Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 11
Figure 9. Voltage waveform at the output of a PWM inverter
with and without overmodulation
(after filtering).
The Procedure is divided into the following sections:
-frequency
component
in the voltage (unfiltered) at the output of a three-phase PWM
inverter
the frequency on the induction motor speed and
magnetizing
current
Operation at motor nominal frequency. Operation at 4/3 the
motor
nominal frequency. Operation at 1/2 the motor nominal
frequency.
High voltages are present in this laboratory exercise. Do not
make or modify any
banana jack connections with the power on unless otherwise
specified.
Setup and connections
In this part of the exercise, you will set up and connect the
equipment.
1. Refer to the Equipment Utilization Chart in Appendix A to
obtain the list of
equipment required to perform the exercise.
Install the equipment in the Workstation.
Mechanically couple the Four-Pole Squirrel Cage Induction
Motor to the
Four-Quadrant Dynamometer/Power Supply using a timing belt.
PROCEDURE OUTLINE
PROCEDURE
Voltage waveform without overmodulation
Voltage waveform with overmodulation
Sine wave modulating the duty
cycle (with overmodulation)
/2
duty cycle = 100%
/2
duty cycle = 0%
Time
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Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
12 © Festo Didactic 86368-00
2. Make sure that the ac and dc power switches on the Power
Supply are set to
the O (off) position, then connect the Power Supply to a three-
phase
ac power outlet.
Make sure that the main power switch on the Four-Quadrant
Dynamometer/
Power Supply is set to the O (off) position, then connect its
Power Input to an
ac power outlet.
3. Connect the Power Input of the Data Acquisition and Control
Interface to
a 24 V ac power supply.
Connect the Low Power Input of the Chopper/Inverter to the
Power Input of
the Data Acquisition and Control Interface. Turn the 24 V ac
power supply
on.
4. Connect the USB port of the Data Acquisition and Control
Interface to a
USB port of the host computer.
Connect the USB port of the Four-Quadrant
Dynamometer/Power Supply to
a USB port of the host computer.
5. Turn the Four-Quadrant Dynamometer/Power Supply on, then
set the
Operating Mode switch to Dynamometer.
6. Turn the host computer on, then start the LVDAC-EMS
software.
In the LVDAC-EMS Start-Up window, make sure that the Data
Acquisition
and Control Interface and the Four-Quadrant
Dynamometer/Power Supply
are detected. Make sure that the Computer-Based
Instrumentation and
Chopper/Inverter Control functions for the Data Acquisition and
Control
Interface are available. Select the network voltage and
frequency that
correspond to the voltage and frequency of your local ac power
network, then
click the OK button to close the LVDAC-EMS Start-Up
window.
7. Connect the Digital Outputs of the Data Acquisition and
Control
Interface (DACI) to the Switching Control Inputs of the
Chopper/Inverter
using a DB9 connector cable.
On the Chopper/Inverter, set the Dumping switch to the O (off)
position. The
Dumping switch is used to prevent overvoltage on the dc bus of
the
Chopper/Inverter. It is not required in this exercise.
8. Set up the circuit shown in Figure 10. Use the diodes in the
Rectifier and
Filtering Capacitors to implement the three-phase full-wave
rectifier. Use the
Chopper/Inverter to implement the Three-phase inverter.
Connect the
Thermistor Output of the Four-Pole Squirrel Cage Induction
Motor to the
Thermistor Input of the Four-Quadrant Dynamometer/Power
Supply.
Notice that the prefix IGBT
has been left out in this
manual when referring to
the IGBT Chopper/Inverter
module.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 13
Figure 10. Three-phase, variable-frequency induction-motor
drive (with three-phase filter).
Measurement of the rms value of the fundamental-frequency
component in
the voltage (unfiltered) at the output of a three-phase PWM
inverter
In this part of the exercise, you will use the Harmonic Analyzer
in LVDAC-EMS to
measure the rms value of the fundamental-frequency component
in the unfiltered
voltage at the output of a three-phase PWM inverter. You will
also observe that
when the load connected to the three-phase PWM inverter
output is inductive
(like a motor), the current waveform at the output of the three-
phase
PWM inverter remains sinusoidal even with no filter.
a The Four-Quadrant Dynamometer/Power Supply is not used in
this part of the exercise although it is mechanically coupled to
the Four-Pole Squirrel Cage
Induction Motor.
9. In LVDAC-EMS, open the Chopper/Inverter Control window,
then make the
following settings:
-Phase PWM inverter.
-2-
3).
nominal frequency
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel.
Three-phase filter
Three-phase
inverter
Three-phase
rectifier
Three-
phase
induction
machine
Switching control
signals from DACI
Brake
N
2 mH
2 mH
2 mH
5 F 5 F 5 F
5 F
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
14 © Festo Didactic 86368-00
the
modulation index m, i.e., it sets the amplitude of the sine-wave
signal
that modulates the duty cycle of the switching control signals.
When
the Peak Voltage parameter is set to 100%, the amplitude of the
modulating signal is set to obtain a peak output voltage
corresponding to 100% of half the dc bus voltage (100% of /2).
In other words, this sets the value of the modulation index to 1
(m = 1).
Pulse-
Width Modulation.
-Phase PWM Inverter.
10. On the Power Supply, turn the three-phase ac power source
on. The motor
should start to rotate.
11. In LVDAC-EMS, open the Oscilloscope. Use channel 1 to
display the dc bus
voltage (input E1), channels 2 and 3 to display the line voltage
at the output
of the three-phase PWM inverter before and after filtering
(inputs E2 and E3,
respectively), and channel 4 to display the current flowing in
the motor stator
windings (input I1).
Select the Continuous Refresh mode, set the time base to
display only one
complete cycle of the voltage and current waveforms (this
provides more
precision for observing the trains of rectangular bipolar pulses
produced by
the three-phase PWM inverter), and set the trigger controls so
that the
Oscilloscope triggers when the waveform of the motor stator
current passes
through 0 A with a positive slope.
Select convenient vertical scale and position settings to
facilitate observation
of the waveforms.
12. Print or save the waveforms displayed on the Oscilloscope
screen for future
reference. It is suggested that you include these waveforms in
your lab
report.
13. Describe the waveform of the voltage at the output of the
three-phase
PWM inverter before and after filtering.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 15
14. Measure and record the rms value of the line voltage at the
output of the
three-phase PWM inverter before and after filtering.
RMS value of the line voltage at the output of the three-phase
PWM inverter before filtering: V
RMS value of the line voltage at the output of the three-phase
PWM inverter after filtering: V
15. Are the rms values of the line voltage at the output of the
three-phase
PWM inverter measured before and after filtering using the
Oscilloscope
equal? Explain briefly.
16. Describe the waveform of the current flowing in each stator
winding of the
three-phase motor (input I1).
17. In LVDAC-EMS, open the Harmonic Analyzer window, then
make the
following settings:
nominal
frequency indicated on the Four-Pole Squirrel Cage Induction
Motor
front panel.
r is set to 40.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
16 © Festo Didactic 86368-00
18. Measure and record the rms value of the fundamental-
frequency
component (1f) in the line voltage at the output of the three-
phase
PWM inverter after filtering (input E3), indicated in the
Harmonic Analyzer
display.
RMS value of the fundamental-frequency component in the line
voltage
at the output of the three-phase PWM inverter after filtering: V
a Compare the rms value of the fundamental-frequency
component in the line voltage at the output of the three-phase
PWM inverter after filtering measured
using the Harmonic Analyzer with the rms value of the line
voltage measured
previously using the Oscilloscope. The values should be
identical because the
voltage waveform is sinusoidal.
19. In the Harmonic Analyzer window, set the Input parameter
to E2 to measure
the line voltage at the output of the three-phase PWM inverter
before filtering.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter before
filtering (input E2), indicated in the Harmonic Analyzer
display.
RMS value of the fundamental-frequency component in the line
voltage
at the output of the three-phase PWM inverter before filtering:
V
a The rms value of the fundamental-frequency component in the
line voltage at the output of the three-phase PWM inverter
before filtering should be slightly
higher than that after filtering because of the voltage drop
across the three-
phase filter.
20. Do your measurements confirm that the rms value of the
fundamental-
frequency component in the line voltage at the outputs of the
three-phase
PWM inverter before filtering can be measured using the
Harmonic
Analyzer? Explain briefly.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 17
21. In the Chopper/Inverter Control window, stop the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
Remove the three-phase filter from the circuit and disconnect
voltage
input E3. The circuit should be as shown in Figure 11.
Figure 11. Three-phase, variable-frequency induction-motor
drive (without three-phase filter).
22. In the Chopper/Inverter Control window, start the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source on.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter indicated in
the Harmonic Analyzer display.
RMS value of the fundamental-frequency component in the line
voltage
at the output of the three-phase PWM inverter: V
a Compare the rms value of the fundamental-frequency
component in the line voltage measured without filter to that
measured in step 19 when the filter was in
the circuit.
Three-phase
inverter
Three-phase
full-wave rectifier
Three-
phase
induction
machine
Switching control
signals from DACI
Brake
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
18 © Festo Didactic 86368-00
23. On the Oscilloscope, use channel 1 to display the dc bus
voltage (input E1),
channel 2 to display the voltage at the output of the three-phase
PWM inverter (input E2), and channel 3 to display the current
flowing in one
of the motor stator windings (input I1).
Select convenient vertical scale and position settings to
facilitate observation
of the waveforms.
24. Print or save the waveforms displayed on the Oscilloscope
screen for future
reference. It is suggested that you include these waveforms in
your lab
report.
25. Explain why the waveform of the motor stator current
displayed on the
Oscilloscope screen is sinusoidal even if the filter is removed.
Overmodulation
In this part of the exercise, you will increase the rms value of
the line voltage at
the output of a three-phase PWM inverter by increasing the
amplitude of the sine
wave that modulates the duty cycle of the switching transistors
of the
PWM inverter to a level that causes overmodulation.
a For the remaining of this exercise, the rms value of the line
voltage at the output of the three-phase PWM inverter always
refers to the rms value of the
fundamental-frequency component in the line voltage (i.e., the
rms value of
the 1f component measured with the Harmonic Analyzer).
26. Compare the rms value of the line voltage at the output of
the three-phase
PWM inverter measured in step 22 with the nominal line voltage
of the
Four-Pole Squirrel Cage Induction Motor (indicated on its front
panel). Does
the maximum line voltage which can be obtained at the outputs
of the three-
phase PWM inverter without overmodulation (i.e., with
modulation index set
to 1) allow the induction motor to be operated at its nominal
voltage?
27. In order to increase the voltage at the output of the three-
phase
PWM inverter (i.e., the voltage applied to the motor windings),
set the Peak
Voltage parameter in the Chopper/Inverter Control window to
117% of the
dc bus voltage/2 (this corresponds to the maximum Peak
Voltage value
available in the Chopper/Inverter Control window). This setting
increases the
amplitude of the sine-wave signal that modulates the duty
cycles of the
switching control signals to a level that causes overmodulation.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 19
28. Using the Harmonic Analyzer, measure and record the rms
value of the line
voltage at the output of the three-phase PWM inverter obtained
when
overmodulation is used.
RMS value of the line voltage at the output of the three-phase
PWM inverter (with overmodulation): V
29. Is the value measured in the previous step close to the
nominal line voltage
of the Four-Pole Squirrel Cage Induction Motor?
30. Print or save the waveforms displayed on the Oscilloscope
screen for future
reference. It is suggested that you include these waveforms in
your lab
report.
31. Compare the voltage waveforms at the output of the three-
phase
PWM inverter obtained with and without overmodulation (i.e.,
obtained in
step 30 and step 24, respectively). Do you observe that the
voltage
waveform obtained with overmodulation contains less
rectangular pulses,
indicating that overmodulation occurs?
32. Do your observations confirm that overmodulation allows
the rms value of the
maximum voltage at the output of a three-phase PWM inverter
to be
increased?
33. In the Chopper/Inverter Control window, stop the Three-
Phase
PWM Inverter.
Effect of the frequency on the induction motor speed and
magnetizing
current
In this part of the exercise, you will use the same circuit as in
the previous
section (see Figure 11) to observe the effect of the frequency on
the speed and
magnetizing current of the three-phase induction motor.
34. In LVDAC-EMS, open the Four-Quadrant
Dynamometer/Power Supply
window, then make the following settings:
Positive Constant-Torque
Prime
Mover/Brake.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
20 © Festo Didactic 86368-00
This
allows the torque command of the prime mover/brake to be
controlled manually.
ensuring
that the Four-Pole Squirrel Cage Induction Motor operates with
no
mechanical load.
The first and second
numbers in this parameter specify the number of teeth on the
pulley
of the Four-Quadrant Dynamometer/Power Supply and the
number
of teeth on the pulley of the machine under test, respectively.
The
pulley ratio between the Four-Quadrant Dynamometer/Power
Supply
and the Four-Pole Squirrel Cage Induction Motor is 24:24.
is
required to match the characteristics of the thermistor in the
Four-
Pole Squirrel Cage Induction Motor with the Thermistor Input
in the
Four-Quadrant Dynamometer/Power Supply.
-Torque Primer Mover/Brake by
setting
the Status parameter to Started or by clicking the Start/Stop
button.
de of the meters by clicking
on the
corresponding button.
Operation at motor nominal frequency
35. In the Chopper/Inverter Control window, make the
following settings:
-Phase
PWM inverter.
he Switching Frequency parameter to 2000 Hz.
-2-
3).
nominal
frequency indicated on the Four-Pole Squirrel Cage Induction
Motor
front panel.
Pulse-
Width Modulation.
-Phase PWM Inverter.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
dure
© Festo Didactic 86368-00 21
36. The induction motor should rotate at a speed slightly below
the synchronous
speed of the three-phase induction motor. Record the motor
speed of
rotation indicated by the Speed meter in the Four-Quadrant
Dynamometer/Power Supply window in Table 2.
a The synchronous speed of the Four-Pole Squirrel Cage
Induction Motor is 1500 r/min at a local ac power network
frequency of 50 Hz and 1800 r/min at
a local ac power network frequency of 60 Hz.
Table 2. Effect of the frequency on the induction motor speed
and magnetizing current.
Frequency Motor speed of
rotation
(r/min)
DC bus voltage
(V)
RMS value of the
motor magnetizing
current
(A)
Amplitude of the
motor magnetizing
current (peak value)
(A)
Nominal
frequency
4/3 the nominal
frequency
1/2 the nominal
frequency
37. On the Oscilloscope, use channel 1 to display the dc bus
voltage (input E1),
and channel 2 to display the motor stator current (input I1).
Select convenient vertical scale and position settings to
facilitate observation
of the waveforms.
38. Measure and record the dc bus voltage as well as the rms
value and
amplitude of the motor magnetizing current in Table 2.
Operation at 4/3 the motor nominal frequency
39. In the Chopper/Inverter Control window, gradually increase
the frequency of
the three-phase PWM inverter up to about 4/3 the motor
nominal frequency
while observing the motor speed and magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
40. Gradually decrease the frequency of the three-phase PWM
inverter to the
motor nominal frequency.
The stator current meas-
ured when an induction
motor operates with no
mechanical load is virtually
equal to the motor magnet-
izing current.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
22 © Festo Didactic 86368-00
41. Compare the motor speed measured at 4/3 the nominal
frequency to that
measured at the motor nominal frequency. Does the motor speed
increase in
direct proportion with the increase in frequency?
42. Compare the motor magnetizing current measured at 4/3 the
motor nominal
frequency to that measured at the motor nominal frequency.
How does the
motor magnetizing current vary when the frequency is
increased? Explain
why.
43. How does the motor magnetizing current variation affect the
maximum
magnetic flux density in the motor?
Operation at 1/2 the motor nominal frequency
In the next steps, the current in the motor stator windings will
exceed the nominal value.
Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
44. Gradually decrease the frequency of the three-phase PWM
inverter down to
about 1/2 the motor nominal frequency while observing the
motor speed and
magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
45. In the Chopper/Inverter Control window, stop the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 23
46. Compare the motor speed measured at 1/2 the motor
nominal frequency to
that measured at the motor nominal frequency. Does the motor
speed
decrease in direct proportion with the decrease in frequency?
47. How does the motor magnetizing current vary when the
frequency is
decreased? Explain why.
48. Compare the magnetizing current measured at 1/2 the motor
nominal
frequency to that measured at the motor nominal frequency as
well as to the
motor full-load current rating indicated on the Four-Pole
Squirrel Cage
Induction Motor front panel. What are the consequences of
decreasing the
frequency to 1/2 the motor nominal frequency while keeping the
voltage
constant?
Saturation curves
In this part of the exercise, you will plot the saturation curves
of the three-phase
induction motor at the motor nominal frequency, 2/3 the
nominal frequency,
1/2 the nominal frequency, and 1/3 the nominal frequency.
In the next steps, the current in the motor stator windings will
exceed the nominal value.
Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
49. Remove the timing belt that mechanically couples the Four-
Pole Squirrel
Cage Induction Motor to the Four-Quadrant
Dynamometer/Power Supply.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
24 © Festo Didactic 86368-00
50. In the Chopper/Inverter Control window, make the
following settings.
-Phase
PWM inverter.
Hz.
Phase Sequence parameter is set to Fwd (1-2-
3).
frequency indicated on the Four-Pole Squirrel Cage Induction
Motor
front panel.
e-Phase PWM Inverter.
51. On the Power Supply, turn the three-phase ac power source
on.
For each value of the Peak Voltage parameter shown in Table 3,
measure
the rms value of the line voltage applied to the motor stator
windings using
the Harmonic Analyzer and the peak value of the current
flowing in the motor
stator windings (peak magnetizing current) using the
Oscilloscope (use the
horizontal cursors to make the measurement). To obtain optimal
results,
begin your measurements with the highest Peak Voltage setting
as shown in
Table 3.
To reduce the data acquisition time and to prevent the motor
from
overheating, it is strongly recommended to use the Data Table
in
LVDAC-EMS to record the circuit parameters measured.
Table 3. RMS value of the motor stator winding voltage (at the
fundamental frequency) and
peak magnetizing current of the three-phase induction motor at
various frequencies.
Peak voltage
(% of dc
bus/2)
(%)
/ / /
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
117
110
100
90
80
70
60
50
40
30
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 25
52. Repeat the previous step at the following three other
frequencies:
2 3⁄ , 1/2 , and 1/3 . As mentioned earlier, make sure
that the motor peak current never exceeds four times the
nominal current
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel or the
temperature never exceeds 80°C (176°F), particularly at 1/2 and
1/3 the
nominal motor operating frequency.
a Use the 40 A terminal of input I1 on the DACI and set the
Range of input I1 to High in the Data Acquisition and Control
Settings window of LVDAC-EMS as
soon as the motor stator current reaches 4 A. Temporarily turn
the ac power
source off before making these changes.
53. Plot on the same graph the saturation curves (rms value of
the motor stator
winding voltage as a function of the peak magnetizing current)
of the Four-
Pole Squirrel Cage Induction Motor at each of the frequencies
in Table 3.
a You will probably not observe any saturation at nominal
frequency because the voltage applied to the motor windings
cannot be increased sufficiently.
54. Do the saturation curves you plotted in the previous step
show that, when the
motor stator winding voltage is increased, the peak magnetizing
current
increases in similar proportions until the voltage reaches a value
at which
saturation in the stator core of the induction motor starts to
occur, causing
the peak magnetizing current to increase much more rapidly
than the motor
stator winding voltage?
55. Do your observations confirm that increasing the frequency
allows higher
voltages to be applied to the motor stator windings before
saturation starts to
occur?
56. Close LVDAC-EMS, turn off all equipment, and remove all
leads and cables.
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
26 © Festo Didactic 86368-00
In this exercise, you learned that a three-phase, variable-
frequency induction-
motor drive consists of a power diode, three-phase full-wave
rectifier and a three-
phase PWM inverter. You learned that varying the operating
frequency of the
PWM inverter varies the frequency of the ac voltage applied to
the induction
motor, and thus, the motor speed. You also learned that varying
the modulation
index of the PWM inverter allows the voltage applied to the
motor stator windings
to be adjusted. You were introduced to the use of
overmodulation in the
PWM inverter to increase the maximum voltage that can be
applied to the motor
stator windings. You saw that for a given frequency, the
maximum flux density
( .) in the stator of the motor is directly proportional to the rms
value of
voltage applied to the motor stator windings. You also saw that
when the voltage
increases and reaches a certain value, saturation occurs in the
stator causing the
motor magnetizing current to start increasing at a rate which
largely exceeds that
of the motor voltage. You learned that for a given voltage, the
maximum flux
density is inversely proportional to the frequency of the ac
voltage applied to the
motor windings, and consequently, the magnetizing current of
the motor
decreases when the frequency increases and vice versa. You
were introduced to
the use of a harmonic analyzer to measure the rms value of the
fundamental-
frequency component of a non-sinusoidal signal (e.g., the
unfiltered voltage at
the output of a three-phase PWM inverter).
1. How can the ac voltage at the output of a three-phase PWM
inverter be
varied?
2. How does the magnetizing current vary when saturation starts
to occur in the
stator of an induction motor?
3. What should be done for an induction motor to be able to
produce the
highest possible torque?
4. How do the maximum flux density ( .) and peak magnetizing
current of an
induction motor vary when the PWM inverter frequency
decreases and the
voltage at the PWM inverter output (motor stator voltage)
remains constant?
CONCLUSION
REVIEW QUESTIONS
Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 27
5. Explain why the rms value of the fundamental-frequency
component in the
voltage (unfiltered) at the output of a three-phase PWM inverter
cannot be
measured using a conventional voltmeter.
Rates and Predictors of Postpartum Depression by Race
and Ethnicity: Results from the 2004 to 2007 New York City
PRAMS Survey (Pregnancy Risk Assessment Monitoring
System)
Cindy H. Liu • Ed Tronick
Published online: 25 October 2012
� Springer Science+Business Media New York 2012
Abstract The objective of this study was to examine
racial/ethnic disparities in the diagnosis of postpartum
depression (PPD) by: (1) identifying predictors that account
for prevalence rate differences across groups, and (2) com-
paring the strength of predictors across groups. 3,732 White,
African American, Hispanic, and Asian/Pacific Islander
women from the New York City area completed the Preg-
nancy Risk Assessment Monitoring System from 2004 to
2007, a population-based survey that assessed sociodemo-
graphic risk factors, maternal stressors, psycho-education
provided regarding depression, and prenatal and postpartum
depression diagnoses. Sociodemographic and maternal
stressors accounted for increased rates in PPD among Blacks
and Hispanics compared to Whites, whereas Asian/Pacific
Islander women were still 3.2 times more likely to receive a
diagnosis after controlling for these variables. Asian/Pacific
Islanders were more likely to receive a diagnosis after their
providers talked to them about depressed mood, but were less
likely than other groups to have had this conversation. Pre-
natal depression diagnoses increased the likelihood for PPD
diagnoses for women across groups. Gestational diabetes
decreased the likelihood for a PPD diagnosis for African
Americans; a trend was observed in the association between
having given birth to a female infant and increased rates of
PPD diagnosis for Asian/Pacific Islanders and Whites. The
risk factors that account for prevalence rate differences in
postpartum diagnoses depend on the race/ethnic groups
being compared. Prenatal depression is confirmed to be a
major predictor for postpartum depression diagnosis for all
groups studied; however, the associations between other
postpartum depression risk factors and diagnosis vary by
race/ethnic group.
Keywords Postpartum depression � Health status
disparities � Asian Americans � Prenatal depression �
Gestational diabetes
Introduction
Postpartum depression (PPD) is a serious health concern
affecting approximately 13 % of all women [1]. At least
19.2 % of women experience depression within 12 months
after giving birth [2]. The associations between prenatal
depression and PPD depression are well documented [3–5].
Psychosocial factors including high stress, low social sup-
port, and low marital satisfaction are also predictors [4, 5].
Surprisingly little is known about the extent to which
postpartum depression varies by race and ethnicity, given the
effects of culture on the experiences and manifestations of
depression [6, 7]. This dearth of information on postpartum
depression in ethnic minorities is well recognized. In a
published review of maternal depression, the Agency for
Healthcare Research and Quality found ‘‘screening instru-
ments [to be] poorly representative of the U.S. population,’’
and that ‘‘populations [from studies] were overwhelmingly
Caucasian’’ [8]. A review by O’Hara found that meta-anal-
yses on postpartum depression had omitted race and eth-
nicity as risk factors for postpartum depression [4].
Research studies on postpartum depression that have
included ethnic minorities generally compare African
C. H. Liu (&)
Beth Israel Deaconess Medical Center, Harvard Medical School,
75 Fenwood Road, Boston, MA 02115, USA
e-mail: [email protected]
E. Tronick
Child Development Unit, University of Massachusetts,
100 Morrissey Blvd, Boston, MA 02125, USA
e-mail: [email protected]
123
Matern Child Health J (2013) 17:1599–1610
DOI 10.1007/s10995-012-1171-z
Americans and Hispanics with Whites. In these studies,
group differences in prevalence rates have shown to be
inconsistent. Across studies, the rates of postpartum
depression in African American and Hispanic women were
found to be higher [9], lower [10], or no different [11]
compared to Whites. What accounts for observed racial and
ethnic differences in prevalence is unclear. In some studies,
sociodemographic risk variables were associated with
higher levels of depressive symptomatology among Afri-
can Americans, raising the possibility that sociodemo-
graphic variables rather than race and ethnicity account for
different levels of postpartum depression [12–14]. In con-
trast, others have shown greater levels of depressive
symptomatology among African Americans and Hispanics
than Whites, after accounting for sociodemographic factors
[9]. While certain social factors could increase risk, some
factors might buffer against postpartum depression within
groups. For instance, low income foreign-born Hispanic
women with social support exhibited lower rates of post-
partum depression [15], whereas bilingual Hispanic women
were at greater risk than those who spoke only Spanish
[11]. It is possible that factors such as social support or
nativity and its effect on the likelihood of postpartum
depression differ by race/ethnicity because they express
different meanings or incur different implications for each
group. Moreover, stigmas about psychological problems
and help-seeking may have an effect on identifying post-
partum depression, resulting in a subsequent effect on
reported prevalence of postpartum depression rates [6, 16].
Given the mixed picture across groups, this study aimed to
systematically determine the extent to which prevalence
rates across race and ethnicity are explained by factors
associated with postpartum depression.
This study uniquely includes Asian/Pacific Islander
(API) women within the U.S. As the fastest growing ethnic
minority group, over 16 million APIs are estimated to be
living in the U.S [17, 18]. The research on API postpartum
experiences is limited, which is striking given that API
women may hold several risk factors.
If psychiatric history is a major predictor, API women
may be at greatest risk: those between the ages of
15–24 years have the highest rate of depression and su-
icidality compared to any other ethnicity, gender, or age
[19–21]. One study showed APIs to be at lower risk for
postpartum depressive symptoms compared to Whites,
African Americans, and Hispanics [14], while another
study reported a greater percentage of APIs with post-
partum symptoms compared to White Americans [22].
Analyses conducted by the New York City Department of
Health and Mental Hygiene on data from the 2004 to 2007
New York City (NYC) Pregnancy Risk Assessment Mon-
itoring System (PRAMS) revealed a higher rate of PPD
diagnoses among APIs compared to other groups [23–25].
From the most recent sample in 2007, 10.4 % of API
received a PPD diagnosis compared to 1.7 % of non-His-
panic White women [26]. These findings suggest a poten-
tial risk for postpartum depression in APIs.
This study examines racial/ethnic disparities in PPD
diagnosis by identifying predictors accounting for preva-
lence differences. Because previous studies have either
focused mostly on small samples of one group, or did not
examine these risk factors by race/ethnicity, we hypothe-
size that associations of risk factors and PPD differ by race/
ethnic group. The risk factors evaluated were selected
based on the current literature [27–31]. Our study also
sought to explain disparities in PPD rates from a published
report by the NYC Department of Health and Mental
Hygiene. We utilized the study’s comprehensive popula-
tion-based dataset. We also sought to determine the
strength of predictors within each group and differences
across groups. Accordingly, we stratified our analyses by
race/ethnicity. Determining the strength of predictors by
group is essential for identifying individuals most at risk,
and may inform the possible causes of depression for dif-
ferent groups. Unique to this study was the use of diagnosis
as an outcome measure, the inclusion of information on
whether providers talked to women about depressed mood,
and an adequate sample size of APIs. This allowed us to
also examine disparities in psycho-education and diagnosis
across groups.
Methods
Sample
This study used the NYC PRAMS from 2004 to 2007, a
population-based survey administered to postpartum
women from NYC. Coordinated by the Centers for Disease
Control and Prevention and state health departments,
PRAMS’ goal is to monitor maternal behaviors and expe-
riences of women before, during, and after live birth
pregnancies. The dataset was provided by the NYC
Department of Health and Mental Hygiene (DOHMH).
The participants were part of an ongoing population-
based random sampling of NYC live births. NYC mothers
of approximately 180 infants with registered birth certifi-
cates that gave birth during the previous 2–4 months were
contacted for participation monthly. Eighty-three percent
responded by mail and 17 % by phone. The sample was
randomized without replacement and stratified by birth
weight. The final dataset was weighted for stratification,
nonselection, and nonresponse.
According to the DOHMH, a total of 4,813 responses
were received with response rates of at least 70 % from
July to December of 2004, May to December of 2005, and
1600 Matern Child Health J (2013) 17:1599–1610
123
January to December of 2006. A rate of 65 % was achieved
from January to December of 2007. For 2004–2005,
responses were weighted to represent 138,266 live births.
For 2006 and 2007, responses represented 119,079 and
122,222 live births, respectively. Based on the DOHMH
analysis, respondents differed from non-respondents on
some key sociodemographic variables (p  .05). APIs
compared to other racial and ethnic groups, younger
women, and women with less education were less likely to
respond to the survey.
Measures
The birth certificate provided information on maternal race/
ethnicity and nativity (i.e., U.S. or non-U.S. born mothers).
Women were classified as Hispanic or non-Hispanic based
on self-report. Non-Hispanic women were categorized in
one of the following groups: White, African American,
Asian/Pacific Islander, and American Indian/Alaskan
Native. Maternal age, nativity (U.S. Born versus Foreign
Born) and education (categorized as: 0–8, 9–11, 12, 13–15,
and [16 years) were based at the time of infant birth from
information in the birth certificate. Mean infant age at the
time of survey completion was 9.7 months; there were no
significant differences in infant age across groups.
The PRAMS survey itself provided information for
remaining variables. To obtain income, women were asked
to indicate ‘‘total household income before taxes in the
12 months before the new baby was born’’ by checking off
one of the following options: $10,000, $10,000–$14,999,
$15,000–$19,999, $20,000–$24,999, $25,000–$34,999,
$35,000–$49,999, $50,000–$74,999, and [$75,000. Stress-
ful events during pregnancy were obtained by ‘‘yes’’ or
‘‘no’’ responses to events that may have occurred during
the last 12 months before the new baby was born. Exam-
ples include ‘‘I moved to a new address,’’ ‘‘I had a lot of
bills to pay,’’ ‘‘I got separated or divorced from my hus-
band or partner,’’ and ‘‘Someone very close to me died.’’
