Exam 1 - All Class PowerPoint Slides for stats

2
Mark A. Kaelin, Ed.D.
kaelinm@mail.montclair.edu
Science of Public Health: Epidemiology
Orientation and Expectations
January 17, 2017

3
1. What is epidemiology?
2. Enduring Epidemiological Understandings
3. Syllabus
4. Assigned Readings
January 17, 2018

4
Epidemiology is what epidemiologists do.
(Gilliam, 1963)
What is epidemiology?

5
A particular or detached incident or fact
of an interesting nature; a biographical incident or fragment;
a single passage of private life.
Anecdote

6
Anecdote
Science
DZ
Transforming Anecdote to Science

7
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Healthy
People
-
Healthy
People
E
Random
Assignment
E
DZ
DZ
DZ
DZ
Controlled Trial
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Healthy
People
-
Healthy
People
E
E
DZ
DZ
DZ
DZ
Cohort Study
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Case-Control Study
-
DZ
DZ
E
E
E
E
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Cross-Sectional Study
-
E
E
DZ
DZ

8
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Healthy
People
-
Healthy
People
E
Random
Assignment
E
DZ
DZ
DZ
DZ
Controlled Trial
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Healthy
People
-
Healthy
People
E
E
DZ
DZ
DZ
DZ
Cohort Study
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Case-Control Study
-
DZ
DZ
E
E
E
E
Time
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Cross-Sectional Study
-
E
E
DZ
DZ
d
b
c
a

9
Epidemiologists make rates,
compare rates,
and makes inferences
based on their similarities or differences.

10
Compare
Divide
Count

11
Detectives
Investigate crimes
Look for clues at a
crime scene
Judge quality of
evidence
Form hypotheses
Identify suspects
Present evidence
in court
Help control crime
Investigate health-related events
Look for clues in the
populations
Judge quality of
evidence
Form hypotheses
Identify suspected causes
Present evidence in scientific
journals and at scientific meetings
Help control disease
Epidemiologists

12
The term epidemiology is derived from the Greek:
epi : on, upon
demos : the people
logos : theory, source, the study of
(Webster's Unabridged Dictionary)

13
Disease
Frequency
Distribution
Determinants
Methods
Populations
Prevention
Review students’ definitions of epidemiology.

14
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)

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16

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18

19

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3. Syllabus
January 17, 2018

This course was developed by following curriculum design principles advocated
by Grant Wiggins and Jay McTighe in their text Understanding by Design. Their
contention is that effective curricula are created by identifying enduring
understandings and essential questions.
Enduring understandings are the big ideas that reside at the heart of a
discipline and have lasting value outside the classroom.
The essential questions are the questions that, when answered, create the
enduring understanding in the first place.
The teacher’s challenge is to create experiences that develop students' abilities
to answer the essential questions and, in doing so, develop the their own
enduring understandings.
Essential Questions and Enduring Epidemiological Understandings

22
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.

23
How can hypotheses be tested?
Is there an association between the hypothesized cause and the
health-related outcome?
A hypothesis can be tested by comparing the frequency of health-
related outcome in selected groups of people with and without an
exposure to determine if the exposure and the outcome are associated.
When an exposure is hypothesized to have a beneficial effect, studies
can be designed in which a group of people is intentionally exposed to
the hypothesized cause and compared to a group that is not exposed.
When an exposure is hypothesized to have a detrimental effect, it is not
ethical to intentionally expose a group of people. In these
circumstances, studies can be designed that observe groups of free-
living people with and without the exposure.
2.

24
Why did the association occur?
One possible explanation for finding an association is that the exposure
causes the outcome. Because studies are complicated by factors not
controlled by the investigator, other explanations also must be
considered.
3.

25
Is the association causal?
Judgments about whether an exposure causes a health-related
outcome are developed by examining a body of epidemiologic evidence
as well as evidence from other scientific disciplines.
4.

26
What should be done
when a cause of a health-related outcome is found?
Individual and societal decisions about what should be done to improve
health and prevent disease are based on more than scientific evidence.
Social, economic, ethical, environmental, cultural, and / or political
factors are also considered in decision-making.
5.

27
Did what was done work?
The effectiveness of a health-promoting strategy can be evaluated by
comparing the frequency of a health-related outcome in selected
groups of people who were and were not exposed to the strategy.
Costs, trade-offs, and alternative solutions must also be considered in
evaluating the strategy.
6.

28
Can epidemiological thinking be helpful
in exploring a non-health-related outcome?
An understanding of non-health related phenomena can be developed
through epidemiologic thinking, by identifying their patterns in
populations, formulating causal hypotheses, and testing those
hypotheses by making group comparisons.
7.

29
How can epidemiology contribute to exploring public health issues?
The causes of health are discoverable by systematically and rigorously
identifying their patterns in populations, formulating causal hypotheses,
and testing those hypotheses by making group comparisons. These
methods lie at the core of the science of epidemiology. Epidemiology is
the basic science of public health, a discipline responsible for improving
health and preventing disease in populations.
8.

30
“Learning with understanding is facilitated
when new and existing knowledge is structured around
the major concepts and principles of a discipline.”
National Research Council, (2002), Learning and Understanding.
Washington, D.C.: National Academy Press.
Enduring Epidemiological Understandings

31
Learners “… presented with vast amounts of content knowledge
that is not organized into meaningful patterns
are likely to forget what they have learned
and to be unable to apply the knowledge
to new problems or unfamiliar contexts.”
National Research Council , Learning and Understanding

32
Ken Bain, What the Best College Teachers Do
“… they can distinguish between foundational concepts
and elaborations or illustrations of those ideas.”

33
“… to see past the surface
features of any problem to
the deeper, more
fundamental principles of
the discipline.”
National Research Council
Learning and Understanding

34
To understand something
as a specific instance
of a more general case
… is to have learned
not only a specific thing
but also a model for
understanding other things like
it that one may encounter.”
Jerome Bruner,
The Process of Education, 1960

To understand something as a specific instance of a more general case
… is to have learned not only a specific thing but also a model
for understanding other things like it that one may encounter.
Jerome Bruner, The Process of Education, 1960
Students Shown Dangers of Texting while Driving

What Science Says about Marijuana

39

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Grant Wiggins and Jay McTighe, authors of Understanding by Design,
call the major concepts and principles of a discipline enduring understandings.
• Enduring, big ideas, having lasting value outside the classroom
• Big ideas and core processes at the heart of the discipline
• Abstract, counterintuitive, and often misunderstood ideas
• Big ideas embedded in facts, skills, and activities
Enduring Understandings

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3. Syllabus
January 17, 2018

Exam 1 - All Class PowerPoint Slides for stats

44
• Definition or Description / Relationship
Exam 1 Preparation

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• Selected PowerPoint Slides
• Form a Hypothesis
• Assigned Readings, Videos and Podcasts
Exam 1 Preparation

Explain the relationship between the article, XXXXXXXXXX, and the content
of this course to date.
• Adult Use of Prescription Opioid Pain Medications — Utah, 2008
(MMWR)
• Colombia’s Data-Driven Fight Against Crime (NYT)
• Community Outbreak of HIV Infection Linked to Injection Drug Use
of Oxymorphone — Indiana, 2015 (MMWR)
• Gun Violence Archive - General Methodology
(http://www.gunviolencearchive.org/methodology)
• Measles Outbreak — California, December 2014–February 2015
(MMWR)
• Unintentional Strangulation Deaths from the "Choking Game"
Among Youths Aged 6--19 Years - United States, 1995-2007
(MMWR)

49
• New Article
• Calculations
Exam 1 Preparation

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outcome?
these outcomes.
1.

