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Observational Study
. 2020 Oct;76(4):413-426.
doi: 10.1016/j.annemergmed.2020.07.035. Epub 2020 Jul 23.

Excess Out-of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of Emergency Medical Services Data in the COVID-19 Crisis in Tijuana, Mexico

Affiliations
Observational Study

Excess Out-of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of Emergency Medical Services Data in the COVID-19 Crisis in Tijuana, Mexico

Joseph Friedman et al. Ann Emerg Med. 2020 Oct.

Abstract

Study objective: Emergency medical services (EMS) may serve as a key source of real-time data about the evolving health of coronavirus disease 2019 (COVID-19)-affected populations, especially in low- and middle-income countries with less rapid and reliable vital statistics registration systems. Although official COVID-19 statistics in Mexico report almost exclusively inhospital mortality events, excess out-of-hospital mortality has been identified in other countries, including 1 EMS study in Italy that showed a 58% increase. Additionally, EMS and hospital reports from several countries have suggested that silent hypoxemia-low Spo2 in the absence of dyspnea-is associated with COVID-19. It is unclear, however, how these phenomena can be generalized to low- and middle-income countries. We assess how EMS data can be used in a sentinel capacity in Tijuana, a city on the Mexico-United States border with earlier exposure to COVID-19 than many low- and middle-income country settings.

Methods: In this observational study, we calculated numbers of weekly out-of-hospital deaths and respiratory cases handled by EMS in Tijuana, and estimated the difference between peak epidemic rates and expected trends based on data from 2014 to 2019. Results were compared with official COVID-19 statistics, stratified by neighborhood socioeconomic status, and examined for changing demographic or clinical features, including mean Spo2.

Results: An estimated 194.7 excess out-of-hospital deaths (95% confidence interval 135.5 to 253.9 deaths) occurred during the peak window (April 14 to May 11), representing an increase of 145% (95% CI 70% to 338%) compared with expected levels. During the same window, only 5 COVID-19-related out-of-hospital deaths were reported in official statistics. This corresponded with an increase in respiratory cases of 236.5% (95% CI 100.7% to 940.0%) and a decrease in mean Spo2 to 77.7% from 90.2% at baseline. The highest out-of-hospital death rates were observed in low-socioeconomic-status areas, although respiratory cases were more concentrated in high-socioeconomic-status areas.

Conclusion: EMS systems may play an important sentinel role in monitoring excess out-of-hospital mortality and other trends during the COVID-19 crisis in low- and middle-income countries. Using EMS data, we observed increases in out-of-hospital deaths in Tijuana that were nearly 3-fold greater than increases reported in EMS data in Italy. Increased testing in out-of-hospital settings may be required to determine whether excess mortality is being driven by COVID-19 infection, health system saturation, or patient avoidance of health care. We also found evidence of worsening rates of hypoxemia among respiratory patients treated by EMS, suggesting a possible increase in silent hypoxemia, which should be met with increased detection and clinical management efforts. Finally, we observed social disparities in out-of-hospital death that warrant monitoring and amelioration.

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Figures

Figure 1
Figure 1
Weekly case breakdown by triage priority code, 2019 to 2020. A, Only out-of-hospital mortality cases and patients in critical condition who required urgent hospitalization. B, The full distribution of patients. Both A and B refer to nontrauma patients and include data from 2019 through June 2020. The vertical black line marks the week of March 31, when respiratory morbidity cases began to increase.
Figure 2
Figure 2
Long-term EMS-documented out-of-hospital mortality and respiratory cases, 2014 to 2020. Expected values (black line) and 95% prediction intervals (gray band) based on model fit on data from 2014 to 2019, with forecasts through June 2020. Both series exclude trauma patients. A, EMS-documented out-of-hospital mortality. B, EMS-documented respiratory cases.
Figure 3
Figure 3
EMS-documented out-of-hospital mortality and respiratory cases compared with official COVID-19 case and mortality numbers, March 17 to June 29. A, EMS-documented out-of-hospital mortality, with the observed and expected number shown in text. B, EMS-documented respiratory cases, with the observed and expected number shown in text. C, Deaths among patients with confirmed COVID-19, according to official national government statistics, with the number reported as managed in the outpatient setting and the total reported in text. D, Number of patients with confirmed COVID-19, according to official national government statistics, with the number reported as managed in the outpatient setting and the total reported in text. A to D, Weekly totals. A and B, Expected values (black line) and 95% prediction intervals (gray band) based on forecasted trends from 2014 to 2019; trauma patients are excluded.
Figure 4
Figure 4
Trends in Spo2 and percentage of patients presenting alert among EMS-documented respiratory cases. The distribution of Spo2 values over time is visualized weekly from March 31 to May 11, 2020, and compared with all data from 2019. Respiratory cases were divided into 5 quintiles of Spo2 values, and the median of each quartile is plotted. The color reflects the percentage of individuals in each quartile who presented as alert, which is also plotted as text next to each point.
Figure 5
Figure 5
Out-of-hospital mortality and respiratory cases by neighborhood and neighborhood socioeconomic status. The categoric socioeconomic status of each basic statistical unit (basic geostatistical area) is mapped for Tijuana. Overlaid is the out-of-hospital mortality occurring during April 14 to May 11 (A) and respiratory cases occurring during March 31 to May 11 (B). The number of cases in each neighborhood is shown as a point, with the size reflecting the magnitude. In the middle column, the points are organized by neighborhood socioeconomic status. On the right, the number of cases is shown as a rate per 100,000 people for each quintile of neighborhood socioeconomic status. SES, Socioeconomic status.

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