Evidence map›Paper›PMID 39180044›Full record

ArticlePopulation health metrics2024

The quality of routine data for measuring facility-based maternal mortality in public and private health facilities in Kampala City, Uganda.

Catherine Birabwa, Aduragbemi Banke-Thomas, Aline Semaan, Josefien van Olmen, Rornald Muhumuza Kananura, Emma Sam Arinaitwe, Peter Waiswa, Lenka Beňová

Abstract read
In one paragraph

Article in Population health metrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Measurement of maternal mortality, United Republic of Tanzania.Bulletin of the World Health Organization · 2026
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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.

Catherine BirabwaDepartment of Health Policy, Planning and Management, Makerere University School of Public Health, Kampala, Uganda. cathybirabwa3@gmail.com.
Aduragbemi Banke-ThomasFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Aline SemaanDepartment of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
Josefien van OlmenDepartment of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.
Rornald Muhumuza KananuraDepartment of Health Policy, Planning and Management, Makerere University School of Public Health, Kampala, Uganda.
Emma Sam ArinaitweMinistry of Health, Kampala, Uganda.
Peter WaiswaDepartment of Health Policy, Planning and Management, Makerere University School of Public Health, Kampala, Uganda.
Lenka BeňováDepartment of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.

Funding

the Research Foundation - Flanders (Fonds Wetenschappelijk Onderzoek) 1234820N
6 · The paper itself

Abstract

backgroundRoutine health facility data are an important source of health information in resource-limited settings. Regular quality assessments are necessary to improve the reliability of routine data for different purposes, including estimating facility-based maternal mortality. This study aimed to assess the quality of routine data on deliveries, livebirths and maternal deaths in Kampala City, Uganda.

methodsWe reviewed routine health facility data from the district health information system (DHIS2) for 2016 to 2021. This time period included an upgrade of DHIS2, resulting in two datasets (2016-2019 and 2020-2021) that were managed separately. We analysed data for all facilities that reported at least one delivery in any of the six years, and for a subset of facilities designated to provide emergency obstetric care (EmOC). We adapted the World Health Organization data quality review framework to assess completeness and internal consistency of the three data elements, using 2019 and 2021 as reference years. Primary data were collected to verify reporting accuracy in four purposively selected EmOC facilities. Data were disaggregated by facility level and ownership.

resultsWe included 255 facilities from 2016 to 2019 and 247 from 2020 to 2021; of which 30% were EmOC facilities. The overall completeness of data for deliveries and livebirths ranged between 53% and 55%, while it was < 2% for maternal deaths (98% of monthly values were zero). Among EmOC facilities, completeness was higher for deliveries and livebirths at 80%; and was < 6% for maternal deaths. For the whole sample, the prevalence of outliers for all three data elements was < 2%. Inconsistencies over time were mostly observed for maternal deaths, with the highest difference of 96% occurring in 2021.

conclusionsRoutine data from childbirth facilities in Kampala were generally suboptimal, but the quality was better in EmOC facilities. Given likely underreporting of maternal deaths, further efforts to verify and count all facility-related maternal deaths are essential to accurately estimate facility-based maternal mortality. Data reliability could be enhanced by improving reporting practices in EmOC facilities and streamlining reporting processes in private-for-profit facilities. Further qualitative studies should identify critical points where data are compromised, and data quality assessments should consider service delivery standards.

Indexed as

Data AccuracyHealth FacilitiesMaternal MortalityDelivery, ObstetricFemaleHumansMaternal Health ServicesPregnancyPrivate FacilitiesUgandaData qualityDHIS2Health management information systemsInstitutional maternal mortalityMaternal healthMaternal mortalityRHISRoutine health facility dataSub-saharan AfricaUrban health

Identifiers

PMID39180044
PMCPMC11342531

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.