Stormy weather: a retrospective analysis of demand for emergency medical services during epidemic thunderstorm asthma

OBJECTIVES To describe the demand for emergency medical assistance during the largest outbreak of thunderstorm asthma reported globally, which occurred on 21 November 2016. DESIGN A time series analysis was conducted of emergency medical service caseload between 1 January 2015 and 31 December 2016. Demand during the thunderstorm asthma event was compared to historical trends for the overall population and across specific subgroups. SETTING Victoria, Australia. MAIN OUTCOME MEASURES Number of overall cases attended by emergency medical services, and within patient subgroups. RESULTS On 21 November 2016, the emergency medical service received calls for 2954 cases, which was 1014 more cases than the average over the historical period. Between 6 pm and midnight, calls for 1326 cases were received, which was 2.5 times higher than expected. A total of 332 patients were assessed by paramedics as having acute respiratory distress on 21 November, compared with a daily average of 52 during the historical period. After adjustment for temporal trends, thunderstorm asthma was associated with a 42% (95% confidence interval 40% to 44%) increase in overall caseload for the emergency medical service and a 432% increase in emergency medical attendances for acute respiratory distress symptoms. Emergency transports to hospital increased by 17% (16% to 19%) and time critical referrals from general practitioners increased by 47% (21% to 80%). Large increases in demand were seen among patients with a history of asthma and bronchodilator use. The incidence of out-of-hospital cardiac arrest increased by 82% (67% to 99%) and pre-hospital deaths by 41% (29% to 55%). CONCLUSIONS An unprecedented outbreak of thunderstorm asthma was associated with substantial increase in demand for emergency medical services and pre-hospital cardiac arrest. The health impact of future events may be minimised through use of preventive measures by patients and predictive early warning systems.