These events were counted and categorized into the fol-
lowing: 0, 1–2, 3–5, and 6–13 events. A ‘‘yes’’ or ‘‘no’’
response was also used to obtain information on following:
gestational diabetes (‘‘High blood sugar (diabetes) that
started during this pregnancy’’), social support from partner
(responses of ‘‘My husband or partner’’ to the question
‘‘During your most recent pregnancy, who would have
helped you if a problem had come up’’), NICU (Neonatal
Intensive Care Unit) (‘‘After your baby was born, was he or
she put in an intensive care unit?’’), unintended pregnancy
(‘‘When you got pregnant with your new baby, were you
trying to get pregnant?’’). The NYC PRAMS included
additional questions related to depression. Mothers were
asked to respond ‘‘yes’’ or ‘‘no’’ regarding prenatal
depression (‘‘At any time during your most recent
pregnancy, did a doctor, nurse, or other health care worker
diagnose you with depression?’’), and discussion about
mood (‘‘At any time during your most recent pregnancy or
after delivery, did a doctor, nurse, or other health care
worker talk with you about ‘‘baby blues’’ or postpartum
depression?’’). In addition, mothers were asked about PPD
diagnosis (‘‘Since your new baby was born, has a doctor,
nurse, or other health care worker diagnosed you with
depression?’’). The response to this item was the outcome
variable used for the analyses in this study.
The language of the survey (English or Spanish version)
was also noted.
Variables
Covariates included maternal age, household income,
maternal education, nativity, and infant age at the time the
mother completed the questionnaire. Variables considered
as potential stressors included: gestational diabetes,
stressful events, social support, NICU, intention for preg-
nancy, and prenatal depression. Discussion about mood
served as an additional predictor of PPD diagnosis.
Responses with missing variables of interest for this
study were eliminated. Variables with less than a 100 %
response rate included household income (86.9 %),
maternal education (99.3 %), maternal age (97.0 %), and
PPD diagnosis (99.4 %) resulting in an unweighted study
sample of 3,732.
Statistical Analyses
To account for the stratified and weighted sample, the data
was analyzed using the complex samples module of SPSS
version 17.0 (SPSS Inc., Chicago, IL). A non-race stratified
model was conducted to determine the likelihood of
receiving a PPD diagnosis for each race/ethnic group with
Whites as the reference group. A series of four logistic
regression models were employed where the variables of
interest (race/ethnicity, sociodemographic, stressors, and
discussion about mood) were sequentially added to the
model, allowing incremental examination of the variables’
effects in identifying factors that explain racial/ethnic
disparities in PPD.
Prevalence estimates within each group were generated
according to predictors. To compare the characteristics of
those with and without PPD and to understand associated
predictors, race-stratified logistic regressions incorporated
all predictors, with sociodemographic variables as covari-
ates. Adjusted odds ratios for each predictor were gener-
ated by race/ethnic group. Note that our models failed to
converge with the inclusion of language, nativity, and
NICU variables because of low cell sizes; thus, these
variables were dropped from our analyses. Unless
Matern Child Health J (2013) 17:1599–1610 1601
123
otherwise noted, all reported proportions represent weigh-
ted averages.
Results
Compared to other groups, API women showed the highest
rate for PPD, followed by Hispanics and African Ameri-
cans. White women had the lowest rate of PPD. The high
rate of a PPD diagnosis among API women is consistent
with previous analyses from this dataset, which utilized a
larger sample size than the dataset here, as this set includes
only women with complete data on the predictor variables.
Other racial/ethnic differences among assessed variables
are presented (Table 1).
A major objective was to determine whether sociode-
mographic variables, stressor variables, and discussion
about mood accounted for PPD differences. In the unad-
justed model, likelihood estimates indicate that API women
were 4.6 times more likely and Hispanic women 2.7 times
more likely than Whites to receive a PPD diagnosis.
African American were 1.7 times more likely to receive the
diagnosis than Whites, although this was not statistically
significant (Table 2). Once sociodemographic factors were
entered, African Americans were no more likely to receive
a diagnosis than Whites. For Hispanics, the greater likeli-
hood for a diagnosis compared to Whites was less pro-
nounced after accounting for sociodemographic factors and
was eliminated with the inclusion of stressors. The diag-
nosis likelihood was slightly reduced for APIs after
accounting for sociodemographic factors, and significantly
reduced with stressor variables, although diagnosis likeli-
hood was still more than double the rate of Whites and
African Americans. In contrast to the other groups, diag-
nosis likelihood for APIs increased to 3.2 times relative to
Whites, after accounting for reports of having discussed
mood with a provider. Prenatal depression was by far the
strongest predictor for all women compared to other
stressors, although women who gave birth to females were
more likely to receive a diagnosis than women with male
infants. Overall, those who had a discussion about mood
were also more likely to receive a diagnosis.
Profiles of women with PPD diagnoses compared to
women without a diagnosis differed by race/ethnicity. The
majority of White women reporting a PPD diagnosis
received a postgraduate education, while API and African
American women with the diagnosis tended to be high
school graduates. Approximately half of the White women
with PPD had household incomes above $75,000 per year.
Among APIs, Hispanics, and African Americans, more
women with PPD had less than $15,000 of household
income per year than those without a diagnosis (Table 3).
With regard to stressors, we found a significantly higher
rate of gestational diabetes among those with PPD than
those without PPD, but only for White women. However,
after controlling for sociodemographic variables through
our race-stratified adjusted model, gestational diabetes did
not significantly predict PPD in White, API, or Hispanic
women (Table 4). In fact, African American women with
gestational diabetes were less likely to receive a diagnosis
of PPD.
Compared to those without PPD, there was a higher
percentage among APIs and Hispanics with the diagnosis
who had an unintended pregnancy. In addition, the
majority of APIs with PPD had a diagnosis of prenatal
depression compared to the other groups. Stressful events
were not associated with greater likelihood for PPD, but
API women who reported having 6–13 stressful events
were significantly more likely to have PPD, a rate that was
statistically significant. The association between prenatal
depression and PPD persisted for all groups, even after
controlling for sociodemographic variables.
Overall, there was a higher rate of women with PPD
who had a discussion about mood with their providers than
women without the diagnosis. However, the association
between PPD and discussion about mood with providers
was specific to only API and African American women in
the adjusted model.
Women from all groups who received a diagnosis of
PPD were more likely to have given birth to females
although the differences were not statistically significant.
However, having a female infant seemed to slightly
increase the likelihood of a PPD diagnosis among White
and API women based on the race-stratified analyses.
Discussion
This study assessed PPD estimates and identified predictors
of PPD as defined by women’s reports of receiving a
diagnosis from a health care provider. We included API
women and used race-stratified analyses, allowing us to
determine whether predictors varied by race/ethnicity.
This study also sought to identify factors that explained
racial/ethnic disparities obtained in a previous analysis of
the dataset by the NYC Department of Health and Mental
Hygiene. As with other studies, we found that sociode-
mographic factors accounted for the higher rates of PPD
among African Americans and Hispanics. Based on such
findings, some have argued for prevention or intervention
programs to provide resources (e.g., financial support,
education) in addressing the racial/ethnic disparities of
PPD for African Americans and Hispanics [12]. However,
unlike other studies that primarily assessed reported
symptoms [9, 12, 14], we used the diagnosis of PPD as the
1602 Matern Child Health J (2013) 17:1599–1610
123
outcome measure. This raises the possibility that sociode-
mographic status accounts for the rates at which one
receives a diagnosis; in our study, African Americans and
Hispanics with lower sociodemographic statuses were less
likely to receive a diagnosis compared to Whites. If race/
ethnic disparities are found among rates of diagnosis, then
the diagnostic process may be another area to target for
improvement among lower sociodemographic status
groups.
Among ethnic minorities in our study, API women were
the most likely to receive a PPD diagnosis, and unlike
African Americans and Hispanics, the likelihood of
receiving a PPD diagnosis for APIs remained significantly
higher even after accounting for other variables (e.g.,
sociodemographic factors). Prenatal depression was asso-
ciated with PPD for all groups in our study, but the like-
lihood was highest for APIs. Although psychiatric history
for depression was not available, the strong association
between prenatal depression and PPD observed among the
API women in our sample adds to the growing concern of
depression experiences and its effects on API women
during motherhood [19–21]. A number of factors specific
to API women’s experiences are potentially associated
with later postpartum mood. The high rate of depression
and suicidal ideation during adolescence and young
adulthood may reflect family and societal pressures faced
by young women to uphold high academic standards and
traditional gender roles [32]. These young women likely
must negotiate their cultural values and beliefs when
assuming a mother’s identity [33, 34]. In addition, the
cultural preference for male infants may affect PPD.
Table 1 Weighted percentage distribution of mothers who
recently
gave birth that completed the NYC PRAMS from 2004 to 2007,
by
characteristic, according to race/ethnicity
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)
Maternal age
20 2.4a 0.9a 9.9b 6.9c
20–34 70.1
a
75.4
b
76.8
b
73.8
a,b
C35 27.5
a
23.7
a,b
13.3
c
19.3
b,d
Maternal education
0–8 1.7
a
2.7
a
11.7
b
1.6
a
9–11 4.2
a
10.7
b
19.6
c
15.8
d
12 22.6
a
26.1
a
34.4
b
32.1
b
13–15 16.2
a
14.7
a
21.1
b
28.1
c
C16 55.4
a
45.8
b
13.2
c
22.4
d
Income
10,000 10.0a 20.4b 40.3c 29.2d
10,000–14,999 6.7
a
15.1
b
14.3
b
10.3
c
15,000–19,999 4.6
a
8.0
b
8.8
b
8.6
c
20,000–24,999 4.7
a
5.8
a
6.8
b
9.2
c
25,000–34,999 6.8
a
5.7
a
9.5
b
13.2
c
35,000–49,999 8.7
a
6.0
a
6.7
a
10.1
a
50,000–74,999 12.1
a
9.9
a
6.3
a
10.2
a
C75,000 46.4
a
29.1
b
7.1
c
9.0
d
Maternal nativity
U.S. born 68.4
a
11.1
b
34.1
c
56.3
d
Non-U.S. born 31.1 88.9 65.6 43.0
Missing data 0.5 0 0.3 0.7
Language of questionnaire
English 99.1
a
99.5
a
51.2
b
98.8
a
Spanish 0 0 48.5 0
Missing data 0.5 0.5 0.3 1.2
NICU
Yes 5.1 5.9 6.4 14.4
No 94.9
a
94.1
a
93.6
a
85.5
b
Don’t know 0 0.1 0 0.1
Gender
Male 49.3
a
52.1
a
51.1
a
52.0
a
Female 50.7 47.9 48.9 48.0
Diabetes
No 92.4 85.1 89.9 89.9
Yes 7.6
a
14.9
b
10.1
c
10.1
c
Stresses
0 45.1
a
49.1
a
31.6
b
26.5
c
1–2 41.8
a
38.7
a
41.5
a
42.8
a
3–5 12.1
a
11.3
a
23.3
b
25.2
b
6–13 1.1
a
0.8
a
3.6
b
5.5
c
Social support
No 90.4 90.8 76.9 75.2
Yes 9.6
a
9.2
a
23.1
b
24.8
b
Table 1 continued
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)
Intention for pregnancy
No 30.9
a
35.1
a
59.0
b
66.5
c
Yes 69.1 64.9 41.0 33.5
Prenatal depression diagnosis
No 97.2 87.6 92.4 94.5
Yes 2.8
a
12.4
b
7.6
c
5.5
d
Discussion about mood
No 46.0 61.4 42.7 39.3
Yes 54.0
a
38.6
b
57.3
a,c
60.7
c
Postpartum depression diagnosis
No 97.4 89.3 93.6 96.3
Yes 2.6
a
10.7
b
6.4
c
3.7
a
Lower case superscripts that differ across each row represent
statistically
different values across racial/ethnic groups. Conversely, groups
within a
row that share the same superscript demonstrate no statistically
significant
difference in values within p  .05
Matern Child Health J (2013) 17:1599–1610 1603
123
Table 2 Logistic regression models of race/ethnicity, other
sociodemographic factors, stressors, and discussion of mood
with provider, with
adjusted odds of postpartum depression diagnosis
Model 1 Model 2 Model 3 Model 4
OR CI OR CI OR CI OR CI
Race
White 1.0 1.0 1.0 1.0
Asian/Pacific Islander 4.6*** 2.6–8.2 4.0*** 2.2–7.2 2.7** 1.4–
4.9 3.2*** 1.7–6.0
Hispanic 2.7*** 1.7–4.5 1.8* 1.0–3.1 1.5 0.9–2.7 1.5 0.9–2.7
Black 1.7
�
1.0–3.0 1.2 0.6–2.2 0.9 0.5–1.8 0.9 0.4–1.8
Maternal age
20 1.0
20–34 0.5 0.3–1.1 0.5 0.2–1.1 0.5 0.2–1.2
C35 0.7 0.3–1.6 0.7 0.3–1.7 0.7 0.3–1.9
Maternal education
0–8 1 1 1
9–11 0.8 0.3–1.9 1.2 0.4–3.2 1.1 0.4–3.0
12 1.0 0.5–2.1 1.6 0.7–4.1 1.6 0.7–4.0
13–15 1.1 0.5–2.5 1.6 0.6–4.2 1.6 0.6–4.3
C16 0.8 0.4–1.8 1.5 0.6–4.0 1.6 0.6–4.2
Income
10,000 1.0 1.0 1.0
10,000–14,999 1.2 0.7–2.1 1.5* 0.8–2.8 1.5* 0.8–2.8
15,000–19,999 0.8* 0.3–1.6 1.1 0.5–2.4 1.0 0.5–2.2
20,000–24,999 0.5 0.2–1.2 0.6 0.3–1.4 0.6 0.2–1.3
25,000–34,999 0.6 0.3–1.3 0.7 0.3–1.7 0.7 0.3–1.6
35,000–49,999 0.3 0.1–0.7 0.3 0.1–0.9 0.3 0.1–0.8
50,000–74,999 0.4 0.2–0.9 0.5 0.2–1.3 0.5 0.2–1.3
C75,000 0.5 0.3–1.0 0.7 0.3–1.5 0.7 0.3–1.4
Gender
Male 1.0 1.0
Female 1.6* 1.1–2.4 1.7* 1.1–2.5
Diabetes
No 1.0 1.0
Yes 0.8 0.4–1.5 0.8 0.4–1.6
Stresses
0 1.0 1.0
1–2 0.8 0.5–1.3 0.8 0.5–1.3
3–5 1.0 0.6–1.8 1.0 0.6–1.8
6–18 1.8
�
0.7–4.9 2.0
�
0.8–5.1
Social support
No 1.0 1.0
Yes 1.1 0.7–1.9 1.2 0.7–2.0
Intention for pregnancy
No 1.0 1.0
Yes 1.2 0.8–1.8 1.2 0.8–1.8
Prenatal depression diagnosis
No 1.0 1.0
Yes 17.3*** 10.9–27.5 15.0*** 9.4–23.8
Discussion about mood
No 1.0
Yes 2.6*** 1.6–4.1
�
p  0.1; * p  .05; ** p  .01; *** p  .001
1604 Matern Child Health J (2013) 17:1599–1610
123
Table 3 Weighted percentage of mothers who completed the
NYC PRAMS from 2004 to 2007, by characteristic according to
race/ethnicity and
postpartum depression diagnosis
White Asian/Pacific Islander Hispanic Black
No PPD PPD No PPD PPD No PPD PPD No PPD PPD
(n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n =
979) (n = 48)
Maternal age
20 2.3 5.9 1 0 9.6 13.4 6.2 25.2***
20–34 70.4 62 74.1 86.1
�
77.7 63.7** 74.2 63.4
�
C35 27.4 32.1 24.9 13.9 12.6 22.9** 19.6 11.5
Maternal education
0–8 1.7 0 2.4 4.9 11.5 15.8 1.7 0.7
9–11 4 9 10.6 11.4 19.3 23 15.9 12.5
12 22.9 12.1 23 52.8*** 34.9 25.9
�
31.4 49.3***
13–15 16.5 5.3 14.5 16.5 20.7 27.7 28.2 26.2
C16 54.9 73.6
�
49.5 14.3*** 13.6 7.6 22.8 11.4
Income
10,000 9.9 15.4 18.7 34.2* 40.1 44.2 28.6 46.9***
10,000–14,999 6.7 4.7 14.8 18.2 13.5 27*** 9.8 21.8***
15,000–19,999 4.8 0 7.3 13.5 9.1 5.3 8.4 14.9*
20,000–24,999 4.8 0 6 4.5 6.7 8.5 9.6 0.6**
25,000–34,999 7 0.5 4.6 15.3 9.7 5.8 13.5 4.8*
35,000–49,999 8.5 15.1 6.7 0.3 7.1 0.5* 10.5 1.7**
50,000–74,999 12.1 11.5 10.7 3.6 6.5 4.4 10.6 0.5**
C75,000 46.2 52.7 31.3 10.3** 7.3 4.3 9 8.8
Maternal nativity
U.S. born 68.4 66.7 12.3 100*** 35.0 23.1* 56.4 54.2
Non-U.S. born 31.1 30.3 87.7 0 64.7 76.9 42.9 45.8
Missing data 0.5 0.3 0 0 0.3 0 0.7 0
Language of questionnaire
English 99.2 97.0 99.5 100 50 46.2 98.8 100
Spanish 0 0 0.1 0 50 53.8 1.1 0
Missing data 0.8 3.0 0.4 0 0 0 0.1 0
NICU
No 94.9 94.2 93.6 98.1 93.7 91.9 85.7 82.3
Yes 5.1 5.8 6.3 1.9 6.3 8.1 14.3 17.3
Don’t know 0 0 0.1 0 0 0 0.1 0.5
Gender
Male 49.5 40.2 53.2 43 51.7 43.8 52.3 44.7
Female 50.5 59.8 46.8 57 48.3 56.2 47.7 55.3
Diabetes
No 92.5 90 85.6 81.4 90.2 86.2 89.6 98.9
Yes 7.5 10* 14.4 18.6 9.8 13.8 10.4 1.1
Stresses
0 45.6 26.4* 47.6 61.2 31.6 30.8 27 12.5**
1–2 41.4 54.3 39.9 28.8 42.6 25 42.9 39.4
3–5 12 14.2 12.1 5.4 22.7 32.1 24.6 41.2
6–13 1 5.1* 0.4 4.5** 3 12.1 5.4 6.9
Social support
No 9.4 15.3 8.6 13.7 22.7 28.3 24.6 31.4
Yes 90.6 84.7 91.4 86.3 77.3 71.7 75.4 68.6
Intention for pregnancy
Matern Child Health J (2013) 17:1599–1610 1605
123
Chinese women with a female infant were more likely to
experience PPD [35, 36]. In another study on Indian
women, having a female infant increased the effects of
other risk factors [37]. Recent findings have also demon-
strated a greater likelihood for Asian women to develop
gestational diabetes, which is associated with PPD [38–40].
Other explanations for Asian American depression in the
literature range from biological [41] to social [42]. Toge-
ther, these explanations may represent a general vulnera-
bility for depression generalizing to API women’s
depressed mood during the postpartum period. Future
studies in PPD research may want to specifically examine
the association between psychiatric history and PPD by
race/ethnicity to determine if psychiatric history predicts
PPD more strongly in API women.
Furthermore, discussing depressed mood with providers
increased the likelihood for women to receive a diagnosis.
This was especially true for APIs where the likelihood of
receiving a diagnosis was 3.2 times more than White
women after our analyses considered such discussions as a
factor. These high rates could reflect the quality of the
diagnostic processes that take place between API women
and their providers. The use of a diagnostic criterion by the
NYC PRAMS to assess PPD is unlike other prevalence
studies that typically use structured assessments for PPD
(e.g., a single question on depressive mood during preg-
nancy, multiple items covering symptomatology, etc.) [9,
12–14]. APIs tend to endorse somatic experiences rather
than psychological symptoms [43, 44]. Conversations with
a provider could increase sensitivity during the assessment,
thus facilitating a positive diagnosis. Increased research on
the diagnostic process within a health care setting would
greatly enhance understanding of how dialogues between
provider and patient result in diagnoses. In particular,
future research should consider differences in the charac-
teristics of providers and clinics among those who did and
did not receive a PPD diagnosis, and the nature of the
actual exchanges occurring between providers and patients.
It was particularly striking that approximately half of the
providers did not talk to women about PPD. Racial/ethnic
disparities were also found when assessing these rates.
While the majority of African American, Hispanic, and
White women reported having had a conversation with
their providers, only 38.6 % of API women in our study
reported this. Given that Asians tend to minimize their
psychological distress [6, 16], providers may not realize
distress nor recognize the need to bring up depressed mood.
APIs who had a conversation were 9.1 times more likely to
receive a diagnosis than APIs without, regardless of their
sociodemographic background. Thus, although APIs were
the group most likely to benefit from information about
depressed mood, they were the least likely to be provided
with it. Additionally, African Americans showed the
highest rate of having been presented with information
about mood compared to the other groups; those with a
conversation were 5.8 times more likely to receive a
diagnosis.
Altogether, and of greatest concern were the low rates of
assessment for all groups, and especially for APIs. Our
findings suggest that the information presented by a pro-
vider has powerful implications for determining diagnosis,
especially for APIs and African Americans. This finding
has implications for studies obtaining prevalence rates
without considering racial/ethnic disparities within the
screening or diagnostic process. Differences in prevalence
rates may be attributed to the lack of medical information
and treatment opportunities available to certain groups.
Our inclusion of known predictors for PPD in race-
stratified analyses allowed us to compare the strength of
stressors across groups. Most of the group differences in
the predicted likelihood for PPD were not statistically
significant suggesting few group differences in the
Table 3 continued
White Asian/Pacific Islander Hispanic Black
No PPD PPD No PPD PPD No PPD PPD No PPD PPD
(n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n =
979) (n = 48)
No 31.1 26.3 33.4 49.2 58.2 69.8 66.4 67.6
Yes 68.9 73.7 66.6 50.8* 41.8 30.2** 33.6 32.4
Prenatal depression diagnosis
No 98 67.1 93.8 35.8 95 54.4 95.4 71
Yes 2 32.9*** 6.2 64.2*** 5 45.6*** 4.6 29***
Discussion about mood
No 46.5 26.1 66.2 21.1 43.5 30.5 40.5 8.6
Yes 53.5 73.9* 33.8 78.9*** 56.5 69.5** 59.5 91.4***
�
p  0.1; * p  .05; ** p  .01; *** p  .001
1606 Matern Child Health J (2013) 17:1599–1610
123
association between stressors and PPD. Furthermore,
stressful events were not associated with a greater likeli-
hood at a statistical level, with the exception of API
women; those who reported 6-13 stressful events were
significantly more likely to receive a diagnosis. The
explanation may reside in the distribution of reported
stressful events for APIs; compared to other groups, the
majority of API women reported zero stressful events. As
such, the few APIs who disclosed high numbers of stressful
events may have been the most likely to receive a diag-
nosis. APIs may still minimize their experience of stress
despite being asked to state the occurrence of stressful
events given their tendency to minimize psychological
problems in general [6, 16]. Providers may want to inquire
further about actual events and how it affects their API
patients both psychologically and physically.
A number of associations between stressors and PPD
require clarification through further research. There was a
trend for increased PPD rates among API and White
women who gave birth to female infants. Few studies have
included infant gender in PPD studies within the U.S.;
those that have find no association [45, 46]. Given these
studies’ small samples (n 200), any effects may have
been too small to detect. One study did find increased self-
esteem in mothers of male infants, although this association
was mediated by paternal support [47]. The statistical trend
in our data may indicate actual preferences for infant
gender, but it could also reflect other factors moderated by
infant gender. Our findings demonstrate the need to include
infant gender in future studies and to identify mechanisms
that explain this association.
In addition, we did not find a general link between
gestational diabetes and PPD, despite a previous study’s
results [39]. When examining groups separately, APIs in
our study were more likely to have gestational diabetes;
however, this did not predict PPD. Instead, we found a
decrease in the likelihood for PPD diagnoses among
African Americans with gestational diabetes. There is
evidence to suggest that African Americans may be less
inclined to disclose symptoms even though providers speak
Table 4 Race/ethnicity stratified logistic regression showing
adjusted odds of postpartum depression diagnosis per predictor
by race/ethnic
group
White Asian/Pacific Islander Hispanic Black
OR CI OR CI OR CI OR CI
Gender
Male 1.0 1.0 1.0 1.0
Female 2.2
�
0.9–5.8 2.6
�
0.9–7.2 1.5 0.8–2.7 1.5 0.5–4.1
Diabetes
No 1.0 1.0 1.0 1.0
Yes 1.0 0.3–3.8 0.8 0.2–3.7 1.4 0.6–3.3 0.1** 0.0–0.5
Stresses
0 1.0 1.0 1.0 1.0
1–2 2.3
�
0.8–6.7 0.8 0.3–2.2 0.4* 0.2–0.9 1.8 0.3–10.2
3–5 1.2 0.2–8.5 0.1 0.0–1.1 0.8
�
0.4–1.8 3.1 0.4–21.7
6–13 5.2 0.9–30.8 2.7* 0.5–15.8 2.5 0.7–9.7 1.5 0.1–15.6
Social support
No 1.0 1.0 1.0 1.0
Yes 1.6 0.4–6.0 1.9 0.4–9.5 0.9 0.4–1.7 1.5 0.5–4.6
Intention for pregnancy
No 1.0 1.0 1.0 1.0
Yes 0.9 0.3–3.3 2.2 0.8–6.4 1.4 0.7–2.6 0.7 0.2–1.9
Prenatal depression diagnosis
No 1.0 1.0 1.0 1.0
Yes 29.4*** 8.5–101.4 52.1*** 16.4–166.0 15.3*** 7.6–30.9
8.1*** 2.9–22.8
Discussion about mood
No 1.0 1.0 1.0 1.0
Yes 1.7 0.6–4.8 9.1** 2.5–33.4 1.3 0.7–2.6 5.8** 2.1–15.9
Only adjusted odds ratios are presented because race/ethnic
stratified analyses did not converge when including unadjusted
factors in the model.
This was due to zero to small sample sizes in race 9
sociodemographic contingency tables
�
p  0.1; * p  .05; ** p  .01; *** p  .001
Matern Child Health J (2013) 17:1599–1610 1607
123
with them about prenatal depression and PPD at a higher
rate [48]. Mistrust and perceived discrimination within the
medical care setting may prevent disclosure about depres-
sion [22, 49]. In particular, some studies have found that
among those with depressive mood accompanied with
diabetes, African Americans were less likely to be recog-
nized as depressed and to receive depression treatment
[49–51]. Given our initial findings, the association between
gestational diabetes and PPD may not be generalizable,
although further research is needed to fully understand the
relationship. Studies that do not stratify by race may
overlook differences in the effect of gestational diabetes on
depression by race/ethnicity.
Interpretation of results should be made with caution in
light of our limitations. As with any self-report, inaccura-
cies in this data are possible given recall problems. In
addition, prenatal and PPD diagnoses were used in our
study. It would have been far preferable to obtain corrob-
orating information from medical records; however, this
information was unavailable within this survey study. It is
possible that providers employed different standards for
diagnoses, which may be reflected in this data, for instance,
the consideration of ‘‘baby blues’’ or the inclusion of dif-
ferent methods to assess depression (e.g., questionnaire,
verbal report). Furthermore, these diagnoses may not nec-
essarily reflect actual depression rates, but as discussed,
may be more of a reflection of provider sensitivity to
detecting symptoms in a particular group. Finally, the race/
ethnic categories are a proxy for a culture, and are com-
prised of heterogeneous subgroups. For instance, the
unique experiences of Chinese, Japanese or Filipino groups
may have been overlooked since they were combined into
one race/ethnic category.
Conclusion
Our results highlight racial/ethnic disparities in PPD and its
diagnosis, inviting a more nuanced approach in the con-
sideration of PPD risk factors. Although we relied on broad
race/ethnic categories, these findings demonstrate at a basic
level, the possibility of differential effects in the risk fac-
tors associated with PPD. Explanations for racial/ethnic
disparities in diagnosis compared to Whites differ by group
and are not necessarily due to sociodemographic status or
stress, factors that usually explain racial/ethnic disparities.
While prenatal depression seems to be a major risk factor
for PPD across all groups, the extent to which a factor is a
‘‘risk’’ for a particular racial/ethnic group needs to be
evaluated. These associations point to the possibility of
group-specific mechanisms leading to a PPD diagnosis.
Universal postpartum depression screening as a single
approach may not be adequate given the role that provider-
patient interactions might have as suggested by these study
findings. Rather, this study broadly reveals a need to con-
sider the diagnostic process between provider-patient by
race/ethnicity to better understand the source of treatment
disparities.
Acknowledgments The authors would like to acknowledge the
NYC Department of Health and Mental Hygiene Bureau of
Maternal,
Infant and Reproductive Health PRAMS Team, Bureau of Vital
Statistics, and the CDC PRAMS Team, Program Services and
Development Branch, Division of Reproductive Health. Support
during the preparation of this manuscript was provided through
a
grant from the Sackler Foundation for Psychobiological
Research and
through the Stuart T. Hauser Clinical Research Training
Fellowship
(2T32MH016259-30).
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Rates and Predictors of Postpartum Depression by Race and
Ethnicity: Results from the 2004 to 2007 New York City
PRAMS Survey (Pregnancy Risk Assessment Monitoring
System)AbstractIntroductionMethodsSampleMeasuresVariables
Statistical
AnalysesResultsDiscussionConclusionAcknowledgmentsReferen
ces
CLINICAL ISSUES
The effect of nurse–patient interaction on anxiety and
depression in
cognitively intact nursing home patients
Gørill Haugan, Siw T Innstrand and Unni K Moksnes
Aims and objectives. To test the effects of nurse–patient
interaction on anxiety and depression among cognitively intact
nursing home patients.
Background. Depression is considered the most frequent mental
disorder among the older population. Specifically, the
depression rate among nursing home patients is three to four
times higher than among community-dwelling older people,
and a large overlap of anxiety is found. Therefore, identifying
nursing strategies to prevent and decrease anxiety and depres-
sion is of great importance for nursing home patients’ well-
being. Nurse–patient interaction is described as a fundamental
resource for meaning in life, dignity and thriving among nursing
home patients.
Design. The study employed a cross-sectional design. The data
were collected in 2008 and 2009 in 44 different nursing
homes from 250 nursing home patients who met the inclusion
criteria.
Methods. A sample of 202 cognitively intact nursing home
patients responded to the Nurse–Patient Interaction Scale and
the Hospital Anxiety and Depression Scale. A structural
equation model of the hypothesised relationships was tested by
means of LISREL 8.8 (Scientific Software International Inc.,
Lincolnwood, IL, USA).
Results. The SEM model tested demonstrated significant direct
relationships and total effects of nurse–patient interaction on
depression and a mediated influence on anxiety.
Conclusion. Nurse–patient interaction influences depression, as
well as anxiety, mediated by depression. Hence, nurse–
patient interaction might be an important resource in relation to
patients’ mental health.
Relevance to clinical practice. Nurse–patient interaction is an
essential factor of quality of care, perceived by long-term nurs-
ing home patients. Facilitating nurses’ communicating and
interactive skills and competence might prevent and decrease
depression and anxiety among cognitively intact nursing home
patients.
