The relationship of type of disability and employment status in the United States from the Behaviorial Risk Factor Surveillance System.

The most recent census data on persons with disabilities in the United States reveals that 49.7 million people reported some type of long lasting condition or disability (U.S. Bureau of Census, 2000). Furthermore, according to the 2004 Disability Status Report (Rehabilitation Research and Training Center on Disability Demographics and Statistics [StatsRRTC], 2005), information from the American Community Surveys, 12.1% of working-age people currently had a disability. Within this population, 2.8% had a sensory disability, 7.6% had a physical disability, 4.2% had a mental disability, 2.0% had a self care disability, 3.0% had a disability that affected their ability to go outside the home and 7.0% had a disability that affected their ability to work.

Additional information from the Disability Status Report (StatsRRTC, 2005) reveals that the employment rate of working age people with disabilities decreased from 37.9% in 2003 to 37.5% in 2004, while the employment rate for working-age people without disabilities increased from 77.6% in 2003 to 77.8% in 2004. Within the population of persons with disabilities, 47.3% had a sensory disability, 31.8% had a physical disability, 28.5% had a mental disability, 17.6% had a self-care disability, 16.7% had a disability that affected their ability to go outside and 17.3% had a disability that affected their ability to work. The percentage of working-age people with disabilities working full-time/full-year decreased from 23.0% in 2003 to 22.4% in 2004, while the percentage of working-age people without disabilities decreased from 56.4% in 2003 to 56.2% in 2004. Lastly, the percentage of working-age people with sensory, physical and/or mental disabilities that reported an employment disability (difficulty working at a job or business) increased from 53.2% in 2003 to 54.2% in 2004.

The National Organization on Disability (N.O.D.)/Harris Survey of Americans with Disabilities (2000) surveyed 997 adults with disabilities and 953 adults without disabilities in May and June 2000. Among working age adults (18-64) with disabilities, three out of ten (32%) work full or part-time, compared to eight out often (81%) of those without disabilities, a gap of 49%. This is a slight increase over the 1998 Lou Harris poll which showed an employment rate of 79% for persons without a disability and 29% for persons with a disability (Harris and Associates, 1998).

When measuring employment, it may not be appropriate to look at all people with disabilities aged 18-64 since there are a significant number of people who say they are completely unable to work due to their disabilities. It is noteworthy that over the past fourteen years, the percentage of people with disabilities who say they are unable to work has risen steadily from 29% to 43% (Harris Interactive, Inc., 2000). Among the unemployed, more than two out of three people with disabilities (67%) would prefer to work. When looking only at people with disabilities who say they are able to work, the employment rate has increased gradually from 46% in 1986 to 56% in 2000. Some respondents believed that this increase could be attributed, at least in part, to the Americans with Disabilities Act (ADA). Other factors that are likely to have sparked this change are a strong economy and a growth in technology, both of which allow companies to make accommodations that they had not made in the past.

Employment by Type of Disability

Baldwin (1999) noted that because of differences in the nature and onset of health conditions, it was important to study the labor market experiences of different impairment groups separately, rather than treat workers with a disability and potential workers as a single group. The author studied data from the 1984 and 1990 panels of the Survey of Income and Program Participation to analyze trends in the employment and wages of six impairment groups in the years immediately preceding the ADA. She found that persons with mental conditions consistently had the poorest labor market outcomes. Their employment rates and wages were the lowest among all impairment groups and showed no improvement between 1984 and 1990. Musculoskeletal conditions were the most common impairment category and persons in this group had comparatively high average wages. However, employment rates for persons with musculoskeletal conditions were only at the median for the groups with disabilities.

The most significant changes in employment and wages between 1984 and 1990 occurred for persons with sensory and respiratory conditions. Employment rates declined for both groups, and persons with sensory impairments experienced decreasing relative wages as well. Finally, persons with cardiovascular conditions made some labor market gains between 1984 and 1990, but the relative prevalence of these conditions declined considerably. Baldwin further stated that these results confirm the diversity of labor market experiences within the population with disabilities and suggests that policies designed to improve labor market outcomes for workers with disabilities should be targeted to the different needs of different impairment groups.

Zwerling, et al. (2002) found that persons with cardiovascular, musculoskeletal, and respiratory disabilities were less likely to work than other Americans with disabilities. Persons with psychiatric disabilities showed considerable variety in their likelihood to work. Those with schizophrenia and paranoid delusional disorder, bipolar disorder and major depression were also less likely to procure and maintain work. Further, persons with self-reported alcohol abuse were more likely to work compared persons with self-reported drug abuse, other than alcohol, who were not as likely to work. Kaye (2001) reported that of all the groups analyzed, people with severe mobility impairments were the least likely to be employed (< 24%). Approximately 32% of people with mental retardation, 33% of people with mental health disability, 34% of people with severe visual impairments and 35% of people with communication difficulties were employed.

