Abstract
Objective:
To identify how pre-diagnosis employment, education, demographic statuses, and disease factors relate to job retention among people with multiple sclerosis (MS).
Design:
Cross-sectional. Logit model.
Setting:
Data were collected at an academic Medical University and a specialty hospital, both in the Southeastern USA.
Participants:
People with MS (n = 1,126) who were employed at the time of MS diagnosis.
Interventions:
Not applicable.
Main Outcome Measure(s):
Job retention was measured by employment status at the time of follow-up assessment.
Results:
Pre-diagnostic educational attainment was predictive of job retention. Among several pre-diagnostic employment characteristics, only working in production, transportation, and materials moving was significantly related to a lower odds of job retention compared with those working in professional/managerial occupations. Aging factors were strongly related to job retention, with declines in job retention observed with increasing age and years since diagnosis. Non-Hispanic Black and Hispanic participants reported lower odds of job retention compared with non-Hispanic whites, although there were no observed effects of sex. A significantly lower job retention rate was observed among those with progressive MS, compared with relapsing remitting. Job retention was also less likely among people with greater MS severity and fatigue.
Conclusion:
Job retention strategies and interventions should target people with greater MS complications and severity, as well as non-Hispanic Blacks and Hispanics, as these characteristics are more highly related to job retention than our pre-diagnostic employment and vocational history.
Keywords: employment, rehabilitation, vocational, multiple sclerosis, fatigue, cognitive dysfunction
Multiple sclerosis (MS) is a neurologic condition that affects the central nervous system (CNS) and often leads to complex physical and cognitive symptoms.1 MS is commonly initially classified as relapsing-remitting (where symptoms or flare-ups come and go, often with several months in between) and then may advance to secondary-progressive or primary-progressive, where symptoms progress and do not remit. MS is typically diagnosed between the ages of 20 and 50,2 which is the time of life that is associated with career establishment and advancement. MS symptoms and complications frequently impede career plans and vocational outcomes, as individuals often experience both visible and invisible symptoms which can undermine the quality of employment, such as earnings levels and benefits. These symptoms may include motor impairment as well as less visible symptoms such as fatigue, cognitive impairment, visual impairment, issues with depressive symptoms, and diminished strength and stamina.1,3
Despite the importance of employment to quality-of-life among people with MS,4–7 the rate of employment after MS remains well below that of the general population,6 and declines with the person’s age. Jobless rates observed in MS average around 60%, and research from the United States suggests that at any given time, less than half (41–48%) of adults with MS are employed.8,9 Employment retention declines over time, with only 20–30% remaining employed 5–17 years post-diagnosis.10–13 Severity and progression of MS symptoms have also been shown to decrease participation in employment.14,15 For instance, an employment rate of just under 7% was observed for people with severe and progressive MS, compared with 76% for those with very mild symptoms and 77.3% for those with no symptoms.16 A study by Conradsson et. al17 showed that the largest decline in employment status from baseline to follow-up assessment occurred in those with the largest decrease in cognitive function and increase in physical impact of the disease.
There are multiple potential barriers to employment for people with MS, and these may differ from people who have other types of CNS conditions, such as spinal cord injury. The most frequently self-identified barriers to employment among a cohort of 1,234 participants with MS recruited through a specialty clinical center included fatigue, impaired cognition, endurance, and job stress.18 Pre-MS employment history and education may also be important predictors of job retention. Rumrill and Nissen (2012) reported that people with MS who worked in professional, technical, and managerial jobs prior to MS onset and who were viewed as high-status employees by their employers fared much better in their long-term job retention prospects than did workers with MS in other types of jobs that were perceived as lower in status by their employers.18
Existing research using econometric modeling indicates that the severity of MS is consistently related to poor outcomes, as are other factors. For instance, age, years since diagnosis, MS diagnosis and severity, and race-ethnicity have been associated with differential job benefits19 and earnings.9 However, these studies have not focused on job retention per se, looking only at those who were employed at the time of diagnosis, nor have they focused on pre-MS education and other employment history variables at the time of MS diagnosis. In combination with the investigation of demographic and disease factors, identifying the relationship of educational and employment status at MS diagnosis will help us to understand those factors that specifically affect job retention in the years and decades after MS onset, establishing a foundation for interventions to address this problem.
