11 th EPIET Epidemiology Course Menorca, October 2 2006 Environmental Epidemiology (introduction) Dr...

1111thth EPIET Epidemiology EPIET Epidemiology CourseCourse

Menorca, October 2 2006Menorca, October 2 2006

Environmental EpidemiologyEnvironmental Epidemiology

(introduction)(introduction)

Dr Georges SalinesDr Georges Salines

Institut de Veille SanitaireInstitut de Veille Sanitaire

Département Santé EnvironnementDépartement Santé Environnement

I.I. ObjectivesObjectives

To provide a basic knowledge ofTo provide a basic knowledge of : :► The definitions of environmental health, The definitions of environmental health,

environmental epidemiology, environmental environmental epidemiology, environmental risksrisks

► The concept of low-risk and the links The concept of low-risk and the links between relative risk, prevalence of between relative risk, prevalence of exposure and attributable riskexposure and attributable risk

► The limits of epidemiology in environmental The limits of epidemiology in environmental health health

► How to deal with these limitsHow to deal with these limits

DefinitionsDefinitions

► Epidemiology is the study of the distribution Epidemiology is the study of the distribution and determinants of health-related states or and determinants of health-related states or events in specified populationsevents in specified populations

► The environment is all the physical, chemical The environment is all the physical, chemical and biological factors external to a person, and biological factors external to a person, and all the related behaviours. (WHO)and all the related behaviours. (WHO)

► The environment is the sum of all external The environment is the sum of all external conditions affecting the life, development conditions affecting the life, development and survival of an organism (US EPA) and survival of an organism (US EPA)

► The environment is everything that is not me The environment is everything that is not me (Einstein)(Einstein)

Traditional exclusionsTraditional exclusions

►Genetics factors Genetics factors (except interactions (except interactions genes/environment)genes/environment)

►Behaviours Behaviours (except behaviours (except behaviours modifying exposures)modifying exposures)

►Social factors Social factors (except links between (except links between SES and physical environment)SES and physical environment)

► Infectious diseases Infectious diseases (except those (except those transmitted through exposure to media)transmitted through exposure to media)

RiskRisk

► 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 risksHigh risks

► occupational environmentoccupational environment aromatic amines and bladder canceraromatic amines and bladder cancer asbestos fibres and mesotheliomaasbestos fibres and mesothelioma cadmium and kidney diseasescadmium and kidney diseases benzene and leukaemiabenzene and leukaemia pesticides and infertilitypesticides and infertility organic solvents and neurological disordersorganic solvents and neurological disorders etc ... etc ...

► general environmentgeneral environment

December 1952 - LondonDecember 1952 - London

December 1952 - LondonDecember 1952 - London

1953 - Minamata1953 - Minamata

December 1984 - BhopalDecember 1984 - Bhopal

1986 - Tchernobyl1986 - Tchernobyl

Thyroid cancer in childrenThyroid cancer in children

0

20

40

60

80

100

120

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

Years

Inc

ide

nc

e R

ate

pe

r m

illi

on

Belarus

Ukraine

Briansk

Gomel

2003 - Paris2003 - Paris

Mortality and mean temperature in Paris

1999-2002 versus 2003

Peak: Aug 13th

2005 - Katrina2005 - Katrina

2006 Abidjan2006 Abidjan

►

► anthropogenic activitiesanthropogenic activities London 1952London 1952 Minamata 1953 …Minamata 1953 …

► natural originnatural origin Heat wavesHeat waves hurricanes…hurricanes…

► mixed originmixed origin UV and melanomaUV and melanoma tremolite and mesothelioma in New Caledoniatremolite and mesothelioma in New Caledonia erionite and mesothelioma in Turkey ...erionite and mesothelioma in Turkey ...

