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11 th EPIET Epidemiology Course Menorca, October 2 2006

11 th EPIET Epidemiology Course Menorca, October 2 2006. Environmental Epidemiology (introduction). Dr Georges Salines Institut de Veille Sanitaire Département Santé Environnement. I. Objectives. To provide a basic knowledge of :

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11 th EPIET Epidemiology Course Menorca, October 2 2006

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  1. 11th EPIET Epidemiology CourseMenorca, October 2 2006 Environmental Epidemiology (introduction) Dr Georges Salines Institut de Veille Sanitaire Département Santé Environnement

  2. I. Objectives To provide a basic knowledge of : • The definitions of environmental health, environmental epidemiology, environmental risks • The concept of low-risk and the links between relative risk, prevalence of exposure and attributable risk • The limits of epidemiology in environmental health • How to deal with these limits

  3. Definitions • Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations • The environment is all the physical, chemical and biological factors external to a person, and all the related behaviours. (WHO) • The environment is the sum of all external conditions affecting the life, development and survival of an organism (US EPA) • The environment is everything that is not me (Einstein)

  4. Traditional exclusions • Genetics factors (except interactions genes/environment) • Behaviours (except behaviours modifying exposures) • Social factors (except links between SES and physical environment) • Infectious diseases (except those transmitted through exposure to media)

  5. Risk • A measure of the probability that damage to life, health, property, and/or the environment will occur as a result of a given hazard (US EPA) • Rylander classification • RR > = 10 : people themselves recognize the risk • RR de 9 à 2 : « comfort zone » for epidemiology • RR < 2 : zone where epidemiology reaches its limits...

  6. High risks • occupational environment • aromatic amines and bladder cancer • asbestos fibres and mesothelioma • cadmium and kidney diseases • benzene and leukaemia • pesticides and infertility • organic solvents and neurological disorders • etc ... • general environment

  7. December 1952 - London

  8. December 1952 - London

  9. 1953 - Minamata

  10. December 1984 - Bhopal

  11. 1986 - Tchernobyl

  12. Thyroid cancer in children

  13. 2003 - Paris

  14. Mortality and mean temperature in Paris 1999-2002 versus 2003 Peak: Aug 13th

  15. 2005 - Katrina

  16. 2006 Abidjan

  17. Nature of high risks in general environment • anthropogenic activities • London 1952 • Minamata 1953 … • natural origin • Heat waves • hurricanes… • mixed origin • UV and melanoma • tremolite and mesothelioma in New Caledonia • erionite and mesothelioma in Turkey ...

  18. Characteristics of high risks • High RR • benzidine / bladder cancer RR = 500 • asbestos / mesothelioma RR = 50 • tobacco (>25g/d) / lung cancer RR = 30 • Usually severe and often specific health endpoints • “well defined” populations • in space, in time • socio-demographic characteristics • relatively small populations

  19. Lowrisks • urban air pollution and short-term respiratory diseases • RR = 1.1 - 1.5 • chlorinated water supplies and bladder cancer • RR = 1.4 • electromagnetic fields and children leukemia • RR = 1.3 ...

  20. Small relative risks do not mean small health impacts • Relative risk and attributable risk • relative risk • ratio measure : “it is an indicator for epidemiologist ” • attributable risk • FRA = p * ( RR -1) / [ 1+ p * ( RR - 1) ]“if the relation is causal, it estimates the proportion (amount) of diseases that we can attribute to the exposure”

  21. Health impact

  22. Health impact

  23. May be not that low after all low risks or weak associations ?

  24. Theoretical baseline situation(the wonderful world) E0 = non exposed, E1=low exposure, E2=high exposure * Incidence : x /100.000, ** RR : true Relative Risk

  25. Heterogeneity in the population’s sensitivityto the exposure 50% 50% * (S) : high sensitivity. (s) : low sensitivity

  26. Non specific definition of the health outcome * (D) : disease specifically related to exposure. (d) : disease not related to exposure

  27. Errors in the exposure classification E0 E1 E2 Prevalence 50% 35% 15% Incidence 150 214.3 250 RR 1.0 1.43 1.67 20% of non exposed (E0) are categorised E1 and 10% of non-exposedare categorised E2.

