Abstract
Introduction:
Degenerative cervical myelopathy (DCM) is a progressive condition that results in significant neurological decline and disability. Racial and ethnic disparities in healthcare access and outcomes are well-documented, yet their impact on DCM patients remains insufficiently explored. This study aims to investigate racial disparities in the self-reported health status and Quality of Life (QoL), health literacy, and healthcare access among individuals with DCM using data from the All of Us Research Program (AoURP).
Methods:
In this retrospective study, we analyzed AoURP participants with a diagnosis of DCM based on ICD-9/10 codes. Race and ethnicity were categorized as White/Caucasian (WC), Black/African American (BAA), and Non-White Hispanic (NWH). Participants’ demographic characteristics, socioeconomic status, self-reported health status and QoL, health literacy, and healthcare utilization patterns were assessed through survey responses. To assess whether SES mediates the association between race and outcomes, a causal mediation analysis was conducted, operationalizing SES as a composite of standardized income, education, and employment measures. Statistical analyses were conducted using chi-square and independent t-tests to compare categorical and continuous variables, respectively.
Results:
Among 3,092 DCM patients, 26% identified as BAA, 64% as WC, and 10% as NWH. Significant socioeconomic disparities were observed, with WC participants reporting higher educational attainment, income, and homeownership rates (p < 0.001). Healthcare access varied substantially, with BAA and NWH participants reporting lower rates of insurance coverage, specialist consultations, and primary care access compared to WC (p < 0.05). Financial and transportation barriers to care access were more frequently reported among minority groups. BAA and NWH participants also had lower health literacy, reporting greater difficulty understanding medical information, completing medical forms, and requiring assistance with health materials (p < 0.001). Furthermore, both BAA and NWH groups reported poorer self-perceived health and QoL, and higher pain levels (p<0.001). Causal mediation analysis demonstrated that SES partially mediated the relationship between race and key outcomes, including health literacy, healthcare access, and self-perceived health, indicating that socioeconomic disadvantage explains much, but not all, of the observed disparities.
Conclusion:
This study highlights substantial racial disparities in healthcare access, health literacy, and self-reported health status and QoL among DCM patients, which are partially mediated by socioeconomic factors. Recognizing and addressing these disparities is essential to improving DCM outcomes and ensuring equitable care.
Keywords: Racial Disparity, Health Literacy, Healthcare utilization, Degenerative Cervical Myelopathy, All of US Research Program
Introduction
Degenerative cervical myelopathy (DCM) is a progressive and debilitating condition characterized by compression of the cervical spinal cord, leading to neurological decline, chronic pain, and impairment in motor function.1 As a leading cause of spinal cord dysfunction in adults, DCM places a considerable burden on individuals and health care systems alike, with high risk of long-term disability and reduced quality of life (QoL).2 While DCM can have profound impacts on mobility and overall well-being, growing evidence suggests that the burden of this disease – and access to timely and effective care – varies significantly across racial and ethnic groups.3
In the United States, Black and Hispanic populations have historically faced structural inequities in access to health care. These groups are more likely to be uninsured, experience economic challenges and encounter various barriers that limit their access to high-quality medical care.4,5 These disparities are especially concerning in DCM, a condition where natural progression can lead to irreversible neurological damage without timely diagnosis and intervention.1 Delayed diagnosis in minority populations may lead to more advanced disease at presentation and increasing the risk of adverse outcomes. Recent studies have shown that minority patients and others from disadvantaged backgrounds are more likely to experience diagnostic delays, increased disability, greater dissatisfaction, as well as higher healthcare costs.6–8 Therefore, recognizing the factors contributing to these disparities is essential for promoting equitable healthcare, improving patient outcomes, and controlling unnecessary healthcare expenditures.
Level of health literacy and health care utilization are health determinants that physicians often overlook. Research has shown for example that limited health literacy is independently linked to poorer outcomes.9 This relationship is established in a number of disease such as heart failure,10 asthma,11 diabetes,12 and others. Few studies have examined the impact of racial disparities on health literacy, and access to care in DCM patients. Exploring these factors may provide insight into race-based differences in DCM outcomes.
The All of Us Research Program (AoURP), funded by the National Institutes of Health,13 aims to enhance the representation of underrepresented populations in research and captures social and behavioral determinants of health.14 The program’s goal is to enroll at least one million participants who consent to sharing electronic health records (EHR), providing biospecimens, completing surveys, and undergoing physical assessments.15 This study utilizes AoURP survey data to explore racial disparities in self-reported health status and QoL, health literacy, and healthcare access among patients with DCM.
