December 2008 Policy Brief
University of Kansas Medicaid Intrastructure Change Evaluation Project
Number 11, December 2008
THE KANSAS DEMONSTRATION TO MAINTAIN INDEPENDENCE AND EMPLOYMENT:
PREVENTING OR FORSTALLING DISABILITY AMONG PARTICIPANTS IN THE
KANSAS HIGH RISK INSURANCE POOL
By Jean P. Hall, Ph.D. and Jan Moore, MA, MBA, MSW
BACKGROUND
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.
THE KANSAS DMIE
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.
EARLY FINDINGS
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).
UNDERINSURANCE
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.
TABLE ONE INFORMATION:
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.
TABLE TWO INFORMATION:
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.
TABLE THREE INFORMATION:
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.
TABLE FOUR INFORMATION:
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
TABLE FIVE INFORMATION:
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.
REFERENCES
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. http://das. kucrl.org/dmie.shtml.
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., http://www. worksupport.com/research/viewContent.cfm/509.
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. The Policy Brief and other information regarding the Working Healthy program can be found online at http://www.workinghealthy.org
Additional copies and copies in alternate formats are available upon request by calling
1-800-449-1439 or emailing pixie@ku.edu
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