assignment 9- Ethics

Asada et al. International Journal for Equity in Health (2015) 14:11 DOI 10.1186/s12939-015-0138-2

RESEARCH Open Access

Unexplained health inequality – is it unfair? Yukiko Asada1*, Jeremiah Hurley2, Ole Frithjof Norheim3 and Mira Johri4,5

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.

Keywords: Health inequalities, Health disparities, Health inequities, Measurement, Ethics

Introduction Inequalities and inequities in health care and health out- comes continue to be in the center stage of health policy in many jurisdictions. Accurate measurement of inequal- ities and inequities is indispensable to track progress or to identify needs for policy interventions [1,2]. Regular reporting of health inequalities and inequities requires ongoing data and methodological improvement. Meas- urement of health inequities is more challenging than that of health inequalities not only for their require- ments for data on determinants of health [3] but also for ethical considerations. Health inequities are a subset of

* Correspondence: [email protected] 1Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, Nova Scotia B3H1V7, Canada Full list of author information is available at the end of the article

© 2015 Asada et al.; licensee BioMed Central. Commons Attribution License (http://creativec reproduction in any medium, provided the o Dedication waiver (http://creativecommons.or unless otherwise stated.

ethically problematic health inequalities – differences in health –, and their measurement demands a definition of health inequity and operationalization of the chosen definition in the measurement exercises [4,5]. To date, no single, agreed-up definition of health inequi-

ties exists. Alternative definitions of health inequity can be distinguished by the sources of health inequality each classified as ethically acceptable and unacceptable. For example, Braveman and Gruskin define health equity as “the absence of systematic disparities in health … between social groups who have different levels of underlying social advantage/disadvantage” ([6], p. 254). This view thus regards inequalities associated with social advantage as ethically unacceptable. In contrast, equal opportunity for health, a definition gaining popularity in the health eco- nomics literature [7-10], considers health inequality due

This is an Open Access article distributed under the terms of the Creative ommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and riginal work is properly credited. The Creative Commons Public Domain g/publicdomain/zero/1.0/) applies to the data made available in this article,

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 2 of 12

to factors beyond individual control is unfair. In this view, factors within individual control are ethically acceptable sources of inequality. Given that the ultimate focus of policy concern is

health inequities, a key empirical task is to measure the extent to which observed inequality in health is inequit- able. This requires an integration of the conceptual and empirical literatures on health inequity [4,5,11,12]. Em- pirically operationalizing definitions of health inequity has generated an important question not considered in the conceptual literature on health inequity noted above [7,11,12]. Empirical analysis can explain only a portion of observed health inequality. The presence of large un- explained variation in health regression models is no news to methodologists, who typically consider it as a data or methodological limitation. However, in the measurement of health inequity, a question arises as to how we should classify unexplained health inequality – fair or unfair. This ethical question is unavoidable in such empirical exercises, and different answers to this question can result in divergent health inequity results, some of which are fundamental to health equity policy, such as how much health inequity exists in the popula- tion, and to what degree observed health inequality is in- equitable. Despite potentially large policy implications, the issue of unexplained health inequality has not re- ceived sufficient attention in health services and popula- tion health research and policy. The goal of this paper is to demonstrate that the answer

to the ethical question – whether unexplained health in- equality is unfair – determines the choice of the standardization method and can lead to potentially diver- gent estimates of health inequity. In the next section, we explain how this question arises in the assessment of health inequities and articulate how answers to this ques- tion lead to particular methodological choices. We then demonstrate the importance of this ethical question em- pirically using the Joint Canada/United States Survey of Health (JCUSH) [13], which is typical of the data available for health inequity analysis. Our analysis shows that differ- ent ethical judgments regarding unexplained health inequality lead to substantial differences in estimates of health inequity. We conclude by discussing future re- search directions to enhance understanding of this issue.

Ethical judgments regarding unexplained health inequality in health inequity analysis The issue of unexplained health inequality arises in an effort to be transparent and explicit about the definition of health inequity when empirically measuring health in- equity. Measuring health inequity requires individual- level data to model variation in health at the individual level. Assuming that we have such individual-level health survey data, we begin by quantifying the amount of

inequality in the distribution of observed health across individuals. We can use a univariate inequality index (e.g., Gini index). This provides a measure of the total amount of health inequality in the population. To measure health inequity, we must quantify the distri-

bution of unfair health across individuals in the population, that is, unfair health inequality. Unfair health, however, is not directly observable. To estimate it from observed health, we first model variation in health. The goal is to sta- tistically explain as much variation in health as possible with the data at hand. This enables us to partition variation in health into that attributable to factors considered fair, or legitimate, sources of variation, and that attributable to fac- tors considered unfair, or non-legitimate, source of vari- ation. In other words, to define health inequities we need to look at causes of health inequalities [11]. As an example, let us consider a popular definition of

