## 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 --- ## Epidemiology in triads  --- ## Epidemiology triad - Distribution of diseases - Determinants of diseases - Use of the information --- ## Distribution of diseases - Person - Place - Time --- ## Distribution of diseases person  --- ## Distribution of diseases with place  --- ## Distribution of diseases over time  --- ## Measures of disease distribution - Prevalence (proportion) - Incidence (rate) - Ratio (standardised mortality ratio) --- ## 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 --- ## Example of prevalence  --- ## 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 --- ## 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? --- ## 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?) --- ## Concept of Incidence - How many NEW cases of the disease in the community - How many people were AT RISK? - Over WHAt period of time? --- ## 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 --- ## 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 --- ## 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 --- ## Example of incidence rate  --- ## Question for the class 2 Based on the data presented below, is the incidence - (A) increasing or - (B) Falling off --- ## Incidence of the disease in the data  --- ## 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 --- ## Example of incidence rate in real life  --- ## 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 --- ## 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 --- ## Question for class - which population has higher incidence?  --- ## Concept of age-standardised rates  --- ## Question for class - which population has NOW higher incidence? --- ## 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 --- ## Break for 10 minutes --- ## Determinants of diseases --- ## 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? --- ## How do we know that X causes Y? Observe facts --> Frame theories --> Test with new facts --- ## 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 --- ## 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 --- ## 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 --- ## 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! --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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? --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## Tests of association: control for confounding variable --- ## What is a confounding variable?  --- ## Example of a confounding variable  --- ## 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! --- ## How do we control for confounding variable? - Matching - Multivariable analysis - Allocation to groups being compared using Random Numbers Table --- ## How do we find causal linkage using Bradford-Hill Criteria  --- ## Break for a couple of minutes --- ## Putting these ideas together: study designs --- ## 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 --- ## Case control studies  --- ## Cohort studies  --- ## Rounding up everything we learned with Snow's Cholera investigation --- ## Cholera Epidemic https://www.youtube.com/watch?v=KvHL0dHj3RM --- ## The ghost map  --- ## Snow's table  --- ## Summary ---