Pain Medicine 2015; 16: 2222–2225 Wiley Periodicals, Inc.
PERSPECTIVE & COMMENTARY Commentary Controlling for Socioeconomic Status in Pain Disparities Research: All-Else-Equal Analysis When “All Else” Is Not Equal
Colleagues who conduct pain disparities research would firmly acknowledge that a common, if not the most common, question asked in trying to make sense of racial disparities literature is “did you control for socioeconomic status (SES)?” The question is often based on the assumption that the race effect on disparities is indeed an SES effect (i.e., SES is a confounder). This assumption receives further support from the observation that in some studies the effect of race disappears in a model controlling for SES. When this happens, most conclude that race does not matter in disparitiesrelated outcomes. This commentary illustrates why such an interpretation may be misguided given the complex relationship of race and SES in the United States. We begin with defining the concept of confounders and mediators, we then make a case for why SES in the United States context may be best conceptualized as a mediator of race and health relation, we then apply this understanding to an example from pain disparities literature to illustrate the cautions in interpreting the race effects on pain outcomes when controlling for SES. First, a Word about Statistical Control and Disparities Research In general, the statistical control or adjustment implies removing bias or unexplained variability in estimating the effect of an independent variable (X) on an outcome (Y) by holding all presumed covariates and confounders as constant (i.e., when all else is equal or the effect of X on Y when controlling for independent factors). While this is 2222
a useful analytical technique, it has several rather obvious conceptual limitations for disparities research. The broad category of SES includes variables that are social determinants of health such as income, education, wealth, and neighborhood characteristics. The goal of racial disparities research is to explain a social phenomenon constructed of the variables whose levels are systematically organized across different subgroups. The relationship of race and SES is a case in point: According to the American Community Survey, the U.S. Census Bureau’s national random sample survey, over one in four (28%) blacks or African Americans have an income below the national poverty level when compared with 11% for non-Hispanic Whites [4]. They live in geographical areas of high segregation, which some scholars believe to be the root cause of health inequalities [5]. For example, in a study of pain medications’ availability in randomly selected Michigan pharmacies, 97% of the zip codes were amenable to classification as either white zip codes (70% white residents) or nonwhite zip codes (70% non-white residents) [6] confirming continued racial residential segregation. Other pain disparities’ researchers have illustrated the compounding disadvantage in chronic pain outcomes generated by the overlap between race and lack of education, income, insurance, and segregation in the United States [7]. Thus, systematic differences in race and SES highly co-occur but is SES a confounder or a mediator of race and pain outcomes? SES: A Confounder or a Mediator of Race and Health? For a variable to be considered a confounder, the following conditions are necessary: The variable: 1) must be associated with the independent variable under study; 2) must be associated with the outcome; and 3) should not be an intermediate variable in the causal pathway between the independent variable and the outcome [8] (Figure 1A). Field studies such as “Are Emily and Greg More Employable than Lakisha and Jamal?” demonstrate that
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Despite improvements in the overall health in the United States, gaps in quality of care and health outcomes remain stark for racial minorities [1]. Racial disparities in the prevalence of pain and pain treatment are consistently reported since early 1990s and these concerning disparities occur across settings and types of pain [2]. The Institute of Medicine has called for promoting dialogue to improve “the visibility of racial and ethnic disparities in health and health care as a national problem” [3]. Improving the visibility of this intricate problem first and foremost requires correct application and interpretation of research pertaining to racial disparities in health.
Controlling for Socioeconomic Status
despite the same credentials, employment and labor market opportunities differ vastly and fairly homogenously across occupations, industries, and even among equal opportunity employers [9]. Similarly, studies conducted in a variety of other social settings, support that SES is not a confounder as it is in the causal pathway (intermediate variable) between race and health (Figure 1B). In an adjusted analysis, a partial mediation by SES would often lead to attenuation of race effect and a complete mediation would lead to disappearance of race effect. This cannot be interpreted as race being unimportant as different groups have systematically different chance at SES, and thus, have differential risks for worse health outcomes mediated by SES. In their
classic article, Greenland and Robins argue that applying the principle of covariate control is inappropriate in situations where the covariate is an intermediate variable and may produce bias even more severe than the original bias the researcher intends to remove using an adjusted analysis [8]. Careful Interpretation of Findings When Race Effect Disappears in an Analysis Controlling for Race The studies simultaneously controlling for race and SES require careful interpretation based on how SES variables are distributed across groups. A useful illustration of this 2223
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Figure 1 Understanding relationships among race, SES, and health disparities. (A) Illustration of SES as a confounder. The relationship between race (independent variable) and health disparities (outcome variable) is spurious and confounded by the third variable (SES), which is associated with both race and health disparities. Note that for SES to be a confounder, it cannot be an intermediate variable between race and health disparities. (B) Illustration of SES as a mediator. Race is associated with SES and SES is associated with health disparities. Since race systematically relates to SES opportunities, SES is in the causal pathway (mediator) between race and health, and is therefore not a confounder and (C) illustration of SES as an independent predictor. Race (Independent variable) is not completely defined by SES, but they are both independently associated with health disparities (outcome variable). If this is the case, it can only be teased out by obtaining an adequate sample of all races within each SES strata.