health inequity, policy amenability, which argues that health inequality due to factors amenable to policy inter- vention is unfair [14]. We classify each variable in our data as a legitimate (ethically acceptable) source of inequality – that is, it is not amenable to policy intervention – or an illegitimate (ethically unacceptable) source of inequality – that is, it is amenable to policy intervention. Table 1 is an example of such legitimate-illegitimate classification based on the perspective of policy amenability. We assume age largely represents the biological association with health and treat it as the only variable that is not amenable to policy intervention, and thus, a legitimate source of vari- ation in health. We classify all other variables as amenable to policy because: (a) it is possible to change the distribu- tion of the variable (e.g., education, income), or (b) even when it is not possible to change the distribution of the variable, it is in principle possible to change how society treats people with the characteristic (e.g., for race and sex, it is possible to eliminate racial or sex discrimination). Age and sex capture both biology and social policy, and the asymmetrical treatment stems from our assumption as to which effect each of these variables represents most. Classifications like that in Table 1 generate intense de-

bate for at least two reasons. First, defining health inequity in this way assumes causality between health and the other variables, which cannot always be established empir- ically due to data and methodological limitations. Second, people debate passionately whether a particular source is legitimate or illegitimate. Our particular choices presented in Table 1 are only for illustrative purposes. The key point here is that, to estimate unfair health, one needs to classify variables as legitimate or illegitimate according to a chosen definition of health inequity. Having classified each variable, we then remove the

influence of the fair component – legitimate variables according to a chosen definition of health inequity – on the observed health through fairness-standardization.

Table 1 Legitimate-illegitimate classification of variables according to the perspective of policy amenability

Variable Legitimate vs. illegitimate classification

Demographics status

Age Legitimate

Sex Illegitimate

Marital status Illegitimate

Race Illegitimate

Country of birth Illegitimate

Health behaviour

Smoker type and history Illegitimate

BMI Illegitimate

Frequency of physical activity Illegitimate

Socioeconomic status

Household income Illegitimate

Education Illegitimate

Health care factors

Has regular medical doctor Illegitimate

Unmet need Illegitimate

High blood pressure management

Illegitimate

Asthma medication management

Illegitimate

Pharmaceutical insurance Illegitimate

Health insurance type Illegitimate

BMI: body mass index. Variables are those we include in our analysis using the Joint Canada/ United States Survey of Health (JCUSH). “Policy amenability” argues that health inequality due to factors amenable to policy intervention is unfair [14]. A legitimate source of health inequality means that the variable is not amenable to policy, thus, resulting health inequality is ethically acceptable. An illegitimate source of health inequality means that the variable is amenable to policy, thus, resulting health inequality is ethically unacceptable.

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This leaves us with only inequality due to unfair sources. Fairness-standardization is similar to age-standardization in epidemiological studies, which removes the influence of age when estimating mortality rates, but in this context, standardization removes the influence of all legitimate sources of inequality. Consequently, the standardization generates the inequitable distribution of health in the population. The amount of inequity is then quantified by applying the same inequality index as above to this dis- tribution of unfair health. Despite the use of the same mathematical index, the measure here is an index of in- equity, as opposed to simply inequality, as it quantifies the distribution of unfair health. For fairness-standardization, two methods are avail-

able: direct and indirect. As we show below, the choice of the standardization method is closely connected to ethical judgments regarding unexplained inequality.

Both direct and indirect standardization methods are based on the notion that the observed health consists of legitimate, illegitimate, and unexplained components:

Observed HUI ¼ Legitimate þ Illegitimate þ Unexplained

Using the direct standardization method, we predict unfair health directly by allowing only the illegitimate variables alone to influence the predictions. To do so, we purge the influence of legitimate variables by setting the value of these variables constant (expressed with the bar in the equation) during the prediction and ignore the unexplained component:

Unfair̂ HUIdirect ¼ Legitimate �

þ Illegitimate As is clear, this produces a distribution in which the

only source of variation in predicted levels of health arises from variation across individuals in illegitimate factors. Using the indirect standardization, we first predict fair

health by allowing only the legitimate variables to influ- ence the predictions. To do so, we purge the influence of illegitimate variables (by holding their values at a con- stant during prediction) and ignore the unexplained component:

Fair ^

HUI ¼ Legitimate þ Illegitimate�

We then calculate unfair health by subtracting the es- timate of fair health from the observed health and add- ing the mean health of the population:

Unfair̂ HUIindirect

¼ Observed HUI−Fair^HUI þ Population0s mean HUI ¼ Legitimate þ Illegitimate þ Unexplainedð Þ−Legitimate þ Population0s mean HUI

