Influence of the Spatial Resolution of the Exposure Estimate in Determining the Association between Heat Waves and Adverse Health Outcomes. Issue 3 (4th May 2019)

• Record Type: Journal Article
• Authors: Wu, Connor Y. H.; Zaitchik, Benjamin F.; Swarup, Samarth; Gohlke, Julia M.
• Is Part Of: Annals of the American Association of Geographers. Volume 109:Issue 3(2019)
• Page Start: 875
• Page End: 886
• Publication Date: 2019-05-04
• Subjects: case–crossover design -- heat waves -- nonaccidental death -- preterm birth -- temperature; 交叉案例设计 -- 热浪 -- 非意外死亡 -- 早产 -- 温度。; alumbramiento prematuro -- diseño de caso cruzado -- muerte no accidental -- olas de calor -- temperatura; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2018.1511411
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : Area-level estimates of temperature might lead to exposure misclassification in studies examining associations between heat waves and health outcomes. Our study compared the association between heat waves and preterm birth (PTB) or nonaccidental death (NAD) using exposure metrics at varying levels of spatial resolution: ZIP codes, 12.5 km, and 1 km. Using geocoded residential addresses on birth (1990–2010) and death (1997–2010) records from Alabama, we implemented a time-stratified case–crossover design to examine the association between heat waves and PTB or NAD. ZIP code and 12.5-km heat wave indexes (HIs) were derived using air temperatures from Phase 2 of the North American Land Data Assimilation System (NLDAS–2). We downscaled NLDAS–2 data, using land surface temperatures from the Moderate Resolution Imaging Spectroradiometer product, to estimate fine spatial resolution HIs (1 km). The association between heat waves and PTB or NAD was significant and positive using ZIP code, 12.5-km, and 1-km exposure metrics. Moreover, results show that these three exposure metric analyses produced similar effect estimates. Urban heat islands were evident with the 1-km metric. When analyses were stratified by rurality, we found that associations in urban areas were more positive than those in rural areas. Comparing results of models with a varying spatial resolution of the exposure metric allows for examination of potential bias associated with exposure misclassification.