Using spatial data analytics to identify associations between home healthcare services' accessibility and socioeconomic factors

• Published in: IISE transactions on healthcare systems engineering Vol. 8; no. 4; pp. 250 - 267
• Main Authors: Kilinc, Mehmet Serdar, Stamm, Jessica Heier, Milburn, Ashlea Bennett
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.10.2018; Taylor & Francis Ltd
• Subjects: Accessibility; Analytics; Home healthcare; Mathematical models; Population (statistical); Social factors; Socioeconomic data; socioeconomic disparities; Socioeconomic factors; space-varying coefficient models; spatial accessibility; Spatial data; spatial data analytics; Statistical inference; Statistical methods; Statistical models
• ISSN: 2472-5579; 2472-5587
• DOI: 10.1080/24725579.2018.1502222

Home healthcare can improve health outcomes and reduce healthcare costs. However, realizing its benefits is only possible if the service is accessible for patients who need it. Thus, evaluating and understanding the inequities in accessibility of home healthcare services are necessary to improve the healthcare system and overall population health. This article utilizes detailed healthcare system and socioeconomic data in a spatial statistical modeling framework, from which we make inference about spatially explicit relationships between home healthcare accessibility and socioeconomic factors, including rural/urban status, income, education, and race/ethnicity. This is accomplished via space-varying coefficient models that account for the spatial autocorrelation in both the response and predictor variables, as well as capture spatial heterogeneity in the relationships between accessibility and socioeconomic factors. We find statistically significant and spatially varying relationships between accessibility of home healthcare services and several socioeconomic variables at the zip code level. Our results have the potential to inform policies aimed at addressing disparities in spatial accessibility, promoting healthcare effectiveness, and improving outcomes. This work makes important contributions to improving healthcare outcomes using a spatial analytics framework that integrates healthcare systems data at a fine spatial scale with advanced statistical methods.