Abstract
Objective
We assessed whether race or ethnicity was associated with the incidence of high-impact chronic low back pain (cLBP) among adults consulting a primary care provider for acute low back pain (aLBP).
Methods
In this secondary analysis of a prospective cohort study, patients with aLBP were identified through screening at seventy-seven primary care practices from four geographic regions. Incidence of high-impact cLBP was defined as the subset of patients with cLBP and at least moderate disability on Oswestry Disability Index [ODI >30]) at 6 months. General linear mixed models provided adjusted estimates of association between race/ethnicity and high-impact cLBP.
Results
We identified 9,088 patients with aLBP (81.3% White; 14.3% Black; 4.4% Hispanic). Black/Hispanic patients compared to White patients, were younger and more likely to be female, obese, have Medicaid insurance, worse disability on ODI, and were at higher risk of persistent disability on STarT Back Tool (all P < .0001). At 6 months, more Black and Hispanic patients reported high-impact cLBP (30% and 25%, respectively) compared to White patients (15%, P < .0001, n = 5,035). After adjusting for measured differences in socioeconomic and back-related risk factors, compared to White patients, the increased odds of high-impact cLBP remained statistically significant for Black but not Hispanic patients (adjusted odds ration [aOR] = 1.40, 95% confidence interval [CI]: 1.05–1.87 and aOR = 1.25, 95%CI: 0.83–1.90, respectively).
Conclusions
We observed an increased incidence of high-impact cLBP among Black and Hispanic patients compared to White patients. This disparity was partly explained by racial/ethnic differences in socioeconomic and back-related risk factors. Interventions that target these factors to reduce pain-related disparities should be evaluated.
ClinicalTrials.gov Identifier
Keywords: Back Pain, Acute Pain, High-Impact Chronic Pain, Disparity, Equity
Introduction
High-impact chronic pain, defined as chronic pain that interferes with daily life or work activities, impacts 19.6 million Americans [1]. Chronic low back pain (cLBP) is the leading cause of disability and health care costs in the United States [2, 3]. Preventing transition of acute low back pain (aLBP) to high-impact cLBP can reduce adverse effects of cLBP on individuals, health systems, and society at-large.
Recently we reported on the TARGET Trial (Targeted Interventions to Prevent Chronic Low Back Pain in High‐Risk Patients), which was embedded in a cohort of over 9,000 adult primary care patients with aLBP [4]. In this cohort, we found that nearly one-third (32%) of aLBP patients reported cLBP at 6 months [5]. We developed a multivariable prediction model to identify patient characteristics associated with aLBP to cLBP transition (i.e., incident cLBP) [5]. As hypothesized, the Subgroups for Targeted Treatment (STarT) Back Tool, a prognostic screening measure for persistent back-related disability, was a strong predictor of incident cLBP [5]. Furthermore, our data revealed Black patients were more likely to develop cLBP than White patients.
While the use of race/ethnicity as an independent cause of pain outcomes is cautioned, consideration of the underlying historical context of discrimination and racism may be helpful in understanding and addressing the social and psychological factors that sustain racial/ethnic disparities in pain outcomes [6, 7]. A better understanding of these complex relationships may inform policies or public health programs that can improve long-term pain-related outcomes. Differences in treatment of acute pain by race/ethnicity are well documented (e.g., non-White patients are less likely to receive medication/opioids, imaging, or subspecialty referrals) [8–14]. Previous studies have also demonstrated provider beliefs about biological differences between racial groups (e.g., that Black people’s skin is thicker than White people’s skin) or other biases that may result in undertreatment of pain among non-White patients [14, 15]. However, the association of race/ethnicity with long term aLBP outcomes is understudied. Whether socioeconomic, back-related risk factors, or treatment recommendations by primary care providers (PCPs) explain race/ethnicity differences in the transition from acute to chronic back pain is unclear. Additionally, we are not aware of previous studies assessing the incidence of high-impact cLBP by race and ethnicity.
To inform these knowledge gaps, we used a causal inference approach to evaluate whether racial/ethnic differences in the incidence of cLBP and high-impact cLBP in the TARGET cohort can be explained by race/ethnicity differences in socioeconomic and back-related risk factors [16, 17]. Additionally, we assessed whether PCP recommended treatment for aLBP differed across racial/ethnic groups.