Methods
Study participants
Adults were enrolled in AoURP at affiliated clinics or medical centers following electronic informed consent. The details of AoURP have been described in previous publications.14 For this study, we used the registered tier dataset version 7 (R2022Q4R9 Cured Data Repository), which includes participants enrolled between May 2018 and July 2022, incorporating physical measurements, survey responses, and EHR data. Age, sex, and race data were extracted from basic surveys and were largely complete. In cases when race was missing from the survey data, we filled it from the demographic AoURP table if available. Patients were categorized as: White/Caucasian (WC), Black/African American (BAA), and Non-White Hispanic (NWH). Patients who did not report a race, reported other, or reported multiple races were excluded. Exact racial distribution of participants with DCM is shown in E-Table 1. We identified patients with DCM using International Classification of Diseases, 9th and 10th revision (ICD-9 and ICD-10) codes (E-Table 2).
Assessment of Outcomes
Responses to surveys assessing overall health status and QoL, health literacy, and barriers to healthcare were extracted. To ensure a more accurate reflection of DCM severity and its impact on QoL and health perception, only patients who completed the overall health status and QoL survey within six months of their DCM diagnosis were included in the health status and QoL analysis. Survey questionnaires are presented in (E-Table 3–5)
Causal Mediation Analysis
To further examine whether socioeconomic status (SES) mediates the association between race and outcomes, a causal mediation analysis was conducted using the mediation package in R. Race (White vs. BAA or NWH) was treated as the exposure, SES as the mediator, and healthcare access, health literacy, and self-perceived health and quality-of-life outcomes as dependent variables. SES was operationalized as a continuous composite score derived by standardizing and summing z-scores of participants’ income, educational attainment, and employment status. Each outcome variable was numerically coded so that higher values represented more favorable responses. For each outcome, two linear regression models were fit: one estimating SES as a function of race (mediator model), and another estimating the outcome as a function of both race and SES (outcome model). The average causal mediation effect (ACME), average direct effect (ADE), and total effect of race were then estimated using nonparametric bootstrapping with 1,000 simulations. This framework allowed quantification of the proportion of the total effect of race that operates indirectly through socioeconomic pathways, providing a more accurate causal interpretation than conventional multivariable adjustment.
Statistical analysis
Participant demographic and clinical characteristics were summarized using descriptive statistics, with continuous variables represented by mean and standard deviation (SD) or median and interquartile range (IQR) and categorical variables by frequency counts. Categorical data were compared with the chi-squared test or Fisher’s exact test when expected values less than 5. Continuous data were compared using independent T-test. Missing values were excluded from the analysis. P values <0.05 was considered significant. All analyses were performed using R version 4.4.0.
Results:
Patients Characteristics
Among the 410,264 AoURP participants with survey data, 3,092 participants were identified with diagnosis of DCM, of whom 802 (26%) identified as BAA, 1,974 (64%) as WC, and 316 (10%) as NWH. Gender distribution was similar across the three cohorts, with the majority identifying as female. Educational attainment differed significantly among groups, with a higher proportion of WC participants holding a college degree compared to BAA participants (51.1% vs. 20.1%, p < 0.001) and NWH participants (51.1% vs. 24.9%, p < 0.001). Employment status also varied, with a greater proportion of WC participants reporting current employment compared to BAA participants (29.4% vs. 17.2%, p < 0.001) and NWH participants (29.4% vs. 21.7%, p < 0.001). Furthermore, annual income distribution showed a significant disparity across groups. A greater proportion of WC participants reported an annual income exceeding $200,000 compared to BAA participants (11.8% vs. 1.7%, p < 0.001) and NWH participants (11.8% vs. 1.6%, p < 0.001). Homeownership was most prevalent in the WC cohort (69.3% vs. 29.4% in the BAA cohort, p < 0.001, and 23.1% in the NWH cohort, p < 0.001), whereas renting was more common among BAA and NWH participants (p < 0.001). (Table 1)
Table 1:
Baseline Demographics and Socioeconomic Status of the DCM Cohort.
