The Health Opportunity Index: Understanding the Input to Disparate Health Outcomes in Vulnerable and High-Risk Census Tracts

Chinonso N. Ogojiaku, Allen Jc, Rexford Anson-Dwamena, Kierra S. Barnett, Olorunfemi Adetona, Wansoo Im and Darryl B. Hood
Additional contact information
Chinonso N. Ogojiaku: Division of Environmental Health Sciences, College of Public Health, Ohio State University, 408 Cunz Hall, 1841 Neil Ave., Columbus, OH 43210, USA
Allen Jc: Office of Health Equity, Ohio Department of Health, Columbus, OH 43215, USA
Rexford Anson-Dwamena: Office of Health Equity, Virginia Department of Health, Richmond, VA 23219, USA
Kierra S. Barnett: The Kirwan Institute for the Study of Race and Ethnicity, Ohio State University, Columbus, OH 43201, USA
Olorunfemi Adetona: Division of Environmental Health Sciences, College of Public Health, Ohio State University, 408 Cunz Hall, 1841 Neil Ave., Columbus, OH 43210, USA
Wansoo Im: Division of Public Health, Meharry Medical College, Nashville, TN 37208, USA
Darryl B. Hood: Division of Environmental Health Sciences, College of Public Health, Ohio State University, 408 Cunz Hall, 1841 Neil Ave., Columbus, OH 43210, USA

IJERPH, 2020, vol. 17, issue 16, 1-17

Abstract: The Health Opportunity Index (HOI) is a multivariate tool that can be more efficiently used to identify and understand the interplay of complex social determinants of health (SDH) at the census tract level that influences the ability to achieve optimal health. The derivation of the HOI utilizes the data-reduction technique of principal component analysis to determine the impact of SDH on optimal health at lower census geographies. In the midst of persistent health disparities and the present COVID-19 pandemic, we demonstrate the potential utility of using 13-input variables to derive a composite metric of health (HOI) score as a means to assist in the identification of the most vulnerable communities during the current pandemic. Using GIS mapping technology, health opportunity indices were layered by counties in Ohio to highlight differences by census tract. Collectively we demonstrate that our HOI framework, principal component analysis and convergence analysis methodology coalesce to provide results supporting the utility of this framework in the three largest counties in Ohio: Franklin (Columbus), Cuyahoga (Cleveland), and Hamilton (Cincinnati). The results in this study identified census tracts that were also synonymous with communities that were at risk for disparate COVID-19 related health outcomes. In this regard, convergence analyses facilitated identification of census tracts where different disparate health outcomes co-exist at the worst levels. Our results suggest that effective use of the HOI composite score and subcomponent scores to identify specific SDH can guide mitigation/intervention practices, thus creating the potential for better targeting of mitigation and intervention strategies for vulnerable communities, such as during the current pandemic.

Keywords: health opportunity index; health equity; health disparities; social determinants of health; principal component analysis; GIS; Ohio; thematic mapping; disease convergence; public health exposome (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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