Health Insurance Markets &
Managed Care
Call for Papers
New Perspectives on Market Competition
& Health Plan Design
Implications for Policy, Delivery, or Practice: Policies aimed
at controlling prices and spending in the private sector need
to recognize the role of market forces.
Primary Funding Source: GAO
●The Contribution of Medicaid Managed Care to the
Increasing Undercount of Medicaid Beneficiaries in the
Current Population Survey
Arpita Chattopadhyay, Ph.D., Andrew B. Bindman, M.D.
Chair: Dennis Scanlon, Pennsylvania State University
Sunday, June 25 • 8:30 am – 10:00 am
●Competition and Other Factors Linked to Wide Variation
in Health Care Prices
Christine Brudevold, Ph.D., A. Bruce Steinwald, Michael
Kendix, Jennifer Rellick, Leslie Gordon, Ann Tynan
Presented By: Christine Brudevold, Ph.D., Assitant Director,
Health Care, GAO, 441 G Street, NW, Washington, DC 20548;
Tel: 202-512-2669; Fax: 202-512-4778;
Email: BrudevoldC@GAO.GOV
Research Objective: This study examined prices and
spending in the Federal Employees Health Benefits Program
(FEHBP) Preferred Provider Organizations (PPOs) to
determine (1) the extent to which hospital and physician prices
varied geographically, (2) which factors were associated with
geographic variation in hospital and physician prices, and (3)
the extent to which hospital and physician price variation
contributed to geographic variation in spending.
Study Design: We compared hospital and physician prices
among metropolitan areas in the United States using FEHBP
health claims data from 2001. We grouped claims by the
metropolitan area where care was delivered and adjusted
claims for the cost of providing services and differences in
case mix. To determine which factors might be associated
with geographic differences in price, we examined the
relationship between price and indicators of market
competition, health maintenance organization price
bargaining leverage and cost-shifting pressures for each
metropolitan area. To examine how prices affected spending,
we calculated measures of utilization and spending for
metropolitan areas, and compared the contribution of price
and utilization to spending in the metropolitan areas in
highest and lowest spending quartiles.
Population Studied: Enrollees and their dependents in the
FEHBP who were under age 65 and participated in one of the
study PPOs.
Principal Findings: FEHBP PPOs paid substantially different
prices for hospital inpatient physician services across
metropolitan areas. The variation in prices appeared to be
affected by market characteristics. In general, less
competition and less HMO capitation were associated with
higher prices. For hospital and physician services, price
contributed to about one-third of the variation in spending
between metropolitan areas in the highest and lowest
spending quartiles.
Conclusions: Understanding price variation is essential to
understanding why some areas have higher or lower spending
in the private sector. Market forces, not just the underlying
costs of doing business, help to determine the prices FEHBP
PPOs ultimately pay hospitals and physicians.
Presented By: Arpita Chattopadhyay, Ph.D., Senior
Statistician, Medicine, Division of General Internal Medicine,
University of California at San Francisco, Box 1364 Parnasus
Avenue, San Francisco, CA 94143; Tel: (415) 206-6166; Fax:
(415) 206-5586; Email: achat@medsfgh.ucsf.edu
Research Objective: To determine whether the penetration of
Medicaid managed care is associated with the magnitude of
the underestimate of Medicaid beneficiaries in the CPS. The
Current Population Survey (CPS) is an important source of
data for comparing beneficiaries across insurance groups.
However, the CPS routinely underestimates the Medicaid
population, which correspondingly results in an overestimate
of one or more of the other insurance categories. Not only is
the underreporting substantial, several studies indicate that
the extent of Medicaid underreporting in CPS has been rising
over time.
Study Design: Pooled cross-sectional comparison of survey
and administrative databases for the years 1995-1999,
corresponding to a period of substantial Medicaid managed
care expansion. We compared the CPS estimates of Medicaid
person-years in California in 1995-1999 with the gold-standard
number derived from the Medicaid eligibility file for the same
time period and examined the association between the CPS
underestimate and penetration of managed care in the
beneficiary’s county, controlling for secular changes in the
design and administration of the CPS and Medicaid policy
that may impact Medicaid reporting in the CPS. A mixed
model methodology, which accounts for clustered
observations, was used to estimate the association between
CPS undercount and Medicaid managed care.
Population Studied: All California Medicaid beneficiaries less
than 65 years old.
Principal Findings: The CPS underestimated the Medicaid
population by approximately a third. At the county level,
errors in estimated numbers of Medicaid beneficiaries in the
CPS increased in association with the penetration of Medicaid
managed care. The unadjusted correlation coefficient between
CPS undercount and mediciad managed care penetration is
0.23 (p=0.01). After controlling for secular changes, each
percentage point increase in the penetration of managed care
was associated with an underestimate in the CPS of 0.4
percentage points (p=0.006). Projecting the results in
California to the growth of Medicaid managed care nationally
during the same time period suggests that Medicaid managed
care might explain more than 80% of the increasing
underestimates of Medicaid beneficiaries in the CPS.
Conclusions: A substantial portion of the increase in the
underestimates of the number of Medicaid beneficiaries in the
Current Population Survey can be explained by the growth of
Medicaid managed care.
Implications for Policy, Delivery, or Practice: Steps must be
taken to improve the Current Population Survey if this survey
is to remain useful for making accurate estimates of
Americans’ health insurance status.
Primary Funding Source: No Funding
●What are the Effects of Medicaid and SCHIP Managed
Care on Children with Chronic Health Conditions?
Amy Davidoff, Ph.D., Ian Hill, M.P.A., M.S.W., Emerald
Adams, BA, Brigette Courtot, BA
Presented By: Amy Davidoff, Ph.D., Assistant Professor,
Public Policy, University of Maryland Baltimore County, 1000
Hilltop Circle, Baltimore, MD 21250; Tel: 410-455-6561; Fax:
410-455-1172; Email: davidoff@umbc.edu
Research Objective: Managed care (MC) is an established
feature of many Medicaid programs but is relatively new for
some enrolled children with chronic health conditions (CHC).
The effects of MC may be particularly strong for children with
CHC, for whom health plans have strong financial incentive to
coordinate care. Alternatively, elevated baseline use by
children with CHC may be appropriate given their greater
needs; MC may exert its effects by disrupting established
provider relationships. The use by capitated plans of
behavioral health or specialty “carve-outs,” while intended to
direct children to appropriate systems of care, may create
fragmentation and access barriers. MC programs designed
specifically for children with CHC may ameliorate negative
effects. The objective of this study is to examine the effect of
different types of mandatory MC programs within Medicaid
and SCHIP on children with CHC.
Study Design: Pooled data from the National Health
Interview Survey (1997-2002) were supplemented with county,
year, and population specific data on Medicaid and SCHIP
MC program types, assembled from annual CMS Medicaid
MC Enrollment Reports and other sources. Children with CHC
were identified based on parent report of diagnosed
conditions or activity limitations. MC data were linked to
children based on state and year specific eligibility for
Medicaid or SCHIP. Linear probability models estimated the
effects of MC program types relative to fee-for-service (FFS)
on access and use for publicly insured children, with and
without CHC.
Population Studied: Medicaid and SCHIP eligible and
enrolled children.
Principal Findings: Relative to FFS, mandatory capitated
programs without carve-outs are associated with decreased
physician visits, reduced likelihood of a specialist visit (-7.2%),
ER visits (-7.6%) and hospital stays (-3.1%). When MC
programs include carve-outs we also observe reduced
probability of mental health specialty visits (-7.8%), vision care
visits (-6.4%) and prescription drug use (-9.6%). Special MC
programs for children with CHC are associated with increased
physician visits. Few significant effects are identified for
children without CHC.
Conclusions: Our results suggest that the effects of MC in
Medicaid and SCHIP operate primarily on children with CHC.
Relative to FFS, mandatory MC programs are associated with
reduced use of services commonly used by children with CHC.
The addition of behavioral health or specialty carve-outs is
associated with even greater reductions in use. Reductions in
ER and hospital use are suggestive of improved outpatient
management; it is not possible to determine whether
reductions in other services represent better care
management or inadequate care. However, despite the
reductions in use, we did not observe a corresponding
increase in perceived unmet need, thus, the net change may
represent improved care management.
Implications for Policy, Delivery, or Practice: Much debate
surrounds the issue of whether MC can work for children with
CHC. In theory, MC could improve coordination and
integration of care, but advocates are concerned that
incentives to control costs may lead plans to under-serve
these vulnerable children. This study does not resolve this
debate, but suggests that MC is associated with improved
outpatient management without increases in unmet need, and
that specialty managed care models can facilitate access to
physician care.
Primary Funding Source: HRSA
●Primary Care Physician Response to a Mental Health
Carve-Out: An Economic Analysis
Ashley Dunham, MSPH, Jennifer L. Troyer, Ph.D., William P.
Brandon, Ph.D.
Presented By: Ashley Dunham, MSPH, Ph.D. Student, Public
Policy, UNC-Charlotte, 528 Maupin Avenue, Salisbury, NC
281441; Tel: 704-239-6039; Email: adunham@uncc.edu
Research Objective: The first research objective was to
examine changes in depression treatment after the
implementation of a Medicaid mental health carve-out that
precluded reimbursement of primary care treatment for
mental health. A second research objective was to use these
results to test, in this particular experiment, two competing
economic theories that may explain the way physicians
behave: the principal-agent theory and the theory of wealth
maximization.
Study Design: The creation of a mandatory Medicaid mental
health carve-out in Mecklenburg County established an
experimental setting that allowed a test of physician behavior
when faced with a contradiction between financial incentives
created by managed care arrangements (the mental health
carve-out) and current "best practice" that supports the
treatment of depression in primary care. In 1996, the State of
North Carolina received permission to implement mandatory
enrollment in risk-contracting HMOs under a 1915(b) waiver
in Mecklenburg County, North Carolina, which contains the
city of Charlotte. As a result of the waiver, Medicaid recipients
were shifted from a fee-for-service system to managed care
plans, but mental health and prescripton drugs were "carvedout" of the premium and responsibility of the HMO. Medicaid
claims data for 1995 through 1998 (for both the experimental
county - Mecklenburg, and a control county - New Hanover)
contain prescription drug claims for anti-depressants and
claims for mental health visits coded for depression
treatment. Claims data from both counties before and after
HMO enrollment allowed the construction of two dependent
variables that measured any change in depression treatment
after the carve-out. Using monthly data, a difference-indifference model tested if the implementation of a carve-out in
Mecklenburg county explained any changes in either referrals
to mental health providers for depression treatment or antidepressant prescribing activity.
Population Studied: A sample of Medicaid recipients in
Mecklenburg County, North Carolina (n=3765) who were
enrolled in Medicaid from January 1995 to December 1998. In
addition, a sample of Medicaid recipients from New Hanover
County, North Carolina who were not enrolled in any managed
care arrangement (n=1004) served as the control county.
Principal Findings: Results showed a significant increase in
depression claims from mental health providers as well as a
significant decrease in anti-depressant claims after HMO
implementation in Mecklenburg County.
