---
title: Introduction to Epidemiology
author: Arindam Basu
date: 2020-02-22
tags: Epidemiology, teaching, introduction
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## Introduction to Epidemiology: a tour

> Epidemiology is the study of distribution and determinants of diseases in populations and use of this knowledge for the improvement in public health

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## Epidemiology in triads

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## Epidemiology triad
- Distribution of diseases
- Determinants of diseases
- Use of the information

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## Distribution of diseases

- Person
- Place
- Time

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## Distribution of diseases person

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## Distribution of diseases with place


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## Distribution of diseases over time


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## Measures of disease distribution

- Prevalence (proportion)
- Incidence (rate)
- Ratio (standardised mortality ratio)

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## Concept of prevalence
- total cases of a disease in the population OVER
- total number of people there
- MULTIPLIED by factor of 100 (percent), 1000 or 10, 000
- At a FIXED PERIOD of TIME

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## Example of prevalence


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## Advantage of prevalence
- Simple snapshot of a health condition in the population
- For a single population, at one point in time
- You can use it to compare two or more populations

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## Limitation of prevalence
- Does not provide any information about how the disease spreads in the community
- Is the disease increasing?
- Is it getting worse?
- Is it getting better?

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## Question for the class - what data do we need?
- What additional data do we need for that?
- (Question for the class, what do you think?)

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## Concept of Incidence
- How many NEW cases of the disease in the community
- How many people were AT RISK?
- Over WHAt period of time?

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## Concept of Person-time
- If you follow 1 person for 1 year,
- You get 1 person-year
- If you follow 100 people for 1 year,
- You get 100 person-year
- If you follow 100 people for 10 years,
- You get 1000 person-years

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## Question for the class

Let's say we follow 1000 people for 5 years, which of the following is correct?

- (X) We have 5000 person-years
- (Y) We have 1000 person-years
- (Z) We have 5 person-years

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## Concept of incidence rate
- Incidence is a rate because it has TIME as DENOMINATOR
- Number of NEW CASES OVER Person-years
- REQUIRES FOLLOW UP of people

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## Example of incidence rate


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## Question for the class 2
Based on the data presented below, is the incidence
- (A) increasing or
- (B) Falling off

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## Incidence of the disease in the data


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## Advantage of Incidence rates
- Used for charting epidemics
- Helps you to understand whether an epidemic is getting better or worse
- Helps to chart data in real time

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## Example of incidence rate in real life

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## Question for the class - 3
How can we use the information on the COVID Epidemic Curve?

- (1) Test whether the infection is rising or falling
- (2) Get an idea when to introduce lockdown or other containment measures
- (3) Find out whether lockdown or containment measures are working or not
- (4) All of the above

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## Limitation of Incidence
- We need longitudinal data
- Without follow-up data we cannot estimate incidence
- Incidence is too simple if we want to compare different populations that differ in age groups

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## Question for class - which population has higher incidence?


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## Concept of age-standardised rates


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## Question for class - which population has NOW higher incidence?

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## Summary for distribution of diseases
- Three measures: prevalence, incidence, and standardised ratios
- Prevalence is used for static time
- Incidence is used in the context of person-year as denominator
- Incidnece is used for measuring how disease increases or decreases over time
- Standardised ratios used to compare two different populations

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## Break for 10 minutes

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## Determinants of diseases

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## What does determinants of diseases in populations mean?
- This means what cause diseases in populations?
- Examples of some questions:
- Does cigarette smoking cause lung cancer? How do we know?
- Does long term sitting and sedentary lifestyle lead to heart diseases?
- Do particular food items such as chicken salad lead to gastroenteritis?
- How do we know?

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## How do we know that X causes Y?

Observe facts --> Frame theories --> Test with new facts

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## What is meant by exposure and outcome
- Consider a disease, any disease, call it O
- We all Y an OUTCOME (health outcome)
- Examples: Diabetes, High blood pressure, lung cancer, so on
- Consider something to which people are exposed
- Example: air pollution, smoking, drinking, gambling, reckless driving
- All of these are called EXPOSURE, give it a name E

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## What is association?
- If high levels of E leads to high levels of O, then
- We call that a positive association, or RISK
- That is, those with high levels of E will have high incidence of O
- Example: people who smoke lots of cigarettes end up with lung cancer
- We say cigarette smoking is associated with lung cancer
- Or, we say Smoking is a RISK for Lung Cancer

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## How do we measure associations?
- Risk Ratio (RR) Or
- Odds Ratio (OR)
- Risk Ratio = Risk of Disease among Exposed OVER Risk of Disease among non-Exposed
- Odds Ratio = Odds of Exposure among Diseased OVER Odds of Exposure among those without the disease

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## Decisions about RRs and ORs
- If RR > 1, the risk is high, OR, association is positive
- Otherwise, if RR = 1, then we cannot say anything
- Else, if RR < 1, there is BENEFICIAL effect!