51
outcome?
population. There are patterns to their occurrence. These patterns
can be identified through the surveillance of populations. Examining
these patterns can help formulate hypotheses about the possible
causes of these outcomes.
1.

1. a form or model proposed for imitation
2. something designed or used as a model for making things
(a dressmaker's pattern)
3. an artistic, musical, literary, or mechanical design or form
4. a natural or chance configuration (frost patterns, the
pattern of events)
5. a length of fabric sufficient for an article (as of clothing)
6. the distribution of shrapnel, bombs on a target, or shot from
a shotgun / the grouping made on a target by bullets
7. a reliable sample of traits, acts, tendencies, or other
observable characteristics of a person, group, or institution
(a behavior pattern, spending patterns)
Pattern
Merriam-Webster Online
Patterns of Health and Disease

1. a form or model proposed for imitation
2. something designed or used as a model for making things
(a dressmaker's pattern)
3. an artistic, musical, literary, or mechanical design or form
4. a natural or chance configuration (frost patterns, the
pattern of events)
5. a length of fabric sufficient for an article (as of clothing)
6. the distribution of shrapnel, bombs on a target, or shot from
a shotgun / the grouping made on a target by bullets
7. a reliable sample of traits, acts, tendencies, or other
observable characteristics of a person, group, or institution
(a behavior pattern, spending patterns)
Merriam-Webster Online
Patterns of Health and Disease
Pattern

Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Patterns of Health-Related Outcomes

56
1. Logistics
2. Review
3. Identifying Patterns and Formulating Hypotheses
Surveillance, Patterns and Hypotheses
January 22, 2018

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1. Autism Rates Have Stabilized in US Children
2. The Census and Right-Wing Hysteria
3. Refusing Vaccinations
4. Why Are White Death Rates Rising
5. One Simple Way to Reduce Some Suicides by 90%
6. Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
7. Pregnancy Boom at Gloucester High
8. CDC Reports a Record Jump in Overdose Deaths
9. Rise in US Traffic Deaths Reported for a Second Year
10.Deadliest Counties in the US
11.What Explains US Mass Shootings?
12.Teenage Suicides Bewilder an Island and the Experts
Attendance Sheet

58
1. Logistics
2. Review
January 22, 2018

59
outcome?
these outcomes.
1.

61
The aim of the course is to create a more scientifically literate person,
someone who:
… can ask, find, or determine answers to questions derived from
curiosity about everyday experiences. … has the ability to describe,
explain, and predict natural phenomenon. … is able to read with
understanding articles about science in the popular press and to
engage in social conversation about the validity of their
conclusions. … can identify scientific issues underlying national and
local decisions and express positions that are scientifically and
technologically informed. … (is) able to evaluate the quality of
scientific information on the basis of its source and the methods used
to generate it. … (has) the capacity to pose and evaluate arguments
based on evidence and to apply conclusions from such arguments
appropriately.
National Research Council, National Science Education Standards. Washington, DC: National Academy Press, 1996.
Scientific Literacy

Identify:
related event
Identifying Patterns and Formulating Hypotheses

The criteria used to establish a specific diagnosis
Case Definition

Identify:
2. Statements that describe the pattern of the particular
health-related event

Characterizes the amount and distribution
of health and disease within a population
Descriptive Epidemiology

Identify:
related event
3. Statements that attempt to explain the reason for the
pattern

A supposition,
arrived at from observation or reflection,
that leads to refutable predictions
Any conjecture
cast in a form that will allow it to be tested and refuted
Hypothesis

Identify:
related event
4. Statements that describe the methodology that
generated the data from which the pattern was identified

69
The ongoing systematic
collection, analysis, and
interpretation of outcome-
specific data
for use in planning,
implementation, and
evaluation of public health
practice
closely integrated with the
timely dissemination of
these data to those who
need to know.
Public Health Surveillance

Autism Rates Have Stabilized in US Children
The Census and Right-Wing Hysteria
Refusing Vaccinations
Why Are White Death Rates Rising?
One Simple Way to Reduce Some Suicides by 90%
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
Pregnancy Boom at Gloucester High
CDC Reports a Record Jump in Overdose Deaths
Rise in US Traffic Deaths Reported for a Second Year
Deadliest Counties in the US
What Explains US Mass Shootings?
Teenage Suicides Bewilder an Island and the Experts

A set of diagnostic criteria that must be fulfilled
in order to identify a person as a case of a particular disease.
Case Definition

https://www.washingtonpost.com/graphics/national/mass-shootings-in-america/

The Number of ‘Mass Shootings’ in the U.S.
Depends on How You Count
https://www.washingtonpost.com/graphics/business/wonkblog/mass-shooting-definition/

2. Statements that describe
the pattern of the particular health-related event

Pattern

Pattern
DZ
DZ
DZ
DZ
DZ
DZ

Pattern
DZ
DZ
DZ
DZ
DZ
DZ
Pattern
Person
Who?
Time
When?
Place
Where?

81
Descriptive Epidemiological Factors
Person Place Time
Sex
Occupation
Age
SES
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset

84
1. Review
3. Reading and Simulation Assignments
January 24, 2018

86
1. Review
January 24, 2018

A supposition,
arrived at from observation or reflection,
that leads to refutable predictions
Hypothesis

89
The ongoing systematic
collection, analysis, and
interpretation of outcome-
specific data
for use in planning,
implementation, and
evaluation of public health
practice
closely integrated with the
timely dissemination of
these data to those who
need to know.

93
1. Review
January 24, 2018

1.
Identify:

1.
Identify:
4. Methodology that generated the data

The number of events,
that is, instances of a given disease or other condition,
in a given population at a designated time
Prevalence

The number of new events of a disease,
in a given population within a specified period of time
Incidence

Prevalence Pot

Prevalence Sink

• Increase in Occurrence of New Cases
• Immigration of Ill Cases
• Immigration of Susceptible Cases
• Emigration of Healthy Cases
• Prolongation of Life of Cases without Cure
Factors that Increase Prevalence

When you can measure what you are speaking about, and express
it in numbers, you know something about it.
But when you cannot measure it, when you cannot express it in
numbers, your knowledge is of a meager and unsatisfactory kind.
Lord Kelvin

Not everything that counts can be counted;
And not everything that can be counted counts.
Albert Einstein

5. The incidence rate of a disease is five times greater in women than
in men, but the prevalence rates show no sex difference. The best
explanation is that:
a. The crude all-cause mortality rate is greater in women
b. The case-fatality rate for this disease is greater in women
c. The case-fatality rate for this disease is lower in women
d. The duration of this disease is shorter in men
e. Risk factors for the disease are more common in women
5 x

1. At an initial examination in Oxford, Mass., migraine headache was
found in 5 of 1,000 men aged 30 to 35 years and in 10 of 1,000
women aged 30 to 35 years. The inference that women have a two
times greater risk of developing migraine headache than do men in
this age group is:
a. correct
b. incorrect, because a ratio has been used to compare male and
female rates
c. incorrect, because of failure to recognize the effect of age in the
two groups
d. incorrect, because no data for a comparison or control group
are given
e. incorrect, because of failure to distinguish between incidence
and prevalence

4. The mortality rate from disease X in city A is 75/100,000 in persons
65 to 69 years old. The mortality rate from the same disease in city
B is 150/100,000 in persons 65 to 69 years old. The inference that
disease X is two times more prevalent in persons 65 to 69 years old
in city B than it is in persons 65 to 69 years old in city A is:
a. Correct
b. Incorrect, because of failure to distinguish between prevalence
and mortality
c. Incorrect, because of failure to adjust for differences in age
distributions
d. Incorrect, because of failure to distinguish between period and
point prevalence
e. Incorrect, because a proportion is used when a rate is required
to support the inference
A B

10. For a disease such as pancreatic cancer, which is highly fatal and
of short duration:
a. Incidence rates and mortality rates will be similar
b. Mortality rates will be much higher than incidence rates
c. Incidence rates will be much higher than mortality rates
d. Incidence rates will be unrelated to mortality rates
e. None of the above

Identify:
2.

