Epidemiology.pptx

Epidemiology in Public Health
Prof. (Mr.) Asokan R.
HOD of Medical Surgical Nursing &
Incharge of Research & Development,
Kalinga Institute of Nursing Sciences,
KIIT Deemed to be University,
Bhubaneswar, Odisha.

Introduction
Epidemiology is the study of the distribution and determinants
of health and disease in populations.
It aims to identify patterns of disease occurrence and investigate
factors that contribute to disease development.
Epidemiology plays a significant role in shaping public health
policy and practice.

Outline of the Presentation
• Definition
• Importance
• Brief history of epidemiology
• The Epidemiologic Approach
• Key Concepts in Epidemiology
• Epidemiologic Measures
• Study Designs
• Advances in Epidemiological Methods
• Data Collection and Management
• The Use of Big Data in Epidemiology
• Applications of Epidemiology
• Limitations and Challenges of
Epidemiology
• Importance of continued innovation in
epidemiology for improving public health
• Future directions in epidemiology

Definition of epidemiology
• Epidemiology is the study of how diseases spread and affect populations.
• Epidemiology helps us understand why and how diseases affect certain
groups of people.
• It involves examining patterns and trends in the occurrence and
distribution of diseases, as well as identifying risk factors and
possible causes.
• Epidemiologists use this information to develop strategies for
preventing and controlling the spread of diseases.

Importance of Epidemiology in public health
1.Disease prevention and control: Epidemiology helps us identify the risk factors
and causes of diseases.
2.Public health policy: Epidemiological data is used to inform public health policies
and guidelines that can help improve the health of populations.
3.Outbreak investigation: During an outbreak, epidemiologists play a critical role in
investigating the source and spread of the disease, which is important in
containing the outbreak and preventing further transmission.
4.Health promotion: Epidemiological research can help identify the most effective
ways to promote healthy behaviors and prevent disease.

6. Resource allocation: Epidemiological data can be used to guide resource
allocation decisions in healthcare, such as the distribution of vaccines and
medical supplies.
7. Disease Surveillance: Epidemiology helps to track the spread of diseases,
and monitor their incidence and prevalence.
8. Evaluation of Health Interventions: Epidemiology provides the tools to
evaluate the effectiveness of health interventions and programs.
9. Predict and Control Outbreaks: Epidemiology helps to predict and control
outbreaks of infectious diseases.

Recent Importance of epidemiology in public health research
• COVID-19 pandemic: Epidemiologists have been instrumental in tracking
the transmission of the virus, identifying risk factors for severe illness, and
developing public health interventions to control the pandemic.
• Designing research studies: Epidemiologists use their knowledge of study
design and statistical methods to design research studies that can answer
specific research questions related to public health.

• Assessing health disparities: Epidemiological research can help identify
disparities in health outcomes among different population groups, such as
racial and ethnic minorities or people living in certain geographic areas.
• Chronic diseases: Epidemiology is also critical in understanding the causes
and risk factors for chronic diseases such as cancer, diabetes, and heart
disease.
• Emerging infectious diseases: Epidemiology is essential in identifying and
responding to emerging infectious diseases. By monitoring disease outbreaks
and analyzing patterns of transmission, epidemiologists can identify the
source of an outbreak and develop strategies to prevent further spread.

Brief history of epidemiology
• Epidemiology roots can be traced back to the 4th century BCE when
Hippocrates, the Greek physician, recognized the importance of
environmental factors and lifestyle choices in the development of disease.
• Modern epidemiology emerged in the mid-19th century, during the cholera
epidemics in Europe and North America.
• In 1854, John Snow, a British physician, used epidemiological methods to
trace the source of a cholera outbreak in London to a contaminated water
pump in Soho, providing evidence for the waterborne transmission of the
disease.

• During the early 20th century, epidemiology expanded rapidly, with the development
of statistical methods and the application of epidemiology to the study of infectious
diseases, chronic diseases, and environmental health.
• In the second half of the 20th century, epidemiology continued to evolve, with a
greater focus on chronic diseases such as cancer, cardiovascular disease, and diabetes.
The development of new technologies and analytical methods, such as molecular
epidemiology and genetic epidemiology.
• Today, epidemiology plays a crucial role in public health, informing disease prevention
and control efforts, shaping health policy, and advancing our understanding of the
complex interplay between genetics, environment, and lifestyle factors in the
development of disease.

