The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to


Journal

Environmental health perspectives
ISSN: 1552-9924
Titre abrégé: Environ Health Perspect
Pays: United States
ID NLM: 0330411

Informations de publication

Date de publication:
07 2022
Historique:
entrez: 29 7 2022
pubmed: 30 7 2022
medline: 3 8 2022
Statut: ppublish

Résumé

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM In this simulation study, we use daily We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. Our findings suggest that the causal determination for long-term

Sections du résumé

BACKGROUND
Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.
OBJECTIVES
We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM
METHODS
In this simulation study, we use daily
RESULTS
We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.
DISCUSSION
Our findings suggest that the causal determination for long-term

Identifiants

pubmed: 35904519
doi: 10.1289/EHP10389
pmc: PMC9337229
doi:

Substances chimiques

Air Pollutants 0
Particulate Matter 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

77006

Subventions

Organisme : NIEHS NIH HHS
ID : P30 ES000002
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG074357
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES032418
Pays : United States

Commentaires et corrections

Type : CommentIn

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Auteurs

Yaguang Wei (Y)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Xinye Qiu (X)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Mahdieh Danesh Yazdi (MD)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Alexandra Shtein (A)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Liuhua Shi (L)

Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Jiabei Yang (J)

Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island, USA.

Adjani A Peralta (AA)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Brent A Coull (BA)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Joel D Schwartz (JD)

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

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