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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: J Nurs Meas. 2021 Sep 13;30(3):433–448. doi: 10.1891/JNM-D-20-00126

The Area Deprivation Index Corresponds Effectively With Other Measures of Objective Socioeconomic Status in Adults With Chronic Low Back Pain

Pamela Jackson 1, Burel R Goodin 1, D Leann Long 1, Rita Jablonski 1, Terence M Penn 1, Andrew M Sims 1, Tammie Quinn 1, Demario S Overstreet 1, Mirjam-Colette Kempf 1, Deanna D Rumble 1, Edwin N Aroke 1
PMCID: PMC10472843  NIHMSID: NIHMS1842830  PMID: 34518395

Abstract

Background and Purpose:

How the Area Deprivation Index (ADI) performs compared to other measures of socioeconomic status (SES) is unknown. The study purpose is to compare the ADI and other measures of SES in their ability to predict pain severity/interference.

Methods:

Four measures of SES were compared—ADI, income, education, and subjective social status (SSS).

Results:

Pain severity/interference correlated positively with ADI (r = .396/r = .33), and negatively with income (r = −.507/r = −.428) and education (r = −.271/r = −.102). Criterion scores of the pain severity model suggest income performs best (AIC = 428.29/BIC = 436.22), followed by ADI (AIC = 437.24/BIC = 445.17), with education performing least well (AIC = 446.35/BIC = 454.29). Similar results were seen for the pain interference model.

Conclusions:

Neighborhood-level factors warrant consideration along with individual-level factors when attempting to understand the impact of SES on chronic low back pain.

Keywords: social class, residence characteristics, social environment, chronic pain

Chronic pain has been recognized as a significant cause of suffering in the world (International Association for the Study of Pain, 2017), with an estimated 11% of American adults experiencing pain every day and an annual economic cost approaching $635 billion (National Center for Complementary and Integrative Health, 2018). Chronic pain is a major cause of disability (Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education, 2011), negatively affecting a person’s quality of life and daily activities of work and family (Dueñas et al., 2016). Low back pain is one of the most common painful conditions and is the leading cause of disability worldwide (GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018). It is consistently among the top five reasons for health-care visits ( M. L. Ferreira et al., 2010; Maher et al., 2017). Although many individuals with acute low back pain recover, about one in five progress to chronic low back pain (CLBP), lasting more than 3 months (Maher et al., 2017). The societal cost of CLBP is very high (Dutmer et al., 2019); it is associated with decreased quality of life (Husky et al., 2018), depressive symptoms (Pinheiro et al., 2016; Robertson et al., 2017), and decreased work productivity (Amirdelfan et al., 2018; Dutmer et al., 2019; Vlaeyen et al., 2018). To date, CLBP remains a major public health problem despite concerted efforts to address it.

Prior studies have documented the relationship between socioeconomic status (SES) and pain outcomes (Dorner et al., 2011; Dorner et al., 2018; Ikeda et al., 2019; Morgan et al., 2011; Riskowski, 2014; van Hecke et al., 2013). Generally, lower SES has been associated with virtually every aspect of poor chronic pain outcomes, including pain severity (Morgan et al., 2011), pain interference (Dorner et al., 2011), and decreased quality of life (Mielck et al., 2014). This trend toward worser pain outcomes in chronic pain has also be documented in CLBP (Hartvigsen et al., 2018; Ikeda et al., 2019; Jonsdottir et al., 2019). Specifically, higher rates of CLBP correlate with low SES and low educational attainment (Ikeda et al., 2019; Jonsdottir et al., 2019).

Investigators frequently use demographic variables to define SES, but the operational measurement of SES varies between studies. Individual level SES has been operationalized using objective and subjective factors (Jackman & Jackman, 1973). Frequently used objective measures of SES include education, income, occupation, and wealth. Research suggests that an individuals’ highest attained level of education is associated with income, wealth, living conditions, and health outcomes (Galobardes et al., 2007). Subjective social status (SSS) is the internally derived perception of one’s position in society (Jackman & Jackman, 1973) and predicts various health outcomes after accounting for objective measures of SES. However, it has been suggested that SSS may differ by sex, race, and ethnicity (Shaked et al., 2016; Wolff et al., 2010). Subjective social status is frequently measured using the MacArthur Scale of Subjective Social Status (or MacArthur Ladder) (W. Ferreira et al., 2018; Hoebel et al., 2017; Stanford University Department of Psychology, n.d.; Subramanyam et al., 2012; Zell et al., 2018). Prior work from our group shows that lower SSS correlates with more severe CLBP and increased pain interference (Aroke et al., 2020).