Conclusions: The results showed that the physicians did
respond to the mental health carve-out with more referrals to
mental health providers for depression treatment than before
the carve-out. With fewer antidepressant prescriptions and
more claims from mental health providers, we assume that
physicians acted as wealth maximizers, referring out treatment
that was not included in their capitated arrangement.
Implications for Policy, Delivery, or Practice: If Medicaid
programs (as well as other health insurance organizations)
want primary care physicians to treat patients for depression,
reimbursement arrangements must be tailored to maximize
both patient and physician utility, including financial
incentives for the primary care physician to implement clinical
practice guidelines for depression treatment. Further research
should explore the willingness of primary care physicians to
treat depression, as well as the effectiveness of depression
treatment in the primary care setting. In addition, research
should test which reimbursement arrangements maximize
both physician and patient utility with regards to depression
treatment.
Primary Funding Source: No Funding
●Using Williness to Pay to Evaluate Hospital Merger:
Results from 16 mergers
Richard Lindrooth, Ph.D., David Dranove, Ph.D, Mark
Satterthwaite, Ph.D.
Presented By: Richard Lindrooth, Ph.D., Associate Professor,
Department of Health Administration and Policy, Medical
University of South Carolina, 151B Rutledge Ave P.O. Box
250961, Charleston, SC 29425; Tel: 8437922192; Fax:
8437921358; Email: lindrorc@musc.edu
Research Objective: To test whether the methodology
proposed by Capps, Dranove and Satterthwaite (2003),
hereafter CDS, can be used to prospectively identify mergers
that will lead to large price increases.
Study Design: First, we estimate the CDS measure for
hospitals and systems in nine markets. This measure is tied
closely to economic theory and is estimated using simulations
based on the parameter estimates of a hospital choice model.
Next, we regress net inpatient revenues from the Medicare
Cost Reports on the CDS measure and control variables and
hospital fixed effects. The parameter estimate on the CDS
measure reflects the increase in net inpatient revenue as a
result of changes in market power associated with a merger.
We identify the effect of self-selected mergers, thus our results
are predictive rather than causitive.
Population Studied: We examine hospitals and systems in
the following markets: Bakersfield, CA; Buffalo, NY; Daytona,
FL ; Denver, CO ; Jacksonville, FL; and Rochester, NY. The
CDS measure is estimate using HCUP-SID data in the year
prior to the first merger in the market. Variables from the
AHA Annual Survey are included to estimate the patient
choice model that underlies the CDS measure. There were 16
involving over 45 hospitals between 1995 and 2000 in these
markets.
Principal Findings: The average increase in the CDS
measures for these mergers was 1162 Units. Based on the
regressions results this translates into an average $7,730,183
increase it net inpatient revenue. The total increase in net
inpatient revenue associated with the mergers is about $124
million. Note that this increase is solely due to the merger, it
is over and above what hospitals would have recieved if they
were independent.
Conclusions: The CDS measure performed reasonably well in
this analysis. About 7 of the 16 mergers led to a greater than
10% increase in market power. The R-squared in the second
part of the analysis was much lower than what was observed
in the CDS analysis of a single market. Though this is not
surprising given we pool 6 years of data over multiple
markets.
Implications for Policy, Delivery, or Practice: This paper has
direct implications for the Federal Trade Commission's
merger policy. We have validated an alternative approach to
measuring market power that in contrast to current (much
critisized) approaches is directly tied to economic theory. This
approach shows promise for future merger cases.
Primary Funding Source: RWJF
Call for Papers
Markets, Financial Incentives & Quality of Care
Chair: Jon Gabel, Center for Studying Health System Change
Tuesday, June 27 • 10:30 am – 12:00 pm
●The Role of Quality in the Formation of Exclusive
Networks for Kidney Transplantation
David Howard, Ph.D.
Presented By: David Howard, Ph.D., Assistant Professor,
Department of Health Policy and Management, Emory
University, 1518 Clifton Road NE, Atlanta, GA 30322; Tel: 404727-3907; Fax: 404-727-9198; Email: david.howard@emory.edu
Research Objective: Almost all large insurers restrict
enrollees’ choice of kidney transplant center under the guise of
“centers of excellence” programs. We determine the role of
quality, as measured by patient outcomes, in the formation of
health plans’ exclusive transplant networks. According to
plans’ promotional literature, hospitals are selected based on
quality.
Study Design: From the Scientific Registry of Transplant
Recipients we obtained a measure of quality for every kidney
transplant hospital in the U.S. (N = 203). The measure is
observed kidney graft survival at one year post-transplant
minus expected graft survival. Graft survival indicates that the
patient is alive and the kidney graft is still functioning.
Expected graft survival is computed by the Registry based on a
regression model with extensive patient controls. Higher
values indicate better performance. We perform two analyses.
First, we compare observed-expected graft survival between innetwork and out-of-network hospitals for five large national
health plans. Lists of networks were obtained from plans’
websites. Second, using patient level data obtained from the
Registry, we estimated a conditional logit model of transplant
center choice. We constructed choice sets based on historical
registration patterns and distance. Two center attributes,
distance from the patients’ home and observed-expected graft
survival, were fully interacted with patient characteristics,
including insurance type (Medicare versus private). We used
the conditional logit model parameters to determine if
privately-insured patients are more likely to register at high
quality centers. The sample size was 32,652 patients.
Principal Findings: Observed-expected graft survival was
higher in in-network hospitals by 2-4 percentage points for all
five health plans (p<0.05 for each). There was a substantial
degree of overlap in the networks, and most networks
included the larger, well-established transplant centers (for
example, UCLA, University of Pittsburgh). Simulations using
the parameters from the patient-level conditional logit model
show that for patients with Medicare, a one-standard
deviation increase in the graft-failure rate is associated with a
2% decline in patient demand. However, for privately-insured
patients, a one-standard deviation increase in the graft-failure
rate is associated with a 15% decline in demand.
Conclusions: Findings indicate that transplants centers with
good outcomes are more likely to be included in plans
“centers of excellence” networks, consistent with plans’
claims.
Implications for Policy, Delivery, or Practice: Harvard
Business School Professor Michael Porter and other thought
leaders have recently questioned whether exclusive provider
networks provide value to patients. This study, as well as
previous studies examining the role of quality in the formation
of provider networks for cardiac surgery, indicate that insurers
use networks to steer patients to high quality facilities.
Primary Funding Source: NIDDK
●Are Insurance Gaps Costly for Diabetes Patients? Who
Pays?
Hsou Mei Hu, Ph.D., Emily C. Shelton, MAE, David M. Cutler,
Ph.D., Allison B. Rosen, M.D., Sc.D.
Presented By: Hsou Mei Hu, Ph.D., Research Associate,
Internal Medicine, University of Michigan, 300 Ingall, Room
7C27, Ann Arbor, MI 48103; Tel: (734) 615-7975;
Email: hsoumeih@med.umich.edu
Research Objective: Continuity of care is crucial for diabetes
patients in preventing costly complications. As health
insurance premiums rise, more chronically ill individuals
experience lapses in insurance coverage which, by limiting
access, may result in worse health outcomes and require more
costly care. The purpose of this paper is to examine the effect
of insurance gaps on diabetic patients’ use and costs of
inpatient and emergency room (ER) care.
Study Design: We used data from three panels (spanning
from 2000 to 2003) of the Medical Expenditure Panel Survey
(MEPS). Respondents reporting diabetes in the first year of
each of the two year panels were dichotomized into those with
continuous coverage (n=1901) and those with gaps in
coverage (n=369). Those who were uninsured for the entire
two year period (n=182) were excluded. We examined the
association between coverage gaps and the use and costs of
care. Poisson regression was used to assess differences by
continuity of coverage in ER visits and hospitalizations, as
these utilization events are markers of worse health outcomes.
Differences in ER and inpatient spending and total spending
(inflated to 2003 dollars) between the two groups were
examined using ordinary least squares regression. Spending
was further explored by private vs. public payers. All analyses
controlled for demographic characteristics, socioeconomic
status, and comorbidities, and adjusted for the MEPS complex
sample design using STATA 9.1.
Population Studied: A weighted population of 6,381,214
civilian noninstitutionalized individuals with diabetes in the
US.
Principal Findings: Diabetes patients experiencing gaps in
insurance coverage had significantly more ER visits (0.99 vs.
0.71) and were more likely to be hospitalized (1.03 vs. 0.71)
during the 24-month period than those with continuous
coverage. Respondents with insurance gaps incurred higher
ER, inpatient, and overall costs than those without insurance
gaps (ER & inpatient costs $9,748 vs. $7,129, and total costs
$19,187 vs. $17,803, respectively). When examining spending
by payer type, public programs (including Medicaid, Medicare
and Tricare) paid more for diabetics with insurance gaps than
for those without gaps in coverage ($9,085 vs. $7,953,
respectively), although private insurers paid comparable
amounts for the two groups.
Conclusions: Insurance gaps are associated with increased
ER visits and hospitalizations, suggesting a possible adverse
impact on the health of individuals with diabetes. This
increased use is mirrored by concomitant increases in ER and
hospital spending. These effects may be more pronounced
for public payers.
Implications for Policy, Delivery, or Practice: Policies to fill
insurance gaps may improve health and potentially even save
money if applied in sicker population, such as those with
diabetes. Further studies are needed to explore the impact on
both health and economic outcomes of policies aimed at
addressing these coverage gaps in chronically ill populations.
Primary Funding Source: NIA, The Harvard Interfaculty
Program for Health Systems Improvement, the Lasker
Foundation
●The Effect of Hospital Safety Reports and a Tiered
Hospital Network on Inpatient Referrals
Dennis Scanlon, Ph.D., MA, BA, Jon B. Christianson, Ph.D.,
Coleen Lucas, RN, Eric Ford, Ph.D.
Presented By: Dennis Scanlon, Ph.D., MA, BA, Associate
Professor of Health Policy & Administration, Health Policy &
Administration, The Pennsylvania State University, 119B
Henderson Building, University Park, PA 16802-6500; Tel: 814865-1925; Fax: 814-863-2905; Email: dpscanlon@psu.edu
Research Objective: Recent years have seen a movement
towards both ‘consumer directed’ and ‘pay for performance’
programs in health care. Many of these programs utilize
‘tiered networks’ for hospital care, where consumer out-ofpocket co-payments vary based on the hospital chosen. While
most of the early tiering efforts were based on hospitals’
charges only, there is an increasing movement towards
placing hospitals into tiers based on efficiency and
quality/safety indicators. However, little has been published
about consumer response to tiered hospital benefits or the
impact of tiered networks on hospital admissions and
revenues. This paper examines the effect of a tiered hospital
benefit in an employed, commercially insured population.
Study Design: In conjunction with its major labor unions, the
Boeing Company instituted the Hospital Safety Incentive
(HSI) for union (i.e., hourly) employees enrolled in Boeing’s
Traditional Medical Plan (TMP). The TMP, which took effect in
July 2004, is an ERISA, self-funded health plan administered
for Boeing by Regence Blue Shield of Washington. The HSI is
unique because it gives patients a financial incentive to
choose hospitals that meet the Leapfrog Group’s three patient
safety "leaps". While the TMP’s standard coverage for
hospital care is 95% of allowed hospital charges (up to the
annual out-of-pocket maximum), union beneficiaries enrolled
in the TMP can achieve a benefit of 100% for hospital care if
admitted to a hospital that meets the Leapfrog standards.