Methods
Study Design
A detailed study protocol for the TARGET Trial was approved by four institutional review boards and published elsewhere [18]. Patients with aLBP were included in a prospective cohort study. Patients at high risk for persistent disability on the STarT Back Tool were additionally included in a multi-site, pragmatic, cluster-randomized trial comparing psychologically informed physical therapy to usual care [18]. No intervention effect was observed [4]. Therefore, for this secondary analysis of a prospective cohort study we collapsed all high, medium, and low risk patients into a single cohort [4, 5].
Patients
The TARGET prospective cohort consists of 9,730 patients with aLBP seen from May 2016 until June 2018 at one of 77 primary care practices in healthcare delivery systems from four US-based geographic regions (Baltimore, MD; Boston, MA; Pittsburgh, PA; Salt Lake City, UT). Patients were followed through March 2019, and clinical outcomes were assessed at 6 months. Adults seen by their PCP for low back pain were eligible if an ICD-9 or ICD-10-CM diagnostic code for “axial low back pain” or “low back pain with leg pain” was documented in electronic medical record (EMR). We excluded adults with cLBP at the time of the PCP index visit, defined as a “yes” answer to both of the following questions: 1) Has your pain been present for more than three months?; and 2) Have you experienced back pain interfering with ability to do regular daily activities at least half of the days in past 6 months? This definition of cLBP is consistent with recommendations by the National Institutes of Health (NIH) Task Force on Research Standards for cLBP [5, 18, 19]. Patients with “red flag” diagnoses indicating a serious underlying cause of back pain (i.e., vertebral fracture, cancer, infection, cauda equina syndrome) were also excluded.
Ascertainment of Race/Ethnicity
Data on race (White/Black/other) and ethnicity (Hispanic/non-Hispanic) were collected from EMR. Patients with Hispanic ethnicity were characterized as Hispanic and race information determined the other two categories, that is, non-Hispanic White and Black patients. Patients with missing or “other” race/ethnicity data were excluded from analyses due to small size of each group (Figure 1).
Figure 1.
Flow diagram.
Outcome Measurement
Acquisition of 6-month outcome data occurred electronically, verbally, or via mail by research personnel. We identified patients with cLBP (using above NIH definition) and high-impact cLBP defined as cLBP with at least moderate disability on the Oswestry Disability Index (ODI score, >30) [20]. The ODI includes 10 items to assess how LBP affects common daily activities. ODI scores range from 0 “no disability due to LBP” to 100 “completely disabled due to LBP” [20]. Our high-impact cLBP definition mirrored the two-question approach used to define high-impact chronic pain in the National Health Interview Survey [21].
Recommendations by PCP
We extracted information on PCP recommendations for aLBP from EMR. Recommendations were characterized as early (0 to 21 days after index visit) or delayed (22 days to 6 months) [5]. These included prescribed medications, referrals to nonpharmacologic treatments or medical specialists, and imaging orders.
Socioeconomic Risk Factors
Type of healthcare insurance was collected. Medicaid status served as a proxy for low individual-level socioeconomic status [22]. Area Deprivation Index (ADI) was used as a measure of neighborhood-level socioeconomic status. The ADI, which measures disadvantage for a particular census block group, is ranked against scores across the United States [23, 24]. Higher percentile rankings on ADI indicate greater neighborhood disadvantage, which is associated with a range of poor health outcomes [25, 26]. We used ArcGIS Desktop version 10.5 (ESRI, Redlands, CA) to obtain the geographical identifier of each individual’s home address, which was then linked to ADI for their census block group. Patients with insufficient information to generate an ADI for their home address (1,388/9,088 patients, 15.3%) were assigned an ADI based on their clinic’s address, the approach used in our previous analyses [5].
Back-related Risk Factors
The nine-item STarT Back Tool was used to determine risk of persistent disability [27]. STarT Back Tool total scores range from 0 to 9, with higher scores indicating a greater number of risk factors for persistent disability. Psychological subscale scores range from 0 to 5, capturing information on anxiety, depression, catastrophizing, fear avoidance beliefs, and bothersomeness. Total and psychological subscale scores are used to categorize patients as low risk (total score ≤3), medium risk (total score ≥4 and subscale score ≤3), or high risk (total score ≥4 and subscale score ≥4) for persistent disability [27]. Body mass index (BMI) at the index PCP visit was identified from EMR.
Analysis
Baseline characteristics are presented by race/ethnicity category. Associations between race/ethnicity and baseline categorical factors were tested with χ2 tests adjusted for clustering at clinic level using Taylor series linearization for variance estimation. Associations with continuous variables were tested with linear mixed models that controlled for clustering with a random clinic effect.
