Evidence map›Paper›PMID 40230976›Full record

ArticleOxford open digital health2024

Assessing the adoption and utilization of the Rwanda COVID-19 data analytics system: a mixed methods approach.

Hassan Mugabo, Gilbert Rukundo, Jean Claude S Ngabonziza, Jean-Baptiste Mazarati, Joseph Aghatise, Olukunle Akinwusi

Abstract read
In one paragraph

Article in Oxford open digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hassan MugaboResearch Innovation and Data Science, Rwanda Biomedical Center, Kigali, Rwanda (RBC), KG 644 St, Kigali, Kimihuruna, P.O. Box 7162, Kigali, Rwanda.ORCID https://orcid.org/0000-0003-4788-4539
Gilbert RukundoResearch Innovation and Data Science, Rwanda Biomedical Center, Kigali, Rwanda (RBC), KG 644 St, Kigali, Kimihuruna, P.O. Box 7162, Kigali, Rwanda.
Jean Claude S NgabonzizaResearch Innovation and Data Science, Rwanda Biomedical Center, Kigali, Rwanda (RBC), KG 644 St, Kigali, Kimihuruna, P.O. Box 7162, Kigali, Rwanda.
Jean-Baptiste MazaratiDigital Health, Foundation for Innovative New Diagnostics, Geneva, Switzerland Chemin du Pommier 40 1218 Grand-Saconnex, Switzerland.
Joseph AghatiseDigital Health, Foundation for Innovative New Diagnostics, Geneva, Switzerland Chemin du Pommier 40 1218 Grand-Saconnex, Switzerland.ORCID https://orcid.org/0009-0008-6472-4813
Olukunle AkinwusiDigital Health, Foundation for Innovative New Diagnostics, Geneva, Switzerland Chemin du Pommier 40 1218 Grand-Saconnex, Switzerland.ORCID https://orcid.org/0000-0002-6111-5958

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRwanda has been widely lauded for its exceptional response to the COVID-19 pandemic. However, although Rwanda established a national system for COVID-19 testing and vaccination data, concerns have been raised about data fragmentation which requires linkage of various data sources, access to data for real-time decision-making, and data completeness.

methodsWe assessed the adoption of the Rwanda COVID-19 data Analytics System (RCAS) for public health staff that employ data from various platforms to generate evidence for policy- and decision-making. A random sample of 56 participants was drawn from the 98 who attended the 2022 RCAS training for data managers from the Rwanda Biomedical Center, technical partners, and health facilities. Of the selected participants, 42 completed the online self-administered questionnaire within the 14-day data collection period. Key informant interviews were then conducted with a subset of 14 respondents.

resultsA strong positive relationship (χ

conclusionRCAS had a positive impact on improvements in data linkage, access to individual-level data for analyses, and progress toward harmonization of health data beyond COVID-19 in Rwanda. Users noted the usability, acceptability, and interoperability of the system. Recommendations for further improvement and scaling of the intervention are discussed.

Indexed as

digital healthdisease surveillanceimpact assessmentinteroperabilityusability

Identifiers

PMID40230976
PMCPMC11932416

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LicenceCC BY
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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.