Boeing’s actuaries have estimated the average value of the 5%
payment to be approximately $450 per admission.
We estimate the effect of the HSI on patients’ selection of
hospital in two of Boeing’s major employment hubs (Seattle,
WA and Wichita, KS). We utilize a pre-post study design and
take advantage of the fact that the HSI did not apply to nonunion (i.e., salaried) employees. We identify the effect by
comparing the change in hospital admissions of hospitalized
hourly and salaried beneficiaries after the HSI went into effect.
To gauge awareness of the HSI, we also examine differences,
pre-post, between hourly and salaried non-hospitalized
beneficiaries. We identify enrolled beneficiaries from claims
data, and while we examine changes in hospital market
shares, our primary outcome variables come from answers
collected during a 20 minute telephone survey. The telephone
survey was necessary since patients are referred to hospitals
by their physicians, and thus it is not clear if admissions
decisions are made by patients, physicians, or jointly.
Targeted respondents were randomly sampled from four
groups in each period (union/non-union, hospitalized/nonhospitalized). The key outcomes include questions regarding
the degree to which the patient was involved in the choice of
hospital and awareness of the HSI. We completed
approximately 1,200 interviews in each period and achieved a
60% survey participation rate.
Population Studied: Salaried and hourly employees of the
Boeing Company and their beneficiaries in two markets
(Seattle, WA and Wichita, KS).
Principal Findings: Preliminary results indicate the HSI did
not influence the admissions decisions of hourly hospitalized
beneficiaries. Answers to other survey questions provide
insight about why. In particular, patient-physician
relationships are very important, and admission to a Leapfrog
compliant hospital would often require switching physicians.
In addition, only 20% of hospitals qualified for the PSI during
the study period.
Conclusions: While benefit plans that include tiered provider
networks (e.g., hospital and physicians) aimed at increasing
consumer sensitivity to cost, quality and safety have grown in
popularity and are theoretically attractive, little is known about
how they work operationally. Our study provides important
insight regarding the practical limitations of these benefit
structures as well as consumer response to them.
Implications for Policy, Delivery, or Practice: Our findings
are important for informing the emerging health care
consumerism and pay for performance movements.
Primary Funding Source: AHRQ
●The Effect of Cost-Sharing on the Adherence of
Antihypertensive Medications
Matthew Solomon, Ph.D., Jose J Escarce, M.D., Ph.D., Dana P
Goldman, Ph.D., Geoffrey F Joyce, Ph.D.
Presented By: Matthew Solomon, Ph.D., Medical Student,
David Geffen School of Medicine at UCLA, 3332 Hamilton
Way, Los Angeles, CA 90026; Tel: 323 669 1550;
Email: mattsol@ucla.edu
Research Objective: To control rapidly rising prescription
drug costs, nearly all health plans have increased their costsharing requirements. Although many studies have
established a link between cost-sharing and the utilization of
prescription drugs, there is little evidence that directly explores
the mechanism underlying such cost-mediated utilization
changes. Preliminary research has indicated that increased
cost-sharing levels are associated with delays in the adoption
of antihypertensive therapy. This study aims to understand
the effect of cost-sharing on adherence to antihypertensive
treatment regimens for those patients with newly diagnosed
hypertension who initiate drug therapy.
Study Design: This is a retrospective study conducted from
1997 to 2002 examining pharmacy and medical claims data
that are matched to the health benefits from 13 large
employers and 31 health plans. Outcomes explored are (1) the
proportion of days (PDC) covered in the first year and first two
years after initiation of antihypertensive therapy, and (2) the
number of days until a 60- or 90-day gap in coverage after
initiation of antihypertensive therapy.
Population Studied: The sample includes 5022 elderly
individuals newly diagnosed with hypertension who have
employer-based retiree insurance benefits, including insurance
for prescription drugs.
Principal Findings: The mean PDC for the overall sample was
75.0% (S.D. 29.2%), and the proportion of patients with a
PDC greater than 80%, a common measure of "good"
adherence, was 60.1% (S.D. 48.9%). In multivariate logistic
regression models, cost-sharing levels were not associated
with having a PDC greater than 80% in either the first year
(P=0.719) or first two years (P=0.765) after the initiation of
antihypertensive medication. Sensitivity analyses revealed that
cost-sharing was similarly not associated with having a PDC
greater than 70%, 60%, 50%, 40%, or 30% in either the first
year or two years after initiation. However, nonantihypertensive drug use at the time of initiation was
associated with an increased likelihood of good adherence in
the first year (O.R.=1.39; P=0.000) and first two years
(O.R.=1.45; P=0.000). Similarly, in multivariate Cox models,
cost-sharing was not associated with the time to a 60- or 90day gap in coverage (P=0.503 and P=0.521, respectively), and
concurrent medication use was associated with a decreased
hazard of having a 60- (H.R.=0.816; P=0.000) or 90-day gap
(H.R.=0.814; P=0.000).
Conclusions: Cost-sharing levels were not associated with
adherence to antihypertensive medication regimens among
newly diagnosed elderly hypertensive patients. However,
concurrent medication use at the time of initiation was a
strong predictor of good adherence. The lack of an
association between cost-sharing and adherence indicates that
adherence may not account for a substantial portion of the
observed cost-related utilization reduction of prescription
drugs found in the literature. Future research should continue
to explore the relationship between prior drug use and
adherence.
Implications for Policy, Delivery, or Practice: These results
will help policy makers understand how drug benefits affect
antihypertensive use, and will help physicians address factors
that may improve adherence to the treatment regimens they
prescribe for their hypertensive patients.
Primary Funding Source: AHRQ, NIH Medical Scientist
Training Program at UCLA
●Effects of Hospital Price Competition on Quality of Care
for 4 High-mortality Conditions
Kevin Volpp, M.D., Ph.D., R. Tamara Konetzka, Ph.D., Julie
Sochalski, Ph.D., FAAN, RN, Jingsan Zhu, M.B.A.
Presented By: Kevin Volpp, MD, Ph.D., CHERP, Philadelphia
VA Medical Center, 3900 Woodland Ave., 9th Floor,
Philadelphia, PA 19104; Tel: 215-573-9718; Fax: 215-573-8778;
Email: volpp70@mail.med.upenn.edu
Research Objective: A significant body of health economics
research indicates that hospital competition shifted from a
quality/amenity basis to a price basis with the growth of
managed care in the 1980s and 1990s, lowering the rate of
increase in hospital costs. However, in recent years costs
have begun to rise at rapid rates and it is less clear that
managed care is still effectively controlling costs. In this
analysis, we examine effects of price competition and
managed care on changes in quality over two time periods:
1991-1997, a period in which price competition and managed
care reduced the rate of increase in hospital costs and 19972001, a period in which our previous work shows that price
competition still reduced the rate of increase in hospital costs
but managed care no longer had these effects.
Study Design: Retrospective Observational Study. We assess
the effects of price competition and managed care penetration
on 30-day mortality for several high-mortality conditions while
controlling for patient severity and changes in hospital volume
and case-mix. We use hospital-level fixed effects in a longdifferences framework to assess whether the effects on quality
of being in a market with high managed care penetration
and/or high market competition have changed over time.
Population Studied: Patients with principal diagnoses of
AMI, stroke, hip fracture, or GI bleed admitted to California
hospitals between 1991 through 2001. We use OSHPD data
linked with state death certificates and hospital financial data.
Principal Findings: Neither the unadjusted nor the adjusted
results suggest a shift in the effect of managed care on
mortality corresponding to the shift in the effect on hospital
cost growth. In both the earlier and later periods, the greatest
decline in mortality is found in markets with high competition
but low managed care. The smallest decline in mortality (or
largest increase) is found in markets with low competition and
high managed care. This would suggest that high competition
is associated with improvements in quality but that high
managed care penetration interferes with rather than
enhances that effect. Higher managed care penetration alone
is associated with relatively worse mortality rates regardless of
competition, but the size of the difference does not differ
substantially between the two periods.
Conclusions: If the fact that higher MCP ceased to have an
effect on costs after the managed care backlash circa 1997
were due to a shift away from competition on price toward
competition on quality, we might expect to see relative
declines in mortality in high-MCP markets in the post-backlash
period. For four common conditions treated in inpatient
settings, we find no evidence of this. These results are
consistent with qualitative evidence that hospitals have simply
gained negotiating power relative to managed care
organizations, and that any shift toward quality competition
consists of competition on amenities and services that have
little influence on clinical outcomes.
Implications for Policy, Delivery, or Practice: Provision of
data on risk-adjusted outcomes in different hospitals might
increase the degree to which hospitals and managed care
organizations compete in improving outcomes.
Primary Funding Source: Doris Duke Charitable Foundation
Related Posters
Health Insurance Markets & Managed Care
Poster Session B
Monday, June 26 • 5:30 pm – 7:00 pm
●Affordability of Health Insurance: Do Assets and Net
Wealth Explain the Demand for Health Insurance Better
than Income?
Didem Bernard, Ph.D. Economics, Jessica Banthin, Ph.D.
Economics, Willaim Encinosa, Ph.D. Economics
Presented By: Didem Bernard, Ph.D. Economics, Senior
Economist, CFACT, AHRQ, 540 Gaither Road, Rockville, MD
20850; Tel: 301-427-1682; Fax: 301-427-1276; Email:
dbernard@ahrq.gov
Research Objective: Understanding the affordability of
coverage is important for evaluating the role of policy in
reducing the number of uninsured workers. We study worker
health insurance take-up and coverage decisions using data
from the Medical Expenditure Panel Survey (MEPS) from 1997
to 2003.
Study Design: Unlike previous studies which control only for
current income, we include information on the presence and
value of family-level assets such as home ownership, vehicles,
savings accounts, stocks, bonds, retirement accounts, as well
as liabilities, to estimate the effect of net wealth on insurance
purchase decisions. Unlike most studies which focus on
“worker” take-up, we also take into account the availability of
insurance offers through the spouse’s employer in estimating
enrollment decisions of workers and their families. We
estimate worker demand for employer sponsored health
insurance as a function of the premium as well as worker,
family, employer and plan characteristics. We use two
approaches to deal with the lack of premium data for workers
who decline coverage. The first approach uses a sub sample in
MEPS from 1997-1999 with linked data from the Household
Component (HC) and the Insurance Component (IC) which is
a survey of employers. Although the HC-IC link sample is not
nationally representative, it contains data on the premiums for
takers and decliners as well as the availability of choice of
health plans, and types of plans offered. The second approach
uses simulated premiums from the Insurance Component List
Sample. Using the nationally representative sample of
employers in the MEPS-IC, we estimate average plan
premiums as a function of predictor variables available on
both the employer and household surveys, including location
(state, MSA), firm size, industry, and plan types offered. We
then use this model to predict premiums for workers in the
household survey.