Meghani and Chittams point can be found in a population-based study of pain by Portenoy et al. [10]. Using multivariable logistic regression (outcome: presence of disabling pain), the researchers found that the effect of race did not remain significant in an adjusted model; rather, variables that are social determinants of health (total family income, education, and employment) were statistically significant [10] (p. 325). Income was the strongest predictor of the outcome; compared with those earning >$75,000, those earing $75,000) with strong negative association with disabling pain. Furthermore, almost one in two African Americans (44% vs 28% Whites) reported earning
PERSPECTIVE & COMMENTARY Commentary Controlling for Socioeconomic Status in Pain Disparities Research: All-Else-Equal Analysis When “All Else” Is Not Equal
Colleagues who conduct pain disparities research would firmly acknowledge that a common, if not the most common, question asked in trying to make sense of racial disparities literature is “did you control for socioeconomic status (SES)?” The question is often based on the assumption that the race effect on disparities is indeed an SES effect (i.e., SES is a confounder). This assumption receives further support from the observation that in some studies the effect of race disappears in a model controlling for SES. When this happens, most conclude that race does not matter in disparitiesrelated outcomes. This commentary illustrates why such an interpretation may be misguided given the complex relationship of race and SES in the United States. We begin with defining the concept of confounders and mediators, we then make a case for why SES in the United States context may be best conceptualized as a mediator of race and health relation, we then apply this understanding to an example from pain disparities literature to illustrate the cautions in interpreting the race effects on pain outcomes when controlling for SES. First, a Word about Statistical Control and Disparities Research In general, the statistical control or adjustment implies removing bias or unexplained variability in estimating the effect of an independent variable (X) on an outcome (Y) by holding all presumed covariates and confounders as constant (i.e., when all else is equal or the effect of X on Y when controlling for independent factors). While this is 2222
a useful analytical technique, it has several rather obvious conceptual limitations for disparities research. The broad category of SES includes variables that are social determinants of health such as income, education, wealth, and neighborhood characteristics. The goal of racial disparities research is to explain a social phenomenon constructed of the variables whose levels are systematically organized across different subgroups. The relationship of race and SES is a case in point: According to the American Community Survey, the U.S. Census Bureau’s national random sample survey, over one in four (28%) blacks or African Americans have an income below the national poverty level when compared with 11% for non-Hispanic Whites [4]. They live in geographical areas of high segregation, which some scholars believe to be the root cause of health inequalities [5]. For example, in a study of pain medications’ availability in randomly selected Michigan pharmacies, 97% of the zip codes were amenable to classification as either white zip codes (70% white residents) or nonwhite zip codes (70% non-white residents) [6] confirming continued racial residential segregation. Other pain disparities’ researchers have illustrated the compounding disadvantage in chronic pain outcomes generated by the overlap between race and lack of education, income, insurance, and segregation in the United States [7]. Thus, systematic differences in race and SES highly co-occur but is SES a confounder or a mediator of race and pain outcomes? SES: A Confounder or a Mediator of Race and Health? For a variable to be considered a confounder, the following conditions are necessary: The variable: 1) must be associated with the independent variable under study; 2) must be associated with the outcome; and 3) should not be an intermediate variable in the causal pathway between the independent variable and the outcome [8] (Figure 1A). Field studies such as “Are Emily and Greg More Employable than Lakisha and Jamal?” demonstrate that
Downloaded from http://painmedicine.oxfordjournals.org/ by guest on June 7, 2016
Despite improvements in the overall health in the United States, gaps in quality of care and health outcomes remain stark for racial minorities [1]. Racial disparities in the prevalence of pain and pain treatment are consistently reported since early 1990s and these concerning disparities occur across settings and types of pain [2]. The Institute of Medicine has called for promoting dialogue to improve “the visibility of racial and ethnic disparities in health and health care as a national problem” [3]. Improving the visibility of this intricate problem first and foremost requires correct application and interpretation of research pertaining to racial disparities in health.
Controlling for Socioeconomic Status
despite the same credentials, employment and labor market opportunities differ vastly and fairly homogenously across occupations, industries, and even among equal opportunity employers [9]. Similarly, studies conducted in a variety of other social settings, support that SES is not a confounder as it is in the causal pathway (intermediate variable) between race and health (Figure 1B). In an adjusted analysis, a partial mediation by SES would often lead to attenuation of race effect and a complete mediation would lead to disappearance of race effect. This cannot be interpreted as race being unimportant as different groups have systematically different chance at SES, and thus, have differential risks for worse health outcomes mediated by SES. In their
classic article, Greenland and Robins argue that applying the principle of covariate control is inappropriate in situations where the covariate is an intermediate variable and may produce bias even more severe than the original bias the researcher intends to remove using an adjusted analysis [8]. Careful Interpretation of Findings When Race Effect Disappears in an Analysis Controlling for Race The studies simultaneously controlling for race and SES require careful interpretation based on how SES variables are distributed across groups. A useful illustration of this 2223
Downloaded from http://painmedicine.oxfordjournals.org/ by guest on June 7, 2016
Figure 1 Understanding relationships among race, SES, and health disparities. (A) Illustration of SES as a confounder. The relationship between race (independent variable) and health disparities (outcome variable) is spurious and confounded by the third variable (SES), which is associated with both race and health disparities. Note that for SES to be a confounder, it cannot be an intermediate variable between race and health disparities. (B) Illustration of SES as a mediator. Race is associated with SES and SES is associated with health disparities. Since race systematically relates to SES opportunities, SES is in the causal pathway (mediator) between race and health, and is therefore not a confounder and (C) illustration of SES as an independent predictor. Race (Independent variable) is not completely defined by SES, but they are both independently associated with health disparities (outcome variable). If this is the case, it can only be teased out by obtaining an adequate sample of all races within each SES strata.
Meghani and Chittams point can be found in a population-based study of pain by Portenoy et al. [10]. Using multivariable logistic regression (outcome: presence of disabling pain), the researchers found that the effect of race did not remain significant in an adjusted model; rather, variables that are social determinants of health (total family income, education, and employment) were statistically significant [10] (p. 325). Income was the strongest predictor of the outcome; compared with those earning >$75,000, those earing $75,000) with strong negative association with disabling pain. Furthermore, almost one in two African Americans (44% vs 28% Whites) reported earning
