Epidemiology stringently focuses on the application of appropriate study designs and analysis to draw an inference. 

1. Epidemiological Methods
Epidemiology stringently focuses on the application of
appropriate study designs and analysis to draw an inference.
Bhoj R Singh
Division of Epidemiology
Indian Veterinary Research Institute,
Izatnagar-243122, India

2. Types of Methods used
Bayesian Methods: A general tool to explicitly incorporate prior knowledge and fit more
complicated regression models.
Causal Inference: Epidemiologists address causality as a primary target. The
counterfactual theory of causation has provided a unified way of conceiving of,
implementing and analyzing epidemiologic studies.
Latent Variable Modelling: Many of the constructs in our studies are not directly
measurable. Latent variables often called confounders are one way to combine multiple,
incomplete measures of these constructs into usable variables in our models.
Longitudinal Data Analysis: Longitudinal or repeated measures data are ubiquitous in
epidemiologic research. Numerous techniques are used to analyze these data, including
covariance pattern models, generalized estimating equations, random
coefficient/growth curve modelling, survival analysis, and time series modelling, to
name a few.
Meta Analysis: Observational and interventional studies often provide the best available
evidence, however individual studies may lack power to provide either definitive or
hypothesis-generating knowledge. Thus, statistical techniques for combining data from
several studies with similar hypotheses is becoming increasingly popular in
epidemiology as Meta-analytical methods.

3. Study methods/designs in Epidemiology
• Observational study Designs [Descriptive studies (occurrence and
distribution) and analytical studies (testing validity of hypothesis)
– Static Studies: Case report, case series and Cross-sectional Studies:
Show only static description of a occurrence of the disease, determines
point prevalence, some times help is formation of causal hypothesis.
– Follow-up studies: To estimate period prevalence, and incidence and for
development of causal and interventional hypothesis. Cohort study
designs.
• Experimental/ Interventional study designs: (to confirm the
hypothesis) Have experimental/ intervention and control groups.
• Quasi-experimental Study Designs
– Case-control
– Nested case-control
– Case-cohort

4. When and where? Which study?
• Descriptive:
– Little is known about the problem
– Rely on pre-existing data pertaining to who/,
When? & Where?
– To establish potential causal hypothesis.
• Analytical:
– Used when insight into various aspects of problem
are already known.
– Rely on generation/ development of new data.
– To find the answer of Why?
– To evaluate the causal hypothesis.

5. Indices used in different Methods
Relative risk (RR): used in cohort studies to measure the strength of
an association.
RR = (incidence in exposed) / (incidence in non-exposed)
Attributable risk (AR): Number of cases attributable to the putative
risk factor
AR = (incidence in exposed) - (incidence in non-exposed)
Attributable fraction (AF): Maximum proportion of a disease in a
population that can be attributed to a risk factor (or maximum
proportion of a disease that would be eliminated in the absence of
the risk factor)
AF = (prevalence of exposure) x (incidence in exposed - incidence in non-
exposed) / (incidence in the overall population)
Odds ratio (OR): Used in case-control studies for estimation of relative
risk.
OR = (cases in exposed group x non-cases in not exposed) / (non-cases
in exposed group x cases in not exposed group)
Short term fluctuations: Epidemic Curves
Long term Fluctuations: Trends

6. Observational study design measures of disease, measures of
risk, and temporality
Study design Measures of disease Measures of risk Temporality
Ecological
Prevalence (rough
estimate)
Prevalence ratio Retrospective
Proportional
mortality
Proportional
mortality
Standardized mortality
Proportional mortality
ratio
Standardized mortality
ratio
Retrospective
Case-crossover None Odds ratio Retrospective
Cross-sectional
Point prevalence
Period prevalence
Odds ratio
Prevalence odds ratio
Prevalence ratio
Prevalence difference
Retrospective
Case-control None Odds ratio Retrospective
Retrospective and
prospective cohort
Point prevalence
Period prevalence
Incidence
Odds ratio
Prevalence odds ratio
Prevalence ratio
Prevalence difference
Attributable risk
Incidence rate ratio
Relative risk
Risk ratio Hazard ratio
Retrospective only
Both retrospective
and prospective,
Prospective only