Population Studied: We study workers' (aged 18 to 64) health
insurance take-up and coverage decisions using data from the
Medical Expenditure Panel Survey (MEPS) from 1997 to 2003.
Principal Findings: Among adults living in families with
health insurance offers in 2001 and 2002, 7.6 percent did not
take up private insurance. As expected, probability of take up
declined with income: 8.6 percent of adults with middle
income, 19.9 percent of adults with low income, and 32.4
percent of poor and near poor adults did not take up private
insurance. (Bernard and Selden, 2005) Preliminary work based
on this sample, suggests that assets and net wealth play a
significant role in insurance coverage decisions. Controlling
for income, adults who did not take up health insurance were
significantly less likely to have assets. For example, among
poor and low income adults, the decliners were less likely to
own homes (46% vs. 54%), cars (78% vs. 86%), checking
accounts (37% vs. 51%), stocks (1% vs. 5%), and individual
retirement accounts (13% vs. 22%).
Conclusions: Preliminary results suggest that assets and net
wealth play a significant role in insurance take up decisions.
Implications for Policy, Delivery, or Practice: Research
using affordability thresholds based on income has shown
that health insurance was affordable to 25% to 75% of the
uninsured in 2000. (Bundorf and Pauly, 2002) Our
preliminary results suggest that affordability thresholds based
solely on income may be inadequate in explaining health
insurance purchase decisions.
Primary Funding Source: AHRQ
●Ambulatory Surgery Facility Access and Health Care Cost
and Utilization
William Cecil, MBA, John Barnes, M.A., M.B.A., Steven L.
Coulter, M.D.
Presented By: William Cecil, MBA, Director, Health Policy
Research, Health Policy Research, BlueCross BlueShield of
Tennessee, 801 Pine Street - 1E, Chattanooga, TN 37402; Tel:
(423) 763-3372; Fax: (423) 755-5100; Email:
bill_cecil@bcbst.com
Research Objective: Since January of 2000, the ASF capacity
in Tennessee has increased by 70 new facilities at a CON
approved cost of $208 million. Since January of 1999,
outpatient surgical plan paid costs for BCBST have risen from
$7.03 PMPM to $15.19, as of June 2005; a 116% increase. OP
surgery utilization has risen from 89.4 per 1,000 members to
144.6 per 1,000; a 61.7 % increase. The paid cost per visit has
risen from $943 to $1,228, a 30.2% increase. BCBST TennCare
OP surgery paid costs have risen from $4.49 to $12.04, a
168% increase. TennCare OP surgery utilization has risen
from 88.2 per 1,000 to 189, a 114% increase. TennCare paid
cost per visit has risen from $610 to $785, a 28.7% increase.
According to the State of Tennessee Department of Labor,
Ambulatory Care Facility employment is projected to grow at
about 493 jobs per year. First quarter 2005 average annual
wage was $43,732. Listings of employers available at the state
web site showed 72% of firms in the ACF category were
ambulatory surgery facilities. A principal justification of
ambulatory surgery facility use for outpatient surgery
compared to hospital based outpatient surgery is lower costs.
The objective of this paper is to investigate the relationship
between ambulatory surgery capacity, access, cost and
utilization and health care costs and utilization.
Study Design: An observational study that makes benefit of
increases in network ambulatory surgery capacity over a four
year period from 2001 through 2004 to test the accessutilization/cost relationships. We examined 357,172 claims
records using standard methods to compare plan paid costs
and utilization of both hospital based and ambulatory surgery
facility based outpatient surgery utilizers.
Population Studied: A commercially insured population of
74,959 members from 2001, 89,484 from 2002, 98,219 from
2003 and 94,510 from 2004, all residing within Tennessee.
Principal Findings: Both theoretical and empirical models
confirm the positive relationship between capacity, access and
utilization. Empirical models show higher adjusted plan paid
costs for those undergoing outpatient surgery in an
ambulatory surgery facility compared to hospital outpatient
surgery. Higher use of primary care office visits, specialist
office visits and imaging in all service locations were
associated with ambulatory surgery facility based outpatient
surgery compared to hospital based outpatient surgery. Actual
and theoretical marginal access improves by 0.07 and 0.08
miles respectively with the addition of a single ambulatory
surgery facility. Marginal ambulatory surgery facility access
gains become less than a mile at 18.6 % of 2004 network
capacity.
Conclusions: The distance to an ambulatory surgery facility is
a function of the number of network ambulatory surgery
facilities. Ambulatory surgery facility utilization is a function of
distance to the facility in addition to out of pocket cost
sharing, age, gender and DCG score. We find higher total plan
paid costs with the use of ambulatory surgery facility based
outpatient surgery.
Implications for Policy, Delivery, or Practice: Careful
consideration should be given to network composition for
outpatient surgery services given the higher cost associated
with ASF use. ASF network management may also play an
important role in imaging cost management.
Primary Funding Source: BlueCross BlueShield of Tennessee
●A Review and Synthesis of the Cost and Benefits of
Health Insurance Regulation in the U.S.
Christopher Conover, Ph.D., Ilse Wiechers, M.P.P., M.D.
Presented By: Christopher Conover, Ph.D., Assistant
Research Professor, Terry Sanford Institute of Public Policy,
Duke University, Box 90253, Durham, NC 27708; Tel: (919)
613-9369; Fax: (919) 684-6246; Email:
conoverc@hpolicy.duke.edu
Research Objective: The central objective of this paper is to
develop a comprehensive estimate of the costs and benefits of
health insurance regulation derived by summing the results of
fine-grained cost estimates in 19 different domains of health
insurance regulation; a companion objective is to identify
domains of health insurance regulation in which regulation
currently appears not to be cost-effective.
Study Design: A formal literature search was conducted for
each major domain of health insurance regulation. For each
domain, a synthetic estimate of regulatory costs is obtained
using a standardized procedure that systematically assembles
evidence from the literature regarding government regulatory
costs (such as monitoring and enforcement costs) and
compliance costs (including both industry costs and patient
time losses), and indirect costs such as costs associated with
health losses (morbidity and mortality losses, all monetized
using a common value of statistical life year) or increases in
uninsured risk (monetized using a standardized estimate of
the external cost of being uninsured and the monetized value
of the increased mortality risk faced by the uninsured).
Corresponding monetized estimates of benefits were
developed based on empirical evidence of benefits derived
from the literature, including efficiency gains, quality
improvements or reductions in uninsured risk. Lower and
upper bound estimates were derived using minimum and
maximum parameter estimates for the various components
used to calculate regulatory costs.
Population Studied: The synthesis focused on 19 domains of
insurance regulation, including regulations focused on access
to care (e.g., high risk pools), cost control (e.g., HIPAA
administrative simplification) and quality (e.g., patient bill of
rights).
Principal Findings: The preliminary base case result for the
cost of health insurance regulation in the U.S. is $99.3 billion
(2002 $); the minimum estimate is $68.2 billion and the
upper bound estimate is $172.7 billion. Estimated benefits are
$84.9 billion, but this ranges from a minimum of $49.7 billion
and upper bound of $180.9 billion. [final estimates updated to
2004 $ to be available in early February]
Conclusions: On balance, the net costs of insurance
regulation exceed $14 billion annually. The domains of health
insurance regulation having the highest net cost include
continuation of coverage requirements, benefit mandates and
patient bill of rights.
Implications for Policy, Delivery, or Practice: The 12
domains in which costs exceeded benefits are areas
warranting closer examination to determine how regulatory
objectives might be achieved more cost-effectively.
Primary Funding Source: ASPE, DHHS
●Generic Switch Study
Melissa Godfrey, MS, William Westerfield, MA, Soyal Momin,
MS, M.B.A., Ray Phillippi, Ph.D., Terence Shea, PharmD
Presented By: Melissa Godfrey, MS, Research Analyst, Health
Services Research, BlueCross BlueShield of Tennessee, 801
Pine Street, 3E, Chattanooga, TN 37402; Tel: (423) 752-6471;
Fax: (423) 785-8083; Email: melissa_godfrey@bcbst.com
Research Objective: The primary purpose of this study was to
estimate the brand-to-generic “switch rate” for a health plan’s
members who were offered a generic co-pay waiver from
October 18 to December 31, 2004. A secondary objective was
to create a statistical model predicting members’ decisions to
switch.
Study Design: Healthcare claims data were analyzed to
identify the health plan’s members who met study inclusion
criteria. Those who switched brand prescriptions to generics
during this time were assigned to the “switcher” group, and
those who did not were assigned to the “non-switcher” group.
The “switch rate” was calculated as the ratio of switchers to
the study population.
The Prescription Drug Survey was created to measure factors
– such as perceptions of generic drugs and interaction with
healthcare professionals – that previous research has linked to
consumers’ decisions to use generic drugs. The survey was
mailed to 1,027 switchers and 1,027 non-switchers. A total of
586 surveys were returned (response rate=28.5%). Survey
responses were combined with healthcare claims data, and
binomial logistic regression was used to create a statistical
model to predict switching.
Population Studied: Participants were members of the
commercially insured population of a large single-state health
insurance carrier in the southeastern United States who met
the following criteria: (1) received the generic co-pay waiver,
(2) purchased the brand version of a multi-source
maintenance drug between July 1 and October 17, 2004, (3)
purchased either the brand or the generic version of the same
multi-source maintenance drug during the co-pay waiver
period, (4) were at least 18 years old, (5) were either a
subscriber or spouse of a subscriber, (6) were enrolled at the
time of survey mailing (June 2005), and (7) qualified for one of
the two groups (“switcher” and “non-switcher”).
Principal Findings: The brand-to-generic switch rate for the
study population during the co-pay waiver period was 11%.
The following factors were associated with members’
decisions to switch: brand co-pay (OR=1.024, p=.0164),
perceptions of generic drugs (OR=1.264, p<.01) approaching a
healthcare professional about switching (OR=2.492, p<.01),
taking estrogens (OR=0.207, p<.01), and taking thyroid
hormones (OR=0.579, p<.01).
Conclusions: Eleven percent of members who were eligible
for inclusion in the Generic Switch Study took advantage of
the co-pay waiver. Members with high brand co-pays, positive
perceptions of generic drugs, and those who approached a
healthcare professional about switching were more likely to
switch than their counterparts. Members who took estrogens
and thyroid hormones were less likely to switch than those
who took other drugs.
Implications for Policy, Delivery, or Practice: Managed care
organizations can encourage members to switch brand
prescriptions to generics by offering financial incentives,
educating members about the benefits of using generics, and
encouraging them to approach healthcare professionals about
cost-effective treatments. Directing these tactics to members
who take estrogens and thyroid hormones should increase
generic adoption. The 11% switch rate can be used as a
baseline measure for future analyses.
Primary Funding Source: BlueCross BlueShield of Tennessee
●Is Cost-sharing Associated with Seeing Out-of-Network
Physicians among Children with Asthma or Diabetes?
David Grembowski, Ph.D., William Trejo, BA, Paula Diehr,
Ph.D., James Stout, M.D., M.P.H., Frederick Connell, M.D.,
M.P.H.
