Epidemiology E2200b. Dr. John Koval Professor of Biostatistics Department of Epidemiology & Biostatistics University of Western Ontario

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1 Epidemiology E2200b Dr. John Koval Professor of Biostatistics Department of Epidemiology & Biostatistics University of Western Ontario

2 With thanks to Dr. Mark Speechley Professor of Epidemiology Department of Epidemiology & Biostatistics

3 Course Objectives You will be able to: understand methodological foundations of applied human health research critically appraise original articles about things that are claimed to be good and bad for us perform fundamental calculations using published data discuss why studies of the same question can get different answers (and why this doesn t mean the science is flawed) list the bases for criticisms and misunderstandings of the science of epidemiology (know the true rather than the imagined limitations)

4 Objectives of Lectures 1&2 You will learn definitions, key concepts, history, modern applications You will be able to: correctly use some terminology describe historical roots, evolution of modern epidemiology recognize epidemiology as a basic science for clinical medicine, public health, health services research, outcomes research, etc.

5 Epidemiology: Informal definition The branch of medical science that helps us identify factors that: Keep us healthy (part of health promotion ) Make us sick (etiologic research) Help us get better again (therapeutic research) Identifying factors is NOT the same as understanding causal mechanisms

6 Some Results Epidemiological methods have discovered numerous causal factors of health outcomes These findings underlie: massive behavioural change after 1950 evidence-based health care & health policy Disagreements among studies are inevitable and do not signify weaknesses of the methodology.

Make us sick: deficiencies of the above factors; smoking; (some) bacteria,

7 Identify factors that: Keep us healthy: physical activity, fruits and vegetables in diet, vitamins and minerals, clean air and water, vaccines Make us sick: deficiencies of the above factors; smoking; (some) bacteria, viruses, parasites Help us get better again: pharmaceuticals, surgery, rehabilitation

8 Putative (potential) causal factors precede causal mechanisms Often begins with a clinical observation 1774 Dr. Percival Pott noticed cancer of the scrotum in chimney sweeps, implicated something in soot

9 Chimney Sweeps (con'd) Chimney Sweeps Act : Sweeps must be at least 8 years old Sweeps must be provided suitable clothes and accommodation Mechanistic knowledge takes years to develop; we now know that soot contains polycyclic aromatic hydrocarbons that lead to squamous-cell carcinoma

10 Other causal risk factors that began with clinical hunches Exposure Cigarette smoke Disease/outcome Lung cancer (1940s) Significance? Lung cancer was once very rare. Beginning of epidemic observed among soldiers who had started smoking in WWI. Became the leading cancer death.

11 rubella Exposure Maternal rubella (red measles) Disease/outcome Birth defects (1940s) Viruses not previously known to cause birth defects ( teratogenic ). All women planning pregnancy now immunized

12 (Unfortunately, we cannot reverse time.) What is causation? Experiment 1: Fred is exposed to A.. [time passes] Fred gets Disease B [turn back the clock, hold everything else constant] Experiment 2: Fred is not exposed to A [same time passes] Fred does not get Disease B We can define a causal exposure as i) one that is followed by a disease outcome ii) that would not have occurred had the exposure not occurred iii) all else held constant. The perfect research design. Proves 100% causal certainty in individuals.

If it doesn t increase your marks by one full letter grade, return the unused portion of the product and I ll

13 Dr. Mark s Magic Potion (A late night infomercial) Hi, Friend. Want to ace your grades in university? Well, Dr. Mark has been teaching for years and has concocted a Magic Study Potion in his kitchen laboratory. If it doesn t increase your marks by one full letter grade, return the unused portion of the product and I ll cheerfully refund the unspent portion of your money!.and that s not all!!...order now and you ll receive absolutely free.

14 Evaluating causal claims The Magic Potion claims to causally increase students grades.. Is there a way to prove with 100% certainty that any student s grade was or was not affected by the Magic Potion?