Identify:
3.

The resistance of a group
to invasion and spread of an infectious agent
based on the resistance to infection
of a high proportion of individual members of the group.
Herd Immunity

http://www.theguardian.com/society/ng-interactive/2015/feb/05/-sp-watch-how-measles-outbreak-spreads-when-kids-get-vaccinated

Doctors Group Urges Measles Shots as Disneyland Outbreak Spreads
http://ti.me/1BW2GU4

128

4.
Identify:

139
Reference Group Theory
In the fourth quarter of 2015, the
median weekly earnings of white men
aged 25 to 54 were $950, well above
the same figure for black men ($703)
and Hispanic men ($701). But for
some whites — perhaps the ones who
account for the increasing death rate
— that may be beside the point. Their
main reference group is their parents’
generation, and by that standard they
have little to look forward to and a lot
to lament.

5.
Identify:

144

145
Three Interventions to Deter Suicides at Hotspots
1. Putting up barriers and structural interventions around
the site
2. Encouraging suicidal people to seek help, by putting
up signs or phone lines for suicide crisis services
3. Putting cameras or trained staff in hotspots to
increase the likelihood that a third party would
intervene before a suicide happened

6.
Identify:

Identify:
6.

7.
Identify:

8.
Identify:

9.
Identify:

10.
Identify:

168
https://vizhub.healthdata.org/subnational/usa

169
“The annual health rankings use a measure called
‘premature age-adjusted mortality’ from the Centers for
Disease Control and Prevention as one of their main
indicators of overall health. This factor uses statistical
methods to adjust for the overall distribution of ages in a
county, so that one can compare mortality in any two
counties independent of whether one has an overall
younger population than the other.”

A measure of the effect of diseases and injuries
in reducing the life span
below national or a hypothetical ideal life expectancy.
Potential Years of Life Lost

A procedure for adjusting rates designed to
minimize the effects of differences in age composition
when comparing rates from different populations.
Age Adjustment / Age Standardization

3. Age-adjusted death rates are used to:
a. Correct death rates for errors in the statement of age
b. Determine the actual number of deaths that occurred in
specific age groups in a population
c. Correct death rates for missing age information
d. Compare deaths in persons of the same age group
e. Eliminate the effects of differences in the age distributions of
populations in comparing death rates

Calculate the age-adjusted death rate for disease Z in communities X
and Y by the direct method, using the total of both communities as the
standard population.

Community X Community Y
Age
Age
Age
#
People
#
Z
Deaths
#
People
#
Z
Deaths
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
What is the age-adjusted death rate from disease Z in community X?

Age
Age
Age
#
People
#
Z
Deaths
#
People
#
Z
Deaths
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
Total 19,000 184 8,000 108

Age
Age
Age
#
People
#
Z
Deaths
Death
Rate
per
1,000
#
People
#
Z
Deaths
Death
Rate
per
1,000
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
Total 19,000 184 9.7 8,000 108 13.5

Age
Age
Age
#
People
#
Z
Deaths
Death
Rate
per
1,000
#
People
#
Z
Deaths
Death
Rate
per
1,000
Young 8,000 69 8.6 5,000 48 9.6
Old 11,000 115 10.5 3,000 60 20.0
Total 19,000 184 9.7 8,000 108 13.5

Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000
Old 14,000
Total 27,000

Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6
Old 14,000 10.5
Total 27,000

Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6 111.8
Old 14,000 10.5 147.0
Total 27,000 258.8

Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6 111.8
Old 14,000 10.5 147.0
Total 27,000 258.8 9.6

Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000
Old 14,000
Total 27,000

Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6
Old 14,000 20.0
Total 27,000

Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6 124.8
Old 14,000 20.0 280.0
Total 27,000 404.8

Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6 124.8
Old 14,000 20.0 280.0
Total 27,000 404.8 15.0

Community X Community
Y
Age
Age
Age
#
People
#
Z
Deaths
Death
Rate
per
1,000
#
People
#
Z
Deaths
Death
Rate
per
1,000
Young 8,000 69 8.6 5,000 48 9.6
Old 11,000 115 10.5 3,000 60 20
Total 19,000 184 9.7 8,000 108 13.5
9.6 15.0

Calculate the age-adjusted death rate for disease Z in communities X
and Y by the direct method, using the total of both communities as the
standard population.
The age-adjusted death rate from disease Z for community X is:
9.6 / 1,000

11.
Identify:

197

198

12.
Identify:

203

207
(Gordis)

Texas Sharp Shooter Effect

209
outcome?
these outcomes.
1.

210
The aim of the course is to create a more scientifically literate person,
someone who:
… can ask, find, or determine answers to questions derived from
curiosity about everyday experiences. … has the ability to describe,
explain, and predict natural phenomenon. … is able to read with
understanding articles about science in the popular press and to
engage in social conversation about the validity of their
conclusions. … can identify scientific issues underlying national and
local decisions and express positions that are scientifically and
technologically informed. … (is) able to evaluate the quality of
scientific information on the basis of its source and the methods used
to generate it. … (has) the capacity to pose and evaluate arguments
based on evidence and to apply conclusions from such arguments
appropriately.
National Research Council, National Science Education Standards. Washington, DC: National Academy Press, 1996.
Scientific Literacy

211
1. Review
January 24, 2018

213
https://stacks.cdc.gov/view/cdc/1261
Pneumocystis Pneumonia - Los Angeles
Explain the relationship between the article, XXXXXXXXXX,
and the content of this course to date.

215
1. Logistics
2. Review
3. Surveillance, Patterns and Hypotheses
• Pneumocystis Pneumonia - Los Angeles
• Whistles
• Adult Obesity
4. Exam 1 Preparation - 2/12
5. PPT Assignment
January 29, 2018

To me, that summed up the whole problem of dealing with AIDS in the media.
Obviously, the reason I covered AIDS from the start was that...
it was never something that happened to those other people.
Randy Shilts in 1983

https://www.youtube.com/watch?v=ua5RrxvfVJU

218
“CDC Gets List of Forbidden Words: Fetus, Transgender, Diversity”

219
Anecdote
Science
DZ

Identify:

222

Identify:

225
outcome?
these outcomes.
1.

226
1. Logistics
2. Review
• Whistles
• Adult Obesity
4. Exam 1 Preparation - 2/12
5. PPT Assignment
January 29, 2018

Explain the relationship between the article, XXXXXXXXXX, and the content of this course to
date.
Epidemiology - A Science for the People (Lancet) (Canvas)
Colombia’s Data-Driven Fight Against Crime (Canvas)
Adult Use of Prescription Opioid Pain Medications — Utah, 2008 (MMWR) (pages 153-157)
(Canvas)
Gun Violence Archive (Online)
• Home (http://www.gunviolencearchive.org/)
• About Us (http://www.gunviolencearchive.org/about)
• General Methodology (http://www.gunviolencearchive.org/methodology)
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Oregon’s Death with Dignity Act: The First Year’s Experience (pages 1-3 and 7-10) (Canvas)
Oregon Death with Dignity Act Data summary 2016 (pages 3-7) (Canvas)

230
• New Article
Exam Preparation

232
1. Review / Exam 1 Preparation - 2/12
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
January 31, 2018

233
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
January 31, 2018

235

AIDS
NOW NO ONE IS SAFE FROM

National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
HIV Surveillance –
Adolescents and Young Adults
Division of HIV/AIDS Prevention

Diagnoses of HIV Infection among Adolescents and Young Adults Aged 13–24 years,
by Race/Ethnicity, 2010–2015—United States and 6 Dependent Areas
Note. Data include persons with a diagnosis of HIV infection regardless of stage of disease at diagnosis.
a Hispanics/Latinos can be of any race.