Our purpose was to identify the relationships of pre-diagnostic educational and vocational factors with job retention after MS, while simultaneously evaluating the relationships between demographic and MS severity characteristics with job retention. We define job retention as maintenance of gainful employment after MS and at the time of follow-up, rather than retention of a specific job. Two research questions guided this study:
Do pre-MS diagnosis education and employment factors such as type of occupation, work hours, and earnings relate to post-MS diagnosis job retention?
To what extent do age (age and years since diagnosis), race-ethnicity, relationship status, and MS factors including diagnosis, severity, cognition, and fatigue relate to job retention?
Pre-diagnostic occupation is nonmodifiable, yet, it is important for vocational rehabilitation (VR) counselors to be able to predict how occupation at diagnosis will affect post diagnostic outcomes, including job retention. Similarly, although demographic and diagnostic factors have been the focus of previous research, it is important to understand how they specifically relate to job retention among those employed at diagnosis, as well as enhancing the body of knowledge regarding factors related to employment after MS.
Method
Participants and design
A total of 1,332 potential participants completed self-report assessments from a pool of 3,291 (686 could not be contacted by phone and 1,273 were nonrespondents). For the current analysis, data for all those who were not employed at the time of MS diagnosis were eliminated, reducing the final cohort to 1,126. Participants were enrolled in this cross-sectional study from a specialty hospital in the southeastern United States, using the following eligibility criteria: (a) adult (≥ 18 y/o) with MS, (b) at least 1-year post MS diagnosis, and (c) under the traditional retirement age of 65 at the time of diagnosis. The specialty hospital treats people with SCI, MS, and other conditions in a community setting, without primary affiliation to an academic institution. The data were collected between 2016 and 2017.
Data Collection Procedures
Institutional review board approval was obtained prior to beginning data collection. Recruitment letters were sent to prospective participants to describe the study and to alert them that materials would be forthcoming. Assessment packages were mailed 4–6 weeks later. Follow-up mailings were sent to nonrespondents, as were follow-up phone calls. Participants had the option to complete the materials online. Remuneration for participating was $50.
Measures
A self-report assessment was used to evaluate employment outcomes after MS.9,19 It elicited information on multiple demographic characteristics (age, sex, race-ethnicity), educational status, MS factors (type, severity, fatigue, time since diagnosis), pre-diagnostic educational attainment, post diagnostic education, and pre-diagnostic employment characteristics (number of work hours, earnings, occupation). MS course was measured by asking participants to indicate which of five categories best described their MS, with the responses recoded into three groups of 1) progressive, 2) relapsing-remitting, and 3) other – unsure. Severity was measured on a 5-point scale and recoded into the following groups: 1) no current symptoms, 2) some symptoms that affect daily functioning, and 3) multiple, severe symptoms significantly limiting daily functioning. Fatigue was measured by asking the participants’ level of agreement with the statement, “I get fatigued easily and this makes maintaining a job difficult”, which was dichotomized as strongly disagree, disagree, or neutral and strongly agree or agree).18 Cutting points for other variables are summarized in table 1. Occupation was defined according to the Standard Industrial Classification (SIC) Codes from the Department of Labor,20 which identified five occupational groups of: 1) management/professional, 2) service, 3) sales/office, 4) natural resources, construction, and maintenance, and 5) production/transportation/materials moving. To measure job retention, individuals were asked to indicate their employment status at the time of completion of the self-report assessment (yes/no), so it reflects the retention of employment to the time of follow-up, rather than a specific job.
Table 1.
Description of variables, breakdown of categories, and the number and portion of participants in each category.