Another study showed that applicants and employees with physical disabilities were viewed more favorably than those with mental, emotional or communication disabilities in almost every area of recruitment, selection, acceptance and performance expectation (Greenwood, Johnson, and Schriner, 1991). MacDonald-Wilson, Rogers and Massaro (2003) performed a multi-site, longitudinal study of 191 employees and 22 supported employment programs across three states during a one year study period. They found that the most frequent functional limitations among this group of employed persons with psychiatric disabilities were cognitive in nature, followed by social, physical and emotional/other. There was a significant relationship between the types of functional limitation and the number and type of accommodations received.

Gouvier, Sytsma-Jordan and Mayville (2003) evaluated the effects of disability type, job complexity and public contact on hiring decisions. Findings indicated disparities in ratings of employability as a function of disability type. Loprest and Maag (2003) found that those with less education and previous work experience, and those with more severe activity limitations had the most difficulty searching for jobs. Conversely, Zwerling et al. (2002) found that persons with disabilities who were more educated were more likely to be employed. Married men were more likely to be employed than unmarried men and African-Americans less likely than Caucasians.

In addition, there have been several studies looking at the relationship of function instead of "clinical diagnosis", and employment. A study by Price, Kendall, Amsters, and Pershouse (2004) examined the factors perceived to change or threaten quality of life among individuals with long duration spinal cord injury. Results found that pain and loss of strength were perceived to have caused changes in function in 11.9% and 14.3% of participants respectively, while these same factors were perceived to have caused changes in quality of life in 19.0% and 17.9% of participants respectively. McDermott, Richards, Ankers, Harmer, and Moran (2004) presented the results of an audit of clinical outcomes from an occupational therapist led service for patients with chronic fatigue syndrome. The service offered group outpatient lifestyle management sessions, in which patients are encouraged to restructure lifestyle patterns in order to facilitate improvements in fatigue and function. Among those treated consumers, 42% reported new part-time or full-time employment, voluntary work or training. Finally, a study by Ross and Stone (2004) sought evidence in the published literature on how best to measure, monitor and treat disability in consumers with chronic fatigue syndrome. The authors found that only cognitive behavior therapy, individualized functional rehabilitation and exercise therapy intervention were associated with restoring the ability to work. They further concluded that simple and consistent evaluations of functional capacity in patients with chronic fatigue syndrome were needed.

These studies illustrate that persons with disabilities continue to experience significantly lower rates of employment than persons without disabilities. However, it is not clear which variables, or combination of variables, such as type of disability, gender, age, level of education, and cultural influence, might increase or decrease the likelihood of employment for persons with disabilities. In addition, the impact of policy issues such as social security disability insurance, personal injury lawsuits or workers compensation needs to be taken into consideration as they can affect a person's desire and ability to return to work. These issues are relevant, but outside the scope of the present study as the information is not available in the dataset. Therefore, this study investigated what types of disabilities are more likely to predict unemployment for persons with disabilities and which factors or combination of factors increase the risk of unemployment for persons with disabilities. The hypothesis is that there are specific types of disabilities or conditions that will increase the risk of unemployment. Also hypothesized is that there are specific types of disabilities or conditions will more strongly predict unemployment.

Methodology

This study was a quantitative study using data from the Behavioral Risk Factor Surveillance System (BRFSS), developed by the Centers for Disease Control and Prevention (CDC). The BRFSS is an annual random survey conducted on a state-wide basis. The survey can be accessed through the CDC website at www.cdc.gov. For this study, disability status was determined by answers to disability related questions on the BRFSS from 1995-2002. The premise of the BRFSS was to collect data on actual behaviors, rather than on attitudes or knowledge, that would be especially useful for planning, initiating, supporting, and evaluating health promotion and disease prevention programs. The BRFSS questionnaire was designed by a working group of state coordinators and CDC staff. Currently the questionnaire has three parts: (1) the core component, consisting of the fixed core, rotating core, and emerging core, (2) optional modules, and (3) state-added questions. All health departments must ask the core component questions without modification in wording; however, the modules are optional.

Data from the BRFSS were chosen as optimal for this study because the BRFSS has consistent methodology meaning that similar or same questions asked annually and the interviews are conducted in a consistent manner. The BRFSS has been shown to be both reliable and valid, substantiated by several studies (Nelson, Holtzman, Bolen, Stanwyck and Mack, 2001; Nelson, Powell-Griner, Town and Kovar, 2003; Ronaldo, et al., 2003). Yearly training of interviewers and checks for consistency are also mandated. Questions addressing disability were consistent across time and clearly related to disability issues. Additionally, the BRFSS dataset contained measures of type of disability as well as the control variables such as gender, education, marital status, race/ethnicity and age. (CDC, 2004).

