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
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.
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
Controlling for Socioeconomic Status
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.
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
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
<$25,000 were 2.54 times more likely to report disabling
pain (95% CI, 1.39 2 4.64, P 5 0.000) [10] (p. 325).
A strict statistical interpretation of these findings will be
that race does not matter but income does. However, a
closer look at Portenoy et al.’s demographics table [10]
(p. 320) shows that only 3% of African Americans
reported an income level (>$75,000) with strong negative association with disabling pain. Furthermore, almost
one in two African Americans (44% vs 28% Whites)
reported earning <$25,000—an income level with the
strongest positive association with the presence of disabling pain [10] (p. 320). Another way to interpret the
findings is that majority of African Americans shared a
social characteristic (i.e., low income levels) that would
predispose them to disparities in pain outcomes [10].
While some may argue that disentangling SES effect is
key in understanding addressable mechanisms (as race
and ethnicity are immutable characteristics), “entangling”
the relationship of race and SES is key in making sense
of the burden of an outcome and disproportionate
amount of resources needed to address the outcome in
different groups.
Further, the concept of adjusting for independent covariates from a statistical standpoint assumes that there is
enough data in all combinations of race and SES to effectively obtain reliable estimates of the model adjusted main
effects. When data is collected from a convenient sample,
this is often not the case resulting in inconclusive and
potentially misleading results. When interpreting results
from analyses of this type of data careful consideration
should be given to reviewing the distribution of participants in all combinations of race and SES categories.
Ignoring these issues can result in misinterpreting the
race effect on the outcome of interest.
In designing studies for which it is an important aim to
distinguish the effect of SES separated from race, care
should be taken to ensure that sufficient data is collected from all combinations of race and SES classification. As there is an overlap between race/ethnicity and
lower SES in the United States, oversampling of racial
and ethnic groups from higher SES strata may be
needed to achieve sufficient power to effectively assess
the two factors independently (Figure 1C).
also subject to lack of equality of social opportunities.
Most existing studies explaining racial disparities in pain
care have employed adjusted regression types of analyses that estimate the independent effect of race when
other variables are held constant. In the context of disparities, the correct interpretation of the contribution of
race (both in the presence and absence of statistical
significance) requires a deeper understanding of an intricate social phenomenon in making statistical inferences.
SALIMAH H. MEGHANI, PhD, MBE, FAAN and
JESSE CHITTAMS, MA
Department of Biobehavioral Health Sciences;
NewCourtland Center for Transitions & Health,
School of Nursing, University of Pennsylvania,
Philadelphia, Pennsylvania, USA
Reprint requests to: Salimah H. Meghani, PhD, MBE,
FAAN, Department of Biobehavioral Health Sciences;
NewCourtland Center for Transitions & Health, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Tel: (215) 573-7128; Fax: (215) 573-7507; E-mail:
[email protected].
Conflict of interest/Disclosures: Authors disclose no
financial or nonfinancial conflicts of interest.
References
1 Gold M. Mathematica Policy Research. Reducing
health care disparities: Where are we now? Robert
Wood Johnson Foundation; 2014:1–7.
2 Meghani SH, Byun E, Gallagher RM. Time to take
stock: A meta-analysis and systematic review of
analgesic treatment disparities for pain in the United
States. Pain Med 2012;13(2):150–74.
3 Institute of Medicine. Roundtable on the Promotion
of Health Equity and the Elimination of Health Disparities. 2013. Available at: http://www.iom.edu/
Activities/SelectPops/HealthDisparities.aspx
(accessed March 2015).
4 U.S. Census Bureau. Poverty Status in the Past 12
Months: 2012 American Community Survey 1-Year
Estimates (Table S1701). 2012. Available at: http://
factfinder2.census.gov/faces/tableservices/jsf/pages/
productview.xhtml?pid=ACS_12_1YR_S1701&prod
Type=table (accessed June 2014).
In Sum
5 Williams DR, Mohammed SA, Leavell J, Collins C.
Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Ann NY Acad Sci 2010;1186:69–101.
The category of SES includes variables such as education, income, insurance, and place of residence that are
6 Green CR, Ndao-Brumblay SK, West B, Washington
T. Differences in prescription opioid analgesic
2224
Controlling for Socioeconomic Status
availability: Comparing minority and white pharmacies
across Michigan. J Pain 2005;6(10):689–99.
7 Meghani SH. Corporatization of pain medicine:
Implications for widening pain care disparities. Pain
Med 2011;12(4):634–44.
8 Greenland S, Robins JM. Confounding and misclassification. Am J Epidemiol 1985;122(3):495–506.
9 Bertrand M, Mullaninathan S. Are Emily and Greg
more employable than Lakisha and Jamal? A field
experiment on labor market discrimination. Am Econ
Rev 2004;94:991–1013.
10 Portenoy RK, Ugarte C, Fuller I, Haas G. Population-based survey of pain in the United States: Differences among white, African American, and
Hispanic subjects. J Pain 2004;5(6):317–28.
2225