| BAA Vs. WC | NWH Vs. WC | ||||||
|---|---|---|---|---|---|---|---|
| Variables | levels | BAA | WC | p | NWH | WC | p |
| Total | 802 | 1974 | 316 | 1974 | |||
| age mean SD | 56.10 (10.64) | 59.62 (11.79) | <0.001 | 55.25 (10.69) | 59.62 (11.79) | <0.001 | |
| Follow up duration | 6.89 (5.36) | 6.88 (5.93) | 0.96 | 5.73 (5.06) | 6.88 (5.93) | 0.001 | |
| gender | Female | 464 (59.0) | 1087 (55.6) | 0.113 | 187 (60.1) | 1087 (55.6) | 0.154 |
| Male | 322 (41.0) | 867 (44.4) | 124 (39.9) | 867 (44.4) | |||
| education | no college | 349 (45.2) | 341 (17.5) | <0.001 | 164 (53.1) | 341 (17.5) | <0.001 |
| some college | 268 (34.7) | 603 (31.0) | 68 (22.0) | 603 (31.0) | |||
| college degree | 155 (20.1) | 1004 (51.5) | 77 (24.9) | 1004 (51.5) | |||
| Employment | No | 649 (82.8) | 1380 (70.6) | <0.001 | 242 (78.3) | 1380 (70.6) | 0.007 |
| Yes | 135 (17.2) | 574 (29.4) | 67 (21.7) | 574 (29.4) | |||
| Income | less 10k | 196 (33.2) | 91 (5.5) | <0.001 | 55 (29.7) | 91 (5.5) | <0.001 |
| 10k 25k | 172 (29.2) | 273 (16.4) | 48 (25.9) | 273 (16.4) | |||
| 25k 35k | 63 (10.7) | 129 (7.7) | 21 (11.4) | 129 (7.7) | |||
| 35k 50k | 50 (8.5) | 190 (11.4) | 19 (10.3) | 190 (11.4) | |||
| 50k 75k | 59 (10.0) | 243 (14.6) | 18 (9.7) | 243 (14.6) | |||
| 75k 100k | 18 (3.1) | 185 (11.1) | 8 (4.3) | 185 (11.1) | |||
| 100k 150k | 19 (3.2) | 252 (15.1) | 9 (4.9) | 252 (15.1) | |||
| 150k 200k | 3 (0.5) | 106 (6.4) | 4 (2.2) | 106 (6.4) | |||
| more 200k | 10 (1.7) | 197 (11.8) | 3 (1.6) | 197 (11.8) | |||
| Living Place | Rent | 467 (61.0) | 486 (25.0) | <0.001 | 204 (68.2) | 486 (25.0) | <0.001 |
| Own | 225 (29.4) | 1346 (69.3) | 69 (23.1) | 1346 (69.3) | |||
| Other Arrangement | 73 (9.5) | 109 (5.6) | 26 (8.7) | 109 (5.6) | |||
Health Care Utilization
A lower proportion of BAA participants reported having health insurance coverage compared to WC participants (96.4% vs. 98.7%, p < 0.001). Similarly, BAA participants were more likely to have been denied insurance coverage compared to WC participants, though this difference was not statistically significant. Among Non-Hispanic White (NWH) participants, insurance rejection was significantly higher than in the WC cohort (20.4% vs. 10.1%, p = 0.004).
Regarding sources of healthcare advice, BAA participants were more likely to report not having a usual place for seeking health advice compared to WC participants (5.0% vs. 1.0%, p < 0.001), a trend also observed in NWH participants (7.5% vs. 1.0%, p < 0.001). Most participants across all cohorts reported obtaining healthcare advice from a single source, with WC participants reporting the lowest proportion of participants with no health place to go (1% vs. 5% in BAA, 7.5 % in NWH). Furthermore, a greater proportion of WC participants utilized more than one source of healthcare advice (16.2% vs. 10.0% in BAA, 10.8% in NWH).
Differences were also noted in the primary place of healthcare utilization. BAA participants were more likely to use the emergency room as their main healthcare source compared to WC participants (6.7% vs. 1.0%, p < 0.001), a pattern similarly seen in NWH participants (3.5% vs. 1.0%, p = 0.05). Conversely, WC participants were more likely to have a primary care physician as their usual healthcare provider compared to both BAA (94.4% vs. 88.6%, p < 0.001) and NWH (94.4% vs. 89.5%, p < 0.05). Specialist care access also varied significantly, with BAA participants being less likely to have consulted a specialist within the last year compared to WC participants (67.9% vs. 81.0%, p < 0.001). Similarly, NWH participants had lower specialist consultation rates compared to WC participants (66.7% vs. 81.0%, p = 0.004). Financial barriers to healthcare access were also evident. A significantly higher proportion of BAA participants reported difficulty affording medication compared to WC participants. Affording emergency room visits was also more challenging for BAA participants than WC participants. While NWH participants reported similar difficulties affording medication and emergency care compared to WC participants, these differences were not significant.