Disability status for this study was determined by response to the following BRFSS question, "Are you limited in any way in any activities because of an impairment or health problem?" Responses include: (1) yes, (2) no, (7) don't know/not sure or (9) refused (CDC, 2004). These responses were dummy coded as l=disability, 0=no disability. This question was chosen because of its consistency across all surveys. This definition of disability is very broad, similar to Nagi's definition, in that the person has an identified health problem, which leads to an impairment, which causes a disability (Nagi, 1998). To further define type of condition/disability, the BRFSS question used was "What is the major impairment or health problem that limits your activity?" Positive "yes" responses to the condition or disability were coded as one and negative "no" responses were coded as zero.

In order to examine the job status of persons with various disabilities, rates of unemployment must be measured. For the purpose of this study, the term "unemployment" represented those not employed and was not be used in the traditional economic sense (that population searching, but unable to find employment). Therefore, one of the dependent variables included in this analysis was employment status which was determined by response to the BRFSS question, "Are you currently: [employed for wages, self-employed, out of work, a homemaker, student, retired, unable to work?]" Employment status was coded as "employed" for those who responded that they were "employed for wages" or "self-employed." All others were coded as "not employed." For cross tabulation, demographic information on employment was further subdivided into "not employed" and "unable to work." For regression analysis, all of the employment variables were dummy coded with "yes" (employed) responses coded as zero and "no" (unemployed) responses coded as one.

Control variables were also used in this analysis and were available in all datasets. Gender is an important factor when one is looking at employment issues for persons with disabilities (Burkhauser, Havemen, and Wolfe, 1990; Reed, 1999). Gender was categorized as 1 = female and 0= male in all surveys. Other control factors included in this study were determined through literature review and step-wise analysis. Educational attainment was measured with the following categories over the eight year period as a ranked categorical variable beginning with those who had: (1) no schooling or kindergarten only through eighth grade, (2) some high school, (3) high school graduate, (4) some college, and (5) college graduate.

Race and ethnicity, as reviewed in the literature, are considered an important control variable as persons with disabilities who were also members of other minority groups or women frequently encounter dual discrimination (Burkhauser, Havemen, and Wolfe, 1990; Reed, 1999). The term "simultaneous oppression" as introduced by Stuart (1992) refers to the intermingled effects of race, gender and disability discrimination. Simultaneous oppression should always be considered as a confounding variable. The U.S. Bureau of Census (1994/1995) reported that more than one-third of all severely disabled working-age Americans were minority group members. In the current study, race and ethnic status were coded as white race, black race, Hispanic ethnicity, Asian/Pacific Islander and Native American/Alaska native.

Marital status was examined as a control variable as well. It was assumed that disabled persons who were married (especially women) might voluntarily choose not to work. In addition, married persons who are disabled have more resources to access if they work. Responses were coded as 1- coupled and 0= uncoupled. Age is the final control variable noted in the literature. Age was categorized into four groups, (1) 18-24, (2) 25-34, (3) 35-49 and (4) 50-65 years of age (CDC, 2004).

The analytical strategy used in this study was to examine if rates of unemployment had changed for groups of persons with various types of disabilities. Also examined were the disability-related risk factors for unemployment. Finally, types of disability will be examined regarding their predictive effect on rates of unemployment. Gender, education, race/ethnic status, and age were used as control variables to determine their impact.

Results

Cross Tabulation

When examining demographics by condition, (see Table 1) it was found that those with stroke, diabetes, heart problems, cancer and emotional problems were the least employed. These results also show that there as been no significant change in employment across time for any condition or disability, despite the legislation of the ADA.

Other cross tabulation results (data not shown) show that women were represented in the categories of arthritis/rheumatism, hypertension/high blood pressure, cancer and emotional problems, whereas men were more represented in orthopedic (back/neck problems, fractures/bone/joint injuries) or circulatory (heart problems and stroke) conditions. Caucasians appear to have more cancer, but African-Americans have more hypertension/high blood pressure, stroke and diabetes. Those who needed the most help with personal care and routine needs were those with stroke, diabetes and cancer. Those who experienced the most significant depressive symptoms were individuals with diabetes, cancer and emotional problems.

Regression Analysis

Table 2 shows the results of regression analysis of the variables on the outcome of unemployment. Because gender was shown to be predictive of employment status, the population was also divided by gender for additional analysis. For the total population, activity limitation and gender were shown to be predictors of unemployment. Conditions that were more strongly predictive of unemployment were stroke, diabetes, cancer and emotional problems. Needing help with personal care and routine needs were also predictive for unemployment, with needing help for routine needs being the stronger predictor. Educational status was a strong negative predictor of unemployment (i.e. less education is more predictive of unemployment). The R square shows that the aforementioned factors predict 19% of unemployment for persons with disabilities.

