Identifying individual risk factors and documenting the pattern of heat-related illness through analyses of hospitalization and patterns of household cooling

• Published in: PloS one Vol. 10; no. 3; p. e0118958
• Main Authors: Schmeltz, Michael T, Sembajwe, Grace, Marcotullio, Peter J, Grassman, Jean A, Himmelstein, David U, Woolhandler, Stephanie
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.03.2015; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Age; Aged; Aged, 80 and over; Air conditioners; Air conditioning; Body temperature; Census of Population; Child; Child, Preschool; Cities; Climate Change; Cluster analysis; Cooling; Cross-Sectional Studies; Diabetes; Environmental aspects; Environmental risk; Extreme Heat; Extreme high temperatures; Fatalities; Female; Forecasts and trends; Health aspects; Health risks; Heat; Heat Stress Disorders - epidemiology; Heat Stress Disorders - etiology; Heatstroke; Hospitalization; Housing; Humans; Illnesses; Infant; Infant, Newborn; Male; Males; Middle Aged; Morbidity; Mortality; Older people; Populations; Public health; Risk analysis; Risk Factors; Rural areas; Studies; Temperature effects; Urban areas; Young Adult; United Kingdom > UK; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0118958

As climate change increases the frequency and intensity of extreme heat events researchers and public health officials must work towards understanding the causes and outcomes of heat-related morbidity and mortality. While there have been many studies on both heat-related illness (HRI), there are fewer on heat-related morbidity than on heat-related mortality. To identify individual and environmental risk factors for hospitalizations and document patterns of household cooling. We performed a pooled cross-sectional analysis of secondary U.S. data, the Nationwide Inpatient Sample. Risk ratios were calculated from multivariable models to identify risk factors for hospitalizations. Hierarchical modeling was also employed to identify relationships between individual and hospital level predictors of hospitalizations. Patterns of air conditioning use were analyzed among the vulnerable populations identified. Hospitalizations due to HRI increased over the study period compared to all other hospitalizations. Populations at elevated risk for HRI hospitalization were blacks, males and all age groups above the age of 40. Those living in zip-codes in the lowest income quartile and the uninsured were also at an increased risk. Hospitalizations for HRI in rural and small urban clusters were elevated, compared to urban areas. Risk factors for HRI include age greater than 40, male gender and hospitalization in rural areas or small urban clusters. Our analysis also revealed an increasing pattern of HRI hospitalizations over time and decreased association between common comorbidities and heat illnesses which may be indicative of underreporting.