↓ Skip to main content

Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda

Overview of attention for article published in Malaria Journal, October 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
11 X users
facebook
1 Facebook page
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
184 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
Published in
Malaria Journal, October 2016
DOI 10.1186/s12936-016-1546-5
Pubmed ID
Authors

Alberto Larocca, Roberto Moro Visconti, Michele Marconi

Abstract

Rural 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. GIS 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. About 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. The 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 184 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 184 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 42 23%
Student > Bachelor 25 14%
Researcher 21 11%
Student > Doctoral Student 13 7%
Student > Ph. D. Student 13 7%
Other 25 14%
Unknown 45 24%
Readers by discipline Count As %
Medicine and Dentistry 31 17%
Nursing and Health Professions 17 9%
Engineering 16 9%
Computer Science 15 8%
Social Sciences 14 8%
Other 39 21%
Unknown 52 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 March 2023.
All research outputs
#3,361,279
of 24,217,496 outputs
Outputs from Malaria Journal
#810
of 5,793 outputs
Outputs of similar age
#56,432
of 318,583 outputs
Outputs of similar age from Malaria Journal
#18
of 101 outputs
Altmetric has tracked 24,217,496 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,583 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.