15 Causal Certainty Necessity and Sufficiency Criteria Necessary Cause: The Magic Potion is necessary for increased grades: only students who took my potion increased their marks by a full letter grade; none others did. Sufficient Cause: The Magic Potion is sufficient for increased grades: every student who took my potion increased their grades.

16 A perfect correlation! Took potion Yes Increased one letter grade Yes Sufficiency (all exposed have outcome) No n/a Necessity (no unexposed have outcome) How many biological, psychological or sociological causes can you name that meet both necessity and sufficiency criteria? How many can you name that meet even one of these criteria? No n/a

17 Causation and Correlation Causation occurs when factor A leads to (or causes) factor B Correlation happens when factor A and factor B are related, so that when factor A is present, factor B often is present, and visa versa

18 You can t prove causation with correlation True, but: You don't need to know the exact cause before doing something We don t need to understand a causal mechanism to act to reduce exposure Dozens of examples exist where epidemiologic associations have subsequently been demonstrated to be causal. If an association is causal, every day we fail to act out of scientific prejudice, people will needlessly get ill or even die.

19 We will be wrong sometimes. Example: in an investigation of an outbreak of Hepatitis A hot dog sausages were implicated. The whole consignment of sausages was thrown out. People were spared from the disease, although the actual mechanism was not clear. It turns out that the source of the bacteria was actually the relish.

20 You can t prove causation with correlation is true, but You can t prove causation without correlation either. All identified causes began with observed correlations. The problem isn t correlation, it s failure to control for CONFOUNDING other explanations that could account for the correlation.

21 How to prove causation? Approaches Best: expose Person A, observe; go back in time, remove exposure, observe and compare 2 nd Best: random assignment to exposure (Experimental) 3 rd Best: observe people in different exposure groups (Observational) Limitations Can t do time travel: counterfactual Unethical with negative outcomes Often impractical (time) Potential for confounding* *Latin, confundere (pour together; confuse)

22 2 nd best: The RCT (Random Controlled Trial) Randomize students to Magic or Placebo Potion: All known and unknown factors are distributed by chance Collect data on factors that could affect grades, compare two groups at baseline, should be similar as the sample size increases If imbalanced, can statistically adjust final estimates Observe between-group difference in grades

23 3 rd best: Observational Designs Are not true experiments People select themselves into exposures Unknown or unmeasured factors (confounders) could be the true cause of any observed difference As our theory improves (as we can explain a larger portion of the variation in outcomes) so does our ability to estimate the true causal effect of any single factor

24 The role of confounding Non-causal association: heavy smokers tend to be heavy coffee drinkers Cigarette Smoking Coffee consumption Spurious association True causal effect Disease Smoking, a true cause of disease, will confound (bias) the association between coffee and disease. The apparent association with coffee is due to the correlation between coffee and smoking.

25 Confounding (con'd) If you measure association of smoking and cancer in the presence of a measurement of coffee consumption, the true effect of smoking will be diminished Coffee consumption is a confounder of the Smoking lung cancer relationship Determination of actual risk factor and actual confounder depends on other (clinical) studies

26 Malaria ( bad air ): A classic case of confounded association Highlands (fresh air) Swamps (musty air) Spurious association No Malaria Malaria Confounder True cause Solution; leave swanp, What s the true cause (vector) of malaria?

27 Epidemiology* (definition) the study of the occurrence and distribution of health-related states or events in specified populations, including the study of determinants influencing such states, and the application of this study to control health problems (Porta M. A Dictionary of Epidemiology, 5 th ed, 2008:81). (emphases added) *From Greek; epi (upon) dēmos (people), logos (word, reason)

28 distribution (Porta, 2008) The complete summary of the frequencies of the values or categories of a measurement made on a group of persons. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have. Usually presented broken down by characteristics such as person, place, and time.