Key words: anxiety, depression, nurse–patient interaction,
nursing home, structural equation model analysis
Accepted for publication: 11 September 2012
Introduction
With advances in medical technology and improvement in the
living standard globally, the life expectancy of people is
increasing worldwide. The document An Aging World (US
Census Bureau 2009) highlights a huge shift to an older popu-
lation and its consequences. Within this shift, the most rapidly
growing segment is people over 80 years old: by 2050, the per-
centage of those 80 and older would be 31%, up from 18% in
1988 (OECD 1988). These perspectives have given rise to the
notions of the ‘third’ (65–80 years old) and the ‘fourth age’
(over 80 years old) in the lifespan developmental literature
(Baltes & Smith 2003). These notions are also referred to as
the ‘young old’ and the ‘old old’ (Kirkevold 2010).
Authors: Gørill Haugan, PhD, RN, Associate Professor, Faculty
of
Nursing, Research Centre for Health Promotion and Resources,
Sør-Trøndelag University College, HIST, Trondheim; Siw T
Innstrand, PhD, Associate Professor, Research Centre for Health
Promotion and Resources Norwegian University of Science and
Technology, NTNU, Trondheim; Unni K Moksnes, PhD, RN,
Associate Professor, Faculty of Nursing, Research Centre for
Health Promotion and Resources, Sør-Trøndelag University
College, HIST, Trondheim, Norway
Correspondence: Gørill Haugan, Associate Professor, Research
Centre for Health Promotion and Resources, HIST/NTNU,
NTNU,
SVT/ISH, 7491 Trondheim, Norway. Telephone:
+47 73 55 29 27.E-mail: [email protected]
© 2013 Blackwell Publishing Ltd
2192 Journal of Clinical Nursing, 22, 2192–2205, doi:
10.1111/jocn.12072
For many of those in the fourth age, issues such as physi-
cal illness and approaching mortality decimates their func-
tioning and subsequently lead to the need for nursing home
(NH) care. A larger proportion of older people will live for
shorter or longer time in a NH at the end of life. This
group will increase in accordance with the growing popula-
tion older than 65, and in particular for individuals older
than 80 years. Currently, 1�4 million older adults in the
USA live in long-term care settings, and this number is
expected to almost double by 2050 (Zeller & Lamb 2011).
In Norway, life expectancy by 2050 is 90�2 years for men
and 93�4 years for women (Statistics of Norway 2010).
Depression is one of the most prevalent mental health
problems facing European citizens today (COM 2005);
and, the World Health Organization (WHO 2001) has esti-
mated that by 2020, depression is expected to be the high-
est ranking cause of disease in the developed world.
Moreover, depression is described to be one of the most
frequent mental disorders in the older population and is
particularly common among individuals living in long-term
care facilities (Choi et al. 2008, Karakaya et al. 2009,
Lattanzio et al. 2009, Drageset et al. 2011, Phillips et al.
2011). A linear increase in prevalence of depression with
increasing age is described (Stordal et al. 2003); the three
strongest explanatory factors on the age effect of depression
are impairment, diagnosis and somatic symptoms, respec-
tively (Stordal et al. 2001, 2003). Worse general medical
health is seen as the strongest factor associated with depres-
sion among NH patients (Djernes 2006, Barca et al. 2009).
A review that included 36 studies from various countries,
reported a prevalence rate for major depression ranging
from 6–26% and from 11–50% for minor depression.
However, the prevalence rate for depressive symptoms ran-
ged from 36–49% (Jongenelis et al. 2003). Twice as many
women are likely to be affected by depression than men
(Kohen 2006), and older people lacking social and emo-
tional support tend to be more depressed (Grav et al.
2012). A qualitative study on successful adjustment among
women in later life identified three main areas as being the
main obstacles for many; these were depression, maintain-
ing intimacy through friends and family and managing the
change process associated with older age (Traynor 2005).
Significantly more hopelessness, helplessness and depres-
sion are found among patients in NHs compared with those
living in the community (Ron 2004). Jongenelis et al.
(2004) found that depression was three to four times higher
in NH patients than in community-dwelling adults. Moving
to a NH results from numerous losses, illnesses, disabilities,
loss of functions and social relations, and approaching mor-
tality, all of which increases an individual’s vulnerability
and distress; in particular, loneliness and depression are iden-
tified as risks to the well-being of older people (Routasalo
et al. 2006, Savikko 2008, Drageset et al. 2012). The NH
life is institutionalised, representing loss of social relation-
ships, privacy, self-determination and connectedness.
Because NH patients are characterised by high age, frailty,
mortality, disability, powerlessness, dependency and vulner-
ability, they are more likely to become depressed. A recent
literature review showed several studies reporting prevalence
of depression in NHs ranging from 24–82% (Drageset et al.
2011). Also, with a persistence rate of more than 50% of
depressed patients still depressed after 6–12 months, the
course of major depression and significant depressive symp-
toms in NH patients tend to be chronic (Rozzini et al.
1996, Smalbrugge et al. 2006a).
Moreover, studies in NHs report a large co-occurrence of
depression and anxiety (Beekman et al. 2000, Kessler et al.
2003, Smalbrugge et al. 2005, Van der Weele et al. 2009,
Byrne & Pachana 2010). A recent review concerning anxi-
ety and depression reports a paucity of findings on anxiety
in older people (Byrne & Pachana 2010). Hence, more
research is urgently required into anxiety disorders in older
people, as these are highly prevalent and associated with
considerable disease burden (ibid.).
Depression and anxiety in NH patients are associated
with negative outcomes such as poor functioning in
activities of daily living and impaired quality of life (QoL)
(Smalbrugge et al. 2006b, Diefenbach et al. 2011, Drageset
et al. 2011), substantial caregiver burden and worsened
medical outcomes (Bell & Goss 2001, Koenig & Blazer
2004, Sherwood et al. 2005), increased risk of hospital
admission (Miu & Chan 2011), a risk of increased demen-
tia (Devanand et al. 1996) and a higher mortality rate
(Watson et al. 2003, Ahto et al. 2007). Accordingly, efforts
to prevent and decrease depression and anxiety are of great
importance for NH patients’ QoL.
Social support and relations to significant others are
found to be a vital resource for QoL and thriving among
NH patients (Bergland & Kirkevold 2005, 2006, Drageset
et al. 2009a, Tsai et al. 2010, Tsai & Tsai 2011), as well
as the nurse–patient relationship (Haugan Hovdenes 2002,
Cox & Bottoms 2004, Franklin et al. 2006, Medvene &
Lann-Wolcott 2010, Burack et al. 2012). The perspective
of promoting health and well-being is fundamental in nurs-
ing and a major nursing concern in long-term care (Nakrem
et al. 2011, Drageset et al. 2009b). However, low rates of
recognition of depression by staff nurses is found (Bagley
et al. 2000, Volkers et al. 2004).
Through the last decades, the importance of establishing
the nurse–patient relationship as an integral component of
© 2013 Blackwell Publishing Ltd
Journal of Clinical Nursing, 22, 2192–2205 2193
Clinical issues Nurse-patient-interaction, depression, and
anxiety
nursing practice has been well documented (Nåden &
Eriksson 2004, Arman 2007, Carpiac-Claver & Levy-
Storms 2007, Granados Gámez 2009, Rchaidia et al. 2009,
Fakhr-Movahedi et al. 2011). Excellent nursing care is
characterised by a holistic view with inherent human values
and moral; thus, excluding the patient as a unique human
being should be regarded as noncaring and amoral practice
(Haugan Hovdenes 2002, Nåden & Eriksson 2004, Aust-
gard 2008, Watson 2008). NH patients are in general
extremely vulnerable and hence the nurse–patient relation-
ship and the nurse–patient interaction are critical to their
experience of dignity, self-respect, sense of self-worth and
well-being (Dwyer et al. 2008, Harrefors et al. 2009,
Heliker 2009). NH patient receiving self-worth therapy
showed statistically significantly reduced depressive symp-
toms relative to control groups members 2 months after
receiving the intervention (Tsai et al. 2008). Self-worth
therapy comprised establishment of a therapeutic relation-
ship offering feedback and focusing the patient’s dignity,
emotional and mental well-being (ibid.).
Caring nurses engage in person-to-person relationships
with the NH patients as unique persons. Good nursing care
is defined by the nurses’ way of being present together with
the patient while performing nursing activities, in which
attitudes and competence are inseparately connected. ‘Pres-
ence’, ‘connectedness’ and ‘trust’ are described as funda-
mental cores of holistic nursing care (McGilton & Boscart
2007, Potter & Frisch 2007, Carter 2009) in the context of
the nurse–patient relationship in which the nurse–patient
interaction is taking place. Trust is seen as a confident
expectation that the nurses can be relied upon to act with
good will and to secure what is best for the individuals
residing in the NH. Hence, trust is the core moral ingredi-
ent in nurse–patient relationships; even more basic than
duties of beneficence, respect, veracity, and autonomy
(Carter 2009).
Caring is a context-specific interpersonal process that is
characterised by expert nursing practice, interpersonal sen-
sitivity, and intimate relationships (Finfgeld-Connett 2008)
which increases patient’s well-being (Nakrem et al. 2011,
Hollinger-Samson & Pearson 2000, Cowling et al. 2008,
Rchaidia et al. 2009, Reed 2009). The relationship between
NH staff attention and NH patients’ affect and activity par-
ticipation have been assessed among depressed NH
patients, showing that positive staff engagement was signifi-
cantly related to patients’ interest, activity participating,
and pleasure (Meeks & Looney 2011). These results suggest
that staff behaviour and engagement could be a reasonable
target for interventions to increase positive affect among
NH patients (ibid.).
In summary, the literature suggests depression as a com-
mon mental disorder among older people characterised by
high age, impairment, and somatic symptoms. In addition,
a large overlap of anxiety is reported. The patients’ sense
of loss of independency and privacy, feelings of isolation
and loneliness, and lack of meaningful activities are risk
factors for depression in NH patients. Nurse–patient inter-
action might be a resource for preventing and decreasing
depression among NH patients. To the authors’ knowl-
edge, previous research has not examined these relation-
ships in NHs by means of structural equation modelling
(SEM).
Aims
The main aim of this study was to investigate the relation-
ships between nurse–patient interaction, anxiety and
depression among cognitively intact NH patients by means
of SEM. Based on the theoretical and empirical knowledge
of depression, anxiety and nurse–patient interaction our
research question was: ‘Does the nurse–patient interaction
affect anxiety and depression in cognitively intact NH
patients?’ The following hypotheses were formulated:
� Hypothesis 1 (H1): nurse–patient interaction positively
affects anxiety.
� Hypothesis 2 (H2): nurse–patient interaction positively
affects depression.
� Hypothesis 3 (H3): depression negatively affects anxiety.
Methods
Design and ethical considerations
The study employed a cross-sectional design. The data was
collected in 2008 and 2009 in 44 different NHs from 250
NH patients who met the inclusion criteria: (1) local
authority’s decision of long-term NH care; (2) residential
time six months or longer; (3) informed consent compe-
tency recognised by responsible doctor and nurse; and (4)
capable of being interviewed. Two counties comprising in
total 48 municipalities in central Norway were selected,
from which 25 (at random) were invited to contribute in
this study. In total, 20 municipalities were partaken. Then,
all the NHs in each of the 20 municipalities was asked to
participate. A total of 44 NHs took part in the study. To
include as many participants from rural and central NHs,
respectively, the NHs was one by one invited to participate,
until the minimum of n = 200 was reached. The NH
patients were approached by a head nurse they knew
well. The nurse presented them with oral and written
© 2013 Blackwell Publishing Ltd
2194 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
information about their rights as participants and their
right to withdraw at any time. Each participant provided
informed consent. Because this population has problems
completing a questionnaire independently, three trained
researchers conducted one-on-one interviews in the patient’s
room in the actual NH. Researchers with identical profes-
sional background were selected (RN, MA, trained and
experienced in communication with older people, as well as
teaching gerontology at an advanced level) and trained to
conduct the interviews as identically as possible. Inter-rater
reliability was assessed by comparing mean scores between
interviewers by means of Bonferroni-corrected one-way
ANOVAs. No statistically significant differences were found
that were not accounted for by known differences between
the areas in which the interviewers operated.
The questionnaires relevant for the present study were part
of a questionnaire comprising 130 items. The interviews
lasted from 45–120 minutes due to the individual partici-
pant’s tempo, form of the day, and need for breaks. Inter-
viewers held a large-print copy of questions and possible
responses in front of the participants to avoid misunder-
standings. Approval by the Norwegian Social Science Data
Services was obtained for a licence to maintain a register
containing personal data (Ref. no. 16443) and likewise we
attained approval from The Regional Committee for
Medical and Health Research Ethics in Central Norway
(Ref. no. 4.2007.645) as well as the directory of the 44 NHs.
Participants
The total sample comprised 202 (80�8%) of 250 long-term
NH patients representing 44 NHs. Long-term NH care was
defined as 24-hour care; short-term care patients, rehabilita-
tions patients, and cognitively impaired patients were not
included. Participants’ age was 65–104, with a mean of
86 years (SD = 7�65). The sample comprised 146 women
(72�3%) and 56 men (27�7%), where the mean age was
87�3 years for women and 82 years for men. A total of 38
(19%) were married/cohabitating, 135 (67%) were widows/
widowers, 11 (5�5%) were divorced, and 18 (19%) were
single. Duration of time of NH residence when interviewed
was at mean 2�6 years for both sexes (range 0�5–13 years);
117 were in rural NHs, while 85 were in urban NHs. In
all, 26�1% showed mild to moderate depression, only one
woman scored >15 indicating severe depression, 70�4%
was not depressed, and nearly 88% had no anxiety disor-
der. Missing data was low in frequency and was handled
by means of the listwise procedure; for the nurse–patient
interaction 4�0% and for anxiety and depression 5�0% had
some missing data.
Measures
The Nurse–Patient Interaction Scale (NPIS) was developed
to identify important characteristics of NH patients’ experi-
ences of the nurse–patient interaction. The NPIS comprises
14 items identifying essential relational qualities stressed in
the nursing literature (Watson 1988, Martinsen 1993,
Eriksson 1995a,b, Nåden & Eriksson 2004, Nåden &
Sæteren 2006, Levy-Malmberg et al. 2008). Examples of
NPIS-items include ‘Having trust and confidence in the staff
nurses’; ‘The nurses take me seriously’, ‘Interaction with
nurses makes me feel good’ as well as experiences of being
respected and recognised as a person, being listened to and
feel included in decisions. The items were developed to
measure the NH patients’ ability to derive a sense of well-
being and meaningfulness through the nurse–patient inter-
action (Haugan Hovdenes 1998, 2002, Hollinger-Samson
& Pearson 2000, Finch 2006, Rchaidia et al. 2009). The
NPIS has shown good psychometric properties with good
content validity and reliability among NH patients;
(Haugan et al. 2012). The NPIS is a 10-points scale from 1
(not at all)–10 (very much); higher numbers indicating
better nurse–patient interaction (Appendix 1). Cronbach’s
Table 1 Means (M), standard deviations (SD), Cronbach’s
alpha, and correlation coefficients for the study variables
Construct M SD Cronbach’s alpha NPIS HADS-A HADS-D
NPIS (10 items) 8�19 1�73 0�92 –
HADS-A (5 items) 0�40 0�50 0�79 �0�114 –
HADS-D (5 items) 0�74 0�58 0�66 �0�294* 0�340* –
HADS (14 items) 2�85 0�34 0�78
*p < 0�01.
NPIS, Nurse–Patient Interaction Scale; HADS, Hospital Anxiety
and Depression Scale; HADS-A, Hospital Anxiety and
Depression Scale -
Anxiety; HADS-D, Hospital Anxiety and Depression Scale -
Depression.
© 2013 Blackwell Publishing Ltd
Journal of Clinical Nursing, 22, 2192–2205 2195
Clinical issues Nurse-patient-interaction, depression, and
anxiety
a = 0�92 (Table 1) and composite reliability = 0�92
(Table 2) of the NPIS construct was good.
Anxiety and depression were assessed by the Hospital
Anxiety and Depression Scale (HADS), comprising 14 items
(Appendix 2), with subscales for anxiety (HADS-A; seven
items) and depression (HADS-D seven items). Each item is
rated from 0–3, where higher scores indicate more anxiety
and depression. The maximum score is 21 on each subscale.
The ranges of scores for cases are as follows: 0–7 normal,
8–10 mild disorder, 11–14 moderate disorder, and 15–21
severe disorder (Snaith & Zigmond 1994). HADS has been
tested extensively and has well-established psychometric
properties (Herrmann 1997). To increase acceptability and
avoid individuals feeling as though they are being tested for
mental disorders, symptoms of severe psychopathology
have been excluded. This makes HADS more sensitive to
milder psychopathology (Stordal et al. 2003). HADS is
translated into Norwegian and found to be valid for older
people (Stordal et al. 2001, 2003).
Examples of sample-items are for depression: ‘I still enjoy
the things I used to enjoy’, ‘I can laugh and see the funny side
of things’, ‘I feel cheerful’, ‘I have lost interest in my appear-
ance’, and ‘I look forward with enjoyment to things’, and for
anxiety: ‘I feel tense and wound up’, ‘I get a sort of frightened
feeling as if something awful is about to happen’, ‘Worrying
thoughts go through my mind’, ‘I get a sort of frightened feel-
ing like ‘butterflies’ in the stomach’, and ‘I get sudden feeling
of panic’. The items were scored on a four-point scale ranging
from totally disagrees to totally agree. The internal consis-
tence of the anxiety and depression constructs (Table 1) was
satisfactory; a = 0�79 and a = 0�66, respectively. Composite
reliability (qc) displayed values between 0�70–0�92 (Table 2);
values >0�60 are desirable, whereas values >0·70 are good
(Diamantopolous & Siguaw 2008, Hair et al. 2010).
Statistical analysis
A structural equation model (SEM) of the hypothesised
relations between the latent constructs of depression and
self-transcendence was tested by means of LISREL 8.8 (Scien-
tific Software International Inc., Lincolnwood, IL, USA)
(Jøreskog & Sørbom 1995). Using SEM accounts for ran-
dom measurement error and the psychometric properties of
the scales in the model are more accurately derived. Since
the standard errors are estimated under non-normality, the
Satorra–Bentler scaled chi-square statistic was applied as a
goodness-of-fit statistic, which is the correct asymptotic
mean even under non-normality (Jøreskog et al. 2000). In
line with the rules of thumb of conventional cut-off criteria
(Schermelleh-Engel et al. 2003), the following fit indices
were used to evaluate model fit: chi-square (v2); a small v2
and a non-significant p-value corresponds to good fit
(Jøreskog & Sørbom 1995). Further we used the root mean
square error of approximation (RMSEA) and the standar-
dised root mean square residual (SRMS) with values below
0�05 indicating good fit, while values smaller than 0�08 are
interpreted as acceptable (Hu & Bentler 1998, Schermelleh-
Engel et al. 2003). The comparative fit index (CFI) and the
non-normed fit index (NNFI) with an acceptable fit at 0�95,
and good fit at 0�97 and above were used, and the normed
fit index (NFI) with an acceptable fit at 0�90, while a good
fit was set to 0�95 (ibid.).
Before examining the hypothesised relationships in the
SEM analysis, the measurement models were tested by con-
firmatory factor analysis (CFA). The CFA provided a good
fit to the observed data for the nurse–patient interaction
construct comprising ten items (v2 = 92�32, df = 77,
Table 2 Measurement models included in Model 1: nurse–
patient
interaction (NPIS) to anxiety (HADS-A) and depression
(HADS-D)
Items Parameter Lisrel estimate t-value R2
NPIS
NPIS1 kx1,1 0�63 6�04** 0�39
NPIS2 kx2,1 0�74 8�99** 0�55
NPIS3 kx3,1 0�74 10�41** 0�55
NPIS4 kx4,1 0�81 12�84** 0�65
NPIS5 kx5,1 0�66 6�16** 0�43
NPIS7 kx6,1 0�72 8�25** 0�51
NPIS9 kx7,1 0�77 14�39** 0�60
NPIS11 kx8,1 0�77 11�36** 0�59
NPIS12 kx9,1 0�69 8�18** 0�47
NPIS13 kx10,1 0�78 9�45** 0�61
HADS-A
HADS1 ky5,2 0�62 – 0�39
HADS3 ky7,2 0�73 7�04** 0�53
HADS5 ky11,2 0�62 4�65** 0�39
HADS9 ky13,2 0�69 5�60** 0�40
HADS13 kx14,2 0�66 6�00** 0�43
HADS-D
HADS2 ky1,1 0�74 – 0�54
HADS4 ky2,1 0�67 7�43** 0�45
HADS6 ky3,1 0�65 5�86** 0�42
HADS10 ky5,1 0�20 2�33* 0�04
HADS12 ky6,1 0�51 4�94** 0�26
qc NPIS 10 items qc 0�92 – –
qc HADS-A 5 items qc 0�80 – –
qc HADS-D 5 items qc 0�70 – –
Standardised factor loadings and t-values. Squared multiple
correla-
tions (R2).
†Composite reliability, qc ¼
P
kð Þ2
P
kð Þ2þP hð Þ
� � (Hair et al. 2010).
*p < 0�05; **p < 0�01.
HADS, Hospital Anxiety and Depression Scale; NPIS, Nurse–
Patient Interaction Scale.
© 2013 Blackwell Publishing Ltd
2196 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
p < 0�0110, RMSEA = 0�032, SRMR = 0�045, NFI = 0�97,
NNFI = 0�99, CFI = 1�00) and the two-factor construct
(HADS) of anxiety and depression comprising 10 items
(v2 = 54�22, df = 34, p < 0�015, RMSEA = 0�056,
SRMR = 0�071, NFI = 0�93, NNFI = 0�96, CFI = 0�97).
All parameter estimates were significant (p < 0�05) and
loaded positively and clearly on their intended latent vari-
able with standardised factor loadings between 0�20–0�81.
For scaling, the first factor loadings of each dependent
latent variable were set to 1.
Results
Descriptive analysis
Table 1 displays the means (M), standard deviations (SD),
Cronbach’s a and Pearson’s correlation matrix for the con-
structs of nurse–patient interaction, anxiety and depression.
The correlations between the measures were in the expected
direction. Moderate correlations were found between the
latent constructs included in the SEM model (Table 1). The
a-levels for the various measures indicate an acceptable
level of inter-item consistency in the measures (Nunally &
Bernstein 1994) with Cronbach’s a coefficients of 0�66 or
higher.
Structural equation modelling (SEM)
To investigate how the nurse–patient interaction related to
anxiety and depression, model-1 was estimated. Figure 1
shows Model-1 with its measurement and structural
models, while Table 2 displays the factor loadings, R2 and
t-values. All estimates were significant (p < 0�05) and the
factor loadings ranged between 0�51–0�81 (except from
item HADS10 ‘I have lost interest in my appearance’ with
factor loading = 0�20 and R2 = 0�04) and R2 values
between 0�26–0�65. Model-1 fit well with the data:
v2 = 211�44, p = 0�011, df = 167, RMSEA = 0�037, p-
value = 0�92, NFI = 0�94, NNFI = 0�99, CFI = 0�99, and
SRMR = 0�060.
Table 3 shows the standardised regression coefficients of
the directional relationships and the total and indirect
effects between the latent constructs in Model-1. As
hypothesised, the directional paths from nurse–patient
interaction to depression displayed a significant negative
relationship (c1,1 = �0�37). The path between nurse–
patient interaction and anxiety was not significant
(c1,2 = �0�09); however, a significant path from depression
to anxiety (b1,2 = 0�55) was found, indicating a mediated
effect (by depression) on anxiety (Table 3).
A scrutiny of the total effects of nurse–patient interaction
revealed statistical significant total effects on depression
(�0�37), as well as a significant total effect on anxiety from
depression (0�55). Also, a significant indirect (mediated)
effect from nurse–patient interaction on anxiety (�0�20)
was displayed (Table 3).
Discussion
The aim of this study was to explore the associations
between nurse–patient interaction, anxiety, and depression
in cognitively intact NH patients. By doing so we sought to
contribute to a holistic nursing perspective of promoting
well-being in NH patients in three ways:
1 This study supplies empirical knowledge to the growing
body of nurse–patient interaction knowledge by exploring
Figure 1 SEM Model-1. Measurement models
and directional relationships for nurse–patient
interaction (NPIS) to anxiety (HADS-A) and
depression (HADS-D).
© 2013 Blackwell Publishing Ltd
Journal of Clinical Nursing, 22, 2192–2205 2197
Clinical issues Nurse-patient-interaction, depression, and
anxiety
cognitively intact NH patient’s’ experiences of the nurse–
patient interaction.
2 This study provides empirical insight to the associations
between, nurse–patient interaction, anxiety, and depres-
sion in a NH population, and
3 By means of advanced statistical analysis such as struc-
tural equation modelling (SEM), the result from this
study suggests a guideline for clinical nursing strategies
promoting well-being and decreasing depression and anx-
iety in NH patients. Finding ways to improve NH staff
nurses’ way of being present, connecting, and interacting
with the patients might be beneficial in this matter.
More specifically, three hypotheses were tested, from
which two were supported (H1, H3). We found that the
hypothesised relationship between nurse–patient interaction
and depression was fully supported; good nurse–patient
interaction was negatively associated with depression; the
better nurse–patient interaction the less depression. The
path from nurse–patient interaction to anxiety was not sig-
nificant; however, a significant relation between depression
and anxiety was found. Accordingly, also an indirect effect
of nurse–patient interaction on anxiety was displayed, med-
iated by the influence on depression. Hence, the model
tested indicates that nurse–patient interaction influences
both depression and anxiety. These findings are consistent
with previous research demonstrating significantly decrease
in depression for NH patients receiving self-worth therapy
and positive attention from NH staff (Tsai et al. 2008,
Meeks & Looney 2011).
Consequently, nursing approaches facilitating NH
patient’s confidence and trust in the staff nurses might pre-
vent and decrease depression in NH patients. In accordance
with former research, trust is a core moral ingredient in
helping relationships (Carter 2009). Therefore, facilitating
patients’ confidence that the staff nurses make all possible
effort to relieve ones’ plagues appear to be crucial for pre-
serving dignity (Cochinov 2002) and prevent depression.
Professional nursing care is determined by the way nurses
are using their knowledge, attitudes, behaviour and com-
munication skills to appreciate the uniqueness of the person
being cared for (Warelow et al. 2008). Accordingly, nurse–
patient interaction fostering experiences of being respected
and recognised as a person, being listened to and taken seri-
ously are positively associated with lower depression scores
among NH patients.
Previous research underlines that the nurse–patient
relationships and the nurse–patient interaction are critical
to patients’ sense of dignity, self-respect, feelings of self-
worth, meaning in life, and well-being (Haugan Hovdenes
2002, Dwyer et al. 2008, Harrefors et al. 2009, Heliker
2009). Moreover, dignity in NH patients has been differen-
tiated into intrapersonal dignity and relational dignity,
socially constructed by the act of recognition (Pleschberger
2007). Thus, nurse–patient interaction facilitating patients’
sense of being taken seriously and recognised as a unique
person might provide a sense of dignity, self-worth, and
thereby prevent and decrease depression among NH
patients.
Consequently, taking time for interestingly listening to
the NH patient appears as vital for preventing and decreas-
ing depression. Former research has pointed to continuity
of care provider to be critical for developing relationships
with patients’ overtime (McGilton 2002). Moreover, mutu-
ality in individuals’ relationships confirming women’s exis-
tence and value has been described as a major influence on
depression in women, whereas depressive symptoms results
Table 3 Structural equation modelling analysis: Model-1,
standar-
dised gamma, total and indirect effects of nurse–patient
interaction
on nursing home patients’ anxiety and depression
Construct Parameter Lisrel estimate t-value
NPIS to HADS-A c 1,1 �0�09 �0�84
NPIS to HADS-D c 1,2 �0�37 �4�58**
HADS-D to HADS-A b 1,2 0�55 4�05**
NPIS t-value
Total effects of nurse–patient interaction on anxiety and
depression
HADS-D
HADS2 �0�23 �4�58**
HADS4 �0�21 �4�32**
HADS6 �0�18 �3�62**
HADS10 �0�08 �2�03*
HADS12 �0�18 �3�48**
HADS-A
HADS1 �0�05 �1�30
HADS3 �0�06 �1�34
HADS5 �0�05 �1�27
HADS9 �0�05 �1�25
HADS13 �0�05 �1�36
Indirect effect of nurse–patient interaction on anxiety and
depression
Anxiety �0�20 �3�11**
Depression – –
*Significant at the 5% level.
**Significant at the 1% level.
Model 1: comprising six HADS-variables and 10 NPIS-
variables.
Standardised gamma: standardised regression coefficients
represent-
ing directional relationships between NPIS, anxiety, and
depression.
Total effects: represents the total influence of the explanatory
variable on anxiety and depression.
Indirect effects: mediated influence.
HADS, Hospital Anxiety and Depression Scale; NPIS, Nurse–
Patient Interaction Scale.
© 2013 Blackwell Publishing Ltd
2198 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
from violating their sense of worthiness (Hedelin & Jonsson
2003). Consequently, nurses must be aware that their atti-
tude, appearance and behaviour are interpreted as a confir-
mation of the patient’s worthiness or worthlessness (ibid.).
In a recent study investigating the concept of receiving care,
one main theme was identified; ‘being of value despite any
potential disadvantages’ (Lundgren & Berg 2011). NH
patients are particularly vulnerable and dependent, thus
there are not many choices available. Receiving care high-
lights the human mode of being, which includes experiences
of being exposed resulting in an increased sense of vulnera-
bility; in turn, this motivates a seeking for valued and
appreciated mutual interactions within a caring process
(ibid.). Thus, taking time, ensuring continuity, and being
educated in interactional skills are not enough to enhance
well-being, a sense of meaning in life, and decrease depres-
sion; the care provider must be engaged in some way, such
as learning about the person through life histories (McGil-
ton & Boscart 2007, Walent 2008, Heliker 2009, Heliker
& Hoang Thanh 2010, Medvene & Lann-Wolcott 2010,
Wright 2010). The NH patient needs to feel understood,
acknowledged, confirmed, and valued, all of which provides
a sense of meaning in life, self-worth, and alleviates suffer-
ing (Haugan Hovdenes 2002, Medvene & Lann-Wolcott
2010).
Nursing homes are unique social environments; tradition-
ally, they offer limited privacy opportunities. Accordingly
NH patients may have infrequent contact with friends and
family members. Thus, NH staff nurses are particularly the
most important providers of social reinforcement (Haugan
Hovdenes 2002, Drageset et al. 2012). However, research
illustrates that NH staff rarely engages in social interac-
tions during mealtimes and does not appreciate this as an
important part of their duties (Pearson & Fitzgerald 2003),
as well as hardly responds to patients’ social engagement,
and seldom displays engagement-supportive behaviour
(Stabell et al. 2004, Meeks & Looney 2011). Caregiving
relationships involve all kinds of social interaction during the
course of which the patient’s sense of self-worth can either
be enhanced or thwarted (Haugan Hovdenes 2002, Hedelin
& Jonsson 2003, Halldorsdottir 2008). The nurse–patient
relationship has been designated as a sense of spiritual con-
nection which is experienced as a bond of energy (Hall-
dorsdottir 2008); a life-giving nurse–patient interaction
which is greatly empowering for the patient. By confirma-
tion, recognising, and empowering the older individuals’
views of who they are and would like to be, NH staff
nurses can positively influence NH patients well-being
(Randers et al. 2002, Tsai et al. 2008, Haugan et al.
2012), thriving (Bergland & Kirkevold 2005), and
consequently depression (Haugan & Innstrand 2012) and
anxiety.
Strengths and limitations
A notable strength of this research is the empirical exami-
nation of associations that have not been tested previously.
