Climate Change, Emergency System Strain, and Code Black Events: A Data-Driven Evaluation of Contributing Factors and Predictive Insights
1
Faculty of Health, University of Waterloo, Waterloo, Canada
Popul. Med. 2026;8(Supplement Supplement 1):A1828
ABSTRACT
INTRODUCTION:
Code Black ambulance events occur when emergency medical services are unable to respond to new calls due to a lack of available paramedic units.1 These events most often result from delays in transferring patients from paramedic care to hospital staff in emergency departments and pose a serious risk to public health and safety.1,2 At the Thunder Bay Regional Health Sciences Centre and within Superior North Emergency Medical Services, Code Black events have become increasingly frequent and deadly.3 This research investigates the operational factors contributing to Code Black events and examines the influence of climate change and extreme weather conditions on emergency medical services demand in Northern Ontario.
METHODS:
This study uses a retrospective observational design integrating emergency department records, emergency medical services dispatch data, and Canadian climate data. Descriptive statistics and data visualization are employed to characterize patterns in Code Black events and overall emergency system performance. Exploratory machine learning models are applied to identify key operational and environmental predictors and to assess the feasibility of predicting periods of elevated system strain.
RESULTS:
Preliminary and expected findings suggest that Code Black events are associated with emergency department crowding, prolonged ambulance offload delays, and fluctuations in emergency medical services demand. Seasonal trends and extreme weather conditions like heat waves and cold snaps are anticipated to further exacerbate these system pressures. Machine learning techniques are expected to demonstrate predictive capability in identifying periods of heightened risk of ambulance blackout periods.
CONCLUSIONS:
By integrating health system and climate data, this study aims to inform data-driven strategies to improve emergency medical services resilience. Although focused on Northern Ontario, the findings will have broader relevancy by highlighting how systemic and environmental pressures can shape access to timely emergency health care in underserved populations.
Code Black ambulance events occur when emergency medical services are unable to respond to new calls due to a lack of available paramedic units.1 These events most often result from delays in transferring patients from paramedic care to hospital staff in emergency departments and pose a serious risk to public health and safety.1,2 At the Thunder Bay Regional Health Sciences Centre and within Superior North Emergency Medical Services, Code Black events have become increasingly frequent and deadly.3 This research investigates the operational factors contributing to Code Black events and examines the influence of climate change and extreme weather conditions on emergency medical services demand in Northern Ontario.
METHODS:
This study uses a retrospective observational design integrating emergency department records, emergency medical services dispatch data, and Canadian climate data. Descriptive statistics and data visualization are employed to characterize patterns in Code Black events and overall emergency system performance. Exploratory machine learning models are applied to identify key operational and environmental predictors and to assess the feasibility of predicting periods of elevated system strain.
RESULTS:
Preliminary and expected findings suggest that Code Black events are associated with emergency department crowding, prolonged ambulance offload delays, and fluctuations in emergency medical services demand. Seasonal trends and extreme weather conditions like heat waves and cold snaps are anticipated to further exacerbate these system pressures. Machine learning techniques are expected to demonstrate predictive capability in identifying periods of heightened risk of ambulance blackout periods.
CONCLUSIONS:
By integrating health system and climate data, this study aims to inform data-driven strategies to improve emergency medical services resilience. Although focused on Northern Ontario, the findings will have broader relevancy by highlighting how systemic and environmental pressures can shape access to timely emergency health care in underserved populations.
| eISSN: | 2654-1459 |
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