Evidence map›Paper›PMID 36271649›Full record

ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2022

Utility of prehospital call center ambulance dispatch data for COVID-19 cluster surveillance: A retrospective analysis.

Srinivasa Rao Janagama, Matthew C Strehlow, Ramana G V Rao, Michael A Kohn, Jennifer A Newberry

Open access · greenAbstract read
In one paragraph

Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Utility of prehospital call center ambulance dispatch data for COVID-19 cluster surveillance: A retrospective analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Srinivasa Rao JanagamaDepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0001-8009-2037
Matthew C StrehlowDepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-0049-9716
Ramana G V RaoEmergency Medicine Learning Centre, GVK Emergency Management Research Institute, Secunderabad, Telangana, India.ORCID https://orcid.org/0000-0002-0197-0231
Michael A KohnDepartment of Epidemiology & Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0001-5459-5044
Jennifer A NewberryDepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-2948-0111
Stanford University · USGVK Emergency Management and Research Institute · INUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCluster 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.

methodsWe 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.

resultsOn 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).

conclusionsThe 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.

Indexed as

Call CentersCOVID-19Influenza, HumanAmbulancesHumansRetrospective Studiesclusterhotspotoutbreak surveillancesurveillance

Identifiers

PMID36271649
PMCPMC9874460
OpenAlexW4307091395

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.