Presented By: David Grembowski, Ph.D., Professor, Health
Services, University of Washington, Box 357660, Seattle, WA
98195-7660; Tel: (206) 616-2921; Fax: (206) 543-3964; Email:
grem@u.washington.edu
Research Objective: To determine the association between
out-of-network and in-network cost-sharing and the likelihood
of seeing an out-of-network physician among children with
asthma or diabetes in managed care plans. The single,
common element across all types of managed care
organizations (MCOs) is a provider network. To control their
costs, MCOs often build networks composed of physicians
with lower fees and/or lower-cost practice styles, and then
route enrollees to network physicians through greater out-ofnetwork cost-sharing. Because children with chronic
conditions have greater needs and see multiple physicians, it
is unclear whether greater out-of-network cost-sharing reduces
the likelihood of seeing an out-of-network physician, and
whether this matters in terms of expenditures and quality of
care.
Study Design: Cross-sectional cohort study.
Population Studied: 123,645 children/adolescents with 2
years of continuous coverage (July 1999 to June 2001) in a
commercial health insurance firm in Washington state. Of
these, 301 children met diabetes eligibility criteria (> 1
diagnosis for diabetes or 2+ insulin prescriptions), and 2,242
children met asthma eligibility criteria (> 2 diagnoses for
asthma separated by at least a week). Dependent variable was
whether a child saw an out-of-network physician in Year 2
(excluding anesthesiologists, radiologists and emergency
room physicians). Cost-sharing was measured by out-ofnetwork and in-network cost-sharing indexes for the child’s
health plan in Year 2. Data sources include physician network
directories, American Medical Association Masterfile, child
characteristics from health plan, asthma severity in Year 1,
sociodemographic characteristics of child’s Zip code
tabulation area, and HMO penetration in child’s county.
Principal Findings: Children with asthma saw an average of
2.4 physicians in Year 2 (excluding anesthesiologists,
radiologists and emergency room physicians), and children
with diabetes saw an average of 2.2 physicians. About 22% of
children with asthma and 24% of children with diabetes had
emergency room visits, and 5% of children with asthma and
12% of children with diabetes were hospitalized. About 4% of
children with asthma and 9% children with diabetes saw an
out-of-network physician in Year 2. Over half of the out-ofnetwork physicians had primary care specialties. Logistic
regression revealed that greater in-network plan coverage was
associated weakly (p=.07) with lower odds of seeing an out-ofnetwork physician for children with diabetes but not for
children with asthma. The out-of-network cost-sharing index
was not associated with seeing an out-of-network physician.
Greater HMO penetration was associated with greater odds of
seeing an out-of-network physician (p<.001) for children with
asthma.
Conclusions: Cost-sharing is not associated generally with
seeing an out-of-network physician among children with
diabetes or asthma in health plans with provider networks.
Greater HMO penetration is associated with seeing an out-ofnetwork physician among children with asthma.
Implications for Policy, Delivery, or Practice: For children
with asthma or diabetes, cost-sharing does not appear to be a
barrier to seeing out-of-network physicians. Greater HMO
market penetration may be related to less physician
participation in plan provider networks, which may increase
utilization of out-of-network physicians.
Primary Funding Source: AHRQ
●The Role of State Regulation in Consumer-Driven Health
Care
Timothy Jost, J.D., Mark Hall, .JD.
Presented By: Timothy Jost, J.D., Robert Willett Family
Professor, College of Law, Washington and Lee University,
Lewis Hall, Lexington, VA 22802; Tel: (540) 458-8510; Fax:
(540) 458-8488; Email: jostt@wlu.edu
Research Objective: To examine the role that the states are
taking in regulating health savings accounts (HSAs) and high
deductible health plans (HDHPs) for which federal tax
subsidies are available under the Medicare Modernization Act
(MMA).
Study Design: Interviews with state regulators, insurance
company and trade association representatives, independent
experts, and HSA advocacy groups, supplemented with a
literature review and legal analysis.
Population Studied: State regulatory programs and insurance
companies.
Principal Findings: Most states have responded affirmatively
to the incentives offered by the MMA for consumer-driven
health care by removing regulatory barriers to qualified
HDHPs such as minimum deductible mandates Many states
have gone further, adopting or modifying state income tax
laws to supplement the federal incentives provided through
the MMA for HSAs and HDHPs with state incentives. With a
few notable exceptions, the states seem generally supportive
of consumer-directed health care and reluctant to impede its
progress with regulatory burdens. In embracing consumerdriven health care, however, the states seem to have lost sight
of their traditional regulatory concerns about financial
accountabilty, on the one hand, and access to insurance, on
the other. The states have also not adequately worked through
the interface between consumer-driven healthcare and state
managed care regulation.
Conclusions: The states have generally embraced the federal
initiative to encourage consumer-driven health care, but in
doing so have largely abdicated their traditional responsibility
for insurance regulation. They have not carefully examined the
potential problems presented by HSAs and HDHPs and
designed an appropriate regulatory response.
Implications for Policy, Delivery, or Practice: Public policy
with respect to consumer-driven health care seems at this
point to be driven exclusively by the federal government. The
states have traditionally been responsible for regulating health
insurance in our federal system, and need to consider their
responsibility for regulating consumer driven health care. This
paper raises the issues that the states need to work through.
Primary Funding Source: RWJF
●Effects of Health Insurance on Physical Activity of US
Adults
Eric Keuffel, M.P.H.
Presented By: Eric Keuffel, M.P.H., Graduate Student, Health
Care Systems Department, Wharton - University of
Pennsylvania, 6 Reaney Court, Philadelphia, PA 19103; Tel:
(215) 546-9286; Email: ekeuffel@wharton.upenn.edu
Research Objective: This study estimates the effects of health
insurance status on physical activity decisions.
Epidemiological evidence links physical activity with
reductions in relative risk for a variety of costly chronic
diseases. In theory, the expected effect of insurance coverage
on primary prevention effort is ambiguous. Moral hazard may
reduce the probability of engaging in prevention behaviors by
beneficiaries. However, many insurers now offer benefits and
programs that promote physical activity. Prior research found
a complementary relationship between coverage and physical
activity. This study disaggregates effects by source (public vs.
private) and type (HMO vs. Non-HMO) of insurance.
Study Design: Physical activity (PA) is defined as “vigorous
activity 3 or more times per week for at least 30 minutes per
session”. Logit models regressed the bivariate physical
activity measure in 2002 (1-yes, 0-no) on insurance variables,
demographic characteristics, occupation, income, education,
health status measures and medical expenditures.
Independent variables either measure 2002 levels or changes
(when appropriate) and help extract out variation due to
underlying health status, demographic, educational and
occupational factors. Variance Inflation Factors (VIF) from
linear specifications reject excessively collinear specifications.
Marginal effects (ME) are estimated using mean values and
reflect the change in probability of being physical active for an
“average” individual for each one unit increase in the relevant
independent variable. The analysis was conducted using
STATA version 9.2.
Population Studied: The Individual Component of the
Medical Expenditure Panel Survey (MEPS) Panel 6 (20012002) is a weighted representative panel of the civilian noninstitutionalized US population. PA was only measured for
adults
Principal Findings: Just over half of adults are physically
active (.55). Although the primary specification results in a
positive, but insignificant, association between insurance
coverage and physical activity; the specification which
differentiated between public and private coverage shows that
private insurance complements physical activity (+.03
marginal effect, p=.06) while public insurance has
insignificant effects. Interaction models indicate that, after
accounting for type of insurance (public and private), those
enrolled in HMO plans within Medicaid have significantly
higher probabilities for PA (+.06 ME, p=.03). Much of the
effect associated with private insurance depends on
employment status. The main private insurance coefficient is
positive (+.08 ME, p<.01), but the interaction term between
private insurance and employment is negative (-.07 ME,
p<.01). Lastly exposure to out-of-pocket costs (measured as a
ratio to total health expenditures) is significant but of modest
magnitude (+.03 ME, p=.05)
Conclusions: To the extent that the covariates eliminate
important confounders such health status and account for
selection, these regressions suggest that private plans and
HMOs increase probability of physical activity for at least
portions of the population. Greater exposure to out-of-pocket
costs increases the probability of PA, but only modestly.
Implications for Policy, Delivery, or Practice: Incentives
embedded in private and HMO style plans appear to increase
PA behavior modestly. While the precise mechanism is
unknown, future identification of these economic fulcrums
that promote physical activity and potentially reduce long run
health care costs may affect both private and government
payers. Further study on the mechanism of action and
subgroup analyses are warranted.
Primary Funding Source: NRSA
●Cost-Based Physician Profiling: Increasing Efficiency
through Accountability
Antonio Legorreta, M.D., M.P.H., Yingxu Zhao, Ph.D., William
Wright, BA, Elizabeth Lord, BA, Antonio Legorreta, M.D.,
M.P.H.
Presented By: Antonio Legorreta, M.D., M.P.H., Adjunct
Professor, Health Services, University of California, Los
Angeles School of Public Health, 21650 Oxnard St. Suite 550,
Woodland Hills, CA 91637; Tel: 818-676-2872; Fax: 818-7159934; Email: legorreta@ucla.edu
Research Objective: To enhance existing methods used by
commercial payers to profile physicians based on cost of care
delivery in a combined health maintenance organization and
preferred provider organization setting.
Study Design: Two years of administrative facility, physician,
and pharmacy claims ending in first quarter 2005 were
employed to identify complete episodes of care using the
Symmetry Episode Treatment Groups (ETG)™ grouper
software. Episodes that were cost outliers, assigned to
members not continuously enrolled during the report period,
or did not occur in the most recent year were excluded.
Episodes with one physician responsible for a majority of total
costs were attributed to that provider. The total episode set
was then filtered to include only episodes in the top 50th
percentile in terms of both cost and frequency for each
specialty as long as 80% of total costs were also included. If
this criteria was not met then the top 25th, or 5th percentile
was used in stepwise fashion as needed.
The traditional ETG risk adjustment approach estimates the
expected value for individual episodes by averaging the costs
of all episodes within the same type of ETG. The ratio
(Observed/Expected) or the standardized cost difference
(SCD) between the average actual and expected costs for each
physician is then calculated to characterize physician practice
efficiency. To improve predictive accuracy, we refined this
mean approach by computing the predicted costs from a
Gamma regression model based on each “Super ETG”
category—a group of similar types of ETGs (e.g., chronic
bronchitis with and without complications and comorbidities)with additional adjustment for demographic and disease
severity characteristics such as age, gender, physician
specialty, complication, level of comorbidity , and medication
burden. The predictive accuracy of the risk adjustment model
was assessed by the R-squared statistic (percentage of
variation explained).
Population Studied: A large Midwest commercial health plan
including approximately 2 million member lives and 10,000
physician groups was examined.
Principal Findings: We identified nearly 1.3 million episodes
which corresponded to 1,620 physician groups in 50
specialties. ETG risk adjustment models with the traditional
mean approach predicted medical costs with a R-squared of
0.633. The regression modeling approach based on SuperETGs explained more total variance (R-squared=0.882).
Conclusions: Regression modeling techniques adjusting for
demographics and disease severity improved the predictive
accuracy of the existing risk adjustment system. Their
application, in combination with the common risk adjustment
methodology (ETG), is suggested for use in economic cost
profiling of physicians.
