Heat-Related Illness Is Associated with Lack of Air Conditioning and Pre-Existing Health Problems in Detroit, Michigan, USA: A Community-Based Participatory Co-Analysis of Survey Data

Jacqueline E. Cardoza, Carina J. Gronlund, Justin Schott, Todd Ziegler, Brian Stone and Marie S. O’Neill
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Jacqueline E. Cardoza: School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
Carina J. Gronlund: Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
Justin Schott: EcoWorks, Detroit, MI 48219, USA
Todd Ziegler: Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
Brian Stone: Georgia Institute of Technology School of City and Regional Planning, Atlanta, GA 30332, USA
Marie S. O’Neill: School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA

IJERPH, 2020, vol. 17, issue 16, 1-11

Abstract: The objective of the study was to investigate, using academic-community epidemiologic co-analysis, the odds of reported heat-related illness for people with (1) central air conditioning (AC) or window unit AC versus no AC, and (2) fair/poor vs. good/excellent reported health. From 2016 to 2017, 101 Detroit residents were surveyed once regarding extreme heat, housing and neighborhood features, and heat-related illness in the prior 5 years. Academic partners selected initial confounders and, after instruction on directed acyclic graphs, community partners proposed alternate directed acyclic graphs with additional confounders. Heat-related illness was regressed on AC type or health and co-selected confounders. The study found that heat-related illness was associated with no-AC ( n = 96, odds ratio (OR) = 4.66, 95% confidence interval (CI) = 1.22, 17.72); living ≤5 years in present home ( n = 57, OR = 10.39, 95% CI = 1.13, 95.88); and fair/poor vs. good/excellent health ( n = 97, OR = 3.15, 95% CI = 1.33, 7.48). Co-analysis suggested multiple built-environment confounders. We conclude that Detroit residents with poorer health and no AC are at greater risk during extreme heat. Academic-community co-analysis using directed acyclic graphs enhances research on community-specific social and health vulnerabilities by identifying key confounders and future research directions for rigorous and impactful research.

Keywords: climate change; heat wave; heat exhaustion; community-based participatory research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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