| Variables | N (%) | |
|---|---|---|
| Sex | Male | 268 (23.82%) |
| Female | 857 (76.18%) | |
| Race-ethnicity | White, non-Hispanic; Hispanic | 728 (66.36%) |
| Black, non-Hispanic | 302 (27.53%) | |
| Hispanic | 38 (3.46%) | |
| Other | 29 (2.64%) | |
| Age at diagnosis | 39 or less | 278 (24.69%) |
| 40–49 | 372 (33.04%) | |
| 50–59 | 332 (29.48%) | |
| 60> | 144 (12.79%) | |
| Time since diagnosis | 9 years or less | 486 (43.16%) |
| 10–19 years | 435 (38.63%) | |
| 20 or more years | 205 (18.21%) | |
| Relationship status | Single | 707 (64.51%) |
| Married or with partner | 224 (20.44%) | |
| Separated, widowed, or divorced | 165 (15.05%) | |
| Educational status | No more than a high school certificate | 283 (25.25%) |
| Two-year degree or completion of trade school | 260 (23.19%) | |
| Four-year degree | 335 (29.88%) | |
| Postsecondary education | 243 (21.68%) | |
| Increase in education from pre-diagnosis to the time of the study | Yes | 120 (11.35%) |
| No | 937 (88.65%) | |
| Hours per week spent working | Less than 30 hours | 77 (6.84%) |
| 30 or more hours | 1,049 (93.17%) | |
| Earnings at the time of diagnosis | <$40,000 | 530 (52.22%) |
| $40,000-$74,999 | 382 (37.64%) | |
| $75,000 or more | 103 (10.15%) | |
| Standard Industrial Codes from the Department of Labor | Production/transportation/material moving | 39 (3.59%) |
| Management/professional | 494 (45.49%) | |
| Service | 101 (9.30%) | |
| Sales/office | 397 (36.56%) | |
| Natural resources, construction, and maintenance | 55 (5.06%) |
Variables and data analysis
We estimated a logit model with robust standard errors of the association of demographic, clinical and vocational covariates with job retention. We used STATA 15 software and reported odds ratios, 95% confidence intervals (CI) and P-values. The odds ratio indicates the ratio of the probability of job retention given one characteristic compared to a reference group (e.g., the odds of job retention for females compared with males). An odds ratio greater than 1.0 indicates a greater probability of job retention compared to the reference group, whereas an odds ratio of less than 1.0 indicates a lower probability of job retention compared with the reference group. The logistic regression was also used to calculate the predicted probabilities of job retention at follow-up for each characteristic, holding all other characteristics constant. The 95% CI and P-values associated with the predicted probabilities are testing the hypothesis that the predicted probability is different from zero. The covariates included demographic, MS factors, and educational factors (see table 1). The variables include: sex, race-ethnicity, age at MS diagnosis, years since diagnosis, relationship status, educational status at diagnosis, and education since diagnosis. Three employment indicators measured to reflect status at the time of diagnosis, including the hours per week spent working, earnings, and occupational categories according to the Standard Industrial Classification (SIC) Codes from the Department of Labor.20
Results
Descriptive
Most participants were women (76.1%), 66.4% were white non-Hispanic, 27.5% were Black non-Hispanic, 6.1% were Hispanic or “other” (e.g., American Indian, more than one race) (table 1). The majority of the participants were between the ages of 40 – 49 at injury (33.0%), with the lowest percentage being 60 and older (12.8 %). In terms of years since diagnosis, the majority of the participants were nine years or less (43.2 %). Approximately 63% were married or cohabitating; 14.7% were single and 19.9% were separated widowed or divorced. Just over 11% of the participants increase their education after MS, whereas there were relatively equal portions of people with pre-MS education of less than a four-year degree and those with more than a four-year degree.
Logistic regression analysis
The pseudo-R squared for the full model was .348. We used McFadden’s Pseudo R-Squared, which is one minus the ratio of two log likelihoods. The numerator is the log likelihood of the logit model selected and the denominator is the log likelihood if the model only had an intercept. The MacFadden Pseudo-R-Square is an analogue to the OLS R-squared. Table 2 summarizes the odds ratios from the logistic regression and table 3 summarizes the predicted probabilities of employment. Compared to those 39 years old or less, those ages 50–59 and those in the highest age group 60+ reported substantially lower odds of job retention (OR = 0.49 and OR = 0.45 respectively). The range of predicted probabilities of job retention was 0.44 – 0.56. Years since diagnosis was also related to a lower odds of job retention, with those having 20 or more years since diagnosis reporting only a 0.15 odds ratio of job retention (32% predicted probability of job retention) compared to the reference group of 9 years or less (61.2% predicted probability of job retention). Race-ethnicity was associated with job retention, as both non-Hispanic Blacks and Hispanic participants reporting lower odds of job retention compared with non-Hispanic whites (OR = 0.52 and OR = 0.45 respectively). Sex was not related to job retention.
Table 2.
