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Fine scale mapping of malaria infection clusters by using routinely collected health facility data in urban Dar es Salaam, Tanzania

Overview of attention for article published in Geospatial health, May 2017
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  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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1 tweeter

Citations

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14 Dimensions

Readers on

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48 Mendeley
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Title
Fine scale mapping of malaria infection clusters by using routinely collected health facility data in urban Dar es Salaam, Tanzania
Published in
Geospatial health, May 2017
DOI 10.4081/gh.2017.494
Pubmed ID
Authors

Yeromin P. Mlacha, Prosper P. Chaki, Alpha D. Malishee, Victoria M. Mwakalinga, Nicodem J. Govella, Alex J. Limwagu, John M. Paliga, Daniel F. Msellemu, Zawadi D. Mageni, Dianne J. Terlouw, Gerry F. Killeen, Stefan Dongus

Abstract

This study investigated whether passively collected routine health facility data can be used for mapping spatial heterogeneities in malaria transmission at the level of local government housing cluster administrative units in Dar es Salaam, Tanzania. From June 2012 to January 2013, residential locations of patients tested for malaria at a public health facility were traced based on their local leaders' names and geo-referencing the point locations of these leaders' houses. Geographic information systems (GIS) were used to visualise the spatial distribution of malaria infection rates. Spatial scan statistics was deployed to detect spatial clustering of high infection rates. Among 2407 patients tested for malaria, 46.6% (1121) could be traced to their 411 different residential housing clusters. One small spatially aggregated cluster of neighbourhoods with high prevalence was identified. While the home residence housing cluster leader was unambiguously identified for 73.8% (240/325) of malaria-positive patients, only 42.3% (881/2082) of those with negative test results were successfully traced. It was concluded that recording simple points of reference during routine health facility visits can be used for mapping malaria infection burden on very fine geographic scales, potentially offering a feasible approach to rational geographic targeting of malaria control interventions. However, in order to tap the full potential of this approach, it would be necessary to optimise patient tracing success and eliminate biases by blinding personnel to test results.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 19%
Student > Bachelor 8 17%
Researcher 7 15%
Student > Ph. D. Student 5 10%
Lecturer 3 6%
Other 8 17%
Unknown 8 17%
Readers by discipline Count As %
Medicine and Dentistry 15 31%
Nursing and Health Professions 5 10%
Agricultural and Biological Sciences 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Environmental Science 2 4%
Other 10 21%
Unknown 11 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 May 2017.
All research outputs
#8,940,498
of 11,194,639 outputs
Outputs from Geospatial health
#72
of 126 outputs
Outputs of similar age
#191,143
of 266,356 outputs
Outputs of similar age from Geospatial health
#1
of 6 outputs
Altmetric has tracked 11,194,639 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 126 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 266,356 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them