ArticleMalaria journal2016
Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda.
Article in Malaria journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed, 38 citations in OpenAlex.
- Strengthening geospatial surveillance for malaria control and elimination in Indonesia: a meeting report.Malaria journal · 2026Article
- Hotspot Mapping, Spatiotemporal Clustering and Forecasting of Malaria Incidence in Uganda, 2014-2023: Strategies to Inform Targeted Public Health Interventions.Journal of epidemiology and global health · 2026Article
- The limitations of mobile phone data for measuring movement patterns of populations at risk of malaria.Malaria journal · 2025Observational
- Designing and deploying caller tunes on mobile phones to promote malaria vaccine uptake in Africa: can the technology acceptance model (TAM) help?Malaria journal · 2024Review
- Service availability and readiness of malaria surveillance information systems implementation at primary health centers in Indonesia.PloS one · 2023Article
- Article
- Global eHealth capacity: secondary analysis of WHO data on eHealth and implications for kidney care delivery in low-resource settings.BMJ open · 2022Article
- Mobile Apps Leveraged in the COVID-19 Pandemic in East and South-East Asia: Review and Content Analysis.JMIR mHealth and uHealth · 2021Review
- Strengthening Surveillance Systems for Malaria Elimination by Integrating Molecular and Genomic Data.Tropical medicine and infectious disease · 2019Review
- Interfacing Pathogen Detection with Smartphones for Point-of-Care Applications.Analytical chemistry · 2019Review
- Exploring Antimalarial Herbal Plants across Communities in Uganda Based on Electronic Data.Evidence-based complementary and alternative medicine : eCAM · 2019Review
- Geospatial Science and Point-of-Care Testing: Creating Solutions for Population Access, Emergencies, Outbreaks, and Disasters.Frontiers in public health · 2019Review
- Obesity Among Peruvian Children Under Five Years of Age in 2017: a Geographic Information System Analysis.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2018Review
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles.
methodsGIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact.
resultsAbout 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network.
conclusionsThe application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.