Implications for Policy, Delivery, or Practice: Measuring
providers on both quality and cost is of critical importance in
light of the continued interest to curb rising healthcare costs
through improved efficiency. Peer-to-peer comparison of the
cost of providing care can help increase physician awareness
and accountability of how practice patterns drive increased
health care spending. This type of profiling may be used by
commercial payers to identify physicians who provide
improved value in health care.
Primary Funding Source: No Funding
●Measuring Managed Care and Its Environment Using
National Surveys: a Review and Assessment
Su-Ying Liang, Ph.D., Kathryn A Phillips, Ph.D., Jennifer Haas,
M.D.
Presented By: Su-Ying Liang, Ph.D., Associate Researcher,
Clinical Pharmacy, University of California San Francisco, 3333
California Street, Suite 420, San Francisco, CA 94143; Tel: 415514-0457; Fax: 415-502-0792; Email:
liangs@pharmacy.ucsf.edu
Research Objective: (1) to review empirical measures and
analytical examples for examining the impact of managed
care, managed care markets, and other characteristics of the
area where an individual resides using two widely used
national surveys, the Medical Expenditure Panel Survey
(MEPS) and the National Health Interview Survey (NHIS);
and (2) to discuss practical issues common in the design,
analysis, and the interpretation of studies using these surveys.
Study Design: We adopt a previously developed framework of
health plan factors that guides our review of managed care
concepts. This framework is based on a review of literature
from 1990-2000 and is updated to incorporate newly
developed managed care concepts from the 2001-2005
literature. We map the managed care concepts onto available
data sources. We summarize empirical measures of managed
care in MEPS and NHIS and measures of area characteristics
in other datasets linkable to MEPS/NHIS including the Area
Resource File and US census data. We provide empirical
applications of these measures and discuss analytical issues
that should be considered.
Population Studied: Synthesis of dataset documentation,
reports, and research articles.
Principal Findings: The framework of health plan factors
identifies thirteen categories of measures from MEPS and
NHIS that can be used to examine health plan characteristics
relating to managed care. The framework also identifies the
areas in which these national surveys can be improved to
advance research on managed care. We discuss the following
analytical issues: (1) categorization and interpretation of
measures of health plan characteristics; (2) defining markets
and relevant areas to measure area characteristics; and (3)
analyzing data at multiple levels, including statistical modeling
and software options.
Conclusions: Despite numerous analytical challenges and the
lack of data from the provider perspective, MEPS and NHIS
are rich sources of data for examining the impact of health
plans and the characteristics of markets or areas on health
care expenditures and outcomes.
Implications for Policy, Delivery, or Practice: National
surveys may consider collecting additional data to allow
examination of new innovations of managed care, including
consumer-directed health plans, pay for performance, and
multi-tiered networks.
Primary Funding Source: NCI
●How Does A Specialty Pharmacy Provider Program
Impact a Health Insurer?
Andrea McAllister, B.S., Andrea DeVries, Ph.D.
Presented By: Andrea McAllister, B.S., Decision Support
Specialist, Knowledge Discovery, Highmark Inc., 120 Fifth
Avenue, Pittsburgh, PA 15222; Tel: 412-544-0664; Fax: 412-5440700; Email: andrea.mcallister@highmark.com
Research Objective: To study the impact of the
implementation of a specialty pharmacy program for
Remicade in Highmark’s Managed Care and PPO products.
Study Design: Costs, including administration (admin) fees,
and utilization were compared for the twelve months prior
and post program which was enacted April 2004. The two
main diagnoses of interest were Crohn’s/Ulcerative Colitis and
Arthritis. Various measures were calculated (number of
claims per patient, Highmark cost per claim, and Highmark
cost per patient per month (PPPM)) and were stratified by
procedure type (treatment, admin), claim type (outpatient,
professional), region of service delivery (Western vs. NonWestern), and Insurance category (Manages Care, PPO, and
Traditional) as well as other indicators. Additionally, an
analysis was completed examining “total visit” cost. In this
case the cost of a “total visit” was equivalent to the treatment
cost plus any admin fees occurring during the seven days
subsequent to the service date of the treatment claim.
Population Studied: All Highmark members receiving
Remicade treatment twelve months prior and post program
implementation.
Principal Findings: Remicade treatment claim costs
decreased by 3% among the Western Managed Care Products
for both Arthritis and Crohn’s disease. Of the approximately
10,000 Remicade claims during the post program period,
about 30% were provided by the specialty pharmacy provider.
Major sources of the non-specialty pharmacy provider claims
were categories in which the specialty pharmacy provider
cannot be used as a provider: Out-of-area (Blue Card),
Western region outpatient facility, and Central region
(professional and outpatient facility). There was an 11%
decrease in outpatient facilities claims coupled with a 4.7%
increase in professional office visit claims after the program
went into effect.
Conclusions: The specialty pharmacy provider program
resulted in decreased costs to Highmark as well as a shift in
site-of-service as seen by opposing changes in outpatient
facility and professional office visit utilization. There was an
added incentive to professional providers in the form of
increased administration fees. This resulted in increased total
professional visit costs but is offset by the decrease in
outpatient facility claims which cost more. Compliance to the
program appeared to be quite high at 30% considering large
categories of claims could not be accessed by the specialty
pharmacy provider. The impact of the specialty provider
program in Highmark’s Western Region for Managed Care
products on site-of-service for Remicade treatments translates
to a total savings to Highmark of $535k per year.
Implications for Policy, Delivery, or Practice: When taking
this total savings result into consideration with the fact that a
specialty provider would responsible for multiple drug
treatments at an insurer the potential for savings due to shift
in site-of-service alone is great. This result can be used as
evidence to support the use of a specialty pharmaceutical
provider by an insurance plan for all drug treatments of which
administration can be performed in either the outpatient or
professional office setting. Considering specialty
pharmaceuticals are greater than a 20% share of entire
pharmacy budgets for an insurer these type of strategies for
cost savings are highly desirable.
Primary Funding Source: No Funding
●Life Style Coaching: Does the Telephone Work?
James Naessens, M.P.H., James Purvis, MS, Rachel Carroll,
BA, Barbara Kreinbring, RN, M.B.A., William Litchy, M.D.,
Holly Van Houten, BA
Presented By: James Naessens, M.P.H., Clinical Associate,
Health Care Policy & Research, Mayo Clinic, Pavilion 3,
Rochester, MN 55905; Tel: (507)284-5592; Fax: (507)284-1731;
Email: naessens@mayo.edu
Research Objective: To assess the effectiveness of telephonic
life style coaching on subjects enrolled in weight control,
stress, exercise and nutrition programs.
Study Design: A pre-post cohort study comparing baseline
and 6 month post-intervention subject-reported status on
general health, confidence, healthy practices and outcome
variables was performed for each of four lifestyle coaching
programs. Primary data was collected with telephone
interviews at enrollment, during intervention and at 6 months.
The intervention consisted of a life style coaching program
with regular telephone contact between subject and coach
over a 13 week period. Assessment variables were created by
comparing 6 month to baseline values or, where appropriate,
analyzing a reported variable reflecting the subject’s
assessment of change from baseline. Paired analysis was
performed with t-tests, rank-sum tests and Chi-square tests,
as appropriate. An intent-to-treat analysis was attempted with
inclusion of all participants completing the follow-up
interview.
Population Studied: Life style coaching enrollees were eligible
for employee-sponsored life style coaching benefits and were
identified with appropriate risk factors through a health risk
appraisal. All enrollees came from across the US from a single
multinational company in 2004. Weight control included 1114
participants, exercise included 334 participants, nutrition
included 259 participants and stress management included
281 participants.
Principal Findings: Significant outcome improvements were
seen in weight control (drops in BMI and weight), exercise
(improvement in energy levels), stress (reporting fewer stressrelated problems), and nutrition (increased energy and drop in
BMI). The average weight loss for the 938 participants with
starting and ending weights was 4 pounds. 58% of weight loss
participants had at least a 5% or 5 pound weight loss.
Significant improvements in confidence dealing with their risk
factor were seen among the participants in weight control and
stress. Those in the exercise program were inadvertently asked
how confident they were at increasing their exercise level.
Nutrition program participants had a significant drop in
confidence. Significant improvements in healthy practices
were seen among participants in all four programs. Significant
improvements in self-reported general health were seen
among participants in weight control, nutrition and exercise.
Conclusions: Telephonic life style coaching appears to be
effective in improving outcomes and healthy practices for
participants who enter those programs with identified health
risks. Participants in weight control, nutrition and exercise
programs reported significant improvements in general
health.
Implications for Policy, Delivery, or Practice: Telephonic
delivery of life style coaching would expand the reach of
programs to improve health and reduce risk. Further research
should be performed to determine whether participants’
improvements were due to program content or frequent
attention.
Primary Funding Source: No Funding
●Examining Health Reimbursement Account Exhaust in a
Consumer Driven Health Plan
Ross Owen, M.P.A., Regina Levin, M.P.H.
Presented By: Ross Owen, M.P.A., Research Analyst,
Research, Definity Health/United Health Group, 1600 Utica
Ave. S., St. Louis Park, MN 55416; Tel: 952-277-6013; Fax: 952277-5502; Email: ross_owen@uhc.com
Research Objective: The central feature of consumer driven
health plans (CDHPs) is the presence of an account from
which enrollees receive “first dollar” coverage as well as the
opportunity to roll funds over from year to year. Despite the
increasing prevalence of such plans, relatively little is known
about how the health reimbursement account (HRA)
functions differently for members based upon their
characteristics and utilization behaviors. This study evaluates
the impact of a large commercial group of CDHP enrollees'
traits and utilization on their length of time to HRA account
exhaust.
Study Design: This retrospective cohort analysis uses Cox
Regression to examine enrollees over a one year period
(calendar 2004) as long as their HRA dollars remained. The
number of months to HRA exhaust serves as the dependent
variable, and enrollee characteristics and behavior during the
HRA period serve as the covariates in the proportional
hazards model.
Population Studied: The study group is composed of a
cohort of commercial (Definity Health) CDHP enrollees
grouped at the family level (n=79,050 families, representing
~188,000 lives). The sample contains all continuously
enrolled employees from Definity’s 2004 book of business
who had plan years that started on January 1st of the study
year, as well as any spouses and dependents enrolled with
them.
Principal Findings: The hazard ratios for each covariate in the
Cox Regression model quantify the expected change in the
hazard (or instantaneous risk) of a family exhausting their
HRA given a unit change in that variable with all other
variables held constant. The utilization variables, as expected,
increase the hazard of exhaust in proportion to the intensity of
utilization. Three additional non-utilization variables have
sizable hazard ratios: family size (hazard ratio = 1.295, 95%
C.I. = 1.285 – 1.306), chronic disease prevalence (hazard ratio
= 1.287, 95% C.I. = 1.270 – 1.303), and the amount of HRA
funds rolled over from the previous year (hazard ratio = .899,
95% C.I. = .896 - .902).
