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Unexplained health inequality--is it unfair?

Asada Y, Hurley J, Norheim OF, Johri M - Int J Equity Health (2015)

Bottom Line: 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).The 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, Nova Scotia, B3H1V7, Canada. yukiko.asada@dal.ca.

ABSTRACT

Introduction: Accurate 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.

Methods: We 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.

Results: Our 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).

Conclusions: The 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.

No MeSH data available.


Magnitude of health inequality and health inequity estimated by the direct and indirect fairness standardization. Data source: Joint Canada/United States Survey of Health (JCUSH). Analysis is weighted. Standard errors are adjusted for the complex survey design. Gini coefficient takes values between zero (most equal) and one (most unequal). The use of the direct standardization implicitly regards unexplained variation in inequality as ethically acceptable, and the use of the indirect standardization implicitly regards it as unfair.
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Fig1: Magnitude of health inequality and health inequity estimated by the direct and indirect fairness standardization. Data source: Joint Canada/United States Survey of Health (JCUSH). Analysis is weighted. Standard errors are adjusted for the complex survey design. Gini coefficient takes values between zero (most equal) and one (most unequal). The use of the direct standardization implicitly regards unexplained variation in inequality as ethically acceptable, and the use of the indirect standardization implicitly regards it as unfair.

Mentions: The far left data point of FigureĀ 1 shows the magnitude of health inequality. The Gini coefficient for the distribution of the observed HUI is 0.094 (95% CI: 0.089, 0.100), and the mean HUI value for this distribution is 0.880 (95% confidence interval [CI]: 0.873, 0.886). Based on this information, the expected mean difference in the HUI of two randomly selected individuals is 0.165, which notably larger than the minimally policy significant difference in the HUI of 0.030.Figure 1


Unexplained health inequality--is it unfair?

Asada Y, Hurley J, Norheim OF, Johri M - Int J Equity Health (2015)

Magnitude of health inequality and health inequity estimated by the direct and indirect fairness standardization. Data source: Joint Canada/United States Survey of Health (JCUSH). Analysis is weighted. Standard errors are adjusted for the complex survey design. Gini coefficient takes values between zero (most equal) and one (most unequal). The use of the direct standardization implicitly regards unexplained variation in inequality as ethically acceptable, and the use of the indirect standardization implicitly regards it as unfair.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4318200&req=5

Fig1: Magnitude of health inequality and health inequity estimated by the direct and indirect fairness standardization. Data source: Joint Canada/United States Survey of Health (JCUSH). Analysis is weighted. Standard errors are adjusted for the complex survey design. Gini coefficient takes values between zero (most equal) and one (most unequal). The use of the direct standardization implicitly regards unexplained variation in inequality as ethically acceptable, and the use of the indirect standardization implicitly regards it as unfair.
Mentions: The far left data point of FigureĀ 1 shows the magnitude of health inequality. The Gini coefficient for the distribution of the observed HUI is 0.094 (95% CI: 0.089, 0.100), and the mean HUI value for this distribution is 0.880 (95% confidence interval [CI]: 0.873, 0.886). Based on this information, the expected mean difference in the HUI of two randomly selected individuals is 0.165, which notably larger than the minimally policy significant difference in the HUI of 0.030.Figure 1

Bottom Line: 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).The 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, Nova Scotia, B3H1V7, Canada. yukiko.asada@dal.ca.

ABSTRACT

Introduction: Accurate 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.

Methods: We 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.

Results: Our 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).

Conclusions: The 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.

No MeSH data available.