Diagnoses of HIV Infection among Adolescents and Young Adults Aged 13–24 Years,
by Transmission Category, 2010–2015—United States and 6 Dependent Areas
Note. Data have been statistically adjusted to account for missing transmission category. “Other” transmission category not displayed as it comprises less
than 1% of cases.
a Heterosexual contact with a person known to have, or to be at high risk for, HIV infection.

Adolescents and Young Adults Aged 13–24 Years Living with Diagnosed HIV Infection,
by Sex and Race/Ethnicity, Year-end 2015—United States and 6 Dependent Areas
a Includes Asian/Pacific Islander legacy cases.
b Hispanics/Latinos can be of any race.

Adolescents and Young Adults Aged 13–24 Years Living with Diagnosed HIV Infection
by Sex and Transmission Category, Year-end 2015—United States
and 6 Dependent Areas
Note. Data have been statistically adjusted to account for missing transmission category. “Other” transmission category not displayed as it comprises 1% or less
of cases.
b Includes hemophilia, blood transfusion, and risk factor not reported or not identified.

Rates of Adolescents Aged 13–19 Years Living with Diagnosed HIV Infection
Year-end 2015—United States and 6 Dependent Areas
N = 5,753 Total Rate = 19.4
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
21.0
0.0
39.8
Note. Data are based on address of residence as of December 31, 2015 (i.e., most recent known address).

Rates of Young Adults Aged 20–24 Years Living with Diagnosed HIV Infection
Year-end 2015—United States and 6 Dependent Areas
N = 31,208 Total Rate = 135.8
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
25.3
155.2
0.0
159.6

Stage 3 (AIDS) Classifications among Adolescents Aged 13–19 Years, by Sex and Year
of Classification, 1985–2015—United States and 6 Dependent Areas

Stage 3 (AIDS) Classifications Among Young Adults Aged 20–24 Years, by Sex and Year
of Classification, 1985–2015—United States and 6 Dependent Areas

Rates of Adolescents Aged 13–19 Years Living with Diagnosed HIV Infection Ever
Classified as Stage 3 (AIDS), Year-end 2015—United States and 6 Dependent Areas
N = 1,124 Total Rate = 3.8
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
5.4
0.0
26.6

Rates of Young Adults Aged 20–24 Years Living with Diagnosed HIV Infection Ever
Classified as Stage 3 (AIDS), Year-end 2015—United States and 6 Dependent Areas
N = 6,827 Total Rate = 29.7
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
35.3
0.0
59.9

Diagnoses of HIV Infection among Persons Aged 13 Years and Older,
by Sex and and Age Group, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay.

Diagnoses of HIV Infection Among Male Adolescents and Young Adults, by Age
Group and Transmission Category, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay. Data have been statistically adjusted to account for missing transmission category.
b Includes hemophilia, blood transfusion, perinatal exposure, and risk factor not reported or not identified.
c Because column totals for numbers were calculated independently of the values for the subpopulations, the values in each column may not sum to the column total

Diagnoses of HIV Infection Among Female Adolescents and Young Adults, by Age
Group and Transmission Category, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay. Data have been statistically adjusted to account for missing transmission category.
b Includes hemophilia, blood transfusion, perinatal exposure, and risk factor not reported or not identified.
c Because column totals for numbers were calculated independently of the values for the subpopulations, the values in each column may not sum to the column total.

Diagnoses of HIV Infection and Population among Adolescents
Aged 13–19 Years, by Race/Ethnicity, 2016—United States

Diagnoses of HIV Infection and Population among Young Adults
Aged 20–24 Years, by Race/Ethnicity 2016—United States

Rates of Diagnoses of HIV Infection among Adolescents Aged 13–19 Years
2016—United States and 6 Dependent Areas
N = 1,688 Total Rate = 5.7
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
4.1
0.0
0.0

Rates of Diagnoses of HIV Infection among Young Adults Aged 20–24 Years
2016—United States and 6 Dependent Areas
N = 6,848 Total Rate = 30.2
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
7.4
0.0
27.8
53.2
65.8

Stage 3 (AIDS) Classifications among Persons Aged 13 Years and Older
with Diagnosed HIV Infection, by Race/Ethnicity and Age Group, 2016
United States and 6 Dependent Areas
a Includes Asian/Pacific Islander legacy cases
b Hispanics/Latinos can be of any race.

Rates of Diagnosed HIV Infection Classified as Stage 3 (AIDS) among Adolescents
Aged 13–19 Years, 2016—United States and 6 Dependent Areas
N = 224 Total Rate = 0.8
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.6
0.0
0.0
0.0
0.0

Rates of Diagnosed HIV Infection Classified as Stage 3 (AIDS) among Young Adults
Aged 20–24 Years, 2016—United States and 6 Dependent Areas
N = 1,266 Total Rate = 5.6
American Samoa
Guam
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
7.4
0.0
5.4
0.0
0.0

259
https://www.cdc.gov/mmwr/index.html

260
https://wwwn.cdc.gov/nndss/conditions/notifiable/2017/

• Frequency
• Severity
• Cost
• Preventability
• Communicability
• Public Interest
Does the health problem justify surveillance?

In December 2010, the Department of Health and Human Services
launched Healthy People 2020, which has four overarching goals:
1.Attain high-quality, longer lives free of preventable disease,
disability, injury, and premature death;
2.Achieve health equity, eliminate disparities, and improve the
health of all groups;
3.Create social and physical environments that promote good
health for all; and
4.Promote quality of life, healthy development, and healthy
behaviors across all life stages.

Jay Z: 'The War on Drugs Is an Epic Fail'
https://www.nytimes.com/video/opinion/100000004642370/jay-z-the-war-on-drugs-is-an-epic-fail.html

Active Surveillance
Denotes a system in which project staff are recruited to carry out a surveillance
program. It may involve interviewing physicians and patients, reviewing medical
records, and, in developing countries, surveying villages …. Surveillance is
generally more accurate when active … because (it) is conducted by individuals
who have been specifically employed to carry out this responsibility.

