The Kansas Demonstration to Maintain Independence and Employment

December, 2008

University of Kansas Medicaid Intrastructure Change Evaluation Project

Number 11, December 2008

The Kansas Demonstration to Maintain Independence and Employment: Preventing or Forestalling Disability among Participants in the Kansas High Risk Insurance Pool 

By Jean P. Hall, Ph.D. and Jan Moore, MA, MBA, MSW


The Ticket to Work and Work Incentives Improvement Act of 1999 (TWWIIA) was passed to address employment and health care issues for people with disabilities, with an emphasis on making it possible for people with disabilities to work without fear of losing health care coverage. Medicaid Buy-In programs, like the Working Healthy program in Kansas, were authorized under section 201 of the Act. These programs allow people who meet the Social Security definition of disability to work and maintain eligibility for Medicaid. They were designed primarily as a means to encourage people who are already receiving disability benefits to return to work or increase employment efforts. Section 204 of TWWIIA authorized the development of another program targeted at disability prevention. Demonstrations to Maintain Independence and Employment (DMIEs) provide health care coverage to working people with potentially disabling conditions to test the hypothesis that providing health care and other supports can prevent or forestall the onset of full disability and eventual dependence on federal disability programs. 


In 2005, Kansas was awarded funding from the Centers for Medicare and Medicaid Services (CMS) for a Demonstration to Maintain Independence and Employment program. The DMIE in Kansas provides supplementary Medicaid-like coverage and enhanced health services to employed individuals with potentially disabling conditions enrolled in the State high risk health insurance pool. Historically, people in this pool have transitioned to federal disability programs at a rate eight times that of the general population (Hall and Moore, 2006).

People qualify for the high risk pool if they have been turned down for coverage by two different insurance companies, offered health insurance that permanently excludes coverage for a pre-existing condition, or are unable to find private health insurance that is cheaper than the pool’s. In addition, participants cannot be eligible for Medicaid or Medicare. Premiums for the pool are currently 133% of the standard rate charged for similar coverage in the private insurance market. Most people enrolled in the pool have annual deductibles ranging from $2,500 to $10,000.

Participation in the DMIE was limited to persons enrolled in the Kansas high risk pool for at least six months who were between ages 18 and 60, working at least 40 hours per month, and experiencing a potentially disabling health condition. Determination of whether a condition was potentially disabling was based on categories of conditions recognized by the Social Security Administration. The DMIE uses an experimental design in which we randomly assigned a total of about 400 participants to either a control or intervention group. Participants in the control group retain standard high risk pool insurance while those in the intervention group receive Medicaid-like coverage as a wraparound to their high risk pool coverage. Benefits include premiums that are subsidized to a flat $152 per month; no deductibles or coinsurance; copayments of only $3 per service; dental, vision, and hearing coverage; increased coverage for services such as mental health, prescription drugs, home health, and preventive care; and vocational rehabilitation and worksite assessment services. The first cohort of participants began receiving services in April 2006. 


Demographic information for DMIE participants is provided in Table 1. Overall, participants represent a well-educated and middle-class population. Incomes vary widely, however, with 40% earning less than 300% of the Federal Poverty Level (FPL). The great majority (70%) are self-employed, making it difficult for them to access employer-based group health insurance. Their occupations are shown in Table 2.

Participants experience a wide range of serious and potentially disabling conditions (Table 3).Using predictive modeling software, we found that their projected medical costs and disease burden are four times that of the general population (Hall and Moore, 2008). Upon enrollment in the study, 55% of the DMIE survey respondents reported difficulty with at least one activity of daily living (ADL) or instrumental activity of daily living (IADL). ADLs include basic activities such as walking, bathing and dressing, while IADLs include activities such as preparing meals or cleaning house. Although participants in the Kansas DMIE do not yet meet the federal definition of disability, they generally fall in between Kansans with and without disabilities on measures of general health status. In fact, DMIE participants actually fare worse than Kansans with disabilities on some measures, such as rates of diabetes, obesity and hypertension (Table 4). 


In light of the many health conditions experienced by the DMIE population and the cost structure of the plans of available through the high risk pool, we wondered whether these individuals would meet one or more definitions of being underinsured. Schoen et al (2008) recently found that some 28% of adults in the U.S. are underinsured. Using two of their measures of underinsurance—deductibles amounting to 5% or more of family income and total out-of-pocket medical expenses amounting to 10% or more of family income—we examined underinsurance within the DMIE population (Table 5). Overall, 94% of the population exhibits one or more indicators of underinsurance. 