Besides personal (objective and subjective) measures of SES, the SES of an individuals’ environment also affect their living conditions and health (Ross & Mirowsky, 2008). Living in disadvantaged neighborhoods has been associated with poor health outcomes, including cardiovascular disease, obesity, diabetes, and chronic pain (Barber et al., 2016; Brennan & Turrell, 2012; Ulirsch et al., 2014). The Area Deprivation Index (ADI) is a well-validated instrument that measures neighborhood SES. Prior studies have used the ADI to examine the effects of neighborhood SES on several health outcomes (Chamberlain et al., 2020; Durfey et al., 2019; Kind et al., 2014; Kurani et al., 2020; Oates et al., 2019). For example, low neighborhood SES has been found to be associated with multimorbidity of chronic diseases (Chamberlain et al., 2020), diabetes, and blood pressure control (Durfey et al., 2019), and the presence of methicillin-resistant Staphylococcus aureus in pediatric patients with cystic fibrosis (Oates et al., 2019). The ADI assesses SES by measuring social deprivation and neighborhood disadvantage (Kind & Buckingham, 2018). Of relevance to this study, emerging evidence suggests that the SES of the neighborhood in which a person lives (i.e., the level of neighborhood disadvantage) is an important factor in pain outcomes (Green & Hart-Johnson, 2012; Maly & Vallerand, 2018). However, little is known about the utility of various measures of SES to assess the association of SES on chronic pain outcomes. While measures of SES are meant to measure the same overarching concept, they may not be interchangeable. The specific measure chosen to examine relationships between SES and back pain outcomes may influence its predictive ability (Fliesser et al., 2017). Informed by the social determinants of health, this paper will generally examine which measure of SES better predicts CLBP outcomes. The purpose of this study was to compare the ADI with other measures of SES in predicting pain severity and interference in adults with CLBP.

BACKGROUND AND CONCEPTUAL FRAMEWORK

An individual’s SES is an essential factor for understanding the personal and societal impact of chronic pain. Chronic pain occurrence and severity increases as objective measures of SES decreases (van Hecke et al., 2013). Other investigators have reported that low SES is linked to poor pain outcomes, while high SES is associated with better results in various pain conditions, including back pain (Dorner et al., 2011; Dorner et al., 2018; Grol-Prokopczyk, 2017; Gurung et al., 2015; Hoy et al., 2010; Janevic et al., 2017; Yu et al., 2020).

In 2010, the World Health Organization published the social determinants of health (SDOH) framework that systematically maps out determinants of health to structural intermediary and health system levels (Solar & Irwin, 2010). SDOH include the conditions in which people live, work, play, and grow. Many SDOH factors tend to cluster among individuals living in underprivileged circumstances and tend to interact with each other. As such, personal factors influence an individual’s health and the environment. In turn, their environment affects both personal factors (e.g., health behaviors) and health outcomes (Artiga & Hinton, 2018). The SDOH framework has been used to explore how various chronic conditions are affected by social (personal and environmental) factors. For instance, in 2000, hundreds of thousands of deaths were attributable to negative SDOH, including low education, individual-level poverty, and area-level poverty (Galea et al., 2011).

As depicted in Figure 1, an individual’s SES (objective and subjective measures of SES) influences CLBP outcomes. Conceptually, objective measures of SES include individual (income and education) and environmental (ADI) factors (Galobardes et al., 2007; Krieger et al., 1997; Ross & Mirowsky, 2008; Singh, 2003). These objective measures of SES influence and are influenced by SSS (MacArthur’s social status ladder).

Figure 1.

Figure 1.

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Relationship of objective and subjective SES measures and chronic low back pain.

Note. ADI = Area Deprivation Index; 1 = Individual-Level, Subjective; 2 = Individual-Level, Objective; 3 = Area-Level, Objective.