Our primary outcomes were incidence of cLBP and high-impact cLBP at 6 months. Generalized linear mixed models with a logit link controlling for cluster design with a random clinic effect and fixed site effect provided adjusted estimates of the association between race/ethnicity and cLBP or high-impact cLBP.
Potential confounders and mediator variables were selected for inclusion in models evaluating the association of race/ethnicity with cLBP outcomes based on relevant causal inference literature and development of a causal graph tailored to our research question (Figure 2) [16, 28].
Figure 2.
Causal graph. This graph shows, in a cohort of patients with acute low back pain, the hypothesized relationship between race and the incidence of chronic low back pain over time. Time precedes from left to right. The graph is acyclic, that is, arrows are only allowed to point to the right, forward in time. Patients were identified from an index visit (time=0) where PCP made first assessment0 of acute LBP. The incidence of cLBP was identified on a follow-up (time=1) assessment1 6 months later. Treatment recommendations by the PCP for LBP from assessment0 to assessment1 were extracted from the electronic medical record. Our first model estimated whether there was a disparity in back-related outcomes by race/ethnicity in the TARGET cohort, adjusting for only differences in age0 and sex0. Our second model additionally adjusted for socioeconomic factors measured at index visit. Specifically, we adjusted for differences in Medicaid status0 and ADI scores0 which represent individual- and neighborhood-level socioeconomic status, respectively. Thus, this attempts to see how estimates would change if distributions of the socioeconomic status measures were set to be equal across race/ethnicity groups [16]. Our third model additionally adjusted for back-related risk factors. Specifically, this attempts to see how estimates would change if distributions of the back-related factors (STarT Back scores0, ODI scores0, and BMI0) were set to be equal across race/ethnicity groups (in addition to socioeconomic status measures) [16].
Our first model estimated whether there was a disparity in back-related outcomes by race/ethnicity in the TARGET cohort, adjusting for only differences in age and sex. Health disparities are defined as differences across socially privileged vs socially marginalized groups that society considers inequitable, avoidable, and unjust [29]. Thus, socioeconomic and clinical variables should not be included in models assessing disparities as differences in these baseline characteristics are not equitable, unavoidable, or just [28]. For example, structural racism is one potential cause of lower socioeconomic status that may lead to risk factors (e.g., obesity, mental health conditions) for persistent disability [30]. In this example, socioeconomic and back-related risk factors are on the causal pathway from race to cLBP and therefore were not included in the first model, as adjusting for these characteristics would underestimate a disparity in the incidence of cLBP.
Our second model additionally adjusted for socioeconomic factors. Specifically, we adjusted for differences in Medicaid status and ADI scores which represent individual- and neighborhood- level socioeconomic status, respectively. Thus, this attempts to see how estimates would change if distributions of the Medicaid insurance and ADI scores were set to be equal across race/ethnicity groups [16].
Our third model additionally adjusted for back-related risk factors. Specifically, STarT Back scores, baseline ODI scores, and BMI. Associations between race/ethnicity or socioeconomic status and back-related risk factors, such as psychological health [31, 32] and obesity [33, 34], have been documented previously. These variables were hypothesized to be downstream factors or mediators on the causal pathway between race/ethnicity and cLBP [35, 36]. Thus, this model attempts to see how estimates would change if distributions of the measured back-related factors were set to be equal across race/ethnicity groups in addition to socioeconomic status measures [16]. Missing information on ODI/BMI were imputed using multiple Markov chain Monte Carlo method with 10 imputed data sets [37].
Sensitivity Analyses
Potential bias due to missing 6-month data was addressed by applying stabilized inverse probability weights to the three models in primary analysis [38]. First, we identified predictors of missing outcome data at 6 months among participants with complete baseline ODI questionnaires (n = 8,841). These predictors were included in a logistic regression predicting response (defined as having 6-month outcome data). The inverse of these predicted probabilities was then used as weights in analysis of patients with complete baseline and outcome data (n = 4,899). This approach results in larger weights being assigned to patients whose characteristics are similar to patients without 6-month outcome data.
A sensitivity analysis of our high-impact cLBP definition was also performed. An ODI score from 20 to 40 indicates moderate back-related disability [20]. Thus, our primary analysis defined high-impact cLBP using the midpoint of this range. High-impact cLBP was alternatively defined as cLBP with 6-month ODI score of >20, the lower bound of moderate disability, and >40, which would indicate more severe disability.
Secondary Outcomes
We evaluated the association of race/ethnicity with PCP referrals, imaging orders, or prescribed medications. We report the frequency and percentage for each recommendation during early and delayed periods [5]. Associations were tested using χ2 tests adjusted for clustering with Taylor series linearization. The association of race/ethnicity with most of the PCP recommendations varied by site requiring stratification of analyses by site.
