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

• Published in: Journal of exposure science & environmental epidemiology 2022-07, Vol.32 (4), p.637-643
• Main Authors: Jun, Yoon-Bae, Song, Insang, Kim, Ok-Jin, Kim, Sun-Young
• Format: Article
• Language: English
• Subjects: Air pollution; Bias; Birth weight; Centroids; Data analysis; Epidemiology; Estimates; Exposure; Impact analysis; Low birth weight; Mathematical models; Medicine; Medicine & Public Health; Particulate emissions; Particulate matter; Pollution studies; Prediction models; Predictions; Simulation; Spatial distribution
• ISSN: 1559-0631; 1559-064X
• DOI: 10.1038/s41370-022-00412-1

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 (PM 10 ) and low birth weight (LBW). Methods We generated true annual average concentrations of PM 10 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 PM 10 with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM 10 and nine exposure prediction methods that provide different sets of predicted PM 10 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 PM 10 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 pra