METHODS

Study Design and Participants

This methodological study employed a secondary analysis of data collected for an ongoing study: Examining Racial and Socioeconomic Disparities in Chronic Low Back Pain (ERASED) (R01MD010441). The goal of the ERASED study is to characterize racial differences in CLBP outcomes using a socioeconomic framework. Details of the ERASED study have previously been published (Aroke et al., 2020; Penn et al., 2020). Briefly, adults between the ages of 19 and 85 years were recruited. Respondents were included if they had nonspecific low back pain lasting more than 12 weeks. Exclusion criteria included the presence of other conditions that may confound result interpretation, such as pain conditions (e.g., ankylosing spondylitis, cancer pain, fibromyalgia), neurological diseases (e.g., Parkinson’s, epilepsy), or medical conditions (e.g., uncontrolled hypertension or poorly controlled diabetes). Participants included in the current study were recruited between November 2017 and November 2019. Only participants with data for all four measures (ADI, income, education and SSS) of SES being examined in this study were included for analysis. The Institutional Review Board at the University of Alabama at Birmingham reviewed and approved the ERASED study. All procedures were carried out following guidelines for the ethical conduct of research.

Measures

Demographic data included age, sex (male vs. female), and self-identified race (non-Hispanic Black, non-Hispanic White, and other). Measures were chosen to represent both area-level and individual SES, as well as objective and subjective SES. The goal was to capture a broad range of aspects of SES with which to compare the ADI. Income and education—measured as self-reported annual household income after taxes and the highest level of education, respectively—served as objective measures of SES at the individual level. Several valid and reliable instruments were used to assess neighborhood disadvantage, SSS, pain severity, and pain interference—the ADI, MacArthur Ladder of Subjective Social Status, and Brief Pain Inventory (BPI) respectively.

Area Deprivation Index.

The ADI is a validated objective measure of SES at the area level, using neighborhood disadvantage (Kind & Buckingham, 2018; Singh, 2003). Each participant’s address was linked to its respective census block, which is publicly available data. The ADI score is created using 17 census indicators of SES using domains such as income, housing, employment, and education (Kind & Buckingham, 2018; Kind et al., 2014). For each participants’ census block, we used the 2015 ADI v.2.0 (available at www.neighborhoodatlas.medicine.wisc.edu) that assigns a decile (i.e., 1–10) ADI score at the state level. For analysis, the Alabama state-level ADI decile scores were stratified into quintile rankings (i.e., 1–5), whereby the highest ranking “5” represented the greatest level of neighborhood disadvantage and “1” represented the lowest level of neighborhood disadvantage. The ADI has demonstrated high internal consistency (i.e., Cronbach’s alpha .94) (Singh, 2003; Singh et al., 2002; Singh & Siahpush, 2002) and a high test-retest reliability of 0.89 (Singh et al., 2002)

MacArthur Ladder of SSS.

The MacArthur ladder is a commonly used instrument that measures SSS at the individual level using a visual analog of a ladder (Adler & Stewart, 2007). Participants compare their self-perceived social status relative to others based on the following instructions:

Think of this ladder as representing where people stand in the United States. At the top of the ladder are the people who are the best off – those who have the most money, the most education and the most respected jobs. At the bottom are the people who are the worst off – who have the least money, least education, and the least respected jobs, or no job. The higher up you are on the ladder, the closer you are to people at the very top; the lower you are, the closer you are to the people at the very bottom. Please place a large “X” on the rung of the ladder for where you think you stand at this time in your life, relative to other people in the United States.

Participants ranked their position on the rungs of the ladder 0 to 10 corresponding with their subjective impression of their level of social status. The MacArthur ladder is a widely used instrument and has been translated into many languages (W. Ferreira et al., 2018). It has a moderate test-retest reliability (ρ = 0.62) (Operario et al., 2004), face validity, moderate concurrent validity (Kappa = 0.55–0.67) (W. Ferreira et al., 2018) and strong construct validity (Cundiff et al., 2013).

Brief Pain Inventory—Short Form.

The BPI is a valid and reliable self-administered 11-item questionnaire to assess clinical pain with an internal consistency reliability of greater than 0.85 (W. Ferreira et al., 2018; Majedi et al., 2017; Song et al., 2016; Tan et al., 2004). It measures two dimensions of pain: pain severity (intensity) and pain interference. The construct validity has previously been reported using factor analysis (K. A. Ferreira et al., 2011; Song et al., 2016; Tan et al., 2004). Prior work by our team indicates that BPI has internal consistency α = 0.94 in our sample (Aroke et al., 2020). Participants were asked to rate their pain at its worst, least, and average for the last 24 hours and at the time of the study at a scale from 0 to 10. The mean of these 4 items correspond to the BPI-pain severity score. Using the same type of 0 to 10 scales, participants rated separately how their pain interferes with their life in the following seven domains: (a) mood, (b) relations with other people, (c) enjoyment, (d) ability to concentrate, (e) appetite, (f) general activity, and (g) walking. Both pain severity and interference items are scored from 0 to 10.

