Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Rena R. Jones; Francis P. Boscoe; Danielle N. Medgyesi; Edward F. Fitzgerald; Syni An Hwang; Shao Lin

doi:10.1016/j.sste.2019.100322

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Rena R. Jones
, Francis P. Boscoe
, Danielle N. Medgyesi
, Edward F. Fitzgerald
, Syni An Hwang
, Shao Lin

Environmental Health Sciences

University at Albany

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM_2.5) and respiratory hospitalizations in New York State (2000–2005). We observed non-significantly higher PM_2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (−0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

Original language	English
Article number	100322
Journal	Spatial and Spatio-temporal Epidemiology
Volume	32
DOIs	https://doi.org/10.1016/j.sste.2019.100322
State	Published - Feb 2020

Keywords

Administrative data
Environmental exposure assessment
Exposure misclassification
Geographic imputation
Spatial error

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10.1016/j.sste.2019.100322

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title = "Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization",

abstract = "Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM2.5) and respiratory hospitalizations in New York State (2000–2005). We observed non-significantly higher PM2.5 exposures, high accuracy of binary exposure assignment (89 to 99\%), and marginally different hazard ratios (HRs) (−0.2 to 0.7\%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.",

keywords = "Administrative data, Environmental exposure assessment, Exposure misclassification, Geographic imputation, Spatial error",

author = "Jones, \{Rena R.\} and Boscoe, \{Francis P.\} and Medgyesi, \{Danielle N.\} and Fitzgerald, \{Edward F.\} and Hwang, \{Syni An\} and Shao Lin",

note = "Publisher Copyright: {\textcopyright} 2019",

year = "2020",

month = feb,

doi = "10.1016/j.sste.2019.100322",

language = "English",

volume = "32",

journal = "Spatial and Spatio-temporal Epidemiology",

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TY - JOUR

T1 - Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

AU - Jones, Rena R.

AU - Boscoe, Francis P.

AU - Medgyesi, Danielle N.

AU - Fitzgerald, Edward F.

AU - Hwang, Syni An

AU - Lin, Shao

PY - 2020/2

Y1 - 2020/2

N2 - Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM2.5) and respiratory hospitalizations in New York State (2000–2005). We observed non-significantly higher PM2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (−0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

AB - Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM2.5) and respiratory hospitalizations in New York State (2000–2005). We observed non-significantly higher PM2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (−0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

KW - Administrative data

KW - Environmental exposure assessment

KW - Exposure misclassification

KW - Geographic imputation

KW - Spatial error

UR - https://www.scopus.com/pages/publications/85077948774

U2 - 10.1016/j.sste.2019.100322

DO - 10.1016/j.sste.2019.100322

M3 - Article

C2 - 32007283

SN - 1877-5845

VL - 32

JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

M1 - 100322

ER -

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

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Keywords

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