Evidence map›Paper›PMID 40845028›Full record

ArticlePloS one2025

Effectiveness and challenges of digital tools implementation for enhancing infectious disease surveillance data quality in low- and middle-income countries: A systematic review protocol.

Oluwatosin Olu-Abiodun, Aderinsola Faturoti, Akinmade Adepoju, Davies Adeloye, Akindele Adebiyi, Olumide Abiodun

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Oluwatosin Olu-AbiodunDepartment of Nursing, Crescent University, Abeokuta, Ogun State, Nigeria.
Aderinsola FaturotiDepartment of Community Medicine, Babcock University Teaching Hospital, Ilishan-Remo, Ogun State, Nigeria.
Akinmade AdepojuDepartment of Community Medicine, Babcock University Teaching Hospital, Ilishan-Remo, Ogun State, Nigeria.ORCID 0000-0001-7564-8782
Davies AdeloyeSchool of Health and Life Sciences, Nursing &Midwifery, Teesside University, United Kingdom.
Akindele AdebiyiDepartment of Community Medicine, University of Ibadan, Oyo State, Nigeria.
Olumide AbiodunDepartment of Community Medicine, Babcock University Teaching Hospital, Ilishan-Remo, Ogun State, Nigeria.ORCID 0000-0002-3458-8776

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMonitoring infectious diseases is essential for preventing and controlling outbreaks, especially in low- and middle-income countries (LMICs), where issues like poor infrastructure, lack of trained staff, and limited resources can make data collection challenging. Digital tools such as mobile health apps and electronic reporting systems show promise in addressing these problems. However, it's still unclear how well these tools actually improve the quality of data, like how quickly information is reported, how accurate it is, whether all necessary data is captured, and if the data can be trusted.

objectivesThis review aims to explore three main points: (1) how digital tools influence the quality of infectious disease data in LMICs; (2) what factors help or hinder their successful use; and (3) what recommendations can be made for policymakers and health workers based on the evidence.

methodsWe will search several databases, including PubMed/MEDLINE, EMBASE, Scopus, CINAHL, and Google Scholar, for studies published from January 2000 to July 2025. To further reduce publication bias, we will search the following institutional repositories (African Health Observatory and Indian Council of Medical Research). The types of studies are randomised trials, quasi-experimental studies, and mixed-methods evaluations that compare digital solutions with traditional methods in LMIC settings. Data extracted will include outcomes such as delays in reporting, error rates, and completeness, and factors like infrastructure and workforce readiness. The quality of each study will be assessed using ROBINS-I for non-randomized studies and ROB2 for randomized controlled trials. Where possible, we will combine data statistically using meta-analysis and analyse qualitative findings for deeper insights. EXPECTED OUTCOMES: This review will offer a clear picture of how effective digital tools are in improving disease surveillance. It will identify common challenges, such as poor connectivity and issues with system integration, and emphasize factors that lead to success, like proper training and government support. Overall, the findings will help shape better strategies to strengthen digital disease monitoring, finally contributing to stronger global health security.

Indexed as

Communicable DiseasesData AccuracyPopulation SurveillanceDeveloping CountriesHumansMobile ApplicationsSystematic Reviews as Topic

Identifiers

PMID40845028
PMCPMC12373227

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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.