¼ Illegitimate þ Unexplained þ Population0s mean HUI

This addition of the mean health of the population is conventional [15] and ensures that the distributions of the observed health and the unfair health have the same mean value. For both standardization methods, we can choose any

values at which to hold the relevant variables constant (le- gitimate variables for direct standardization and illegitim- ate for indirect standardization). But the choice reflects an ethical and policy judgment regarding the reference attri- butes by which we assess health inequity. For example, for the definition of policy amenability discussed above, we can hold each relevant variable at the category to which policies might reasonably aim (e.g., education at “high school”), or we could set the level to the healthiest cat- egory in the population (e.g., education at “university or college certificate”). Whether we should assess health

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 4 of 12

inequity against a modest or ambitious goal depends on which differences in health we consider as unfair and what reference we consider as an appropriate policy goal. Just as the legitimate-illegitimate classification of vari- ables, the choice of reference values can generate de- bate. For the purpose of this paper, which compares the two standardization methods, we set reference values equal to the modest goals as an example. Importantly for the focus of this paper, notice that the

unfair health estimated by the direct standardization does not include the unexplained component while the unfair health estimated by the indirect standardization does. The larger the unexplained component is, the greater the discrepancy is between unfair health esti- mated by these two standardization methods. Notice further that the choice of the standardization

methods implies ethical judgments: using the direct standardization, we regard unexplained variation in in- equality as ethically acceptable, and using the indirect standardization, we regard it as unfair. Although this issue has been raised in the health economics literature [7,11,12], there has been little appreciation for these ethical judgments in the public health and health policy literatures. Below we illustrate how much difference these standardization methods can make in estimates of health inequity using typically available survey data.

Methods Data We estimate health inequity using the 2002–03 Joint Canada/United States Survey of Health (JCUSH), a cross-sectional population health survey jointly con- ducted by Statistics Canada and the U.S. National Center for Health Statistics [13]. The JCUSH questionnaire in- cluded questions regarding health status, health care utilization, health behaviour, socioeconomic status, and health insurance status. The target population was non- institutionalized Canadian and U.S. household residents aged 18 and older. The JCUSH used a complex sampling design with stratification by geographic region and over- sampling of respondents aged 65 and over. For simplicity and the ease of exposition, in this paper

we present the results for the American sample only. The analysis using the Canadian sample yielded the same key methodological findings (available from the au- thors upon request). The original American sample of the JCUSH is 5,183 (response rate: 50.2%). We exclude observations with missing values (typically less than 4% of observations), except income (19.8%), for which we create “income missing” category. We also exclude 48 observations with scores of the Health Utilities Index (HUI), our measure of health, less than or equal to zero. The final sample size for our analysis is 4,328.

Variables Health We measure health by the Health Utilities Index Mark 3 (HUI), a well validated and widely used generic health- related quality of life measure [16]. The HUI measures the respondent’s functional levels in eight dimensions (vision, hearing, speech, mobility, dexterity, emotion, cognition, and pain) and converts his or her functional levels into a health-related quality-of-life score based on preferences of the general public (as opposed to the re- spondent’s preferences) over health states. One advan- tage of the HUI is that it is possible to identify when a difference in scores is meaningful for policy purposes. A difference of 0.030 or greater is meaningful or important [17], indicating the difference large enough to justify a recommendation for an intervention to achieve such an increment in health [18]. The observed distribution of HUI scores in the full sample range from −0.360 to 1.000 on a scale in which 0.000 represents being dead and 1.000 represents perfect health, and negative scores indicates health states worse than dead. For our analysis, we use only observations with zero or positive HUI scores as the Gini index, by which we measure univari- ate inequality and inequity, allows only non-negative values for the variable being analyzed [19].

Attributes known to be associated with health We use a number of attributes known to be associated with health and available from the JCUSH: demographic status, health behaviour, socioeconomic status, and health care system factors, including the availability of basic health care, quality of health care, and health care insurance. We tested for interactions among these vari- ables and retained the interaction terms between smoking and income and between body mass index (BMI) and edu- cation, which remain statistically significant at the 5% level in the final model.

