A Multistate Study on Housing Factors Influential to Heat-Related Illness in the United States

Bibliographic Data

ID15470285
AuthorsMing Hu (0000-0003-2583-1161, University of Maryland, College Park, corresponding author), Kai Zhang (0000-0002-7401-2931, University at Albany, State University of New York), Quynh C Nguyen (0000-0003-4745-6681, University of Maryland, College Park), Tolga Tasdizen (0000-0001-6574-0366, University of Utah), Krupali Krusche (0000-0003-0092-9816, University of Notre Dame)
Year2022
Volume19
Issue23
Pages15762-15762
Publication date2022-11-26
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192315762
PMID36497839
OpenAlexW4310379741
LanguageEN
Citations received5
References cited8

As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, a four-step approach was used to collect, extract and analyze data from twenty-seven states in the United States. Three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal inertia) and eight variables were extracted from the American Housing Survey database, ResStock database and CDC's National Environmental Public Health Tracking Network. Multivariable regression models were used to understand the influential variables, a multicollinearity test was used to determine the dependence of those variables, and then a logistic model was used to verify the results. Three variables-housing age (HA), housing crowding ratio (HCR), and roof condition (RC)-were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also reinforce the concept of the built environment determined health and demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions

Econometrics · Economics · Environmental health · Geography · Heat illness · Logistic regression · Logit · Meteorology · Multicollinearity · Overheating (electricity · Public health · Regression analysis · Statistics · Variables · Air Quality and Health Impacts · Climate Change and Health Impacts · Engineering · Mathematics · Medicine · Noise Effects and Management

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