This study expands previous studies by testing the asso-
ciations between nurse–patient interaction, anxiety, and
depression among NH patients by using structural equa-
tion modelling. Using SEM accounts for random measure-
ment error and the psychometric properties of the scales
in the model are more accurately derived. The study builds
on a strong theoretical foundation with use of question-
naires demonstrating good psychometrical properties. Nev-
ertheless, the findings of this study must be discussed with
some limitations in mind.
First, Model-1 comprises 20 variables, indicating a desir-
able n = 200, while in the present study, n = 191. Informa-
tion input to the SEM estimation increases both with more
indicators per latent variable, as well as with more sample
observations (Westland 2010). The latent variables in the
model are measured by five and ten indicators that
strengthen the reliability. In this respect, the sample size in
the present study is suitable. Nevertheless, a larger sample
would significantly increase statistical power of the tests.
The present sample included fewer men than women,
reflecting the gender composition among the population of
that age in NHs.
Second, the cross-sectional design does not allow us to
determine conclusion regarding causality. A longitudinal
design would have strengthened the study by allowing
changes to be assessed and compared over time.
A third limitation concerns the use of self-reported data,
which implies a certain risk that the findings are based on
common-method variance (Podsakoff et al. 2003).
The fact that the researchers visited the participants to
help fill in the questionnaires might have introduced some
bias into the respondents’ reporting. The questionnaires
were part of a battery of questionnaires comprising 130
items. Thus, frail, older NH patients might tire when com-
pleting the questionnaires; this represents a possible bias to
their reporting. To avoid such a bias, experienced research-
ers were carefully selected and trained in conducting the
interviews following a standardised procedure including
taking small breaks on specific points during the process.
This procedure worked out very well; in just three cases the
interview had to be completed the next day due to respon-
dent’s fatigue. Actually, most participants were even more
vigorous after completing the interview.
© 2013 Blackwell Publishing Ltd
Journal of Clinical Nursing, 22, 2192–2205 2199
Clinical issues Nurse-patient-interaction, depression, and
anxiety
Relevance to clinical nursing
According to the European Commission’s Green Paper on
mental health (COM 2005), depression is one of the most
prevalent mental health problems facing European citizens
today. Taking into account the highly chronic nature of these
psychological states, we consider our findings noteworthy in
their suggestion that nurse–patient interaction might be an
important resource in relation to NH patients’ mental health.
Knowledge of how nurse–patient interaction, anxiety and
depression relate to each other in this respect is important for
researchers, nurses, nursing educators and clinicians.
This study demonstrates that nurse–patient interaction
influences depression as well as anxiety mediated by depres-
sion. Accordingly, facilitating nurse–patient interaction to
provide patients’ sense of worthiness, meaning in life, self-
acceptance and adjustment to the life situation and one’s
disabilities would promote integrity and well-being and pre-
vent despair and depression.
Due to a combination of factors such as patients’ commu-
nication impairment, clinicians’ focus on treating medical
conditions, normalisation of depression in later life and a
lack of training in mental health among staff in NHs, depres-
sion can easily go undetected among the NH population
(Bagley et al. 2000, Martin et al. 2007). Therefore, facilitat-
ing nurse–patient interaction and the staff nurses’ awareness
in assessing patients’ mood and connectedness resources
appear to be crucial. Offering connectedness might be a cen-
tral aspect of NH care (Lundman et al. 2010); enhancing
inner strength by acceptance of the self, death and one’s life
situation might prevent and decrease depression among NH
patients (Haugan & Innstrand 2012).
The interpersonal relationship in nurse–patient interac-
tions has been found to be an essential factor of quality of
care, as perceived by long-term care patients (Haugan Hovd-
enes 2002, Bergland & Kirkevold 2006, Brown Wilson &
Davies 2009). Nurse–patient interaction can enhance both
intrapersonal and interpersonal self-transcendence (Haugan
et al. 2012) and help NH patients preserve their dignity,
identity and integrity (Coughlan & Ward 2007, Tsai et al.
2008, Burack et al. 2012). By means of listening to the
patients, communicating and treating the patients with
respect, by using empathic understanding, and acknowledg-
ing him/her as a person who is to be taken seriously and
attended to, staff nurses might positively influence depres-
sion, anxiety, and well-being (Hollinger-Samson & Pearson
2000, Haugan Hovdenes 2002, Asmuth 2004, Finch 2006,
Jonas-Simpson et al. 2006, Haugan et al. 2012).
Therefore, NH staff nurses should be given more time
available interacting with their patients. A philosophical
shift from care and protection of the body to a person-cen-
tred care would be beneficial (Medvene & Lann-Wolcott
2010, Wright 2010, Jones 2011). In addition, some factors
seem crucial regarding quality of nurse–patient interaction;
in general, staffing levels are low while staff turnover is
high (Baughman & Smith 2010). Further, staff members
are generally poorly trained in nurse–patient interaction
providing well-being, and often they perceive a lack of
autonomy in job performance, feeling that they are not
respected for management (Castle & Engberg 2007, Caspar
& O’Rourke 2008, Bishop et al. 2009). To become a car-
ing caregiver, one must first be treated in a caring way
(Sikma 2006, Tellis-Nayak 2007). Hence, moving from the
traditional institutional model to a responsive, patient-cen-
tred homelike approach might have benefits for both NH
patients and staff (Jones 2010). Educational nursing curric-
ula should underline and facilitate nurse–patient interaction
in order of advancing staff nurses’ presence to assess,
prevent and decrease depression and anxiety among NH
patients. Also, essential is for nurses to develop confidence
in using the therapeutic tools available to create the best
mental health outcomes for the older person.
Acknowledgements
The authors wish to acknowledge the Sør-Trøndelag
University College, Faculty of Nursing, Trondheim, Nor-
way, for supporting this study, as well as the older patients
who voluntarily participated in the study.
Contributions
Study design: GH; data collection and analysis: GH and
manuscript preparation: GH, STI, UKM.
Conflict of interest
No conflict of interest has been declared by the authors.
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Appendix 1. Measurement instrument: Nurse–Patient Interaction
Scale (NPIS) means score and
standard deviation
NPIS item
Total
n = 199
Men
n = 56
Women
n = 143
Mean SD Mean SD Mean SD
NPIS1 Having confidence and trust in the nurses 8�75 1�948
8�84 1�886 8�71 1�977
NPIS2 The nurses take me seriously 8�44 2�185 8�43 2�214
8�44 2�181
NPIS3 Interaction with the nurses makes me feel good 8�02
2�252 8�21 2�230 7�94 2�264
NPIS4 The nurses understand me 7�65 2�419 7�71 2�341
7�63 2�457
NPIS5 The nurses make all possible effort to relieve my plagues
8�80 1�969 9�02 1�753 8�71 2�047
NPIS6 The nurses involve me in decisions regarding my daily
life 6�96 2�984 6�45 2�923 7�16 2�993
NPIS7 The nurses treat me with respect 8�68 2�175 8�70
2�097 8�68 2�213
NPIS8 The nurses ask me how I am 7�70 2�959 7�82 2�924
7�66 2�981
NPIS9 The nurses are listening interestingly to me 7�47 2�743
7�74 2�474 7�37 2�840
NPIS10 I often get hurt or sad from how the nurses interact*
8�29 2�510 8�15 2�825 8�35 2�386
NPIS11 Interaction with the nurses contributes to meaning in
my life 7�83 2�531 8�04 2�449 7�75 2�567
NPIS12 The nurses pay attention to me as a person 8�07 2�266
8�13 2�281 8�05 2�268
NPIS13 I am satisfied with the communication with the nurses
8�32 2�126 8�54 2�097 8�23 2�138
NPIS14 Interaction with nurses is the most important to my
thriving 8�86 2�066 9�09 1�890 8�78 2�131
NPIS: total means score 8�13 1�630 8�21 1�654 8�11 1�626
The NPIS is based on a 10-point scale ranging from 1 (not at
all) to 10 (very much).
The concept of nurses involves all staff caregivers.
*The item NPIS10 is reversed.
© 2013 Blackwell Publishing Ltd
2204 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
Appendix 2. Measurement instrument: Hospital Anxiety and
Depression Scale (HADS) means score
and standard deviation
HADS item
Total
n = 198
Men
n = 56
Women
n = 142
Mean SD Mean SD Mean SD
HADS1 I feel tense and wound up 0�460 0�680 0�339 0�581
0�507 0�712
HADS2 I still enjoy the things I used to enjoy 0�879 0�822
0�893 0�779 0�873 0�841
HADS3 I get a sort of frightened feeling as if something awful
is about to happen 0�434 0�709 0�400 0�655 0�447 0�731
HADS4 I can laugh and see the funny side of things 0�621
0�845 0�589 0�826 0�634 0�855
HADS5 Worrying thoughts go through my mind 0�394 0�710
0�339 0�640 0�416 0�737
HADS6 I feel cheerful 0�533 0�759 0�582 0�686 0�514
0�788
HADS7 I can sit at ease and feel relaxed 0�459 0�689 0�611
0�738 0�401 0�664
HADS8 I feel as I’m slowed down 1�209 1�075 1�226 1�050
1�203 1�088
HADS9 I get a sort of frightened feeling like ‘butterflies’ in the
stomach 0�369 0�613 0�339 0�581 0�380 0�627
HADS10 I have lost interest in my appearance 0�803 1�079
0�946 1�119 0�747 1�061
HADS11 I feel restless as if have to be on the move 0�523
0�793 0�636 0�825 0�479 0�778
HADS12 I look forward with enjoyment to things 0�864 0�943
0�857 0�999 0�866 0�924
HADS13 I get sudden feeling of panic 0�347 0�658 0�357
0�699 0�343 0�643
HADS14 I can enjoy a good book or TV programme 0�561
0�857 0�464 0�713 0�599 0�907
HADS anxiety sum score 3�03 2�903 3�03 2�903 2�99
3�383
HADS depression sum score 5�57 3�238 5�57 3�238 5�42
3�879
HADS: total means score 8�59 5�079 8�59 5�079 8�40
6�078
The STS is based on a 4-point scale ranging from 0 (not at all),
1 (very little), 2 (somewhat) to 3 (very much).
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Journal of Clinical Nursing, 22, 2192–2205 2205
Clinical issues Nurse-patient-interaction, depression, and
anxiety
J O U R N A L O F T R A U M A N U R S I N G
WWW.JOURNALOFTRAUMANURSING.COM 17
R E S E A R C H
ABSTRACT
A retrospective study examined in-hospital antidepressant
medication (ADM) use in adult trauma patients with an
intensive care unit stay of 5 or more days. One fourth of
patients received an ADM, with only 33% of those patients
having a documented history of depression. Of patients
who received their first ADM from a trauma or critical care
physician, only 5% were discharged with a documented
plan for psychiatric follow-up. The study identified a need for
standardized identification and management of depressive
symptoms among trauma patients in the inpatient setting.
Key Words
antidepressant medication , critical care , depression ,
injury ,
psychiatry , trauma
Author Affiliations: UnityPoint Health, Des Moines, Iowa
(Ms Spilman
and Drs Smith and Tonui); and Fort Sanders Regional Medical
Center,
Knoxville, Tennessee (Dr Schirmer).
The abstract was presented at 47th Annual Society for
Epidemiological
Research (SER) Meeting, Seattle, Washington, June 24–27,
2014.
None of the authors have any conflicts of interest to disclose.
Correspondence: Sarah K. Spilman, MA, Trauma Services,
Iowa Methodist
Medical Center, 1200 Pleasant St, Des Moines, IA 50309 (
[email protected]
unitypoint.org ).
Evaluation and Treatment of Depression in Adult
Trauma Patients
Sarah K. Spilman , MA ■ Hayden L. Smith ,
PhD ■ Lori L. Schirmer , PharmD ■ Peter M.
Tonui , MD
approaches require resources and training of hospital
personnel. 5 Regardless of the method, however, assess-
ment of depression is often confounded by the variable
nature of depressive symptoms. Some depressive symp-
toms (eg, fatigue, insomnia, weight loss) can be similar
to symptoms of other medical illnesses or may resemble
temporary conditions, such as delirium or adjustment dis-
order. 6 , 7 In addition, trauma patients in the intensive care
unit (ICU) may often lack the ability to display or report
classic depressive symptoms due to the effects of medica-
tion, pain, or sleep deprivation. 8 , 9
A major issue, though, is that many hospitals do not
routinely screen for depression or assess depressive
symptoms during hospitalization. To our knowledge,
there is no consensus as to when assessments (and re-
assessments) are appropriate. Symptoms of depression
most often are noted through subjective observation by
family or nurses and reported to physicians. Because of
limited resources, mental health experts are often only
involved in the most severe or complicated cases. This is
a fundamental problem in that large numbers of patients
may be overlooked because of the subjective nature and
timing of these observations. Findley and colleagues 4
found that when a psychiatrist was actively involved in
the trauma service, identification and treatment of psy-
chopathology were increased by 78%. While the rate of
mood and anxiety disorders recognized by trauma phy-
sicians remained unchanged, involvement of psychiatry
resulted in a broader range of psychiatric diagnoses and
more than doubled the treatment of substance abuse or
dependence.
Complicating matters further, many trauma patients
present with preexisting depression. Traumatic injury is
related to depression as both a causal factor and a result-
ing condition. 2 , 4 , 10 If patients are unable to self-report
their
health history, the trauma team relies on family report
or pharmacy records. This presents challenges in timely
reinitiation of medications.
STUDY RATIONALE
A review of the medical literature found no relevant
published research on physician and medical team re-
sponse to depressive symptoms during the patient’s ini-
tial hospitalization within settings where mental health
screening is not the standard of care. Current research DOI:
10.1097/JTN.0000000000000102
I
t is well-established in the literature that critically ill
trauma patients can often suffer from depression and
posttraumatic stress disorder in the months and years
following hospitalization. 1-3 Many hospitals may not
have a standardized process for assessing and treat-
ing trauma patients with depressive symptoms. 3-5 During
the acute phase of recovery, the trauma team is primarily
in charge of treating the injuries and preparing to dis-
charge the patient to the next phase of recovery. With-
out a standardized process for recognizing, screening,
and treating the psychological and emotional needs of
the patient, there may be increased risk that depression
will go unrecognized and untreated or misinterpreted and
improperly treated.
Formal assessment of depression can be accom-
plished through clinical interview or screening tools; both
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that examines depression screening has been primarily
funded by grant dollars, which provide hospitals with
resources to staff special assessment teams (eg, Dicker
et al 2 ) and may not represent practices at many hospi-
tals. These studies have established the importance of
early detection of depression, although this may be ex-
tremely difficult in hospitals that do not have protocols
for managing depression in the critically ill or special
teams for assessing, treating, and reassessing mental
health symptoms.
The purpose of this study was to examine how a trau-
ma team recognizes and treats depression in the absence
of a screening tool and to document antidepressant medi-
cation (ADM) usage and prescribing patterns. Study data
can assist in the evaluation and understanding of institu-
tion processes and possibly help design protocols to miti-
gate some of the long-term mental health issues that can
result from traumatic injury.
METHODS
Study Design and Patient Sample
A retrospective study was performed at an urban tertiary
hospital in the Midwestern region of the United States.
The hospital’s trauma registry was used to identify adult
patients (aged 18 years or older) who met trauma criteria
during the 5-year study period of 2008 to 2012. A trauma
patient was defined as an individual who sustained a
traumatic injury with an International Classification of
Diseases, 9th Revision, Clinical Modification code rang-
ing from 800 and 959.9, excluding codes for late effects
of injury (905-909.9), superficial injuries (910-924.9), and
foreign bodies (930-939.9). Patients were included in the
study if they were admitted to the hospital and stayed in
the ICU for 5 or more days. The study was approved by
the hospital’s institutional review board.
Study Data
Study variables were grouped into 3 categories: patient
and injury characteristics, depression diagnoses, and
ADM use. Patient characteristics included gender, race,
age, hospital length of stay (LOS), ICU LOS, and mechani-
cal ventilator days. Discharge status was coded as alive or
deceased, while discharge location was coded as home
or institutional setting (including hospice facility, rehabili-
tation facility, skilled nursing facility, federal hospital, or
intermediate care facility).
Injury characteristics included the Injury Severity Score,
which is an anatomical coding system ranging from 0 (no
injury) to 75 (most severe). Finally, mechanism of injury
was recorded on the basis of the External Causes of In-
jury and Poisoning Code (E-Code): Vehicle accident (810-
848), Accidental Fall (880-888), or Other.
Depression diagnoses were assessed retrospectively
through chart review. Patients were classified as having
a documented history of depression if it was specifically
noted in the medical history or if the patient was taking
an ADM at the time of hospital admission. If the patient’s
history was not obtained at admission, the patient was
considered to be on a prior ADM if he or she received
a dose within the first 72 hours of the hospital stay. We
also noted if a patient received a psychiatric consultation
during their stay and if the patient was discharged with
a plan for psychiatric follow-up. The latter was used to
indicate whether or not discharge instructions included
directions for psychiatry follow-up.
The ADM use was ascertained through pharmacy dis-
pensing records. Specifically, it was recorded if a patient
received any of the following drugs: selective seroto-
nin reuptake inhibitors (SSRIs; citalopram, escitalopram,
fluoxetine, fluvoxamine, paroxetine, sertraline); selective
norepinephrine reuptake inhibitors (SNRIs; desvenlafax-
ine, duloxetine, venlafaxine); dopamine reuptake inhibi-
tors (bupropion); and alpha-2 antagonists (mirtazapine).
Some ADMs were excluded from the study, including
tricyclics and monoamine oxidase inhibitors, which can
be used to treat other diagnoses in addition to depres-
sion; vilazodone, which was not approved by the Food &
Drug Administration until 2011; trazodone because it can
be prescribed as a sleep aid; and milnacipran because its
Food & Drug Administration indication is for fibromyalgia.
The first dispensed ADM was used for basic descrip-
tive purposes. For example, if a patient received multiple
ADMs during the stay, only the first ADM was used to
describe patient treatment. If an ADM was not a medica-
tion taken prior to admission, it is hereafter referred to as
a new ADM. Days between hospital admission and first
ADM dose were used to calculate time of initiation. If an
ADM medication was listed in the discharge summary or
the patient received a dose of the medication on the last
day of the stay, then the patient was classified as being
discharged on an ADM.
Statistical Procedures
Descriptive statistics were reported for continuous data as
medians with interquartile ranges; normality was tested
using the Shapiro-Wilk test. Categorical data were re-
ported as counts with percentages. Comparative statistics
were conducted by stratifying patients based on whether
or not they received an ADM during their hospital stay.
These groups were examined using the Fisher exact test
(nominal variables) and the independent samples Mann-
Whitney U Test (2-group medians). All analyses were
2-tailed and based on a 0.05 significance level. Analy-
ses were performed with IBM SPSS Basic Statistics for
Windows, version 19.0 (IBM Corp, 2010; Armonk, NY).
Copyright © 2015 Society of Trauma Nurses. Unauthorized
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Trauma Patients Admitted During
Study Period (N=4947)
ICU Length of Stay < 5 Days
(n=4635)
ICU Length of Stay > 5 Days
(n=312)
Received No Antidepressant
Medication (n=230)
Restarted Antidepressant
Medication (n=55)
Received New Antidepressant
Medication (n=27)
Figure 1. Trauma patients admitted to the hospital during the
study period, 2008-2012. ICU indicates intensive care unit.
RESULTS
There were 4947 trauma patients admitted to the hospital
during the 5-year study period, with 312 (6.3%) staying in
the ICU for 5 or more days (see Figure 1 ). Patient char-
acteristics are presented in Table 1 . More than two-thirds
of the patients in the study sample were male, and the
majority of patients were white. Fifteen percent of the
patients died.
There were 82 patients (26.3%) who received an ADM
during the hospital stay (see Table 2 ). Bivariate analy-
ses (not shown) revealed significant differences in age,
with older patients more likely to receive an ADM than
younger patients ( P = .002). Men were less likely to re-
ceive an ADM. There were no significant bivariate differ-
ences between patients based on hospital LOS, ICU LOS,
ventilator days, Injury Severity Score, discharge location,
or injury mechanism.
Patients who received an ADM during the hospital stay
were significantly more likely to have a documented his-
tory of depression upon admission to the hospital. Specif-
ically, 67.1% of patients who received an ADM during the
hospital stay were taking an ADM prior to admission and
19.5% had depression mentioned in their medical history.
Patients who received an ADM were also significantly
more likely to receive a psychiatric consultation during
the hospital stay and were more likely to be discharged
with a plan for psychiatric follow-up.
Of the 82 trauma patients who received an ADM dur-
ing hospitalization, 9 (11.0%) were initiated by a psychia-
trist and 73 (89.0%) were initiated by a critical care or
other nonpsychiatric physician (see Table 3 ). One-third
of patients who received an ADM during their stay were
prescribed a new ADM; 29.6% of these new prescriptions
were initiated by psychiatry and 70.3% were initiated by
a nonpsychiatric physician. There were no significant
differences in ADM choice based on the physician who
initiated the medication.
Patients whose ADM was prescribed by a psychiatrist
received their first dose many days later in the hospital
stay than those patients whose ADM was prescribed by
a critical care or other physician. Patients whose ADM
was prescribed by psychiatry were also more likely to be
discharged with a plan for psychiatric follow-up. Nearly
all patients who received an ADM during hospitalization
were discharged with the medication, regardless of the
provider who initiated it.
DISCUSSION
Study data revealed that 26.3% of trauma patients spend-
ing 5 of more days in the ICU received an ADM during
the hospital stay; 33% of these patients did not have a
documented history of depression or ADM use upon ad-
mission. This is considerably higher than ADM use in the
general population, which is estimated at 10% to 11%. 11 , 12
Female trauma patients were more likely to receive an
ADM than male patients, which is consistent with trends
in the general population. 12
Trauma or critical care physicians were the practition-
ers most likely to continue home ADMs and initiate new
ADMs, compared with psychiatry physicians. However,
TABLE 1 Descriptive Characteristics for
Trauma Patients With Intensive Care
Unit Length of Stay 5 or More Days,
2008-2012 (N = 312) a
All Trauma Patients
(N = 312)
Male 218 (70.1%)
White 271 (86.9%)
Median age, y 55.00 (39.75-69.00)
Median hospital length of stay, d 17 (10-25)
Median intensive care unit length
of stay, d
8.5 (6-14)
Median ventilator days 5 (1.5-10)
Deceased 48 (15.4%)
Discharged to home 68 (25.8%)
Median injury severity score 25 (15.5-33.25)
Injury mechanism
Vehicle accident 174 (55.8%)
Fall 105 (33.7%)
Other 31 (9.9%)
a Data are presented in medians (interquartiles) and counts
(percentages).
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20 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 |
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leads to oversights in posttrauma care. Primary care
physicians or other health care providers may not be
alerted to the task of titrating the medication, monitor-
ing adherence to the medication, and/or evaluating the
appropriateness of the ADM in the months and years
after the trauma.
when the ADM was initiated by trauma or critical care
physicians, patients were frequently discharged without
a plan for psychiatric follow-up. This may be a critical
omission, especially for patients started on new ADMs.
Since the trauma or critical care physician manages care
during the acute phase of the injury, this potentially
TABLE 2 Prescription of Antidepressant Medication in
Trauma Patients (N = 312) a
ADM Received During
Stay (n = 82)
ADM Not Received During
Stay (n = 230)
Fisher Exact
Test, P b
Documented history of depression 56 (68.3%) 17 (7.4%) < .001
Taking ADM prior to admission 55 (67.1%) 7 (3.0%) < .001
Depression mentioned in medical history 16 (19.5%) 13 (5.7%)
< .001
Received psychiatric consultation visit
during stay
17 (20.7%) 18 (7.8%) .004
Discharged with plan for psychiatric
follow-up c
8 (12.1%) 6 (3.0%) .009
Abbreviation: ADM, antidepressant medication.
a Data are presented in medians (interquartiles) and counts
(percentages).
b p values are presented for comparisons between patients
based on whether or not they received an ADM during their
hospital stay.
c Excludes patients who expired.
TABLE 3 Prescribing Patterns for Patients Who Received an
Antidepressant Medication,
2008-2012 (n = 82) a
First Dose Prescribed
by Psychiatry (n = 9)
First ADM Prescribed by
Other Physician (n = 73) P b
Taking ADM prior to admission 1 (11.1%) 54 (74.0%) < .001
Median days between hospital
admission and first dose
12 (7.25-19.75) 2.5 (2-7) .010
New ADM during hospitalization 8 (88.9%) 19 (26.0%) < .001
Escitalopram 5 (62.5%) 6 (31.6%) .206
Citalopram 2 (25.0%) 7 (36.8%) .676
Sertraline 1 (12.5%) 2 (10.5%) 1.00
Paroxetine … 2 (10.5%) …
Mirtazapine … 1 (5.3%) …
Venlafaxine … 1 (5.3%) …
Discharged with plan for
follow-up c
4 (44.4%) 4 (5.5%) 0.005
Discharged with prescription for
ADM c
9 (100.0%) 62 (93.9%) 1.00
Abbreviation: ADM, antidepressant medication.
a Data are presented in medians (interquartiles) and counts
(percentages).
b p values are presented for comparisons between patients
based on whether the first dose of an ADM was authorized by a
psychiatrist or another
physician.
c Excludes patients who expired.
Copyright © 2015 Society of Trauma Nurses. Unauthorized
reproduction of this article is prohibited.
JTN-D-14-00071.indd 20JTN-D-14-00071.indd 20 06/01/15
8:45 PM06/01/15 8:45 PM
J O U R N A L O F T R A U M A N U R S I N G
WWW.JOURNALOFTRAUMANURSING.COM 21
The study explores ADM use in the trauma and acute
care setting. While ADM use is not a direct measure of de-
pression, it serves as a proxy for how trauma and critical
care teams recognize and respond to depression in the
absence of screening tools. Many hospitals lack a stand-
ardized process for assessing and treating trauma patients
with depressive symptoms; therefore, we expect that
these results are generalizable to other facilities where
mental health screening is not standard of care. When
a patient is taking an ADM prior to hospitalization, the
trauma team must ensure that the medications are restart-
ed when the patient is hemodynamically stable. But the
situation is less clear in prescribing new ADMs. Initiation
of a new ADM may be done when the physician, nurse,
or family members recognize emerging symptoms of de-
pression or as a preventative approach for symptoms that
are likely to emerge in the future.
We recognize that pharmacological intervention
should not be the first-line treatment to manage depres-
sion. However, since our hospital does not have a stand-
ardized screening tool for depression and does not have
a mental health care professional embedded in the core
trauma team, we believe that these findings are similar to
patterns at other hospitals. It is presumed that patients are
started on ADMs based on feedback from nursing and
family members or recognition of depressive symptoms
during the recovery process, but further analysis is war-
ranted to determine how these decisions are made. Some
patients may be placed on an ADM without warrant, and
conversely, depressed patients may go untreated. Both
scenarios may complicate recovery and lead to adverse
psychological and physical outcomes following the trau-
matic injury.
It deserves mention that it would be ideal for all trau-
ma patients to be screened for depression and mental
health issues. This could potentially improve their recov-
ery process and reduce the likelihood of traumatic injury
in the future, 2 but such an approach may not be practical
in many settings. It is unclear how often patients would
need evaluation to detect change, and how well they can
self-report their symptoms in the first place. Future re-
search efforts should be directed at prospective evaluation
of increased involvement of psychiatrists in the treatment
of trauma patients, as well as increased use of screening
tools for early detection of depressive symptoms. Because
our trauma population is primarily older adults, additional
analyses should focus on the mental health needs of this
aging population and the role that preexisting depression
plays in their injury patterns and recoveries.
Limitations
This study had several limitations. Identification of the
prevalence of depression and ADM use was performed
retrospectively. Reliance on medical documentation to
Acknowledgments
The authors thank Catherine Hackett Renner, James
Rasmussen, and Eric Barlow for assistance in data collec-
tion, analysis, and interpretation.
REFERENCES
1. Bryant RA , O’Donnell ML , Creamer M ,
McFarlane AC , Clark
CR , Silove D . The psychiatric sequelae of traumatic
injury . Am J
Psychiatry . 2010 ; 167 : 312-320 .
2. Dicker RA , Mah J , Lopez D , et al.
Screening for mental illness
in a trauma center: rooting out a risk factor for unintentional
injury . J Trauma . 2011 ; 70 : 1337-1344 .
3. O’Donnell ML , Creamer M , Bryant RA ,
Schnyder U , Shalev
A . Posttraumatic disorders following injury: an empirical
and methodological review . Clin Psych Rev . 2003 ; 23 :
587-603 .
4. Findley JK , Sanders KB , Groves JE .
The role of psychiatry in the
management of acute trauma surgery patients . J Clin
Psychiatry .
2003 ; 5 : 195-200 .
5. Steel JL , Dunlavy AC , Stillman J ,
Paper HC . Measuring depression
and PTSD after trauma: common scales and checklists . Injury .
2011 : 42 : 288-300 .
6. Casey P , Bailey S . Adjustment disorders:
the state of the art .
World Psychiatry . 2011 ; 10 : 11-18 .
7. Jackson JC , Mitchell N , Hopkins RO .
Cognitive functioning,
mental health, and quality of life in ICU survivors: an overview
.
Crit Care Clin . 2009 ; 25 : 615-628 .
determine history of depression may be inaccurate in in-
stances for patients with an undocumented history of de-
pression or patients who received depression diagnoses
based on inadequate clinical assessments. Other mental
health diagnoses may have been present, such as anxi-
ety or adjustment disorder, but they too may have been
misdiagnosed, underdiagnosed, or misdocumented. Re-
latedly, information was lacking from the medical record
if the patient or a family member was unable to provide
a medical history upon admission to the emergency de-
partment. Given the study design, it was not possible to
access compliance with home medications. In particular,
an ADM in a patient’s medical history may not neces-
sarily depict whether the patient was actively taking the
medication prior to admission. Finally, the main focus of
the study was to examine how physicians assess and treat
depression in trauma patients admitted to the ICU for 5
or more days, which does not allow for generalizations
toward general trauma populations.
CONCLUSIONS
Despite difficulties in the diagnosis of depression in
trauma patients, critical care physicians and psychiatrists
do initiate ADMs in patients who exhibit symptoms of
clinical depression. This study identifies a need to more
accurately identify depressive symptoms among trauma
patients and reveals a need for protocols to assess for
mental health diagnoses and manage ADM use among
trauma patients in the inpatient setting and postdis-
charge.
Copyright © 2015 Society of Trauma Nurses. Unauthorized
reproduction of this article is prohibited.
JTN-D-14-00071.indd 21JTN-D-14-00071.indd 21 06/01/15
8:45 PM06/01/15 8:45 PM
22 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 |
Number 1 | January-February 2015
8. Beliles K , Stoudemire A .
Psychopharmacologic treatment
of depression in the medically ill . Psychosomatics . 1998 : 39
:
S2S19 .
9. Jackson JC , Hart RP , Gordon SM ,
Hopkins RO , Girard TD , Ely
EW . Post-traumatic stress disorder and post-traumatic stress
symptoms following critical illness in medical intensive care
unit patients: assessing the magnitude of the problem . Crit
Care. 2007 ; 11 : R27 .
10. O’Donnell ML , Creamer M , Pattison P
. Posttraumatic stress
disorder and depression following trauma: understanding
comorbidity . Am J Psychiatry . 2004 ; 161 : 1390-1396 .