Additional Analyses
To explore intersectionality, we evaluated incidence of cLBP by race/ethnicity group stratified by sex with frequencies and percentages [39]. To explore the role of opioids in managing aLBP we similarly stratified our main findings (i.e., cLBP outcomes) by patients who were or were not prescribed an opioid in the first 21 days from index visit.
All analyses were performed using SAS version 9.4 (SAS, Cary, NC).
Results
Sample
Our analytic sample included 9,088 patients with complete information on baseline back pain and treatment recommendations by PCPs over the 6-month follow-up period (Figure 1). Outcome data were available for 5,035 (55%) patients. Follow-up rates were lower among Black and Hispanic, compared to White patients (46%, 47%, and 57%, respectively, P < .0001).
White patients were older than Black and Hispanic patients (mean age: 51.8, 47.5, 44.0, respectively, P < .0001) (Table 1). White patients were less likely than Black and Hispanic patients to be covered by Medicaid insurance (7%, 16%, and 19%, respectively, P < .0001). Deprivation levels using ADI were higher for White and Black patients, compared to Hispanic patients (47.0, 44.7, and 35.6, respectively, P < .0001). Black patients were more likely than White or Hispanic patients to be obese. Compared to White patients, Black and Hispanic patients at baseline had more disabling back pain (ODI scores: 32.5, 34.7, and 36.3, respectively, P < .0001) and higher STarT Back scores (22%, 30%, and 36% were classified as “high risk” of persistent disability, respectively, P < .0001). STarT Back scores were driven by higher psychological subscale scores, with more Black and Hispanic patients than White patients reporting symptoms of anxiety, depression, catastrophizing, and fear avoidance beliefs.
Table 1.
Patient characteristics by race/ethnicity
| Patients, no. (%)* |
||||
|---|---|---|---|---|
| White | Black | Hispanic | ||
| Characteristics | n = 7,391 | n = 1,293 | n = 404 | P value |
| Age, years, mean (SD) | 51.8 (17.4) | 47.5 (14.5) | 44.0 (14.1) | <.0001 |
| Female | 4,154 (56) | 845 (65) | 243 (60) | <.0001 |
| Geographic location | <.0001 | |||
| Baltimore, MD | 195 (3) | 530 (41) | 137 (34) | |
| Boston, MA | 947 (13) | 414 (32) | 84 (21) | |
| Pittsburgh, PA | 4,415 (60) | 331 (26) | 47 (12) | |
| Salt Lake City, UT | 1,834 (25) | 18 (1) | 136 (34) | |
| Area Deprivation Index | ||||
| Percentile, mean (SD) | 47.0 (24.1) | 44.7(29.2) | 35.6 (22.4) | <.0001 |
| Deprivation tertiles | <.0001 | |||
| Lowest | 2,048 (28) | 587 (45) | 203 (50) | |
| Middle | 2,672 (36) | 256 (20) | 120 (30) | |
| Highest | 2,671 (36) | 450 (35) | 81 (20) | |
| Health Insurance | <.0001 | |||
| Private | 3,907 (53) | 541 (42) | 184 (46) | |
| Medicare | 1,718 (23) | 137 (11) | 30 (7) | |
| Medicaid | 542 (7) | 202 (16) | 78 (19) | |
| Missing/Other | 1,224 (17) | 413 (32) | 112 (28) | |
| Body Mass Index, mean (SD) | 30.1 (6.8) | 32.2 (7.5) | 31.0 (6.5) | <.0001 |
| Normal/underweight BMI <25 | 1,529 (21) | 158 (12) | 62 (15) | <.0001 |
| Overweight 25≤ BMI <30 | 2,193 (30) | 248 (19) | 105 (26) | |
| Obese, BMI ≥30 | 2,906 (39) | 576 (45) | 170 (42) | |
| Unknown/missing | 763 (10) | 311 (24) | 67 (17) | |
| Diagnoses | ||||
| Depression | 213 (3) | 30 (2) | 11 (3) | .66 |
| Anxiety | 331 (4) | 36 (3) | 8 (2) | .01 |
| Radiculopathy | 1909 (26) | 315 (24) | 96 (24) | .60 |