Transportation barriers were more frequently reported in NWH participants compared to WC participants (18.1% vs. 9.0%, p = 0.007), while the difference between BAA and WC participants did not reach statistical significance (12.4% vs. 9.0%, p = 0.168). (Table 2)
Table 2:
Barriers to Healthcare Access in the DCM Cohort.
| BAA Vs. WC | NWH Vs. WC | ||||||
|---|---|---|---|---|---|---|---|
| Variables | levels | BAA | White | p | NWH | WC | p |
| health care plan | No | 28 (3.6) | 25 (1.3) | <0.001 | 6 (1.9) | 25 (1.3) | 0.521 |
| Yes | 743 (96.4) | 1925 (98.7) | 306 (98.1) | 1925 (98.7) | |||
| insurance rejected | No | 180 (89.1) | 1045 (89.9) | 0.818 | 74 (79.6) | 1045 (89.9) | 0.004 |
| Yes | 22 (10.9) | 117 (10.1) | 19 (20.4) | 117 (10.1) | |||
| place health advice | No | 10 (5.0) | 12 (1.0) | <0.001 | 7 (7.5) | 12 (1.0) | <0.001 |
| Yes | 171 (85.1) | 954 (82.7) | 76 (81.7) | 954 (82.7) | |||
| More Than One | 20 (10.0) | 187 (16.2) | 10 (10.8) | 187 (16.2) | |||
| health place | No One Place Most Often | 2 (1.0) | 18 (1.6) | <0.001 | 2 (2.3) | 18 (1.6) | 0.05 |
| Some Other Place | 0 (0.0) | 5 (0.4) | 2 (2.3) | 5 (0.4) | |||
| Emergency Room | 13 (6.7) | 12 (1.0) | 3 (3.5) | 12 (1.0) | |||
| Urgent Care | 7 (3.6) | 29 (2.5) | 2 (2.3) | 29 (2.5) | |||
| Doctors Office | 171 (88.6) | 1084 (94.4) | 77 (89.5) | 1084 (94.4) | |||
| Talked to a specialist | No | 50 (32.1) | 174 (19.0) | <0.001 | 26 (33.3) | 174 (19.0) | 0.004 |
| Yes | 106 (67.9) | 740 (81.0) | 52 (66.7) | 740 (81.0) | |||
| Afford Med | No | 158 (79.8) | 1014 (87.6) | 0.004 | 80 (85.1) | 1014 (87.6) | 0.581 |
| Yes | 40 (20.2) | 143 (12.4) | 14 (14.9) | 143 (12.4) | |||
| Afford ER | No | 174 (93.5) | 1058 (97.7) | 0.004 | 86 (96.6) | 1058 (97.7) | 0.787 |
| Yes | 12 (6.5) | 25 (2.3) | 3 (3.4) | 25 (2.3) | |||
| Transportation Barrier | No | 177 (87.6) | 1032 (91.0) | 0.168 | 77 (81.9) | 1032 (91.0) | 0.007 |
| Yes | 25 (12.4) | 102 (9.0) | 17 (18.1) | 102 (9.0) | |||
Health Literacy
A greater proportion of BAA participants reported requiring assistance with health materials compared to WC participants. Specifically, 49.2% of BAA participants never required assistance, compared to 66.4% of WC participants (p < 0.001). Conversely, BAA participants were more likely to always require assistance (6.2% vs. 2.7%). NWH participants were significantly more likely to require assistance, with a higher proportion reporting that they “always” need assistance compared to WC participants.
Confidence in completing medical forms also differed significantly across groups. BAA participants were less likely to report being “extremely” confident compared to WC participants (51.6% vs. 68.4%, p < 0.001), with higher proportions reporting lower confidence levels. A similar trend was observed among NWH participants, where fewer reported being “extremely” confident (42.5% vs. 68.4%, p < 0.001), and a greater proportion reported lower confidence levels.
Understanding health-related information was also a more frequent challenge for both BAA and NWH participants compared to WC participants. BAA participants were more likely to report that they “always” (3.7% vs. 1.3%, p < 0.001) had difficulty understanding health information. Similarly, NWH participants were significantly more likely to report “always” (8.6% vs. 1.3%, p < 0.001) struggling with comprehension. In contrast, WC participants were the least likely to report difficulty, with 69.2% stating they “never” had difficulty compared to 51.9% of BAA participants and 43.5% of NWH participants (Table 3).
