# Association of Shift, Day, Month and Year with Mortality: Observational Study of Spanish and USA Emergency Care Cohorts

## TL;DR

This study found that the time of day, month, and year affect mortality in emergency medical services, with differences between the U.S. and Spain.

## Contribution

The study identifies specific high-risk temporal patterns for mortality in EMS across two countries.

## Key findings

- Mortality was higher during 06:00–12:00 and 18:00–24:00 shifts.
- Mortality increased in 2022 and 2023 compared to 2018.
- U.S. and Spain showed distinct patterns, with higher U.S. mortality during 12:00–18:00 and in October.

## Abstract

Background/Objectives: Emergency medical services (EMSs) are essential for reducing mortality among critically ill patients. This study aims to evaluate the influence of temporal factors, such as time of day, day of the week, month, and year, on mortality in EMS activations, comparing health systems in the U.S. and Spain. Methods: This multicenter observational study, which is based on two databases (Spain’s Sacyl and the U.S.’s NEMSIS), analyzed EMS activation in high-priority adult patients (>18 years) between 2018 and 2023. Demographic variables, transport characteristics, and response times were included. Short-term mortality was the primary outcome. Results: A total of 54,981 EMS activations (11,713 from the Sacyl dataset and 43,268 from the NEMSIS dataset) were analyzed. Mortality was higher among older patients and males, with significant increases during shifts from 06:00 to 12:00 and from 18:00 to 24:00. Mortality also varied by year, with higher rates in 2022 and 2023 than in 2018. Notable differences were observed between the U.S. and Spain, especially in shifts and months, with higher mortality during the 12:00 to 18:00 shift and in October in the NEMSIS cohort. Conclusions: These findings have direct implications for emergency medical service operations, suggesting that resource allocation, staffing models, and clinical protocols should be strategically optimized based on temporal risk patterns to improve patient outcomes during identified high-risk periods.