11th EPIET Epidemiology Course Menorca, October 2 2006

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 gt 10 people themselves recognize the risk
• RR de 9 à 2  comfort zone  for epidemiology
• RR lt 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 (gt25g/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
Low risks

• 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, E1low exposure, E2high
exposure Incidence x /100.000, RR true
Relative Risk
25
Heterogeneity in the populations 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
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 198944(4)252-259
32
Example (2)
Lynch et al, Arch Env Health 198944(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 199385(14)1159-64
38
Improving statistical power

• Increasing sample size

• Number of cases and controls (1/1) for 1- b
  80, a 5, H0 OR1

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

51
Interpretation of geographical studies

• Measures of geographical associations
• Very difficult to extrapolate at the individual
  level
• Causality generaly out of reach of those designs
• Useful for generating hypotheses

52
Time series

• Statistical power
• Control of confounding factors
• Non time-dependant Population is its own control
• Time-dependant modelling techniques

53
Exemple PSAS9 I
D day
Exposed population
Indicator of exposure
Indicator of effect
All people living in Marseilles
SO2 mg/m3 (Daily mean of 3 monitoring stations)
Daily number of deaths
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
54
Raw curves
Mean levels of air pollution Marseilles,
1990-1995
Daily counts of deaths, Marseilles, 1990-1995
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
55
Time-dependant counfounding factors
Serial correlation fonction of daily mortality
Fonction totale.
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
56
Time-dependant counfounding factors
Filleul et coll., Rev. Mal. Respir., 2001
57
Non time-dependant counfounding factors
Filleul et coll., Rev. Mal. Respir., 2001
58
Modeling Strip-tease of the curves

• Taking into acount long-term trends (iedecrease
  of mortality)
• Taking into acount seasonal variations (Higher
  mortality during winter)
• Taking into acount the day of the week
• Taking into acount co-factors (Meteorological
  data, Flu epidemics, Pollinic data...)

59
Long-term trends
Predicted value of total mortality by
trend-modeling.
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
60
Seasonal variations
Predicted value of mortality by modelization of
seasonal variations
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
61
Meteorological data
Naperian Logarithm of Relative Risk of the
interaction temperature-humidity on total
mortality
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
62
Day of the week
Predicted value of total mortality by
modelization of a  day of the week  holidays
effect
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
63
Full Monty
Residual values of total mortality after
modelization of trend, seasonal variatipons, Flu
epidemics, temperature, humidity, day of the week
holidays
Serial correlation fonction of daily mortality
after modelization of trend, seasonal
variatipons, Flu epidemics, temperature,
humidity, day of the week holidays
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
64
Result
dose-response curve of total mortality in
relation to SO2 levels
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
65
Interpretation of time-series studies

• Establishing causation is possible after a
  careful discussion of Hill criteria
• Strength.
• Consistency.
• Specificity.
• Temporality.
• Biological gradient (dose-response).
• Plausibility.
• Coherence.
• Experiment.
• Analogy.

66
V. Conclusion

• Aspects of the study design that involves
  measurements of variables are critical,
  especially in environmental epidemiology where
  risks from exposure are likely to be small,
  difficult to detect, and perhaps not clinically
  significant, yet maybe of public health
  importance
• Epidemiology is not always the only answer of
  even the more relevant one to questions submitted
  to environmental epidemiologists Risk analysis
  for example, is a very useful and cost-effective
  method
• ...but this is another story.