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81_18- Env Epidemiology 2006 - 4th EPIET Epidemiology Course EPIET

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81_18- Env Epidemiology 2006 - 4th EPIET Epidemiology Course EPIET Powered By Docstoc
					11th 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 :
► 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
                   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)
        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)
                         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...
                        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
December 1952 - London
December 1952 - London
1953 - Minamata
December 1984 - Bhopal
1986 - Tchernobyl
                                   Thyroid cancer in children

                             120
Incidence Rate per million




                             100

                             80                                                                     Belarus
                                                                                                    Ukraine
                             60
                                                                                                    Briansk
                             40                                                                     Gomel

                             20

                              0
                                    1986


                                           1987


                                                  1988


                                                         1989


                                                                 1990


                                                                        1991


                                                                               1992


                                                                                      1993


                                                                                             1994
                                                                Years
2003 - Paris
Mortality and mean temperature in
               Paris
          1999-2002 versus 2003


                         Peak: Aug 13th
2005 - Katrina
    2006 Abidjan
►
   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 ...
       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
                     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 ...
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”
           Health impact
            Exposure prevalence
Relative     0,01       0,10      1,00
 Risks

  10         0,08       0,47      0,90

   5         0,04       0,29      0,80

   2         0,01       0,09      0,50

  1,5          -        0,05      0,33

  1,2          -        0,02      0,17

  1,1          -        0,01      0,09
           Health impact
           Exposure prevalence
Relative    0,01       0,10      1,00
 Risks

  10        0,08       0,47      0,90

   5        0,04       0,29      0,80

   2        0,01       0,09      0,50

  1,5         -        0,05      0,33

  1,2         -        0,02      0,17

  1,1         -        0,01      0,09
May be not that low after all


                low risks
                   or
           weak associations ?
Theoretical baseline situation
   (the wonderful world)
                     E0               E1               E2


Prevalence          80%              15%               5%


Incidence*           100              300              500


RR**                 1.0              3.0              5.0


E0 = non exposed, E1=low exposure, E2=high exposure
* Incidence : x /100.000, ** RR : true Relative Risk
Heterogeneity in the population’s sensitivity
             to the exposure
                                       E0                E1    E2


       Prevalence                     80%                15%   5%

 50%
       Incidence (S)                   100               300   500
 50%
       Incidence (s)                   100               200   300

                                       100               250   400


       RR                              1.0               2.5   4.0
       * (S) : high sensitivity. (s) : low sensitivity
Non specific definition of the health
              outcome
                            E0                  E1                 E2


Prevalence                 80%                15%                  5%


Incidence (D)               100                250                400

Incidence (d)                50                 50                     50
                            150                300                450


RR                          1.0                2.0                 3.0
 * (D) : disease specifically related to exposure. (d) : disease not
   related to exposure
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-exposed
  are categorised E2.
     Inaccuracy in the exposure
             categories

                 E0         E1


Prevalence      50%         50%


Incidence       150         225


RR               1.0        1.5
Epidemiology and weak associations


           ► Improve   data quality
              exposure
              health endpoints
              co-factors
           ► Improve   statistical power
              Meta-analysis & Multi centres
           ► Ecological   designs
 Improving assessment of exposure:
 better use of environmental data


 appropriate selection of sources and routes of
  exposure
 taking account:
  ►criticalperiods of exposure
  ►individual history of exposure : behaviour, space-
   time activities …
           Example
  Exposure = last place of residence, assuming entire life at
this residence and consumption of the town’s drinking water

Chlorination                 0              1-25      26-50        50+
exposure

Cases (286)                 84               31         79          92

Controls (658)             193              104        188         173
OR                         1.0              0.7        1.0         1.2
IC 95%                                   0.4 - 1.1   0.7 - 1.4   0.9 - 1.8
Lynch et al, Arch Env Health 1989;44(4):252-259
             Example (2)
   Exposure = entire lifetime residential history combined with the
            town’s drinking water chlorination history

Chlorination                  0              1-25       26-50        50+
exposure

Cases (286)                  65                   92     102          27

Controls (658)              211               211        195          41
OR                           1.0              1.4        1.7*        2.1*
IC 95%                                     1.1 - 2.1   1.2 - 2.5   1.2 - 3.7
Lynch et al, Arch Env Health 1989;44(4):252-259
Improving assessment of exposure:
personal exposure monitoring

 technical, logistical and financial limits …
 depends on sensibility / specificity of the
  method
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
       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)
      Measuring confounders
      and effect modifiers

 “as much attention” as exposure and
  disease variables
 Biomarkers of susceptibility
         Example
  Combined risk of bladder cancer associated with smoking
       exposure and GSTµ1 genotype among whites

                                      Smoking exposure


  Genotype                0                   1-50            > 50
                    (non smokers)         (pack-years)    (pack-years)
   GTSµ1 (-)               1.3                    4.3          5.9
                       [0.6 - 2.7]          [2.1 - 3.5]    [2.6 - 13.0]


  GTSµ1 (+)                1.0                    2.2          3.5
                          [Ref]             [1.1 - 4.5]    [1.5 - 8.0]


Bell D.A. J Nat Cancer Inst 1993;85(14):1159-64
 Improving statistical power
► Increasing          sample size
    Odds ratio          Prevalence of the exposure in the population