Table 3:
Health Literacy of the DCM Cohort
| BAA Vs. WC | NWH Vs. WC | ||||||
|---|---|---|---|---|---|---|---|
| Variables | levels | BAA | WC | p | NWH | WC | p |
| Health Material Assistance | Never | 394 (49.2) | 1306 (66.4) | <0.001 | 133 (42.2) | 1306 (66.4) | <0.001 |
| Occasionally | 115 (14.4) | 354 (18.0) | 41 (13.0) | 354 (18.0) | |||
| Sometimes | 172 (21.5) | 151 (7.7) | 60 (19.0) | 151 (7.7) | |||
| Often | 53 (6.6) | 73 (3.7) | 24 (7.6) | 73 (3.7) | |||
| Always | 50 (6.2) | 53 (2.7) | 42 (13.3) | 53 (2.7) | |||
| Medical Form Confidence | Not At All | 32 (4.0) | 22 (1.1) | <0.001 | 11 (3.5) | 22 (1.1) | <0.001 |
| A Little Bit | 34 (4.2) | 28 (1.4) | 31 (9.8) | 28 (1.4) | |||
| Somewhat | 138 (17.2) | 141 (7.2) | 50 (15.9) | 141 (7.2) | |||
| Quite A Bit | 165 (20.6) | 408 (20.7) | 79 (25.1) | 408 (20.7) | |||
| Extremely | 413 (51.6) | 1345 (68.4) | 134 (42.5) | 1345 (68.4) | |||
| Difficult Understanding Info | Always | 30 (3.7) | 26 (1.3) | <0.001 | 27 (8.6) | 26 (1.3) | <0.001 |
| Often | 44 (5.5) | 50 (2.5) | 24 (7.6) | 50 (2.5) | |||
| Sometimes | 178 (22.2) | 148 (7.5) | 74 (23.5) | 148 (7.5) | |||
| Occasionally | 114 (14.2) | 349 (17.7) | 43 (13.7) | 349 (17.7) | |||
| Never | 416 (51.9) | 1362 (69.2) | 137 (43.5) | 1362 (69.2) | |||
Health Status and QoL
A total of 2058 patients who had survey within 6 month of DCM diagnosis were included in this analysis. Among them, 554 (27%), 1298 (63%), and 206 (10%) identified as BAA, WC, and NWH respectively. BAA and NWH participants reported worse health status and QoL compared to WC participants across multiple dimensions. Both BAA and NWH groups reported significantly higher pain levels (4.47/10 vs. 6.25/10 in BAA, 5.67/10 in NWH) and greater difficulty with walking and dressing. They also perceived their overall health, quality of life, physical health, and mental health to be poorer compared to WC participants. BAA and NWH participants were more likely to report limitations in daily activities and social roles. The trend indicates that WC individuals consistently rated their health and quality of life more favorably than BAA and NWH individuals, highlighting disparities in self-perceived well-being. (Table 4)
Table 4:
Perception of Self-Health Status in DCM cohort.
| BAA Vs. WC | NWH Vs. WC | ||||||
|---|---|---|---|---|---|---|---|
| Variables | levels | BAA | WC | p | NWH | WC | p |
| Total | 554 | 1298 | 206 | 1298 | |||
| Pain | 6.25 (2.83) | 4.47 (2.83) | <0.001 | 5.67 (3.09) | 4.47 (2.83) | <0.001 | |
| Difficulty Walking | No | 77 (47.8) | 336 (65.8) | <0.001 | 32 (42.7) | 336 (65.8) | <0.001 |
| Yes | 84 (52.2) | 175 (34.2) | 43 (57.3) | 175 (34.2) | |||
| Difficulty Dressing | No | 123 (76.9) | 443 (87.0) | 0.003 | 52 (72.2) | 443 (87.0) | 0.002 |
| Yes | 37 (23.1) | 66 (13.0) | 20 (27.8) | 66 (13.0) | |||
| Health Status | Poor | 68 (12.5) | 108 (8.5) | <0.001 | 32 (15.7) | 108 (8.5) | <0.001 |
| Fair | 214 (39.3) | 313 (24.6) | 74 (36.3) | 313 (24.6) | |||
| Good | 171 (31.4) | 465 (36.5) | 58 (28.4) | 465 (36.5) | |||
| Very Good | 68 (12.5) | 307 (24.1) | 32 (15.7) | 307 (24.1) | |||
| Excellent | 24 (4.4) | 80 (6.3) | 8 (3.9) | 80 (6.3) | |||
| Quality of Life | Poor | 23 (4.3) | 55 (4.3) | <0.001 | 13 (6.5) | 55 (4.3) | <0.001 |
| Fair | 143 (26.9) | 224 (17.6) | 57 (28.6) | 224 (17.6) | |||
| Good | 221 (41.5) | 391 (30.8) | 80 (40.2) | 391 (30.8) | |||
| Very Good | 96 (18.0) | 412 (32.4) | 36 (18.1) | 412 (32.4) | |||
| Excellent | 49 (9.2) | 188 (14.8) | 13 (6.5) | 188 (14.8) | |||
| Physical Health | Poor | 70 (13.1) | 138 (10.8) | <0.001 | 28 (13.7) | 138 (10.8) | <0.001 |
| Fair | 204 (38.2) | 364 (28.6) | 86 (42.2) | 364 (28.6) | |||
| Good | 168 (31.5) | 442 (34.7) | 53 (26.0) | 442 (34.7) | |||
| Very Good | 76 (14.2) | 273 (21.4) | 30 (14.7) | 273 (21.4) | |||
