Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Risk factors for direct heat-related hospitalization during the 2009 Adelaide heatwave: A case crossover study|
|Citation:||Science of the Total Environment, 2013; 442:1-5|
|Publisher:||Elsevier Science BV|
|Ying Zhang, Monika Nitschke, Peng Bi|
|Abstract:||Adelaide experienced an extreme and prolonged 13 days heatwave in summer 2009. The health impacts of this heatwave included an almost 14-fold increase in direct heat-related hospital admissions. This study aims to investigate the risk factors for this extra health burden. A case crossover study was conducted in metropolitan Adelaide to compare the characteristics of patients from the heatwave (exposure) period and non-heatwave (control) periods before and after. Direct heat-related hospitalizations were identified based on the ICD-10 codes (X30, T67, and E86). Patients' data, including age, gender, indicators of health status, living conditions and socio-economic status, were collected from the South Australian Department of Health and patients' case-notes from seven major Adelaide hospitals. Multivariate logistic regression model was used to estimate the odd ratios (OR) and the 95% confidence intervals (CI). Results indicate that living at residential aged care (OR=0.41, 95% CI: 0.15-0.70) and having higher number of co-morbidities (OR=0.89, 95% CI: 0.83-0.95) reduced the risk of hospital admission for direct heat-related illnesses during the heatwave, while having renal problems (OR=1.72, 95% CI: 1.07-2.94), reporting a fall prior to hospitalization (OR=2.04, 95% CI: 1.10-3.77), receiving assistance from community (OR=2.31, 95% CI: 1.24-4.30), living alone (OR=2.41, 95% CI: 1.32-4.40), socio-economic disadvantage (OR=2.10, 95% CI: 1.09-4.04) and no private health insurance (OR=1.82, 95% CI: 1.05-3.16) increased the risk. In conclusion, the people most at risk during the 2009 heatwave in Adelaide were those who lived alone, received help from community services, with co-existing renal problems or a risk of falls, and with a lower socio-economic status. Findings will assist in refining heat-health response systems and developing intervention programmes.|
|Keywords:||Humans; Heat Stroke; Hospitalization; Patient Admission; Multivariate Analysis; Logistic Models; Risk Factors; Case-Control Studies; Cross-Over Studies; Climate; Socioeconomic Factors; Aged; Urban Population; South Australia; Female; Male; Extreme Heat|
|Rights:||© 2012 Elsevier B.V. All rights reserved.|
|Appears in Collections:||Public Health publications|
Environment Institute publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.