Characterizing thunderstorm asthma emergency department presentations using natural language processing for improved real-time surveillance

Thunderstorm asthma events can overwhelm emergency departments within hours, making rapid detection critical for public health preparedness and patient safety. We examined whether thunderstorm asthma presentations show distinctive patterns compared to routine asthma cases in emergency department data, with an aim of enhancing the detection of thunderstorm asthma incidence. Using Victoria's SynSurv surveillance system, we analyzed triage notes from three thunderstorm asthma events occurring between 2022 and 2024 in Victoria, Australia, comparing 687 thunderstorm-related presentations with 687 presentations from eight control days. The study population comprised of emergency department attendees with respiratory presentations. Triage notes were processed using natural language processing and manual review, with demographic, symptom-onset, and medical-history data compared using chi-square tests and temporal analysis. Thunderstorm cases showed significantly higher rates of acute symptom onset within 24 hours (63.0% versus 47.2%), with presentations peaking 3-8 hours after storms and returning to baseline within 24 hours. The affected population skewed markedly toward young adults aged 21-40 years (35.9% versus 15.6% in controls). Immunological and allergic comorbidities were three times more prevalent in thunderstorm cases (16.6% versus 5.6%). These distinct demographic, temporal, and allergic profiles could enhance real-time surveillance and emergency preparedness through refined detection models.