7. Observational study design strengths and weaknesses
Study design Strengths Weaknesses
Ecological
Very inexpensive
Fast
Easy to assign exposure levels
Inaccuracy of data
Inability to control for confounders
Difficulty identifying or quantifying denominator
No demonstrated temporality
Proportional mortality
Very inexpensive
Fast
Outcome (death) well captured
Utilize deaths only
Inaccuracy of data (death certificates)
Inability to control for confounders
Case-crossover
Reduces some types of bias
Good for acute health outcomes with a defined
exposure
Cases act as their own control
Selection of comparison time point difficult
Challenging to execute
Prone to recall bias
No demonstrated temporality
Cross-sectional
Inexpensive
Timely
Individualized data
Ability to control for multiple confounders
Can assess multiple outcomes
No temporality
Not good for rare diseases
Poor for diseases of short duration
No demonstrated temporality
Case-control
Inexpensive
Timely
Individualized data
Ability to control for multiple confounders
Good for rare diseases
Can assess multiple exposures
Cannot calculate prevalence
Can only assess one outcome
Poor selection of controls can introduce bias
May be difficult to identify enough cases
Prone to recall bias
No demonstrated temporality
Retrospective and
prospective cohort
Temporality demonstrated
Individualized data
Ability to control for multiple confounders
Can assess multiple exposures
Can assess multiple outcomes
Expensive
Time intensive
Not good for rare diseases

8. Outbreak investigation
Descriptive Epidemiology
Why to investigate? Identify the source (and eliminate it), Develop
strategies to prevent future outbreaks, Evaluate existing prevention
strategies, Describe new diseases and learn more about known
diseases, Address public concern, It’s your job!
10 Steps in the Process
1. Identify investigation team and resources
2. Establish existence of an outbreak
3. Verify the diagnosis
4. Construct case definition
5. Find cases systematically and develop line listing
6. Perform descriptive epidemiology/develop hypotheses
7. Evaluate hypotheses/perform additional studies as necessary
8. Implement control measures
9. Communicate findings
10. Maintain surveillance

9. Census, Survey and Screening
• Census: No sampling
• Surveys: To map a disease or problem. Sampling required.
– Cross Sectional (cross sectional studies) and
– Longitudinal (cohort studies, panel studies and trend studies)
– Methods
• Questionnaires
– Advantages: Ideal for asking closed-ended questions; effective for market or consumer research
– Disadvantages: Limit the researcher’s understanding of the respondent’s answers; requires budget for
reproduction of survey questionnaires. No response problem
• Interviews
– Advantages: Follow-up questions can be asked; provide better understanding of the answers of the
respondents
– Disadvantages: Time-consuming; many target respondents have no public-listed phone numbers or no
telephones at all. Interviewer effect.
• Screening:
– Mass screening. No sampling. It is the application of a test to detect a potential
disease or unidentified disease or condition or problem in a group, a farm or a
population who has no known signs of that disease or condition i.e., apparently
healthy. Aim is to size/map the iceberg of the disease.
– Targeted screening/ selected or High risk screening: No sampling
– Multipurpose: For more than one problem using more than one test
– Multi-phasic: Different tests are used as for Brucellosis MRT, Slide test, STAT, ELISA
– Opportunistic: case finding to bring the case for treatment, most of the modern
hospital do it by free camp etc.

10. Concerns in Surveys
• Population issues: Is individual identity there? All members of
population are equally available or accessible for sampling?
Geographical barriers? Co-operation!
• Sampling issues: Accessibility for re-sampling, finding of the
sample, availability for sampling etc.
• Question issues: Formulation of case definitions, questions and way
of asking questions! Type of questions, Response and response
scale.
• Bias issues
• Administrative issues
• Financial issues
• Temporal issues (time available)
• Personnel issues
• Facilities

11. Screening Diagnosis
Done on apparently healthy individuals Done on diseased or sick individuals
Applied on groups, farms or population Applied on individuals
Results are arbitrary and final Diagnosis is never final
Based on one criteria and cut-off limit Based on diagnostic test results,
laboratory findings & a number of signs
and symptoms
Based on less accurate, highly sensitive
tests with moderate specificity
More accurate
Less expensive and quick More expensive
Not the basis of treatment Used as basis of treatment
Initiative comes from investigator,
authorities or administrators
Initiative comes from patient owner or
on recommendation from a clinician
Screening versus Diagnosis

12. Sample and Sampling Theories
• Random sampling: In this case each individual is chosen entirely by chance and each
member of the population has an equal chance, or probability, of being selected.
• Systematic sampling: Individuals are selected at regular intervals from a list of the
whole population.
• Stratified sampling: In this method, the population is first divided into sub-groups (or
strata) who all share a similar characteristic and then on each strata either random or
systematic sampling can be done.
• Clustered sampling: In a clustered sample, sub-groups of the population are used as
the sampling unit, rather than individuals.
• Quota sampling: This method of sampling is often used by market researchers, each
researcher is given a defined quota for taking survey.
• Sampling of ease or Convenience sampling: Convenience sampling is perhaps the
easiest method of sampling, because participants are selected in the most
convenient way, and are often allowed to chose or volunteer to take part.
• Snowball sampling: This method is commonly used when it is hard to reach target
or population groups. Existing samples or individuals in the study are asked to
nominate further subjects known to them, so the sample increases in size like a
rolling snowball.
• Strategic Sampling or Targeted sampling
• Sequential sampling
• Multistage sampling