1. Identification of the health problem: The first step to identify disease
outbreak or a particular health condition. This may involve reviewing
medical records, surveying a population, or conducting laboratory testing.
2. Characterization of the problem: To determining its scope and severity.
This may involve assessing the number of cases, the age and sex distribution of
those affected, and any patterns or trends over time.
3. Determination of the risk factors: To determine the risk factors that
contribute to the problem. This may involve conducting surveys or
observational studies to identify common factors or exposures among those who
are affected.

4. Development of a hypothesis: Based on the data collected,
epidemiologists develop a hypothesis to explain the problem and its
associated risk factors.
5. Testing of the hypothesis: Epidemiologists use a laboratory testing,
case-control studies, cohort studies, and randomized controlled trials.
6. Drawing conclusions: epidemiologists draw conclusions based on the
data collected by identifying the causal relationship between a risk factor
and a health outcome, or determining the effectiveness of a particular
intervention.

7. Implementation of interventions: If an intervention is found to be
effective, it can be implemented by developing public health campaigns,
providing treatment or preventive services, or implementing policy changes.
8. Evaluation of the intervention: Finally, the intervention is evaluated to
determine its effectiveness in reducing the health problem.
9. Communicate findings: Finally, the findings from the epidemiologic
approach should be communicated to the public, healthcare professionals,
and policymakers.

Key Concepts in Epidemiology
A. Measures of disease frequency
1.Prevalence
2.Incidence
B. Measures of association
1.Relative risk
2.Odds ratio
C. Bias and confounding
1.Types of bias
2.Methods for controlling bias
3.Types of confounding
4.Methods for controlling confounding

A. Measures of disease frequency
• Prevalence is the proportion of individuals in a population who have a
particular disease or condition at a specific point in time.
=Number of people with the disease /total number of people in the
population.
Prevalence is useful in determining the burden of a disease in a population.
• Incidence is the number of new cases of a disease or condition that occur in a
population over a specified period of time.
=Number of cases per 100,000 people per year.
Incidence is useful in understanding the risk of developing a disease in a
particular population.

B. Measures of association
Relative risk and odds ratio: to assess the relationship between exposure to a risk factor
and the occurrence of a disease. Relative risk (RR) is a measure of the strength of
association between an exposure and a disease.
=Incidence of the disease in the exposed group / incidence of the disease in the
unexposed group.
A relative risk of 1 indicates no association
A relative risk greater than 1 indicates a positive association (i.e. the risk of
disease is higher in the exposed group)
A relative risk less than 1 indicates a negative association (i.e. the risk of disease
is lower in the exposed group).

• Odds ratio (OR) is another measure of the association between exposure
to a risk factor and the development of a disease or health condition.
=odds of exposure among people with the disease or condition / odds
of exposure among people without the disease or condition.
Odds ratio is commonly used in case-control studies.
• An odds ratio of 1 indicates no association,
• An odds ratio greater than 1 indicates a positive association (i.e. the odds
of exposure are higher in cases than in controls)
• An odds ratio less than 1 indicates a negative association (i.e. the odds of
exposure are lower in cases than in controls).

C. Bias and confounding
Bias refers to systematic errors that affect the accuracy and validity of study results.
confounding occurs when a third variable affects the relationship between the independent
and dependent variables being studied. Types of Bias:
1.Selection Bias: occurs when the study population is not representative of the target
population.
2.Information Bias: arises from errors in the measurement or recording of data.
3.Recall Bias: occurs when the study subjects cannot remember past events accurately.
4.Performance Bias: results from differences in the way interventions are implemented.
5.Publication Bias: occurs when studies with positive results are more likely to be
published than those with negative results.

Methods for Controlling Bias:
1.Randomization: the process of randomly assigning subjects to intervention and
control groups.
2.Blinding: involves concealing information from the study subjects, researchers,
or both.
3.Matching: involves selecting study subjects who are similar in key
characteristics.
4.Stratification: involves analyzing data separately for different subgroups.
5.Sensitivity Analysis: tests the robustness of study results to different
assumptions and study designs.

Types of Confounding:
1.Common Confounding: occurs when the confounding variable is related to
both the independent and dependent variables.
2.Reverse Confounding: occurs when the dependent variable affects the
confounding variable.
3.Collider Confounding: occurs when the confounding variable is affected
by both the independent variable and a third variable.

