Post-Pandemic Growth in 9-1-1 Paramedic Calls and Emergency Department Transports Surpasses Pre-Pandemic Rates in the COVID-19 Era: Implications for Paramedic Resource Planning
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic led to a decline in emergency department (ED) visits and a subsequent return to baseline pre-pandemic levels. It is unclear if this trend extended to paramedic services and if patient cohorts accessing paramedics changed. We examined trends and associations between paramedic utilization (9-1-1 calls and ED transports) and the COVID-19 timeframe. METHODS: We conducted a retrospective cross-sectional study using paramedic call data from the Hamilton Paramedic Services from January 2016 to December 2023. We included all 9-1-1 calls where paramedics responded to an incident, excluding paramedic interfacility transfers. We calculated lines of best fit for the pre-pandemic period (January 2016 to January 2020) and compared their predictions to the actual volumes in the post-pandemic period (May 2021 to December 2023). We used an interrupted time series regression model to determine the association between pandemic timeframes (pre-, during-, post-COVID-19) and paramedic utilization (9-1-1 calls and ED transports), while testing for annual seasonality. RESULTS: During the study timeframe, 577,278 calls for paramedics were received and 413,491 (71.6%) were transported to the ED. Post-pandemic, 9-1-1 calls exceeded predicted pre-pandemic levels by 1,298 per month, while ED transports exceeded by 543 per month. The pandemic significantly reduced monthly 9-1-1 calls (-588.2, 95% CI -928.8 to -247.5) and ED transports (-677.3, 95% CI -927.0 to -427.5). Post-pandemic, there was a significant and sustained resurgence in monthly 9-1-1 calls (1,208.0, 95% CI 822.1 to 1,593.9) and ED transports (868.8, 95% CI 585.8 to 1,151.7). Both models exhibited seasonal variations. CONCLUSIONS: Post-pandemic, 9-1-1-initiated paramedic calls experienced a substantial increase, surpassing pre-pandemic growth rates. ED transports returned to pre-pandemic levels but with a steeper and continuous pattern of growth. The resurgence in paramedic 9-1-1 calls and ED transports post-COVID-19 emphasizes an urgent necessity to expedite development of new care models that address how paramedics respond to 9-1-1 calls and transport to overcrowded EDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".