For women, having an activity limitation, diabetes, cancer, and emotional problems were the largest predictors of unemployment. Help with personal care and routine needs was also strongly predictive of unemployment. Educational status was negatively predictive and age was positively predictive (i.e. less education and higher age are most predictive of unemployment). The R square shows that 17% of unemployment can be predicted by these factors for women. For men, having an activity limitation was a stronger predictor of unemployment than it was for women. Stroke was a stronger predictor for unemployment for men than for women, whereas diabetes and emotional problems were weaker predictors for unemployment for men though still significant. Needing help with routine needs was a stronger predictor for unemployment for men than for women. Education was a weaker predictor and age was a stronger predictor of unemployment for men than for women, though still significant (i.e. less education was a stronger predictor of unemployment for women, higher age was a stronger predictor of unemployment for men). The R square shows that 23% of unemployment can be predicted by these factors for men.

Risk of Unemployment

Table 3 displays the odds ratio and risk of conditions and other variables to unemployment for the total population and also for genders specifically. For the total population, risk of unemployment increases one and one half times with stroke, diabetes, cancer, and emotional problems. Also, the need for help with personal care and routine needs increases the risk of unemployment by 1.72 and 2.90 respectively. The length of time of one's limitations between 1 and 30 years increases the risk of unemployment. Additionally, increased length of emotional problems also increases the risk of unemployment.

For females, stroke, diabetes, cancer and emotional problems increased the risk of unemployment almost one and one half times. Need for help with personal care and routine needs increased the risk of unemployment 1.79 and 2.62 times respectively. Length of time of the limitation from 6-20 years increased risk 1.5 times. Having an emotional problem for 15-31 days also increased the risk of unemployment 1.5 times.

For males, stroke, diabetes, cancer and emotional problems also increased the risk of unemployment by almost one and one half times. Needing help with routine needs increased the risk of unemployment by almost four times. Increased length of limitation from 6 months to 50 years increased the risk of unemployment, with the highest risk for those who experienced 6-10 years of limitation. Emotional problems lasting 22-30 days increased the risk of unemployment almost twice as much.

Discussion

As in previous studies, persons with limitations continued to show a lower level of employment (StatsRRTC, 2005; U.S. Bureau of Census, 2000; Harris Interactive, Inc., 2000). Stroke, fractures, bone and joint injuries, diabetes, cancer and emotional problems were the conditions that were consistently the most predictive of unemployment for the total population, across gender. However, it is interesting to note that needing help with personal care and more so, needing help with routine needs is more predictive of unemployment than any one condition. Therefore, it is probable that it is not so much the condition, but the resulting loss of function that predicts whether or not a person will be employed. The implications of this finding are important, especially when looking at medical and rehabilitative care. It is clearly not enough to treat the medical characteristics of the condition or disability, but, for employment purposes and integration into society, it is also important to treat the functional aspects, including personal care and routine needs.

When considering conditions that increase the risk of unemployment, stroke, diabetes, cancer and emotional problems increase the risk of unemployment one and one half times. However, again, needing help with personal care and routine needs show a much higher risk of not be employed than any one condition. For males, needing help with routine needs increases the risk of unemployment four times compared to those males who do not need help with routine needs. This clearly shows that the risk of unemployment is stronger when one has a functional limitation no matter the condition. As expected, length of limitation is also related to increased risk of being unemployed, especially for males. Also, length of time a person has experienced an emotional condition increases the risk of being unemployed. The implication here is that both medical and rehabilitative treatment should begin as soon as possible and focus on functional limitations and needs.

Stroke may result in an increased likelihood of unemployment because of the physical, cognitive, and therefore functional limitations that are characteristic of this diagnosis. Persons with a stroke may be less likely to be able to care for their own personal and routine needs, which is highly predictive of unemployment. Diabetes may result in stroke, parathesias, and amputations which also can cause difficulties in function with regard to personal care and routine needs. These results support those of earlier studies that show that persons with cardiovascular disabilities were less likely to work (Zwerling, et al., 2002). Persons with cancer may have decreased endurance and increased pain, making functional tasks difficult. Results showing that persons with emotional problems have a higher chance of unemployment also support previous findings. However, these persons may also have difficulty completing personal care and routine tasks, which are high predictors of unemployment (Baldwin, 1999; Greenwood, Johnson, and Schriner, 1991). Similar to previous studies these results show that education appears to be a protective factor for conditions and disabilities with regard to employment (Zwerling, et al., 2002; Gouvier, Sytsma-Jordan, Mayville, 2003).

