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Small area estimation of under-5 mortality in Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia using spatially misaligned data

Overview of attention for article published in Population Health Metrics, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

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Title
Small area estimation of under-5 mortality in Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia using spatially misaligned data
Published in
Population Health Metrics, August 2018
DOI 10.1186/s12963-018-0171-7
Pubmed ID
Authors

Laura Dwyer-Lindgren, Ellen R. Squires, Stephanie Teeple, Gloria Ikilezi, D. Allen Roberts, Danny V. Colombara, Sarah Katherine Allen, Stanley M. Kamande, Nicholas Graetz, Abraham D. Flaxman, Charbel El Bcheraoui, Kristjana Asbjornsdottir, Gilbert Asiimwe, Ângelo Augusto, Orvalho Augusto, Baltazar Chilundo, Caroline De Schacht, Sarah Gimbel, Carol Kamya, Faith Namugaya, Felix Masiye, Cremildo Mauieia, Yodé Miangotar, Honoré Mimche, Acácio Sabonete, Haribondhu Sarma, Kenneth Sherr, Moses Simuyemba, Aaron Chisha Sinyangwe, Jasim Uddin, Bradley H. Wagenaar, Stephen S. Lim

Abstract

The under-5 mortality rate (U5MR) is an important metric of child health and survival. Country-level estimates of U5MR are readily available, but efforts to estimate U5MR subnationally have been limited, in part, due to spatial misalignment of available data sources (e.g., use of different administrative levels, or as a result of historical boundary changes). We analyzed all available complete and summary birth history data in surveys and censuses in six countries (Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia) at the finest geographic level available in each data source. We then developed small area estimation models capable of incorporating spatially misaligned data. These small area estimation models were applied to the birth history data in order to estimate trends in U5MR from 1980 to 2015 at the second administrative level in Cameroon, Chad, Mozambique, Uganda, and Zambia and at the third administrative level in Bangladesh. We found substantial variation in U5MR in all six countries: there was more than a two-fold difference in U5MR between the area with the highest rate and the area with the lowest rate in every country. All areas in all countries experienced declines in U5MR between 1980 and 2015, but the degree varied both within and between countries. In Cameroon, Chad, Mozambique, and Zambia we found areas with U5MRs in 2015 that were higher than in other parts of the same country in 1980. Comparing subnational U5MR to country-level targets for the Millennium Development Goals (MDG), we find that 12.8% of areas in Bangladesh did not meet the country-level target, although the country as whole did. A minority of areas in Chad, Mozambique, Uganda, and Zambia met the country-level MDG targets while these countries as a whole did not. Subnational estimates of U5MR reveal significant within-country variation. These estimates could be used for identifying high-need areas and positive deviants, tracking trends in geographic inequalities, and evaluating progress towards international development targets such as the Sustainable Development Goals.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 23%
Student > Doctoral Student 8 14%
Researcher 7 13%
Student > Ph. D. Student 4 7%
Student > Bachelor 3 5%
Other 7 13%
Unknown 14 25%
Readers by discipline Count As %
Medicine and Dentistry 12 21%
Nursing and Health Professions 7 13%
Social Sciences 7 13%
Agricultural and Biological Sciences 3 5%
Computer Science 2 4%
Other 8 14%
Unknown 17 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 14 August 2018.
All research outputs
#4,048,593
of 23,099,576 outputs
Outputs from Population Health Metrics
#116
of 392 outputs
Outputs of similar age
#77,935
of 330,840 outputs
Outputs of similar age from Population Health Metrics
#5
of 5 outputs
Altmetric has tracked 23,099,576 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 392 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has gotten more attention than average, scoring higher than 68% 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 330,840 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 76% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.