Evidence map›Paper›PMID 31263554›Full record

ArticleJournal of global health2019

A mixed-methods quasi-experimental evaluation of a mobile health application and quality of care in the integrated community case management program in Malawi.

Simone Peart Boyce, Florence Nyangara, Joy Kamunyori

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In one paragraph

Article in Journal of global health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 5 pooled it
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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

17 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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

3 authors.

Simone Peart BoyceICF, Atlanta, Georgia, USA.
Florence NyangaraICF, Rockville, Maryland, USA.
Joy KamunyoriICF, Washington, DC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of mobile health (mHealth) technology to improve quality of care (QoC) has increased over the last decade; limited evidence exists to espouse mHealth as a decision support tool, especially at the community level. This study presents evaluation findings of using a mobile application for integrated community case management (iCCM) by Malawi's health surveillance assistants (HSAs) in four pilot districts to deliver lifesaving services for children.

methodsA quasi-experimental study design compared adherence to iCCM guidelines between HSAs using mobile application (n = 137) and paper-based tools (n = 113), supplemented with 47 key informant interviews on perceptions about QoC and sustainability of iCCM mobile application. The first four sick children presenting to each HSA for an initial consultation of an illness episode were observed by a Ministry of Health iCCM trainer for assessment, classification, and treatment. Results were compared using logistic regression, controlling for child-, HSA-, and district-level characteristics, with Holm-Bonferroni-adjusted significance levels for multiple comparison.

resultsHSAs using the application tended to assess sick children according to iCCM guidelines more often than HSAs using paper-based tools for cough (adjusted proportion, 98% vs 91%;

conclusionGenerally, the mobile application is a promising tool for improving adherence to the iCCM protocol for assessing sick children and classifying illness by HSAs. Limited effects on treatments and inconsistent use suggest the need for more studies on mHealth to improve QoC at community level.

Indexed as

Delivery of Health Care, IntegratedAdultCase ManagementChild, PreschoolCommunity Health ServicesCommunity Health WorkersDiarrheaFemaleGuideline AdherenceHumansInfantMalariaMalawiMalePneumoniaPractice Guidelines as Topic

Identifiers

PMID31263554
PMCPMC6594718

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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.