Table 2 Heatwave impacts on health service demand (n = 16)
From: Systematic review of the impact of heatwaves on health service demand in Australia
Location/Data Source | Heatwave Definition | Study type | Effect size (95% CI) | Reference |
|---|---|---|---|---|
Hospital Admissions | ||||
 Perth, Western Australia | EHF | Retrospective population-based | RR = 1.58 (1.18, 2.11) * | Scalley et al. (2015) [31] |
 Adelaide, South Australia | ≥35 °C, 3+ days | Case-series analysis | IRR = 1.07 (0.99, 1.16) | Nitschke et al. (2007) [32] |
Emergency Department Presentations | ||||
 Tasmania | EHF | Case-crossover analysis | OR = 1.05 (1.01, 1.09) * | Campbell et al. (2019) [33] |
 Perth. Western Australia | EHF | Population-based time series | OR = 1.05 (1.05, 1.06) * | Patel et al. (2019b) [34] |
 Perth, Western Australia | EHF | Retrospective population-based | RR = 1.04 (1.04, 1.05) * | Scalley et al. (2015) [31] |
 Western Australia | EHFSevere/extreme | Time series analysis | RR = 1.05 (1.04, 1.06) * | Xiao et al. (2017) [35] |
 New South Wales | EHFIntense | Time series analysis | IRR = 1.04 (1.02, 1.05) * | Jegasothy et al. (2017) [36] |
 Sydney, New South Wales | BOM identified | Time series analysis | RR = 1.02 (1.01, 1.03) * | Schaffer et al. (2012) [37] |
 Brisbane, Queensland | > 37 °C, 2+ days | Case-crossover analysis | OR = 1.15 (1.08, 1.24) * | Wang et al. (2012) [38] |
 Brisbane, Queensland | > 37 °C, 2+ days | Time-stratified case-crossover analysis | OR = 1.14 (1.06, 1.23) * | Tong et al. (2012) [39] |
 Brisbane, Queensland | ≥95th percentile, 2+ days | Time series analysis | RR = 1.10 (1.08, 1.13) * | Tong et al. (2014) [40] |
 Brisbane, Queensland | ≥95th percentile, 3+ days | Case-crossover analysis | OR = 1.04 (1.02, 1.06) * | Tong et al. (2010) [41] |
Ambulance call outs | ||||
 Perth, Western Australia | EHF | Population-based time series | RR = 1.02 (1.01, 1.02) * | Patel et al. (2019a) [42] |
 Sydney, New South Wales | EHF | Time-series analysis | RR = 1.14 (1.11, 1.16) * | Schaffer et al. (2012) [37] |
 New South Wales | EHFIntense | Time series analysis | IRR = 1.05 (1.04, 1.06) * | Jegasothy et al. (2017) [36] |
 Adelaide, South Australia | EHFIntense | Case-crossover analysis | RR = 1.21 (0.81, 1.81) | Varghese et al. (2019) [43] |
 Adelaide, South Australia | BOM identified | Retrospective population-based | RR = 1.11 (1.08, 1.13) * | Williams et al. (2011) [44] |
 Adelaide, South Australia | ≥35 °C, 3+ days | Case-series analysis | IRR = 1.04 (1.01, 1.07) * | Nitschke et al. (2007) [32] |
Mortality | ||||
 Sydney, New South Wales | EHF | Time-series analysis | RR = 1.13 (1.06, 1.22) * | Schaffer et al. (2012) [37] |
 New South Wales | EHFIntense | Time series analysis | IRR = 1.02 (1.01, 1.04) * | Jegasothy et al. (2017) [36] |
 Adelaide, South Australia | BOM identified | Retrospective population-based | IRR = 1.06 (1.00, 1.11)* | Williams et al. (2011) [44] |
Mortality Con’t | ||||
 Adelaide, South Australia | ≥35 °C, 3+ days | Case-series analysis | IRR = 0.95 (0.90, 1.01) | Nitschke et al. (2007) [32] |
 Brisbane, Queensland | > 37 °C, 2+ days | Case-crossover analysis | OR = 1.46 (1.21, 1.77) * | Wang et al. (2012) [38] |
 Brisbane, Queensland | > 37 °C, 2+ days | Time-stratified case-crossover analysis | RR = 1.92 (1.40, 2.11) * | Tong et al. (2012) [39] |
 Brisbane, Queensland | >95th percentile, 2+ days | Time series analysis | RR = 1.05 (1.03, 1.08) * | Wang et al. (2015) [45] |
 Melbourne, Victoria | >95th percentile, 2+ days | Time series analysis | RR = 1.03 (1.01, 1.05) * | Wang et al. (2015) [45] |
 Sydney, New South Wales | >95th percentile, 2+ days | Time series analysis | RR = 1.04 (1.02, 1.06) * | Wang et al. (2015) [45] |
 Brisbane, Queensland | ≥95th percentile, 2+ days | Time series analysis | RR = 1.17 (1.10, 1.25) * | Tong et al. (2014) [40] |
 Brisbane, Queensland | ≥95th percentile, 3+ days | Case-crossover analysis | OR = 1.10 (1.03, 1.18) * | Tong et al. (2010) [41] |