Quantifying health inequality and inequity We use the Gini coefficient to quantify health inequality and inequity [4,20]. The Gini coefficient takes values be- tween zero (perfectly equal distribution) and one (most unequal). The Gini coefficient is widely used in the in- come inequality literature and has also been applied to quantify the distribution of health [21]. The Gini coeffi- cient assumes that the underlying variable is measured at the ratio scale level. The HUI is an interval-scale measure, so our application of the Gini to the HUI vio- lates this assumption. In practice, however, many in- equality analyses apply the Gini to health measures that do not strictly satisfy this assumption, and given that the choice of inequality measure is not central to the main focus of our analysis, we believe our use of the Gini is rea- sonable. Although the 0–1 index of the Gini coefficient

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 5 of 12

itself does not give an intuitive interpretation, twice the value of the Gini coefficient indicates the proportion of the expected mean difference between two randomly se- lected persons in the population [22]. For example, a value of 0.100 for the Gini coefficient with the mean HUI, 0.800, indicates that the expected difference in the HUI from two randomly drawn persons in this population is twice 0.100 (i.e., 0.200) of the mean HUI, 0.800 (i.e., 0.160). When the Gini coefficient in the population indicates the expected difference in the HUI from two randomly drawn persons equal to or greater than 0.030, the minimum mag- nitude for a difference in HUI scores to be policy relevant, we consider this inequality or inequity as policy relevant.

Analysis The analysis proceeds with the following three steps. First, we estimate the magnitude of inequality in the observed HUI across individuals using the Gini coefficient. Second, we model variation in the observed HUI. Third, based on the definition of policy amenability, as discussed above, and using the direct and indirect standardization methods, we estimate unfair HUI for each individual and quantify the magnitude of inequity using the Gini coefficient. In both standardization methods, we hold relevant variable at the category to which policies might reasonably aim (see Additional file 1). Modeling the distribution of the HUI is challenging

because the HUI is bounded (between 0.000 and 1.000), it spikes at 1.0 (in our JCUSH sample, about 25% of the observations have HUI=1), and it is left-skewed. Re- searchers have recommended a number of alternative statistical methods to empirically model the distribution of HUI, including Ordinary Least Squares (OLS), Tobit, censored least absolute deviation (CLAD), two-part models, and latent class models, with no consensus re- garding the best approach [23-26]. In this paper we present results from the OLS because OLS performed well relative to two-part models and CLAD in our sensi- tivity analysis and is easier to understand than the alternativesa. We weight all analyses using the sample weights pro-

vided by the JCUSH. To estimate variance accounting for the JCUSH’s complex survey design, we use the bal- anced repeated replication methods with balanced re- peated replication weights provided by Statistics Canada and the US National Center for Health Statistics. We consider p<0.05 as statistically significant. We use Stata 11 for all analyses [27,28].

Results Sample characteristics Sample characteristics and the average HUI across sub- groups mostly follow expected patterns (Table 2). The average HUI is lower among older age groups; those

separated, divorced, or widowed; black or other racial group; those with unmet need; those without pharma- ceutical insurance; and those with Medicaid only. The average HUI does not differ much by sex or country of birth. Those with healthy behaviours and high socioeco- nomic status, measured by income or education, have higher average HUI. Those with no regular medical doc- tor and no health insurance have higher average HUI than those with regular medical doctor and health insur- ance, which may indicate younger age and less demand for health care among this group. High demand for health care may be a factor for lower average HUI among those with high blood pressure or asthma and re- ceived treatment or medication in the last 12 months than those with such conditions but who did not obtain treatment or medication.

Modeling variation in health (HUI) The fit of our model is comparable to other work de- scribing the variation in the HUI (adjusted R2: 0.258, Table 3) [29,30]. Among the demographic variables, only age is statistically significant. Lack of statistical signifi- cance of race is somewhat counter-intuitive but confirms other studies using the JCUSH (e.g., [30]). When we add socioeconomic variables to demographic variables, race becomes statistically insignificant, and, after introducing health care supply variables, the sign of the coefficient for black flips from negative to positive. All health be- haviour variables (smoker type, BMI, and physical activ- ity) and socioeconomic variables (income and education) show statistically significant effects on the HUI, either individually or through interactions. All health care sup- ply variables are statistically significant, with the unmet need variable showing the largest coefficient (−0.110), followed by health insurance type (−0.092 for Medicaid only with no insurance as the reference).

Health inequality The far left data point of Figure 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 infor- mation, 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.

Health inequity – the direct vs. indirect fairness- standardization method As shown in Figure 1, the choice of the standardization method makes a substantial difference in estimates of health inequity. Using the direct standardization, the magnitude of health inequity, expressed by the Gini

Table 2 Sample characteristics

N (%) HUI

Total sample 4, 328(100) 0.869

Demographics status

Age (year)