| Baseline Oswestry score, mean† (SD) | 32.5 (19.4) | 34.7 (19.8) | 36.3 (20.1) | <.0001 |
| STarT Back Tool | ||||
| Total score, mean (SD) | 4.3 (2.3) | 4.7 (2.4) | 5.0 (2.5) | <.0001 |
| Psychological subscore, mean (SD) | 2.2 (1.5) | 2.5 (1.6) | 2.7 (1.6) | <.0001 |
| Risk category | <.0001 | |||
| Low | 2,703 (37) | 455 (35) | 118 (29) | |
| Moderate | 3,035 (41) | 450 (35) | 139 (34) | |
| High | 1,653 (22) | 388 (30) | 147 (36) | |
| Individual risk factors | ||||
| Pain radiating to the leg | 3,764 (51) | 680/1,292 (53) | 231 (57) | .07 |
| Shoulder or neck pain | 2,893 (39) | 552/1,292 (43) | 178/403 (44) | .02 |
| Walking limitation | 4,213 (57) | 732/1,292 (57) | 247 (61) | .26 |
| Dressed more slowly | 5,121 (69) | 883/1,292 (68) | 288 (71) | .72 |
| Fear avoidance beliefs | 2,736/7,390 (37) | 556/1,292 (43) | 184/402 (46) | <.0001 |
| Anxiety | 2,793 (38) | 658 (51) | 237 (59) | <.0001 |
| Catastrophizing | 1,665/7,390 (23) | 418/1,292 (32) | 161 (40) | <.0001 |
| Depression | 4,019 (58) | 752 (58) | 252 (62) | .003 |
| Very/extremely bothersome | 4,730/7,390 (64) | 798/1,289 (62) | 250/399 (63) | .57 |
| No risk factors (STarT Back score = 0) | 429 (6) | 49 (4) | 16 (4) | .01 |
Data are presented as number (percentage) of patients unless otherwise indicated.
Higher scores on the Oswestry Disability Index (ODI) indicate worse disability. A total of 8,841 patients completed the.
ODI at baseline (7,217 White, 1,235 Black, and 389 Hispanic patients).
The characteristics of patients with complete 6-month outcome data are shown in Supplementary Data Appendix Table 1.
Primary Outcomes
Compared to White patients, Black and Hispanic patients were more likely to develop cLBP (30% vs 42% and 37%, respectively, P = .003) and high-impact cLBP (15% vs 30% and 25%, respectively, P = .0001) at 6 months (Figure 3). Stratified by sex, risk of poor aLBP outcomes was highest among Black women (46% and 33% of Black women reported cLBP and high-impact cLBP, respectively).
Figure 3.
The incidence of chronic pain and high impact by race/ethnicity, overall and stratified by sex. The incidence of chronic low back pain (upper left) and high-impact chronic low back pain (upper right) is shown for 5,035 patients by race/ethnicity group (4,248 White, 596 Black, and 191 Hispanic patients). Sex-stratified incidence of chronic low back pain (lower left) and high-impact chronic low back pain (lower right) is shown for 2,127 male (1,838 White, 212 Black, and 77 Hispanic) and 2,908 female (2,410 White, 384 Black, 114 Hispanic) patients.
Our first model identified a disparity in cLBP outcomes by race/ethnicity (Table 2). Compared to White patients, adjusting for differences in age and sex, Black and Hispanic patients were more likely to develop high-impact cLBP (odds ratio [OR] = 1.80, 95% confidence interval [CI]: 1.38 to 2.35; and OR = 1.69, 95% CI: 1.17 to 2.46, respectively). In our second model adjusting for socioeconomic status, the association with high-impact cLBP was partially attenuated among Black patients (OR = 1.52, 95% CI: 1.16 to 1.70) and no longer statistically significant for Hispanic patients (OR = 1.42, 95% CI: 0.97 to 2.09). Including additional information on back-related risk factors in our third model further attenuated the association of high-impact cLBP among Black vs White patients (OR = 1.40, 95% CI: 1.05 to1.87).
Table 2.
