Title: 11th EPIET Epidemiology Course Menorca, October 2 2006
111th EPIET Epidemiology CourseMenorca, October 2
2006
- Environmental Epidemiology
- (introduction)
Dr Georges Salines Institut de Veille
Sanitaire Département Santé Environnement
2I. 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
3Definitions
- 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)
4Traditional 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)
5Risk
- 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...
6High 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
7December 1952 - London
8December 1952 - London
91953 - Minamata
10December 1984 - Bhopal
111986 - Tchernobyl
12Thyroid cancer in children
132003 - Paris
14Mortality and mean temperature in Paris
1999-2002 versus 2003
Peak Aug 13th
152005 - Katrina
162006 Abidjan
17Nature 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 ...
18Characteristics 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
19Low 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 ...
20Small 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
21Health impact
22Health impact
23May be not that low after all
- low risks
- or
- weak associations ?
24Theoretical baseline situation(the wonderful
world)
E0 non exposed, E1low exposure, E2high
exposure Incidence x /100.000, RR true
Relative Risk
25Heterogeneity in the populations sensitivityto
the exposure
50
50
(S) high sensitivity. (s) low sensitivity
26Non specific definition of the health outcome
(D) disease specifically related to exposure.
(d) disease not related to exposure
27Errors 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.
28Inaccuracy in the exposure categories
29Epidemiology and weak associations
- Improve data quality
- exposure
- health endpoints
- co-factors
- Improve statistical power
- Meta-analysis Multi centres
- Ecological designs
30Improving 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
31Example
Lynch et al, Arch Env Health 198944(4)252-259
32Example (2)
Lynch et al, Arch Env Health 198944(4)252-259
33Improving assessment of exposure personal
exposure monitoring
- technical, logistical and financial limits
- depends on sensibility / specificity of the method
34Improving 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
35Improving 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)
36Measuring confounders and effect modifiers
- as much attention as exposure and disease
variables - Biomarkers of susceptibility
37Example
Bell D.A. J Nat Cancer Inst 199385(14)1159-64
38Improving statistical power
- Number of cases and controls (1/1) for 1- b
80, a 5, H0 OR1
39Improving statistical power
- Mammoth studies
- Expansive
- Complex
- Pooling data
- Meta-analysis (or combined analysis)
- Multi centres studies
- heterogeneity ?
40Ecological studies principle
- Agregated data
- Statistical unit group
- Group exposure
- Mean exposure, environmental proxy
- Group effect
- Frequency of disease in the statistical unit,
SIR, SMR
41Avantages 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
42Geographical 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
43Limits Biases and fallacies
- Classification
- Surveillance
- Selection
- Ecological fallacy
44Classification errors
M
M
E
E
Often non differential Risk dilution toward 1
(bias toward false negative)
45Surveillance 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)
46Selection Bias
- Example 1 Texas Sharpshooter (Bias toward false
positive) - Example 2 Flight of the sick people (Bias toward
false negative)
47Ecological Fallacy in Geographical study
Incidence rate
Area A
Area B
Area C
Environmental exposure
48Ecological Fallacy
Incidence rate
population A
?
?
?
?
?
?
population B
?
?
?
?
?
?
population C
?
?
?
?
?
?
?
?
Individual exposure
49Example
- 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
50But
- 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
51Interpretation 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
52Time series
- Statistical power
- Control of confounding factors
- Non time-dependant Population is its own control
- Time-dependant modelling techniques
53Exemple 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
54Raw 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
55Time-dependant counfounding factors
Serial correlation fonction of daily mortality
Fonction totale.
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
56Time-dependant counfounding factors
Filleul et coll., Rev. Mal. Respir., 2001
57Non time-dependant counfounding factors
Filleul et coll., Rev. Mal. Respir., 2001
58Modeling 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...)
59Long-term trends
Predicted value of total mortality by
trend-modeling.
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
60Seasonal variations
Predicted value of mortality by modelization of
seasonal variations
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
61Meteorological data
Naperian Logarithm of Relative Risk of the
interaction temperature-humidity on total
mortality
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
62Day 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
63Full 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
64Result
dose-response curve of total mortality in
relation to SO2 levels
Source Surveillance épidémiologique air et
santé, rapport InVS, mars 1999
65Interpretation 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.
66V. 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.