DATA ANALYSIS

The ADI and other measures of SES were compared using Spearman correlations, coefficient of multiple determination partial R2, and goodness-of-fit tests Akaike information criterion (AIC) and Bayesian information criteria (BIC). The strength of the correlation coefficients was classified as follows: negligible (ρ < 0.10), weak (0.10–0.39), moderate (0.40–0.69), strong (0.70–0.89), and very strong (0.90–1.00) (Schober et al., 2018). Linear regression modeling was used to determine the relative contributions of ADI, income, education, and SSS to pain severity/interference. For comparison, partial R2 of .02 to .14 were considered as small, .15 to .34 as medium, and .35 or greater as large (Cohen, 1992). Also, lower AIC/BIC scores were considered better, and an internal consistency Cronbach’s ɑ = .7 to .95 was considered acceptable (Tavakol & Dennick, 2011). Test-retest reliability of the ADI was assessed using a Bland-Altman plot. A power analysis using G*Power 3.1 showed that a sample size of 104 eligible was sufficient to detect a weak correlation (ρ = 0.27) and a ΔR2 of .08 with 80% statistical power and alpha level of .05 (Faul et al., 2009). Data were analyzed using R version 4.0.2 statistical software.

RESULTS

Pain severity and interference data were available for 129 participants. Of these, data for all variables of interest were available for 104 participants with CLBP, who were included in the final analyses. Participants were on average, 45 years old and tended to be women (58.7%), and African American (56.7%). The median household income was $35K–39.9K, falling below the Alabama State median income ($48K) and national median income level ($60K) (www.census.gov/quickfacts). Approximately 74% of participants received some college education, graduated from college, or attended graduate school. On average, participants rated their social status as 5.1 (SD = ±2.0) out of 10. ADI scores were available for 97% of participants representing 85 census block groups, across five counties (Jefferson, Shelby, Bibb, Walker, and Winston) in Central Alabama. Table 1 summarizes the characteristics of participants included in the study.

TABLE 1.

Characteristics of Participants (N = 104)

Characteristic Mean (SD) or % (n)
Age in years 45 ± 13
Sex
 Female 58.7% (61)
 Male 41.3% (43)
Ethnicity
 African American 56.7% (59)
 Caucasian 41.3% (43)
 Unknown 1.9% (2)
Area Deprivation Index (1–5) 2.7 (1.5)
Income (Median) $35K–$39.9K
Education
 Partial High School 3.8% (4)
 High School Graduate 22.1% (23)
 Partial College 31.7% (33)
 College Graduate 24% (25)
 Graduate/Professional School 18.2% (19)
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Correlations of Objective and SSS

As depicted in Table 2, there was a statistically significant moderate negative relationship between the objective measures of social status (income and education) and the ADI, ρ = −0.57 and −0.45, respectively. However, ADI and SSS were not significantly correlated (p = .335). In addition, both pain severity and pain interference negatively correlated with individual measures of social status and positively correlated with ADI. In contrary, the relationship between SSS and pain severity was not statistically significant (p = .11). Similarly, the relationship between pain interference and both education and SSS were not statistically significant

TABLE 2.

Spearman Correlations Between Measures

1. 2. 3. 4. 5. 6.
1. ADI –
2. Income −.566** –
3. Education −.449** .455** –
4. SSS −.096 .311** .181 –
5. Pain Severity .396** −.507** −.271* −.16 –
6. Pain Interference .33** −.428** −.102 −.152 .627** –
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Note. ADI = Area Deprivation Index; SSS = Subjective Social Status.

*

p < .05.

**

p ≤ .001.

Comparing Predictors of Pain Severity and Interference

The average pain severity and interference scores were 5.1 (SD ± 2.1) and 4.1 (SD ± 2.3), respectively. The objective measures that showed significant correlations with pain outcomes were fitted into different linear models to compare their ability to predict pain severity/interference (Figure 2). For pain severity, the model’s goodness-of-fit AIC and BIC suggested that income outperforms other measures of objective SES (AIC = 428.29/ BIC = 436.22), followed by ADI (AIC = 437.24/BIC = 445.17), with education performing least well (AIC = 446.35/BIC = 454.29). Likewise, for pain interference, AIC and BIC suggested that income performs best (AIC = 456.08/BIC = 464.01), followed closely by ADI (AIC = 4 63.96/BIC = 471.90).