11. Olfson M , Marcus SC . National patterns in
antidepressant
medication treatment . Arch Gen Psychiatry. 2009 ; 66 : 848-
856 .
12. Pratt LA , Brody DJ , Gu Q .
Antidepressant use in persons
aged 12 and over: United States, 2005-2008 . NCHS Data Brief
.
October 2011 : 76 .
Copyright © 2015 Society of Trauma Nurses. Unauthorized
reproduction of this article is prohibited.
JTN-D-14-00071.indd 22JTN-D-14-00071.indd 22 06/01/15
8:45 PM06/01/15 8:45 PM
Copyright of Journal of Trauma Nursing is the property of
Society of Trauma Nurses and its
content may not be copied or emailed to multiple sites or posted
to a listserv without the
copyright holder's express written permission. However, users
may print, download, or email
articles for individual use.
Data Companion for a VFD Induction Motor Drive
Procedure
Step
12
22 RMS value of the fundamental-frequency component in the
line voltage at the output of the three-phase PWM inverter
without filtering: 177 V
24
36
51

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Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor D.docx

  • 1. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 11 Figure 9. Voltage waveform at the output of a PWM inverter with and without overmodulation (after filtering). The Procedure is divided into the following sections: -frequency component in the voltage (unfiltered) at the output of a three-phase PWM inverter the frequency on the induction motor speed and magnetizing current Operation at motor nominal frequency. Operation at 4/3 the motor nominal frequency. Operation at 1/2 the motor nominal frequency.
  • 2. High voltages are present in this laboratory exercise. Do not make or modify any banana jack connections with the power on unless otherwise specified. Setup and connections In this part of the exercise, you will set up and connect the equipment. 1. Refer to the Equipment Utilization Chart in Appendix A to obtain the list of equipment required to perform the exercise. Install the equipment in the Workstation. Mechanically couple the Four-Pole Squirrel Cage Induction Motor to the Four-Quadrant Dynamometer/Power Supply using a timing belt. PROCEDURE OUTLINE PROCEDURE Voltage waveform without overmodulation Voltage waveform with overmodulation Sine wave modulating the duty cycle (with overmodulation) /2 duty cycle = 100% /2
  • 3. duty cycle = 0% Time V o lta g e a t th e o u tp u t o f a t h re e -p
  • 4. h a se P W M in ve rt e r Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 12 © Festo Didactic 86368-00 2. Make sure that the ac and dc power switches on the Power Supply are set to the O (off) position, then connect the Power Supply to a three- phase ac power outlet. Make sure that the main power switch on the Four-Quadrant Dynamometer/ Power Supply is set to the O (off) position, then connect its Power Input to an ac power outlet.
  • 5. 3. Connect the Power Input of the Data Acquisition and Control Interface to a 24 V ac power supply. Connect the Low Power Input of the Chopper/Inverter to the Power Input of the Data Acquisition and Control Interface. Turn the 24 V ac power supply on. 4. Connect the USB port of the Data Acquisition and Control Interface to a USB port of the host computer. Connect the USB port of the Four-Quadrant Dynamometer/Power Supply to a USB port of the host computer. 5. Turn the Four-Quadrant Dynamometer/Power Supply on, then set the Operating Mode switch to Dynamometer. 6. Turn the host computer on, then start the LVDAC-EMS software. In the LVDAC-EMS Start-Up window, make sure that the Data Acquisition and Control Interface and the Four-Quadrant Dynamometer/Power Supply are detected. Make sure that the Computer-Based Instrumentation and Chopper/Inverter Control functions for the Data Acquisition and Control Interface are available. Select the network voltage and frequency that
  • 6. correspond to the voltage and frequency of your local ac power network, then click the OK button to close the LVDAC-EMS Start-Up window. 7. Connect the Digital Outputs of the Data Acquisition and Control Interface (DACI) to the Switching Control Inputs of the Chopper/Inverter using a DB9 connector cable. On the Chopper/Inverter, set the Dumping switch to the O (off) position. The Dumping switch is used to prevent overvoltage on the dc bus of the Chopper/Inverter. It is not required in this exercise. 8. Set up the circuit shown in Figure 10. Use the diodes in the Rectifier and Filtering Capacitors to implement the three-phase full-wave rectifier. Use the Chopper/Inverter to implement the Three-phase inverter. Connect the Thermistor Output of the Four-Pole Squirrel Cage Induction Motor to the Thermistor Input of the Four-Quadrant Dynamometer/Power Supply. Notice that the prefix IGBT has been left out in this manual when referring to the IGBT Chopper/Inverter module.
  • 7. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 13 Figure 10. Three-phase, variable-frequency induction-motor drive (with three-phase filter). Measurement of the rms value of the fundamental-frequency component in the voltage (unfiltered) at the output of a three-phase PWM inverter In this part of the exercise, you will use the Harmonic Analyzer in LVDAC-EMS to measure the rms value of the fundamental-frequency component in the unfiltered voltage at the output of a three-phase PWM inverter. You will also observe that when the load connected to the three-phase PWM inverter output is inductive (like a motor), the current waveform at the output of the three- phase PWM inverter remains sinusoidal even with no filter. a The Four-Quadrant Dynamometer/Power Supply is not used in this part of the exercise although it is mechanically coupled to the Four-Pole Squirrel Cage Induction Motor. 9. In LVDAC-EMS, open the Chopper/Inverter Control window, then make the following settings:
  • 8. -Phase PWM inverter. -2- 3). nominal frequency indicated on the Four-Pole Squirrel Cage Induction Motor front panel. Three-phase filter Three-phase inverter Three-phase rectifier Three- phase induction machine Switching control signals from DACI Brake N
  • 9. 2 mH 2 mH 2 mH 5 F 5 F 5 F 5 F Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 14 © Festo Didactic 86368-00 the modulation index m, i.e., it sets the amplitude of the sine-wave signal that modulates the duty cycle of the switching control signals. When the Peak Voltage parameter is set to 100%, the amplitude of the modulating signal is set to obtain a peak output voltage corresponding to 100% of half the dc bus voltage (100% of /2). In other words, this sets the value of the modulation index to 1 (m = 1). Pulse- Width Modulation. -Phase PWM Inverter. 10. On the Power Supply, turn the three-phase ac power source
  • 10. on. The motor should start to rotate. 11. In LVDAC-EMS, open the Oscilloscope. Use channel 1 to display the dc bus voltage (input E1), channels 2 and 3 to display the line voltage at the output of the three-phase PWM inverter before and after filtering (inputs E2 and E3, respectively), and channel 4 to display the current flowing in the motor stator windings (input I1). Select the Continuous Refresh mode, set the time base to display only one complete cycle of the voltage and current waveforms (this provides more precision for observing the trains of rectangular bipolar pulses produced by the three-phase PWM inverter), and set the trigger controls so that the Oscilloscope triggers when the waveform of the motor stator current passes through 0 A with a positive slope. Select convenient vertical scale and position settings to facilitate observation of the waveforms. 12. Print or save the waveforms displayed on the Oscilloscope screen for future reference. It is suggested that you include these waveforms in your lab report. 13. Describe the waveform of the voltage at the output of the
  • 11. three-phase PWM inverter before and after filtering. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 15 14. Measure and record the rms value of the line voltage at the output of the three-phase PWM inverter before and after filtering. RMS value of the line voltage at the output of the three-phase PWM inverter before filtering: V RMS value of the line voltage at the output of the three-phase PWM inverter after filtering: V 15. Are the rms values of the line voltage at the output of the three-phase PWM inverter measured before and after filtering using the Oscilloscope equal? Explain briefly. 16. Describe the waveform of the current flowing in each stator winding of the three-phase motor (input I1).
  • 12. 17. In LVDAC-EMS, open the Harmonic Analyzer window, then make the following settings: nominal frequency indicated on the Four-Pole Squirrel Cage Induction Motor front panel. r is set to 40. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 16 © Festo Didactic 86368-00 18. Measure and record the rms value of the fundamental- frequency component (1f) in the line voltage at the output of the three- phase
  • 13. PWM inverter after filtering (input E3), indicated in the Harmonic Analyzer display. RMS value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter after filtering: V a Compare the rms value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter after filtering measured using the Harmonic Analyzer with the rms value of the line voltage measured previously using the Oscilloscope. The values should be identical because the voltage waveform is sinusoidal. 19. In the Harmonic Analyzer window, set the Input parameter to E2 to measure the line voltage at the output of the three-phase PWM inverter before filtering. Measure and record the rms value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter before filtering (input E2), indicated in the Harmonic Analyzer display. RMS value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter before filtering: V a The rms value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter
  • 14. before filtering should be slightly higher than that after filtering because of the voltage drop across the three- phase filter. 20. Do your measurements confirm that the rms value of the fundamental- frequency component in the line voltage at the outputs of the three-phase PWM inverter before filtering can be measured using the Harmonic Analyzer? Explain briefly. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 17 21. In the Chopper/Inverter Control window, stop the Three- Phase PWM Inverter. On the Power Supply, turn the three-phase ac power source off. Remove the three-phase filter from the circuit and disconnect
  • 15. voltage input E3. The circuit should be as shown in Figure 11. Figure 11. Three-phase, variable-frequency induction-motor drive (without three-phase filter). 22. In the Chopper/Inverter Control window, start the Three- Phase PWM Inverter. On the Power Supply, turn the three-phase ac power source on. Measure and record the rms value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter indicated in the Harmonic Analyzer display. RMS value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter: V a Compare the rms value of the fundamental-frequency component in the line voltage measured without filter to that measured in step 19 when the filter was in the circuit. Three-phase inverter Three-phase full-wave rectifier Three-
  • 16. phase induction machine Switching control signals from DACI Brake Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 18 © Festo Didactic 86368-00 23. On the Oscilloscope, use channel 1 to display the dc bus voltage (input E1), channel 2 to display the voltage at the output of the three-phase PWM inverter (input E2), and channel 3 to display the current flowing in one of the motor stator windings (input I1). Select convenient vertical scale and position settings to facilitate observation of the waveforms. 24. Print or save the waveforms displayed on the Oscilloscope screen for future reference. It is suggested that you include these waveforms in your lab report. 25. Explain why the waveform of the motor stator current displayed on the
  • 17. Oscilloscope screen is sinusoidal even if the filter is removed. Overmodulation In this part of the exercise, you will increase the rms value of the line voltage at the output of a three-phase PWM inverter by increasing the amplitude of the sine wave that modulates the duty cycle of the switching transistors of the PWM inverter to a level that causes overmodulation. a For the remaining of this exercise, the rms value of the line voltage at the output of the three-phase PWM inverter always refers to the rms value of the fundamental-frequency component in the line voltage (i.e., the rms value of the 1f component measured with the Harmonic Analyzer). 26. Compare the rms value of the line voltage at the output of the three-phase PWM inverter measured in step 22 with the nominal line voltage of the Four-Pole Squirrel Cage Induction Motor (indicated on its front panel). Does the maximum line voltage which can be obtained at the outputs of the three- phase PWM inverter without overmodulation (i.e., with modulation index set to 1) allow the induction motor to be operated at its nominal voltage?
  • 18. 27. In order to increase the voltage at the output of the three- phase PWM inverter (i.e., the voltage applied to the motor windings), set the Peak Voltage parameter in the Chopper/Inverter Control window to 117% of the dc bus voltage/2 (this corresponds to the maximum Peak Voltage value available in the Chopper/Inverter Control window). This setting increases the amplitude of the sine-wave signal that modulates the duty cycles of the switching control signals to a level that causes overmodulation. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 19 28. Using the Harmonic Analyzer, measure and record the rms value of the line voltage at the output of the three-phase PWM inverter obtained when overmodulation is used. RMS value of the line voltage at the output of the three-phase PWM inverter (with overmodulation): V 29. Is the value measured in the previous step close to the nominal line voltage of the Four-Pole Squirrel Cage Induction Motor?
  • 19. 30. Print or save the waveforms displayed on the Oscilloscope screen for future reference. It is suggested that you include these waveforms in your lab report. 31. Compare the voltage waveforms at the output of the three- phase PWM inverter obtained with and without overmodulation (i.e., obtained in step 30 and step 24, respectively). Do you observe that the voltage waveform obtained with overmodulation contains less rectangular pulses, indicating that overmodulation occurs? 32. Do your observations confirm that overmodulation allows the rms value of the maximum voltage at the output of a three-phase PWM inverter to be increased? 33. In the Chopper/Inverter Control window, stop the Three- Phase PWM Inverter. Effect of the frequency on the induction motor speed and magnetizing current In this part of the exercise, you will use the same circuit as in
  • 20. the previous section (see Figure 11) to observe the effect of the frequency on the speed and magnetizing current of the three-phase induction motor. 34. In LVDAC-EMS, open the Four-Quadrant Dynamometer/Power Supply window, then make the following settings: Positive Constant-Torque Prime Mover/Brake. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 20 © Festo Didactic 86368-00 This allows the torque command of the prime mover/brake to be controlled manually. ensuring that the Four-Pole Squirrel Cage Induction Motor operates with no mechanical load. The first and second numbers in this parameter specify the number of teeth on the pulley of the Four-Quadrant Dynamometer/Power Supply and the number
  • 21. of teeth on the pulley of the machine under test, respectively. The pulley ratio between the Four-Quadrant Dynamometer/Power Supply and the Four-Pole Squirrel Cage Induction Motor is 24:24. is required to match the characteristics of the thermistor in the Four- Pole Squirrel Cage Induction Motor with the Thermistor Input in the Four-Quadrant Dynamometer/Power Supply. -Torque Primer Mover/Brake by setting the Status parameter to Started or by clicking the Start/Stop button. de of the meters by clicking on the corresponding button. Operation at motor nominal frequency 35. In the Chopper/Inverter Control window, make the following settings: -Phase PWM inverter. he Switching Frequency parameter to 2000 Hz. -2- 3).
  • 22. nominal frequency indicated on the Four-Pole Squirrel Cage Induction Motor front panel. Pulse- Width Modulation. -Phase PWM Inverter. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor dure © Festo Didactic 86368-00 21 36. The induction motor should rotate at a speed slightly below the synchronous speed of the three-phase induction motor. Record the motor speed of rotation indicated by the Speed meter in the Four-Quadrant Dynamometer/Power Supply window in Table 2. a The synchronous speed of the Four-Pole Squirrel Cage Induction Motor is 1500 r/min at a local ac power network frequency of 50 Hz and 1800 r/min at a local ac power network frequency of 60 Hz. Table 2. Effect of the frequency on the induction motor speed and magnetizing current.
  • 23. Frequency Motor speed of rotation (r/min) DC bus voltage (V) RMS value of the motor magnetizing current (A) Amplitude of the motor magnetizing current (peak value) (A) Nominal frequency 4/3 the nominal frequency 1/2 the nominal frequency 37. On the Oscilloscope, use channel 1 to display the dc bus
  • 24. voltage (input E1), and channel 2 to display the motor stator current (input I1). Select convenient vertical scale and position settings to facilitate observation of the waveforms. 38. Measure and record the dc bus voltage as well as the rms value and amplitude of the motor magnetizing current in Table 2. Operation at 4/3 the motor nominal frequency 39. In the Chopper/Inverter Control window, gradually increase the frequency of the three-phase PWM inverter up to about 4/3 the motor nominal frequency while observing the motor speed and magnetizing current. Measure and record the motor speed, the dc bus voltage, as well as the rms value and amplitude of the motor magnetizing current in Table 2. 40. Gradually decrease the frequency of the three-phase PWM inverter to the motor nominal frequency. The stator current meas- ured when an induction motor operates with no mechanical load is virtually equal to the motor magnet- izing current.
  • 25. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 22 © Festo Didactic 86368-00 41. Compare the motor speed measured at 4/3 the nominal frequency to that measured at the motor nominal frequency. Does the motor speed increase in direct proportion with the increase in frequency? 42. Compare the motor magnetizing current measured at 4/3 the motor nominal frequency to that measured at the motor nominal frequency. How does the motor magnetizing current vary when the frequency is increased? Explain why. 43. How does the motor magnetizing current variation affect the maximum magnetic flux density in the motor? Operation at 1/2 the motor nominal frequency In the next steps, the current in the motor stator windings will exceed the nominal value.
  • 26. Perform your manipulations rapidly to prevent motor overheating. Also, make sure that the motor peak current never exceeds four times the nominal current shown on the Four-Pole Squirrel Cage Induction Motor front panel or the motor temperature never exceeds 80°C (176°F). 44. Gradually decrease the frequency of the three-phase PWM inverter down to about 1/2 the motor nominal frequency while observing the motor speed and magnetizing current. Measure and record the motor speed, the dc bus voltage, as well as the rms value and amplitude of the motor magnetizing current in Table 2. 45. In the Chopper/Inverter Control window, stop the Three- Phase PWM Inverter. On the Power Supply, turn the three-phase ac power source off. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 23 46. Compare the motor speed measured at 1/2 the motor nominal frequency to that measured at the motor nominal frequency. Does the motor speed
  • 27. decrease in direct proportion with the decrease in frequency? 47. How does the motor magnetizing current vary when the frequency is decreased? Explain why. 48. Compare the magnetizing current measured at 1/2 the motor nominal frequency to that measured at the motor nominal frequency as well as to the motor full-load current rating indicated on the Four-Pole Squirrel Cage Induction Motor front panel. What are the consequences of decreasing the frequency to 1/2 the motor nominal frequency while keeping the voltage constant? Saturation curves In this part of the exercise, you will plot the saturation curves of the three-phase induction motor at the motor nominal frequency, 2/3 the nominal frequency,
  • 28. 1/2 the nominal frequency, and 1/3 the nominal frequency. In the next steps, the current in the motor stator windings will exceed the nominal value. Perform your manipulations rapidly to prevent motor overheating. Also, make sure that the motor peak current never exceeds four times the nominal current shown on the Four-Pole Squirrel Cage Induction Motor front panel or the motor temperature never exceeds 80°C (176°F). 49. Remove the timing belt that mechanically couples the Four- Pole Squirrel Cage Induction Motor to the Four-Quadrant Dynamometer/Power Supply. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 24 © Festo Didactic 86368-00 50. In the Chopper/Inverter Control window, make the following settings. -Phase PWM inverter. Hz. Phase Sequence parameter is set to Fwd (1-2- 3).
  • 29. frequency indicated on the Four-Pole Squirrel Cage Induction Motor front panel. e-Phase PWM Inverter. 51. On the Power Supply, turn the three-phase ac power source on. For each value of the Peak Voltage parameter shown in Table 3, measure the rms value of the line voltage applied to the motor stator windings using the Harmonic Analyzer and the peak value of the current flowing in the motor stator windings (peak magnetizing current) using the Oscilloscope (use the horizontal cursors to make the measurement). To obtain optimal results, begin your measurements with the highest Peak Voltage setting as shown in Table 3. To reduce the data acquisition time and to prevent the motor from overheating, it is strongly recommended to use the Data Table in LVDAC-EMS to record the circuit parameters measured. Table 3. RMS value of the motor stator winding voltage (at the fundamental frequency) and peak magnetizing current of the three-phase induction motor at various frequencies.
  • 30. Peak voltage (% of dc bus/2) (%) / / / Motor line voltage (V) Peak magnetizing current (A) Motor line voltage (V) Peak magnetizing current (A) Motor line voltage (V)
  • 32. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 25 52. Repeat the previous step at the following three other frequencies: 2 3⁄ , 1/2 , and 1/3 . As mentioned earlier, make sure that the motor peak current never exceeds four times the nominal current indicated on the Four-Pole Squirrel Cage Induction Motor front panel or the temperature never exceeds 80°C (176°F), particularly at 1/2 and 1/3 the nominal motor operating frequency. a Use the 40 A terminal of input I1 on the DACI and set the Range of input I1 to High in the Data Acquisition and Control Settings window of LVDAC-EMS as soon as the motor stator current reaches 4 A. Temporarily turn the ac power source off before making these changes. 53. Plot on the same graph the saturation curves (rms value of the motor stator winding voltage as a function of the peak magnetizing current) of the Four- Pole Squirrel Cage Induction Motor at each of the frequencies in Table 3. a You will probably not observe any saturation at nominal frequency because the voltage applied to the motor windings cannot be increased sufficiently.
  • 33. 54. Do the saturation curves you plotted in the previous step show that, when the motor stator winding voltage is increased, the peak magnetizing current increases in similar proportions until the voltage reaches a value at which saturation in the stator core of the induction motor starts to occur, causing the peak magnetizing current to increase much more rapidly than the motor stator winding voltage? 55. Do your observations confirm that increasing the frequency allows higher voltages to be applied to the motor stator windings before saturation starts to occur? 56. Close LVDAC-EMS, turn off all equipment, and remove all leads and cables. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor 26 © Festo Didactic 86368-00 In this exercise, you learned that a three-phase, variable- frequency induction- motor drive consists of a power diode, three-phase full-wave
  • 34. rectifier and a three- phase PWM inverter. You learned that varying the operating frequency of the PWM inverter varies the frequency of the ac voltage applied to the induction motor, and thus, the motor speed. You also learned that varying the modulation index of the PWM inverter allows the voltage applied to the motor stator windings to be adjusted. You were introduced to the use of overmodulation in the PWM inverter to increase the maximum voltage that can be applied to the motor stator windings. You saw that for a given frequency, the maximum flux density ( .) in the stator of the motor is directly proportional to the rms value of voltage applied to the motor stator windings. You also saw that when the voltage increases and reaches a certain value, saturation occurs in the stator causing the motor magnetizing current to start increasing at a rate which largely exceeds that of the motor voltage. You learned that for a given voltage, the maximum flux density is inversely proportional to the frequency of the ac voltage applied to the motor windings, and consequently, the magnetizing current of the motor decreases when the frequency increases and vice versa. You were introduced to the use of a harmonic analyzer to measure the rms value of the fundamental- frequency component of a non-sinusoidal signal (e.g., the unfiltered voltage at the output of a three-phase PWM inverter).
  • 35. 1. How can the ac voltage at the output of a three-phase PWM inverter be varied? 2. How does the magnetizing current vary when saturation starts to occur in the stator of an induction motor? 3. What should be done for an induction motor to be able to produce the highest possible torque? 4. How do the maximum flux density ( .) and peak magnetizing current of an induction motor vary when the PWM inverter frequency decreases and the voltage at the PWM inverter output (motor stator voltage) remains constant? CONCLUSION REVIEW QUESTIONS
  • 36. Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor © Festo Didactic 86368-00 27 5. Explain why the rms value of the fundamental-frequency component in the voltage (unfiltered) at the output of a three-phase PWM inverter cannot be measured using a conventional voltmeter. Rates and Predictors of Postpartum Depression by Race and Ethnicity: Results from the 2004 to 2007 New York City PRAMS Survey (Pregnancy Risk Assessment Monitoring System) Cindy H. Liu • Ed Tronick Published online: 25 October 2012 � Springer Science+Business Media New York 2012 Abstract The objective of this study was to examine racial/ethnic disparities in the diagnosis of postpartum depression (PPD) by: (1) identifying predictors that account
  • 37. for prevalence rate differences across groups, and (2) com- paring the strength of predictors across groups. 3,732 White, African American, Hispanic, and Asian/Pacific Islander women from the New York City area completed the Preg- nancy Risk Assessment Monitoring System from 2004 to 2007, a population-based survey that assessed sociodemo- graphic risk factors, maternal stressors, psycho-education provided regarding depression, and prenatal and postpartum depression diagnoses. Sociodemographic and maternal stressors accounted for increased rates in PPD among Blacks and Hispanics compared to Whites, whereas Asian/Pacific Islander women were still 3.2 times more likely to receive a diagnosis after controlling for these variables. Asian/Pacific Islanders were more likely to receive a diagnosis after their providers talked to them about depressed mood, but were less likely than other groups to have had this conversation. Pre- natal depression diagnoses increased the likelihood for PPD diagnoses for women across groups. Gestational diabetes
  • 38. decreased the likelihood for a PPD diagnosis for African Americans; a trend was observed in the association between having given birth to a female infant and increased rates of PPD diagnosis for Asian/Pacific Islanders and Whites. The risk factors that account for prevalence rate differences in postpartum diagnoses depend on the race/ethnic groups being compared. Prenatal depression is confirmed to be a major predictor for postpartum depression diagnosis for all groups studied; however, the associations between other postpartum depression risk factors and diagnosis vary by race/ethnic group. Keywords Postpartum depression � Health status disparities � Asian Americans � Prenatal depression � Gestational diabetes Introduction Postpartum depression (PPD) is a serious health concern affecting approximately 13 % of all women [1]. At least 19.2 % of women experience depression within 12 months after giving birth [2]. The associations between prenatal
  • 39. depression and PPD depression are well documented [3–5]. Psychosocial factors including high stress, low social sup- port, and low marital satisfaction are also predictors [4, 5]. Surprisingly little is known about the extent to which postpartum depression varies by race and ethnicity, given the effects of culture on the experiences and manifestations of depression [6, 7]. This dearth of information on postpartum depression in ethnic minorities is well recognized. In a published review of maternal depression, the Agency for Healthcare Research and Quality found ‘‘screening instru- ments [to be] poorly representative of the U.S. population,’’ and that ‘‘populations [from studies] were overwhelmingly Caucasian’’ [8]. A review by O’Hara found that meta-anal- yses on postpartum depression had omitted race and eth- nicity as risk factors for postpartum depression [4]. Research studies on postpartum depression that have included ethnic minorities generally compare African C. H. Liu (&)
  • 40. Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA 02115, USA e-mail: [email protected] E. Tronick Child Development Unit, University of Massachusetts, 100 Morrissey Blvd, Boston, MA 02125, USA e-mail: [email protected] 123 Matern Child Health J (2013) 17:1599–1610 DOI 10.1007/s10995-012-1171-z Americans and Hispanics with Whites. In these studies, group differences in prevalence rates have shown to be inconsistent. Across studies, the rates of postpartum depression in African American and Hispanic women were found to be higher [9], lower [10], or no different [11] compared to Whites. What accounts for observed racial and ethnic differences in prevalence is unclear. In some studies, sociodemographic risk variables were associated with
  • 41. higher levels of depressive symptomatology among Afri- can Americans, raising the possibility that sociodemo- graphic variables rather than race and ethnicity account for different levels of postpartum depression [12–14]. In con- trast, others have shown greater levels of depressive symptomatology among African Americans and Hispanics than Whites, after accounting for sociodemographic factors [9]. While certain social factors could increase risk, some factors might buffer against postpartum depression within groups. For instance, low income foreign-born Hispanic women with social support exhibited lower rates of post- partum depression [15], whereas bilingual Hispanic women were at greater risk than those who spoke only Spanish [11]. It is possible that factors such as social support or nativity and its effect on the likelihood of postpartum depression differ by race/ethnicity because they express different meanings or incur different implications for each group. Moreover, stigmas about psychological problems
  • 42. and help-seeking may have an effect on identifying post- partum depression, resulting in a subsequent effect on reported prevalence of postpartum depression rates [6, 16]. Given the mixed picture across groups, this study aimed to systematically determine the extent to which prevalence rates across race and ethnicity are explained by factors associated with postpartum depression. This study uniquely includes Asian/Pacific Islander (API) women within the U.S. As the fastest growing ethnic minority group, over 16 million APIs are estimated to be living in the U.S [17, 18]. The research on API postpartum experiences is limited, which is striking given that API women may hold several risk factors. If psychiatric history is a major predictor, API women may be at greatest risk: those between the ages of 15–24 years have the highest rate of depression and su- icidality compared to any other ethnicity, gender, or age [19–21]. One study showed APIs to be at lower risk for
  • 43. postpartum depressive symptoms compared to Whites, African Americans, and Hispanics [14], while another study reported a greater percentage of APIs with post- partum symptoms compared to White Americans [22]. Analyses conducted by the New York City Department of Health and Mental Hygiene on data from the 2004 to 2007 New York City (NYC) Pregnancy Risk Assessment Mon- itoring System (PRAMS) revealed a higher rate of PPD diagnoses among APIs compared to other groups [23–25]. From the most recent sample in 2007, 10.4 % of API received a PPD diagnosis compared to 1.7 % of non-His- panic White women [26]. These findings suggest a poten- tial risk for postpartum depression in APIs. This study examines racial/ethnic disparities in PPD diagnosis by identifying predictors accounting for preva- lence differences. Because previous studies have either focused mostly on small samples of one group, or did not examine these risk factors by race/ethnicity, we hypothe-
  • 44. size that associations of risk factors and PPD differ by race/ ethnic group. The risk factors evaluated were selected based on the current literature [27–31]. Our study also sought to explain disparities in PPD rates from a published report by the NYC Department of Health and Mental Hygiene. We utilized the study’s comprehensive popula- tion-based dataset. We also sought to determine the strength of predictors within each group and differences across groups. Accordingly, we stratified our analyses by race/ethnicity. Determining the strength of predictors by group is essential for identifying individuals most at risk, and may inform the possible causes of depression for dif- ferent groups. Unique to this study was the use of diagnosis as an outcome measure, the inclusion of information on whether providers talked to women about depressed mood, and an adequate sample size of APIs. This allowed us to also examine disparities in psycho-education and diagnosis across groups.
