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. 2021 Aug 30;50(4):1091-1102.
doi: 10.1093/ije/dyab094.

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia

Isabel R Fulcher  1   2 , Emma Jean Boley  3 , Anuraag Gopaluni  4 , Prince F Varney  3 , Dale A Barnhart  1   5 , Nichole Kulikowski  1 , Jean-Claude Mugunga  5 , Megan Murray  6 , Michael R Law  7 , Bethany Hedt-Gauthier  1   4 ; Cross-site COVID-19 Syndromic Surveillance Working Group
Affiliations

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia

Isabel R Fulcher et al. Int J Epidemiol. .

Abstract

Background: Early detection of SARS-CoV-2 circulation is imperative to inform local public health response. However, it has been hindered by limited access to SARS-CoV-2 diagnostic tests and testing infrastructure. In regions with limited testing capacity, routinely collected health data might be leveraged to identify geographical locales experiencing higher than expected rates of COVID-19-associated symptoms for more specific testing activities.

Methods: We developed syndromic surveillance tools to analyse aggregated health facility data on COVID-19-related indicators in seven low- and middle-income countries (LMICs), including Liberia. We used time series models to estimate the expected monthly counts and 95% prediction intervals based on 4 years of previous data. Here, we detail and provide resources for our data preparation procedures, modelling approach and data visualisation tools with application to Liberia.

Results: To demonstrate the utility of these methods, we present syndromic surveillance results for acute respiratory infections (ARI) at health facilities in Liberia during the initial months of the COVID-19 pandemic (January through August 2020). For each month, we estimated the deviation between the expected and observed number of ARI cases for 325 health facilities and 15 counties to identify potential areas of SARS-CoV-2 circulation.

Conclusions: Syndromic surveillance can be used to monitor health facility catchment areas for spikes in specific symptoms which may indicate SARS-CoV-2 circulation. The developed methods coupled with the existing infrastructure for routine health data systems can be leveraged to monitor a variety of indicators and other infectious diseases with epidemic potential.

Keywords: COVID-19; Syndromic surveillance; disease monitoring; infectious disease; time series modelling.

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Figures

Figure 1
Figure 1
(A) Number and (B) proportion of acute respiratory infection cases at JJ Dossen Hospital in Maryland County, Liberia. The black line represents the observed value and grey line the predicted counts with 95% prediction intervals in light grey
Figure 2
Figure 2
(A) Number and (B) proportion of acute respiratory infections in the Maryland County model with the black lines representing the observed value and grey line the predicted counts with 95% prediction intervals in light grey
Figure 3
Figure 3
Residual, autocorrelation function and partial autocorrelation function plots corresponding to the baseline period in Figure 1 (JJ Dossen Hospital) and Figure 2 (Maryland County)
Figure 4
Figure 4
Proportion of facilities with complete data in 2020 compared to 2019 by county
Figure 5
Figure 5
Deviation in number of acute respiratory infections standardized per 100 000 persons in each county from January to August 2020. The black dotted lines represent the difference between the observed and predicted counts (deviation), with corresponding 95% prediction intervals in light grey. County population sizes scaled to account for excluded facilities.
Figure 6
Figure 6
Proportion of acute respiratory infections for each county from January to August 2020. The black lines represent the observed count and grey line the predicted counts, with 95% prediction intervals in light gray
See this image and copyright information in PMC

References

    1. Murthy S, Leligdowicz A, Adhikari NK.. Intensive care unit capacity in low-income countries: a systematic review. PloS One 2015;10:e0116949. - PMC - PubMed
    1. Davies J, Abimiku AL, Alobo M. et al. Sustainable clinical laboratory capacity for health in Africa. Lancet Glob Health 2017;5:e248–49. - PubMed
    1. Kavanagh MM, Erondu NA, Tomori O. et al. Access to lifesaving medical resources for African countries: COVID-19 testing and response, ethics, and politics. Lancet 2020;395:1735–38. - PMC - PubMed
    1. Wagenaar BH, Sherr K, Fernandes Q, Wagenaar AC.. Using routine health information systems for well-designed health evaluations in low-and middle-income countries. Health Policy Plan 2016;31:129–35. - PMC - PubMed
    1. Hung YW, Hoxha K, Irwin BR, Law MR, Grépin KA.. Using routine health information data for research in low-and middle-income countries: a systematic review. BMC Health Serv Res 2020;20:1–5. - PMC - PubMed

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