Odds ratios, confidence intervals, and p-values for demographic, MS, educational, and employment characteristics related to job retention
| Characteristic | Odds ratio | 95% CI | p-value | |
|---|---|---|---|---|
| Age at diagnosis (ref: 39 or less) | ||||
| 40–49 | .89 | .529 | 1.488 | .649 |
| 50–59 | .49 | .276 | .871 | .015 |
| 60> | .45 | .202 | .990 | .047 |
| Time since diagnosis (ref: 9 years or less) | ||||
| 10–19 years | .39 | .257 | .604 | <.001 |
| 20 or more years | .15 | .079 | .268 | <.001 |
| Sex (ref: male) | ||||
| Female | .81 | .461 | 1.407 | 0.449 |
| Race (ref: White, non-Hispanic) | ||||
| Black, non-Hispanic | .52 | .339 | .813 | .004 |
| Hispanic | .45 | .194 | 1.064 | .069 |
| Other | .93 | .235 | 3.677 | .918 |
| Relationship status (ref: never married) | ||||
| Divorced/widowed/separated | .62 | .381 | 1.011 | .055 |
| Couple | .70 | .385 | 1.280 | .248 |
| MS course (ref: relapsing-remitting) | ||||
| Progressive | .54 | .309 | .959 | .035 |
| Unknown | .63 | .342 | 1.163 | .140 |
| MS severity (ref: no symptoms) | ||||
| Some symptoms | .44 | .280 | .702 | .001 |
| Multiple, severe symptoms | .12 | .069 | .255 | <.001 |
| Cognitive impairment (ref: normal cognition) | ||||
| Minimal-mild cognitive disability | .80 | .280 | .702 | .394 |
| Moderate to total cognitive disability | .65 | .069 | .225 | .192 |
| Fatigue | .23 | .149 | .345 | <.001 |
| Hours spent working (ref: <30 hours) | ||||
| ≥ 30 hours | 1.47 | .585 | 3.689 | .413 |
| Income (ref: <$40,000) | ||||
| $40,000 - $74,999 | 1.34 | .865 | 2.073 | .191 |
| >$75,000 | 1.11 | .481 | 2.567 | .804 |
| Education (ref: high school certificate or less) | ||||
| Two-year degree or completion of trade school | 1.78 | 1.046 | 3.024 | .033 |
| Four-year degree | 1.84 | 1.047 | 3.235 | .034 |
| Postsecondary education | 1.96 | 1.023 | 3.767 | .043 |
| Increase in education from pre-diagnosis to time of study | .98 | .507 | 1.899 | .954 |
| Occupational field (ref: production/transportation/material moving, never employed) | ||||
| Service | .88 | .414 | 1.867 | .737 |
| Management/professional | .91 | .590 | 1.409 | .678 |
| Sales/office | .42 | .150 | 1.170 | .097 |
| Natural resources, construction, and maintenance | .44 | .200 | .959 | .039 |
Pseudo R2 = .348
Table 3.
Predicted probability of job retention, marginal probability, standard error, and confidence intervals.
| Characteristic | Margin (%) | Std. Err. | 95% CI | |
|---|---|---|---|---|
| Age at diagnosis/injury | ||||
| 39 or less | 55.6 | .03 | .49 | .62 |
| 40–49 | 53.9 | .02 | .49 | .58 |
| 50–59 | 45.2 | .02 | .40 | .50 |
| 60> | 43.9 | .05 | .35 | .53 |
| Race-ethnicity | ||||
| Non-Hispanic White | 54.0 | .02 | .50 | .58 |
| Non-Hispanic Black | 44.6 | .03 | .40 | .50 |
| Hispanic | 42.5 | .06 | .31 | .54 |
| Other | 53.0 | .10 | .33 | .73 |
| Marital status | ||||
| Never married | 52.8 | .02 | .50 | .56 |
| Divorced/widowed/separated | 45.8 | .03 | .40 | .52 |
| Couple | 47.6 | .04 | .40 | .56 |
| Sex | ||||
| Female | 49.9 | .02 | .47 | .60 |
| Male | 53.0 | .04 | .46 | .53 |
| MS Course | ||||
| Progressive | 52.6 | .02 | .49 | .56 |
| Relapsing-remitting | 43.5 | .04 | .36 | .51 |
| Other/unsure | 45.7 | .04 | .37 | .54 |
| Severity | ||||
| No symptoms | 65.4 | .03 | .59 | .72 |
| Some symptoms | 51.1 | .02 | .46 | .58 |
| Multiple, severe symptoms | 29.5 | .04 | .22 | .37 |
| Time since diagnosis/injury | ||||
| 9 years or less | 61.2 | .02 | .57 | .66 |
| 10–19 years | 46.7 | .002 | .42 | .51 |
| 20 or more years | 32.0 | .04 | .25 | .39 |
| Cognitive impairment | ||||
