Evidence map›Paper›PMID 34974837›Full record

ArticleBMC health services research2022

A data quality assessment of the first four years of malaria reporting in the Senegal DHIS2, 2014-2017.

Pierre Muhoza, Roger Tine, Adama Faye, Ibrahima Gaye, Scott L Zeger, Abdoulaye Diaw, Alioune Badara Gueye, Almamy Malick Kante, Andrea Ruff, Melissa A Marx

Abstract read
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Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

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0cells of the map it votes in
20citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Assessing the impacts of sufficient and reliable electricity in low-resource healthcare facilities.Proceedings of the National Academy of Sciences of the United States of America · 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

10 authors.

Pierre MuhozaDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA. pmuhoza1@jhmi.edu.
Roger TineDépartement de Parasitologie, Faculté de Médecine, de Pharmacie et d'Odontologie, Université Cheikh Anta Diop de Dakar, Dakar, Senegal.
Adama FayeInstitut de Santé et Développement, Université Cheikh Anta Diop de Dakar, Dakar, Senegal.
Ibrahima GayeInstitut de Santé et Développement, Université Cheikh Anta Diop de Dakar, Dakar, Senegal.
Scott L ZegerDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Abdoulaye DiawDirection de la Planification, de la Recherche et des Statistiques/ Division du Système d'Information Sanitaire et Sociale, Ministère de la Santé et de l'Action Sociale (MSAS), Dakar, Senegal.
Alioune Badara GueyeProgramme National de Lutte Contre le Paludisme, Ministère de la Santé et de l'Action Sociale (MSAS), Dakar, Senegal.
Almamy Malick KanteDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Andrea RuffDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Melissa A MarxDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the global burden of malaria decreases, routine health information systems (RHIS) have become invaluable for monitoring progress towards elimination. The District Health Information System, version 2 (DHIS2) has been widely adopted across countries and is expected to increase the quality of reporting of RHIS. In this study, we evaluated the quality of reporting of key indicators of childhood malaria from January 2014 through December 2017, the first 4 years of DHIS2 implementation in Senegal.

methodsMonthly data on the number of confirmed and suspected malaria cases as well as tests done were extracted from the Senegal DHIS2. Reporting completeness was measured as the number of monthly reports received divided by the expected number of reports in a given year. Completeness of indicator data was measured as the percentage of non-missing indicator values. We used a quasi-Poisson model with natural cubic spline terms of month of reporting to impute values missing at the facility level. We used the imputed values to take into account the percentage of malaria cases that were missed due to lack of reporting. Consistency was measured as the absence of moderate and extreme outliers, internal consistency between related indicators, and consistency of indicators over time.

resultsIn contrast to public facilities of which 92.7% reported data in the DHIS2 system during the study period, only 15.3% of the private facilities used the reporting system. At the national level, completeness of facility reporting increased from 84.5% in 2014 to 97.5% in 2017. The percentage of expected malaria cases reported increased from 76.5% in 2014 to 94.7% in 2017. Over the study period, the percentage of malaria cases reported across all districts was on average 7.5% higher (P < 0.01) during the rainy season relative to the dry season. Reporting completeness rates were lower among hospitals compared to health centers and health posts. The incidence of moderate and extreme outlier values was 5.2 and 2.3%, respectively. The number of confirmed malaria cases increased by 15% whereas the numbers of suspected cases and tests conducted more than doubled from 2014 to 2017 likely due to a policy shift towards universal testing of pediatric febrile cases.

conclusionsThe quality of reporting for malaria indicators in the Senegal DHIS2 has improved over time and the data are suitable for use to monitor progress in malaria programs, with an understanding of their limitations. Senegalese health authorities should maintain the focus on broader adoption of DHIS2 reporting by private facilities, the sustainability of district-level data quality reviews, facility-level supervision and feedback mechanisms at all levels of the health system.

Indexed as

Health Information SystemsMalariaChildData AccuracyHumansIncidenceSenegal

Identifiers

PMID34974837
PMCPMC8722300

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Registered trials

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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.