Nature of high risks in general Nature of high risks in general environmentenvironment

► High RRHigh RR benzidine / bladder cancer benzidine / bladder cancer RR = 500RR = 500 asbestos / mesotheliomaasbestos / mesothelioma RR = 50RR = 50 tobacco (>25g/d) / lung cancertobacco (>25g/d) / lung cancer RR = 30RR = 30

► Usually severe and often specific health Usually severe and often specific health endpointsendpoints

► ““well defined” populationswell defined” populations in space, in timein space, in time socio-demographic characteristicssocio-demographic characteristics relatively small populationsrelatively small populations

Characteristics of high risksCharacteristics of high risks

► urban air pollution and short-term urban air pollution and short-term respiratory diseasesrespiratory diseases RR = 1.1 - 1.5RR = 1.1 - 1.5

► chlorinated water supplies and bladder chlorinated water supplies and bladder cancercancer RR = 1.4RR = 1.4

► electromagnetic fields and children electromagnetic fields and children leukemialeukemia RR = 1.3RR = 1.3 ... ...

LowLow risksrisks

►Relative risk and attributable riskRelative risk and attributable risk relative riskrelative risk

►ratio measure : “ratio measure : “it is an indicator for it is an indicator for epidemiologist ”epidemiologist ”

attributable riskattributable risk►FRA = p FRA = p ** ( RR -1) / [ 1+ p ( RR -1) / [ 1+ p ** ( RR - 1) ] ( RR - 1) ]

““if the relation is causalif the relation is causal, it estimates the , it estimates the proportion (amount) of diseases that we proportion (amount) of diseases that we can attribute to the exposure”can attribute to the exposure”

Small relative risks do not mean Small relative risks do not mean small health impactssmall health impacts

Exposure prevalence

Relative

Risks

0,01 0,10 1,00

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

Risks

0,01 0,10 1,00

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 impactHealth impact

May be not May be not thatthat low after all low after all

low risks low risks

oror

weak associations ?weak associations ?

Theoretical baseline situationTheoretical baseline situation((the wonderful worldthe 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 exposureE0 = non exposed, E1=low exposure, E2=high exposure* Incidence : x /100.000, * Incidence : x /100.000, ** RR : true Relative Risk** RR : true Relative Risk

Heterogeneity in the population’s Heterogeneity in the population’s sensitivitysensitivity

to the exposureto the exposure E0 E1 E2

Prevalence 80% 15% 5%

Incidence (S) 100 300 500

Incidence (s) 100 200 300

100 250 400

RR 1.0 2.5 4.0

50%50%

50%50%

* (S) : high sensitivity. (s) : low sensitivity

Non specific definition of the health Non specific definition of the health outcomeoutcome

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 classificationErrors in the exposure classification

20% of non exposed (E0) are categorised E1 and 10% of non-exposedare categorised E2.

E0 E1 E2

Prevalence 50% 35% 15%

Incidence 150 214.3 250

RR 1.0 1.43 1.67

Inaccuracy in the exposure Inaccuracy in the exposure categoriescategories

E0 E1

Prevalence 50% 50%

Incidence 150 225

RR 1.0 1.5

Epidemiology and weak Epidemiology and weak associationsassociations

► Improve data qualityImprove data quality exposureexposure health endpointshealth endpoints co-factorsco-factors

► Improve statistical powerImprove statistical power Meta-analysis & Multi Meta-analysis & Multi

centrescentres

►Ecological designsEcological designs

appropriate selection of sources and appropriate selection of sources and routes of exposureroutes of exposure

taking account:taking account:►critical periods of exposurecritical periods of exposure►individual history of exposure : behaviour, individual history of exposure : behaviour,

space-time activities …space-time activities …

Improving assessment of exposure:Improving assessment of exposure:better use of environmental databetter use of environmental data

Exposure = last place of residence, assuming entire life at this residence and consumption of the town’s drinking water Chlorination exposure

0 1-25 26-50 50+

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

ExampleExample

Exposure = entire lifetime residential history combined with the town’s drinking water chlorination history

Chlorination exposure

0 1-25 26-50 50+

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

Example (2)Example (2)

technical, logistical and financial limits …technical, logistical and financial limits … depends on sensibility / specificity of the depends on sensibility / specificity of the

methodmethod

Improving assessment of exposure:Improving assessment of exposure:personal exposure monitoringpersonal exposure monitoring

cellular, biochemical, molecular alterationscellular, biochemical, molecular alterations► measurable in biological media (human tissues, cells or measurable in biological media (human tissues, cells or

fluids)fluids) advantages advantages

► measurement of a dose (effectively absorbed)measurement of a dose (effectively absorbed)► integration of all the routes of exposure and sources of integration of all the routes of exposure and sources of

absorptionabsorption► avoids subjects’ lack of knowledge, memory failure, avoids subjects’ lack of knowledge, memory failure,

biased recall, deliberate misinformation …biased recall, deliberate misinformation … limitslimits

