The Usefulness of Maximum Daily Temperatures Versus Defined Heatwave Periods in Assessing the Impact of Extreme Heat on ED Admissions for Chronic Conditions
Abstract: Objective: To compare a heatwave based exposure classification with a maximum daily temperature based exposure classification in assessing the associations between increased heat and emergency department (ED) admissions for chronic conditions.
Methods: ED admission data was collected from 4 public hospitals in South Australia from 2007 to 2009. Effects of 5 heatwave periods were examined using conditional logistic regression (heatwave versus non-heatwave) whilst effects of maximum daily temperature were explored using negative binomial regression with temperature classified using <25 °C (reference category) and additional 5 °C increments. Non-linear regression (ED admissions per unit °C) was used to examine possible temperature thresholds for increased ED admissions.
Results: In heatwave/non-heatwave analysis, an increased odds of admission during heatwaves was observed for heat-related complaints [OR=3.2; 95%CI=2.5, 4.11] and renal conditions [OR=1.13; 95%CI=1.05, 1.21] only. In temperature based analysis, mental health related conditions began increasing at 30-34 °C compared to <25 °C [IRR=1.11; 95%CI=1.02, 1.20], heat related conditions were increased at 35-39 °C [IRR=3.4; 95%CI=2.48, 4.64] while CVD admissions were lower above 40 °C [IRR=0.89; 95%CI=0.80-0.99]. Significant threshold temperatures were identified for heat-related conditions at 37.6 °C [p<0.001] and for renal admissions at 39.2 °C [p<0.001].
Conclusions: Using maximum daily temperature was a more sensitive approach to detecting effects of heat on ED admissions for chronic disease and also allowed the detection of temperature threshold effects. Assessing the impact of temperature rather than heatwaves should better identify the weather conditions that increase the risk of events amongst individuals with specific chronic conditions.Keywords: Emergency department admissions, excess heat, temperature threshold, chronic conditions, case-cross over design, conditional logistic regression, negative binomial regression.
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