18-44 1,962(45.33) 0.910

45-64 1,470(33.96) 0.856

65+ 896(20.70) 0.800

Sex

Men 1,899(43.88) 0.881

Women 2,429(56.12) 0.860

Marital status

Married or common law partner 2,443(56.45) 0.888

Separated, divorced, or widowed 1,094(25.28) 0.812

Single 791(18.28) 0.889

Race

White 3,384(78.19) 0.874

Other 500(11.55) 0.842

Black 332(7.67) 0.843

Asian 112(2.59) 0.918

Country of birth

Foreign born 614(14.19) 0.867

Native born 3,714(85.81) 0.869

Health behaviour

Smoker type and history

Never smoked 2,259(52.20) 0.889

Former smoker and started smoking at or after 18 years

717(16.57) 0.858

Former smoker and started smoking before 18 years

342(7.90) 0.817

BMI

Underweight 96(2.22) 0.820

Normal weight 1,864(43.07) 0.890

Overweight 1,455(33.62) 0.880

Obese 913(21.10) 0.813

Frequency of physical activity

Regular 2,518(58.18) 0.907

Occasional 736(17.01) 0.885

Infrequent 1,074(24.82) 0.768

Socioeconomic status

Household income

Lowest income quintile 665(15.37) 0.769

Lower middle income quintile 696(16.08) 0.855

Middle income quintile 620(14.33) 0.894

Higher middle income quintile 726(16.77) 0.909

Highest middle income quintile 763(17.63) 0.930

Income missing 858(19.82) 0.852

Table 2 Sample characteristics (Continued)

Education

Less than high school 431(9.96) 0.756

High school graduate 1,569(36.25) 0.856

Non-university/college certificate 635(14.67) 0.867

University/college certificate 1,693(39.12) 0.911

Health care factors

Has regular medical doctor

No 786(18.16) 0.890

Yes 3,542(81.84) 0.864

Unmet need

No 3,816(88.84) 0.885

Yes 512(11.83) 0.753

With high blood pressure and received treatment in the last 12 months

No 54(1.25) 0.820

Yes 832(19.22) 0.788

No high blood pressure 3,442(79.53) 0.889

With asthma and received medication in the last 12 months

No 190(4.39) 0.882

Yes 280(6.47) 0.784

No asthma 3,858(89.14) 0.875

Has pharmaceutical insurance

No 881(20.36) 0.846

Yes 3,447(79.64) 0.875

Health insurance type (US only)

No insurance 443(10.24) 0.851

Medicaid only 160(3.70) 0.677

Non-Medecaid public only including Medicare 254(5.87) 0.758

Private plus public including Medicare 818(18.90) 0.811

Private only 2,653(61.30) 0.912

Data source: Joint Canada/United States Survey of Health (JCUSH). BMI: body mass index; HUI: Health Utilities Index. BMI is based on the World Health Organization. Underweight: <18.5 kg/m2; normal weight: 18.5-24.9 kg/m2; overweight: 25-30 kg/m2; obese >30 kg/m2. HUI estimates are weighted and unadjusted.

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 6 of 12

coefficient, is 0.059 (95% confidence interval [CI]: 0.058, 0.061), while using the indirect standardization, the Gini coefficient is 0.086 (95% CI: 0.082, 0.091). The large differ- ence between these inequity estimates reflects the large amount of unexplained variation in health – the adjusted R2 for the regression model is 0.258, which indicates that about 75% of the variation in the observed HUI is not explained by the model. The direct standardization method presumes this large unexplained variation is fair, while the indirect method regards this unexplained vari- ation as unfair. Both inequity estimates are policy relevant. The Gini

coefficients of 0.059 (the direct standardization) and of

Table 3 Results of ordinary least squares regression model for the health utilities index

Coefficient (95% CI) p-value

Age (years, reference: 18-44) 0.000

45-64 -0.044(-0.057, -0.030) 0.000

65+ -0.013(-0.041, 0.015) 0.362

Male -0.001(-0.012, 0.010) 0.890

Marital status (reference: single) 0.064

Married or common law partner -0.005(-0.010, 0.019) 0.554

Separated, divorced, or widowed -0.014(-0.034, 0.005) 0.155

Race (reference: White) 0.342

Other -0.006(-0.028, 0.016) 0.601

Black -0.016(-0.005, 0.037) 0.127

Asian -0.011(-0.046, 0.024) 0.546

Foreign born -0.006(-0.013, 0.026) 0.515

Smoking (reference: never smoked) 0.059

Former smoker and started smoking at or after 18 years -0.052(-0.107, 0.002) 0.060

Former smoker and started smoking before 18 years -0.086(-0.170, 0.003) 0.043

Current smoker and started smoking at or after 18 years -0.015(-0.066, 0.036) 0.558