29 Age distribution of percentage of pregnancies ending in miscarriage/stillbirth, by age of women at end of pregnancy, Canada, 1974 and 1992 % All ages Source: Health Reports, Summer 1996, 8:13

Identifying possible (and probable) causal factors is not the same as

30 determinants any factor that brings about change in a health condition or other defined characteristic. (Porta, 2008). Identifying possible (and probable) causal factors is not the same as explaining causal mechanisms If a factor is causal, reducing exposure will reduce outcome even if we don t understand the mechanism

31 study can be: Surveillance (e.g. mandatory disease reporting) Descriptive (hypothesis generating) (e.g. proportion of pregnancies that end in miscarriage/stillbirth, by characteristics of person, place and time) Analytic (hypothesis testing) (estimates of X-Y association from observational studies) Experiments (clinical trials)

32 Analytic Epidemiology: Primary role is etiologic* Exposures ('determinants') For example, Physical (ionizing radiation) Chemical (lead) Biological (needlesticks) Social: educational attainment, poverty Behaviours: tobacco, diet *Greek, aitia (cause) Outcomes ( health related states and events ) For example, Diseases with biological models Illnesses without biological models Injuries Birth outcomes Psychological states such as QOL (Quality of Life)

33 Key concept: Reliable case definition Case definition: A set of criteria (not necessarily diagnostic criteria) that must be fulfilled in order to identify a person as a case of a particular disease (Porta, 2008:32) Clinical or Laboratory criteria or both Scoring systems with points that match disease features (e.g. Multiple sclerosis) Reliability: The degree to which the results obtained by a measurement or procedure can be replicated (Porta, 2008:214)

34 Key concept: Risk RISK(def): The probability that an event will occur, e.g., that an individual will become ill or die within a stated period of time or age. (Porta, 2008:217) Major aim of Epidemiology is to quantify the risk of developing disease or other negative health state posed by various exposures (molecules, microorganisms, environments, behaviors).

Probability Causation of health and illness is extremely complex Even widely agreed upon causes fail to meet necessity and sufficiency: Grandma smoked a pack a day and died peacefully in her

35 Probability Causation of health and illness is extremely complex Even widely agreed upon causes fail to meet necessity and sufficiency: Grandma smoked a pack a day and died peacefully in her sleep at 110, and Uncle Elmo got lung cancer and never smoked. We need to rely on probability statements: the probability of an outcome is 2, 3, 4.. times higher among exposed than unexposed

36 Observed versus predicted probability Average (predicted) risks estimated from groups, used to advise individual patients: (e.g. risk of adverse surgical outcome; risk of cancer recurrence) But! individuals will either have (risk = 100%) or not have (risk = 0%) an outcome over a specified time period (you can t have 32% of a stoke ). Estelle, 28, never-smoker, former Varsity volleyball player, has a stroke. Observed individual risk of stroke for that year = 100% Jerome, 75, high blood pressure, smoker, does not have a stroke. His observed individual risk for that year = 0% People like Estelle face a very low predicted risk; people like Jerome face a much higher predicted risk

37 History of Epidemiology Epidemiology is a young science with ancient roots in the study of epidemics (def: The occurrence in a community or region of cases of an illness, specific health related behavior, or other health-related events, clearly in excess of normal expectancy. Porta, 2008:79) Clearly in excess differs by disease and time frame (e.g. H1N1 or Lung Cancer) Began with communicable diseases; methods have been adapted for chronic diseases and other health states and events (injuries, birth outcomes, etc)

38 Demons, Miasms* and Germs Epidemiologic insights (e.g. events are not random) are clear in the writings of Hippocrates 2500 years ago. Millenia passed before we had the intellectual foundation to scientifically test 2 competing hypotheses about the causes of epidemic diseases Key period: 1850s England: Drs. John Snow (cholera) and William Budd (typhoid fever) *From Greek, miainein (to pollute).

