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Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Overview of attention for article published in Population Health Metrics, May 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#50 of 400)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

policy
2 policy sources
twitter
17 X users
facebook
1 Facebook page

Citations

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96 Dimensions

Readers on

mendeley
304 Mendeley
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Title
Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation
Published in
Population Health Metrics, May 2012
DOI 10.1186/1478-7954-10-8
Pubmed ID
Authors

Andrew J Tatem, Susana Adamo, Nita Bharti, Clara R Burgert, Marcia Castro, Audrey Dorelien, Gunter Fink, Catherine Linard, Mendelsohn John, Livia Montana, Mark R Montgomery, Andrew Nelson, Abdisalan M Noor, Deepa Pindolia, Greg Yetman, Deborah Balk

Abstract

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 304 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 4%
United Kingdom 6 2%
Netherlands 2 <1%
Kenya 2 <1%
Indonesia 1 <1%
Australia 1 <1%
Tanzania, United Republic of 1 <1%
Malaysia 1 <1%
Czechia 1 <1%
Other 2 <1%
Unknown 275 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 20%
Student > Master 54 18%
Researcher 49 16%
Student > Bachelor 21 7%
Student > Doctoral Student 18 6%
Other 55 18%
Unknown 47 15%
Readers by discipline Count As %
Medicine and Dentistry 60 20%
Social Sciences 39 13%
Agricultural and Biological Sciences 38 13%
Environmental Science 19 6%
Earth and Planetary Sciences 19 6%
Other 69 23%
Unknown 60 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 20 December 2021.
All research outputs
#1,999,945
of 24,226,848 outputs
Outputs from Population Health Metrics
#50
of 400 outputs
Outputs of similar age
#12,124
of 167,024 outputs
Outputs of similar age from Population Health Metrics
#2
of 6 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 400 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.2. This one has done well, scoring higher than 87% 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 167,024 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
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 4 of them.