  • 45. Methods Sample This study used the NYC PRAMS from 2004 to 2007, a population-based survey administered to postpartum women from NYC. Coordinated by the Centers for Disease Control and Prevention and state health departments, PRAMS’ goal is to monitor maternal behaviors and expe- riences of women before, during, and after live birth pregnancies. The dataset was provided by the NYC Department of Health and Mental Hygiene (DOHMH). The participants were part of an ongoing population- based random sampling of NYC live births. NYC mothers of approximately 180 infants with registered birth certifi- cates that gave birth during the previous 2–4 months were contacted for participation monthly. Eighty-three percent responded by mail and 17 % by phone. The sample was randomized without replacement and stratified by birth weight. The final dataset was weighted for stratification,
  • 46. nonselection, and nonresponse. According to the DOHMH, a total of 4,813 responses were received with response rates of at least 70 % from July to December of 2004, May to December of 2005, and 1600 Matern Child Health J (2013) 17:1599–1610 123 January to December of 2006. A rate of 65 % was achieved from January to December of 2007. For 2004–2005, responses were weighted to represent 138,266 live births. For 2006 and 2007, responses represented 119,079 and 122,222 live births, respectively. Based on the DOHMH analysis, respondents differed from non-respondents on some key sociodemographic variables (p .05). APIs compared to other racial and ethnic groups, younger women, and women with less education were less likely to respond to the survey. Measures The birth certificate provided information on maternal race/
  • 47. ethnicity and nativity (i.e., U.S. or non-U.S. born mothers). Women were classified as Hispanic or non-Hispanic based on self-report. Non-Hispanic women were categorized in one of the following groups: White, African American, Asian/Pacific Islander, and American Indian/Alaskan Native. Maternal age, nativity (U.S. Born versus Foreign Born) and education (categorized as: 0–8, 9–11, 12, 13–15, and [16 years) were based at the time of infant birth from information in the birth certificate. Mean infant age at the time of survey completion was 9.7 months; there were no significant differences in infant age across groups. The PRAMS survey itself provided information for remaining variables. To obtain income, women were asked to indicate ‘‘total household income before taxes in the 12 months before the new baby was born’’ by checking off one of the following options: $10,000, $10,000–$14,999, $15,000–$19,999, $20,000–$24,999, $25,000–$34,999, $35,000–$49,999, $50,000–$74,999, and [$75,000. Stress- ful events during pregnancy were obtained by ‘‘yes’’ or
  • 48. ‘‘no’’ responses to events that may have occurred during the last 12 months before the new baby was born. Exam- ples include ‘‘I moved to a new address,’’ ‘‘I had a lot of bills to pay,’’ ‘‘I got separated or divorced from my hus- band or partner,’’ and ‘‘Someone very close to me died.’’ These events were counted and categorized into the fol- lowing: 0, 1–2, 3–5, and 6–13 events. A ‘‘yes’’ or ‘‘no’’ response was also used to obtain information on following: gestational diabetes (‘‘High blood sugar (diabetes) that started during this pregnancy’’), social support from partner (responses of ‘‘My husband or partner’’ to the question ‘‘During your most recent pregnancy, who would have helped you if a problem had come up’’), NICU (Neonatal Intensive Care Unit) (‘‘After your baby was born, was he or she put in an intensive care unit?’’), unintended pregnancy (‘‘When you got pregnant with your new baby, were you trying to get pregnant?’’). The NYC PRAMS included additional questions related to depression. Mothers were
  • 49. asked to respond ‘‘yes’’ or ‘‘no’’ regarding prenatal depression (‘‘At any time during your most recent pregnancy, did a doctor, nurse, or other health care worker diagnose you with depression?’’), and discussion about mood (‘‘At any time during your most recent pregnancy or after delivery, did a doctor, nurse, or other health care worker talk with you about ‘‘baby blues’’ or postpartum depression?’’). In addition, mothers were asked about PPD diagnosis (‘‘Since your new baby was born, has a doctor, nurse, or other health care worker diagnosed you with depression?’’). The response to this item was the outcome variable used for the analyses in this study. The language of the survey (English or Spanish version) was also noted. Variables Covariates included maternal age, household income, maternal education, nativity, and infant age at the time the mother completed the questionnaire. Variables considered
  • 50. as potential stressors included: gestational diabetes, stressful events, social support, NICU, intention for preg- nancy, and prenatal depression. Discussion about mood served as an additional predictor of PPD diagnosis. Responses with missing variables of interest for this study were eliminated. Variables with less than a 100 % response rate included household income (86.9 %), maternal education (99.3 %), maternal age (97.0 %), and PPD diagnosis (99.4 %) resulting in an unweighted study sample of 3,732. Statistical Analyses To account for the stratified and weighted sample, the data was analyzed using the complex samples module of SPSS version 17.0 (SPSS Inc., Chicago, IL). A non-race stratified model was conducted to determine the likelihood of receiving a PPD diagnosis for each race/ethnic group with Whites as the reference group. A series of four logistic regression models were employed where the variables of
  • 51. interest (race/ethnicity, sociodemographic, stressors, and discussion about mood) were sequentially added to the model, allowing incremental examination of the variables’ effects in identifying factors that explain racial/ethnic disparities in PPD. Prevalence estimates within each group were generated according to predictors. To compare the characteristics of those with and without PPD and to understand associated predictors, race-stratified logistic regressions incorporated all predictors, with sociodemographic variables as covari- ates. Adjusted odds ratios for each predictor were gener- ated by race/ethnic group. Note that our models failed to converge with the inclusion of language, nativity, and NICU variables because of low cell sizes; thus, these variables were dropped from our analyses. Unless Matern Child Health J (2013) 17:1599–1610 1601 123
  • 52. otherwise noted, all reported proportions represent weigh- ted averages. Results Compared to other groups, API women showed the highest rate for PPD, followed by Hispanics and African Ameri- cans. White women had the lowest rate of PPD. The high rate of a PPD diagnosis among API women is consistent with previous analyses from this dataset, which utilized a larger sample size than the dataset here, as this set includes only women with complete data on the predictor variables. Other racial/ethnic differences among assessed variables are presented (Table 1). A major objective was to determine whether sociode- mographic variables, stressor variables, and discussion about mood accounted for PPD differences. In the unad- justed model, likelihood estimates indicate that API women were 4.6 times more likely and Hispanic women 2.7 times more likely than Whites to receive a PPD diagnosis.
  • 53. African American were 1.7 times more likely to receive the diagnosis than Whites, although this was not statistically significant (Table 2). Once sociodemographic factors were entered, African Americans were no more likely to receive a diagnosis than Whites. For Hispanics, the greater likeli- hood for a diagnosis compared to Whites was less pro- nounced after accounting for sociodemographic factors and was eliminated with the inclusion of stressors. The diag- nosis likelihood was slightly reduced for APIs after accounting for sociodemographic factors, and significantly reduced with stressor variables, although diagnosis likeli- hood was still more than double the rate of Whites and African Americans. In contrast to the other groups, diag- nosis likelihood for APIs increased to 3.2 times relative to Whites, after accounting for reports of having discussed mood with a provider. Prenatal depression was by far the strongest predictor for all women compared to other stressors, although women who gave birth to females were
  • 54. more likely to receive a diagnosis than women with male infants. Overall, those who had a discussion about mood were also more likely to receive a diagnosis. Profiles of women with PPD diagnoses compared to women without a diagnosis differed by race/ethnicity. The majority of White women reporting a PPD diagnosis received a postgraduate education, while API and African American women with the diagnosis tended to be high school graduates. Approximately half of the White women with PPD had household incomes above $75,000 per year. Among APIs, Hispanics, and African Americans, more women with PPD had less than $15,000 of household income per year than those without a diagnosis (Table 3). With regard to stressors, we found a significantly higher rate of gestational diabetes among those with PPD than those without PPD, but only for White women. However, after controlling for sociodemographic variables through our race-stratified adjusted model, gestational diabetes did
  • 55. not significantly predict PPD in White, API, or Hispanic women (Table 4). In fact, African American women with gestational diabetes were less likely to receive a diagnosis of PPD. Compared to those without PPD, there was a higher percentage among APIs and Hispanics with the diagnosis who had an unintended pregnancy. In addition, the majority of APIs with PPD had a diagnosis of prenatal depression compared to the other groups. Stressful events were not associated with greater likelihood for PPD, but API women who reported having 6–13 stressful events were significantly more likely to have PPD, a rate that was statistically significant. The association between prenatal depression and PPD persisted for all groups, even after controlling for sociodemographic variables. Overall, there was a higher rate of women with PPD who had a discussion about mood with their providers than women without the diagnosis. However, the association
  • 56. between PPD and discussion about mood with providers was specific to only API and African American women in the adjusted model. Women from all groups who received a diagnosis of PPD were more likely to have given birth to females although the differences were not statistically significant. However, having a female infant seemed to slightly increase the likelihood of a PPD diagnosis among White and API women based on the race-stratified analyses. Discussion This study assessed PPD estimates and identified predictors of PPD as defined by women’s reports of receiving a diagnosis from a health care provider. We included API women and used race-stratified analyses, allowing us to determine whether predictors varied by race/ethnicity. This study also sought to identify factors that explained racial/ethnic disparities obtained in a previous analysis of the dataset by the NYC Department of Health and Mental
  • 57. Hygiene. As with other studies, we found that sociode- mographic factors accounted for the higher rates of PPD among African Americans and Hispanics. Based on such findings, some have argued for prevention or intervention programs to provide resources (e.g., financial support, education) in addressing the racial/ethnic disparities of PPD for African Americans and Hispanics [12]. However, unlike other studies that primarily assessed reported symptoms [9, 12, 14], we used the diagnosis of PPD as the 1602 Matern Child Health J (2013) 17:1599–1610 123 outcome measure. This raises the possibility that sociode- mographic status accounts for the rates at which one receives a diagnosis; in our study, African Americans and Hispanics with lower sociodemographic statuses were less likely to receive a diagnosis compared to Whites. If race/ ethnic disparities are found among rates of diagnosis, then
  • 58. the diagnostic process may be another area to target for improvement among lower sociodemographic status groups. Among ethnic minorities in our study, API women were the most likely to receive a PPD diagnosis, and unlike African Americans and Hispanics, the likelihood of receiving a PPD diagnosis for APIs remained significantly higher even after accounting for other variables (e.g., sociodemographic factors). Prenatal depression was asso- ciated with PPD for all groups in our study, but the like- lihood was highest for APIs. Although psychiatric history for depression was not available, the strong association between prenatal depression and PPD observed among the API women in our sample adds to the growing concern of depression experiences and its effects on API women during motherhood [19–21]. A number of factors specific to API women’s experiences are potentially associated with later postpartum mood. The high rate of depression
  • 59. and suicidal ideation during adolescence and young adulthood may reflect family and societal pressures faced by young women to uphold high academic standards and traditional gender roles [32]. These young women likely must negotiate their cultural values and beliefs when assuming a mother’s identity [33, 34]. In addition, the cultural preference for male infants may affect PPD. Table 1 Weighted percentage distribution of mothers who recently gave birth that completed the NYC PRAMS from 2004 to 2007, by characteristic, according to race/ethnicity White Asian/ Pacific Islander Hispanic Black (n = 1,043) (n = 425) (n = 1,253) (n = 1,027) Maternal age 20 2.4a 0.9a 9.9b 6.9c 20–34 70.1
  • 62. C16 55.4 a 45.8 b 13.2 c 22.4 d Income 10,000 10.0a 20.4b 40.3c 29.2d 10,000–14,999 6.7 a 15.1 b 14.3 b 10.3 c 15,000–19,999 4.6 a 8.0 b 8.8
  • 65. 34.1 c 56.3 d Non-U.S. born 31.1 88.9 65.6 43.0 Missing data 0.5 0 0.3 0.7 Language of questionnaire English 99.1 a 99.5 a 51.2 b 98.8 a Spanish 0 0 48.5 0 Missing data 0.5 0.5 0.3 1.2 NICU Yes 5.1 5.9 6.4 14.4 No 94.9 a
  • 66. 94.1 a 93.6 a 85.5 b Don’t know 0 0.1 0 0.1 Gender Male 49.3 a 52.1 a 51.1 a 52.0 a Female 50.7 47.9 48.9 48.0 Diabetes No 92.4 85.1 89.9 89.9 Yes 7.6 a 14.9 b
  • 68. 11.3 a 23.3 b 25.2 b 6–13 1.1 a 0.8 a 3.6 b 5.5 c Social support No 90.4 90.8 76.9 75.2 Yes 9.6 a 9.2 a 23.1 b 24.8 b
  • 69. Table 1 continued White Asian/ Pacific Islander Hispanic Black (n = 1,043) (n = 425) (n = 1,253) (n = 1,027) Intention for pregnancy No 30.9 a 35.1 a 59.0 b 66.5 c Yes 69.1 64.9 41.0 33.5 Prenatal depression diagnosis No 97.2 87.6 92.4 94.5 Yes 2.8 a
  • 70. 12.4 b 7.6 c 5.5 d Discussion about mood No 46.0 61.4 42.7 39.3 Yes 54.0 a 38.6 b 57.3 a,c 60.7 c Postpartum depression diagnosis No 97.4 89.3 93.6 96.3 Yes 2.6 a 10.7 b 6.4
  • 71. c 3.7 a Lower case superscripts that differ across each row represent statistically different values across racial/ethnic groups. Conversely, groups within a row that share the same superscript demonstrate no statistically significant difference in values within p .05 Matern Child Health J (2013) 17:1599–1610 1603 123 Table 2 Logistic regression models of race/ethnicity, other sociodemographic factors, stressors, and discussion of mood with provider, with adjusted odds of postpartum depression diagnosis Model 1 Model 2 Model 3 Model 4 OR CI OR CI OR CI OR CI Race White 1.0 1.0 1.0 1.0 Asian/Pacific Islander 4.6*** 2.6–8.2 4.0*** 2.2–7.2 2.7** 1.4–
  • 72. 4.9 3.2*** 1.7–6.0 Hispanic 2.7*** 1.7–4.5 1.8* 1.0–3.1 1.5 0.9–2.7 1.5 0.9–2.7 Black 1.7 � 1.0–3.0 1.2 0.6–2.2 0.9 0.5–1.8 0.9 0.4–1.8 Maternal age 20 1.0 20–34 0.5 0.3–1.1 0.5 0.2–1.1 0.5 0.2–1.2 C35 0.7 0.3–1.6 0.7 0.3–1.7 0.7 0.3–1.9 Maternal education 0–8 1 1 1 9–11 0.8 0.3–1.9 1.2 0.4–3.2 1.1 0.4–3.0 12 1.0 0.5–2.1 1.6 0.7–4.1 1.6 0.7–4.0 13–15 1.1 0.5–2.5 1.6 0.6–4.2 1.6 0.6–4.3 C16 0.8 0.4–1.8 1.5 0.6–4.0 1.6 0.6–4.2 Income 10,000 1.0 1.0 1.0 10,000–14,999 1.2 0.7–2.1 1.5* 0.8–2.8 1.5* 0.8–2.8 15,000–19,999 0.8* 0.3–1.6 1.1 0.5–2.4 1.0 0.5–2.2
  • 73. 20,000–24,999 0.5 0.2–1.2 0.6 0.3–1.4 0.6 0.2–1.3 25,000–34,999 0.6 0.3–1.3 0.7 0.3–1.7 0.7 0.3–1.6 35,000–49,999 0.3 0.1–0.7 0.3 0.1–0.9 0.3 0.1–0.8 50,000–74,999 0.4 0.2–0.9 0.5 0.2–1.3 0.5 0.2–1.3 C75,000 0.5 0.3–1.0 0.7 0.3–1.5 0.7 0.3–1.4 Gender Male 1.0 1.0 Female 1.6* 1.1–2.4 1.7* 1.1–2.5 Diabetes No 1.0 1.0 Yes 0.8 0.4–1.5 0.8 0.4–1.6 Stresses 0 1.0 1.0 1–2 0.8 0.5–1.3 0.8 0.5–1.3 3–5 1.0 0.6–1.8 1.0 0.6–1.8 6–18 1.8 � 0.7–4.9 2.0 �
  • 74. 0.8–5.1 Social support No 1.0 1.0 Yes 1.1 0.7–1.9 1.2 0.7–2.0 Intention for pregnancy No 1.0 1.0 Yes 1.2 0.8–1.8 1.2 0.8–1.8 Prenatal depression diagnosis No 1.0 1.0 Yes 17.3*** 10.9–27.5 15.0*** 9.4–23.8 Discussion about mood No 1.0 Yes 2.6*** 1.6–4.1 � p 0.1; * p .05; ** p .01; *** p .001 1604 Matern Child Health J (2013) 17:1599–1610 123
  • 75. Table 3 Weighted percentage of mothers who completed the NYC PRAMS from 2004 to 2007, by characteristic according to race/ethnicity and postpartum depression diagnosis White Asian/Pacific Islander Hispanic Black No PPD PPD No PPD PPD No PPD PPD No PPD PPD (n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n = 979) (n = 48) Maternal age 20 2.3 5.9 1 0 9.6 13.4 6.2 25.2*** 20–34 70.4 62 74.1 86.1 � 77.7 63.7** 74.2 63.4 � C35 27.4 32.1 24.9 13.9 12.6 22.9** 19.6 11.5 Maternal education 0–8 1.7 0 2.4 4.9 11.5 15.8 1.7 0.7 9–11 4 9 10.6 11.4 19.3 23 15.9 12.5 12 22.9 12.1 23 52.8*** 34.9 25.9 � 31.4 49.3*** 13–15 16.5 5.3 14.5 16.5 20.7 27.7 28.2 26.2
  • 76. C16 54.9 73.6 � 49.5 14.3*** 13.6 7.6 22.8 11.4 Income 10,000 9.9 15.4 18.7 34.2* 40.1 44.2 28.6 46.9*** 10,000–14,999 6.7 4.7 14.8 18.2 13.5 27*** 9.8 21.8*** 15,000–19,999 4.8 0 7.3 13.5 9.1 5.3 8.4 14.9* 20,000–24,999 4.8 0 6 4.5 6.7 8.5 9.6 0.6** 25,000–34,999 7 0.5 4.6 15.3 9.7 5.8 13.5 4.8* 35,000–49,999 8.5 15.1 6.7 0.3 7.1 0.5* 10.5 1.7** 50,000–74,999 12.1 11.5 10.7 3.6 6.5 4.4 10.6 0.5** C75,000 46.2 52.7 31.3 10.3** 7.3 4.3 9 8.8 Maternal nativity U.S. born 68.4 66.7 12.3 100*** 35.0 23.1* 56.4 54.2 Non-U.S. born 31.1 30.3 87.7 0 64.7 76.9 42.9 45.8 Missing data 0.5 0.3 0 0 0.3 0 0.7 0 Language of questionnaire English 99.2 97.0 99.5 100 50 46.2 98.8 100 Spanish 0 0 0.1 0 50 53.8 1.1 0
  • 77. Missing data 0.8 3.0 0.4 0 0 0 0.1 0 NICU No 94.9 94.2 93.6 98.1 93.7 91.9 85.7 82.3 Yes 5.1 5.8 6.3 1.9 6.3 8.1 14.3 17.3 Don’t know 0 0 0.1 0 0 0 0.1 0.5 Gender Male 49.5 40.2 53.2 43 51.7 43.8 52.3 44.7 Female 50.5 59.8 46.8 57 48.3 56.2 47.7 55.3 Diabetes No 92.5 90 85.6 81.4 90.2 86.2 89.6 98.9 Yes 7.5 10* 14.4 18.6 9.8 13.8 10.4 1.1 Stresses 0 45.6 26.4* 47.6 61.2 31.6 30.8 27 12.5** 1–2 41.4 54.3 39.9 28.8 42.6 25 42.9 39.4 3–5 12 14.2 12.1 5.4 22.7 32.1 24.6 41.2 6–13 1 5.1* 0.4 4.5** 3 12.1 5.4 6.9 Social support No 9.4 15.3 8.6 13.7 22.7 28.3 24.6 31.4
  • 78. Yes 90.6 84.7 91.4 86.3 77.3 71.7 75.4 68.6 Intention for pregnancy Matern Child Health J (2013) 17:1599–1610 1605 123 Chinese women with a female infant were more likely to experience PPD [35, 36]. In another study on Indian women, having a female infant increased the effects of other risk factors [37]. Recent findings have also demon- strated a greater likelihood for Asian women to develop gestational diabetes, which is associated with PPD [38–40]. Other explanations for Asian American depression in the literature range from biological [41] to social [42]. Toge- ther, these explanations may represent a general vulnera- bility for depression generalizing to API women’s depressed mood during the postpartum period. Future studies in PPD research may want to specifically examine the association between psychiatric history and PPD by
  • 79. race/ethnicity to determine if psychiatric history predicts PPD more strongly in API women. Furthermore, discussing depressed mood with providers increased the likelihood for women to receive a diagnosis. This was especially true for APIs where the likelihood of receiving a diagnosis was 3.2 times more than White women after our analyses considered such discussions as a factor. These high rates could reflect the quality of the diagnostic processes that take place between API women and their providers. The use of a diagnostic criterion by the NYC PRAMS to assess PPD is unlike other prevalence studies that typically use structured assessments for PPD (e.g., a single question on depressive mood during preg- nancy, multiple items covering symptomatology, etc.) [9, 12–14]. APIs tend to endorse somatic experiences rather than psychological symptoms [43, 44]. Conversations with a provider could increase sensitivity during the assessment, thus facilitating a positive diagnosis. Increased research on
  • 80. the diagnostic process within a health care setting would greatly enhance understanding of how dialogues between provider and patient result in diagnoses. In particular, future research should consider differences in the charac- teristics of providers and clinics among those who did and did not receive a PPD diagnosis, and the nature of the actual exchanges occurring between providers and patients. It was particularly striking that approximately half of the providers did not talk to women about PPD. Racial/ethnic disparities were also found when assessing these rates. While the majority of African American, Hispanic, and White women reported having had a conversation with their providers, only 38.6 % of API women in our study reported this. Given that Asians tend to minimize their psychological distress [6, 16], providers may not realize distress nor recognize the need to bring up depressed mood. APIs who had a conversation were 9.1 times more likely to receive a diagnosis than APIs without, regardless of their
  • 81. sociodemographic background. Thus, although APIs were the group most likely to benefit from information about depressed mood, they were the least likely to be provided with it. Additionally, African Americans showed the highest rate of having been presented with information about mood compared to the other groups; those with a conversation were 5.8 times more likely to receive a diagnosis. Altogether, and of greatest concern were the low rates of assessment for all groups, and especially for APIs. Our findings suggest that the information presented by a pro- vider has powerful implications for determining diagnosis, especially for APIs and African Americans. This finding has implications for studies obtaining prevalence rates without considering racial/ethnic disparities within the screening or diagnostic process. Differences in prevalence rates may be attributed to the lack of medical information and treatment opportunities available to certain groups.
  • 82. Our inclusion of known predictors for PPD in race- stratified analyses allowed us to compare the strength of stressors across groups. Most of the group differences in the predicted likelihood for PPD were not statistically significant suggesting few group differences in the Table 3 continued White Asian/Pacific Islander Hispanic Black No PPD PPD No PPD PPD No PPD PPD No PPD PPD (n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n = 979) (n = 48) No 31.1 26.3 33.4 49.2 58.2 69.8 66.4 67.6 Yes 68.9 73.7 66.6 50.8* 41.8 30.2** 33.6 32.4 Prenatal depression diagnosis No 98 67.1 93.8 35.8 95 54.4 95.4 71 Yes 2 32.9*** 6.2 64.2*** 5 45.6*** 4.6 29*** Discussion about mood No 46.5 26.1 66.2 21.1 43.5 30.5 40.5 8.6 Yes 53.5 73.9* 33.8 78.9*** 56.5 69.5** 59.5 91.4***
  • 83. � p 0.1; * p .05; ** p .01; *** p .001 1606 Matern Child Health J (2013) 17:1599–1610 123 association between stressors and PPD. Furthermore, stressful events were not associated with a greater likeli- hood at a statistical level, with the exception of API women; those who reported 6-13 stressful events were significantly more likely to receive a diagnosis. The explanation may reside in the distribution of reported stressful events for APIs; compared to other groups, the majority of API women reported zero stressful events. As such, the few APIs who disclosed high numbers of stressful events may have been the most likely to receive a diag- nosis. APIs may still minimize their experience of stress despite being asked to state the occurrence of stressful events given their tendency to minimize psychological problems in general [6, 16]. Providers may want to inquire
  • 84. further about actual events and how it affects their API patients both psychologically and physically. A number of associations between stressors and PPD require clarification through further research. There was a trend for increased PPD rates among API and White women who gave birth to female infants. Few studies have included infant gender in PPD studies within the U.S.; those that have find no association [45, 46]. Given these studies’ small samples (n 200), any effects may have been too small to detect. One study did find increased self- esteem in mothers of male infants, although this association was mediated by paternal support [47]. The statistical trend in our data may indicate actual preferences for infant gender, but it could also reflect other factors moderated by infant gender. Our findings demonstrate the need to include infant gender in future studies and to identify mechanisms that explain this association. In addition, we did not find a general link between
  • 85. gestational diabetes and PPD, despite a previous study’s results [39]. When examining groups separately, APIs in our study were more likely to have gestational diabetes; however, this did not predict PPD. Instead, we found a decrease in the likelihood for PPD diagnoses among African Americans with gestational diabetes. There is evidence to suggest that African Americans may be less inclined to disclose symptoms even though providers speak Table 4 Race/ethnicity stratified logistic regression showing adjusted odds of postpartum depression diagnosis per predictor by race/ethnic group White Asian/Pacific Islander Hispanic Black OR CI OR CI OR CI OR CI Gender Male 1.0 1.0 1.0 1.0 Female 2.2 � 0.9–5.8 2.6 � 0.9–7.2 1.5 0.8–2.7 1.5 0.5–4.1
  • 86. Diabetes No 1.0 1.0 1.0 1.0 Yes 1.0 0.3–3.8 0.8 0.2–3.7 1.4 0.6–3.3 0.1** 0.0–0.5 Stresses 0 1.0 1.0 1.0 1.0 1–2 2.3 � 0.8–6.7 0.8 0.3–2.2 0.4* 0.2–0.9 1.8 0.3–10.2 3–5 1.2 0.2–8.5 0.1 0.0–1.1 0.8 � 0.4–1.8 3.1 0.4–21.7 6–13 5.2 0.9–30.8 2.7* 0.5–15.8 2.5 0.7–9.7 1.5 0.1–15.6 Social support No 1.0 1.0 1.0 1.0 Yes 1.6 0.4–6.0 1.9 0.4–9.5 0.9 0.4–1.7 1.5 0.5–4.6 Intention for pregnancy No 1.0 1.0 1.0 1.0 Yes 0.9 0.3–3.3 2.2 0.8–6.4 1.4 0.7–2.6 0.7 0.2–1.9 Prenatal depression diagnosis
  • 87. No 1.0 1.0 1.0 1.0 Yes 29.4*** 8.5–101.4 52.1*** 16.4–166.0 15.3*** 7.6–30.9 8.1*** 2.9–22.8 Discussion about mood No 1.0 1.0 1.0 1.0 Yes 1.7 0.6–4.8 9.1** 2.5–33.4 1.3 0.7–2.6 5.8** 2.1–15.9 Only adjusted odds ratios are presented because race/ethnic stratified analyses did not converge when including unadjusted factors in the model. This was due to zero to small sample sizes in race 9 sociodemographic contingency tables � p 0.1; * p .05; ** p .01; *** p .001 Matern Child Health J (2013) 17:1599–1610 1607 123 with them about prenatal depression and PPD at a higher rate [48]. Mistrust and perceived discrimination within the medical care setting may prevent disclosure about depres- sion [22, 49]. In particular, some studies have found that
  • 88. among those with depressive mood accompanied with diabetes, African Americans were less likely to be recog- nized as depressed and to receive depression treatment [49–51]. Given our initial findings, the association between gestational diabetes and PPD may not be generalizable, although further research is needed to fully understand the relationship. Studies that do not stratify by race may overlook differences in the effect of gestational diabetes on depression by race/ethnicity. Interpretation of results should be made with caution in light of our limitations. As with any self-report, inaccura- cies in this data are possible given recall problems. In addition, prenatal and PPD diagnoses were used in our study. It would have been far preferable to obtain corrob- orating information from medical records; however, this information was unavailable within this survey study. It is possible that providers employed different standards for diagnoses, which may be reflected in this data, for instance,
  • 89. the consideration of ‘‘baby blues’’ or the inclusion of dif- ferent methods to assess depression (e.g., questionnaire, verbal report). Furthermore, these diagnoses may not nec- essarily reflect actual depression rates, but as discussed, may be more of a reflection of provider sensitivity to detecting symptoms in a particular group. Finally, the race/ ethnic categories are a proxy for a culture, and are com- prised of heterogeneous subgroups. For instance, the unique experiences of Chinese, Japanese or Filipino groups may have been overlooked since they were combined into one race/ethnic category. Conclusion Our results highlight racial/ethnic disparities in PPD and its diagnosis, inviting a more nuanced approach in the con- sideration of PPD risk factors. Although we relied on broad race/ethnic categories, these findings demonstrate at a basic level, the possibility of differential effects in the risk fac- tors associated with PPD. Explanations for racial/ethnic
  • 90. disparities in diagnosis compared to Whites differ by group and are not necessarily due to sociodemographic status or stress, factors that usually explain racial/ethnic disparities. While prenatal depression seems to be a major risk factor for PPD across all groups, the extent to which a factor is a ‘‘risk’’ for a particular racial/ethnic group needs to be evaluated. These associations point to the possibility of group-specific mechanisms leading to a PPD diagnosis. Universal postpartum depression screening as a single approach may not be adequate given the role that provider- patient interactions might have as suggested by these study findings. Rather, this study broadly reveals a need to con- sider the diagnostic process between provider-patient by race/ethnicity to better understand the source of treatment disparities. Acknowledgments The authors would like to acknowledge the NYC Department of Health and Mental Hygiene Bureau of Maternal, Infant and Reproductive Health PRAMS Team, Bureau of Vital
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  • 104. 123 Copyright of Maternal & Child Health Journal is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Rates and Predictors of Postpartum Depression by Race and Ethnicity: Results from the 2004 to 2007 New York City PRAMS Survey (Pregnancy Risk Assessment Monitoring System)AbstractIntroductionMethodsSampleMeasuresVariables Statistical AnalysesResultsDiscussionConclusionAcknowledgmentsReferen ces CLINICAL ISSUES The effect of nurse–patient interaction on anxiety and depression in cognitively intact nursing home patients Gørill Haugan, Siw T Innstrand and Unni K Moksnes Aims and objectives. To test the effects of nurse–patient interaction on anxiety and depression among cognitively intact nursing home patients. Background. Depression is considered the most frequent mental
  • 105. disorder among the older population. Specifically, the depression rate among nursing home patients is three to four times higher than among community-dwelling older people, and a large overlap of anxiety is found. Therefore, identifying nursing strategies to prevent and decrease anxiety and depres- sion is of great importance for nursing home patients’ well- being. Nurse–patient interaction is described as a fundamental resource for meaning in life, dignity and thriving among nursing home patients. Design. The study employed a cross-sectional design. The data were collected in 2008 and 2009 in 44 different nursing homes from 250 nursing home patients who met the inclusion criteria. Methods. A sample of 202 cognitively intact nursing home patients responded to the Nurse–Patient Interaction Scale and the Hospital Anxiety and Depression Scale. A structural equation model of the hypothesised relationships was tested by means of LISREL 8.8 (Scientific Software International Inc., Lincolnwood, IL, USA). Results. The SEM model tested demonstrated significant direct relationships and total effects of nurse–patient interaction on depression and a mediated influence on anxiety. Conclusion. Nurse–patient interaction influences depression, as well as anxiety, mediated by depression. Hence, nurse–
  • 106. patient interaction might be an important resource in relation to patients’ mental health. Relevance to clinical practice. Nurse–patient interaction is an essential factor of quality of care, perceived by long-term nurs- ing home patients. Facilitating nurses’ communicating and interactive skills and competence might prevent and decrease depression and anxiety among cognitively intact nursing home patients. Key words: anxiety, depression, nurse–patient interaction, nursing home, structural equation model analysis Accepted for publication: 11 September 2012 Introduction With advances in medical technology and improvement in the living standard globally, the life expectancy of people is increasing worldwide. The document An Aging World (US Census Bureau 2009) highlights a huge shift to an older popu- lation and its consequences. Within this shift, the most rapidly growing segment is people over 80 years old: by 2050, the per- centage of those 80 and older would be 31%, up from 18% in 1988 (OECD 1988). These perspectives have given rise to the
  • 107. notions of the ‘third’ (65–80 years old) and the ‘fourth age’ (over 80 years old) in the lifespan developmental literature (Baltes & Smith 2003). These notions are also referred to as the ‘young old’ and the ‘old old’ (Kirkevold 2010). Authors: Gørill Haugan, PhD, RN, Associate Professor, Faculty of Nursing, Research Centre for Health Promotion and Resources, Sør-Trøndelag University College, HIST, Trondheim; Siw T Innstrand, PhD, Associate Professor, Research Centre for Health Promotion and Resources Norwegian University of Science and Technology, NTNU, Trondheim; Unni K Moksnes, PhD, RN, Associate Professor, Faculty of Nursing, Research Centre for Health Promotion and Resources, Sør-Trøndelag University College, HIST, Trondheim, Norway Correspondence: Gørill Haugan, Associate Professor, Research Centre for Health Promotion and Resources, HIST/NTNU, NTNU, SVT/ISH, 7491 Trondheim, Norway. Telephone: +47 73 55 29 27.E-mail: [email protected] © 2013 Blackwell Publishing Ltd
  • 108. 2192 Journal of Clinical Nursing, 22, 2192–2205, doi: 10.1111/jocn.12072 For many of those in the fourth age, issues such as physi- cal illness and approaching mortality decimates their func- tioning and subsequently lead to the need for nursing home (NH) care. A larger proportion of older people will live for shorter or longer time in a NH at the end of life. This group will increase in accordance with the growing popula- tion older than 65, and in particular for individuals older than 80 years. Currently, 1�4 million older adults in the USA live in long-term care settings, and this number is expected to almost double by 2050 (Zeller & Lamb 2011). In Norway, life expectancy by 2050 is 90�2 years for men and 93�4 years for women (Statistics of Norway 2010). Depression is one of the most prevalent mental health problems facing European citizens today (COM 2005); and, the World Health Organization (WHO 2001) has esti- mated that by 2020, depression is expected to be the high- est ranking cause of disease in the developed world.