Denotes surveillance in which available data on a reportable disease are used,
or in which disease reporting is mandated or requested, with the responsibility for
the reporting often falling on the health care provider or district officer.
The completeness and quality of the data reported
thus largely on this individual and his or her staff,
who often take on the role without additional funds or resources.
Passive Surveillance

266

267
https://www.cdc.gov/flu/weekly/index.htm#S2

268
http://www.nj.gov/health/cd/documents/flu/surveillance/flu_report_wk_03.pdf

269
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
January 31, 2018

270

271
Time: When?
PPT Sheet
Person:
Place:
Time:
Lifeguards, Drum Majors,
Referees , Coaches, Traffic
Policemen
Pools, seashore, gymnasiums,
athletic fields, intersections
Hot days, schooldays, after
school, holidays

272
Hypotheses for Card
Card / Group Hypotheses
1
2
3
4
5
6
7
8
9
10
Grass / Soil
Grass
Grass / Leaves / Dirt
XXXXXXXXXXXXXXXX
Grass / Dirt
Grass / Trees
1

273
Hypotheses for Card
Card / Group Hypotheses
1
2
3
4
5
6
7
8
9
10
Alcoholic beverages
Alcohol
Alcohol / Food poisoning / Drug-smoking
Music
Alcohol / Drinks / Food / Music
XXXXXXXXXXX
2

274
What hypotheses might explain the distribution
of the health-related outcome?
these outcomes.
1.

275
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
January 31, 2018

276
“… major behavioral risks among adults
associated with premature morbidity and mortality.”
Behavioral Risk Factor Surveillance System
(BRFSS)

Definitions:
• Obesity: Body Mass Index (BMI) of 30 or higher.
• Body Mass Index (BMI): A measure of an adult’s
weight in relation to his or her height, specifically the
adult’s weight in kilograms divided by the square of
his or her height in meters.
Obesity Trends Among U.S. Adults
between 1985 and 2009

Obesity Trends Among U.S. Adults
between 1985 and 2009
Source of the data:
• The data shown in these maps were collected
through CDC’s Behavioral Risk Factor Surveillance
System (BRFSS). Each year, state health
departments use standard procedures to collect data
through a series of landline telephone interviews with
U.S. adults.
• Prevalence estimates generated for the maps may
vary slightly from those generated for the states by
BRFSS (http://aps.nccd.cdc.gov/brfss) as slightly
different analytic methods are used.

Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1985
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%

BRFSS, 1986
No Data <10% 10%–14%

BRFSS, 1987
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4”
person)
No Data <10% 10%–14%

BRFSS, 1988
No Data <10% 10%–14%

BRFSS, 1989
No Data <10% 10%–14%

BRFSS, 1990
No Data <10% 10%–14%

BRFSS, 1991
No Data <10% 10%–14% 15%–19%

BRFSS, 1992
No Data <10% 10%–14% 15%–19%

BRFSS, 1993
No Data <10% 10%–14% 15%–19%

BRFSS, 1994
No Data <10% 10%–14% 15%–19%

BRFSS, 1995
No Data <10% 10%–14% 15%–19%

BRFSS, 1996
No Data <10% 10%–14% 15%–19%

BRFSS, 1997
No Data <10% 10%–14% 15%–19% ≥20%

BRFSS, 1998
No Data <10% 10%–14% 15%–19% ≥20%

BRFSS, 1999
No Data <10% 10%–14% 15%–19% ≥20%

BRFSS, 2000
No Data <10% 10%–14% 15%–19% ≥20%

BRFSS, 2001
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

BRFSS, 2002
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

BRFSS, 2003
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

BRFSS, 2004
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

BRFSS, 2005
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

BRFSS, 2006
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

BRFSS, 2007
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

BRFSS, 2008
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

BRFSS, 2009
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

1999
BRFSS, 1990, 1999, 2009
(*BMI 30, or about 30 lbs. overweight for 5’4” person)
2009
1990
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

305
Prevalence estimates
reflect BRFSS methodological changes started in 2011.
These estimates should not be compared to
prevalence estimates before 2011.

Prevalence of Self-Reported Obesity Among
U.S. Adults by State and Territory
Definitions
 Obesity: Body Mass Index (BMI) of 30 or higher.
 Body Mass Index (BMI): A measure of an adult’s
weight in relation to his or her height, calculated by
using the adult’s weight in kilograms divided by the
square of his or her height in meters.

Source of the Data
 The data were collected through the Behavioral
Risk Factor Surveillance System (BRFSS), an
ongoing, state-based, telephone interview survey
conducted by state health departments with
assistance from CDC.
 Height and weight data used in the BMI
calculations were self-reported.

BRFSS Methodological Changes Started in 2011
 New sampling frame that included both landline
and cell phone households.
 New weighting methodology used to provide a
closer match between the sample and the
population.

Exclusion Criteria Used Beginning with 2011 BRFSS Data
Records with the following were excluded:
 Height: <3 feet or ≥8 feet
 Weight: <50 pounds or ≥650 pounds
 BMI: <12 kg/m2 or ≥100 kg/m2
 Pregnant women

Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2011
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.

¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.

Prevalence¶ of Self-Reported Obesity Among U.S. Adults
by State and Territory, BRFSS, 2011
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates
before 2011. Source: Behavioral Risk Factor Surveillance System, CDC.
State Prevalence 95% Confidence Interval
Alabama 32.0 (30.5, 33.5)
Alaska 27.4 (25.3, 29.7)
Arizona 25.1 (23.0, 27.3)
Arkansas 30.9 (28.8, 33.1)
California 23.8 (22.9, 24.7)
Colorado 20.7 (19.7, 21.8)
Connecticut 24.5 (23.0, 26.0)
Delaware 28.8 (26.9, 30.7)
District of Columbia 23.7 (21.9, 25.7)
Florida 26.6 (25.4, 27.9)
Georgia 28.0 (26.6, 29.4)
Guam 27.4 (24.8, 30.2)
Hawaii 21.8 (20.4, 23.4)
Idaho 27.0 (25.3, 28.9)
Illinois 27.1 (25.4, 28.9)
Indiana 30.8 (29.5, 32.3)
Iowa 29.0 (27.6, 30.3)
Kansas 29.6 (28.7, 30.4)
Kentucky 30.4 (28.9, 31.9)
Louisiana 33.4 (32.0, 34.9)
Maine 27.8 (26.8, 28.9)
Maryland 28.3 (26.9, 29.7)
Massachusetts 22.7 (21.8, 23.7)
Michigan 31.3 (30.0, 32.6)
Minnesota 25.7 (24.6, 26.8)
Mississippi 34.9 (33.5, 36.3)
Missouri 30.3 (28.6, 32.0)
Montana 24.6 (23.3, 26.0)
Nebraska 28.4 (27.6, 29.2)
Nevada 24.5 (22.5, 26.6)
New Hampshire 26.2 (24.7, 27.7)
New Jersey 23.7 (22.7, 24.8)
New Mexico 26.3 (25.1, 27.6)
New York 24.5 (23.2, 25.9)
North Carolina 29.1 (27.7, 30.6)
North Dakota 27.8 (26.3, 29.4)
Ohio 29.6 (28.3, 31.0)
Oklahoma 31.1 (29.7, 32.5)
Oregon 26.7 (25.2, 28.3)
Pennsylvania 28.6 (27.3, 29.8)
Puerto Rico 26.3 (25.0, 27.7)
Rhode Island 25.4 (23.9, 27.0)
South Carolina 30.8 (29.6, 32.1)
South Dakota 28.1 (26.3, 30.1)
Tennessee 29.2 (26.8, 31.7)
Texas 30.4 (29.1, 31.8)
Utah 24.4 (23.4, 25.5)
Vermont 25.4 (24.1, 26.8)
Virginia 29.2 (27.5, 30.9)
Washington 26.5 (25.3, 27.7)
West Virginia 32.4 (30.9, 34.0)
Wisconsin 27.7 (25.8, 29.7)
Wyoming 25.0 (23.5, 26.6)

Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2012
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates
before 2011. Source: Behavioral Risk Factor Surveillance System, CDC.
Alabama 33.0 (31.5, 34.4)
Alaska 25.7 (23.9, 27.5)
Arizona 26.0 (24.3, 27.8)
Arkansas 34.5 (32.7, 36.4)
California 25.0 (23.9, 26.0)
Colorado 20.5 (19.5, 21.4)
Connecticut 25.6 (24.3, 26.9)
Delaware 26.9 (25.2, 28.6)
Florida 25.2 (23.6, 26.7)
Georgia 29.1 (27.4, 30.8)
Guam 29.1 (26.3, 31.9)
Hawaii 23.6 (22.0, 25.1)
Idaho 26.8 (24.8, 28.8)
Illinois 28.1 (26.4, 29.9)
Indiana 31.4 (30.1, 32.7)
Iowa 30.4 (29.1, 31.8)
Kansas 29.9 (28.7, 31.0)
Kentucky 31.3 (29.9, 32.6)
Louisiana 34.7 (33.1, 36.4)
Maine 28.4 (27.2, 29.5)
Maryland 27.6 (26.3, 28.9)
Michigan 31.1 (29.8, 32.3)
Minnesota 25.7 (24.7, 26.8)
Mississippi 34.6 (33.0, 36.2)
Missouri 29.6 (28.0, 31.2)
Montana 24.3 (23.1, 25.5)
Nebraska 28.6 (27.7, 29.6)
Nevada 26.2 (24.3, 28.1)
New Hampshire 27.3 (25.8, 28.8)
New Jersey 24.6 (23.6, 25.6)
New Mexico 27.1 (25.9, 28.3)
New York 23.6 (22.0, 25.1)
North Dakota 29.7 (27.9, 31.4)
Ohio 30.1 (29.0, 31.2)
Oklahoma 32.2 (30.8, 33.6)
Oregon 27.3 (25.7, 29.0)
Puerto Rico 28.4 (27.0, 29.7)
Rhode Island 25.7 (24.1, 27.4)
South Dakota 28.1 (26.5, 29.8)
Tennessee 31.1 (29.6, 32.7)
Texas 29.2 (27.8, 30.5)
Utah 24.3 (23.3, 25.3)
Vermont 23.7 (22.3, 25.1)
Virginia 27.4 (26.0, 28.7)
Washington 26.8 (25.8, 27.8)
West Virginia 33.8 (32.2, 35.4)
Wisconsin 29.7 (27.8, 31.6)
Wyoming 24.6 (22.8, 26.4)

¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates before 2011.
Alabama 32.4 (30.8, 34.1)
Alaska 28.4 (26.5, 30.4)
Arizona 26.8 (24.3, 29.4)
Arkansas 34.6 (32.7, 36.6)
California 24.1 (23.0, 25.3)
Colorado 21.3 (20.4, 22.2)
Connecticut 25.0 (23.5, 26.4)
Delaware 31.1 (29.3, 32.8)
Florida 26.4 (25.3, 27.4)
Georgia 30.3 (28.9, 31.8)
Guam 27.0 (24.4, 29.8)
Hawaii 21.8 (20.4, 23.2)
Idaho 29.6 (27.8, 31.4)
Illinois 29.4 (27.7, 31.2)
Indiana 31.8 (30.6, 33.1)
Iowa 31.3 (29.9, 32.7)
Kansas 30.0 (29.2, 30.7)
Kentucky 33.2 (31.8, 34.6)
Louisiana 33.1 (31.1, 35.2)
Maine 28.9 (27.5, 30.2)
Maryland 28.3 (27.0, 29.5)
Michigan 31.5 (30.4, 32.6)
Minnesota 25.5 (24.1, 26.8)
Mississippi 35.1 (33.5, 36.8)
Missouri 30.4 (28.8, 32.1)
Montana 24.6 (23.4, 25.8)
Nebraska 29.6 (28.4, 30.7)
Nevada 26.2 (24.0, 28.6)
New Hampshire 26.7 (25.3, 28.3)
New Jersey 26.3 (25.1, 27.5)
New Mexico 26.4 (25.1, 27.7)
New York 25.4 (24.2, 26.6)
North Dakota 31.0 (29.5, 32.5)
Ohio 30.4 (29.2, 31.6)
Oklahoma 32.5 (31.2, 33.9)
Oregon 26.5 (24.9, 28.1)
Puerto Rico 27.9 (26.4, 29.5)
Rhode Island 27.3 (25.8, 28.8)
South Dakota 29.9 (28.0, 31.8)
Tennessee 33.7 (31.9, 35.5)
Texas 30.9 (29.5, 32.3)
Utah 24.1 (23.2, 25.1)
Vermont 24.7 (23.4, 26.1)
Virginia 27.2 (25.9, 28.5)
Washington 27.2 (26.0, 28.3)
West Virginia 35.1 (33.6, 36.6)
Wisconsin 29.8 (28.0, 31.6)
Wyoming 27.8 (26.2, 29.5)

.
Alabama 33.5 (32.1, 35.0)
Alaska 29.7 (27.8, 31.7)
Arizona 28.9 (27.7, 30.2)
Arkansas 35.9 (33.8, 38.0)
California 24.7 (23.5, 25.9)
Colorado 21.3 (20.4, 22.2)
Connecticut 26.3 (24.9, 27.7)
Delaware 30.7 (28.6, 32.8)
Florida 26.2 (25.0, 27.5)
Georgia 30.5 (28.9, 32.1)
Guam 28.0 (25.6, 30.5)
Hawaii 22.1 (20.7, 23.5)
Idaho 28.9 (27.1, 30.8)
Illinois 29.3 (27.6, 31.1)
Indiana 32.7 (31.6, 34.0)
Iowa 30.9 (29.6, 32.3)
Kansas 31.3 (30.3, 32.2)
Kentucky 31.6 (30.2, 33.1)
Louisiana 34.9 (33.4, 36.4)
Maine 28.2 (26.9, 29.5)
Maryland 29.6 (28.1, 31.1)
Michigan 30.7 (29.4, 32.0)
Minnesota 27.6 (26.8, 28.5)
Mississippi 35.5 (33.4, 37.6)
Missouri 30.2 (28.6, 31.9)
Montana 26.4 (24.9, 27.9)
Nebraska 30.2 (29.2, 31.3)
Nevada 27.7 (25.4, 30.1)
New Hampshire 27.4 (25.8, 29.1)
New Jersey 26.9 (25.7, 28.1)
New Mexico 28.4 (27.0, 30.0)
New York 27.0 (25.6, 28.5)
North Dakota 32.2 (30.5, 34.0)
Ohio 32.6 (31.2, 34.1)
Oklahoma 33.0 (31.7, 34.3)
Oregon 27.9 (26.3, 29.6)
Puerto Rico 28.3 (26.8, 29.8)
Rhode Island 27.0 (25.4, 28.6)
South Dakota 29.8 (27.9, 31.8)
Tennessee 31.2 (29.3, 33.2)
Texas 31.9 (30.6, 33.3)
Utah 25.7 (24.9, 26.6)
Vermont 24.8 (23.5, 26.1)
Virginia 28.5 (27.2, 29.7)
Washington 27.3 (26.0, 28.5)
West Virginia 35.7 (34.2, 37.2)
Wisconsin 31.2 (29.6, 32.8)
Wyoming 29.5 (27.5, 31.5)