| Excellent | 16 (3.0) | 57 (4.5) | 7 (3.4) | 57 (4.5) | |||
| Mental Health | Poor | 27 (5.0) | 39 (3.1) | <0.001 | 15 (7.5) | 39 (3.1) | <0.001 |
| Fair | 124 (23.0) | 184 (14.4) | 47 (23.4) | 184 (14.4) | |||
| Good | 178 (33.0) | 377 (29.5) | 65 (32.3) | 377 (29.5) | |||
| Very Good | 125 (23.2) | 398 (31.2) | 42 (20.9) | 398 (31.2) | |||
| Excellent | 85 (15.8) | 278 (21.8) | 32 (15.9) | 278 (21.8) | |||
| Daily Activity | Not At All | 29 (5.3) | 37 (2.9) | <0.001 | 19 (9.4) | 37 (2.9) | <0.001 |
| A Little | 136 (25.0) | 215 (16.8) | 54 (26.7) | 215 (16.8) | |||
| Moderately | 186 (34.1) | 268 (20.9) | 48 (23.8) | 268 (20.9) | |||
| Mostly | 90 (16.5) | 283 (22.1) | 28 (13.9) | 283 (22.1) | |||
| Completely | 104 (19.1) | 477 (37.3) | 53 (26.2) | 477 (37.3) | |||
| Social Role | Poor | 34 (6.3) | 54 (4.3) | <0.001 | 12 (5.9) | 54 (4.3) | <0.001 |
| Fair | 159 (29.6) | 200 (15.8) | 61 (30.2) | 200 (15.8) | |||
| Good | 161 (30.0) | 339 (26.7) | 58 (28.7) | 339 (26.7) | |||
| Very Good | 97 (18.1) | 382 (30.1) | 41 (20.3) | 382 (30.1) | |||
| Excellent | 86 (16.0) | 294 (23.2) | 30 (14.9) | 294 (23.2) | |||
Causal Mediation
Causal mediation analysis demonstrated that SES, a composite of income, education, and employment, partly mediated the relationship between race and key outcomes (Table 5). For both BAA and NWH participants, SES accounted for a substantial proportion of the disparities in pain, self-reported health and quality-of-life domains, daily activity, social role, and health literacy. Nonetheless, significant direct effects of race persisted for several outcomes, indicating that while SES explains much of the observed difference, additional structural and healthcare-related factors contribute to the residual disparities.
Table 5:
Causal mediation of SES on racial differences in outcomes (vs White)
| Outcome | ACME | ADE | Total Effect | Prop. Med. |
|---|---|---|---|---|
| BAA vs White | ||||
| Pain (higher=worse) | +0.72 [0.60, 0.84] | +1.11 [0.82, 1.39] | +1.83 [1.54, 2.10] | 0.39 [0.32, 0.49] |
| Health status | −0.28 [−0.32, −0.23] | −0.12 [−0.22, −0.02] | −0.40 [−0.49, −0.30] | 0.70 [0.53, 0.93] |
| QoL | −0.29 [−0.34, −0.25] | −0.04 [−0.15, 0.06] | −0.34 [−0.43, −0.24] | 0.87 [0.65, 1.26] |
| Physical health | −0.27 [−0.31, −0.22] | −0.02 [−0.12, 0.08] | −0.28 [−0.38, −0.19] | 0.94 [0.67, 1.41] |
| Mental health | −0.23 [−0.28, −0.19] | −0.11 [−0.22, 0.00] | −0.34 [−0.45, −0.23] | 0.69 [0.50, 1.00] |
| Daily activity | −0.33 [−0.38, −0.28] | −0.24 [−0.35, −0.11] | −0.57 [−0.69, −0.45] | 0.58 [0.47, 0.76] |
| Social role | −0.28 [−0.33, −0.24] | −0.14 [−0.25, −0.02] | −0.42 [−0.54, −0.30] | 0.67 [0.52, 0.94] |
| Place for health advice | −0.019 [−0.035, −0.005] | −0.089 [−0.156, −0.019] | −0.108 [−0.174, −0.041] | 0.18 [0.04, 0.55] |
| Understand info | −0.20 [−0.23, −0.16] | −0.27 [−0.37, −0.17] | −0.47 [−0.57, −0.37] | 0.42 [0.32, 0.55] |
| Med. form confidence | −0.21 [−0.24, −0.18] | −0.19 [−0.28, −0.10] | −0.39 [−0.49, −0.30] | 0.53 [0.41, 0.69] |
| Health material assistance | −0.21 [−0.25, −0.17] | −0.24 [−0.35, −0.11] | −0.44 [−0.56, −0.33] | 0.47 [0.36, 0.66] |
| NWH vs White | ||||
| Pain (higher=worse) | +0.76 [0.59, 0.93] | +0.70 [0.29, 1.12] | +1.46 [1.01, 1.92] | 0.52 [0.39, 0.72] |
| Health status | −0.27 [−0.33, −0.21] | −0.30 [−0.44, −0.16] | −0.57 [−0.73, −0.41] | 0.47 [0.36, 0.64] |
| QoL | −0.30 [−0.37, −0.24] | −0.37 [−0.51, −0.20] | −0.67 [−0.81, −0.51] | 0.45 [0.35, 0.62] |
| Physical health | −0.27 [−0.33, −0.21] | −0.18 [−0.32, −0.02] | −0.45 [−0.60, −0.28] | 0.61 [0.45, 0.91] |
| Mental health | −0.23 [−0.29, −0.18] | −0.29 [−0.46, −0.12] | −0.52 [−0.70, −0.35] | 0.45 [0.32, 0.67] |
| Daily activity | −0.36 [−0.43, −0.28] | −0.29 [−0.50, −0.11] | −0.64 [−0.87, −0.45] | 0.55 [0.41, 0.77] |