Methods for Controlling Confounding:
1.Stratification: involves analyzing data separately for different subgroups based on the
confounding variable.
2.Multivariate Analysis: involves adjusting for the confounding variable in statistical
models.
3.Matching: involves selecting study subjects who are similar in the confounding
variable.
4.Randomization: helps to minimize the effect of confounding by balancing the
distribution of confounding variables between the intervention and control groups.
5.Sensitivity Analysis: a statistical technique that tests the robustness of study results to
different assumptions about the confounding variable.

Epidemiologic Measures
• Incidence and prevalence
• Mortality rates
• Case-fatality rates
• Attack rates
• Relative risk (Risk ratios)
• Odds ratios

Mortality Rates:
• Mortality rate is the number of deaths due to a specific disease or condition in a
given population during a specific time period.
Number of deaths per 100,000 people per year.
Case-fatality Rates:
• Case-fatality rate is the proportion of people with a specific disease or condition
who die from that disease or condition during a specific time period.
Attack Rates:
• Attack rate is the proportion of people who become ill with a specific disease or
condition among those who have been exposed to it during a specific time
period.

Some examples of each of these measures
1. Incidence and Prevalence: Town has a population of 10,000 people. In
the year 2022, 100 new cases of COVID-19 were reported in the town.
The incidence rate would be 100/10,000 = 0.01 or 1% per year.
If, at the same time, there were already 50 cases of COVID-19 in the
town, the prevalence would be 50/10,000 = 0.005 or 0.5%.
2. Mortality Rates: In the year 2022, Town has a population of 1000
people, out of the 100 cases of COVID-19, 10 people died due to the
disease.
The mortality rate would be 10/1000 = 0.01 or 1% per year.

3. Case-fatality Rates: In the same example above,
the case-fatality rate would be 10/100 = 0.1 or 10%.
4. Attack Rates: Group of 50 people attended a picnic and were
exposed to contaminated potato salad. Out of these 50 people, 10
developed symptoms of food poisoning.
The attack rate would be 10/50 = 0.2 or 20%.

5. Risk Ratios: We want to study the association between smoking and lung
cancer.
We follow a group of 1000 people who smoke and 1000 people who don't
smoke for 10 years. During this time, 50 people in the smoking group and 10
people in the non-smoking group develop lung cancer.
The incidence rate of lung cancer in the smoking group is 50/1000 = 0.05
or 5%,
Incidence rate in the non-smoking group is 10/1000 = 0.01 or 1%.
The risk ratio would be 0.05/0.01 = 5, indicating that the incidence of lung
cancer is 5 times higher in smokers than non-smokers.

• 6. Odds Ratios: In the same example as above, we could also calculate the
odds ratio.
The odds of developing lung cancer among smokers would be
50/950 = 0.053,
while the odds among non-smokers would be 10/990 = 0.0101.
The odds ratio would be 0.053/0.0101 = 5.25,
which is very similar to the risk ratio of 5.

Study Designs
• Observational studies
• Cohort studies
• Case-control studies
• Cross-sectional studies
• Experimental studies
• Randomized controlled trials

Observational studies:
Investigator observes and measures outcomes of interest in participants
without manipulating any variables.
Example of observational study:
• A study that examines the relationship between smoking and lung cancer by
observing and comparing the incidence of lung cancer in smokers and non-
smokers over time.

Pros:
• Conducted on large populations, making them cost-effective and efficient.
• These studies can detect associations between risk factors and outcomes
that may not be detected in experimental studies.
• They can be used to generate hypotheses for further investigation.
Cons:
• Observational studies cannot establish causality, as they cannot control for
all potential confounding variables that may influence the outcome.
• There is a risk of selection bias, as participants are not randomly assigned to
groups.
• There may be recall bias, as participants may not accurately recall or report
their exposure to risk factors.

Cohort studies:
Follow a group of individuals over time to examine the incidence of a
particular disease or outcome.
Example of cohort study:
• The Nurses’ Health Study, which follows a cohort of female nurses over
time to investigate the relationship between lifestyle factors and chronic
disease.

Pros:
• Cohort studies can establish a temporal relationship between risk factors and
outcomes.
• They can investigate multiple outcomes over time.
• They can provide information on the incidence and natural history of diseases.
Cons:
• Cohort studies can be costly and time-consuming.
• There may be losses to follow-up, which can affect the generalizability of the results.
• There may be confounding variables that are not measured or controlled for.