Current smoker and started smoking before 18 years -0.070(-0.140, 0.000) 0.050

BMI (reference: normal weight) 0.053

Underweight -0.166(-0.283, 0.048) 0.006

Overweight -0.025(-0.084, 0.034) 0.402

Obese -0.086(-0.084, 0.040) 0.485

Frequency of physical activity (reference; regular) 0.000

Occasional -0.012(-0.024, 0.001) 0.069

Infrequent -0.083(-0.099, -0.066) 0.000

Household income (reference: lowest income quintile) 0.122

Lower middle income quintile -0.021(-0.009, 0.050) 0.169

Middle income quintile -0.038(-0.010, 0.067) 0.009

Higher middle income quintile -0.036(-0.008, 0.064) 0.011

Highest middle income quintile -0.037(-0.008, 0.065) 0.011

Income missing -0.027(-0.001, 0.056) 0.056

Education (reference: less than high school) 0.026

High school graduate -0.007(-0.035, 0.049) 0.737

Non-university/college certificate -0.032(-0.013, 0.078) 0.162

University/college certificate -0.029(-0.013, 0.070) 0.176

Has regular medical doctor -0.021(-0.036,-0.006) 0.005

Presence of self-reported unmet need -0.110(-0.133,-0.087) 0.000

Treatment for high blood pressure in the last 12 months (reference: no treatment) 0.000

Received treatment -0.023(-0.081, 0.034) 0.424

No high blood pressure -0.020(-0.034, 0.073) 0.475

Medication for asthma in the last 12 months (reference: no medication) 0.008

Received medication -0.020(-0.076,-0.004) 0.031

No asthma -0.000(-0.026, 0.026) 0.999

Has pharmaceutical insurance -0.032(-0.051, 0.013) 0.001

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 7 of 12

Table 3 Results of ordinary least squares regression model for the health utilities index (Continued)

Health insurance type (US only, reference: no insurance) 0.000

Medicaid only -0.092(-0.146,-0.039) 0.001

Non-Medicaid public only including Medicare -0.052(-0.092,-0.013) 0.010

Private plus public including Medicare -0.038(-0.074, 0.001) 0.043

Private only -0.035(-0.008, 0.062) 0.010

Smoking x household income (reference: never smoked x lowest income quintile) 0.024

Former smoker and started smoking at or after 18 years

x Lower middle income quintile 0.011(-0.063, 0.085) 0.770

x Middle income quintile 0.038(-0.024, 0.101) 0.225

x Higher middle income quintile 0.050(-0.010, 0.111) 0.101

x Highest middle income quintile 0.062(0.000, 0.124) 0.050

x Income missing 0.086(-0.003, 0.122) 0.064

Former smoker and started smoking before 18 years

x Lower middle income quintile 0.041(-0.059, 0.141) 0.418

x Middle income quintile 0.062(-0.035, 0.160) 0.210

x Higher middle income quintile 0.105(0.015, 0.194) 0.023

x Highest middle income quintile 0.094(0.004, 0.184) 0.041

x Income missing -0.003(-0.110, 0.105) 0.962

Current smoker and started smoking at or after 18 years

x Lower middle income quintile 0.027(-0.035, 0.088) 0.396

x Middle income quintile -0.022(-0.085, 0.040) 0.487

x Higher middle income quintile -0.010(-0.072, 0.053) 0.757

x Highest middle income quintile 0.025(-0.033, 0.084) 0.393

x Income missing 0.002(-0.061, 0.065) 0.955

Current smoker and started smoking before 18 years

x Lower middle income quintile 0.063(-0.016, 0.142) 0.116

x Middle income quintile 0.002(-0.100, 0.103) 0.976

x Higher middle income quintile 0.027(-0.058, 0.112) 0.531

x Highest middle income quintile 0.094(-0.007, 0.180) 0.034

x Income missing 0.002(-0.041, 0.141) 0.282

BMI x education (reference: normal weight x less than high school) 0.005

Underweight

x High school graduate 0.108(-0.038, 0.255) 0.147

x Non-university/college certificate 0.159(-0.021, 0.340) 0.083

x University/college certificate 0.176(-0.052, 0.301) 0.006

Overweight

x High school graduate 0.051(-0.012, 0.113) 0.112

x Non-university/college certificate 0.014(-0.051, 0.080) 0.665

x University/college certificate 0.022(-0.040, 0.083) 0.490

Obese

x High school graduate -0.001(-0.068, 0.067) 0.985

x Non-university/college certificate -0.051(-0.128, 0.025) 0.190

x University/college certificate 0.013(-0.051, 0.078) 0.683

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 8 of 12

Table 3 Results of ordinary least squares regression model for the health utilities index (Continued)

Constant 0.919(0.839, 1.000) 0.000

Sample size 4328

Adjusted R-squared 0.258

Data source: Joint Canada/United States Survey of Health (JCUSH). CI: confidence interval; BMI: body mass index. P-value for each variable category is from t-test; p-value for the reference category is from F-test for all categories of each variable. Analysis is weighted. Standard errors are adjusted for the complex survey design.

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 9 of 12

0.086 (the indirect standardization) translate into the expected mean differences in the HUI of 0.101 and 0.166, respectively, between two randomly selected persons. These HUI values are more than three times larger than the minimally policy relevant difference of the HUI, 0.030.