.
Alabama 35.6 (34.1, 37.2)
Alaska 29.8 (27.5, 32.3)
Arizona 28.4 (26.9, 30.0)
Arkansas 34.5 (32.2, 36.9)
California 24.2 (23.2, 25.2)
Colorado 20.2 (19.1, 21.3)
Connecticut 25.3 (24.1, 26.4)
Delaware 29.7 (27.6, 31.8)
Florida 26.8 (25.5, 28.1)
Georgia 30.7 (28.8, 32.6)
Guam 31.6 (28.2, 35.1)
Hawaii 22.7 (21.3, 24.1)
Idaho 28.6 (26.9, 30.4)
Illinois 30.8 (29.2, 32.4)
Indiana 31.3 (29.5, 33.1)
Iowa 32.1 (30.5, 33.8)
Kansas 34.2 (33.4, 35.0)
Kentucky 34.6 (32.9, 36.3)
Louisiana 36.2 (34.3, 38.1)
Maine 30.0 (28.6, 31.4)
Maryland 28.9 (27.2, 30.7)
Michigan 31.2 (29.9, 32.4)
Minnesota 26.1 (25.3, 27.0)
Mississippi 35.6 (33.8, 37.5)
Missouri 32.4 (30.8, 34.0)
Montana 23.6 (22.1, 25.2)
Nebraska 31.4 (30.3, 32.5)
Nevada 26.7 (24.1, 29.5)
New Hampshire 26.3 (24.8, 27.9)
New Jersey 25.6 (24.3, 26.9)
New Mexico 28.8 (27.1, 30.6)
New York 25.0 (24.0, 26.1)
North Dakota 31.0 (29.3, 32.8)
Ohio 29.8 (28.4, 31.2)
Oklahoma 33.9 (32.2, 35.6)
Oregon 30.1 (28.4, 31.8)
Puerto Rico 29.5 (28.0, 31.1)
Rhode Island 26.0 (24.3, 27.7)
South Dakota 30.4 (28.5, 32.3)
Tennessee 33.8 (31.9, 35.7)
Texas 32.4 (30.9, 33.9)
Utah 24.5 (23.5, 25.5)
Vermont 25.1 (23.8, 26.6)
Virginia 29.2 (27.9, 30.6)
Washington 26.4 (25.5, 27.4)
West Virginia 35.6 (34.1, 37.1)
Wisconsin 30.7 (29.0, 32.4)
Wyoming 29.0 (27.0, 31.1)

.
Alabama 35.7 (34.2, 37.3)
Alaska 31.4 (28.5, 34.4)
Arizona 29.0 (27.5, 30.6)
Arkansas 35.7 (33.3, 38.1)
California 25.0 (23.9, 26.1)
Colorado 22.3 (21.4, 23.2)
Connecticut 26.0 (24.8, 27.2)
Delaware 30.7 (28.7, 32.8)
Florida 27.4 (26.4, 28.5)
Georgia 31.4 (29.7, 33.2)
Guam 28.3 (25.1, 31.7)
Hawaii 23.8 (22.5, 25.2)
Idaho 27.4 (25.6, 29.3)
Illinois 31.6 (29.9, 33.3)
Indiana 32.5 (31.2, 33.8)
Iowa 32.0 (30.5, 33.4)
Kansas 31.2 (30.1, 32.3)
Kentucky 34.2 (32.7, 35.6)
Louisiana 35.5 (33.4, 37.7)
Maine 29.9 (28.5, 31.3)
Maryland 29.9 (28.9, 31.0)
Michigan 32.5 (31.4, 33.6)
Minnesota 27.8 (26.9, 28.6)
Mississippi 37.3 (35.4, 39.1)
Missouri 31.7 (30.0, 33.4)
Montana 25.5 (23.9, 27.2)
Nebraska 32.0 (30.8, 33.2)
Nevada 25.8 (23.9, 27.8)
New Hampshire 26.6 (25.0, 28.2)
New Jersey 27.4 (25.7, 29.1)
New Mexico 28.3 (26.6, 30.1)
New York 25.5 (24.6, 26.5)
North Dakota 31.9 (30.3, 33.6)
Ohio 31.5 (30.2, 32.8)
Oklahoma 32.8 (31.2, 34.3)
Oregon 28.7 (27.3, 30.3)
Puerto Rico 30.7 (29.0, 32.5)
Rhode Island 26.6 (24.9, 28.4)
South Dakota 29.6 (27.6, 31.7)
Tennessee 34.8 (33.0, 36.7)
Texas 33.7 (31.9, 35.4)
Utah 25.4 (24.2, 26.5)
Vermont 27.1 (25.5, 28.7)
Virgin Islands 32.5 (28.6, 36.6)
Virginia 29.0 (27.7, 30.3)
Washington 28.6 (27.6, 29.6)
West Virginia 37.7 (36.3, 39.0)
Wisconsin 30.7 (29.0, 32.5)
Wyoming 27.7 (25.7, 29.8)

Summary
 No state had a prevalence of obesity less than 20%.
 3 states and the District of Columbia had a prevalence of
obesity between 20% and <25%.
 22 states and Guam had a prevalence of obesity between
25% and <30%.
 20 states, Puerto Rico, and Virgin Islands had a prevalence
of obesity between 30% and <35%.
 5 states (Alabama, Arkansas, Louisiana, Mississippi, and
West Virginia) had a prevalence of obesity of 35% or greater.
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to
http://www.cdc.gov/obesity/data/prevalence-maps.html

323
The ongoing and systematic
collection, analysis,
and interpretation
of outcome-specific data
for use in
planning, implementation,
and evaluation
of public health practice
closely integrated
with the timely dissemination
of these data to those who
need to know.

324
The ongoing and systematic
collection, analysis,
and interpretation
of outcome-specific data
for use in
planning, implementation,
and evaluation
of public health practice
closely integrated
with the timely dissemination
of these data to those who
need to know.

325
these outcomes.
1.

326
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
January 31, 2018

327
What hypotheses might explain
the distribution of the health-related outcome?
these outcomes.
1.

328
What hypotheses might explain
the distribution of the health-related outcome?

Hyundai Predicts a Baby Boom in New World Cup Ads
https://www.youtube.com/watch?v=L7v5pf0aN2Q

334
“Other studies have shown, unsurprisingly, that rationality is not always a
key factor in conception. One of the most intense emotions that can be
experienced is the social component of belonging and the self assertion of
a group (also known as you’ll never walk alone). Thus, the act of coming
together can be interpreted on many levels when people feel motivated to
share their euphoria with others.”

https://www.youtube.com/watch?v=5iLL57puZPM

336
“Some authors have shown that circumstances are decisive influences on
human conception or other behaviours. Socioeconomic factors, wars,
epidemics, famines, migrations, and cultural and religious events can drive
or impede procreation every bit as much as candlelight with Julio Iglesias
on the stereo, which - depending on the individuals present - could either
enhance or reduce desire.”

338
1. Follow-Up
• PPT Sheets
• Cholera
• Others
February 5, 2018

343
“Other studies have shown, unsurprisingly, that rationality is not always a
key factor in conception. One of the most intense emotions that can be
experienced is the social component of belonging and the self assertion of
a group (also known as you’ll never walk alone). Thus, the act of coming
together can be interpreted on many levels when people feel motivated to
share their euphoria with others.”

344
“Some authors have shown that circumstances are decisive influences on
human conception or other behaviours. Socioeconomic factors, wars,
epidemics, famines, migrations, and cultural and religious events can drive
or impede procreation every bit as much as candlelight with Julio Iglesias
on the stereo, which - depending on the individuals present - could either
enhance or reduce desire.”

345
“In summary, our results may have several different interpretations. One is
that human emotions on a large scale can profoundly affect demographic
swings in populations, that national or regional events can reduce the
weight of reason and increase the weight of passion. Validation of our
results could contribute to a better understanding of human behaviour,
improve healthcare planning, and even aid government policy makers in
stimulating or reducing birth rates. Ideally, to bridge the gap between
observational and trial data, it would help greatly if Iniesta were willing to
replicate his intervention—although the cost of such a study could be
prohibitive, not to mention harmful to the reference group (Chelsea).”

