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Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression

Overview of attention for article published in International Journal of Health Geographics, July 2017
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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 (75th percentile)

Mentioned by

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2 policy sources
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2 tweeters
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1 Facebook page

Citations

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13 Dimensions

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133 Mendeley
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Title
Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression
Published in
International Journal of Health Geographics, July 2017
DOI 10.1186/s12942-017-0097-5
Pubmed ID
Authors

Jan van de Kassteele, Laurens Zwakhals, Oscar Breugelmans, Caroline Ameling, Carolien van den Brink

Abstract

Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered. We used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level. We estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the direct estimates at the municipal level. Structured additive regression is a useful tool to provide small area estimates in a unified framework. We are able to produce valid nationwide small area estimates of 26 health-related indicators at neighbourhood level in the Netherlands. The results can be used for local policy makers to make appropriate health policy decisions.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 133 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 53 40%
Student > Master 29 22%
Researcher 7 5%
Student > Ph. D. Student 7 5%
Other 4 3%
Other 8 6%
Unknown 25 19%
Readers by discipline Count As %
Nursing and Health Professions 28 21%
Medicine and Dentistry 16 12%
Sports and Recreations 12 9%
Psychology 7 5%
Social Sciences 6 5%
Other 32 24%
Unknown 32 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 18 December 2019.
All research outputs
#2,894,087
of 16,413,158 outputs
Outputs from International Journal of Health Geographics
#125
of 563 outputs
Outputs of similar age
#62,727
of 270,025 outputs
Outputs of similar age from International Journal of Health Geographics
#1
of 1 outputs
Altmetric has tracked 16,413,158 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 563 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has done well, scoring higher than 76% 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 270,025 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 75% 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