39 2 theories of epidemic disease Miasmatic (miasma) Air has a bad quality Rotting organic matter Miasma could be passed from cases to susceptibles in contagious diseases Contagion Invisible entities Spread through direct contact, droplet spread or contaminated fomites Most physicians supported miasma; it explained the facts better: didn t know that asymptomatic people could be infectious ( well carriers ) Didn t know about immunity

40 1850s England: Urbanization, industrialization, poverty, crowding, filth and epidemic disease Increasingly scientific medical profession continued to favour miasmatic theory over contagion: Lack of Sanitation Germs Miasms Disease London, 1854: Cholera epidemic was it miasma or germs? How to prevent/stop epidemic

41 William Farr Deaths from Cholera in 10,000 Inhabitants by Elevation of Residence above Sea Level, London, Elevation above Sea Level (ft) < Number of Deaths Data from Farr W: Vital Statistics: A Memorial Volume of Selections from the Reports and Writings of William Farr (edited for the Sanitary Institute of Great Britain by Noel A. Humphreys). London, The Sanitary Institute, 1885.

John Snow, M.D. (1813-1858) www.ph.ucla.edu/epi/snow.

42 John Snow, M.D. ( ) theory that cholera is communicable and waterborne Used spot maps of cases residences, compared to location of public water pumps > 500 cholera fatalities within 250 yards of Cambridge and Broad Streets in a 10 day period.

43 Snow's diagram

44 Snow (con'd) Eventually convinced Parish authorities to remove Broad Street pump handle during August-September 1854 epidemic

45 Modern day view The pump on Broadwick street Plus the Pump Pub

46 Cases of Cholera by date of onset, London, Aug. 19 Sept. 19, 1854 f Pump handle removed Fatal attacks Deaths August September Epidemic curve adapted from Roht et al, 1982:300

47 Natural Experiment London England, ~ major water suppliers: Lambeth, and Southwark & Vauxhall Lambeth moved their intake to a cleaner section up river Interviewed household members to ascertain which of two companies supplied their water Compared 1853 cholera cases according to water company (retrospective study)

48 Cholera mortality by water supply, 1 st seven weeks of epidemic (Roht et al, 1982:304) Water Supply Southwark & Vauxhall Lambeth Co. Rest of London # houses Cholera Deaths Deaths/ 10,000 houses (risk) 40,046 1, , ,423 1,

49 Epidemiologic measures of association: Relative Risk* One form of Relative Risk = Risk Ratio Deaths/10,000 exposed (S&V) = = 8.4 Deaths/10,000 unexposed (Lambeth) 37.5 Mortality was 8.4 times more common in S&V houses than in Lambeth houses. Based on these non-experimental (non-randomized) findings, who here would choose S&V?

The Establishment reacts: The Lancet: not by any means conclusive Royal College of Physicians: theory as a whole is untenable continued to support foul or damp air as the cause Board of Health

50 The Establishment reacts: The Lancet: not by any means conclusive Royal College of Physicians: theory as a whole is untenable continued to support foul or damp air as the cause Board of Health Medical Inspectors: We see no reason to adopt this belief (1854) far-fetched doctrine (Chapman, 1866) 1884, Robert Koch (Nobel, 1905) identified Vibrio Cholerae, made no mention of Snow s work

51 This is, unfortunately, not unique Many epidemiologic findings, even after multiple replications and systematic testing and rejection of bias explanations, are stubbornly resisted. Why? economic self-interest resistance to behavioral change unwillingness to admit past practices killed people Unfortunately, isolated first findings are often given the most sensationalistic media coverage

Epidemiology E2200b. Dr. John Koval Professor of Biostatistics Department of Epidemiology & Biostatistics University of Western Ontario

Epidemiology E2200b. Dr. John Koval Professor of Biostatistics Department of Epidemiology & Biostatistics University of Western Ontario Epidemiology E2200b Dr. John Koval Professor of Biostatistics Department of Epidemiology & Biostatistics University of Western Ontario With thanks to Dr. Mark Speechley Professor of Epidemiology Department