A small minority of Americans who rely on federal disability programs become seriously ill, injured, or disabled all at once immediately prior to applying for Social Security benefits; the large majority experience a gradual path of worsening medical conditions over time (Miller, 2005). Honeycutt (2004) found that lack of health insurance coverage, or uninsurance, was a risk factor in the trajectory to disability. Other researchers (Seifert and Rukavina, 2006; Wong et al., 2001) indicated that individuals who must share 

in a high proportion of their medical costs or who have medical debt exhibit care-seeking behavior similar to that of uninsured people (i.e., not seeking care when medically appropriate to do so). In other words, significant underinsurance may factor into the disability trajectory in much the same way that uninsurance does (Hall and Moore, 2008). In fact, preliminary results from the DMIE evaluation indicate that people in the intervention group who are receiving the additional Medicaid-like coverage are experiencing less decline in their overall health than are study members in the control group. 


Title - Kansas DMIE Participants' Self-Reported Demographic Characteristics (study sample n=416)

Characteristic = Gender- Female = 50% of study sample

Characteristic = Age Distribute, 18-29 = 4%; 30-39=5%; 40-49=26%; 50-59=51%;60-61=13% (note Maximum age was 60 to exclude those who would age into Medicare in the course of the study; however, some people attained age 61 between application date and study implementation).

Characteristic = Educational Attainment, More than a 4-year college degree=23%;4-year college degree=22%; some college or 2-year degree=36%; high school diploma=18%; less than a high school diploma=2%

Characteristic = Income, Family income less than 200% of the Federal Poverty Level (FPL) = 20%; Family income more than 200%, but less that 300% of FPL=20%; Family income greater than 300% of FPL=60%

Mean own annual income = $49, 970 (SD=62,436)

Mean household annual income = $69,990 (SD=71,436)

Data source for table 1 = Enorllment applications, surveys and KHIA claims.


Title - Kansas DMIE Participants' Occupations (study sample n=380)

Occupational Group = Profressional, technical, managerial = 49%

Clerical and sales = 16%

Service = 10%

Agricultural and related occupations = 14%

Processing occupations = 2%

Machine trades/Benchwork = 4%

Structural work = 13%

Miscellaneous = 8%

Not working = 4% (While employment was required for admission to the study, some individuals later indicated that they were temporarily unemployed, on sick leave or had retired.)

Note for Table 2: Data source is self-report during Round 2 survey. n represents the number of participants who completed the Round 2 survey. Classification system is Dictionary of Occupational Titles.


Title - DMIE Participants' Major Potentially Disabiling Conditions (n=416)

ICD-9 Cateogory = HIV (042), Claims percent of sample = 0 Self-reported percent of sample=1%; Maximum combined percent of sample=1%

ICD-9 Category = Cancers (151-154, 170-175, 179, 182-185, 189, 193, 200, 202, 205-208, 230-239), Claims percent of sample=13.5%, self-reported percent of sample=13.0, maximum combined percent of sample=18.8%

ICD-9 Category = Diabetes (250), claims percent of sample=25.2%, self-reported percent of sample=26.7%, maximum combined percent of sample=29.1%

ICD-9 Category = Mental illnesses (295-301, 306-310, 312-316), claims percent of sample=21.6%, self-reported percent of sample=28.8, maximum combined percent of sample=35.3%

ICD-9 Category=Neurological Disorders (332-338, 340-345, 350-359, 433-438), claims percent of sample=10.1%, self-reported percent of sample=7.7%, maximum combined percent of sample=13.7%

ICD-9 Category=Stroke (433-438), claims percent of sample=1.9%; self-reported percent of sample=2.2%, maximum combined percent of sample=3.1%

ICD-9 Category=Cardiovascular (410-416, 425-428, 441-448), claims percent of sample=19.7%, self-reported percent of sample=20.4%, maximum combined percent of sample=26.4%

ICD-9 Category=Respiratory (491-493, 510-519), claims percent of sample=11.8%, self-reported percent of sample=12.5%, maximum combined percent of sample=18.3%

ICD-9 Category=Gastrointestinal (555-556, 570-573), claims percent of sample=4.8%, self-reported percent of sample=3.8%, maximum combined percent of sample=5.8%

ICD-9 Category=Arthropathies (710-711, 713-719), claims percent of sample=19.5%, self-reported percent of sample=12.0%, maximum combined percent of sample=25.7%

ICD-9 Category=Dorsopathies (720-724), claims percent of sample=25.5%; self-reported percent of sample=9.1%, maximum combined percent of sample=29.8%

Notes for Table 3:Claims data represents conditions for which treatment was received in the year prior to participants' enrollment in teh study. Self-reported data reflect conditions as reported on enrollment applications or during surveys. Maxiumum combined data were computed by cross-tabulation of individual records of clains and self-reported conditions.


