Evidence map›Paper›PMID 37270545›Full record

ArticleBMC health services research2023

Area-specific covid-19 effects on health services utilization in the Democratic Republic of the Congo using routine health information system data.

Gustavo Angeles, Hannah Silverstein, Matt Worges, David R Hotchkiss, Janna M Wisniewski, Paul Samson Lusamba Dikassa, William Weiss, Karar Zunaid Ahsan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

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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

8 authors at 4 institutions in 3 countries.

Gustavo AngelesDepartment of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Hannah SilversteinDepartment of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. silver24@live.unc.edu.
Matt WorgesDepartment of International Health and Sustainable Development, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
David R HotchkissDepartment of International Health and Sustainable Development, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
Janna M WisniewskiDepartment of International Health and Sustainable Development, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
Paul Samson Lusamba DikassaTulane International LLC, Kinshasa, Democratic Republic of the Congo.
William WeissDepartment of International Health, The John Hopkins University, Baltimore, MD, USA.
Karar Zunaid AhsanPublic Health Leadership Program, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC, Chapel Hill, USA.
Tulane University · USUniversity of North Carolina at Chapel Hill · USDepartment of Health · ZAInstitut Supérieur de Commerce de Kinshasa · CD

Funding

United States Agency for International Development D4I Associate Award 7200AA18LA00008
6 · The paper itself

Abstract

backgroundSince March 2020, the COVID-19 pandemic has shocked health systems worldwide. This analysis investigated the effects of the pandemic on basic health services utilization in the Democratic Republic of the Congo (DRC) and examined the variability of COVID effects in the capital city Kinshasa, in other urban areas, and in rural areas.

methodsWe estimated time trends models using national health information system data to replicate pre-COVID-19 (i.e., January 2017-February 2020) trajectories of health service utilization, and then used those models to estimate what the levels would have been in the absence of COVID-19 during the pandemic period, starting in March 2020 through March 2021. We classified the difference between the observed and predicted levels as the effect of COVID-19 on health services. We estimated 95% confidence intervals and p-values to examine if the effect of the pandemic, nationally and within specific geographies, was statistically significant.

resultsOur results indicate that COVID-19 negatively impacted health services and subsequent recovery varied by service type and by geographical area. COVID-19 had a lasting impact on overall service utilization as well as on malaria and pneumonia-related visits among young children in the DRC. We also found that the effects of COVID-19 were even more immediate and stronger in the capital city of Kinshasa compared with the national effect. Both nationally and in Kinshasa, most affected services had slow and incomplete recovery to expected levels. Therefore, our analysis indicates that COVID-19 continued to affect health services in the DRC throughout the first year of the pandemic.

conclusionsThe methodology used in this article allows for examining the variability in magnitude, timing, and duration of the COVID effects within geographical areas of the DRC and nationally. This analytical procedure based on national health information system data could be applied to surveil health service disruptions and better inform rapid responses from health service managers and policymakers.

Indexed as

COVID-19Health Information SystemsChildChild, PreschoolDemocratic Republic of the CongoFacilities and Services UtilizationHumansPandemicsCOVID-19Democratic Republic of the Congo (DRC)DHIS2Health facility dataHealth management information systems (HMIS)Health services utilizationMaternal and child healthUniversal health coverage

Identifiers

PMID37270545
PMCPMC10239053
OpenAlexW4379260949

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.