Introduction to Epidemiology. Introduction to Epidemiology. Introduction to Epidemiology. Introduction to Epidemiology. Introduction to Epidemiology

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1 Executive Veterinary Program University of Illinois December 11 12, 2014 and Causal Inference Dr. Randall Singer Professor of Epidemiology Epidemiology study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems... definition broadened in the past 50 years, from communicable diseases to disease states Epidemiology: information on risk factors for disease cause disease effect if a cause can be prevented, or its effect reduced, disease incidence will also decrease preventive medicine evidence-based medicine public/herd health Epidemiology descriptive disease occurrence, distribution analytical estimates of risk: odds ratio, relative risk diagnostic test evaluation theoretical simulation, predictive models descriptive epidemiology Human West Nile virus disease cases, Texas 2002 Equine West Nile virus disease cases, Texas

2 analytical epidemiology theoretical epidemiology Risk factors, equine West Nile virus disease cases Equine West Nile virus disease case clusters Variable Coefficient SE P-value OR Constant <0.001 Ataxia Recumbent < Vaccinated <12 months Predictive spread of FMD in south Texas, following feral pig introduction Basic Epidemiological Concepts Disease is not random Disease is multifactorial - results from complex interactions of many factors Goal to identify risk factors for outcomes of interest Disease: infectious and non-infectious Health outcomes survival, longevity Productivity milk production per cow, acre, kg of CO2 What is a Cause? What is a Cause? Compare two outcomes: what did happen what would have happened if the same people during the same time period had instead experienced a different exposure level If the outcomes are different, the exposure difference is a cause if exposed if not exposed 2

3 What is a Cause? Causal Effect if exposed if not exposed RR causal Counterfactuals and Substitutes Comparison of disease frequency in one group during one time period, but under two different exposure scenarios Counterfactuals and Substitutes The Counterfactual Model Target population if exposed to the antibiotic Target population if NOT exposed to the antibiotic RR causal = 3

4 The Counterfactual Model Selection Errors Exposure Distribution 1 Exposure Distribution 0 A 1 E 0 Followed Target population if exposed to the antibiotic Target population if NOT exposed to the antibiotic Substitute population NOT exposed to antibiotic No. of New Cases a 11 a 12 e 01 Sampled e 02 Participants a e Analyzed a 14 e 04 b 14 f 04 RR causal = RR observed = Denominator b 13 f 03 b 12 f 02 Confounding = B 1 b 11 f 01 F 0 Questions about an association Hypothesis testing Is it due to chance or statistically significant? 5% of studies will yield a false association Effect estimation What is the magnitude - is it biologically meaningful and important? Causal inference Is it a true cause or due to bias or confounding? Simple models of causation The child and the light switch Repeatedly tests and observes Likely to conclude that flicking switch causes light to come on Does not appreciate that other factors are equally essential for the effect Simple models of causation Biological systems are complex All disease is multifactorial Necessary cause - any factor without which disease would not occur Microbial agent of infectious disease Sufficient cause - minimal set of conditions that inevitably lead to a disease event For biological effects, many components of a sufficient cause are unknown Causal complement Set of conditions that must be present for a factor to have a causal effect Poorly understood in biological systems Causal complement is an equal partner in producing the effect Strength of an effect in a population depends on the prevalence of its causal complement 4

5 Measures of impact Attributable implies causation Attributable fraction Impact of exposure in the exposed group Population attributable fraction Impact of exposure on the whole population Proportion of disease 90% of cancer environmentally caused 40% of cancer from occupational exposures 10-20% of cancer caused by infections Sum > 100% AF/PAF are estimates of the relative roles of factors in a given setting Criteria for Causation Systematic evaluation of evidence Hill s criteria for causation Proposed in 1965 Expanded criteria from US Surgeon general report Nine criteria Evan s criteria 1976 Strength of association Strong association more likely to be causal Indicated by risk ratios > 2 or < 0.5 suggestive Smoking and lung cancer: 18 HPV-16 and cervical cancer: 435 Cannot infer a weak association is not causal How strong is strong? Strength of association Weak association does not eliminate causation Smoking and CHD (RR = 1.3) Passive smoking and lung cancer Strong but not causal Down s syndrome and birth order Birth order confounded with maternal age Rules out association due to weak confounder or modest bias Schoenbach,

6 Strength of association Hill commenting on statistics and p-values to prove causation: No formal tests of significance can answer those questions. Such tests can, and should, remind us of the effects that the play of chance can create, and they will instruct us in the likely magnitude of those effects. Beyond that they contribute nothing to the proof of our hypothesis. Consistency Consistent findings in studies using different methods and in different populations Guards against associations due to chance Chance association unlikely in multiple studies Many studies may be subject to the same biases or problems of confounding Participation bias in HRT studies (Hill, 1965, p. 299) Consistency Lack of consistency does not rule out causation Results may vary across studies due to modifying effects of other variables Different causal complements Different prevalence of causal complement Temporality Cause must precede effect If A comes after B, A did not cause B Can be difficult to establish Long induction and/or latent periods Cross-sectional and case-control studies Biological gradient Biological gradient (dose response) Dose - response Incremental changes in disease rates with changes in exposure Nature of relationship between exposure and disease Non-linear effects Threshold and saturation effects Herd size and disease risks? 6

7 Body mass index and mortality risk Overall mortality of overweight persons (BMI ) is no higher than that of persons of normal weight (BMI ) Plausibility Association makes biological sense Not objective More willing to accept the case for a relationship that is consistent with current knowledge/belief Stress and gastric ulcers Confirmation bias: tendency to selectively gather and process information such that it fits existing beliefs Clinical diagnosis Epidemiological inference Experimental evidence Change in exposure causes change in outcome under controlled conditions Test for causal hypothesis - often not possible Non-human models Intervention studies Fluoridation and caries Clinical trials and evidence based medicine Hormone replacement therapy Analogy Readier to accept arguments similar to others that we accept Function of imagination Cause-and-effect relationship already established for a similar exposure/disease Papillomaviruses and tumors in animals Provides weak (no) evidence Saccharin and rodents Gastric ulcers in pigs and people Coherence and Specificity Coherence Fits with known facts of natural history and biology of disease Fine distinction from plausibility Specificity Single agent for single disease (hang over from germ theory) Presence of specificity supports causation Lack of specificity does not rule out causation Wholly invalid Evan s Unified Concept of Causation Incidence should be higher in individuals exposed to the putative cause Exposure to the putative cause should be more common in cases than in those without the disease Exposure must precede disease 7

8 Evan s Unified Concept of Causation There should be a spectrum of measurable host responses to the agent (infectious diseases) Elimination of the cause should result in lower incidence of the disease Preventing or modifying the host s response should decrease or eliminate the disease The disease should be reproducible experimentally Value of causal criteria None of my 9 viewpoints can bring indisputable evidence for or against the cause and effect hypothesis and none can be regarded as sine qua non (Hill, 1965) No set of criteria replaces judgement in causal inference Gradual accumulation of knowledge and understanding no line in the sand! Causal inference and decision making Inference for establishing etiology Inference for decision making Need for timely action Outbreak investigation, product withdrawal Causal judgments critical for policy making Tobacco, saccharin, coffee, oral contraceptives, handguns, pollution controls, etc. Product withdrawal (food, cars, toys, ) Summary epidemiology focuses on risk factors for disease in populations epidemiologic research informs preventive medicine & public health components of epidemiology research: descriptive, analytical, theoretical goal is to demonstrate causal relationships biases impair our ability to establish causal relationships 8