Case-control studies:
Individuals with a specific outcome (cases) are compared to individuals
without the outcome (controls) to identify potential risk factors.
Example of case-control study:
• A study that investigates the association between alcohol consumption and
liver cancer by comparing the alcohol intake of individuals with liver cancer
to that of individuals without liver cancer.

Pros:
• Case-control studies can investigate rare diseases or outcomes.
• They can be conducted more quickly and cost-effectively than cohort studies.
• They can investigate multiple risk factors for a single outcome.
Cons:
• There is a risk of recall bias, as cases may be more likely to remember their exposure
to risk factors.
• Selection bias can occur if controls are not representative of the population from
which cases were drawn.
• They cannot establish causality, as they rely on retrospectively collected data.

Cross-sectional studies:
Collect data from a population at a single point in time.
Example of cross-sectional study:
• A study that estimates the prevalence of diabetes in a population by
measuring the proportion of individuals with diabetes at a specific point in
time.

Pros:
• Cross-sectional studies can be conducted quickly and cost-effectively.
• They can provide information on the prevalence of diseases or risk factors.
• They can identify potential associations between risk factors and outcomes.
Cons:
• Cross-sectional studies cannot establish causality, as they do not follow participants
over time.
• There may be selection bias if the study population is not representative of the
general population.
• There may be recall bias, as participants may not accurately report their exposure to
risk factors.

Experimental studies:
Investigator manipulates one or more variables to examine their effect on
an outcome of interest.
• A study that examines the effect of a new drug on blood pressure by randomly
assigning participants to receive either the drug or a placebo and measuring
changes in blood pressure.

Pros:
• Experimental studies can establish cause-and-effect relationships between
variables.
• They can control for confounding variables and biases.
• They can be used to test the efficacy of interventions or treatments.
Cons:
• Experimental studies can be costly and time-consuming.
• There may be ethical concerns with randomly assigning participants to
groups.
• The results may not be generalizable to real-world settings.

Randomized controlled trials:
Type of experimental study in which participants are randomly assigned to
either an intervention group or a control group to compare the effects of an
intervention on an outcome of interest.
Example of RCTs include:
• A study that investigates the effectiveness of a new vaccine by randomly assigning
participants to receive either the vaccine or a placebo and monitoring the incidence
of the disease.

Pros:
• RCTs are considered the gold standard for evaluating the effectiveness of
interventions or treatments.
• They can establish cause-and-effect relationships between variables.
• They can control for confounding variables and biases.
Cons:
• RCTs can be costly and time-consuming.
• There may be ethical concerns with randomly assigning participants to
groups.
• The results may not be generalizable to real-world settings.

• The choice of study design can impact the strength of evidence for public
health interventions.
• Observational studies can provide important information on risk factors and
disease patterns, but they cannot establish causality.
• Experimental studies, such as RCTs, are better able to establish causality, but
they may not be feasible or ethical in some cases.
• Therefore, a combination of study designs may be needed to provide the
strongest evidence for public health interventions.

Advances in Epidemiological Methods

New epidemiological methods, including machine learning and
causal inference, are increasingly being used in public health research to
improve the accuracy and precision of estimates of disease risk and causality.
• Machine learning is a branch of artificial intelligence that involves
training algorithms to make predictions based on patterns in data.
• Causal inference is a statistical framework for making causal inferences
from observational data.

Machine learning:
• In a study published in the journal Lancet Digital Health, researchers used
machine learning to develop a model for predicting hospital readmissions
among patients with heart failure.
The model was able to identify high-risk patients with 78% accuracy, which
could help healthcare providers target interventions to prevent readmissions.
• In a study published in the Journal of Clinical Epidemiology, researchers used
machine learning to develop a model for predicting 5-year survival among
breast cancer patients.
The model outperformed traditional prognostic models, indicating that
machine learning could improve personalized cancer care.

Causal inference:
• In a study published in the New England Journal of Medicine, researchers used causal
inference methods to evaluate the effectiveness of a policy to reduce salt intake in
Argentina.
The study found that the policy led to a 12% reduction in salt intake and a 35%
reduction in cardiovascular events, indicating that the policy was effective.
• In a study published in the American Journal of Epidemiology, researchers used
causal inference methods to investigate the relationship between exposure to air
pollution and mortality.
The study found that even low levels of exposure to particulate matter were
associated with an increased risk of mortality, providing strong evidence for the
harmful effects of air pollution.