The hypothesis that there are particular conditions and disabilities that result in an increased chance of unemployment was supported. These are stroke, fractures, bone and joint injuries, diabetes, cancer and emotional problems as shown in the results section and Tables 2 and 3. Variables most predictive of unemployment are having an activity limitation, being female, having less education, being non-white, having a stroke, diabetes, cancer or an emotional problem, needing help with personal care and routine needs and being younger than 24 or older than 54 years of age. Risk factors for unemployment are similar to the predictive factors.

Assisting consumers with the ability to perform personal care tasks and complete routine needs has been the purview of rehabilitation professionals, most notably occupational therapists, nurses and vocational rehabilitation counselors. Policies that support provision of personal care assistants may also decrease the risk of unemployment because of functional loss. It appears from this research that these issues are of extreme importance related to reducing the risk of unemployment for persons with health conditions and disabilities. These results stress the importance of assessing not only clinical symptoms but functional strengths and weaknesses of persons with health conditions and disabilities in order to provide the best intervention for attaining and maintaining employment, consistent with the findings of McDermott, Richards, Ankers, Harmer, and Moran, (2004).

Strengths of this study are that the data come from a reliable and valid dataset. The population studied is large, strengthening the analysis and results are supported by previous studies. Limitations are that the database did not identify certain disabilities that are frequently mentioned in the literature such as developmental disabilities, spinal cord injuries and head injuries. This database focuses on more traditional health conditions as opposed to disabilities per se. Therefore, additional analysis with other databases such as the National Health Interview Survey would provide analyses of other conditions and potentially support results obtained in this study. In addition, the employment variable is limited to "employed" versus "not employed." Data are not available on the type of employment or whether the employment is full or part time. Further analysis of other databases which contain this information would provide opportunity for additional discussion. However, because the results found in this study are so consistent, the author believes that the conclusions made from the analyses are strong.

Conclusion

Persons with disabilities as a whole continue to show a significantly lower employment rate. This rate has not changed significantly since Title I of the Americans with Disabilities Act was passed. This research showed that specific conditions of stroke, fracture, bone and joint injuries, diabetes, cancer and emotional problems are predictive of unemployment and show an increased risk of unemployment. These results suggest that because functional losses with regard to needing help with personal care and routine needs are even more predictive of unemployment than specific conditions, more attention should be given to addressing functional strengths and limitations by medical and rehabilitation professionals than has been the case in the past. Referrals by physicians for these persons to rehabilitation professionals who can provide interventions aimed at adapting the person or the work environment should be provided. An implication for rehabilitation professionals is that intervention should be focused on return of functional skills and not just amelioration of symptoms. Finally, because of the limitations in this database, further research should focus on type of employment (full-time, part-time), type of occupation, and a broader range of disabilities such as spinal cord injury, developmental disabilities and head trauma. Additional information such as whether the person with a disability is receiving Social Security Disability Insurance, involved in a lawsuit or receiving worker's compensation would enhance the data for analysis as these issues can confound the issue of return to work, despite condition or functional status.

Acknowledgement

The author would like to acknowledge the Saint Louis University Research Administration Office for funding this research through the Summer Research Award.

References

Baldwin, M.L. (1999). The effects of impairments on employment and wages: Estimates from the 1984 and 1990 SIPP. Behavioral Sciences Law, 17, 7-27.

Burkhauser, R.V., Havemen, R.H., & Wolfe, B.L. (1990). The changing economic condition of the disabled: A two decade review of economic well-being. Washington, D.C.: National Council on Disability.

Centers for Disease Control and Prevention (CDC) (2004). Behavioral Risk Factor Surveillance System: Frequently Asked Questions. Retrieved July, 2004 from http://www.cdc.gov/brfss/faqs.htm.

Gouvier, W.D., Sytsma-Jordan, W., & Mayville, S. (2003). Patterns of discrimination in hiring job applicants with disabilities: The role of disability type, job complexity, and public contact. Rehabilitation Psychology, 48, 175-181.

Greenwood, R., Johnson, V.A., & Schriner, K.F. (1991). Employer concerns regarding workers with disabilities and the business-rehabilitation partnership: The PWI practitioner's perspective. Journal of Rehabilitation, 6, 14-19.

Harris Interactive, Inc. (2000). 2000 N.O.D./Harris Survey of Americans with disabilities. New York City: National Organization on Disability.

Harris, L. & Associates. (1998). NOD/Harris survey of Americans with disabilities. New York City: National Organization on Disability.

Iachan, R., Schulman, J., Powell-Griner, E., Nelson, D.E., Mariolis, P., & Stanwyck, C. (2001). Pooling state telephone survey health data for national estimates: The CDC Behavioral Risk Factor Surveillance System, 1995, in Cynamon, M.L., & Kulka, R.A., (eds.) Conference on Health Survey Research Methods, 1999, Williamsburg, VA. DHHS Publication No. (PHS) 01-1013: Hyattsville, MD.