  28. E0 E1 Prevalence 50% 50% Incidence 150 225 RR 1.0 1.5 Inaccuracy in the exposure categories

  29. Epidemiology and weak associations • Improve data quality • exposure • health endpoints • co-factors • Improve statistical power • Meta-analysis & Multi centres • Ecological designs

  30. Improving assessment of exposure: better use of environmental data • appropriate selection of sources and routes of exposure • taking account: • critical periods of exposure • individual history of exposure : behaviour, space-time activities …

  31. Example Lynch et al, Arch Env Health 1989;44(4):252-259

  32. Example (2) Lynch et al, Arch Env Health 1989;44(4):252-259

  33. Improving assessment of exposure: personal exposure monitoring • technical, logistical and financial limits … • depends on sensibility / specificity of the method

  34. Improving assessment of exposure: biomarkers of exposure • cellular, biochemical, molecular alterations • measurable in biological media (human tissues, cells or fluids) • advantages • measurement of a dose (effectively absorbed) • integration of all the routes of exposure and sources of absorption • avoids subjects’ lack of knowledge, memory failure, biased recall, deliberate misinformation … • limits • costs • Representativity of a single sample taken at a particular time • In some cases, route of exposure is of the essence

  35. Improving assessment of health endpoints • outcomes specified “as precisely as possible” • subgroups of disease • biomarkers of effects • sub clinical events • predictive value ? • variability • biological, laboratory-related, logistical issues (bias)

  36. Measuring confounders and effect modifiers • “as much attention” as exposure and disease variables • Biomarkers of susceptibility

  37. Example Bell D.A. J Nat Cancer Inst 1993;85(14):1159-64

  38. 20000 15000 10000 5000 0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2 Odds ratio Improving statistical power • Increasing sample size • Number of cases and controls (1/1) for 1- b = 80%, a = 5%, H0: OR=1

  39. Improving statistical power • “Mammoth” studies • Expansive • Complex • Pooling data • Meta-analysis (or combined analysis) • Multi centres studies • heterogeneity ?

  40. Ecological studies : principle • Agregated data • Statistical unit = « group » • Group exposure • Mean exposure, environmental proxy • Group effect • Frequency of disease in the statistical unit, SIR, SMR

  41. Avantages of Ecological studies • Wider exposure contrasts may be found between populations than between individuals within the same population • Large number of observations • Statistical power • Use of existing data • rapid • Cost-effective

  42. Geographical studies • Statistical units = geographical areas • Exposure levels : E1, E2, …, Ei • Prevalence or incidence levels: M1, M2, …., Mi • Resarch of an association between : • Variations of exposure levels • Variation of health indicators

  43. Limits: Biases and fallacies • Classification • Surveillance • Selection • « Ecological fallacy »

  44. Classification errors M M E E Often non differential = Risk dilution toward 1 (bias toward false negative)

  45. Surveillance bias Vicinity of a Nuclear Plant Leukemia Register « Non exposed » Zone All cancers Register Often differential: bias toward false positive (if better sensitivity) or toward false negative (if better specificity)

  46. Selection Bias • Example 1: Texas Sharpshooter (Bias toward false positive) • Example 2: Flight of the sick people (Bias toward false negative)

  47. Ecological Fallacy in Geographical study Incidence rate Area A Area B Area C Environmental exposure

  48. Ecological Fallacy Incidence rate population A       population B       population C         Individual exposure

  49. Example • 1983: leukaemia cluster among children living near the Sellafield nuclear waste reprocessing plant (United Kingdom) • Other leukaemia clusters have since been identified near other nuclear sites, such as Dounreay in Scotland and Krümmel in Germany

  50. But… • In view of current knowledge about the relation between exposure to radiation and the risk of leukemia, dose levels around nuclear sites are incompatible with the excess risks observed … • Studies considering several sites (United Kingdom, France, USA, Germany, Canada, Japan, Sweden, Spain) have not detected any global excess • Leukaemia clusters have been observed in areas far from any nuclear site • There are alternative hypotheses which may explain the leukaemia clusters located near some nuclear sites

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