The odds of incident chronic LBP or high-impact chronic LBP for Black and Hispanic patients compared to White patients
| Complete Case Analysis‖ |
Inverse Probability Weighting Analysis‖| |
|||
|---|---|---|---|---|
| Chronic LBP§ | High-impact Chronic LBP¶ | Chronic LBP§ | High-impact Chronic LBP¶ | |
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Black patients | ||||
| Model 1* | 1.48 (1.18, 1.86) | 1.80 (1.38, 2.35) | 1.49 (1.20, 1.87) | 1.90 (1.46, 2.46) |
| Model 2† | 1.32 (1.05, 1.67) | 1.52 (1.16, 2.00) | 1.34 (1.07, 1.69) | 1.61 (1.24, 2.09) |
| Model 3‡ | 1.22 (0.96, 1.56) | 1.40 (1.05, 1.87) | 1.21 (0.96, 1.53) | 1.47 (1.11, 1.95) |
| Hispanic patients | ||||
| Model 1* | 1.34 (0.97, 1.85) | 1.69 (1.17, 2.46) | 1.26 (0.93, 1.72) | 1.83 (1.28, 2.62) |
| Model 2† | 1.20 (0.87, 1.66) | 1.42 (0.97, 2.09) | 1.15 (0.84, 1.57) | 1.52 (1.06, 2.19) |
| Model 3‡ | 1.10 (0.78, 1.54) | 1.25 (0.83, 1.90) | 0.99 (0.71, 1.38) | 1.36 (0.91, 2.02) |
CI = confidence interval; LBP = low back pain; OR = odds ratio.
Model 1 provides an estimate of the health disparity, i.e., it adjusts only for age and sex differences.
Model 2 provides an estimate if socioeconomic characteristics were set to be equal by race/ethnicity groups, i.e., it adjusts for differences in age, sex, Medicaid status, neighborhood SES (ADI of individual).
Model 3 provides an estimate if socioeconomic and back-related risk factors are set to be equal, i.e., it additionally adjusts for differences in continuous BMI, STarT Back score, and ODI.
Chronic low back pain defined using the NIH definition of chronic low back pain.
High-impact cLBP further capturing individuals with moderate disability on Oswestry Disability Index at 6 months (ODI score >30).
Complete case analysis used data from 5,035 patients with complete information on race/ethnicity status and back pain outcomes. Missing values of BMI and ODI were imputed using the multiple Markov chain Monte Carlo method. Variables included in the imputed model were smoking status, site, race/ethnicity, sex, Medicaid status, anxiety, NIH task force Q1 “how long” at baseline, age, STarT Back score, and referral to physical therapy, pain management, MRI imaging, muscle relaxants, or opioids within 21 days of the index visit. These were chosen because they were associated with either missing BMI or ODI or with the values themselves or because they were in the model for predicting chronicity.
Inverse Probability Weighting (IVPW) analysis used information from 8,841 participants with baseline ODI data to obtain the probability of missing outcome data. The inverse of these predicted probabilities were then used as weights in the analysis of patients with complete data (n = 4,899). Categorical BMI with a “missing” category was used in IVPW analysis.
Sensitivity Analyses
Findings were similar in sensitivity analyses using inverse probability weighting designed to address potential bias due to missing 6-month data (Table 2). For high-impact cLBP, the magnitude of association increased slightly for both Black and Hispanic patients. The socioeconomic model reached statistical significance for high-impact cLBP in Hispanic vs White patients (OR = 1.52, 95% CI: 1.06 to 2.19).
Results were also similar when defining high-impact cLBP with alternative ODI thresholds, although estimates were slightly attenuated in risk factor model for thresholds of 20 and 40 points on ODI when comparing Black to White patients, and no longer statistically significant (Supplementary Data Appendix Table 2).
Secondary Outcomes
Race/ethnicity differences in early care recommendations varied by site (Supplementary Data Appendix Table 3). Recommendation of early nonpharmacologic treatment for 3 sites ranged from 31% to 47% with much lower rates observed at clinics based in Salt Lake City area. Pharmacologic treatment was recommended to more than half of patients (61% to 77%) across all sites. Black and Hispanic patients were less likely than White patients to receive an opioid prescription (11%, 11%, and 20%, respectively, P < .0001). In a post hoc stratified analysis, Black, Hispanic, and White patients were more likely to have high-impact cLBP if prescribed an opioid in first 21 days (n = 980, 45%, 37%, and 22%, respectively) than those not (n = 4,055, 28%, 23%, and 13%, respectively). Similarly, in a post hoc stratified analysis, Black, Hispanic, and White patients were more likely to have cLBP if prescribed an opioid in first 21 days (n = 980, 56%, 63%, and 39%, respectively) than those not (n = 4,055, 40%, 34%, and 28%, respectively). No meaningful race/ethnicity differences in early imaging or specialist referrals were observed.
Data on delayed treatment recommendations are shown in Supplementary Data Appendix Table 4. Overall, most White, Black, and Hispanic patients did not receive additional PCP recommendations for aLBP after the first 21 days (86%, 87%, and 87%, P = .60).