Jans, L, & Stoddard, S. (1999). Chartbook on women and disability in the United States. Washington, D.C.: U.S. National Institute on Disability and Rehabilitation Research (NIDRR).

Kaye, H.S. (2001). Disability watch, volume 2: The status of people with disabilities in the United States. Oakland: Disability Rights Advocates.

Loprest, P. & Maag, E. (2003). The relationship between early disability onset and education and employment. Washington, D.C.: The Urban Institute; 2003.

MacDonald-Wilson, K.L., Rogers, E.S. & Massaro, J. (2003). Identifying the relationships between functional limitations, job accommodations, and demographic characteristics of persons with psychiatric disabilities. Journal of Vocational Rehabilitation, 18, 15-24.

McDermott, C., Richards, S.C.M., Ankers, S., Harmer, J., & Moran, C.J. (2004). An evaluation of a chronic fatigue lifestyle management programme focusing on the outcome of return to work training. British Journal of Occupational Therapy, 67, 269-273.

Mokdad, A.H., Stroup, D.F. & Giles, W.H. (2003). Public health surveillance for Behavioral risk factors in a changing environment: Recommendations from the Behavioral Risk Factor Surveillance team. MMWR, 52 (RR-9), 1-12.

Nagi, S. (1991). Disability concepts revisited: Implications to prevention. In A.M. Pope and A.R. Tarlove (Eds.), Disability in America: Toward a National Agenda for Prevention. Washington, D.C.: National Academy Press.

Nelson, D.E., Holtzman, D., Bolen, J., Stanwyck, C.A., & Mack, K.A. (2001). Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Social and Preventive Medicine, 46 Supplemental (1), S03-S42.

Nelson, D.E., Powell-Griner, E., Town, M., & Kovar, M.G. (2003). A comparison of national estimates from the National Health Interview Survey and the Behavioral Risk Factor Surveillance System. American Journal of Public Health, 93, 1335-1341.

Price, G.L., Kendall, M., Amsters, D.I., & Pershouse, K.J.(2004) Perceived causes of change in function and quality of life among individuals with long duration spinal cord injury. Clinical Rehabilitation, 18, 164-171.

Reed, CA. (1999). Women with disabilities making the transition back to work: Psychosocial barriers and interventions. Work: A Journal of Prevention, Assessment and Rehabilitation, 13, 67-72.

Rehabilitation Research and Training Center on Disability Demographics and Statistics (2005). 2004 Disability Status Reports. Ithaca, NY, Cornell University.

Ross, S.D. & Stone, L.R. (2004). Disability and chronic fatigue syndrome. Archives of Internal Medicine, 164, 1098-1107.

Stuart, O.W. (1992). Race and disability: just double oppression? Disability, Handicap & Society, 7, 177-188.

U.S. Bureau of Census. (1994/1995). Survey of income and program participation. Washington, D.C.: U.S. Bureau of Census.

U.S. Bureau of Census. (1997). Survey of income and program participation. Washington, D.C.: U.S. Bureau of Census.

U.S. Bureau of Census.(2000). Disability status: 2000--census brief Washington, D.C.: U.S. Bureau of Census.

U.S. Bureau of Census. (2001). Americans with disabilities: Household economic status, in Current population report. Washington, D.C.: U.S. Bureau of Census.

U.S. Commission on Civil Rights. (1998). Helping employees comply with the ADA: An assessment of how the U.S. Equal Employment Opportunity Commission is enforcing Title I of the Americans with Disabilities Act. Washington, D.C.: U.S. Commission on Civil Rights.

Zwerling, C., Whitten, P.S., Sprince, N.L., Davis, C.S., Wallace, R.B., Blanck, P.D., et al. (2002). Workforce participation by persons with disabilities: The National Health Interview Survey Disability Supplement, 1994 to 1995. Journal of Occupational and Environmental Medicine, 44, 358-364.

Diane Lynn Smith

University of Illinios at Urbana-Champaign

Diane Lynn Smith, University of Illinois at Ubana-Champaign, Department of Kinesiology and Community Health, 125 Huff Hall, 1204 S. Fourth St., MC 588, Champaign, IL 61820. Email: [e-mail]

Table 1
Employment Status by Condition (1995-2003)
                                                             Unable to
Condition                          Employed   Not Employed      Work

