Extreme heatwaves occurred in Adelaide, South Australia, in the summers of 2008 and 2009. Both heatwaves were unique in terms of their duration (15 days and 13 days respectively), and the 2009 heatwave was also remarkable in its intensity with a maximum temperature reaching 45.7°C. It is of interest to compare the health impacts of these two unprecedented heatwaves with those of previous heatwaves in Adelaide.

Heat waves are considered a health risk and they are likely to increase in frequency, intensity and duration as a consequence of climate change. The effects of heat waves on human health could be reduced if individuals recognise the risks and adopt healthy behaviours during a heat wave. The purpose of this study was to determine the predictors of risk perception using a heat wave scenario and identify the constructs of the health belief model that could predict adaptive behaviours during a heat wave. A cross-sectional study was conducted during the summer of 2012 among a sample of persons aged between 30 to 69 years in Adelaide. Participants’ perceptions were assessed using the health belief model as a conceptual frame. Their knowledge about heat waves and adaptive behaviours during heat waves was also assessed. Logistic regression analyses were performed to determine the predictors of risk perception to a heat wave scenario and adaptive behaviours during a heat wave. Of the 267 participants, about half (50.9%) had a high risk perception to heat waves while 82.8% had good adaptive behaviours during a heat wave. Multivariate models found that age was a significant predictor of risk perception. In addition, participants who were married (OR = 0.21; 95% CI, 0.07–0.62), who earned a gross annual household income of ≥$60,000 (OR = 0.41; 95% CI, 0.17–0.94) and without a fan (OR = 0.29; 95% CI, 0.11–0.79) were less likely to have a high risk perception to heat waves. Those who were living with others (OR = 2.87; 95% CI, 1.19–6.90) were more likely to have a high risk perception to heat waves. On the other hand, participants with a high perceived benefit (OR = 2.14; 95% CI, 1.00–4.58), a high “cues to action” (OR = 3.71; 95% CI, 1.63–8.43), who had additional training or education after high school (OR = 2.65; 95% CI, 1.25–5.58) and who earned a gross annual household income of ≥$60,000 (OR = 2.66; 95% CI, 1.07–6.56) were more likely to have good adaptive behaviours during a heat wave. The health belief model could be useful to guide the design and implementation of interventions to promote adaptive behaviours during heat waves.

Extremely high temperatures over many consecutive days have been linked to an increase in renal disease in several cities. This is becoming increasingly relevant with heatwaves becoming longer, more intense, and more frequent with climate change. This study aimed to extend the known relationship between daily temperature and kidney disease to include the incidence of eight temperature-prone specific renal disease categories – total renal disease, urolithiasis, renal failure, acute kidney injury (AKI), chronic kidney disease (CKD), urinary tract infections (UTIs), lower urinary tract infections (LUTIs) and pyelonephritis. Daily data was acquired for maximum, minimum and average temperature over the period of 1 July 2003 to 31 March 2014 during the warm season (October to March) in Adelaide, South Australia. Data for daily admissions to all metropolitan hospitals for renal disease, including 83,519 emergency department admissions and 42,957 inpatient admissions, was also obtained. Renal outcomes were analyzed using time-stratified negative binomial regression models, with the results aggregated by day. Incidence rate ratios (IRR) and 95% confidence intervals (CI) were estimated for associations between the number of admissions and daily temperature. Increases in daily temperature per 1 °C were associated with an increased incidence for all renal disease categories except for pyelonephritis. Minimum temperature was associated with the greatest increase in renal disease followed by average temperature and then maximum temperature. A 1°C increase in daily minimum temperature was associated with an increase in daily emergency department admissions for AKI (IRR 1.037, 95% CI: 1.026–1.048), renal failure (IRR 1.030, 95% CI: 1.022–1.039), CKD (IRR 1.017, 95% CI: 1.001–1.033) urolithiasis (IRR 1.015, 95% CI: 1.010–1.020), total renal disease (IRR 1.009, 95% CI: 1.006–1.011), UTIs (IRR 1.004, 95% CI: 1.000–1.007) and LUTIs (IRR 1.003, 95% CI: 1.000–1.006). An increased frequency of renal disease, including urolithiasis, acute kidney injury and urinary tract infections, is predicted with increasing temperatures from climate change. These results have clinical and public health implications for the management of renal diseases and demand tailored health services. Future research is warranted to analyze individual renal diseases with more comprehensive information regarding renal risk factors, and studies examining mortality for specific renal diseases.