Health inequality vs. health inequity Figure 1 also shows that the choice of the standardization method influences the comparison between health in- equality and health inequity. The Gini coefficient for the distribution of the observed HUI (0.094; 95% CI: 0.089, 0.100) is 1.6 times larger than the Gini coefficient for health inequity estimated by the direct standardization (0.059; 95% CI: 0.058, 0.061). However, the Gini coeffi- cients for inequality and for inequity estimated by the in- direct standardization (0.086; 95% CI: 0.082, 0.091) are not statistically significantly different. Therefore, the choice of the standardization method offers two contrast- ing results: About 60% of health inequality (the direct standardization) or almost all health inequality (the indir- ect standardization) we observe is inequitable.

0.05

0.06

0.07

0.08

0.09

0.10

0.11

Observed HUI Un (direct st

G in

i

Figure 1 Magnitude of health inequality and health inequity estimate Joint Canada/United States Survey of Health (JCUSH). Analysis is weighted. coefficient takes values between zero (most equal) and one (most unequal variation in inequality as ethically acceptable, and the use of the indirect st

Discussion In the context of the empirical assessments of health in- equities, this paper investigated the empirical import- ance of the ethical question of whether unexplained health inequality is unfair. The classification of unex- plained inequality as fair or unfair is closely connected to the choice of the fairness-standardization methods, a critical step for the measurement of health inequities. As the analysis of the US component of the JCUSH showed, this choice can substantially influence the empirical re- sults regarding how much health inequity exists in the population and the proportion of observed health in- equality that is inequitable. We obtained the same re- sults in analyses using the Canadian sample of the JCUSH and using a different definition of health in- equity, equal opportunity for health (results not shown). The question of how best to treat unexplained health

inequality deserves more extensive consideration in the assessment of health inequities than it currently does. Both direct and indirect fairness-standardization methods are technically valid but can produce different health inequity information and imply different ethical

fair HUI andardization)

Unfair HUI (indirect standardization)

d by the direct and indirect fairness standardization. Data source: Standard errors are adjusted for the complex survey design. Gini ). The use of the direct standardization implicitly regards unexplained andardization implicitly regards it as unfair.

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 10 of 12

stances in regard to unexplained variation. An analogy here may be the choice between direct and indirect age- standardization methods in epidemiological studies [31]. Both of these methods are sound but are known to pro- duce different results. Analysts are therefore advised to be explicit and consistent about their methodological choice. What complicates the choice of the fairness- standardization methods is that it is not merely meth- odological but ethical. Although unexplained health inequality is not an issue

for those who subscribe to the view that all health inequal- ities are inequitable (for whom all observed variation – ex- plained or unexplained – is unfair), it is an unavoidable issue for empirical analysts who do distinguish between pure health inequality and health inequity. Currently avail- able data and modeling techniques enable analysts to ex- plain only a relatively small portion of observed variation in health at the individual level. Because the issue of unex- plained inequality only arises in empirical work, it has rarely been paid attention to in the conceptual discussion regarding definitions of health inequity. Still, some work in the recent detailed philosophical analysis of health in- equity by philosophers, economists, and ethicists provides a hint as to how to consider the ethical significance of un- explained inequality. To examine the ethical significance of unexplained in-

equality, it is useful to recognize that unexplained vari- ation – residuals in a regression context – consists of two types of variation: variation systematically related to unob- served factors and random variation. The issue of un- measured systematic variation stems from methodological limitations. Improved data, such as longitudinal data with a rich array of variables capturing individuals’ life history, and improved modeling techniques can reduce unmeasured systematic variation. As soon as unmeas- ured systematic variation becomes observed systematic variation, the question goes back to a familiar, on-going debate regarding definitions of health inequity, that is, which sources of health inequality are ethically unacceptable. To assess the ethical significance of random variation,

the philosophical literature distinguishes “brute luck” – unfortunate events from which even sensible persons suf- fer, such as being hit by lightning during the commute with no warning, or suffering from a genetic disease by chance (often referred to as genetic lottery) – and “option luck” – unfortunate events associated with voluntary risks, such as being hit by lightning while playing golf with a plenty of warning or getting injured during voluntary bun- gee jumping [32-34]. The philosophical literature offers a wide range of views regarding the ethical significance of brute and option luck. Some scholars consider neither option nor brute luck as unfair because only variations in health associated with known socially distributed

determinants of health are unfair [35,36]. Alternatively, most equality in opportunity theories, also known as luck egalitarianism, consider that inequality caused by brute luck is unfair while that by option luck is fair [37]. Yet an- other view sees both brute and option luck as unfair [38]. To date, this philosophical literature has not caught atten- tion in health services and population health research and policy, but it is an important literature in the face of large unexplained health inequality in empirical work. Advances in data, modeling techniques, and philosoph-