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## Question for the Class - Is this high risk?
You conducted a study on cigarette smoking and risk of lung cancer, and found RR = 2.50; what would you say?

- (1) Cigarette smoking increases the risk of lung cancer
- (2) Cigarette smoking has no effect on lung cancer

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## Establishment of cause and effect
- If we have to show exposure E is a cause of disease O, then
- Show that E has TRUE or REAL association with O
- And show that,
- that Association is one of CAUSE and EFFECT

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## Four things to consider
- Valid Association: did not occur due to chance (rule out chance)
- Observed association could not be due to biases (eliminate biases)
- Observed association cannot be due to a third factor (confounding)
- Examine causal factors using Hill's criteria
- Examine counterfactual theories of causation

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## Rule out the play of chance for exposure disease relationship
- Test with hypothesis testing
- Null Hypothesis: that there is equal chances of disease with and without exposure
- Alternative hypothesis: risk of disease is higher with exposure
- Always test with null hypothesis
- Test p-values and 95% confidence interval

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## What is meant by null hypothesis? (Example: smoking and lung cancer)
- Exposure and disease outcomes are unrelated
- People who smoke and who are non-smokers get lung cancer at the same rate
- Rate of lung cancer among smokers = Rate of smoking among non-smokers
- All epidemiological research is about disproving the null hypothesis

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## Question for the class - what is the correct null hypothesis
Imagine you are investigating whether smoking cause lung cancer. What is the correct null hypothesis?
- (X) Cigarette smoking DOES NOT cause Lung Cancer
- (Y) There is NO ASSOCIATION between cigarette smoking and lung cancer
- (Z) The RISK of Lung Cancer is same for Smokers and Non-smokers

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## p-value and 95%Confidence Interval

- You completed a study of an Exposure E and outcome O
- You were to repeat the study a 100 times!
- You found a measurement of RISK (RR) of 2.5
- You found a p-value of 0.02
- You found 95% Confidence Interval of 2.1 - 4.2
- What do these things mean?

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## Question for the class - what does RR of 2.5 mean?
- (A) E is high risk for O
- (B) E has no association with O

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## Concept of p-value
- Assuming that the null hypothesis is TRUE,
- and suppose you ran this study 100 times,
- Then only in 2 out of those 100 studies,
- you might get the kind of high risk RR you got
- You can reject the null hypothesis

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## Concept of 95% Confidence Interval
- If you repeated this study 100 times,
- In 95 out of 100 times,
- You might get an RR value between 2.1 and 4.2
- Most likely 2.5

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## Question for the class - What does RR of 2.1 mean?
- (A) E is high risk for O
- (B) E has no association with O

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## How do we rule out the play of chance?
- Select a large enough sample suitable for the effect you want to study
- Perform sample size estimation and power calculation ahead of the study
- Deal with it during the planning of the study

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## What is bias?
- Bias = Systematic errors in observation or conduct of the study
- Selection Bias: Where the groups are not comparable the way they were identified
- Response Bias: When the participants of the study provide erroneous information

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## How can you eliminate biases in the study design?
- In the study design phase,
- The investigator should be careful about selection of the sample
- In experimental studies, use randomisation and blinding
- Train the data collectors in the study

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## Tests of association: control for confounding variable

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## What is a confounding variable?

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## Example of a confounding variable

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## Explanation why Age is a confounding variable
- In the study on the association between smoking and lung cancer,
- Age is a confounding variable
- Old people tend to smoke more than younger people, AND
- Old age is also a risk factor for ANY cancer!
- Smoking CANNOT CAUSE Aging!

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## How do we control for confounding variable?
- Matching
- Multivariable analysis
- Allocation to groups being compared using Random Numbers Table

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## How do we find causal linkage using Bradford-Hill Criteria

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## Break for a couple of minutes

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## Putting these ideas together: study designs

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## Epidemiological study designs
- Single Case studies
- Case series (used in Epidemic Surveillance)
- Cross-sectional surveys
- Case control studies (Most widely used study designs)
- Cohort studies

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## Case control studies

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## Cohort studies

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## Rounding up everything we learned with Snow's Cholera investigation

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## Cholera Epidemic
https://www.youtube.com/watch?v=KvHL0dHj3RM

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## The ghost map


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## Snow's table

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## Summary

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