                        20%          10%            5%           2%             1%

        4,0                 38          55           94         211             418
        3,0                 63          96         168          387             755
        2,0              171          279          504          1186           2333
        1,5              532          903        1674           4004           7888
   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

   Number of cases and controls (1/1) for 1- b = 80%, a = 5%, H0: OR=1
 Improving statistical power

► “Mammoth”        studies
   Expansive
   Complex
► Pooling   data
   Meta-analysis (or combined analysis)
   Multi centres studies
    ►   heterogeneity ?
     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
   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
             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
        Limits: Biases and fallacies


► Classification
► Surveillance
► Selection
►«   Ecological fallacy »
        Classification errors

    M                 M
             E


             E
Often non differential = Risk dilution toward 1
        (bias toward false negative)
             Surveillance bias

Vicinity of
                                Leukemia Register
a Nuclear Plant


« Non exposed »
                               All cancers Register
Zone


    Often differential: bias toward false positive
    (if better sensitivity) or toward false negative
    (if better specificity)
             Selection Bias

► Example 1: Texas Sharpshooter (Bias
 toward false positive)
► Example 2: Flight of the sick people (Bias
 toward false negative)
      Ecological Fallacy in Geographical
Incidence            study
rate
                                    Area A


                       Area B

           Area C




                      Environmental exposure
              Ecological Fallacy
Incidence
rate

                                          population A
                                             
                                             
                           population B
                              
                              
            population C
               
                

                           Individual exposure
                  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
                      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
   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
               Time series

           power
►Statistical
►Control of confounding factors
 +++
   Non time-dependant: Population is its own
    control
   Time-dependant: modelling techniques
                      Exemple PSAS9 I
                                            D day



        Exposed                           Indicator                           Indicator
        population                       of exposure                           of effect




       All people                       SO2 mg/m3                               Daily
        living in                     (Daily mean of                          number
       Marseilles                  3 monitoring stations)                     of deaths


Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
                                                           Raw curves
                           Mean levels of air pollution
                                  Marseilles,
                                  1990-1995

                                                                            Daily counts of deaths, Marseilles, 1990-
                                                                            1995
                                    D io x y d e                       de          s oufr e            ( SO2)
0 20 40 µg/m3 60 80 10




                            J 1990 91 92 93 94 95
                               J 19 19 19 19 19
                                  J  J  J
                         J an an an an an an




                                      Fu mé e s                        n o ir e s                 ( FN )
                         Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
        60 80
 Time-dependant counfounding factors
             Serial correlation fonction of daily mortality
                                     Fonction totale.
             0.20
             0.15
             0.10
      PACF


             0.05
             0.0
             -0.05
             -0.10




                     0   5          10         15          20         25      30

                                                    Jour




Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
 Time-dependant counfounding factors

Risk factor                                                      Health outcome


                                     Confounding factor

Daily variation of                                             Daily variation
air pollution                                                  of number of deaths
levels                                                         from respiratory causes




                                 Daily variations of weather


Filleul et coll., Rev. Mal. Respir., 2001
     Non time-dependant counfounding
                 factors
Risk factor                                                 Health outcome


                                     Confounding factor

Daily variation of                                        Daily variation
air pollution                                             of number of deaths
levels                                                    from respiratory causes




                                            Tobacco


Filleul et coll., Rev. Mal. Respir., 2001
    Modeling: Strip-tease of the
              curves
► Taking  into acount long-term trends (ie:decrease
  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...)
                           Long-term trends
                Predicted value of total mortality by trend-modeling.




Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
                        Seasonal variations
     Predicted value of mortality by modelization of seasonal variations




Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
                                                            Meteorological data
           Naperian Logarithm of Relative Risk of the interaction
           temperature-humidity on total mortality
                                       la t i f
                                 0 .0 5
                L n d u r is q u e r e
            - 0 .0 5    0




                                                  20
                                                       15
                                                            10                                                                                               25
                                                                                                                                                        20
                                                             hu              5
                                                                  m                                                                                15
                                                                      id i                                                                    le
                                                                             te   0                                              1 0 inim a
                                                                                                                              r e .m
                                                                                                             5         a tu
                                                                                      -5                          pe r
                                                                                                         0   te m
                                                                                           -1
                                                                                                0   -5




Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
                            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
                                                         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
             0.20




                                                                   mortality after modelization of
             0.15




                                                                   trend, seasonal variatipons, Flu
             0.10
   P A C F




                                                                   epidemics, temperature, humidity,
             0.05




                                                                   day of the week & holidays
             0.0
             -0.10 -0.05




                           0   5   10   15          20   25   30
                                             Jour




Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
                                                 Result
                “dose-response curve” of total mortality in relation to SO2 levels


       1.
       10


       1.
       08
Ri
sq
ue     1.
Re     06
lati
f
       1.
       04
                                               non-paramétrique
       1.
       02                                                                         linéaire


       1.
       00



            0                   20                        40                 60              80
                                               SO2 moyenne 0-1 jours



       Source : Surveillance épidémiologique air et santé, rapport InVS, mars 1999
     Interpretation of time-series
                studies
►   Establishing causation is possible after a careful
    discussion of Hill criteria
    1.   Strength.
    2.   Consistency.
    3.   Specificity.
    4.   Temporality.
    5.   Biological gradient (dose-response).
    6.   Plausibility.
    7.   Coherence.
    8.   Experiment.
    9.   Analogy.
                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.

				
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