  • 109. Moreover, depression is described to be one of the most frequent mental disorders in the older population and is particularly common among individuals living in long-term care facilities (Choi et al. 2008, Karakaya et al. 2009, Lattanzio et al. 2009, Drageset et al. 2011, Phillips et al. 2011). A linear increase in prevalence of depression with increasing age is described (Stordal et al. 2003); the three strongest explanatory factors on the age effect of depression are impairment, diagnosis and somatic symptoms, respec- tively (Stordal et al. 2001, 2003). Worse general medical health is seen as the strongest factor associated with depres- sion among NH patients (Djernes 2006, Barca et al. 2009). A review that included 36 studies from various countries, reported a prevalence rate for major depression ranging from 6–26% and from 11–50% for minor depression. However, the prevalence rate for depressive symptoms ran- ged from 36–49% (Jongenelis et al. 2003). Twice as many women are likely to be affected by depression than men
  • 110. (Kohen 2006), and older people lacking social and emo- tional support tend to be more depressed (Grav et al. 2012). A qualitative study on successful adjustment among women in later life identified three main areas as being the main obstacles for many; these were depression, maintain- ing intimacy through friends and family and managing the change process associated with older age (Traynor 2005). Significantly more hopelessness, helplessness and depres- sion are found among patients in NHs compared with those living in the community (Ron 2004). Jongenelis et al. (2004) found that depression was three to four times higher in NH patients than in community-dwelling adults. Moving to a NH results from numerous losses, illnesses, disabilities, loss of functions and social relations, and approaching mor- tality, all of which increases an individual’s vulnerability and distress; in particular, loneliness and depression are iden- tified as risks to the well-being of older people (Routasalo et al. 2006, Savikko 2008, Drageset et al. 2012). The NH
  • 111. life is institutionalised, representing loss of social relation- ships, privacy, self-determination and connectedness. Because NH patients are characterised by high age, frailty, mortality, disability, powerlessness, dependency and vulner- ability, they are more likely to become depressed. A recent literature review showed several studies reporting prevalence of depression in NHs ranging from 24–82% (Drageset et al. 2011). Also, with a persistence rate of more than 50% of depressed patients still depressed after 6–12 months, the course of major depression and significant depressive symp- toms in NH patients tend to be chronic (Rozzini et al. 1996, Smalbrugge et al. 2006a). Moreover, studies in NHs report a large co-occurrence of depression and anxiety (Beekman et al. 2000, Kessler et al. 2003, Smalbrugge et al. 2005, Van der Weele et al. 2009, Byrne & Pachana 2010). A recent review concerning anxi- ety and depression reports a paucity of findings on anxiety in older people (Byrne & Pachana 2010). Hence, more
  • 112. research is urgently required into anxiety disorders in older people, as these are highly prevalent and associated with considerable disease burden (ibid.). Depression and anxiety in NH patients are associated with negative outcomes such as poor functioning in activities of daily living and impaired quality of life (QoL) (Smalbrugge et al. 2006b, Diefenbach et al. 2011, Drageset et al. 2011), substantial caregiver burden and worsened medical outcomes (Bell & Goss 2001, Koenig & Blazer 2004, Sherwood et al. 2005), increased risk of hospital admission (Miu & Chan 2011), a risk of increased demen- tia (Devanand et al. 1996) and a higher mortality rate (Watson et al. 2003, Ahto et al. 2007). Accordingly, efforts to prevent and decrease depression and anxiety are of great importance for NH patients’ QoL. Social support and relations to significant others are found to be a vital resource for QoL and thriving among NH patients (Bergland & Kirkevold 2005, 2006, Drageset
  • 113. et al. 2009a, Tsai et al. 2010, Tsai & Tsai 2011), as well as the nurse–patient relationship (Haugan Hovdenes 2002, Cox & Bottoms 2004, Franklin et al. 2006, Medvene & Lann-Wolcott 2010, Burack et al. 2012). The perspective of promoting health and well-being is fundamental in nurs- ing and a major nursing concern in long-term care (Nakrem et al. 2011, Drageset et al. 2009b). However, low rates of recognition of depression by staff nurses is found (Bagley et al. 2000, Volkers et al. 2004). Through the last decades, the importance of establishing the nurse–patient relationship as an integral component of © 2013 Blackwell Publishing Ltd Journal of Clinical Nursing, 22, 2192–2205 2193 Clinical issues Nurse-patient-interaction, depression, and anxiety nursing practice has been well documented (Nåden & Eriksson 2004, Arman 2007, Carpiac-Claver & Levy- Storms 2007, Granados Gámez 2009, Rchaidia et al. 2009,
  • 114. Fakhr-Movahedi et al. 2011). Excellent nursing care is characterised by a holistic view with inherent human values and moral; thus, excluding the patient as a unique human being should be regarded as noncaring and amoral practice (Haugan Hovdenes 2002, Nåden & Eriksson 2004, Aust- gard 2008, Watson 2008). NH patients are in general extremely vulnerable and hence the nurse–patient relation- ship and the nurse–patient interaction are critical to their experience of dignity, self-respect, sense of self-worth and well-being (Dwyer et al. 2008, Harrefors et al. 2009, Heliker 2009). NH patient receiving self-worth therapy showed statistically significantly reduced depressive symp- toms relative to control groups members 2 months after receiving the intervention (Tsai et al. 2008). Self-worth therapy comprised establishment of a therapeutic relation- ship offering feedback and focusing the patient’s dignity, emotional and mental well-being (ibid.). Caring nurses engage in person-to-person relationships
  • 115. with the NH patients as unique persons. Good nursing care is defined by the nurses’ way of being present together with the patient while performing nursing activities, in which attitudes and competence are inseparately connected. ‘Pres- ence’, ‘connectedness’ and ‘trust’ are described as funda- mental cores of holistic nursing care (McGilton & Boscart 2007, Potter & Frisch 2007, Carter 2009) in the context of the nurse–patient relationship in which the nurse–patient interaction is taking place. Trust is seen as a confident expectation that the nurses can be relied upon to act with good will and to secure what is best for the individuals residing in the NH. Hence, trust is the core moral ingredi- ent in nurse–patient relationships; even more basic than duties of beneficence, respect, veracity, and autonomy (Carter 2009). Caring is a context-specific interpersonal process that is characterised by expert nursing practice, interpersonal sen- sitivity, and intimate relationships (Finfgeld-Connett 2008)
  • 116. which increases patient’s well-being (Nakrem et al. 2011, Hollinger-Samson & Pearson 2000, Cowling et al. 2008, Rchaidia et al. 2009, Reed 2009). The relationship between NH staff attention and NH patients’ affect and activity par- ticipation have been assessed among depressed NH patients, showing that positive staff engagement was signifi- cantly related to patients’ interest, activity participating, and pleasure (Meeks & Looney 2011). These results suggest that staff behaviour and engagement could be a reasonable target for interventions to increase positive affect among NH patients (ibid.). In summary, the literature suggests depression as a com- mon mental disorder among older people characterised by high age, impairment, and somatic symptoms. In addition, a large overlap of anxiety is reported. The patients’ sense of loss of independency and privacy, feelings of isolation and loneliness, and lack of meaningful activities are risk factors for depression in NH patients. Nurse–patient inter-
  • 117. action might be a resource for preventing and decreasing depression among NH patients. To the authors’ knowl- edge, previous research has not examined these relation- ships in NHs by means of structural equation modelling (SEM). Aims The main aim of this study was to investigate the relation- ships between nurse–patient interaction, anxiety and depression among cognitively intact NH patients by means of SEM. Based on the theoretical and empirical knowledge of depression, anxiety and nurse–patient interaction our research question was: ‘Does the nurse–patient interaction affect anxiety and depression in cognitively intact NH patients?’ The following hypotheses were formulated: � Hypothesis 1 (H1): nurse–patient interaction positively affects anxiety. � Hypothesis 2 (H2): nurse–patient interaction positively affects depression. � Hypothesis 3 (H3): depression negatively affects anxiety.
  • 118. Methods Design and ethical considerations The study employed a cross-sectional design. The data was collected in 2008 and 2009 in 44 different NHs from 250 NH patients who met the inclusion criteria: (1) local authority’s decision of long-term NH care; (2) residential time six months or longer; (3) informed consent compe- tency recognised by responsible doctor and nurse; and (4) capable of being interviewed. Two counties comprising in total 48 municipalities in central Norway were selected, from which 25 (at random) were invited to contribute in this study. In total, 20 municipalities were partaken. Then, all the NHs in each of the 20 municipalities was asked to participate. A total of 44 NHs took part in the study. To include as many participants from rural and central NHs, respectively, the NHs was one by one invited to participate, until the minimum of n = 200 was reached. The NH patients were approached by a head nurse they knew
  • 119. well. The nurse presented them with oral and written © 2013 Blackwell Publishing Ltd 2194 Journal of Clinical Nursing, 22, 2192–2205 G Haugan et al. information about their rights as participants and their right to withdraw at any time. Each participant provided informed consent. Because this population has problems completing a questionnaire independently, three trained researchers conducted one-on-one interviews in the patient’s room in the actual NH. Researchers with identical profes- sional background were selected (RN, MA, trained and experienced in communication with older people, as well as teaching gerontology at an advanced level) and trained to conduct the interviews as identically as possible. Inter-rater reliability was assessed by comparing mean scores between interviewers by means of Bonferroni-corrected one-way ANOVAs. No statistically significant differences were found that were not accounted for by known differences between
  • 120. the areas in which the interviewers operated. The questionnaires relevant for the present study were part of a questionnaire comprising 130 items. The interviews lasted from 45–120 minutes due to the individual partici- pant’s tempo, form of the day, and need for breaks. Inter- viewers held a large-print copy of questions and possible responses in front of the participants to avoid misunder- standings. Approval by the Norwegian Social Science Data Services was obtained for a licence to maintain a register containing personal data (Ref. no. 16443) and likewise we attained approval from The Regional Committee for Medical and Health Research Ethics in Central Norway (Ref. no. 4.2007.645) as well as the directory of the 44 NHs. Participants The total sample comprised 202 (80�8%) of 250 long-term NH patients representing 44 NHs. Long-term NH care was defined as 24-hour care; short-term care patients, rehabilita- tions patients, and cognitively impaired patients were not
  • 121. included. Participants’ age was 65–104, with a mean of 86 years (SD = 7�65). The sample comprised 146 women (72�3%) and 56 men (27�7%), where the mean age was 87�3 years for women and 82 years for men. A total of 38 (19%) were married/cohabitating, 135 (67%) were widows/ widowers, 11 (5�5%) were divorced, and 18 (19%) were single. Duration of time of NH residence when interviewed was at mean 2�6 years for both sexes (range 0�5–13 years); 117 were in rural NHs, while 85 were in urban NHs. In all, 26�1% showed mild to moderate depression, only one woman scored >15 indicating severe depression, 70�4% was not depressed, and nearly 88% had no anxiety disor- der. Missing data was low in frequency and was handled by means of the listwise procedure; for the nurse–patient interaction 4�0% and for anxiety and depression 5�0% had some missing data. Measures The Nurse–Patient Interaction Scale (NPIS) was developed to identify important characteristics of NH patients’ experi- ences of the nurse–patient interaction. The NPIS comprises 14 items identifying essential relational qualities stressed in the nursing literature (Watson 1988, Martinsen 1993,
  • 122. Eriksson 1995a,b, Nåden & Eriksson 2004, Nåden & Sæteren 2006, Levy-Malmberg et al. 2008). Examples of NPIS-items include ‘Having trust and confidence in the staff nurses’; ‘The nurses take me seriously’, ‘Interaction with nurses makes me feel good’ as well as experiences of being respected and recognised as a person, being listened to and feel included in decisions. The items were developed to measure the NH patients’ ability to derive a sense of well- being and meaningfulness through the nurse–patient inter- action (Haugan Hovdenes 1998, 2002, Hollinger-Samson & Pearson 2000, Finch 2006, Rchaidia et al. 2009). The NPIS has shown good psychometric properties with good content validity and reliability among NH patients; (Haugan et al. 2012). The NPIS is a 10-points scale from 1 (not at all)–10 (very much); higher numbers indicating better nurse–patient interaction (Appendix 1). Cronbach’s Table 1 Means (M), standard deviations (SD), Cronbach’s alpha, and correlation coefficients for the study variables
  • 123. Construct M SD Cronbach’s alpha NPIS HADS-A HADS-D NPIS (10 items) 8�19 1�73 0�92 – HADS-A (5 items) 0�40 0�50 0�79 �0�114 – HADS-D (5 items) 0�74 0�58 0�66 �0�294* 0�340* – HADS (14 items) 2�85 0�34 0�78 *p < 0�01. NPIS, Nurse–Patient Interaction Scale; HADS, Hospital Anxiety and Depression Scale; HADS-A, Hospital Anxiety and Depression Scale - Anxiety; HADS-D, Hospital Anxiety and Depression Scale - Depression. © 2013 Blackwell Publishing Ltd Journal of Clinical Nursing, 22, 2192–2205 2195 Clinical issues Nurse-patient-interaction, depression, and anxiety a = 0�92 (Table 1) and composite reliability = 0�92 (Table 2) of the NPIS construct was good. Anxiety and depression were assessed by the Hospital Anxiety and Depression Scale (HADS), comprising 14 items (Appendix 2), with subscales for anxiety (HADS-A; seven items) and depression (HADS-D seven items). Each item is rated from 0–3, where higher scores indicate more anxiety
  • 124. and depression. The maximum score is 21 on each subscale. The ranges of scores for cases are as follows: 0–7 normal, 8–10 mild disorder, 11–14 moderate disorder, and 15–21 severe disorder (Snaith & Zigmond 1994). HADS has been tested extensively and has well-established psychometric properties (Herrmann 1997). To increase acceptability and avoid individuals feeling as though they are being tested for mental disorders, symptoms of severe psychopathology have been excluded. This makes HADS more sensitive to milder psychopathology (Stordal et al. 2003). HADS is translated into Norwegian and found to be valid for older people (Stordal et al. 2001, 2003). Examples of sample-items are for depression: ‘I still enjoy the things I used to enjoy’, ‘I can laugh and see the funny side of things’, ‘I feel cheerful’, ‘I have lost interest in my appear- ance’, and ‘I look forward with enjoyment to things’, and for anxiety: ‘I feel tense and wound up’, ‘I get a sort of frightened feeling as if something awful is about to happen’, ‘Worrying
  • 125. thoughts go through my mind’, ‘I get a sort of frightened feel- ing like ‘butterflies’ in the stomach’, and ‘I get sudden feeling of panic’. The items were scored on a four-point scale ranging from totally disagrees to totally agree. The internal consis- tence of the anxiety and depression constructs (Table 1) was satisfactory; a = 0�79 and a = 0�66, respectively. Composite reliability (qc) displayed values between 0�70–0�92 (Table 2); values >0�60 are desirable, whereas values >0·70 are good (Diamantopolous & Siguaw 2008, Hair et al. 2010). Statistical analysis A structural equation model (SEM) of the hypothesised relations between the latent constructs of depression and self-transcendence was tested by means of LISREL 8.8 (Scien- tific Software International Inc., Lincolnwood, IL, USA) (Jøreskog & Sørbom 1995). Using SEM accounts for ran- dom measurement error and the psychometric properties of the scales in the model are more accurately derived. Since the standard errors are estimated under non-normality, the Satorra–Bentler scaled chi-square statistic was applied as a goodness-of-fit statistic, which is the correct asymptotic
  • 126. mean even under non-normality (Jøreskog et al. 2000). In line with the rules of thumb of conventional cut-off criteria (Schermelleh-Engel et al. 2003), the following fit indices were used to evaluate model fit: chi-square (v2); a small v2 and a non-significant p-value corresponds to good fit (Jøreskog & Sørbom 1995). Further we used the root mean square error of approximation (RMSEA) and the standar- dised root mean square residual (SRMS) with values below 0�05 indicating good fit, while values smaller than 0�08 are interpreted as acceptable (Hu & Bentler 1998, Schermelleh- Engel et al. 2003). The comparative fit index (CFI) and the non-normed fit index (NNFI) with an acceptable fit at 0�95, and good fit at 0�97 and above were used, and the normed fit index (NFI) with an acceptable fit at 0�90, while a good fit was set to 0�95 (ibid.). Before examining the hypothesised relationships in the SEM analysis, the measurement models were tested by con- firmatory factor analysis (CFA). The CFA provided a good fit to the observed data for the nurse–patient interaction construct comprising ten items (v2 = 92�32, df = 77,
  • 127. Table 2 Measurement models included in Model 1: nurse– patient interaction (NPIS) to anxiety (HADS-A) and depression (HADS-D) Items Parameter Lisrel estimate t-value R2 NPIS NPIS1 kx1,1 0�63 6�04** 0�39 NPIS2 kx2,1 0�74 8�99** 0�55 NPIS3 kx3,1 0�74 10�41** 0�55 NPIS4 kx4,1 0�81 12�84** 0�65 NPIS5 kx5,1 0�66 6�16** 0�43 NPIS7 kx6,1 0�72 8�25** 0�51 NPIS9 kx7,1 0�77 14�39** 0�60 NPIS11 kx8,1 0�77 11�36** 0�59 NPIS12 kx9,1 0�69 8�18** 0�47 NPIS13 kx10,1 0�78 9�45** 0�61 HADS-A HADS1 ky5,2 0�62 – 0�39 HADS3 ky7,2 0�73 7�04** 0�53 HADS5 ky11,2 0�62 4�65** 0�39 HADS9 ky13,2 0�69 5�60** 0�40 HADS13 kx14,2 0�66 6�00** 0�43 HADS-D HADS2 ky1,1 0�74 – 0�54 HADS4 ky2,1 0�67 7�43** 0�45 HADS6 ky3,1 0�65 5�86** 0�42 HADS10 ky5,1 0�20 2�33* 0�04 HADS12 ky6,1 0�51 4�94** 0�26 qc NPIS 10 items qc 0�92 – – qc HADS-A 5 items qc 0�80 – –
  • 128. qc HADS-D 5 items qc 0�70 – – Standardised factor loadings and t-values. Squared multiple correla- tions (R2). †Composite reliability, qc ¼ P kð Þ2 P kð Þ2þP hð Þ � � (Hair et al. 2010). *p < 0�05; **p < 0�01. HADS, Hospital Anxiety and Depression Scale; NPIS, Nurse– Patient Interaction Scale. © 2013 Blackwell Publishing Ltd 2196 Journal of Clinical Nursing, 22, 2192–2205 G Haugan et al. p < 0�0110, RMSEA = 0�032, SRMR = 0�045, NFI = 0�97, NNFI = 0�99, CFI = 1�00) and the two-factor construct (HADS) of anxiety and depression comprising 10 items (v2 = 54�22, df = 34, p < 0�015, RMSEA = 0�056, SRMR = 0�071, NFI = 0�93, NNFI = 0�96, CFI = 0�97). All parameter estimates were significant (p < 0�05) and loaded positively and clearly on their intended latent vari-
  • 129. able with standardised factor loadings between 0�20–0�81. For scaling, the first factor loadings of each dependent latent variable were set to 1. Results Descriptive analysis Table 1 displays the means (M), standard deviations (SD), Cronbach’s a and Pearson’s correlation matrix for the con- structs of nurse–patient interaction, anxiety and depression. The correlations between the measures were in the expected direction. Moderate correlations were found between the latent constructs included in the SEM model (Table 1). The a-levels for the various measures indicate an acceptable level of inter-item consistency in the measures (Nunally & Bernstein 1994) with Cronbach’s a coefficients of 0�66 or higher. Structural equation modelling (SEM) To investigate how the nurse–patient interaction related to anxiety and depression, model-1 was estimated. Figure 1 shows Model-1 with its measurement and structural
  • 130. models, while Table 2 displays the factor loadings, R2 and t-values. All estimates were significant (p < 0�05) and the factor loadings ranged between 0�51–0�81 (except from item HADS10 ‘I have lost interest in my appearance’ with factor loading = 0�20 and R2 = 0�04) and R2 values between 0�26–0�65. Model-1 fit well with the data: v2 = 211�44, p = 0�011, df = 167, RMSEA = 0�037, p- value = 0�92, NFI = 0�94, NNFI = 0�99, CFI = 0�99, and SRMR = 0�060. Table 3 shows the standardised regression coefficients of the directional relationships and the total and indirect effects between the latent constructs in Model-1. As hypothesised, the directional paths from nurse–patient interaction to depression displayed a significant negative relationship (c1,1 = �0�37). The path between nurse– patient interaction and anxiety was not significant (c1,2 = �0�09); however, a significant path from depression to anxiety (b1,2 = 0�55) was found, indicating a mediated effect (by depression) on anxiety (Table 3). A scrutiny of the total effects of nurse–patient interaction revealed statistical significant total effects on depression (�0�37), as well as a significant total effect on anxiety from depression (0�55). Also, a significant indirect (mediated)
  • 131. effect from nurse–patient interaction on anxiety (�0�20) was displayed (Table 3). Discussion The aim of this study was to explore the associations between nurse–patient interaction, anxiety, and depression in cognitively intact NH patients. By doing so we sought to contribute to a holistic nursing perspective of promoting well-being in NH patients in three ways: 1 This study supplies empirical knowledge to the growing body of nurse–patient interaction knowledge by exploring Figure 1 SEM Model-1. Measurement models and directional relationships for nurse–patient interaction (NPIS) to anxiety (HADS-A) and depression (HADS-D). © 2013 Blackwell Publishing Ltd Journal of Clinical Nursing, 22, 2192–2205 2197 Clinical issues Nurse-patient-interaction, depression, and anxiety cognitively intact NH patient’s’ experiences of the nurse–
  • 132. patient interaction. 2 This study provides empirical insight to the associations between, nurse–patient interaction, anxiety, and depres- sion in a NH population, and 3 By means of advanced statistical analysis such as struc- tural equation modelling (SEM), the result from this study suggests a guideline for clinical nursing strategies promoting well-being and decreasing depression and anx- iety in NH patients. Finding ways to improve NH staff nurses’ way of being present, connecting, and interacting with the patients might be beneficial in this matter. More specifically, three hypotheses were tested, from which two were supported (H1, H3). We found that the hypothesised relationship between nurse–patient interaction and depression was fully supported; good nurse–patient interaction was negatively associated with depression; the better nurse–patient interaction the less depression. The path from nurse–patient interaction to anxiety was not sig-
  • 133. nificant; however, a significant relation between depression and anxiety was found. Accordingly, also an indirect effect of nurse–patient interaction on anxiety was displayed, med- iated by the influence on depression. Hence, the model tested indicates that nurse–patient interaction influences both depression and anxiety. These findings are consistent with previous research demonstrating significantly decrease in depression for NH patients receiving self-worth therapy and positive attention from NH staff (Tsai et al. 2008, Meeks & Looney 2011). Consequently, nursing approaches facilitating NH patient’s confidence and trust in the staff nurses might pre- vent and decrease depression in NH patients. In accordance with former research, trust is a core moral ingredient in helping relationships (Carter 2009). Therefore, facilitating patients’ confidence that the staff nurses make all possible effort to relieve ones’ plagues appear to be crucial for pre- serving dignity (Cochinov 2002) and prevent depression.
  • 134. Professional nursing care is determined by the way nurses are using their knowledge, attitudes, behaviour and com- munication skills to appreciate the uniqueness of the person being cared for (Warelow et al. 2008). Accordingly, nurse– patient interaction fostering experiences of being respected and recognised as a person, being listened to and taken seri- ously are positively associated with lower depression scores among NH patients. Previous research underlines that the nurse–patient relationships and the nurse–patient interaction are critical to patients’ sense of dignity, self-respect, feelings of self- worth, meaning in life, and well-being (Haugan Hovdenes 2002, Dwyer et al. 2008, Harrefors et al. 2009, Heliker 2009). Moreover, dignity in NH patients has been differen- tiated into intrapersonal dignity and relational dignity, socially constructed by the act of recognition (Pleschberger 2007). Thus, nurse–patient interaction facilitating patients’ sense of being taken seriously and recognised as a unique
  • 135. person might provide a sense of dignity, self-worth, and thereby prevent and decrease depression among NH patients. Consequently, taking time for interestingly listening to the NH patient appears as vital for preventing and decreas- ing depression. Former research has pointed to continuity of care provider to be critical for developing relationships with patients’ overtime (McGilton 2002). Moreover, mutu- ality in individuals’ relationships confirming women’s exis- tence and value has been described as a major influence on depression in women, whereas depressive symptoms results Table 3 Structural equation modelling analysis: Model-1, standar- dised gamma, total and indirect effects of nurse–patient interaction on nursing home patients’ anxiety and depression Construct Parameter Lisrel estimate t-value NPIS to HADS-A c 1,1 �0�09 �0�84 NPIS to HADS-D c 1,2 �0�37 �4�58** HADS-D to HADS-A b 1,2 0�55 4�05**
  • 136. NPIS t-value Total effects of nurse–patient interaction on anxiety and depression HADS-D HADS2 �0�23 �4�58** HADS4 �0�21 �4�32** HADS6 �0�18 �3�62** HADS10 �0�08 �2�03* HADS12 �0�18 �3�48** HADS-A HADS1 �0�05 �1�30 HADS3 �0�06 �1�34 HADS5 �0�05 �1�27 HADS9 �0�05 �1�25 HADS13 �0�05 �1�36 Indirect effect of nurse–patient interaction on anxiety and depression Anxiety �0�20 �3�11** Depression – – *Significant at the 5% level. **Significant at the 1% level. Model 1: comprising six HADS-variables and 10 NPIS- variables. Standardised gamma: standardised regression coefficients
  • 137. represent- ing directional relationships between NPIS, anxiety, and depression. Total effects: represents the total influence of the explanatory variable on anxiety and depression. Indirect effects: mediated influence. HADS, Hospital Anxiety and Depression Scale; NPIS, Nurse– Patient Interaction Scale. © 2013 Blackwell Publishing Ltd 2198 Journal of Clinical Nursing, 22, 2192–2205 G Haugan et al. from violating their sense of worthiness (Hedelin & Jonsson 2003). Consequently, nurses must be aware that their atti- tude, appearance and behaviour are interpreted as a confir- mation of the patient’s worthiness or worthlessness (ibid.). In a recent study investigating the concept of receiving care, one main theme was identified; ‘being of value despite any potential disadvantages’ (Lundgren & Berg 2011). NH
  • 138. patients are particularly vulnerable and dependent, thus there are not many choices available. Receiving care high- lights the human mode of being, which includes experiences of being exposed resulting in an increased sense of vulnera- bility; in turn, this motivates a seeking for valued and appreciated mutual interactions within a caring process (ibid.). Thus, taking time, ensuring continuity, and being educated in interactional skills are not enough to enhance well-being, a sense of meaning in life, and decrease depres- sion; the care provider must be engaged in some way, such as learning about the person through life histories (McGil- ton & Boscart 2007, Walent 2008, Heliker 2009, Heliker & Hoang Thanh 2010, Medvene & Lann-Wolcott 2010, Wright 2010). The NH patient needs to feel understood, acknowledged, confirmed, and valued, all of which provides a sense of meaning in life, self-worth, and alleviates suffer- ing (Haugan Hovdenes 2002, Medvene & Lann-Wolcott 2010).
  • 139. Nursing homes are unique social environments; tradition- ally, they offer limited privacy opportunities. Accordingly NH patients may have infrequent contact with friends and family members. Thus, NH staff nurses are particularly the most important providers of social reinforcement (Haugan Hovdenes 2002, Drageset et al. 2012). However, research illustrates that NH staff rarely engages in social interac- tions during mealtimes and does not appreciate this as an important part of their duties (Pearson & Fitzgerald 2003), as well as hardly responds to patients’ social engagement, and seldom displays engagement-supportive behaviour (Stabell et al. 2004, Meeks & Looney 2011). Caregiving relationships involve all kinds of social interaction during the course of which the patient’s sense of self-worth can either be enhanced or thwarted (Haugan Hovdenes 2002, Hedelin & Jonsson 2003, Halldorsdottir 2008). The nurse–patient relationship has been designated as a sense of spiritual con- nection which is experienced as a bond of energy (Hall-
  • 140. dorsdottir 2008); a life-giving nurse–patient interaction which is greatly empowering for the patient. By confirma- tion, recognising, and empowering the older individuals’ views of who they are and would like to be, NH staff nurses can positively influence NH patients well-being (Randers et al. 2002, Tsai et al. 2008, Haugan et al. 2012), thriving (Bergland & Kirkevold 2005), and consequently depression (Haugan & Innstrand 2012) and anxiety. Strengths and limitations A notable strength of this research is the empirical exami- nation of associations that have not been tested previously. This study expands previous studies by testing the asso- ciations between nurse–patient interaction, anxiety, and depression among NH patients by using structural equa- tion modelling. Using SEM accounts for random measure- ment error and the psychometric properties of the scales in the model are more accurately derived. The study builds
  • 141. on a strong theoretical foundation with use of question- naires demonstrating good psychometrical properties. Nev- ertheless, the findings of this study must be discussed with some limitations in mind. First, Model-1 comprises 20 variables, indicating a desir- able n = 200, while in the present study, n = 191. Informa- tion input to the SEM estimation increases both with more indicators per latent variable, as well as with more sample observations (Westland 2010). The latent variables in the model are measured by five and ten indicators that strengthen the reliability. In this respect, the sample size in the present study is suitable. Nevertheless, a larger sample would significantly increase statistical power of the tests. The present sample included fewer men than women, reflecting the gender composition among the population of that age in NHs. Second, the cross-sectional design does not allow us to determine conclusion regarding causality. A longitudinal
  • 142. design would have strengthened the study by allowing changes to be assessed and compared over time. A third limitation concerns the use of self-reported data, which implies a certain risk that the findings are based on common-method variance (Podsakoff et al. 2003). The fact that the researchers visited the participants to help fill in the questionnaires might have introduced some bias into the respondents’ reporting. The questionnaires were part of a battery of questionnaires comprising 130 items. Thus, frail, older NH patients might tire when com- pleting the questionnaires; this represents a possible bias to their reporting. To avoid such a bias, experienced research- ers were carefully selected and trained in conducting the interviews following a standardised procedure including taking small breaks on specific points during the process. This procedure worked out very well; in just three cases the interview had to be completed the next day due to respon- dent’s fatigue. Actually, most participants were even more
  • 143. vigorous after completing the interview. © 2013 Blackwell Publishing Ltd Journal of Clinical Nursing, 22, 2192–2205 2199 Clinical issues Nurse-patient-interaction, depression, and anxiety Relevance to clinical nursing According to the European Commission’s Green Paper on mental health (COM 2005), depression is one of the most prevalent mental health problems facing European citizens today. Taking into account the highly chronic nature of these psychological states, we consider our findings noteworthy in their suggestion that nurse–patient interaction might be an important resource in relation to NH patients’ mental health. Knowledge of how nurse–patient interaction, anxiety and depression relate to each other in this respect is important for researchers, nurses, nursing educators and clinicians. This study demonstrates that nurse–patient interaction influences depression as well as anxiety mediated by depres-
  • 144. sion. Accordingly, facilitating nurse–patient interaction to provide patients’ sense of worthiness, meaning in life, self- acceptance and adjustment to the life situation and one’s disabilities would promote integrity and well-being and pre- vent despair and depression. Due to a combination of factors such as patients’ commu- nication impairment, clinicians’ focus on treating medical conditions, normalisation of depression in later life and a lack of training in mental health among staff in NHs, depres- sion can easily go undetected among the NH population (Bagley et al. 2000, Martin et al. 2007). Therefore, facilitat- ing nurse–patient interaction and the staff nurses’ awareness in assessing patients’ mood and connectedness resources appear to be crucial. Offering connectedness might be a cen- tral aspect of NH care (Lundman et al. 2010); enhancing inner strength by acceptance of the self, death and one’s life situation might prevent and decrease depression among NH patients (Haugan & Innstrand 2012).