346
This baby was born on 7/4/17 . Her name is Ivy. Why?

347
"Whether it's the natural ebb and flow of labor and delivery or the Cubs celebration?
We can leave that up to the imagination."
A Cubs World Series Baby Boom? Some Parents and Hospitals Think So

348
Italy’s “Fertility Day”
“Beauty has no age limit. Fertility does.”

349
“Don’t let your sperm go up in smoke.”
Italy’s “Fertility Day”

351
“Many working women, without an extended family to care for
a child, face a dilemma, as private child care is expensive.
Some also worry that their job security may be undermined by
missing workdays because of child care issues. Many
companies do not offer flexible hours for working mothers.”

352
“So many young women are even asked to pre-sign a
resignation letter here, especially in small companies,” said
Teresa Potenza, a longtime women’s advocate in Naples,
referring to a practice in which some women are asked to sign
a resignation letter in case of pregnancy before they are hired.
“Even to all those women, that campaign is a punch to the
gut.”

“One of the most intense emotions that can be experienced
is the social component of belonging and the self assertion of a group
(also known as you’ll never walk alone).”

354
outcome?
these outcomes.
1.

355
https://www.youtube.com/watch?v=9KqekigARfE
“Super Bowl Babies Choir”

356
https://www.youtube.com/watch?v=OV5_LQArLa0

357
Explain the relationship between the article, Pneumocystis
Pneumonia - Los Angeles, and the content of this course
to date.
1. Anecdotes / Case Reports

358
Anecdote
Science
DZ

359
to date.
2. Patterns / PPT

361

362
to date.
2. Patterns / PPT
3. Hypotheses

364
to date.
2. Patterns / PPT
3. Hypotheses
4. Surveillance / MMWR

366
to date.
2. Patterns / PPT
3. Hypotheses
5. Epi Understanding 1

367
outcome?
these outcomes.
1.

368
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition

370
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition
7. Incidence / Prevalence

373
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition
8. Texas Sharp Shooter Effect

375
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition
9. Definition of Epidemiology

376

377
these outcomes.
1.

380
• Cholera
• Others
2. Exam 1 Preparation
February 7, 2018

381
• Cholera
• Others
February 7, 2018

382
https://www.youtube.com/watch?v=3P8shnNEXb4

386
John Snow, MD
(1813-1858)
Father of Epidemiology
“On the Mode and Transmission of Cholera”

387
“To solve the riddle of cholera you had to zoom out, look for
broader patterns in the disease’s itinerary through the city.”
Bird’s-Eye View

“What he needed now were aberrations, deviations from the
norm. Pockets of life where you would expect death, pockets of
death where you would expect life.”

391
Epidemiological Factors
Person Place Time
Sex
Occupation
Age
SES
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset

392
http://www.theghostmap.com/

393
“But Snow had not investigated the drinking habits of the
neighborhood residents who had survived the epidemic.”
Residents Who Had Survived

394
“To measure the effects of good or bad water supply, it is
requisite to find two classes of inhabitants living at the same
level, moving in equal space, enjoying an equal share of the
means of subsistence, engaged in the same pursuits, but
differing in this respect, - that one drinks water from Battersea,
the other from Kew …. But of such experimenta crucis the
circumstances of London do not admit”
Two Classes of Inhabitants

397
... the intermixing of the water supply of the Southwark and Vauxhall Company with that of the Lambeth
Company, over an extensive part of London, admitted of the subject being sifted in such a way as to
yield the most incontrovertible proof on one side or the other. In the sub-districts enumerated in the
above table as being supplied by both Companies, the mixing of the supply is of the most intimate kind.
The pipes of each Company go down all the streets, and into nearly all the courts and alleys. A few
houses are supplied by one Company and a few by the other, according to the decision of the owner or
the occupier at the time when the Water Companies were in active competition. In many cases a single
house has a supply different from that on either side. Each company supplies both rich and poor, both
large houses and small; there is no difference either in the condition or occupation of the persons
receiving the water of the different Companies. Now it must be evident that, if the diminution of cholera,
in the districts partly supplied with the improved water, depended on this supply, the houses receiving it
would be the houses enjoying the whole benefit of the diminution of the malady, whilst the houses
supplied with the water from Battersea Fields would suffer the same mortality as they would if the
improved supply did not exist at all. As there is no difference whatever, either in the houses of the
people receiving the supply of the two Water Companies, or in any of the physical conditions with which
they are surrounded, it is obvious that no experiment could have been devised which would more
thoroughly test the effect of water supply on the progress of cholera than this, which circumstances
placed ready made before the observer.
The experiment, too, was on the grandest scale. No fewer than three hundred thousand people of both
sexes, of every age and occupation, and of every rank and station, from gentlefolks down to the very
poor, were divided in to two groups , without their choice, and, in most cases, without their knowledge;
one group being supplied with water containing the sewage of London, and, amongst it, whatever might
have come from the cholera patients, the other group having water quite free from such impurity.
To turn this grand experiment to account, all that was required was to learn the supply of water to each
individual house where a fatal attack of cholera might occur.

403
“You and I may not live to see the day,” Snow explained to the
young curate, “and my name may be forgotten when it comes;
but the time will arrive when great outbreaks of cholera will be
things of the past; and it is the knowledge of the way in which
the disease is propagated which will cause it to disappear.”
May Not Live to See the Day

405
“Victorian London
illustrates how much could be done with bad science;
the continuing existence of cholera in the Third World
shows that even good science is impotent
without the resources, the institutions, and the will to act.”

406
outcome?
these outcomes.
1.

412
John Snow, MD
(1813-1858)
Father of Epidemiology
“On the Mode and Transmission of Cholera”

414
• Cholera
• Others
February 7, 2018

Predictive Analytics - Police Use Analytics to Reduce Crime
https://www.youtube.com/watch?v=iY3WRvXVogo

http://www.npr.org/blogs/health/2012/02/07/146519243/disease-sleuths-surf-for-outbreaks-online

417
Exam Preparation
Tasks
•Open all PowerPoint slides and select slides with words or phrases that are
appropriate for “definition and relationship” questions
•Write three “definition and relationship” questions; one of which is challenging
•Open all PowerPoint slides and select slides that address an important
epidemiology concept and are appropriate for “selected slide” questions
•Write three “selected slide” questions; one of which is challenging
•Copy and read each of the assigned readings. Then highlight and make notes
in the margins so that you have identified and explained the relationship
between the article and eight or more parts of the content of this course to date
that are applicable.

420
outcome?
these outcomes.
1.

427
Epidemiology is what epidemiologists do.
(Gilliam, 1963)
Some Definitions
Epidemiologists make rates, compare rates,
and makes inferences based their similarities or differences.

431
Trachea, Bronchus, and Lung Cancer

435
Under what circumstances
would you have the fewest number of colors on a map?

436
What are the fewest number of colors
that could appear on a map?

448
http://www.whyhere.org/

460
Epidemiological Factors
Person Place Time
Sex
Occupation
Age
SES
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset

461
Health and disease are not distributed haphazardly in a population.
There are patterns to their occurrence. These patterns can be
identified through the surveillance of populations. Examining these
patterns of health and disease can help formulate hypotheses about
their possible causes. (Chapters 1, 3 and 4)
1.
The student will be able to explain the fundamental epidemiological
understandings identified below and apply the understandings to the
interpretation of articles from the popular and scientific press.

463
• New Article
Exam Preparation
Expectations

464
to date.

465
Anecdote
Science
DZ

466
to date.
2. Patterns / PPT

468

469
to date.
2. Patterns / PPT
3. Hypotheses

471
to date.
2. Patterns / PPT
3. Hypotheses

473
to date.
2. Patterns / PPT
3. Hypotheses

474
outcome?
these outcomes.
1.

475
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition

477
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition

480
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition

482
to date.
2. Patterns / PPT
3. Hypotheses
6. Case Definition
9. Definition of Epidemiology

483

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