Discussion
In this secondary analysis of a prospective cohort study of adults with aLBP, Black and Hispanic patients were more likely to develop cLBP or high-impact cLBP than White patients. Adjusting for differences in socioeconomic and back-related risk factors attenuated this association; however, the relationship between Black race and development of high-impact cLBP persisted. Differences in PCP treatment recommendations by race/ethnicity did not appear to account for observed differences. Lastly, Black women experienced the highest rates of cLBP and high-impact cLBP.
A large body of research has characterized relationships between race/ethnicity, socioeconomic status, chronic health conditions, and general well-being [30, 40–42]. Less is known about the impact of race/ethnicity on long-term aLBP outcomes. Previous systematic reviews of risk factors for persistent disabling back pain have largely omitted social constructs of race, ethnicity, or racism [43–46]. A study of over 5,000 older military Veterans found back-related outcomes did not differ by race/ethnicity over 24 months [47]. Cross-sectional national surveys have not observed an association between race/ethnicity and prevalence of high-impact chronic pain in any body region [1, 48]. Our findings may differ due to the prospective design, shorter follow-up period (6 months), and/or use of ODI to define high-impact cLBP, which resulted in exclusion of individuals with relatively mild disability. A recent prospective study of 217 adults with aLBP found pain resolution was more likely among White than Black participants (hazard ratio = 5.4) [49]. Our findings extend this literature in a large prospective multi-site cohort of adults with exclusively aLBP, and is the first to evaluate incidence of high-impact cLBP by racial/ethnic groups.
Approximately a third of the association between race/ethnicity and high-impact cLBP was attenuated by adjusting for differences in the two available measures of SES. Although Black and White patients had similar neighborhood-level SES on ADI scores, Black and Hispanic patients were twice as likely as White patients to be insured by Medicaid (indicating lower individual-level SES). This provides additional data supporting the relationship between socioeconomic status and back pain outcomes [50, 51]. Unmet social and economic needs, which may be more prevalent in Medicaid enrollees, are associated with mental health conditions [31, 32] and obesity [33, 34], which in turn are risk factors for persistent back-related disability [35, 36].
Adjusting for baseline differences in BMI, back-related disability (ODI) and prognosis (STarT Back) also modestly attenuated the association between race/ethnicity and high-impact cLBP. Pain related psychological factors (e.g., catastrophizing), as captured by STarT Back scores, were most prevalent among Black and Hispanic patients. This provides further evidence of racial/ethnic disparities in mental health conditions [31, 32], which in turn are strong predictors of persistent, disabling pain [43]. Patients were screened during their healthcare encounter. Interactions with healthcare settings or providers may contribute to race/ethnicity differences in anxiety and distress captured by STarT Back scores as well as experiences with discrimination outside of the healthcare setting.
Black and Hispanic patients were half as likely as White patients to be prescribed opioids within 21 days of their index visit. This is consistent with a wider body of evidence and a likely indicator of discrimination [52]. However, stratified findings for those with/without an opioid prescription did not modify the association between race/ethnicity and cLBP incidence. This suggests higher opioid use among White patients did not explain their lower cLBP incidence.
After adjusting for socioeconomic and back-related risk factors there was a residual association between Black race and high-impact cLBP. We did not have a direct measure of individual experiences of racism, or structural racism at the neighborhood level, limiting our ability to understand the role of differential experiences of discrimination by Black and White patients and cLBP incidence. Based on our causal diagram (Figure 2), we hypothesized discrimination was an important upstream factor, the primary driver of socioeconomic and back-related risk factors.
Future research should aim to better understand potential mechanisms that mediate the association of race/ethnicity with cLBP incidence. This will rely on a more comprehensive approach to measurement of potential mediators such as interpersonal or institutional discrimination, trauma history and perceived stress. More detailed measurement of social determinants of health (employment, housing, or other social support) that are racially influenced is also needed. Racial bias in pain assessment [15], treatment access, and negative healthcare experiences and fear of discrimination by providers should also be measured. Unmet social needs or experiences of discrimination, more common among non-White individuals [53], can elevate levels of perceived stress (as measured by STarT Back), increasing risk of more persistent and disabling back pain [54]. Further exploration of the intersectionality of race, sex, and other social determinants of health with aLBP outcomes is needed [55–57]. Cumulative discrimination or disadvantage may increase risk of disabling musculoskeletal conditions [58, 59]. Practice policies and trainings that address bias and provide resources for patients experiencing discrimination (e.g., support for trauma, stress management) remain fundamental for clinicians and caregivers to mitigate the role of racism/discrimination in the healthcare setting. Future work should evaluate whether policies or public health interventions that reduce bias and/or improve socioeconomic status also improve aLBP outcomes.
