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.