Environmental Epidemiology

1. Environmental Epidemiology Fred Ellerbusch

2. Objectives • Defining the environment and epidemiology • Exposure and health effects • Mercury poisoning example • Types of epidemiology studies • Relative risk • Odds ratio • Formaldehyde exposure example • Poisoning exercise • Uncertainty • Causation

3. Defining the environment • All non genetic factors • that is everything that is outside of the human genome • Factors that are exogenous to human beings and that that alter patterns of disease and health • Physical • Chemical • Biological • Social • Political • Cultural

5. Common environmental factors that could affect health Chemical Chemicals, dust, drugs, tobacco, foods Psychological Stress, work patterns human relationships Accident Hazards, speed, alcohol, drugs Biological Bacteria, viruses, parasites Physical Noise, climate, lighting, radiation, ergonomic

6. Definition of Epidemiology “The study of the distribution and determinants of health and diseases, morbidity, injuries, disability, and mortality in populations” Friis, 1996

7. Organisms Chemicals exogenous endogenous Nutrients Physical forces Psychological factors Age, sex Disease history Immunologic response Host behavior, diet, activity, nutritional, exposure status Genetic Epidemiologic Triad Environmental Factors Agents • Physical • Temperature • Wind pattern • Altitude • Biologic • Socioeconomic • Geology • Hydrogeology • Additional point sources Host Factors Agents have characteristics such as infectivity, pathogenicity, virulence Source: Smith, Theobald, 1934

8. Epidemiologic Triad serves as useful tool to frame and identify risk factors • Adverse effects are manifested through an interaction of: • the host such as humans (or organisms) • the environment • the agent • This interaction is a function of hazard, exposure, and response

9. The environmental health hazard pathway Traditional Hazards Human activities, natural events Modern Hazards Development activities Emissions Dispersion and Transformation Environmental Concentration Air Water Soil Food Exposure Dose and Target Organ Dose Health Effects Subclinical effects, Morbidity, Mortality Adapted from Briggs et al, 1996

10. Exposures • The concentration of an agent in the environment that contacts an external portion of the human body; which may be ascertained by: • Interviews • Questionnaires • Structured diaries • Measurements of external media • Measurements in microenvironments • Individual doses • Measurements of concentration in human tissues or metabolic products • Markers of physiologic effects

11. The London Smog Epidemic, December 1952

12. Effect of carbon monoxide inhalation

13. World Tobacco Consumption WHO

15. Health Effects • Any case of a given disease • The end point of a causal mechanism, identifying the type of outcome that a cause produces, even behavior http://www.cancer.org/statistics/index.html

16. Health effects of lead in adults and children at various blood lead concentrations

17. Exposure vs. dose • Dose is the quantity of an environmental substance that enters the body • Exposure is affected by • Routes of absorption • Human activity patterns • Physiologic characteristics • Variability over time and space • Through exposure assessment • Who is exposed • Through what medium • At what dose • For how long

18. Exposure vs. Dose

19. Epidemiological Field Investigation Intervention Remove or modify suspected cause and study result • Analytical Epi • State hypothesis • Study design • Design study • Sample Size • Illnesses • Exposures • Confounding • Ethics • Resources • QC • Perform Study • Descriptive Epi • Define illness • Consider hypothesis and biological significance • Define population at risk • Measure disease excess • Establish surveillance Further Studies Collaborating Agencies Working groups No Action Indicated No significant disease excess

20. Mercury poisoning example • Respected laboratory researcher was not steady on her feet, her speech was slurred and hard to understand. • She reported neuropathy in her fingers • She reported that she had been working with Dimethyl Mercury in her laboratory • Several months before she indicated that she cleaned up a small spill • She indicated that she was wearing latex gloves at the time and only a few drops may have gotten on the glove • She died six months later

21. What do we know? • Mercury is usually found in three forms • Elemental • Inorganic as Hg+ or Hg++ • Organic such as Methyl Mercury H3C-Hg+ • Methyl Mercury is highly toxic • 2000 g/liter – IDLH • 50 to 200 g/liter – toxic effects (CNS -> brain) • Some routes of absorption were involved – that is exposure took place in some fashion • Time line: Potential exposure GI Systemic Coma Death Aug Dec Jan Feb June