Conclusions: Controlling for utilization and severity of illness,
the model demonstrates that larger families and families with
more chronic disease had increased risk of exhausting their
HRA. The model also quantifies the impact of rollover dollars
in reducing the hazard of exhaust, exploring the impact of
members’ ability to roll these funds over from year to year on
the HRA’s effectiveness in protecting members from out-ofpocket costs.
Implications for Policy, Delivery, or Practice: This study
provides a rare look at a large sample of commercial CDHP
enrollees’ interaction with their plan’s central feature. Given
the increasing prevalence of CDHP and the amount of focus
given account-based coverage, results like these do much to
evaluate how this plan design functions “on the ground”.
These results will be of interest to practitioners and
policymakers as they relate to benefit design and the suitability
of CDHP for enrollees with widely varying health needs. With
better knowledge about how the HRA will work differently for
individuals and families based on their composition and
behavior, CDHPs can more precisely target financing
mechanisms, communication strategies, and incentives to
encourage clinically beneficial and cost-effective care.
Primary Funding Source: Definity Health/United Health
Group
managed care and government constraints, which are
restraining growth about one percent per year. Summing up,
we estimate that the current stimulus to real per capita
personal health care spending to be in the range of 4.5-5
percent per year.
Conclusions: Because the growth in real per capita GDP is
only expected to be two percent per year or a little more, our
results indicate that the percentage of GDP devoted to health
care will grow from 16 percent in 2004 to about 21 percent in
2014. Serious attempts to slowdown health care spending
growth might consider consumer-friendly ways to reduce
insurance coverage.
Implications for Policy, Delivery, or Practice: High
deductible insurance coupled with health savings accounts
may be a way of lowering coverage in a consumer-friendly way
for the working age population. However, because
government health care spending (46 percent of the total in
2003) is quickly approaching half of all health spending as the
baby-boomers retire, consumer options leading to lower
coverage in this portion of the market might be considered as
well.
Primary Funding Source: American Enterprise Institute
●Health Insurance and the Rising Cost of Health Care
Edgar Peden, Ph.D., Mark S. Freeland, Ph.D.
●Deductible-based Cost-sharing: Informed Consumers or
Uninformed Confusion?
Mary Reed, Dr.P.H., Richard Brand, Ph.D., Joe Newhouse,
Ph.D., Joe Selby, M.D., M.P.H., John Hsu, M.D., M.B.A.
Presented By: Edgar Peden, Ph.D., Author, American
Enterprise Institute, 7509 Oakmont Dr, Frederick, MD 21702;
Tel: (301) 473-8553; Email: rhohat@adelphia.net
Research Objective: An empirical analysis of the factors
driving medical spending growth from 1960 through 2003.
Study Design: Using data from the National Health Accounts,
and other national data, our study is based on a time-series
(regression) analysis of the health spending growth and its
causal factors.
Population Studied: U.S. population and health care market.
Principal Findings: Our analysis of the post World War II
health market experience shows that government policies
have fostered ever greater insurance coverage both indirectly
(employer tax deductibility) and directly (Medicare and
Medicaid). In the late 1940s consumers paid 70 cents on the
dollar out-of-pocket for health care, but in the ensuing 50 plus
years it dropped almost continuously so that in the early
2000s it had fallen to less than 15 cents. This inter-temporal
change has exacerbated the familiar ‘moral hazard’ problem of
over consumption of health care. But more importantly it has
also resulted in ready markets for high cost medical
technology, as consumers have leveraged their out-of-pocket
spending to pay for ever more sophisticated, ever more costly
medical services. The marginal benefits from this higher
spending may be less than the marginal cost.
The econometric model we estimated for 1960-2002, implies
that insurance coverage (the percentage of medical spending
paid by insurers) and its growth are currently driving real per
capita personal health care spending higher at the rate of 3.5
percent per year. This stimulus is being abetted by growth in
real permanent income, non-commercial medical research
spending, provider monopoly power, and the changing
age/gender population mix. Together these account for
another 2-2.5 percent per year. Offsetting these factors are
Presented By: Mary Reed, DrPH, Research Associate,
Division of Research, Kaiser Permanente, 2000 Broadway,
Oakland, CA 94612; Tel: 510.891.3808; Fax: 510.891.3606;
Email: mer@dor.kaiser.org
Research Objective: Deductible-based plans with high levels
of cost-sharing could encourage patients to participate more
actively in their health care. Effective incentives, however,
require that consumers are well-informed. We examined
patient knowledge of their deductible plans in a population
with a new deductible plan.
Study Design: We conducted a cross-sectional telephoneinterview study in a random sample of adult health system
members, with over-sampling of members with chronic
diseases (asthma, coronary artery disease, heart failure,
hypertension, or diabetes). Participants reported whether they
faced any deductible, the amount of their deductible, and
which medical services applied to their deductible. We
compared these patient reports with each respondent’s actual
deductible structures and amounts using the health-plan’s
automated databases.
Population Studied: All 479 study participants had a
deductible: for 56.5%, the deductibles applied only to hospital
care; for the remaining 43.5%, the deductible applied to all
care except office visits. Participants had a mean age of 44.8
years, 52.4% were female, and 76.0% reported being of white
race/ethnicity; 49.7% had “excellent” or “very good” health
status; 49.7% had less than a college-graduate education; and
17.1% had an annual household income of less than $35,000.
Principal Findings: While all respondents actually faced a
deductible, 44.6% of participants were not aware that they
faced any deductible. Among those who reported having a
deductible, 54.7% did not know that they had to pay full price
for hospital care before reaching their deductible. Similarly,
less than half knew that their actual deductible applied to
other services: 34.8% knew that emergency department care
was included in their deductible and 33.9% knew that medical
tests were included. Although no one’s deductible applied to
office visits, 39.3% believed that office visits were included
toward their deductible. In multivariate analyses, respondents
whose deductibles applied to all care (except office visits) were
significantly more likely to know that they faced a deductible
than those whose deductibles applied only to hospital care
(OR: 1.89, 95%CI: 1.23, 2.94). In addition, those with nonwhite race/ethnicity (OR: 0.57, 95%CI: 0.36, 0.89) and who
had “excellent” or “very good” self-reported health (OR: 0.59,
95%CI: 0.38, 0.91) were significantly less likely to be aware of
their deductible.
Conclusions: Almost half of patients did not know that they
faced a deductible at all. Among patients who did know about
their deductible, most were unaware of the specific medical
services that were included or excluded from their deductible.
Implications for Policy, Delivery, or Practice: New
deductible forms of cost sharing have the promise to involve
patients more directly as consumers of healthcare, but
effective patient participation requires detailed knowledge of
their benefits. We found that patients had limited
understanding of their plans. This confusion could inhibit the
ability of these plans to engage patients in their care.
Additionally, these inaccurate perceptions could even lead
some patients to avoid care unnecessarily. More research is
needed to understand how these deductibles affect patient
care-seeking behavior, especially as benefits grow increasingly
complex.
Primary Funding Source: Kaiser Foundation Research
Institute
●More Than Half of Californians in HMOs are Overweight
or Obese
Dylan Roby, Ph.D., Gerald Kominski, Ph.D., Natalya
Mostovskaya, BA
Presented By: Dylan Roby, Ph.D., Senior Research Associate,
Center for Health Policy Research, UCLA, 10911 Weyburn Ave,
Suite 300, Los Angeles, CA 90024; Tel: 310-794-3953; Fax: 310794-2686; Email: droby@ucla.edu
Research Objective: This project examines overweight and
obesity among HMO enrollees in the state of California.
Study Design: Using data from the 2003 California Health
Interview Survey (CHIS), it examines the proportion of private
HMO enrollees who are overweight or obese (based on BMI
and age) in each of the seven major health plans in the state.
The study also calculated rates by different age and ethnic
categories. The UCLA Center for Health Policy Research
collected information on height, weight, and health plan name
during the most recent California Health Interview Survey.
Population Studied: Private HMO Enrollees, ages 12-64.
Principal Findings: More than five million Californians
enrolled in private HMOs - over half of all private HMO
enrollees aged 12-64 - are overweight or obese. Members of
Aetna (59%), Kaiser (54%), and Health Net (53%) reported
higher combined prevalence of overweight and obesity relative
to the statewide rate; Cigna (52%) was comparable to the
state average; while members of Blue Cross (51%), Blue Shield
(49%) and Pacificare (49%) had combined overweight and
obesity rates lower than the statewide average. Aetna had the
highest percentage of obese enrollees (23%), while Blue Cross
had the lowest (17%).
Conclusions: Our estimates provide a baseline measure of
overweight and obesity prevalence rates in California's HMOs,
and thus provide a starting point for assessing whether plans
are making progress in dealing with this important threat to
public health in the future. Since HMOs provide insurance to
almost half of California's population, it is essential that they
take further action to improve the current baseline of
overweight and obesity rates in HMOs.
Implications for Policy, Delivery, or Practice: HMOs face a
serious challenge in addressing the problem of overweight
and obesity among their members, and should actively design
appropriate interventions to address this problem.
Emphasizing diet, exercise, and regular healthy activity are
important steps in confronthing this nationwide epidemic.
Health plans can also take steps to help the process, such as
physician incentives to discuss weight management and
providing educational materials to enrollees. Several health
plans, including LA Care and Kaiser Permanente, are actively
involved in weight management strategies for their members.
Primary Funding Source: California Office of the Patient
Advocate
●The Early Disease Detection Study
Chris Stehno, MBA (magna cum laude), University of Notre
Dame; Graduate Studies in Bio-Statistics, University of Iowa;
BS in Biology, University of Kansas
Presented By: Chris Stehno, MBA (magna cum laude),
University of Notre Dame; Graduate Studies in Bio-Statistics,
University of Iowa; BS in Biology, University of Kansas,
Healthcare Management Consultant, Denver Health Practice,
Milliman, Inc., 1099 18th Street, Suite 3100, Denver, CO
80202-1931; Tel: (303) 299-9400; Fax: (303) 299-9018; Email:
chris.stehno@milliman.com
Research Objective: Disease Management (DM) programs
have been plagued by faulty and judgmental ROI estimations
and conclusions. The fact is that current predictive models
used in DM rarely can predict an event in the early or predisease states wherein it is well known that intervention can
make a significant difference. The problem lies not in the
models themselves, but rather in the data being used to
power these models. This paper will discuss the statistical
significance of lifestyle-based data in relationship to early
disease detection.
Study Design: The use of historical medical data as a
predictor of future events has been the mainstay for the
insurance and healthcare industry for over 50 years. This
study looks at the statistical significance of historical medical
data, lifestyle data, and the combination of the two when
defining the at-risk population for four different events: aortic
aneurysms, atherosclerosis, osteoporosis, and stroke.
Population Studied: Our population involved over 100,000
individuals who participated in a series of medical screenings.
These screenings included: ultrasound imaging measurement
of the abdominal aorta, Ankle Brachial Index (ABI index) test,
Bone Mineral Density (BMD) measurement, and Doppler
ultrasound imaging of the carotid arteries for blood flow
velocity. In addition, participants provided medical history
and self-reported lifestyle-based data elements.
