Measurement error in epidemiologic studies of air pollution based on land-use regression models
Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locati...
| Authors: | ; ; ; ; ; ; ; |
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| Format: | Article |
| Publication date: | 2013 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/174158 |
| Online Access: | https://hdl.handle.net/2117/174158 https://dx.doi.org/10.1093/aje/kwt127 |
| Access Level: | Open access |
| Keyword: | Epidemiology Air -- Pollution Communicable diseases Biometry Air pollution Bias (epidemiology) Measurement error Regression analysis Epidemiologia Aire -- Contaminació Malalties transmissibles Biometria Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica Àrees temàtiques de la UPC::Ciències de la salut::Impacte ambiental |
| Summary: | Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models. |
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