Dataset Information

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study.

ABSTRACT:

Background

Recent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people's homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.

Objective

This simulation study aimed to understand the impact of incomplete addresses on health effect estimation based on the association between particulate matter with diameter ≤10 µm (PM₁₀) and low birth weight (LBW).

Methods

We generated true annual average concentrations of PM₁₀ at 46,007 mothers' homes and their LBW status, using the parameters obtained from our data analysis and a previous study in Seoul, Korea. Then, we hypothesized that mothers' address information is limited to the district and compared the properties of their health effect estimates of PM₁₀ with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM₁₀ and nine exposure prediction methods that provide different sets of predicted PM₁₀ concentrations of mothers.

Results

We observed increased bias and root mean square error consistently across all environmental scenarios and prediction methods using incomplete addresses compared to complete addresses. However, the bias related to incomplete addresses decreased when we used population-representative exposures averaged to the district from predicted PM₁₀ at census tract centroids.

Significance

Our simulation study suggested that individual exposure estimated by prediction approaches and averaged across population-representative points can provide improved accuracy in health effect estimates when complete address data are unavailable.

Impact statement

Our simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people's addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, district, and zip code area. Our findings can help understand the possible consequences of limited address information and provide practical guidance in achieving the accuracy in health effect analysis.

SUBMITTER: Jun YB

PROVIDER: S-EPMC9349037 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study.

Jun Yoon-Bae YB Song Insang I Kim Ok-Jin OJ Kim Sun-Young SY

Journal of exposure science & environmental epidemiology 20220126 4

<h4>Background</h4>Recent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people's homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.<h4>Objective</h4>This simulation study aimed to understand the impact of incomplete addresses on health effect estimatio ...[more]

PMID: 35082387

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Project description:Few birth cohort studies have examined the role of traffic-related air pollution (TRAP) in the development of infantile atopic dermatitis (AD), but none have investigated the role of preventive factors such as green spaces. The aim of this study was to investigate whether exposure to nitrogen dioxide (NO₂) and particulate matter with an aerodynamic diameter of <10 μm (PM10) during pregnancy is associated with increased risk of development of AD in 6-month-old children and also to examine how this association changes with residential green space. This study used prospective data from 659 participants of the Mothers and Children's Environmental Health study. Subjects were geocoded to their residential addresses and matched with air pollution data modeled using land-use regression. Information on infantile AD was obtained by using a questionnaire administered to the parents or guardians of the children. The association between infantile AD and exposure to NO₂ and PM10 was determined using logistic regression models. We assessed the effects of residential green spaces using stratified analyses and by entering product terms into the logistic regression models. The risk of infantile AD significantly increased with an increase in air pollution exposure during the first trimester of pregnancy. The adjusted odds ratio (OR) and 95% confidence interval (CI) were 1.219 (1.023-1.452) per 10 μg/m³ increase in PM10 and 1.353 (1.027-1.782) per 10 ppb increase in NO₂. An increase in the green space within 200 m of residence was associated with a decreased risk of AD (OR = 0.996, 95% CI: 0.993-0.999). The stratified analysis of residential green space revealed stronger associations between infantile AD and PM10 and NO₂ exposure during the first trimester in the areas in the lower tertiles of green space. This study indicated that exposure to TRAP during the first trimester of pregnancy is associated with infantile AD. Less residential green space may intensify the association between TRAP exposure and infantile AD.

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Dataset Information

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study.

Background

Objective

Methods

Results

Significance

Impact statement

Publications

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study.

Similar Datasets

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