| Social role | −0.28 [−0.35, −0.22] | −0.16 [−0.34, 0.01] | −0.45 [−0.63, −0.27] | 0.64 [0.45, 1.03] |
| Place for health advice | −0.019 [−0.038, −0.005] | −0.090 [−0.207, 0.024] | −0.109 [−0.224, 0.004] | 0.18 [−0.18, 1.20] |
| Understand info | −0.19 [−0.24, −0.15] | −0.56 [−0.74, −0.38] | −0.75 [−0.94, −0.56] | 0.26 [0.19, 0.34] |
| Med. form confidence | −0.19 [−0.23, −0.15] | −0.37 [−0.54, −0.20] | −0.56 [−0.74, −0.38] | 0.34 [0.24, 0.50] |
| Health material assistance | −0.19 [−0.24, −0.15] | −0.55 [−0.76, −0.34] | −0.74 [−0.96, −0.52] | 0.26 [0.19, 0.37] |
Values are estimates [95% CI]. ACME = indirect (mediated) effect via SES; ADE = direct effect of race; Total = ACME+ADE; Prop. Med. = proportion of total effect mediated by SES. For all outcomes except pain, negative effects indicate worse status vs White. For pain, positive effects indicate higher pain (worse).
Discussion
Our findings highlight pronounced disparities in self-reported health status and QoL, health literacy, and access to healthcare among patients with DCM in the United States. BAA and NWH patients reported poorer self-perceived health and QoL, lower health literacy, and greater difficulties in obtaining necessary medical care. These findings suggest that systemic healthcare inequities, compounded by broader socioeconomic barriers, may substantially contribute to disparities in disease severity and overall QoL.
The intersection of race, socio-economic status (SES) and health outcomes has been well-documented across a range of medical conditions. BAA and NWH patients in our cohort had lower employment rates, household incomes and educational attainment levels than their WC counterparts. These socioeconomic disadvantages likely contribute to a cascade of health-related challenges leading to worse disease outcomes.16 A study by Rethorn et al. found that individuals with lower SES undergoing DCM surgery experienced poorer post-operative outcomes, including higher levels of disability, pain and reduced QoL.17 Furthermore, other studies have demonstrated that higher deprivation indices, unemployment, and rural residence are associated with more severe disease at presentation, longer delays in diagnosis and worse outcomes.6,18
Moreover, our findings indicate that healthcare literacy is more limited among minority populations, a challenge frequently observed in socioeconomically disadvantaged groups. This deficiency may hinder patients’ ability to comprehend their condition, navigate complex specialty care pathways, and recognize the urgency of their symptoms, potentially contributing to disparities in healthcare access and outcomes. These delays in care may worsen disease progression, particularly in conditions like DCM. A systematic review of 111 studies including various medical conditions consistently demonstrated that limited health literacy is linked to greater healthcare costs, increased hospital admissions, more frequent emergency department visits, poorer overall health outcomes, and higher mortality rates.9 While Lans et al. found that limited health literacy is common among spine patients, their study was not specific to DCM.19 On the other hand, literature has shown that poor health literacy may also lead to unnecessary spinal surgery.20 To our knowledge, no studies have specifically examined the impact of low health literacy in the DCM population. Further research is needed to investigate the underlying causes of racial disparities in health literacy and assess their impact on the poorer healthcare outcomes observed in minority populations. This is particularly critical in DCM, a progressive condition where patient understanding of the disease plays a pivotal role in satisfaction and perceived surgical success.
