Risk factors for deaths during the 2009 heat wave in Adelaide, Australia: a matched case-control study.

The extreme heat wave in Australia in 2009 resulted in significantly increased number of daily deaths. The circumstances that lead to deaths during extreme heat have not been explored before in Australia. This study aims to identify the individual and community risk factors for deaths during this extreme heat wave in Adelaide. A matched case-control study was conducted. Cases were those who died in the Adelaide metropolitan area during the heat wave period. For each case, two community controls were randomly selected, matched by age and gender. Face-to-face or telephone interviews were conducted to collect data of demographic information, living environment, social support, health status and behavioural changes during the heat wave. Descriptive analysis, as well as simple and multiple conditional logistic regressions were performed. In total, 82 deaths and 164 matched community controls were included in the analysis, with a median age of 77.5 (range 26.6-100.7). The multiple logistic regression model indicated that, compared with controls, the risk of death during the heat wave was significantly increased for people living alone (AOR = 42.31, 95 % CI 2.3, 792.8) or having existing chronic heart disease (AOR = 22.4, 95 % CI 1.7, 303.0). In addition, having air conditioning in bedrooms (AOR = 0.004, 95 % CI 0.00006, 0.28) and participating in social activities more than once a week (AOR = 0.011, 95 % CI 0.0004, 0.29) indicated significant protective effects. We have identified factors that could significantly impact on the likelihood of deaths during heat waves. Our findings could assist in the development of future intervention programs and policies to reduce mortality associated with a warmer climate.

Heat Health Messages: A Randomized Controlled Trial of a Preventative Messages Tool in the Older Population of South Australia.

This study explores the efficacy of providing targeted information to older individuals to prevent adverse health outcomes during extreme heat. Participants ≥65 years of age (n = 637) were recruited from previous population-based studies and randomized into intervention and control groups. The intervention group received evidence-based information leaflets and summarised "Beat the Heat" tips. Post summer 2013-2014, participants responded to questions about their behaviours and their health experiences. Chi square analysis and risk ratios (RR) were used to determine the difference in effects. Responses were received from 216 intervention subjects and 218 controls. Behaviour modification during extreme heat was similar in both groups except for significant increases in the use of cooling systems and the use of a wet cloth to cool the skin in the intervention group. Both actions were recommended in the information package. More people in the intervention group also claimed to have had adequate heat health information. After adjusting for confounders, the RR for self-reported heat stress experienced during summer 2014 indicated a 63% (RR 0.37; 95% CI: 0.22-0.63) reduction in the intervention group compared to the control group. Access to intensive prevention information may have contributed to this positive outcome, indicating the potential usefulness of targeted heat-health information for seniors.

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

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.