Figure 2.

Figure 2.

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AIC and BIC scores for SES pain severity and pain interference with SES measures.

Assessment of Independent Contribution to Pain Outcomes

We fitted each objective measure of SES into a multiple regression model to assess its independent contribution to pain outcomes assessed. Results of the model predicting pain severity indicated that income made the largest contribution (partial R2 = .098), followed by ADI (partial R2 = .021). Similar results were obtained from the model predicting pain interference. The model revealed that while the ADI (partial R2 = .029) makes a small independent contribution, the contribution of income (partial R2 = .100) was greatest. Education contributed a small amount to the interference model (partial R2 = .026) and an insignificant amount to the pain severity model (partial R2 = .0003).

Reliability of ADI and BPI

We used ADI decile scores from 2013 and 2015 to determine its test-retest reliability. The test-retest reliability of the ADI was excellent with an intraclass correlation of 0.92 (p < .001; 95% CI: 0.89–0.94). Figure 3 displays the Bland-Altman plot for the differences between the 2013 and 2015 ADI decile scores. Most differences were clustered within two deciles, which are acceptable limits of agreement (Polit & Beck, 2017). Internal consistency of the BPI in our sample was high with a Cronbach’s alpha of 0.95 (95% CI: 0.93–0.96).

Figure 3.

Figure 3.

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Degree of agreement between ADI Deciles for 2013 and 2015.

DISCUSSION

Our study offers preliminary evidence that the ADI is an effective measure of SES and adds to the growing body of literature indicating neighborhood disadvantage is an important predictor of pain outcomes. In direct comparisons between objective and subjective measures of SES, our results showed that income performed best at predicting pain severity and interference. However, the ADI outperformed education and SSS, which are other more well-established measures of SES (Galobardes et al., 2007; Krieger et al., 1997; Singh-Manoux et al., 2005). Our findings are consistent with prior studies, which found lower neighborhood SES is associated with poorer pain outcomes. Living in a disadvantaged neighborhood is associated with increased musculoskeletal pain and increased pain interference with daily activities following motor vehicle accident (Ulirsch et al., 2014). Similarly, greater noninflammatory musculoskeletal pain interference has been reported among individuals living in low SES neighborhoods in Norway (Brekke et al., 2002). Likewise, Fuentes et al. (2007) found that high neighborhood SES is associated with lower chronic pain in adults over age 50 years. However, other investigators have shown that while low neighborhood SES is associated with new-onset chronic widespread pain, that relationship is not robust and does not hold up once psychological comorbidities are considered (Davies et al., 2009). This discrepancy may be related to the fact that the neighborhood SES measure used by Davies and colleagues (2009) did not account for income and poverty, which contribute to ADI scores. To our knowledge, this is the first study to offer evidence that suggest that neighborhood disadvantage correlates with pain severity and pain interference in CLBP. Thus, for adults with CLBP, ADI may be a good predictor of pain outcomes.

Area-Level Versus Individual-Level Measures of SES

Our findings suggest that there is a moderate relationship between individual-level versus area-level measures of SES. Also, the area-level measure of SES (i.e., ADI) performs better than some individual-level measures of SES in predicting pain severity and pain interference. The strength of the relationship in our study is slightly higher than previously reported. Buajitti and colleagues (2020) reported a low correlation between individual and area-level SES among participants in the population-based Canadian Community Health Survey. Diez-Roux et al. (2001) found weak correlations between individual and area-level measures of income in an examination of three U.S.-based cohorts. The differences in the strength of the relationship between individual and area-level measures may be related to the fact that various measures capture different dimensions of SES. Also, it may reflect the relative homogeneity of ADI in our study sample.

While the relationships between ADI and income, as well as the ADI and education were significant, the relationship between the ADI and SSS were not. Thus, supporting the view that area- and individual-level measures of SES are related and multidimensional, but not interchangeable (Galobardes et al., 2007; Geyer et al., 2006; Krieger et al., 1997). It is possible that an individual’s neighborhood may be a reflection of their income and education, which does not necessarily reflect the individual’s perception of their SES. Unfortunately, this complex relationship is under-explored among individuals living with CLBP. Fliesser and colleagues (2017), examined the relationship of chronic back pain outcomes with three different singular, individual-level measures of SES and a multidimensional, individual-level index. While the multidimensional measure predicted pain intensity best, singular measures were better at predicting disability. They concluded that the predictive ability of SES on back pain is variable based on the SES measure that is chosen (Fliesser et al., 2017). Other investigators have argued that it may not be “useful or theoretically compelling to search for a single ‘best’ indicator” of SES because different measures of SES capture different dimensions of the social hierarchy (Galobardes et al., 2007). Therefore, different SES indicators may measure different ways SES impacts health (Galobardes et al., 2007). Ultimately, our results support the inclusion of area-level indicators in addition to individual-level measures to better understand the impact of SES on CLBP.