| Normal cognition | 54.2 | .04 | .47 | .61 |
| Minimal-mild cognitive disability | 50.8 | .02 | .47 | .54 |
| Moderate-total cognitive disability | 47.6 | .03 | .41 | .54 |
| Fatigue | ||||
| Strongly disagree, disagree, neutral | 65.1 | .03 | .60 | .70 |
| Strongly agree or agree | 39.8 | .02 | .35 | .44 |
| Hours per week spent working | ||||
| Less than 30 hours | 45.3 | .07 | .32 | .58 |
| 30 hours or more | 50.9 | .01 | .48 | .54 |
| Earnings | ||||
| <$40,000 | 48.8 | .02 | .47 | .53 |
| $40,000-$74,999 | 53.1 | .02 | .49 | .58 |
| $75,000 or more | 50.3 | .06 | .39 | .61 |
| Education | ||||
| No more than a high school certificate | 43.7 | .03 | .38 | .50 |
| Two-year degree or completion of trade school | 52.2 | .03 | .47 | .58 |
| Four-year degree | 52.7 | .03 | .47 | .58 |
| Postsecondary education | 53.7 | .03 | .47 | .60 |
| Increase in education from pre-diagnosis to the time of the study | ||||
| No | 50.5 | .01 | .48 | .53 |
| Yes | 50.2 | .05 | .41 | .59 |
| Occupational type | ||||
| Production/transportation/material moving | 40.2 | .05 | .30 | .50 |
| Service | 50.3 | .05 | .40 | .60 |
| Management/professional | 52.2 | .02 | .48 | .57 |
| Sales/office | 50.9 | .02 | .46 | .55 |
| Natural resources, construction, and maintenance | 39.5 | .07 | .26 | .53 |
All MS variables were significantly related to job retention, except cognitive impairment. Compared with those who had relapsing-remitting MS, those with progressive MS only had a 0.54 odds of job retention. Compared with those with the least severe MS, the odds of employment decreased to 0.44 for those with moderate severity and 0.12 for those with the most severe MS (Table 2), translating into differences in predicted job retention of 65.4% and 29.5% (Table 3). Fatigue was statistically significant (p<.001) with only a 0.23 odds of job retention compared to those without fatigue (Table 2) and a predicted probability of employment of 65.1% compared with only 39.8%.
In terms of pre-MS education, each of the three groups with more than a high school certificate had a greater odds of job retention compared with those that had no more than a high school certificate (OR ranged from 1.78 – 1.96; table 2). This translated into a range in predicted employment from 43.7% – 53.7%. Additional education after MS was unrelated to job retention. Of the employment variables, only one pre-MS occupation was significant. Those who were working in production, transportation, and material moving at the time of MS diagnosis had a 0.44 odds of job retention compared with those who were working in professional roles at the time of diagnosis (table 2), and a predicted probability of employment of only 40.2% compared with 52.2% for those in professional roles.
Discussion
Job retention is critical to the lives of people with MS, as most people with MS in the traditional working age range are gainfully employed at the time of diagnosis (84.5% in the current study). This study contributed to our understanding of job retention after MS by identifying the relationships between several types of factors and job retention, including pre-diagnostic educational and employment characteristics. The primary findings indicate only limited relationships between education and employment factors at the time of MS onset with job retention, an average of 12 years after MS diagnosis. Stronger relationships were observed between aging factors, race-ethnicity, and MS factors and job retention. The findings underscore the significant barriers associated with MS severity and complications.
