Evidence map›Paper›PMID 31637032›Full record

ReviewBMJ global health2019

Generating statistics from health facility data: the state of routine health information systems in Eastern and Southern Africa.

Abdoulaye Maïga, Safia S Jiwani, Martin Kavao Mutua, Tyler Andrew Porth, Chelsea Maria Taylor, Gershim Asiki, Dessalegn Y Melesse, Candy Day, Kathleen L Strong, Cheikh Mbacké Faye and 5 more

Abstract readReview
In one paragraph

Review in BMJ global health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing 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

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

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16 more citing papers are in PubMed but not listed here.

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

15 authors.

Abdoulaye MaïgaInternational Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-0475-1106
Safia S JiwaniInternational Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Martin Kavao MutuaDepartment of Research, African Population and Health Research Center, Nairobi, Kenya.ORCID 0000-0003-1643-9934
Tyler Andrew PorthDivision of Data, Research and Policy, Data and Analytics Section, UNICEF, New York City, New York, USA.
Chelsea Maria TaylorData Analytics and Delivery, World Health Organization, Geneva, Switzerland.
Gershim AsikiDepartment of Research, African Population and Health Research Center, Nairobi, Kenya.
Dessalegn Y MelesseDepartment of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Candy DayHealth System Trust, Westville, South Africa.ORCID 0000-0002-7290-0855
Kathleen L StrongMaternal, Newborn, Child and Adolescent Health Department, World Health Organization, Geneva, Switzerland.
Cheikh Mbacké FayeWest Africa Regional Office, African Population and Health Research Center, Nairobi, Kenya.ORCID 0000-0002-4028-0575
Kavitha ViswanathanInformation Evidence and Research, World Health Organization, Geneva, Switzerland.
Kathryn Patricia O'NeillInformation Evidence and Research, World Health Organization, Geneva, Switzerland.
Agbessi AmouzouInternational Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-6262-3866
Bob S PondIndependent Consultant, Portland, Oregon, USA.
Ties BoermaCentre for Global Public Health, University of Manitoba, Winnipeg, Manitoba, Canada.ORCID 0000-0001-9922-495X

Funding

World Health Organization 001
6 · The paper itself

Abstract

Health facility data are a critical source of local and continuous health statistics. Countries have introduced web-based information systems that facilitate data management, analysis, use and visualisation of health facility data. Working with teams of Ministry of Health and country public health institutions analysts from 14 countries in Eastern and Southern Africa, we explored data quality using national-level and subnational-level (mostly district) data for the period 2013-2017. The focus was on endline analysis where reported health facility and other data are compiled, assessed and adjusted for data quality, primarily to inform planning and assessments of progress and performance. The analyses showed that although completeness of reporting was generally high, there were persistent data quality issues that were common across the 14 countries, especially at the subnational level. These included the presence of extreme outliers, lack of consistency of the reported data over time and between indicators (such as vaccination and antenatal care), and challenges related to projected target populations, which are used as denominators in the computation of coverage statistics. Continuous efforts to improve recording and reporting of events by health facilities, systematic examination and reporting of data quality issues, feedback and communication mechanisms between programme managers, care providers and data officers, and transparent corrections and adjustments will be critical to improve the quality of health statistics generated from health facility data.

Indexed as

data quality assessmentDHIS2Eastern and Southern Africaroutine health information systems

Identifiers

PMID31637032
PMCPMC6768347

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.