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Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 2 | 15% |
| Canada | 2 | 15% |
| Sweden | 1 | 8% |
| United States | 1 | 8% |
| United Kingdom | 1 | 8% |
| Australia | 1 | 8% |
| Iceland | 1 | 8% |
| Unknown | 4 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 54% |
| Scientists | 3 | 23% |
| Practitioners (doctors, other healthcare professionals) | 3 | 23% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 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 | 106 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 22 | 20% |
| Student > Ph. D. Student | 17 | 15% |
| Researcher | 16 | 14% |
| Professor | 7 | 6% |
| Student > Bachelor | 6 | 5% |
| Other | 29 | 26% |
| Unknown | 14 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 26 | 23% |
| Medicine and Dentistry | 19 | 17% |
| Nursing and Health Professions | 12 | 11% |
| Economics, Econometrics and Finance | 11 | 10% |
| Business, Management and Accounting | 4 | 4% |
| Other | 13 | 12% |
| Unknown | 26 | 23% |
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
#3,891,475
of 23,577,761 outputs
Outputs from International Journal for Equity in Health
#691
of 1,966 outputs
Outputs of similar age
#55,427
of 356,725 outputs
Outputs of similar age from International Journal for Equity in Health
#5
of 23 outputs
Altmetric has tracked 23,577,761 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 1,966 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. 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 356,725 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 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.