► costscosts► Representativity of a single sample taken at a particular Representativity of a single sample taken at a particular

timetime► In some cases, route of exposure is of the essenceIn some cases, route of exposure is of the essence

Improving assessment of exposure: Improving assessment of exposure: biomarkers of exposurebiomarkers of exposure

outcomes specified “as precisely as outcomes specified “as precisely as possible”possible”

►subgroups of diseasesubgroups of disease

biomarkers of effectsbiomarkers of effects►sub clinical eventssub clinical events►predictive value ? predictive value ? ►variabilityvariability

biological, laboratory-related, logistical issues (bias)biological, laboratory-related, logistical issues (bias)

Improving assessment Improving assessment of health endpointsof health endpoints

“ “as much attention” as exposure andas much attention” as exposure and disease variables disease variables

Biomarkers of susceptibilityBiomarkers of susceptibility

Measuring confounders Measuring confounders and effect modifiersand effect modifiers

Combined risk of bladder cancer associated with smoking exposure and GSTµ1 genotype among whites

Smoking exposure

Genotype 0 (non smokers)

1-50 (pack-years)

> 50 (pack-years)

GTSµ1 (-) 1.3

[0.6 - 2.7]

4.3

[2.1 - 3.5]

5.9

[2.6 - 13.0]

GTSµ1 (+) 1.0

[Ref]

2.2

[1.1 - 4.5]

3.5

[1.5 - 8.0]

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

ExampleExample

► Increasing sample sizeIncreasing sample size

Improving statistical powerImproving statistical power

0

5000

10000

15000

20000

1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2

Odds ratio

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

Number of cases and controls (1/1) for 1- = 80%, = 5%, H0: OR=1

►““Mammoth” studiesMammoth” studies ExpansiveExpansive ComplexComplex

►Pooling dataPooling data Meta-analysis (or combined analysis)Meta-analysis (or combined analysis) Multi centres studiesMulti centres studies

► heterogeneity ?heterogeneity ?

Improving statistical powerImproving statistical power

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

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

E

E

M M

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)

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 study

Environmental exposure

Area B

Area C

Area A

Incidence rate

Ecological Fallacy

population B

population C

population A

Individual exposure

Incidence rate

ExampleExample

►1983: leukaemia cluster among 1983: leukaemia cluster among children living near the Sellafield children living near the Sellafield nuclear waste reprocessing plant nuclear waste reprocessing plant (United Kingdom)(United Kingdom)

►Other leukaemia clusters have since Other leukaemia clusters have since been identified near other nuclear been identified near other nuclear sites, such as Dounreay in Scotland sites, such as Dounreay in Scotland and Krümmel in Germanyand Krümmel in Germany

But…But…

► In view of current knowledge about the relation In view of current knowledge about the relation between exposure to radiation and the risk of between exposure to radiation and the risk of leukemia, dose levels around nuclear sites are leukemia, dose levels around nuclear sites are incompatible with the excess risks observed …incompatible with the excess risks observed …

► Studies considering several sites (United Kingdom, Studies considering several sites (United Kingdom, France, USA, Germany, Canada, Japan, Sweden, France, USA, Germany, Canada, Japan, Sweden, Spain) have not detected any global excess Spain) have not detected any global excess

► Leukaemia clusters have been observed in areas Leukaemia clusters have been observed in areas far from any nuclear sitefar from any nuclear site

► There are alternative hypotheses which may There are alternative hypotheses which may explain the leukaemia clusters located near some explain the leukaemia clusters located near some nuclear sitesnuclear 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 seriesTime series

►Statistical powerStatistical power►Control of confounding factors Control of confounding factors