  • 145. The interpersonal relationship in nurse–patient interac- tions has been found to be an essential factor of quality of care, as perceived by long-term care patients (Haugan Hovd- enes 2002, Bergland & Kirkevold 2006, Brown Wilson & Davies 2009). Nurse–patient interaction can enhance both intrapersonal and interpersonal self-transcendence (Haugan et al. 2012) and help NH patients preserve their dignity, identity and integrity (Coughlan & Ward 2007, Tsai et al. 2008, Burack et al. 2012). By means of listening to the patients, communicating and treating the patients with respect, by using empathic understanding, and acknowledg- ing him/her as a person who is to be taken seriously and attended to, staff nurses might positively influence depres- sion, anxiety, and well-being (Hollinger-Samson & Pearson 2000, Haugan Hovdenes 2002, Asmuth 2004, Finch 2006, Jonas-Simpson et al. 2006, Haugan et al. 2012). Therefore, NH staff nurses should be given more time available interacting with their patients. A philosophical
  • 146. shift from care and protection of the body to a person-cen- tred care would be beneficial (Medvene & Lann-Wolcott 2010, Wright 2010, Jones 2011). In addition, some factors seem crucial regarding quality of nurse–patient interaction; in general, staffing levels are low while staff turnover is high (Baughman & Smith 2010). Further, staff members are generally poorly trained in nurse–patient interaction providing well-being, and often they perceive a lack of autonomy in job performance, feeling that they are not respected for management (Castle & Engberg 2007, Caspar & O’Rourke 2008, Bishop et al. 2009). To become a car- ing caregiver, one must first be treated in a caring way (Sikma 2006, Tellis-Nayak 2007). Hence, moving from the traditional institutional model to a responsive, patient-cen- tred homelike approach might have benefits for both NH patients and staff (Jones 2010). Educational nursing curric- ula should underline and facilitate nurse–patient interaction in order of advancing staff nurses’ presence to assess,
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  • 181. Warelow P, Edward KL & Vinek J (2008) Care: what nurses say and what nurses do. Holistic Nursing Practice 22, 146–153. Watson J (1988) Nursing: Human Science and Human Care. A Theory of Nurs- ing. National League for Nursing, New York, NY. Watson J (2008) The Philosophy and Sci- ence of Caring. University Press of Colorado, Boulder, CO. Watson L, Garret J, Sloane P, Gruber-Baldi- ni AL & Zimmerman S (2003) Depres- sion in assisted living: results from a four-state study. American Journal of Geriatric Psychiatry 11, 534–542. Westland JC (2010) Lower bounds on sam- ple size in structural equation model-
  • 182. ing. Electronic Commerce Research and Applications 9, 476–487. WHO (2001) World Health Report 2001. Wright K (2010) Prevention and treatment of depression in care homes. Nursing & Residential Care 12, 188–191. Zeller JM & Lamb K (2011) Mindfulness meditation to improve care quality and quality of life in long-term care settings. Geriatric Nursing 32, 114– 118. Appendix 1. Measurement instrument: Nurse–Patient Interaction Scale (NPIS) means score and standard deviation NPIS item Total n = 199 Men n = 56
  • 183. Women n = 143 Mean SD Mean SD Mean SD NPIS1 Having confidence and trust in the nurses 8�75 1�948 8�84 1�886 8�71 1�977 NPIS2 The nurses take me seriously 8�44 2�185 8�43 2�214 8�44 2�181 NPIS3 Interaction with the nurses makes me feel good 8�02 2�252 8�21 2�230 7�94 2�264 NPIS4 The nurses understand me 7�65 2�419 7�71 2�341 7�63 2�457 NPIS5 The nurses make all possible effort to relieve my plagues 8�80 1�969 9�02 1�753 8�71 2�047 NPIS6 The nurses involve me in decisions regarding my daily life 6�96 2�984 6�45 2�923 7�16 2�993 NPIS7 The nurses treat me with respect 8�68 2�175 8�70 2�097 8�68 2�213 NPIS8 The nurses ask me how I am 7�70 2�959 7�82 2�924 7�66 2�981 NPIS9 The nurses are listening interestingly to me 7�47 2�743 7�74 2�474 7�37 2�840 NPIS10 I often get hurt or sad from how the nurses interact* 8�29 2�510 8�15 2�825 8�35 2�386 NPIS11 Interaction with the nurses contributes to meaning in my life 7�83 2�531 8�04 2�449 7�75 2�567 NPIS12 The nurses pay attention to me as a person 8�07 2�266 8�13 2�281 8�05 2�268 NPIS13 I am satisfied with the communication with the nurses 8�32 2�126 8�54 2�097 8�23 2�138 NPIS14 Interaction with nurses is the most important to my thriving 8�86 2�066 9�09 1�890 8�78 2�131 NPIS: total means score 8�13 1�630 8�21 1�654 8�11 1�626
  • 184. The NPIS is based on a 10-point scale ranging from 1 (not at all) to 10 (very much). The concept of nurses involves all staff caregivers. *The item NPIS10 is reversed. © 2013 Blackwell Publishing Ltd 2204 Journal of Clinical Nursing, 22, 2192–2205 G Haugan et al. Appendix 2. Measurement instrument: Hospital Anxiety and Depression Scale (HADS) means score and standard deviation HADS item Total n = 198 Men n = 56 Women n = 142 Mean SD Mean SD Mean SD HADS1 I feel tense and wound up 0�460 0�680 0�339 0�581 0�507 0�712
  • 185. HADS2 I still enjoy the things I used to enjoy 0�879 0�822 0�893 0�779 0�873 0�841 HADS3 I get a sort of frightened feeling as if something awful is about to happen 0�434 0�709 0�400 0�655 0�447 0�731 HADS4 I can laugh and see the funny side of things 0�621 0�845 0�589 0�826 0�634 0�855 HADS5 Worrying thoughts go through my mind 0�394 0�710 0�339 0�640 0�416 0�737 HADS6 I feel cheerful 0�533 0�759 0�582 0�686 0�514 0�788 HADS7 I can sit at ease and feel relaxed 0�459 0�689 0�611 0�738 0�401 0�664 HADS8 I feel as I’m slowed down 1�209 1�075 1�226 1�050 1�203 1�088 HADS9 I get a sort of frightened feeling like ‘butterflies’ in the stomach 0�369 0�613 0�339 0�581 0�380 0�627 HADS10 I have lost interest in my appearance 0�803 1�079 0�946 1�119 0�747 1�061 HADS11 I feel restless as if have to be on the move 0�523 0�793 0�636 0�825 0�479 0�778 HADS12 I look forward with enjoyment to things 0�864 0�943 0�857 0�999 0�866 0�924 HADS13 I get sudden feeling of panic 0�347 0�658 0�357 0�699 0�343 0�643 HADS14 I can enjoy a good book or TV programme 0�561 0�857 0�464 0�713 0�599 0�907 HADS anxiety sum score 3�03 2�903 3�03 2�903 2�99 3�383 HADS depression sum score 5�57 3�238 5�57 3�238 5�42 3�879 HADS: total means score 8�59 5�079 8�59 5�079 8�40 6�078 The STS is based on a 4-point scale ranging from 0 (not at all), 1 (very little), 2 (somewhat) to 3 (very much).
  • 186. The Journal of Clinical Nursing (JCN) is an international, peer reviewed journal that aims to promote a high standard of clinically related scholarship which supports the practice and discipline of nursing. For further information and full author guidelines, please visit JCN on the Wiley Online Library website: http://wiley onlinelibrary.com/journal/jocn Reasons to submit your paper to JCN: High-impact forum: one of the world’s most cited nursing journals, with an impact factor of 1�118 – ranked 30/95 (Nursing (Social Science)) and 34/97 Nursing (Science) in the 2011 Journal Citation Reports® (Thomson Reuters, 2011). One of the most read nursing journals in the world: over 1�9 million full text accesses in 2011 and accessible in over 8000 libraries worldwide (including over 3500 in developing countries with free or low cost access). Early View: fully citable online publication ahead of inclusion in an issue. Fast and easy online submission: online submission at http://mc.manuscriptcentral.com/jcnur. Positive publishing experience: rapid double-blind peer review with constructive feedback. Online Open: the option to make your article freely and openly accessible to non-subscribers upon publication in Wiley Online Library, as well as the option to deposit the article in your preferred archive.
  • 187. © 2013 Blackwell Publishing Ltd Journal of Clinical Nursing, 22, 2192–2205 2205 Clinical issues Nurse-patient-interaction, depression, and anxiety J O U R N A L O F T R A U M A N U R S I N G WWW.JOURNALOFTRAUMANURSING.COM 17 R E S E A R C H ABSTRACT A retrospective study examined in-hospital antidepressant medication (ADM) use in adult trauma patients with an intensive care unit stay of 5 or more days. One fourth of patients received an ADM, with only 33% of those patients having a documented history of depression. Of patients who received their first ADM from a trauma or critical care physician, only 5% were discharged with a documented plan for psychiatric follow-up. The study identified a need for standardized identification and management of depressive symptoms among trauma patients in the inpatient setting.
  • 188. Key Words antidepressant medication , critical care , depression , injury , psychiatry , trauma Author Affiliations: UnityPoint Health, Des Moines, Iowa (Ms Spilman and Drs Smith and Tonui); and Fort Sanders Regional Medical Center, Knoxville, Tennessee (Dr Schirmer). The abstract was presented at 47th Annual Society for Epidemiological Research (SER) Meeting, Seattle, Washington, June 24–27, 2014. None of the authors have any conflicts of interest to disclose. Correspondence: Sarah K. Spilman, MA, Trauma Services, Iowa Methodist Medical Center, 1200 Pleasant St, Des Moines, IA 50309 ( [email protected] unitypoint.org ). Evaluation and Treatment of Depression in Adult Trauma Patients Sarah K. Spilman , MA ■ Hayden L. Smith , PhD ■ Lori L. Schirmer , PharmD ■ Peter M. Tonui , MD
  • 189. approaches require resources and training of hospital personnel. 5 Regardless of the method, however, assess- ment of depression is often confounded by the variable nature of depressive symptoms. Some depressive symp- toms (eg, fatigue, insomnia, weight loss) can be similar to symptoms of other medical illnesses or may resemble temporary conditions, such as delirium or adjustment dis- order. 6 , 7 In addition, trauma patients in the intensive care unit (ICU) may often lack the ability to display or report classic depressive symptoms due to the effects of medica- tion, pain, or sleep deprivation. 8 , 9 A major issue, though, is that many hospitals do not routinely screen for depression or assess depressive symptoms during hospitalization. To our knowledge, there is no consensus as to when assessments (and re- assessments) are appropriate. Symptoms of depression most often are noted through subjective observation by family or nurses and reported to physicians. Because of limited resources, mental health experts are often only involved in the most severe or complicated cases. This is a fundamental problem in that large numbers of patients may be overlooked because of the subjective nature and timing of these observations. Findley and colleagues 4 found that when a psychiatrist was actively involved in the trauma service, identification and treatment of psy- chopathology were increased by 78%. While the rate of mood and anxiety disorders recognized by trauma phy- sicians remained unchanged, involvement of psychiatry resulted in a broader range of psychiatric diagnoses and more than doubled the treatment of substance abuse or dependence. Complicating matters further, many trauma patients present with preexisting depression. Traumatic injury is related to depression as both a causal factor and a result-
  • 190. ing condition. 2 , 4 , 10 If patients are unable to self-report their health history, the trauma team relies on family report or pharmacy records. This presents challenges in timely reinitiation of medications. STUDY RATIONALE A review of the medical literature found no relevant published research on physician and medical team re- sponse to depressive symptoms during the patient’s ini- tial hospitalization within settings where mental health screening is not the standard of care. Current research DOI: 10.1097/JTN.0000000000000102 I t is well-established in the literature that critically ill trauma patients can often suffer from depression and posttraumatic stress disorder in the months and years following hospitalization. 1-3 Many hospitals may not have a standardized process for assessing and treat- ing trauma patients with depressive symptoms. 3-5 During the acute phase of recovery, the trauma team is primarily in charge of treating the injuries and preparing to dis- charge the patient to the next phase of recovery. With- out a standardized process for recognizing, screening, and treating the psychological and emotional needs of the patient, there may be increased risk that depression will go unrecognized and untreated or misinterpreted and improperly treated. Formal assessment of depression can be accom- plished through clinical interview or screening tools; both Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited.
  • 191. JTN-D-14-00071.indd 17JTN-D-14-00071.indd 17 06/01/15 8:45 PM06/01/15 8:45 PM 18 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 | Number 1 | January-February 2015 that examines depression screening has been primarily funded by grant dollars, which provide hospitals with resources to staff special assessment teams (eg, Dicker et al 2 ) and may not represent practices at many hospi- tals. These studies have established the importance of early detection of depression, although this may be ex- tremely difficult in hospitals that do not have protocols for managing depression in the critically ill or special teams for assessing, treating, and reassessing mental health symptoms. The purpose of this study was to examine how a trau- ma team recognizes and treats depression in the absence of a screening tool and to document antidepressant medi- cation (ADM) usage and prescribing patterns. Study data can assist in the evaluation and understanding of institu- tion processes and possibly help design protocols to miti- gate some of the long-term mental health issues that can result from traumatic injury. METHODS Study Design and Patient Sample A retrospective study was performed at an urban tertiary hospital in the Midwestern region of the United States. The hospital’s trauma registry was used to identify adult patients (aged 18 years or older) who met trauma criteria
  • 192. during the 5-year study period of 2008 to 2012. A trauma patient was defined as an individual who sustained a traumatic injury with an International Classification of Diseases, 9th Revision, Clinical Modification code rang- ing from 800 and 959.9, excluding codes for late effects of injury (905-909.9), superficial injuries (910-924.9), and foreign bodies (930-939.9). Patients were included in the study if they were admitted to the hospital and stayed in the ICU for 5 or more days. The study was approved by the hospital’s institutional review board. Study Data Study variables were grouped into 3 categories: patient and injury characteristics, depression diagnoses, and ADM use. Patient characteristics included gender, race, age, hospital length of stay (LOS), ICU LOS, and mechani- cal ventilator days. Discharge status was coded as alive or deceased, while discharge location was coded as home or institutional setting (including hospice facility, rehabili- tation facility, skilled nursing facility, federal hospital, or intermediate care facility). Injury characteristics included the Injury Severity Score, which is an anatomical coding system ranging from 0 (no injury) to 75 (most severe). Finally, mechanism of injury was recorded on the basis of the External Causes of In- jury and Poisoning Code (E-Code): Vehicle accident (810- 848), Accidental Fall (880-888), or Other. Depression diagnoses were assessed retrospectively through chart review. Patients were classified as having a documented history of depression if it was specifically noted in the medical history or if the patient was taking an ADM at the time of hospital admission. If the patient’s history was not obtained at admission, the patient was considered to be on a prior ADM if he or she received
  • 193. a dose within the first 72 hours of the hospital stay. We also noted if a patient received a psychiatric consultation during their stay and if the patient was discharged with a plan for psychiatric follow-up. The latter was used to indicate whether or not discharge instructions included directions for psychiatry follow-up. The ADM use was ascertained through pharmacy dis- pensing records. Specifically, it was recorded if a patient received any of the following drugs: selective seroto- nin reuptake inhibitors (SSRIs; citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline); selective norepinephrine reuptake inhibitors (SNRIs; desvenlafax- ine, duloxetine, venlafaxine); dopamine reuptake inhibi- tors (bupropion); and alpha-2 antagonists (mirtazapine). Some ADMs were excluded from the study, including tricyclics and monoamine oxidase inhibitors, which can be used to treat other diagnoses in addition to depres- sion; vilazodone, which was not approved by the Food & Drug Administration until 2011; trazodone because it can be prescribed as a sleep aid; and milnacipran because its Food & Drug Administration indication is for fibromyalgia. The first dispensed ADM was used for basic descrip- tive purposes. For example, if a patient received multiple ADMs during the stay, only the first ADM was used to describe patient treatment. If an ADM was not a medica- tion taken prior to admission, it is hereafter referred to as a new ADM. Days between hospital admission and first ADM dose were used to calculate time of initiation. If an ADM medication was listed in the discharge summary or the patient received a dose of the medication on the last day of the stay, then the patient was classified as being discharged on an ADM. Statistical Procedures
  • 194. Descriptive statistics were reported for continuous data as medians with interquartile ranges; normality was tested using the Shapiro-Wilk test. Categorical data were re- ported as counts with percentages. Comparative statistics were conducted by stratifying patients based on whether or not they received an ADM during their hospital stay. These groups were examined using the Fisher exact test (nominal variables) and the independent samples Mann- Whitney U Test (2-group medians). All analyses were 2-tailed and based on a 0.05 significance level. Analy- ses were performed with IBM SPSS Basic Statistics for Windows, version 19.0 (IBM Corp, 2010; Armonk, NY). Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited. JTN-D-14-00071.indd 18JTN-D-14-00071.indd 18 06/01/15 8:45 PM06/01/15 8:45 PM J O U R N A L O F T R A U M A N U R S I N G WWW.JOURNALOFTRAUMANURSING.COM 19 Trauma Patients Admitted During Study Period (N=4947) ICU Length of Stay < 5 Days (n=4635) ICU Length of Stay > 5 Days (n=312) Received No Antidepressant Medication (n=230)
  • 195. Restarted Antidepressant Medication (n=55) Received New Antidepressant Medication (n=27) Figure 1. Trauma patients admitted to the hospital during the study period, 2008-2012. ICU indicates intensive care unit. RESULTS There were 4947 trauma patients admitted to the hospital during the 5-year study period, with 312 (6.3%) staying in the ICU for 5 or more days (see Figure 1 ). Patient char- acteristics are presented in Table 1 . More than two-thirds of the patients in the study sample were male, and the majority of patients were white. Fifteen percent of the patients died. There were 82 patients (26.3%) who received an ADM during the hospital stay (see Table 2 ). Bivariate analy- ses (not shown) revealed significant differences in age, with older patients more likely to receive an ADM than younger patients ( P = .002). Men were less likely to re- ceive an ADM. There were no significant bivariate differ- ences between patients based on hospital LOS, ICU LOS, ventilator days, Injury Severity Score, discharge location, or injury mechanism. Patients who received an ADM during the hospital stay were significantly more likely to have a documented his- tory of depression upon admission to the hospital. Specif- ically, 67.1% of patients who received an ADM during the hospital stay were taking an ADM prior to admission and 19.5% had depression mentioned in their medical history. Patients who received an ADM were also significantly more likely to receive a psychiatric consultation during
  • 196. the hospital stay and were more likely to be discharged with a plan for psychiatric follow-up. Of the 82 trauma patients who received an ADM dur- ing hospitalization, 9 (11.0%) were initiated by a psychia- trist and 73 (89.0%) were initiated by a critical care or other nonpsychiatric physician (see Table 3 ). One-third of patients who received an ADM during their stay were prescribed a new ADM; 29.6% of these new prescriptions were initiated by psychiatry and 70.3% were initiated by a nonpsychiatric physician. There were no significant differences in ADM choice based on the physician who initiated the medication. Patients whose ADM was prescribed by a psychiatrist received their first dose many days later in the hospital stay than those patients whose ADM was prescribed by a critical care or other physician. Patients whose ADM was prescribed by psychiatry were also more likely to be discharged with a plan for psychiatric follow-up. Nearly all patients who received an ADM during hospitalization were discharged with the medication, regardless of the provider who initiated it. DISCUSSION Study data revealed that 26.3% of trauma patients spend- ing 5 of more days in the ICU received an ADM during the hospital stay; 33% of these patients did not have a documented history of depression or ADM use upon ad- mission. This is considerably higher than ADM use in the general population, which is estimated at 10% to 11%. 11 , 12 Female trauma patients were more likely to receive an ADM than male patients, which is consistent with trends in the general population. 12
  • 197. Trauma or critical care physicians were the practition- ers most likely to continue home ADMs and initiate new ADMs, compared with psychiatry physicians. However, TABLE 1 Descriptive Characteristics for Trauma Patients With Intensive Care Unit Length of Stay 5 or More Days, 2008-2012 (N = 312) a All Trauma Patients (N = 312) Male 218 (70.1%) White 271 (86.9%) Median age, y 55.00 (39.75-69.00) Median hospital length of stay, d 17 (10-25) Median intensive care unit length of stay, d 8.5 (6-14) Median ventilator days 5 (1.5-10) Deceased 48 (15.4%) Discharged to home 68 (25.8%) Median injury severity score 25 (15.5-33.25) Injury mechanism Vehicle accident 174 (55.8%)
  • 198. Fall 105 (33.7%) Other 31 (9.9%) a Data are presented in medians (interquartiles) and counts (percentages). Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited. JTN-D-14-00071.indd 19JTN-D-14-00071.indd 19 06/01/15 8:45 PM06/01/15 8:45 PM 20 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 | Number 1 | January-February 2015 leads to oversights in posttrauma care. Primary care physicians or other health care providers may not be alerted to the task of titrating the medication, monitor- ing adherence to the medication, and/or evaluating the appropriateness of the ADM in the months and years after the trauma. when the ADM was initiated by trauma or critical care physicians, patients were frequently discharged without a plan for psychiatric follow-up. This may be a critical omission, especially for patients started on new ADMs. Since the trauma or critical care physician manages care during the acute phase of the injury, this potentially TABLE 2 Prescription of Antidepressant Medication in Trauma Patients (N = 312) a
  • 199. ADM Received During Stay (n = 82) ADM Not Received During Stay (n = 230) Fisher Exact Test, P b Documented history of depression 56 (68.3%) 17 (7.4%) < .001 Taking ADM prior to admission 55 (67.1%) 7 (3.0%) < .001 Depression mentioned in medical history 16 (19.5%) 13 (5.7%) < .001 Received psychiatric consultation visit during stay 17 (20.7%) 18 (7.8%) .004 Discharged with plan for psychiatric follow-up c 8 (12.1%) 6 (3.0%) .009 Abbreviation: ADM, antidepressant medication. a Data are presented in medians (interquartiles) and counts (percentages). b p values are presented for comparisons between patients based on whether or not they received an ADM during their hospital stay.
  • 200. c Excludes patients who expired. TABLE 3 Prescribing Patterns for Patients Who Received an Antidepressant Medication, 2008-2012 (n = 82) a First Dose Prescribed by Psychiatry (n = 9) First ADM Prescribed by Other Physician (n = 73) P b Taking ADM prior to admission 1 (11.1%) 54 (74.0%) < .001 Median days between hospital admission and first dose 12 (7.25-19.75) 2.5 (2-7) .010 New ADM during hospitalization 8 (88.9%) 19 (26.0%) < .001 Escitalopram 5 (62.5%) 6 (31.6%) .206 Citalopram 2 (25.0%) 7 (36.8%) .676 Sertraline 1 (12.5%) 2 (10.5%) 1.00 Paroxetine … 2 (10.5%) … Mirtazapine … 1 (5.3%) … Venlafaxine … 1 (5.3%) … Discharged with plan for follow-up c
  • 201. 4 (44.4%) 4 (5.5%) 0.005 Discharged with prescription for ADM c 9 (100.0%) 62 (93.9%) 1.00 Abbreviation: ADM, antidepressant medication. a Data are presented in medians (interquartiles) and counts (percentages). b p values are presented for comparisons between patients based on whether the first dose of an ADM was authorized by a psychiatrist or another physician. c Excludes patients who expired. Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited. JTN-D-14-00071.indd 20JTN-D-14-00071.indd 20 06/01/15 8:45 PM06/01/15 8:45 PM J O U R N A L O F T R A U M A N U R S I N G WWW.JOURNALOFTRAUMANURSING.COM 21 The study explores ADM use in the trauma and acute care setting. While ADM use is not a direct measure of de- pression, it serves as a proxy for how trauma and critical care teams recognize and respond to depression in the absence of screening tools. Many hospitals lack a stand-
  • 202. ardized process for assessing and treating trauma patients with depressive symptoms; therefore, we expect that these results are generalizable to other facilities where mental health screening is not standard of care. When a patient is taking an ADM prior to hospitalization, the trauma team must ensure that the medications are restart- ed when the patient is hemodynamically stable. But the situation is less clear in prescribing new ADMs. Initiation of a new ADM may be done when the physician, nurse, or family members recognize emerging symptoms of de- pression or as a preventative approach for symptoms that are likely to emerge in the future. We recognize that pharmacological intervention should not be the first-line treatment to manage depres- sion. However, since our hospital does not have a stand- ardized screening tool for depression and does not have a mental health care professional embedded in the core trauma team, we believe that these findings are similar to patterns at other hospitals. It is presumed that patients are started on ADMs based on feedback from nursing and family members or recognition of depressive symptoms during the recovery process, but further analysis is war- ranted to determine how these decisions are made. Some patients may be placed on an ADM without warrant, and conversely, depressed patients may go untreated. Both scenarios may complicate recovery and lead to adverse psychological and physical outcomes following the trau- matic injury. It deserves mention that it would be ideal for all trau- ma patients to be screened for depression and mental health issues. This could potentially improve their recov- ery process and reduce the likelihood of traumatic injury in the future, 2 but such an approach may not be practical in many settings. It is unclear how often patients would
  • 203. need evaluation to detect change, and how well they can self-report their symptoms in the first place. Future re- search efforts should be directed at prospective evaluation of increased involvement of psychiatrists in the treatment of trauma patients, as well as increased use of screening tools for early detection of depressive symptoms. Because our trauma population is primarily older adults, additional analyses should focus on the mental health needs of this aging population and the role that preexisting depression plays in their injury patterns and recoveries. Limitations This study had several limitations. Identification of the prevalence of depression and ADM use was performed retrospectively. Reliance on medical documentation to Acknowledgments The authors thank Catherine Hackett Renner, James Rasmussen, and Eric Barlow for assistance in data collec- tion, analysis, and interpretation. REFERENCES 1. Bryant RA , O’Donnell ML , Creamer M , McFarlane AC , Clark CR , Silove D . The psychiatric sequelae of traumatic injury . Am J Psychiatry . 2010 ; 167 : 312-320 . 2. Dicker RA , Mah J , Lopez D , et al. Screening for mental illness in a trauma center: rooting out a risk factor for unintentional injury . J Trauma . 2011 ; 70 : 1337-1344 . 3. O’Donnell ML , Creamer M , Bryant RA , Schnyder U , Shalev
  • 204. A . Posttraumatic disorders following injury: an empirical and methodological review . Clin Psych Rev . 2003 ; 23 : 587-603 . 4. Findley JK , Sanders KB , Groves JE . The role of psychiatry in the management of acute trauma surgery patients . J Clin Psychiatry . 2003 ; 5 : 195-200 . 5. Steel JL , Dunlavy AC , Stillman J , Paper HC . Measuring depression and PTSD after trauma: common scales and checklists . Injury . 2011 : 42 : 288-300 . 6. Casey P , Bailey S . Adjustment disorders: the state of the art . World Psychiatry . 2011 ; 10 : 11-18 . 7. Jackson JC , Mitchell N , Hopkins RO . Cognitive functioning, mental health, and quality of life in ICU survivors: an overview . Crit Care Clin . 2009 ; 25 : 615-628 . determine history of depression may be inaccurate in in- stances for patients with an undocumented history of de- pression or patients who received depression diagnoses based on inadequate clinical assessments. Other mental health diagnoses may have been present, such as anxi- ety or adjustment disorder, but they too may have been misdiagnosed, underdiagnosed, or misdocumented. Re- latedly, information was lacking from the medical record if the patient or a family member was unable to provide a medical history upon admission to the emergency de- partment. Given the study design, it was not possible to
  • 205. access compliance with home medications. In particular, an ADM in a patient’s medical history may not neces- sarily depict whether the patient was actively taking the medication prior to admission. Finally, the main focus of the study was to examine how physicians assess and treat depression in trauma patients admitted to the ICU for 5 or more days, which does not allow for generalizations toward general trauma populations. CONCLUSIONS Despite difficulties in the diagnosis of depression in trauma patients, critical care physicians and psychiatrists do initiate ADMs in patients who exhibit symptoms of clinical depression. This study identifies a need to more accurately identify depressive symptoms among trauma patients and reveals a need for protocols to assess for mental health diagnoses and manage ADM use among trauma patients in the inpatient setting and postdis- charge. Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited. JTN-D-14-00071.indd 21JTN-D-14-00071.indd 21 06/01/15 8:45 PM06/01/15 8:45 PM 22 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 | Number 1 | January-February 2015 8. Beliles K , Stoudemire A . Psychopharmacologic treatment of depression in the medically ill . Psychosomatics . 1998 : 39 : S2S19 .
  • 206. 9. Jackson JC , Hart RP , Gordon SM , Hopkins RO , Girard TD , Ely EW . Post-traumatic stress disorder and post-traumatic stress symptoms following critical illness in medical intensive care unit patients: assessing the magnitude of the problem . Crit Care. 2007 ; 11 : R27 . 10. O’Donnell ML , Creamer M , Pattison P . Posttraumatic stress disorder and depression following trauma: understanding comorbidity . Am J Psychiatry . 2004 ; 161 : 1390-1396 . 11. Olfson M , Marcus SC . National patterns in antidepressant medication treatment . Arch Gen Psychiatry. 2009 ; 66 : 848- 856 . 12. Pratt LA , Brody DJ , Gu Q . Antidepressant use in persons aged 12 and over: United States, 2005-2008 . NCHS Data Brief . October 2011 : 76 . Copyright © 2015 Society of Trauma Nurses. Unauthorized reproduction of this article is prohibited. JTN-D-14-00071.indd 22JTN-D-14-00071.indd 22 06/01/15 8:45 PM06/01/15 8:45 PM Copyright of Journal of Trauma Nursing is the property of Society of Trauma Nurses and its content may not be copied or emailed to multiple sites or posted to a listserv without the
  • 207. copyright holder's express written permission. However, users may print, download, or email articles for individual use. Data Companion for a VFD Induction Motor Drive Procedure Step 12 22 RMS value of the fundamental-frequency component in the line voltage at the output of the three-phase PWM inverter without filtering: 177 V 24 36 51