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Development of a metric for tracking and comparing population health based on the minimal generic set of domains of functioning and health

Overview of attention for article published in Population Health Metrics, May 2016
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Title
Development of a metric for tracking and comparing population health based on the minimal generic set of domains of functioning and health
Published in
Population Health Metrics, May 2016
DOI 10.1186/s12963-016-0088-y
Pubmed ID
Authors

Cornelia Oberhauser, Somnath Chatterji, Carla Sabariego, Alarcos Cieza

Abstract

The following minimal set of valid health domains for tracking the health of both clinical and general populations has recently been proposed: 1) energy and drive functions, 2) emotional functions, 3) sensation of pain, 4) carrying out daily routine, 5) walking and moving around, and 6) remunerative employment. This study investigates whether these domains can be integrated into a sound psychometric measure to adequately assess, compare, and monitor the health of populations. Data from waves 3 and 4 of the English Longitudinal Study of Ageing (ELSA) were analysed (N = 9779 and 11,050). From ELSA, 12 items operationalizing the six domains of the minimal generic set were identified. The Partial Credit Model (PCM) was applied to create a health metric based on these items. The Item Response Theory (IRT) model assumptions of unidimensionality, local independence, and monotonicity were evaluated, and Differential Item Functioning (DIF) was examined for sex and age groups. The psychometric properties of: 1) internal consistency reliability, 2) construct validity, and 3) sensitivity to change were evaluated to establish the final health metric. IRT model assumptions were found to be fulfilled. None of the items showed DIF by sex or age group. The final health metric demonstrated sound psychometric properties. The health metric developed in this study - based on the domains of the minimal generic set - proved useful for a wide range of health comparisons, especially for different groups of persons, and both cross-sectionally and over time. Monitoring health over time provides especially useful information for health care providers and health policymakers and both in clinical settings and the general population. The developed health metric offers a wide range of applications, including comparisons of levels of health among different groups in the general population, clinical populations, and even populations within and across different countries.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 22%
Student > Master 2 11%
Other 1 6%
Professor 1 6%
Student > Doctoral Student 1 6%
Other 2 11%
Unknown 7 39%
Readers by discipline Count As %
Mathematics 2 11%
Psychology 2 11%
Sports and Recreations 2 11%
Nursing and Health Professions 1 6%
Computer Science 1 6%
Other 3 17%
Unknown 7 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 May 2016.
All research outputs
#14,261,557
of 22,869,263 outputs
Outputs from Population Health Metrics
#279
of 392 outputs
Outputs of similar age
#169,716
of 311,729 outputs
Outputs of similar age from Population Health Metrics
#7
of 9 outputs
Altmetric has tracked 22,869,263 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
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 13.7. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 311,729 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.