Introduction: Cluster surveillance, identification, and containment are primary outbreak management techniques; however, adapting these for low- and middle-income countries is an ongoing challenge. We aimed to evaluate the utility of prehospital call center ambulance dispatch (CCAD) data for surveillance by examining the correlation between influenza-like illness (ILI)-related dispatch calls and COVID-19 cases.
Methods: We performed a retrospective analysis of state-level CCAD and COVID-19 data recorded between January 1 and April 30, 2020, in Telangana, India. The primary outcome was a time series correlation between ILI calls in CCAD and COVID-19 case counts. Secondarily, we looked for a year-to-year correlation of ILI calls in the same period over 2018, 2019, and 2020.
Results: On average, ILI calls comprised 12.9% (95% CI 11.7%-14.1%) of total daily calls in 2020, compared to 7.8% (95% CI 7.6%-8.0%) in 2018, and 7.7% (95% CI 7.5%-7.7%) in 2019. ILI call counts from 2018, 2019, and 2020 aligned closely until March 19, when 2020 ILI calls increased, representing 16% of all calls by March 23 and 27.5% by April 7. In contrast to the significant correlation observed between 2020 and previous years' January-February calls (2020 and 2019-Durbin-Watson test statistic [DW] = 0.749, p < 0.001; 2020 and 2018-DW = 1.232, p < 0.001), no correlation was observed for March-April calls (2020 and 2019-DW = 2.012, p = 0.476; 2020 and 2018-DW = 1.820, p = 0.208). In March-April 2020, the daily reported COVID-19 cases by time series significantly correlated with the ILI calls (DW = 0.977, p < 0.001). The ILI calls on a specific day significantly correlated with the COVID-19 cases reported 6 days prior and up to 14 days after (cross-correlation > 0.251, the 95% upper confidence limit).
Conclusions: The statistically significant time series correlation between ILI calls and COVID-19 cases suggests prehospital CCAD can be part of early warning systems aiding outbreak cluster surveillance, identification, and containment.
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http://dx.doi.org/10.1111/acem.14612 | DOI Listing |
Public Health
January 2025
Agenzia Regionale Emergenza Urgenza Headquarters (AREU HQ), Milan, Italy.
Objectives: Influenza-like illness (ILI) refers to the set of symptoms associated with seasonal influenza infection. In Italy, the syndromic surveillance system RespiVirNet uses both epidemiological and virological data to monitor ILI incidence with a weekly cadence. To estimate ILI incidence in real time, several countries adopted surveillance systems which include data from the emergency-urgency (E-U) system.
View Article and Find Full Text PDFFront Public Health
May 2023
Communicable Diseases Policy Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Commun Dis Intell (2018)
March 2023
Hunter New England Population Health, New South Wales, Australia.
FluTracking provided evidence for an early, long, but moderate influenza season in the Australian community compared to prior years. Influenza-like illness (ILI) activity in 2019 peaked earlier (week ending 16 June) than any season on record in FluTracking data. ILI attack rates were above average early in the 2019 season (peak of 2.
View Article and Find Full Text PDFAcad Emerg Med
December 2022
Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA.
Introduction: Cluster surveillance, identification, and containment are primary outbreak management techniques; however, adapting these for low- and middle-income countries is an ongoing challenge. We aimed to evaluate the utility of prehospital call center ambulance dispatch (CCAD) data for surveillance by examining the correlation between influenza-like illness (ILI)-related dispatch calls and COVID-19 cases.
Methods: We performed a retrospective analysis of state-level CCAD and COVID-19 data recorded between January 1 and April 30, 2020, in Telangana, India.
Prehosp Emerg Care
April 2023
Regions Hospital Emergency Medical Services, Saint Paul, Minnesota.
Objective: Identify if prehospital patient encounters can predict SARS-CoV-2 (COVID-19) related hospital utilization.
Methods: EMS data from COVID-19-related prehospital encounters was pulled from NEMSIS systems in Minnesota. This data was plotted against hospital general medical-surgical bed and ICU bed usage during the initial COVID-19 surge and again during a second surge.
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