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Unexplained health inequality – is it unfair?

Overview of attention for article published in International Journal for Equity in Health, January 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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12 tweeters
facebook
1 Facebook page

Citations

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13 Dimensions

Readers on

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105 Mendeley
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Title
Unexplained health inequality – is it unfair?
Published in
International Journal for Equity in Health, January 2015
DOI 10.1186/s12939-015-0138-2
Pubmed ID
Authors

Yukiko Asada, Jeremiah Hurley, Ole Frithjof Norheim, Mira Johri

Abstract

IntroductionAccurate measurement of health inequities is indispensable to track progress or to identify needs for health equity policy interventions. A key empirical task is to measure the extent to which observed inequality in health ¿ a difference in health ¿ is inequitable. Empirically operationalizing definitions of health inequity has generated an important question not considered in the conceptual literature on health inequity. Empirical analysis can explain only a portion of observed health inequality. This paper demonstrates that the treatment of unexplained inequality is not only a methodological but ethical question and that the answer to the ethical question ¿ whether unexplained health inequality is unfair ¿ determines the appropriate standardization method for health inequity analysis and can lead to potentially divergent estimates of health inequity.MethodsWe use the American sample of the 2002¿03 Joint Canada/United States Survey of Health and measure health by the Health Utilities Index (HUI). We model variation in the observed HUI by demographic, socioeconomic, health behaviour, and health care variables using Ordinary Least Squares. We estimate unfair HUI by standardizing fairness, removing the fair component from the observed HUI. We consider health inequality due to factors amenable to policy intervention as unfair. We contrast estimates of inequity using two fairness-standardization methods: direct (considering unexplained inequality as ethically acceptable) and indirect (considering unexplained inequality as unfair). We use the Gini coefficient to quantify inequity.ResultsOur analysis shows that about 75% of the variation in the observed HUI is unexplained by the model. The direct standardization results in a smaller inequity estimate (about 60% of health inequality is inequitable) than the indirect standardization (almost all inequality is inequitable).ConclusionsThe choice of the fairness-standardization method is ethical and influences the empirical health inequity results considerably. More debate and analysis is necessary regarding which treatment of the unexplained inequality has the stronger foundation in equity considerations.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
Colombia 1 <1%
Chile 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Unknown 100 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 20%
Student > Ph. D. Student 17 16%
Researcher 16 15%
Professor 7 7%
Student > Doctoral Student 5 5%
Other 28 27%
Unknown 11 10%
Readers by discipline Count As %
Social Sciences 26 25%
Medicine and Dentistry 17 16%
Nursing and Health Professions 12 11%
Economics, Econometrics and Finance 11 10%
Business, Management and Accounting 4 4%
Other 12 11%
Unknown 23 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 13 September 2015.
All research outputs
#3,721,363
of 22,785,242 outputs
Outputs from International Journal for Equity in Health
#676
of 1,896 outputs
Outputs of similar age
#54,489
of 353,099 outputs
Outputs of similar age from International Journal for Equity in Health
#5
of 25 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,896 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.2. This one has gotten more attention than average, scoring higher than 64% 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 353,099 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 84% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.