Arthritis/Rheumatism                46.0%        31.0%         23.0%
Back/Neck Problems                  51.9%        24.9%         23.2%
Fractures, Bone/Joint Problems      64.4%        23.5%         12.0%
Walking Problems                    47.1%        27.4%         25.1%
Lung/Breathing Problems             47.4%        29.4%         23.1%
Hearing Problems                    64.4%        28.2%          6.8%
Eye/Vision Problems                 50.3%        26.4%         23.2%
Heart Problems                      35.6%        30.7%         33.4%
Stroke Problem                      18.1%        31.6%         50.3%
Hypertension/High Blood Pressure    43.7%        37.0%         19.3%
Diabetes                            29.8%        32.2%         19.3%
Cancer                              37.2%        26.2%         36.2%
Emotional Problems                  34.8%        22.0%         36.2%
Other                               51.4%        25.0%         23.6%
Need Help with Personal Care        21.3%        26.0%         52.6%
Need Help with Routine Needs        24.3%        27.5%         45.9%

Table 2
Regression of employment on variables (1=not employed)

                                 Logit          Logit
                              co-efficient   co-efficient
                                (Total)        (Female)

Activity limitation             .278 **        .248 **
Gender                          .207 **
Arthritis                       -.560 **       -.572 **
Back/Neck Injury                -.668 **       -.761 **
Fracture/Bone/Jt. Injury        -.951 **       -.954 **
Walking Problem                 -.494 **       -.634 **
Lung/Breathing Problem          -.436 **       -.571 **
Hearing Problem                 -.667 **       -.729 **
Eye/Vision Problem              -.487 **       -.315 *
Heart Problem                   -.143          -.292 *
Stroke                          .459 **        .346
HBP/HTN                         -.548 **       -.599 **
Diabetes                        .524 **        .621 **
Cancer                          .324 **        .349 **
Emotional Problem               .449 *         .489 **
Need Help w/Personal Care       .573 **        .615 **
Need Help w/Routine Needs       1.04 **        .930 **
Length of Limitation            .000 **        .000 *
Length of Emotional Problem     .000           .001
Marital Status                  -.168 **       .051
Educational Status              -.423 **       -.448 **
Race/Ethnicity                  .003           .004
Age                             .450 **        .363 **
-2Log Likelihood                36994.022      23417.778
N                               32034          19611
Reference Point                 .5             .5
Cox & Snell R Square            .19            .17

                                 Logit
                              co-efficient
                                 (Male)

Activity limitation              .331 **
Gender
Arthritis                        -.569 *
Back/Neck Injury                 -.514 **
Fracture/Bone/ft. Injury         -.916 **
Walking Problem                  -.274
Lung/Breathing Problem           -.208
Hearing Problem                  -.561 *
Eye/Vision Problem               -.657 **
Heart Problem                    -.003
Stroke                           .607
HBP/HTN                          -.428
Diabetes                         .412
Cancer                           .314
Emotional Problem                .356 **
Need Help w/Personal Care        .521 **
Need Help w/Routine Needs        1.306 **
Length of Limitation             .000 **
Length of Emotional Problem      -.001
Marital Status                   -.618 **
Educational Status               .393 **
Race/Ethnicity                   .000
Age                              .641
-2Log Likelihood                 13262.562
N                                12423
Reference Point                  .50
Cox & Snell R Square             .23

* p < 0.05  ** p < 0.01

Table 3
Logistic regression of variables to unemployment

                                Total               Female
                                OR      95% CI      OR       95% CI

Activity Limitation             1.345   1.23-1.47   1.28     1.15-1.43
(Limited vs. Not Limited)

Gender (Female vs. Male)        1.259   1.19-1.33

Health Condition/Disability
(vs. no problem)
Arthritis                       .57     .48-.68     .57      .45-.71
Back/Neck Injury                .55     .46-.65     .50      .40-.63
Fracture/Bone/Jt. Injury        .44     .36-.52     .44      .35-.56
Walking Problem                 .66     .54-.80     .59      .46-.76
Lung/Breathing Problem          .60     .50-.73     .54      .42-.69
Hearing Problem                 .52     .37-.73     .48      .31-.76
Eye/Vision Problem              .66     .52-.84     .80      .58-1.10
Heart Problem                   .88     .73-1.07    .77      .59-1.01
Stroke                          1.66    1.23-2.23   1.57     1.04-2.35
HBP/HTN                         .57     .44-.75     .55      .40-.77
Diabetes                        1.58    1.34-1.87   1.72     137-2.16
Cancer                          1.48    1.21-1.82   1.60     1.24-2.07
Emotional Problem               1.38    1.23-1.54   1.44     1.25-1.66

Need Help w/Personal Care       1.72    1.56-1.89   1.79     1.59-2.01
(vs. no help needed)

Need Help w/Routine Care        2.90    2.72-3.09   2.62     2.42-2.82
(vs. no help needed)