ical arguments are ongoing processes, and the measure- ment and monitoring of health inequities for effective policy making cannot wait for their perfection. Three pro- posals are available for the treatment of unexplained health inequality in the current imperfect world that still urges policy making. First, Bago d’Uva, Jones, and van Doorslaer [39] recommend in the context of need- standardization for health care utilization, which faces a directly analogous problem, that analysts always provide two estimates of inequity, the lower bound estimate pro- vided by the direct standardization and the upper bound estimate by the indirect standardization. This is a prag- matic stop-gap solution but passes the difficult ethical question to users of health inequity information. Second, given complex causal relationships between health and its determinants and the fact that we do not understand them fully, we might argue that it would be safer to assume un- explained health inequality is of ethical significance, that is, unfair [40,41]. This judgment, and policy decisions that follow from it, will come with some opportunity cost. Re- sources that are devoted to address health inequity based on this judgment could be directed to competing health or other social issues. We should at least know the nature of such opportunity cost before committing to such judgment. Finally, Garcia-Gomez and colleagues [7] empirically

investigate what unexplained health inequality is. They tested the view articulated by Lefranc and colleagues in the analysis of unexplained income inequality [42]: clas- sify unexplained inequality as luck; examine whether the distribution of luck is uncorrelated with ethically un- acceptable sources of inequality; and if that is the case, consider luck an ethically acceptable source of inequal- ity. In their analysis of inequality in mortality among the Dutch population, they adopted the view of equal oppor- tunity for health as the definition of health inequity, which argues that health inequality due to factors beyond indi- vidual control is unfair. They considered variables such as sex, age, and education as ethically unacceptable sources of inequality while variables such as smoking, exercise, and weight as ethically acceptable sources of inequality. They found that unexplained inequality is distributed dif- ferently across groups of people categorized by sex, age, and education with or without controlling for the health

Asada et al. International Journal for Equity in Health (2015) 14:11 Page 11 of 12

behaviour. In sum, their analysis suggests that unexplained inequality is not an ethically acceptable source of inequality. Most of this emerging empirical work and its authors’

insight in into the importance of ethical discussion are of considerable significance for public health and health pol- icy. Given potentially serious policy implications of the issue of unexplained health inequality, analysts should at least make their methodological choices explicit and re- port both results from both standardization methods whenever they can. Moving beyond this pragmatic solution, however, analysts need to spur more debate and analysis regarding which treatment of the unexplained inequality has the stronger foundation in equity considerations.

Endnote aThe choice of the standardization methods would be-

come even more ethically relevant if we used a non- linear model for the HUI. This means that, in a sense, our results using a linear model provide conservative es- timates of the importance of this choice. We would like to thank an anonymous reviewer for pointing this out.

Additional file

Additional file 1: Categories at which variables are held constant in the fairness-standardization in the analysis.

Abbreviations BMI: Body mass index; CI: Confidence intervals; CLAD: Censored least absolute deviation; HUI: Health utilities index; JCUSH: Joint Canada/United States Survey of Health; OLS: Ordinary least squares; US: United States.

Competing interests The authors declare that they have no competing interests.

Authors’ contributions YA conceived and designed the study and analyzed the data, and JH, OFN, and MJ critically contributed to the conception, design, and analysis. All authors critically contributed to interpret results. YA and JH drafted the manuscript. All authors contributed to critical revisions of the manuscript for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements This study was supported by Nova Scotia Health Research Foundation (8677) and Canadian Institutes of Health Research (MPE 124739). Yukiko Asada was supported by a Canadian Institutes of Health Research New Investigator Award (MSH 87687). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We gratefully acknowledge valuable comments from Dan Hausman, Larry Temkin, and participants of the polinomics seminar at the Centre for Health Economics and Policy Analysis at McMaster University and the Quebec Inter-University Center for Social Statistics conference: the social policy and health inequalities: An International perspective.

Author details 1Department of Community Health and Epidemiology, Dalhousie University, 5790 University Avenue, Halifax, Nova Scotia B3H1V7, Canada. 2Department of Economics and Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, Ontario L8S4M4, Canada. 3Department of Research and Development, Haukeland University Hospital, Jonas Liesvei 65,

5021 Bergen, Norway. 4Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Tour Saint-Antoine, Porte S03-458, 850, rue St-Denis, Montreal, Quebec H2X0A9, Canada. 5Département d’administration de la santé, Université de Montréal, C.P. 6128, succursale Centre-ville, Montreal, Quebec H3C3J7, Canada.

Received: 13 August 2014 Accepted: 7 January 2015

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