Our findings further demonstrated that minorities have significantly poorer self-perceived health and QoL at diagnosis. They tend to report more difficulty with walking and dressing, worse overall mental and physical health, and higher pain scores. The poorer QoL and increased disease severity in minority patients may stem from multiple factors. A study by Kaleta et al, has shown that lower educational level, lower income, and unemployment status were all factors that caused patients to be significantly more likely to poorly perceive their own QoL.21 Furthermore, in a study of patients from both the United Kingdom and United States, Pope et al. found that minority patients with DCM experience treatment delays of 1–2 years compared to white patients, which could explain the higher disease severity at presentation.22 Additionally, our minority cohort reported lower mental health ratings than their white counterparts, which may further contribute to disparities in overall QoL and physical health. Studies have shown that mental health is an important factor that affect patients reported outcomes in spine surgery.23–26 Outcome research is a key focus in spine surgery,27–35 with several studies examining the impact of racial disparities.36,37 Though much of the disparities literature in DCM — like many areas of spine surgery — has focused on postoperative outcomes, more limited research has investigated disparities in preoperative presentation or other factors that might have led to the worse health status before surgery. Our results build on emerging evidence in DCM by leveraging a population-level dataset to examine differences in QoL across racial groups.
One particularly concerning finding in our study is the lower rate of specialist care utilization among BAA and NWH patients. Access to neurological and orthopedic specialists is essential in diagnosing and managing DCM, but these patients were less likely to receive such care. This disparity may be driven by a range of factors, including limited availability of specialists in minority-dense areas,38 implicit bias in referral practices and financial constraints that limit patients’ ability to seek care outside primary care settings.39 Furthermore, transportation barriers were more commonly reported among NWH patients compared to WC patients, suggesting that logistical challenges may further hinder access to necessary services.40 Du et al. examined disparities in hospital resource utilization after elective cervical surgery, finding that minority patients had longer hospital stays, higher costs, and were more likely to be discharged to non-home settings.3 This may be due, in part, to delayed diagnosis and more severe disease at presentation. However, to our knowledge, no study has assessed disparities in overall healthcare access among DCM patients, which may be a key driver of these poorer hospital outcomes and increased costs. Identifying and addressing these structural barriers is crucial to ensuring timely and effective treatment for minority patients, ultimately improving their functional outcomes and QoL.6
These patterns likely reflect the compounding effects of higher SES on multiple dimensions of health care access and utilization. Patients with greater financial resources likely have better access to primary and specialist care, allowing for earlier recognition and treatment of DCM symptoms. They also may face fewer logistical barriers, such as transportation difficulties, and experiences less financial strain when seeking care, enabling them to pursue timely evaluations and follow-up appointments. In contrast, patients with lower SES are often more reliant on overburdened public health systems and may face delays in diagnosis due to reduces access to specialists or longer wait times for appointments. This delay can result in a more advanced disease state by the time treatment is initiated, further contributing to the disparities observed in this study.41–44
Our study reinforces the need to integrate social determinants of health into clinical decision-making and also into structured efforts to improve DCM detection and care access. This could lead to earlier diagnosis, more timely intervention, and better long-term outcomes. Further research should focus on tracking the long-term outcomes of minority patients with DCM to understand how these disparities impact health trajectories over time. Examining innovative health care delivery models – such as mobile health clinics and telemedicine consultations – could provide valuable insights into strategies for reducing access barriers in underserved communities. Finally, future research should assess whether similar disparities in healthcare access, health literacy, and quality of life exist across other degenerative spinal conditions, such as lumbar stenosis or spondylolisthesis, to determine the broader applicability of these findings and guide more comprehensive strategies for equitable spine care.
Limitations
This study has several limitations. First, while our findings reveal significant disparities, they do not elucidate the underlying mechanisms driving these differences. Second, the retrospective nature of data collection limits our ability to control for potential confounders or account for unmeasured variables. Third, the use of EHR data for outcome identification presents inherent challenges, including potential inaccuracies and limitations in the specificity of ICD-9/10 diagnostic coding. Fourth, the use of patient-reported outcomes from AoURP, which employs general QoL assessments, may not provide the same level of precision as validated, condition-specific QoL measurement tools (e.g., EQ-5D)45 or standardized clinical classifications such as the ASA score. Fifth, the AoURP lacks data on symptom duration, treatment delay, and postoperative outcomes, limiting the ability to determine whether disparities in access and health literacy translate into clinically meaningful differences in outcomes. Additionally, exclusion of participants with unclear or multiple race designations may introduce selection bias. Finally, geographic information was not available, preventing calculation of area-level deprivation indices and limiting assessment of within-group socioeconomic variation.
Conclusion
Our findings highlight the persistent need to address racial disparities that may exist in healthcare access, health literacy, and self-perceived health and QoL among DCM patients. These factors should be considered on the individual clinical level and in structured programs focused on detection and treatment access intended to achieve improved health outcomes for all patients with DCM.
Supplementary Material
References:
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