22. Route of absorption analysis

23. Route of absorption analysis

24. Dimethyl Mercury • Molecular Weight • 2 Hg = 200 • 2 H3 = 6 • 2 C = 24 • Total = 230, say about 90% = Hg • Specific Gravity of DMM = 3.2 gram/ml or about 3 grams of Hg per milliliter • 3,000,000 g/ml • ~ 5 drops per ml or ~ 600,000 g per drop • Say that 25% of one drop got through the glove • 150,000 g

25. What was the root cause and what could have been done? • She knew the danger • She may have been careless with cleanup • She had not reported the event until months later • Hair showed spike 3 wks after the event consistent with calculations ~ 1000 ppm • Could there have been a shared responsibility to prevent exposure?

26. Types of epidemiology studies • Descriptive studies (e.g., counts) are useful to identify risk factors, and hypothesis development • Three common analytical studies are: • Cross-sectional: Disease and Exposure are known • Cohort: Exposure is known but no disease is present at start • Suited for rare exposures and temporal associations • Expensive, limited by sufficiency of records for retrospective studies • Case Control: Disease is known but exposure is unknown • Quick, inexpensive, good for long latency disease • Prone to selection and information bias • Randomized Clinical Trial – the Gold Standard

27. Cohort vs. Case Control Cohort Direction of Inquiry Disease Population Subjects w/o disease Exposed No Disease Disease Unexposed No Disease Time Exposure Cases (subjects w/disease) Population No Exposure Exposure Controls (subjects w/o disease) No Exposure Case Control Direction of Inquiry

28. Cohort study design • Exposure precedes disease • Statistic is Relative Risk (RR) • Which is the incidence of disease among exposed subjects divided by incidence of disease among unexposed subjects • Expressed as • The risk of disease in the exposed is X times greater/lesser than in the unexposed • RR > 1.0 means increased risk; > 2 usually important • RR < 1.0 means decreased risk

29. Relative Risk • As a comparative discipline, epidemiology seeks to compare risks for exposed and unexposed populations using a single statistic: Relative Risk Generic 2 x 2 Table Disease Exposure Yes No Total Yes a b a+b No c d c+d Total a+c b+d a+b+c+d RR = a/(a+b) c/(c+d) For Cohort studies, statistic is the incidence of disease among exposed divided by incidence of disease among unexposed

30. Case Control study design • Disease precedes exposure • Statistic is the Odds Ratio (OR) • Which is the ratio of exposed among cases divided by the ratio of exposed among controls • An estimate of the RR when total population is unknown • Expressed as • The risk of exposure in the diseased group is X times greater/lesser than in the non-diseased group • OR > 1.0 means increased risk; > 2 usually important • OR < 1.0 means decreased risk

31. Odds Ratio • As a comparative discipline, epidemiology seeks to compare risks for exposed and unexposed populations using a single statistic: Odds Ratio 2 x 2 Table Exposure Group Yes No Total Cases a b a+b Controls c d c+d Total a+c b+d a+b+c+d OR = (ad)/(bc) For Case Control studies, statistic is an approximation of the RR, or OR = the ratio of exposed among cases divided by the ratio of exposed among controls

32. RR and OR measures are statistical • Therefore they are subject to statistical variation: • Both will produce values of <1, 1, >1 indicating decreased risk, no risk, increased risk • To check that observations did not occur by chance • Usually determined by a confidence interval such as 95% CI indicating that the true value lies in the range with a 95% probability • However, true value could be in the other 5% • If CI contains “1” then chance cannot be ruled out • That a true effect can be detected • Usually determined by the power of the study (function of study size)

33. OR(1 + (Z)/(2)) Representation of a 95% CI 2 = n(ad-bc)2 (a+c)(b+d)(a+b)(c+d) Mean 2.5% 2.5% http://pavlov.psyc.queensu.ca/~flanagan/202_1999/lecture12/lecture12.html

34. A Case Control Example • Formaldehyde exposures were suspected of causing nasal cancer • A study was performed of cancer cases to determine if formaldehyde exposure was associated • The researchers found the following: • Cancer Cases: 91 • Controls: 195 • Of cancer cases: 31 were exposed, 60 not • Of controls: 34 were exposed, 161 not