++++++ Non time-dependant: Population is its Non time-dependant: Population is its

own controlown control Time-dependant: modelling Time-dependant: modelling

techniques techniques

Exemple PSAS9 IExemple PSAS9 I

D day

Exposed population

Indicator of exposure

Indicatorof effect

All people living in

Marseilles

SO2 g/m3

(Daily mean of 3 monitoring stations)

Daily number of deaths

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

Raw curves

Dioxyde de soufre (SO2)

µg

/m3

02

04

06

08

01

00

Jan 1990 Jan 1991 Jan 1992 Jan 1993 Jan 1994 Jan 1995

Fumées noires (FN)

µg

/m3

02

04

06

08

0

Jan 1990 Jan 1991 Jan 1992 Jan 1993 Jan 1994 Jan 1995

Mean levels of air pollution Marseilles, 1990-1995

5

10

15

20

25

30

35

40

Daily counts of deaths, Marseilles, 1990-1995

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

Time-dependant counfounding factors

Jour

PA

CF

0 5 10 15 20 25 30

-0.1

0-0

.05

0.0

0.05

0.10

0.15

0.20

Fonction totale.

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

Serial correlation fonction of daily mortality

Time-dependant counfounding factors

Risk factor Health outcome

Confounding factor

Daily variation of air pollution levels

Daily variation of number of deaths 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 air pollution levels

Daily variation of number of deaths from respiratory causes

Tobacco

Filleul et coll., Rev. Mal. Respir., 2001

Modeling: Strip-tease of the curves

► Taking into acount long-term trends Taking into acount long-term trends (ie:decrease of mortality)(ie:decrease of mortality)

► Taking into acount seasonal variations Taking into acount seasonal variations (Higher mortality during winter)(Higher mortality during winter)

► Taking into acount the day of the weekTaking into acount the day of the week► Taking into acount co-factors Taking into acount co-factors

(Meteorological data, Flu epidemics, Pollinic (Meteorological data, Flu epidemics, Pollinic data...)data...)

Long-term trends

Predicted value of total mortality by trend-modeling.

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

21

21.5

22

22.5

23

23.5

24

Seasonal variations

Predicted value of mortality by modelization of seasonal variations

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

19

21

23

25

27

29

31

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

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

15

17

19

21

23

25

27

29

31

33

Full Monty

-4

-3

-2

-1

0

1

2

3

4

Jour

PA

CF

0 5 10 15 20 25 30

-0.1

0-0

.05

0.0

0.0

50

.10

0.1

50

.20

Serial correlation fonction of daily mortality after modelization of trend, seasonal variatipons, Flu epidemics, temperature, humidity, day of the week & holidays

Residual values of total 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

Result “dose-response curve” of total mortality in relation to SO2 levels

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

SO2 moyenne 0-1 jours

RisqueRelatif

0 20 40 60 80

1.00

1.02

1.04

1.06

1.08

1.10

non-paramétrique

linéaire

Interpretation of time-series Interpretation of time-series studiesstudies

► Establishing causation Establishing causation is possibleis possible after a after a careful discussion of Hill criteriacareful discussion of Hill criteria1.1. Strength. Strength. 2.2. Consistency. Consistency. 3.3. Specificity. Specificity. 4.4. Temporality. Temporality. 5.5. Biological gradient (dose-response). Biological gradient (dose-response). 6.6. Plausibility. Plausibility. 7.7. Coherence. Coherence. 8.8. Experiment. Experiment. 9.9. Analogy. Analogy.

V. ConclusionV. Conclusion

► Aspects of the study design that involves Aspects of the study design that involves measurements of variables are critical, especially measurements of variables are critical, especially in environmental epidemiology where risks from in environmental epidemiology where risks from exposure are likely to be small, difficult to detect, exposure are likely to be small, difficult to detect, and perhaps not clinically significant, yet maybe and perhaps not clinically significant, yet maybe of public health importanceof public health importance

► Epidemiology is not always the only answer of Epidemiology is not always the only answer of even the more relevant one to questions even the more relevant one to questions submitted to environmental epidemiologists: Risk submitted to environmental epidemiologists: Risk analysis for example, is a very useful and cost-analysis for example, is a very useful and cost-effective methodeffective method

► ...but this is another story....but this is another story.