Title-DMIE Participant Health Indicator Comparison

Health Indicator=Report Fair or Poor Overall Health, 22% of DMIE participants, 6% of Kansans without disabilities, 42% of Kansans with disabilities

Health Indicator=Report Poor Mental Health, 14% of DMIE participants, 6% of Kansans without disabilities, 21% of Kansans with disabilities

Health Indicator=Overweight of Obese, 77% of DMIE participants, 59% of Kansans without disabilities, 69% of Kansans with disabilities

Health Indicator=Obese, 44% of DMIE participants, 23% of Kansans without disabilities, 37% of Kansans with disabilities

Health Indicator=Diabetes, 27% of DMIE participants, 5% of Kansans without disabilities, 15% of Kansans with disabilities

Health Indicator=Hypertension, 46% of DMIE participants, 20% of Kansans without disabilities, 42% of Kansans with disabilities


Title-DMIE Participants' Rates of Underinsurance

Underinsurance Indicator=Deductible of greater than 5% of family income, 55% of DMIE sample

Underinsurance Indicator=Out-of-pocket expenses greater than 10% of family income for self, 77% of DMIE sample

Underinsurance Indicator=Out-of-pocket expenses great than 10% of family income for self & family, 91% of DMIE sample

Underinsurance Indicator=One or more of these indicators above, 94% of DMIE sample

Notes for Table 5: indicator 1, n=314 based on self-report; indicators 2-4, n=137 based on self-report

Implications for Policy

Despite its traditional emphasis on productivity, the United States still lacks a national health care policy and system designed to invest in preventive care for people on the pathway to disability (Lerner et al., 2005). On the other hand, employers are increasingly aware that cutting short-term costs of medical coverage can result in much greater long-term costs in lost productivity. (Loeppke et al., 2007). In fact, these authors found that the cost of health-related lost productivity was more than four times that of medical and pharmacy 

costs. Conversely, Hadley (2003) estimated that improving health status from “fair to poor” to “good to excellent” would increase both work effort and earnings by approximately 15% to 20%, increase incomes and tax revenues, and reduce government spending for disability and other health-related programs. 

If we accept the argument that better access to care may prevent the progression to full disability for this population, then how can better access be achieved? Sommers (2007) suggested “the current framework of eligibility for public coverage may need to sever eligibility for public insurance from income support standards, and instead base eligibility for coverage on assessment of medical need” (p. 403). Medicaid Buy-In programs currently operate in 40 states, allowing individuals with disabilities to accumulate greater 

assets, increase earnings, and pay a pro-rated premium to maintain Medicaid coverage. Existing Medicaid Buy-In eligibility guidelines for level of disability and personal assets and income could be expanded to reach many members of the high risk pool population in Kansas and other states. Coverage provided under Medicaid would likely be more comprehensive and less costly to beneficiaries—and it would have the potential to offset greater programmatic costs in the long term if their conditions were stabilized and their 

employment maintained. 


Hadley, J. (2003). Sicker and poorer—the consequences of being uninsured: A review of the research on the relationship between health insurance, medical care use, work, and income. Medical Care Research and Review 60(2):3S-75S. 

Hall, J.P. and J.M. Moore. (2008). Does high- risk pool coverage meet the needs of people at risk for disability? Inquiry 45(3):340-352. 

Hall, J.P. and J.M. Moore. (2006). Historical disability outcomes of enrollees in the Kansas high- risk pool: A white paper presented to CMS by the Kansas DMIE project, January 2006. 

Honeycutt, T. (2004). Program and benefit paths to the Social Security Disability Insurance program. Journal of Vocational Rehabilitation 21(2):83-94. Lerner, D., S. Allaire, and S.

Reisine. (2005). Work disability resulting from chronic health conditions. Journal of Occupational and Environmental Medicine47(3):253-264. 

Loeppke R., M. Taitel, D. Richling, et al. (2007). Health and productivity as a business strategy. Journal of Occupational and Environmental Medicine 49:712- 721. 

Miller, L. (2005). Early intervention and diversion strategies as a means for stemming the growth in Social Security Disability Programs. Paper presented at National Council on Disability, Social Security Study, Consensus Validation Conference, January 26, 2005. Washington, D.C.,

Schoen, C., S. R. Collins, J. L. Kriss, and M. M. Doty. (2008). How many are underinsured? Trends among U.S. adults, 2003 and 2007. Health Affairs Web Exclusive June 10:W298–W309. 

Seifert, R., and M. Rukavina. (2006). Bankruptcy is the tip of a medical-debt iceberg. Health Affairs Web Exclusive February 28:W89-W92. 

Sommers, A. (2007). Access to health insurance, barriers to care, and service use among adults with disabilities. Inquiry 43(4): 393-405. 

Wong, M., R. Andersen, C. Sherbourne, R. Hays, and M. Shapiro. (2001). Effects of cost sharing on care seeking and health status: Results from the medical outcomes study. American Journal of Public Health 91(11):1889-1894.

This Policy Brief is published by the KU-CRL Division of Adult Studies in cooperation with the Kansas Health Policy Authority.

Additional copies and copies in alternate formats are available upon request by calling 1-800-449-1439 or emailing


KU Research Team

Jean P. Hall, Ph.D., Principal Investigator

Noelle K. Kurth, M.S., Project Coordinator

Shawna Carroll, Graduate Research Assistant

Emily Fall, Graduate Research Assistant

Kansas Health Policy Authority

Mary Ellen O'Brien Wright, Working Healthy Program Director

Nancy Scott, Benefits Specialist Team Leader