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Millennium development health metrics: where do Africa’s children and women of childbearing age live?

Overview of attention for article published in Population Health Metrics, July 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

policy
1 policy source
twitter
4 tweeters

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
52 Mendeley
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Title
Millennium development health metrics: where do Africa’s children and women of childbearing age live?
Published in
Population Health Metrics, July 2013
DOI 10.1186/1478-7954-11-11
Pubmed ID
Authors

Andrew J Tatem, Andres J Garcia, Robert W Snow, Abdisalan M Noor, Andrea E Gaughan, Marius Gilbert, Catherine Linard

Abstract

The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation.Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
Japan 1 2%
Unknown 49 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 11 21%
Student > Master 9 17%
Student > Bachelor 4 8%
Other 3 6%
Other 4 8%
Unknown 8 15%
Readers by discipline Count As %
Environmental Science 11 21%
Social Sciences 10 19%
Medicine and Dentistry 5 10%
Agricultural and Biological Sciences 4 8%
Earth and Planetary Sciences 3 6%
Other 9 17%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 01 December 2019.
All research outputs
#3,666,181
of 15,296,995 outputs
Outputs from Population Health Metrics
#127
of 320 outputs
Outputs of similar age
#35,866
of 159,178 outputs
Outputs of similar age from Population Health Metrics
#1
of 1 outputs
Altmetric has tracked 15,296,995 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 60% 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 159,178 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 77% of its contemporaries.
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