Overview of Epidemiology

1. Overview 1 Overview of Epidemiology

2. Overview 2 Epidemiologic Research Assumes Disease occurrence is not random Systematic investigation of different populations can identify causal and preventive factors Making comparisons is the cornerstone of systematic investigations

3. Overview 3 Definition of Epidemiology The study of the distribution and determinants of disease frequency in human populations and the application of this study to control health problems

4. Overview 4 Disease frequency - count cases, need system, records Disease distribution - who, when, where Frequency, distribution, other factors generate hypotheses about determinants A determinant is a characteristic that influences whether or not disease occurs Key Words in Definition

5. Overview 5 Natural Progression in Epidemiologic Reasoning 1st � Suspicion that a factor influences disease occurrence. Arises from clinical practice, lab research, examining disease patterns by person, place and time, prior epidemiologic studies 2nd � Formulation of a specific hypothesis

6. Overview 6 Natural Progression in Epidemiologic Reasoning 3rd � Conduct epidemiologic study to determine the relationship between the exposure and the disease. Need to consider chance, bias, confounding when interpreting the study results. 4th � Judge whether association may be causal. Need to consider other research, strength of association, time directionality

7. Overview 7 Hypothesis Formation and Testing Clues from many sources and imagination lead to hypothesis formation Conduct epidemiologic study to test hypothesis

8. Overview 8 Descriptive Epidemiology Describe patterns of disease by person, place, and time Person: Who is getting the disease? (for example, what is their age, sex, religion, race, educational level etc?)

9. Ex. Mortality rates per 100,000 from diseases of the heart by age and sex (2000) What hypotheses can you generate from these data?

10. Place Where are the rates of disease the highest and lowest? What hypotheses can you generate from this map? Malignant Melanoma of Skin

11. Overview 11 Place What hypotheses can you generate from this map? Cancer of the Trachea, Bronchus and Lung

12. Overview 12 Variation on Place: Migrant StudiesMortality rates (per 100,000) due to stomach cancer. What hypotheses can you generate from these data?

13. Overview 13 Time: Is the present frequency of disease different from the past? What hypotheses can you generate from these data?

14. Overview 14 Solve the Mystery Epidemic The following data relate to an unusual episode that actually occurred. Describe the epidemiologic features of this episode. For example, the overall mortality �rate� was 68.2% Based on the descriptive characteristics, formulate a hypothesis concerning the etiology of this episode.

15. Overview 15 Solve the Mystery Epidemic These data describe the mortality rates associated with the sinking of the Titanic.These data describe the mortality rates associated with the sinking of the Titanic.

16. Overview 16 Main Epidemiologic Study Designs for Testing Hypotheses Cohort study Experimental study Case-control study Each design represents a different way of harvesting information. Selection of one over another depends on the particular research question, concerns of about data quality and efficiency, and practical and ethical considerations

17. Overview 17 Which study design to choose? In theory, it's possible to use each design to test a hypothesis Example: Suppose you want to study the relationship between dietary Vitamin A and lung cancer�.

18. Overview 18 Cohort Study Option Subjects are chosen on the basis of exposure status and followed to assess the occurrence of disease High Vitamin A consumption ---------------> lung cancer or not Low Vitamin A Consumption --------------> lung cancer or not What are the advantages and disadvantages of this option?

19. Overview 19 Experimental Study Option Special type of cohort study in which investigator assigns the exposure to individuals, preferably at random � Investigator assigns exposure to: High Vit A consumption ----------------> lung cancer or not Low Vit A consumption ----------------> lung cancer or not What are the advantages and disadvantages of this option?

20. Overview 20 Case-Control Study Option Cases with the disease and controls who generally do not have the disease are chosen and past exposure to a factor is determined Prior Vitamin A consumption <----------- lung cancer cases Prior Vitamin A consumption <---------- controls What are the advantages and disadvantages of this option?

21. Overview 21 In practice, choice of study design depends on: State of knowledge Frequency of exposure and disease Time, cost and other feasibility considerations Each study design has unique and complementary advantages and disadvantages

22. Overview 22 The result of an epidemiologic study, regardless of type, is an association between the disease under study and an exposure. Two questions must be answered: Is the observed association valid? Is the association causal?

23. Overview 23 Example: Lung Cancer and Smoking Conclusion: The rate of lung cancer among smokers is five times that of nonsmokers. There is an association between smoking and lung cancer in these data.

24. Overview 24 Example: Lung Cancer and Smoking 1st Question: Is the observed association valid?

25. Overview 25 Random Error Inference about experience of entire population based on study sample Random variation from sample to sample Sample size determines degree to which chance affects findings P values and confidence intervals quantify amount of random error

26. Overview 26 Random Error Prior smoking and lung cancer study: Under null hypothesis (no association between smoking and lung cancer) you would expect to see an equal number of lung cancers among smokers and nonsmokers (120/2 = 60). P value tells you probability of seeing 100 vs. 20 lung cancers given that you expected 60 in each group. P value tells you nothing about alternative explanations such as bias

27. Overview 27 Bias Any source of error in the determination of the association between the disease and exposure Selection bias - bias in how subjects are selected Observation bias - bias in how information is obtained Could bias explain the finding?

28. Overview 28 Confounding Not the investigator�s fault, just a fact of life Confounding is a mixing of effects between the association of the disease and third factor (the confounder) What confounding factors could explain the finding?

29. Overview 29 Let�s practice assessing validity. The Good News Survey was administered to readers of the magazine Hippocrates. Nearly 3,000 readers responded with information on their health, diets and habits. The data were analyzed and many associations were seen. Consider the following findings and state whether or not you think that they are valid. If not, give a possible alternative explanation for the association.

30. Overview 30

31. Overview 31 Valid statistical association does not imply cause and effect. You must use your judgment.  Remember: Causality is in the eyes of the beholder.�Many epidemiologists think that Sir A.B. Hill's guidelines are useful for making the assessment.

32. Overview 32 Time sequence established: There is evidence that exposure preceded disease. Strength of association: Stronger associations are more likely to be causal.

33. Overview 33 Consistency: If other investigations using different populations, different study designs show similar results, there is strong support for causality. Biologic credibility: Does association make sense biologically? Dose-response: Does disease risk increase as exposure level increases?