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Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates

Overview of attention for article published in Population Health Metrics, October 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#11 of 306)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

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76 tweeters
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1 Facebook page

Citations

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14 Dimensions

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67 Mendeley
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Title
Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates
Published in
Population Health Metrics, October 2016
DOI 10.1186/s12963-016-0106-0
Pubmed ID
Authors

Elisabeth zu Erbach-Schoenberg, Victor A. Alegana, Alessandro Sorichetta, Catherine Linard, Christoper Lourenço, Nick W. Ruktanonchai, Bonita Graupe, Tomas J. Bird, Carla Pezzulo, Amy Wesolowski, Andrew J. Tatem

Abstract

Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities. We show how mobile phone data can be used to measure seasonal changes in health district population numbers, which are used as denominators for calculating district-level disease incidence. Using the example of malaria case reporting in Namibia we use 3.5 years of phone data to investigate the spatial and temporal effects of fluctuations in denominators caused by seasonal mobility on malaria incidence estimates. We show that even in a sparsely populated country with large distances between population centers, such as Namibia, populations are highly dynamic throughout the year. We highlight how seasonal mobility affects malaria incidence estimates, leading to differences of up to 30 % compared to estimates created using static population maps. These differences exhibit clear spatial patterns, with likely overestimation of incidence in the high-prevalence zones in the north of Namibia and underestimation in lower-risk areas when compared to using static populations. The results here highlight how health metrics that rely on static estimates of denominators from censuses may differ substantially once mobility and seasonal variations are taken into account. With respect to the setting of malaria in Namibia, the results indicate that Namibia may actually be closer to malaria elimination than previously thought. More broadly, the results highlight how dynamic populations are. In addition to affecting incidence estimates, these changes in population density will also have an impact on allocation of medical resources. Awareness of seasonal movements has the potential to improve the impact of interventions, such as vaccination campaigns or distributions of commodities like bed nets.

Twitter Demographics

The data shown below were collected from the profiles of 76 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Australia 1 1%
Unknown 65 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Researcher 13 19%
Student > Master 9 13%
Student > Bachelor 7 10%
Student > Doctoral Student 5 7%
Other 9 13%
Unknown 10 15%
Readers by discipline Count As %
Medicine and Dentistry 10 15%
Agricultural and Biological Sciences 8 12%
Computer Science 6 9%
Social Sciences 6 9%
Nursing and Health Professions 4 6%
Other 21 31%
Unknown 12 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 52. 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 03 April 2017.
All research outputs
#371,561
of 14,369,976 outputs
Outputs from Population Health Metrics
#11
of 306 outputs
Outputs of similar age
#12,551
of 268,101 outputs
Outputs of similar age from Population Health Metrics
#1
of 1 outputs
Altmetric has tracked 14,369,976 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 306 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.0. This one has done particularly well, scoring higher than 96% 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 268,101 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 95% of its contemporaries.
We're also able to compare this research output to 1 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