Weather and temporal models for emergency medical services: An assessment of generalizability

Sriram Ramgopal*, Nalyn Siripong, David D. Salcido, Christian Martin-Gill

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Emergency medical services (EMS) response volume has been linked to weather and temporal factors in a regional EMS system. We aimed to identify if models of EMS utilization incorporating these data are generalizable through geographically disparate areas in the United States. Methods: We performed a retrospective analysis of EMS dispatch data from four regions: New York City, San Francisco, Cincinnati, and Marin County for years 2016–2019. For each model, we used local weather data summarized from the prior 6 h into hourly bins. Our outcome for each model was EMS dispatches as count data. We fit and optimized a negative binomial regression model for each region, to estimate incidence rate ratios. We compared findings to a prior study performed in Western Pennsylvania. Results: We included 5,940,637 EMS dispatches from New York City, 809,405 from San Francisco, 260,412 from Cincinnati, and 77,461 from Marin County. Models demonstrated consistency with the Western Pennsylvania model with respect to temperature, season, wind speed, dew point, and time of day; both in terms of direction and effect size when expressed as incidence rate ratios. Precipitation was associated with increasing dispatches in the New York City, Cincinnati, and Marin County models, but not the San Francisco model. Conclusion: With minor differences, regional models demonstrated consistent associations between dispatches and time and weather variables. Findings demonstrate the generalizability of associations between these variables with respect to EMS use. Weather and temporal factors should be considered in predictive modeling to optimize EMS staffing and resource allocation.

Original languageEnglish (US)
JournalAmerican Journal of Emergency Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • Climate
  • EMS
  • Modeling
  • Prediction model
  • Prehospital
  • Weather

ASJC Scopus subject areas

  • Emergency Medicine

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