Interpretation and implications for VR counselors
For many years, rehabilitation researchers have sought to understand the high rate of workforce attrition that follows the onset of MS.18 A study by Khan et. al provides detail summarizing the literature related to lack of evidence that VR programs are effective in those with MS.21 Our findings are consistent with previous studies that have demonstrated the strong predictive power of demographic and disease-related factors in explaining the tenuous employment outcomes of Americans with MS.7 Specifically, our findings of decreased employment with increasing age and years postdiagnosis are consistent with earlier studies involving National MS Society participants.8,22 Similarly, racial-ethnic disparities in employment have been widely observed, consistent with our findings that non-Hispanic Blacks and Hispanic participants were less likely to be employed at follow-up.16 Furthermore, the findings that job retention is diminished for people with progressive, more severe MS, and those with fatigue, is not surprising and in line with previous research.8,12,13,23,24 However, it was surprising that cognition was unrelated to job retention.. This was not consistent with previous studies of employment after MS that included all people with MS within the working age range, rather than simply those who were employed at the time of diagnosis.9,19
Findings regarding education are consistent with previous research in that education beyond a high school certificate or diploma was significantly related to job retention. However, we did not find the same pattern of an increased likelihood of favorable outcomes with increasing education, as has been found in previous studies.9,19 For instance, in the study of earnings, using the same larger set of data, odds ratios for a four-year degree and for postsecondary education were 1.95 and 2.87 respectively. These findings may relate to our focus on job retention among those who were employed at the time of diagnosis, rather than all people with MS, or it may relate to our use of pre-diagnosis education. Therefore, even though some might conceptualize job retention as equivalent with current employment status, it is important to differentiate those who were not working at the time of diagnosis. The effects of education appeared to be less profound, not simply by the lack of increasing probability of employment with higher education, but also the lack of significance between a positive change in educational milestone after MS and job retention. This finding is difficult to explain, although its very presence may simply underscore the potency of disease-related factors as predictors of employment outcomes; the disease experience likely supersedes all other possible explanations for the low rate of job retention among people with MS, such that educational attainment in the face of a severe disease experience cannot exercise the positive influence it would have on job retention in the absence of an MS diagnosis.
The findings regarding employment history are somewhat surprising. Given the average length of follow-up (12 years), pre-MS employment characteristics simply are not strong predictors of long-term outcomes. Therefore, outside of the observed effect of having someone working as a professional at the time of MS diagnosis, VR specialists should not assume that job hours or earnings at the time of diagnosis will necessarily translate into better long-term outcomes after diagnosis, particularly when considering variations in disease experience.
Methodologic Considerations
We utilized data from (or enrolled) participants who were identified in a clinical setting, rather than from self-selected community-based organizations like the National MS Society. This has several strengths that include: the clinical setting ensures that all participants have MS, establishes a specific target cohort, and allows for more intensive follow-up with multiple mailings to specific participants. On the other hand, individuals identified through a specialty clinic may differ from the overall MS population in other respects that may include more severe symptoms requiring treatment or admission practices that may be affected by factors such as socioeconomic status, race-ethnicity, and/or geographic distance from the center. At a minimum, the setting should be considered when evaluating study results and differential findings between studies.
Second, we relied exclusively on self-report data, which may lead to some inaccuracies, such as with self-reported diagnosis, and inaccurate retrospective recall. However, self-report allows for measurement of a significant amount of information that could not otherwise be obtained from records. We do not have information on reasons for non-participation.
Third, participants had a wide range in the number of years since diagnosis, so there was variation in the amount of time that elapsed between diagnosis and follow-up.
Lastly, the response rate, although relatively high, still raises the possibility of selection bias. Selection bias would be of concern if people with a certain characteristic were systematically more or less likely to participate. For example, it is possible that people with higher education levels are more likely to participate in research and therefore disproportionately responded to the request to participate.
Future Research
Research is needed to identify and test the effectiveness of targeted job retention interventions for people with MS. Whereas, we addressed overall educational level and milestones, there is a need to better understand specific types of training that might promote job retention in people with MS. This is particularly important because few people with MS reenter the labor force after leaving. These findings, combined with the evidence that employment is linked to quality of life in people with MS, suggests that further studies need to look to alternative, proactive VR models for people with MS, since many may never get connected to rehabilitative services, particularly if they choose not to disclose their disability. There also is a need for longitudinal studies to better track job retention and other employment outcomes over time, rather than at a single point in time. In summary, a combination of different approaches is needed for future research to help us understand and promote job retention and other quality outcomes for people with MS.
Conclusion
MS factors, such as severity and complications, are stronger explanatory factors for job retention than pre-diagnostic employment and vocational history. Compared to non-Hispanic whites, non-Hispanic blacks had lower odds of job retention, which may speak to the need for targeted intervention strategies for these individuals.
Support:
The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), 90RT5035. NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the policy of NIDILRR, ADL, or HHS, and you should not assume endorsement by the funding agency or Federal Government.
We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on me or on any organization with which we are associated AND, if applicable, we certify that all financial and material support for this research (e.g., NIH or NHS grants) and work are clearly identified in the title page of the manuscript.
The manuscript submitted does not contain information about medical device(s).
List of abbreviations:
- MS
Multiple sclerosis
- CNS
Central nervous system
- VR
Vocational rehabilitation
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