Length of Limitation
1-7 days (ref.)                 1.0
> 7 days                        .59     .39-.90     .69      .43-1.13
1-4 weeks                       .60     .43-.84     .54      .36-.80
> 4 weeks                       .79     .53-1.17    .79      .49-1.28
1-6 months                      .80     .63-1.02    .68      .51-.92
7-12 months                     .85     .64-1.13    .70      .50-.99
>12 months                      1.15    .83-1.59    .86      .57-1.29
1-5 years                       1.24    .99-1.55    1.10     .84-1.46
6-10 years                      1.59    1.27-2.00   1.40     1.06-1.85
11-20 years                     1.57    1.25-1.99   1.40     1.05-1.86
21-30 years                     1.44    1.13-1.85   1.18     .86-1.62
31-40 years                     1.06    .80-1.41    .98      .69-1.40
41-50 years                     .04     1.02-1.92   1.31     .88-1.95
> 50 years                      .95     .67-1.45    1.12     .70-1.80

Length of Emotional Problem
1-7 days (ref.)                 1.0
8-14 days                       1.23    1.11-1.37   1.18     1.04-1.34
15-21 days                      1.46    1.32-1.60   1.31     1.17-1.47
22-30 days                      1.80    1.65-1.96   1.69     1.52-1.87
None                            1.11    1.56-2.15   1.75     1.43-2.14

Educational Status
No school-grade 8(ref.)         1.0
Some high school                .70     .61-.81     .64      .53-.77
High school grad/GED            .42     .37-.48     .37      .31-.44
Some college                    .33     .29-.38     .28      .23-.33
College grad                    .20     .18-.23     .17      .15-.21

Marital Status                  .94     .89-.99     1.19     1.11-1.27
(coupled vs. uncoupled)

Age
18-24 (ref.)                    1.0
25-34                           .67     .59-.76     .75      .64-.88
35-44                           .67     .60-.76     .66      .57-.77
45-54                           1.02    .91-1.15    .88      .76-1.02
55-65                           3.09    2.74-3.48   2.55     2.20-2.96

Race/Ethnicity
White (ref.)                    1.0                 1.0
Black                           1.21    1.11-1.32   1.15     1.04-1.28
Hispanic                        1.32    1.20-1.45   1.38     1.22-1.56
Asian/Pacific Islander          1.15    .91-1.45    1.36     1.00-1.83
American Indian/Alaska Native   1.01    .83-1.24    1.10     .86-1.41

                                Male
                                OR      95% CI

Activity Limitation             1.46    1.26-1.69
(Limited vs. Not Limited)

Gender (Female vs. Male)

Health Condition/Disability
(vs. no problem)
Arthritis                       .57     .43-.76
Back/Neck Injury                .64     .49-.83
Fracture/Bone/Jt. Injury        .45     .34-.59
Walking Problem                 .79     .59-1.07
Lung/Breathing Problem          .74     .55-1.00
Hearing Problem                 .60     .36-1.00
Eye/Vision Problem              .57     .40-.81
Heart Problem                   1.01    .76-1.34
Stroke                          1.75    1.12-2.74
HBP/HTN                         .65     .40-1.05
Diabetes                        1.43    1.10-1.86
Cancer                          1.36    .96-1.93
Emotional Problem               1.24    1.01-1.53

Need Help w/Personal Care       1.67    1.40-1.98
(vs. no help needed)

Need Help w/Routine Care        3.77    3.34-4.25
(vs. no help needed)

Length of Limitation
1-7 days (ref.)
> 7 days                        .34     .15-.80
1-4 weeks                       .67     .37-1.22
> 4 weeks                       .82     .40-1.68
1-6 months                      1.05    .67-1.63
7-12 months                     1.24    .75-2.07
>12 months                      1.91    1.09-3.36
1-5 years                       1.53    1.01-2.31
6-10 years                      2.05    1.35-3.12
11-20 years                     1.96    1.29-2.99
21-30 years                     1.92    1.23-2.99
31-40 years                     1.25    .76-2.04
41-50 years                     1.55    .90-2.66
> 50 years                      .82     .41-1.66

Length of Emotional Problem
1-7 days (ref.)
8-14 days                       1.31    1.08-1.59
15-21 days                      1.79    1.51-2.13
22-30 days                      2.09    1.80-2.42
None                            1.91    1.46-2.50

Educational Status
No school-grade 8(ref.)
Some high school                .81     .64-1.02
High school grad/GED            .52     .42-.64
Some college                    .44     .36-.55
College grad                    .25     .20-.31

Marital Status                  .58     .53-.64
(coupled vs. uncoupled)

Age
18-24 (ref.)
25-34                           .51     .40-.64
35-44                           .71     .58-.87
45-54                           1.39    1.14-1.70
55-65                           4.86    3.96-5.96

Race/Ethnicity
White (ref.)                    1.0
Black                           1.41    1.21-1.66
Hispanic                        1.28    1.08-1.51
Asian/Pacific Islander          .88     .59-1.29
American Indian/Alaska Native   .86     .61-1.21