The TARGET prospective cohort has highlighted both a concern in the absolute number of patients with aLBP who develop cLBP [5] and concerns about racial/ethnic disparities in aLBP outcomes. We estimated that 3 of 10 White patients and 4 of 10 Black patients with aLBP developed cLBP. As the severity of the outcome increases the disparity widened. For high-impact cLBP, the rate was 1.5 of 10 White patients compared to 3 of 10 Black patients. Given the burden of chronic and disabling pain on patients, providers, and healthcare systems, this difference has important implications for clinicians and policy makers, who should strive to better address risk factors in ways that improve overall aLBP management while addressing racial/ethnic disparities in aLBP outcomes.
Our study has several limitations. Race/ethnicity data were obtained from medical records, which may not always be the same as self-reported race/ethnicity. Missing 6-month data may also introduce bias. We addressed this in part by sensitivity analyses using inverse probability weighting for missing data. While our large sample and pragmatic recruitment approach support the generalizability of findings in study locations, our findings may not be generalizable to other minority groups or geographic regions not represented in our sample. Lastly, the number of Hispanic patients in our sample was relatively small yielding limited statistical power. The association of cLBP incidence among Hispanic patients vs White patients was similar in magnitude to that of Black vs White patients, although a wider confidence interval crossed the null in some of our models.
In conclusion, Black and Hispanic patients were more likely to develop cLBP or high-impact cLBP than White patients. This association was only partially explained by measured socioeconomic and back-related risk factors. Understanding mechanisms of this association and developing strategies to mitigate racial/ethnic disparities in aLBP outcomes is essential for long term health equity in communities.
Authors’ contributions
Authors Smith, Patterson, and Stevans had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Supplementary Material
Contributor Information
Eric J Roseen, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA; Department of Rehabilitation Sciences, MGH Institute for Health Professions, Boston, Massachusetts, USA; Department of Physical Medicine and Rehabilitation, VA Boston Healthcare System, Boston, Massachusetts, USA.
Clair N Smith, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, Pennsylvania, USA.
Utibe R Essien, Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Yvette C Cozier, Slone Epidemiology Center, Boston University School of Public Health, Boston, Massachusetts, USA.
Christopher Joyce, School of Physical Therapy, Massachusetts College of Pharmacy and Health Sciences, Worcester, Massachusetts, USA.
Natalia E Morone, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
Russell S Phillips, Center for Primary Care, Harvard Medical School, Boston, Massachusetts, USA; Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Katherine Gergen Barnett, Department of Family Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
Charity G Patterson, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, Pennsylvania, USA.
Stephen T Wegener, Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Gerard P Brennan, Department of Physical Therapy, Intermountain Healthcare Rehabilitation Services, Murray, Utah, USA.
Anthony Delitto, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, Pennsylvania, USA.
Robert B Saper, Department of Wellness and Preventive Medicine, Cleveland Clinic, Cleveland, Ohio, USA.
Jason M Beneciuk, Department of Physical Therapy, University of Florida College of Public Health and Health Professions, Gainesville, Florida, USA.
Joel M Stevans, Department of Physical Therapy, University of Florida College of Public Health and Health Professions, Gainesville, Florida, USA.
Concept and design: Roseen, Smith, Essien, Cozier, Joyce, Patterson, Delitto, Saper, Beneciuk, Stevans. Acquisition, analysis, or interpretation of data: All authors. Critical revision of the manuscript for important intellectual content: All Authors. Statistical analysis: Roseen, Smith, Patterson. Obtained funding: Delitto, Saper. Administrative, technical, or material support: Roseen, Stevans. Study supervision: Roseen, Stevans.
Additional contributions: We thank The TARGET TRIAL GROUP for reviewing this manuscript.
Conflicts of interest: The authors have no financial or other relationships that would constitute a conflict of interest. The TARGET Trial and Inception Cohort were funded by the Patient Centered Outcomes Research Institute (PCORI contract number NCT02647658). Dr. Roseen is the recipient of a career development award from the National Center for Complementary and Integrative Health (NCCIH, K23-AT010487), which supported his work on this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of PCORI or NCCIH.
Prior presentations: This work was presented, in part, at the Massachusetts General Hospital Scientific Advisory Committee Meeting (April 7, 2021) and the International Forum on Back and Neck Pain Research in Primary Care (November 12, 2021).
Supplementary Data
Supplementary Data may be found online at Pain Medicine online.
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