Advantages
• Improved accuracy and precision of estimates of disease risk and causality.
• Ability to identify complex relationships between multiple risk factors and
outcomes.
• Potential for developing personalized risk prediction models and targeted
interventions.
Limitations
• The need for large and diverse datasets.
• Potential for overfitting and biased predictions.
• Difficulty in interpreting complex models and identifying causal mechanisms.

Data Collection and Management

Sources of Data for Public Health:
1.Vital Statistics: Vital statistics are records of birth, death, marriage, and divorce, which
provide information on demographic characteristics, causes of death, and health trends.
2.Disease Registries: Disease registries collect data on specific diseases or conditions,
such as cancer, diabetes, or HIV/AIDS. These registries provide information on the
incidence, prevalence, and mortality rates of diseases.
3.Surveys: Surveys are used to collect data on health behaviors, risk factors, and health
outcomes. Surveys can be conducted in person, by telephone, or online.
4.Electronic Health Records: EHRs can be used to collect and analyze data on patient
demographics, diagnoses, treatments, and outcomes.

Data Quality and Accuracy:
• Completeness: Completeness refers to the proportion of cases that are reported to a
surveillance system. Incomplete data can lead to underestimates of disease burden
and hinder public health interventions.
• Validity: Validity refers to the accuracy of the data, i.e., whether the data measures
what it is intended to measure. Invalid data can lead to incorrect conclusions and
ineffective interventions.
• Reliability: Reliability refers to the consistency of the data, i.e., whether the data
can be replicated over time and across different contexts.
• Timeliness: Timeliness refers to the speed at which data are collected and
reported.

The Use of Big Data in Epidemiology

Big data has revolutionized epidemiology by providing new
opportunities to analyze and interpret large and complex datasets.
Big data refers to large volumes of structured and unstructured data
that are generated from various sources, such as electronic health records,
social media, mobile devices, and sensors.

Applications of Epidemiology
• Infectious disease epidemiology
• Chronic disease epidemiology
• Environmental epidemiology
• Occupational epidemiology
• Genetic epidemiology

1.Infectious Disease Epidemiology: Used to identify the source of outbreaks, monitor the
spread of infectious diseases, and evaluate the effectiveness of interventions such as
vaccination programs.
2.Chronic Disease Epidemiology: Used to identify risk factors for chronic diseases &
monitor disease trends and evaluate the effectiveness of interventions.
3.Environmental Epidemiology: Used to investigate the relationship between environmental
exposures and health outcomes & identify environmental risk factors for diseases such as air
pollution, lead exposure, and pesticide exposure.
4.Occupational Epidemiology: Used to identify workplace hazards and evaluate the
effectiveness of occupational health interventions & monitor occupational disease trends and
investigate work-related injuries and illnesses.
5.Genetic Epidemiology: Used to identify genetic risk factors for diseases & identify gene-
environment interactions and to evaluate the effectiveness of genetic screening and
counseling programs.

Limitations and Challenges of Epidemiology

1.Causality: Associations can be identified between certain risk factors and health
outcomes, it can be difficult to determine if one factor caused the other.
2.Bias: Selection bias, measurement bias, or confounding.
3.Generalizability: Difficult to generalize findings to other populations.
4.Ethical considerations: Ethical concerns around privacy and confidentiality, vulnerable
groups to harm from participating in epidemiological studies.
5.Resource constraints: Conducting large-scale epidemiological studies can be costly and
time-consuming.
6.Changing disease patterns: Disease patterns change and new diseases emerge.
7.Data quality: Errors in data collection, processing, or analysis, which can impact the
validity of the study findings.

Importance of continued innovation in epidemiology for
improving public health

1.Advancing our understanding of disease
2.Improving public health interventions
3.Addressing health disparities
4.Responding to global health challenges.
5.Supporting evidence-based policymaking

Future directions in epidemiology

1.Integrating big data and artificial intelligence: Identify new risk factors and improve
disease forecasting.
2.Precision medicine: Personalized risk assessments to target preventive measures or
treatments based on an individual's genetic and environmental risks.
3.One Health: Understanding the complex relationships between humans, animals, and
the environment and how they impact disease transmission and spread.
4.Global health security: Developing more effective surveillance systems, improving
emergency response protocols, and enhancing international cooperation to better
prepare for and respond to pandemics.
5.Social determinants of health: Socioeconomic status, education, and access to
healthcare to reduce health disparities and improve overall health outcomes.

Epidemiology.pptx

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