Objective Versus Subjective Measures of SES

In our study, objective measures of SES significantly predicted pain severity and interference, while our subjective SES measure did not. These findings support the fact that objective and subjective measures of SES are not interchangeable (Shaked et al., 2016). Other studies have reported that objective measures of SES are associated with back pain outcomes. Using the objective measures income and education, Dorner et al. (2011) found lower SES to be associated with greater disability from pain. Similarly, a review by Hoy et al. (2010) found that low educational status is a common risk factor for low back pain. Jonsdottir et al. (2019) report an association between low SES and low educational attainment as risk factors for chronic back pain. Similarly, in her study using nationally representative data from the United States, Riskowski (2014) found increased odds of back pain for both men and women with the lowest SES.

Limitations

This study has a number of limitations, including the modest number of participants, homogeneity of participants neighborhood, and idiosyncratic relationship between predictors and pain outcomes. Due to missing data on full address and income, approximately 80% of the original 129 participants were included in the final analysis. While there is no set cutoff for missing data, 5% is considered inconsequential and over 10% may be considered problematic for inference (Dong & Peng, 2013). Also, most of our participants resided in the Birmingham-Hoover, Alabama metropolitan area. The Birmingham-Hoover metropolitan area covers over 5,200 square miles and is the most populous area of the state of Alabama, with an estimated population of over 1.1 million (U.S. Census Bureau, 2018). Future studies should include participants from other geographic areas because measuring SES involves assessment of a social hierarchy, which may vary from one geographic location to another (Jackman & Jackman, 1973; Singh-Manoux et al., 2005). The use of the ADI that relies on census block data is a major limitation. This is because the reliability of the ADI score depends on the accuracy of the census data (Messer & Kaufman, 2006). However, multiple indicators are preferred when measuring SES (Messer & Kaufman, 2006). Thus, the breadth of the census data could be a strength because the ADI uses 17 indicators to assess neighborhood SES. Finally, the cross-sectional approach limited our ability to determine how long the participants had lived in the disadvantaged neighborhood.

RELEVANCE TO NURSING PRACTICE AND RESEARCH

Our findings have important implications for nursing practice and research. These findings suggest that research on SES and pain that encompasses both area-level and individual-level factors will provide valuable perspective on pain outcomes. Considering that SES may play a role in the development of pain and musculoskeletal pain severity (Maly & Vallerand, 2018; Riskowski, 2014), our findings support the adoption of neighborhood disadvantage screening for patients with, or at risk for developing chronic pain. Since the ADI allows for assessment of neighborhood disadvantage using readily available data, it may be a valuable tool in efforts to address chronic pain disparities, especially those based on geographically delineated SES inequality. An understanding of the level of neighborhood disadvantage of individuals and groups may be useful for the planning of patient care because it can help identify patients who would benefit from interventions that address area-level SDOH. Regarding research implications, nurse scientists should investigate the effect of neighborhood on chronic pain outcomes using a longitudinal approach. Also, future studies should compare individual versus area level interventions in addressing SES disparities in chronic pain.

CONCLUSION

Individual and area factors correlate with CLBP outcomes such that lower income, lower education, and living in a disadvantaged neighborhood predicts CLBP severity and interference. To better understand the impact of SES on CLBP outcomes, both area-level and individual-level aspects of SES should be considered. Results generally fit with previously established findings that different measures of SES are related but not interchangeable. Among SES measures, income appears to be the strongest predictors of CLBP outcomes.

Funding.

This study was funded in part by the National Institute on Minority Health and Health Disparities grant R01MD010441, National Institute of Arthritis and Musculoskeletal and Skin Diseases grant R01AR079178, as well as the American Association of Nurse Anesthetists grant 2018-FS-4.

Footnotes

Disclosure. The authors have no relevant financial interest or affiliations with any commercial interests related to the subjects discussed within this article.

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