35. 2 x 2 Table

36. Odds Ratio • Odds of exposure among cases was = a/b or 31/60 • Odds of exposure among controls was = c/d or 34/161 • Odds ratio = (a/b)/(c/d) or (31/60)/(34/161) = 2.45 • Of nasal cancer cases, workers were 2.45 (95%CI 1.4, 4.3) times more likely to have been exposed to formaldehyde • Note CI did not contain unity

37. Class exercise Complete a case study using some epidemiology principles

38. Uncertainty & Certainty • What is uncertainty • What is it composed of • Why is it important • How does it affect risk assessment • What are we certain about • Taxes • Life is finite, we will all die of some cause • Some features of our lives are genetically predetermined • Life’s events will modify life & death within these genetic boundaries • Including exposure to environmental agents

39. Certainty • Physical causes of illness and death are often certain • For example, trauma • Biological causes of illness and death are increasingly certain • Immune system response indicators to biological agents • Chemical causes of illness and death are less certain • Latency periods for onset of illness from initial exposure • Certainty can be increased if: • exposures are high enough to leave evidence • unique biomarkers are manifested such as cellular alterations • unique or rare illness is caused • The degree of certainty is important if causation is to be found

40. Scientific uncertainty affects risk assessments • Driven and Limited by data • It is composed of two components: • Variability • Lack of knowledge • Statistical knowledge provides some insight into data variability and incomplete information

41. A comparison of some of life’s risks Actual vs. Theoretical ~ uncertainty Measured values have low uncertainty

42. Where does uncertainty come from?Risk assessment is an estimate • Limited data on actual exposure • Data collection techniques • Measurement techniques • Modeling limitations • Dose Response assumptions • Bias and Confounders • Incomplete understanding of mechanisms • Assumptions or default values in the absence of data • Variability and natural variation

43. Quantitative: Descriptive errors Measurement Transcription Communication Lack of clarity Lack of data Variability Population Sampling Qualitative Complexity Assumptions Ignorance Qualitative and quantitative aspects of uncertainty

44. Uncertainty can or should affect decision-making Low U. Point estimate An easy decision Low U. Exposure Limit Measured Exposure

45. Uncertainty can or should affect decision-making High U. Point estimate A more difficult decision High U. Exposure Limit Measured Exposure

46. Uncertainty can mask riskWhich is the greater risk? A difficult decision High Point estimate Risk Low

47. Uncertainties in Risk Assessments • Dose = Effect relationship • amount of a chemical exposure and the nature and/or severity of the toxic effect • Data sources on toxic chemicals • For some chemicals no good animal model exists • Laboratory experiments on animals • NOT epidemiology studies of humans • Inferences from bacteria and/or human cells • Inappropriate use of tests • Dr. Bruce Ames, (Ames salmonella microsomal screening test developer), has commented about the inappropriate use of the test

48. Uncertainties in Risk Assessments • Some have argued that these sources of uncertainty can be problematic because: • Animal or cellular testing = a human being • Much animal toxicity testing is short-term • Relatively high exposures are used experimentally to find an effect • Animal models are not as diverse as humans due to: • Genetics • Age • Behavioral such as culture and diet • Occupation • Health status

49. Uncertainties in Risk Assessments • Others have argued that extrapolations from animals to humans are more reliable than epidemiology studies, due to: • Ethical difficulty in testing human populations • Small study populations (lack of "statistical significance“ or power to detect an effect) • Confounders such as age and smoking • Lack of actual exposure data • Inherent differences between study populations and the population to be protected

50. Uncertainties in Risk Assessments • However, no effect levels can be an artifact of test level intervals and therefore: • When no animal effect is observed, does a negligible risk to humans exposed at such a level exist? • e.g., a 1% incidence of any disease in the human population would be equivalent to over 2 million people. • Small size animal studies of n=25 could not detect 1% • Policy has attempted to address this dilemma • Cancer: no safe dose = linear relationship between dose-effect is directly proportional (linear) • Non cancer: a safe dose as a threshold