Does consideration of larger study areas yield more accurate estimates of air pollution health effects? An illustration of the bias-variance trade-off in air pollution epidemiology
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Does consideration of larger study areas yield more accurate estimates of air pollution health effects? An illustration of the bias-variance trade-off in air pollution epidemiology. / Pedersen, Marie; Siroux, Valérie; Pin, Isabelle; Charles, Marie Aline; Forhan, Anne; Hulin, Agnés; Galineau, Julien; Lepeule, Johanna; Giorgis-Allemand, Lise; Sunyer, Jordi; Annesi-Maesano, Isabella; Slama, Rémy; ‘EDEN Mother–Child’ Cohort Study Group.
In: Environment International, Vol. 60, 2013, p. 23-30.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Does consideration of larger study areas yield more accurate estimates of air pollution health effects?
T2 - An illustration of the bias-variance trade-off in air pollution epidemiology
AU - Pedersen, Marie
AU - Siroux, Valérie
AU - Pin, Isabelle
AU - Charles, Marie Aline
AU - Forhan, Anne
AU - Hulin, Agnés
AU - Galineau, Julien
AU - Lepeule, Johanna
AU - Giorgis-Allemand, Lise
AU - Sunyer, Jordi
AU - Annesi-Maesano, Isabella
AU - Slama, Rémy
AU - ‘EDEN Mother–Child’ Cohort Study Group
N1 - © 2013 Elsevier Ltd. All rights reserved.
PY - 2013
Y1 - 2013
N2 - BACKGROUND: Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may also enhance the potential for confounding. We aimed to discuss some analytical approaches to handle this trade-off.METHODS: We modeled NO2 and PM10 concentrations at the home addresses of 1082 pregnant mothers from EDEN cohort living in and around urban areas, using ADMS dispersion model. Simulations were performed to identify the best strategy to limit confounding by unmeasured factors varying with area type. We examined the relation between modeled concentrations and respiratory health in infants using regression models with and without adjustment or interaction terms with area type.RESULTS: Simulations indicated that adjustment for area limited the bias due to unmeasured confounders varying with area at the costs of a slight decrease in statistical power. In our cohort, rural and urban areas differed for air pollution levels and for many factors associated with respiratory health and exposure. Area tended to modify effect measures of air pollution on respiratory health.CONCLUSIONS: Increasing the size of the study area also increases the potential for residual confounding. Our simulations suggest that adjusting for type of area is a good option to limit residual confounding due to area-associated factors without restricting the area size. Other statistical approaches developed in the field of spatial epidemiology are an alternative to control for poorly-measured spatially-varying confounders.
AB - BACKGROUND: Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may also enhance the potential for confounding. We aimed to discuss some analytical approaches to handle this trade-off.METHODS: We modeled NO2 and PM10 concentrations at the home addresses of 1082 pregnant mothers from EDEN cohort living in and around urban areas, using ADMS dispersion model. Simulations were performed to identify the best strategy to limit confounding by unmeasured factors varying with area type. We examined the relation between modeled concentrations and respiratory health in infants using regression models with and without adjustment or interaction terms with area type.RESULTS: Simulations indicated that adjustment for area limited the bias due to unmeasured confounders varying with area at the costs of a slight decrease in statistical power. In our cohort, rural and urban areas differed for air pollution levels and for many factors associated with respiratory health and exposure. Area tended to modify effect measures of air pollution on respiratory health.CONCLUSIONS: Increasing the size of the study area also increases the potential for residual confounding. Our simulations suggest that adjusting for type of area is a good option to limit residual confounding due to area-associated factors without restricting the area size. Other statistical approaches developed in the field of spatial epidemiology are an alternative to control for poorly-measured spatially-varying confounders.
KW - Air Pollutants
KW - Air Pollution
KW - Asthma
KW - Cohort Studies
KW - Computer Simulation
KW - Confounding Factors (Epidemiology)
KW - Environmental Monitoring
KW - Female
KW - Humans
KW - Infant
KW - Logistic Models
KW - Models, Chemical
KW - Models, Statistical
KW - Nitrogen Dioxide
KW - Particulate Matter
KW - Pregnancy
KW - Prenatal Exposure Delayed Effects
KW - Prevalence
KW - Questionnaires
KW - Risk Factors
KW - Rural Population
KW - Sample Size
KW - Urban Health
KW - Urban Population
U2 - 10.1016/j.envint.2013.07.005
DO - 10.1016/j.envint.2013.07.005
M3 - Journal article
C2 - 23994839
VL - 60
SP - 23
EP - 30
JO - Environment international
JF - Environment international
SN - 0160-4120
ER -
ID: 143933540