Risk factors of direct heat-related hospital admissions during the 2009 heatwave in Adelaide, Australia: a matched case-control study

Ying Zhang; Monika Nitschke; Antoinette Krackowizer; Keith Dear; Dino Pisaniello; Philip Weinstein; Graeme Tucker; Sepehr Shakib; Peng Bi

doi:10.1136/bmjopen-2015-010666

Risk factors of direct heat-related hospital admissions during the 2009 heatwave in Adelaide, Australia: a matched case-control study

BMJ Open. 2016 Jun 2;6(6):e010666. doi: 10.1136/bmjopen-2015-010666.

Authors

Ying Zhang¹, Monika Nitschke², Antoinette Krackowizer³, Keith Dear⁴, Dino Pisaniello³, Philip Weinstein⁵, Graeme Tucker², Sepehr Shakib⁶, Peng Bi³

Affiliations

¹ School of Public Health, China Studies Centre, University of Sydney, Sydney, New South Wales, Australia.
² South Australian Department for Health and Ageing (DHA), Adelaide, South Australia, Australia.
³ School of Public Health, University of Adelaide, Adelaide, South Australia, Australia.
⁴ Global Health Institute, Duke University and Duke Kunshan University, Shanghai, China.
⁵ School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.
⁶ Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

Abstract

Objective: The extreme heatwave of 2009 in South Australia dramatically increased morbidity, with a 14-fold increase in direct heat-related hospitalisation in metropolitan Adelaide. Our study aimed to identify risk factors for the excess morbidity.

Design: A matched case-control study of risk factors was conducted.

Setting: Patients and matched community controls were interviewed to gather data on demographics, living environment, social support, health status and behaviour changes during the heatwave.

Participants: Cases were all hospital admissions with heat-related diagnoses during the 5-day heatwave in 2009. Controls were randomly selected from communities.

Outcome measures: Descriptive analyses, simple and multiple conditional logistic regressions were performed. Adjusted ORs (AORs) were estimated.

Results: In total, 143 hospital patients and 143 matched community controls were interviewed, with a mean age of 73 years (SD 21), 96% European ethnicity, 63% retired, 36% with high school or higher education, and 8% institutional living. The regression model indicated that compared with the controls, cases were more likely to have heart disease (AOR=13.56, 95% CI 1.27 to 144.86) and dementia (AOR=26.43, 95% CI 1.99 to 350.73). The protective factors included higher education level (AOR=0.48, 95% CI 0.23 to 0.99), having air-conditioner in the bedroom (AOR=0.12, 95% CI 0.02 to 0.74), having an emergency button (AOR=0.09, 95% CI 0.01 to 0.96), using refreshment (AOR=0.10, 95% CI 0.01 to 0.84), and having more social activities (AOR=0.11, 95% CI 0.02 to 0.57).

Conclusions: Pre-existing heart disease and dementia significantly increase the risk of direct heat-related hospitalisations during heatwaves. The presence of an air-conditioner in the bedroom, more social activities, a higher education level, use of emergency buttons and refreshments reduce the risk during heatwaves.

Keywords: EPIDEMIOLOGY; PREVENTIVE MEDICINE; PUBLIC HEALTH.

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MeSH terms

Aged
Aged, 80 and over
Case-Control Studies
Disasters*
Emergency Service, Hospital / statistics & numerical data*
Extreme Heat*
Female
Humans
Logistic Models
Male
Middle Aged
Morbidity*
Patient Admission / statistics & numerical data*
Protective Factors
Risk Factors
South Australia