Principal Findings: The lifestyle elements provided as good
as or better predictive capabilities than the medical history
elements for at-risk status in all four of the tests studied. The
combination of the two datasets, historical medical and
lifestyle-based, provided the optimal results in all four of the
cases.
Conclusions: Current predictive models and data mining
techniques based solely on historical medical inputs seriously
impede our ability to predict diseases in the early onset or predisease state. The need to look ‘out of the box’ at new
datasets, such as lifestyle-based datasets, is critical to fully
developing disease management and wellness initiatives.
Implications for Policy, Delivery, or Practice: ROI questions
surrounding disease management initiatives will continue to
plague the industry as long as DM applications focus on late
stage disease identification and management techniques.
However, a move to incorporate the early and pre-disease
states will provide significant improvements to both costs and
patient health. In order to achieve these goals, a new way of
thinking and new datasets are necessary to move our
antiquated predictive models into the next generation.
Primary Funding Source: No Funding
●Severe Medical Conditions and Loss of Health Insurance
Coverage: Evidence from the Health and Retirement Study
Hsin-yu Tseng, Ph.D. (expected in June 2006)
Presented By: Hsin-yu Tseng, Ph.D. (expected in June 2006),
Ph.D. candidate, Department of Economics, University of
Chicago, 5550 S. Dorchester Ave. Apt. 607, Chicago, IL 60637;
Tel: 773-256-1392; Email: htseng@uchicago.edu
Research Objective: The diagnosis of severe medical
conditions has competing effects on health insurance
coverage. On the one hand, it increases the demand for
coverage because individuals diagnosed with severe medical
conditions (high-risk individuals) not only face a substantially
higher risk of future health problems, but also need the
coverage to pay for regular checkups helping to manage their
medical conditions. On the other hand, this diagnosis may
place individuals at risk of losing coverage due to declines in
resources and due to risk discrimination in private health
insurance markets. Individuals would want to avoid this risk
because loss of coverage hinders their consumption
smoothing and may exacerbate their medical conditions (e.g.,
Institute of Medicine, 2003; Levy and Meltzer, 2004). Public
insurance and price restrictions in private markets aim to
reduce this risk. However, little is known about whether severe
medical conditions affect the likelihood of losing coverage and
whether these price restrictions affect this likelihood. To
address these issues, this paper conducts two tests. First, I
test whether high-risk individuals, specifically high-risk
policyholders who purchase coverage from the individual
(non-group) health insurance market, continue or lose
coverage. In the individual market, risk discrimination is
prevalent in most states because whether coverage is offered
and at what premium rate depends on risk status. In the
employment-based (group) market, however, federal laws
prohibit individual risk discrimination. Second, I test whether
price restrictions in insurance markets result in an adverse
selection effect leading low-risk policyholders to drop out of
the market. I conduct this test by exploiting state variations in
the degree of risk discrimination in the individual market.
Some states implement price restrictions, while the other
states do not. These restrictions limit the range of variation in
premium rates due to risk status. Thus premium rates
charged to high-risk policyholders are lowered, but rates
charged to low-risk policyholders are increased. In a voluntary
market, these restrictions may result in an adverse selection
effect in which high-risk policyholders retain coverage, but lowrisk policyholders drop out of the market.
Study Design: I use individual data on 50-to-64-year-old
Americans from the Health and Retirement Study (HRS).
Conditional on current insurance status, I focus on changes in
insurance coverage at the extensive margin for two reasons.
First, the HRS statistics indicate that approximately 98% of
the policyholders have comprehensive plans. These statistics
imply that coverage changes at the extensive margin result in
larger financial risk changes than changes at the intensive
margin. Second, HRS provides limited, qualitative measures
on intensive-margin changes. Additionally, the coverage
analysis is augmented by an analysis of premium payment
level because HRS is one of the few public data sets that
contain information on premium payments. I divide high-risk
individuals into two groups: (1) individuals who “just become
high-risk;” (2) individuals who “have been high-risk for two or
more years.” I compare changes in insurance coverage
between each of these two groups and a low-risk reference
group. This division is justified because the diagnosis of
severe medical conditions resembles a permanent health
shock, and the degree of risk discrimination may vary across
the duration of experiencing medical conditions.
Population Studied: 50-to-64-year-old Americans
Principal Findings: The findings indicate that severe medical
conditions have a lagged effect on the probability of becoming
uninsured. Policyholders who become high-risk do not lose
coverage immediately. This finding may be explained by
prohibition of selective increases in premium rates and by
short duration of experiencing medical conditions. In contrast,
policyholders who have been high-risk for two or more years
are 5.1 percentage points (57%) more likely to become
uninsured. Three factors can explain this likelihood, including
risk discrimination, an imperfect safety net, and an income
effect. Four factors, however, cannot, including the crowding
out effect of Medicaid coverage or of uncompensated care,
better health conditions, and fatalism. Findings from premium
payment analysis also support this lagged effect. The findings
further indicate that state price restrictions result in an
adverse selection effect. Price restrictions are associated with
a 4.7 percentage points (45%) increase in low-risk
policyholders’ probability of becoming uninsured, and a 5.5
percentage points (24%) decline in high-risk policyholders’
probability of becoming uninsured. The overall coverage rate
declines by 2.6 percentage points (20%). Robustness checks
indicate that this adverse selection effect is not driven by
declines in income. The findings indicate that insurance
coverage responds to changes in risk status. This response
implies an ex-ante welfare loss. I calibrate this loss and find
that two-thirds of the respondents are willing to pay 14% of
their income to avoid coverage fluctuation. This welfare loss
arises because before diagnosis, risk-averse individuals would
want to insure against the risk of losing coverage if they
become ill. However, this insurance is imperfect in spite of
public insurance and price restrictions in private markets.
Conclusions: High-risk individuals have a higher demand for
health insurance coverage, but they may be at risk of losing
coverage due to risk discrimination in private health insurance
markets and due to declines in resources. This paper
examines whether high-risk individuals, specifically high-risk
policyholders who purchase coverage from the individual
market, continue or lose coverage. The findings suggest that
policyholders who have been high-risk for two or more years
are more likely to become uninsured than their low-risk
counterparts. This likelihood implies imperfect “smoothing” in
coverage so it indicates an ex-ante welfare loss. I calibrate this
loss and find that two-thirds of the policyholders are willing to
pay 14% of their income to avoid coverage fluctuation.
States have implemented price restrictions to narrow the
range of premium rate variation due to risk status. Yet, these
restrictions are prone to market failures in the individual
health insurance market. This paper confirms the premise of
market failure because the findings show that price
restrictions have helped high-risk policyholders retain
coverage, but result in a negative externality leading low-risk
policyholders to drop out of the market. This dropout provides
evidence of adverse selection and implies a welfare tradeoff
between regimes with and without price restrictions. Future
research will employ the joint distribution of risk status and
risk aversion to figure out which regime is associated with a
larger overall welfare loss.
Implications for Policy, Delivery, or Practice: Among
policies that address the aforementioned welfare loss, price
restrictions with compulsory purchase of catastrophic plans
impose the greatest degree of regulation on the health
insurance market. Such policies may reduce welfare since
individuals cannot choose to be uninsured. However, they do
eliminate the risk of losing coverage so welfare may be
improved from an ex-ante point of view. Although an attempt
to implement universal health insurance coverage failed in
1994, coverage expansion remains a key policy issue. This
paper examines why policyholders become uninsured. So, on
the flip side, it analyzes how to help the uninsured obtain
coverage. In addition to the risk discrimination discussed
earlier, the findings imply that the success of coverage
expansion hinges on whether the society has a consensus on
substantial income redistribution. Income effects play a vital
role in dropping insurance coverage; for policyholders who
have been high-risk, low income is associated with an 11.8
percentage points (109%) higher probability of becoming
uninsured. Medical progress has made genetic testing
possible, and it is expected that this testing will become part
of the routine physical examination of the twenty-first century
(Pyenson, 2003). My findings can be applied to genetic testing
because government faces the same challenge when it decides
whether the insurer can use genetic information for risk
discrimination. Information from genetic testing may facilitate
preventive treatments; however, if its use is unregulated, this
information may deprive individuals of insurance possibilities
much before they actually contract the genetic disease (e.g.,
Alzheimer’s disease). If the insurer is prohibited from using
genetic information, but individuals have access to it, this
prohibition may result in an adverse selection effect leading
low-risk individuals to drop out of the market. The extreme
case of this dropout is the collapse of insurance markets.
Primary Funding Source: Chicago Center for Excellence in
Health Promotion Economics
●Why Small Are Firms Less Likely than Big Firms to Offer
Health Insurance?
Chapin White, M.P.P., Ph.D., David Auerbach, Ph.D., Stuart
Hagen, Ph.D.
Presented By: Chapin White, MPP, Ph.D., Analyst, Health and
Human Resources Division, Congressional Budget Office,
Ford House Office Building, Room 424c, Washington, DC
20515; Tel: 202-226-4931; Fax: 202-225-3149; Email:
chapin_white@post.harvard.edu
Research Objective: Small firms are substantially less likely
than large firms to offer health insurance. Three explanations
are commonly offered for this phenomenon are: higher
loading and administrative costs for small firms, differences in
the workforces, and adverse selection. Our first research
objective is to propose an additional explanation for the low
offer rates at small firms, which we term the "dispersion"
effect. Dispersion refers to the variation across firms in the
firm-level aggregate preferences for health insurance. Small
firms will tend to exhibit more dispersion than large firms,
even if there is no sorting of workers into firms based on
worker preferences, due simply to the law of large numbers.
Our second research objective is to gauge the relative
importance of those four explanations for the disparity in offer
rates between small and large firms.
Study Design: We construct a microsimulation model based
on the 2001 Survey of Income and Program Participation
(SIPP). That microsimulation model is used to measure the
magnitude of each of the explanations for the large firm-small
firm disparity in offer rates. Each individual in the SIPP is
assigned a reservation price (based on income, age, sex,
health status, education, Medicaid eligibility and other factors)
and a health insurance premium (based on expected medical
spending multiplied by an appropriate load factor). Workers
are grouped into synthetic firms of various sizes. Those
synthetic firms choose, based on the aggregated preferences
of their workforces, whether to offer health insurance and, if
they offer, what level of employee premium contribution to
impose. Workers, if they are offered health insurance, choose
whether to enroll in an employer-sponsored family policy, an
employer-sponsored single policy, a non-group policy or no
policy. The reservation prices and premiums are calibrated so
that the microsimulation produces firm-level and individuallevel behavior that roughly matches the observed offer and
enrollment behavior reported in the SIPP.
Population Studied: The U.S. non-Medicare population.
Principal Findings: In the microsimulation runs in which all
workers are grouped into synthetic large firms, firm-level offer
rates are substantially higher than in the microsimulation runs
in which all workers are grouped into small firms. The effects
of large-firm versus small-firm loading are minor, as are the
effects of adverse selection.
Conclusions: The two major factors that account for the
discrepancy in offer rates between large and small firms are,
first, the composition of the workforce and, second, the
dispersion effect. The differences in loading, and the effects of
adverse selection account for little of the observed
discrepancy.
Primary Funding Source: No Funding