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Lynn A. Blewett, PhD
Principal Investigator, SHADAC
Professor, Health Policy & Management
University of Minnesota School of Public Health
Supported by a grant from the
Introduction & Overview
3
Health Services Research Theme Issue:
State Health Policy Research
• Funded by the State Health Access Reform Evaluation
• National Program of the Robert Wood Johnson
Foundation
• Co-located with State Health Access Data Assistance
Center
• 43 research grants awarded since 2008
– Focused on State Health Policy Research
• 10 grants recently awarded - October 2014
4
Motivation for Theme Issue
• Many ACA provisions have been implemented at the
state level
– Complex rollout of the law, state variations in implementation
• Effective evaluation of the ACA will depend on rigorous
research conducted at the state level
– Researchers and policy makers eagerly awaiting data
– Importance of state-based research as health insurance
marketplaces and Medicaid expansion initially implemented
• Topic Areas: Changing distribution of coverage; expansion vs.
non-expansion states; FFM vs SBMs; changing individual and small
group insurance markets; changing role of ESI; access to care;
availability of primary care; health outcomes/population health
metrics
5
New and Improved Data for State Level Research
• Improvements
– The Census Bureau’s American Community Survey (ACS)
– Early release of National Health Interview Survey (NHIS)
– Current Population Survey (CPS) monthly insurance coverage
• New non-federal Data Sources
– RWJF-Urban Health Reform Monitoring Survey (HRMS)
– Gallup-Healthways Well-Being Index
– Rand Health Reform Opinion Survey (HROS)
• Ongoing Household Surveys and Marketplace Data
6
Acknowledgements: Advancing State Health Policy
Research
• Robert Wood Johnson Foundation (RWJF)’
– KATHERINE HEMPSTEAD, PhD, SHARE Program
Officer
• Journal of Health Services Research
– ROBIN CAMPBELL, Senior Journal Editor,
– MICHAEL CHERNEW, Senior Associate Editor
Professor, Harvard Medical School
– JACQUELINE S. ZINN, Ph.D, Co-Editor in Chief,
Professor, Risk, Insurance, and Healthcare
Management, Temple, Fox School of Business
7
Resources
• SHADAC Data Center
http://www.shadac.org/datacenter
• RWJF DataHub
http://www.shadac.org/content/stayupdated
• SHARE Program Report
8
Sign up to receive our
newsletter and updates at
www.shadac.org
@shadac
@lynnblewett
9
The Effects of Expanding Public
Insurance to Rural Low-Income
Childless Adults
Marguerite Burns, Ph.D.
University of Wisconsin- Madison
HSR Webinar
February 27, 2015
What Do We Know About the Effects on Use
of Care of Expanding Medicaid to Childless
Adult Populations?
Recent Studies
Massachusetts [Long and Dahlen, 2014]
• Increased likelihood of usual source of care
Oregon [Finkelstein et al.,2012; Baicker et al.,2013; Taubman et al., 2014]
• Increased outpatient visits
• Mixed effects on ED use
• Initial increase in inpatient use that did not persist
Wisconsin [DeLeire et al., 2013]
• Increased outpatient visits
• Increased ED visits
• Decreased inpatient stays
14
BC+ Core Plan
Medicaid-like plan for uninsured adults without
dependent children with incomes < 200% FPL
Wave 1 of enrollment occurred on January 1, 2009
• Automatic enrollment of individuals participating in
Milwaukee County’s safety net program
Wave 2 of enrollment opened July 1, 2009
• Open statewide to eligible individuals
• Enrollment closed suddenly on Oct 9, 2009
• Subsequent applicants placed on a waitlist
How did the Core Plan for childless adults
affect the use of health care?
Methods
Regression discontinuity design
• Use the sudden imposition of a waitlist as the source
of the discontinuity
• Compare care use after imposition of waitlist for
enrolled and waitlisted individuals who applied around
the time of the cutoff
Sharp Regression Discontinuity
Local Linear Regression
For outcome Yi, date Xi cutoff date x0, threshold
indicator Wi
Yi      X i  x0   Wi    X i  x0 Wi   i
where the weights are given by
h  X i  x0
h is the bandwidth in days, and τ is the treatment effect
of interest.
Marshfield
Data—
Marshfield Clinic & Medicaid Administrative
Demographic Characteristics
Core
Enrollees
(All)
Core
Enrollees
(Within 30
Days)
4,280
658
3,262
351
Male
41%
44%
48%
45%
Age, years
43.78
41.00
39.91
39.84
Age<35
30%
39%
45%
44%
Age 35-54
43%
39%
37%
40%
Age 55 +
26%
22%
18%
16%
Number of enrollees
Waitlisted
Waitlisted Applicants
Applicants (Within 30
(All)
Days)
Outpatient Visits
0
2
4
6
Panel A. Outpatient
-20
-10
0
10
Days from Oct 5th (left) or Oct 14th (right)
20
Preventive Care Visits
0
.2
.4
.6
.8
Panel B. Preventive Care
-20
-10
0
10
Days from Oct 5th (left) or Oct 14th (right)
20
Mental Health or Substance Use
0
.5
1
1.5
2
Panel C. Mental Health or Substance Abuse
-20
-10
0
10
Days from Oct 5th (left) or Oct 14th (right)
20
ED Visits
0
.1
.2
.3
.4
Panel D. Emergency
-20
-10
0
10
Days from Oct 5th (left) or Oct 14th (right)
20
Inpatient Visits
0
.05
.1
.15
Panel E. Inpatient
-20
-10
0
10
Days from Oct 5th (left) or Oct 14th (right)
20
Summary of Main Results
Any
Outpatient Preventive
Mental
Health or
Substance
Abuse
Emergency
Inpatient
Baseline
2.783
0.275
0.297
0.056
0.034
Coef
1.076
0.256
-0.064
0.060
0.042
P-Value
0.026
0.000
0.655
0.086
0.081
Notes: All results estimated at a bandwidth of 14 days excluding one week prior to and following
the closing date.
Conclusions
Enrollment into a public insurance for a rural population of
childless adults leads to:
• Increases in OP visits, including preventive care but not mental
health/substance use disorder
• Increases in inpatient stays
• Inconclusive evidence re: ED visits
We should anticipate increases in the use of care following
Medicaid expansions and be attentive to variable effects across
and within states.
Acknowledgments
• Co-authors
Laura Dague, Ph.D., Texas A&M University
Thomas DeLeire, Ph.D., Georgetown University
Lindsey Leininger, Ph.D., Mathematica Policy Research
Gaston Palmucci, Ph.D., Fiscalia Nacional Economica
Donna Friedsam, MPH, University of Wisconsin-Madison
Kristen Voskuil, MA, University of Wisconsin-Madison
John Schmelzer, Ph.D., Marshfield Clinic
Mary Dorsch, RN, Marshfield Clinic
• Funding
NIH NCATS Grant UL1TR000427 to the UW ICTR
NIMH K01 092338
Robert Wood Johnson Foundation SHARE program
WI Department of Health Services
Effects of Massachusetts Health Reform
on Chronic Disease Outcomes
Tomasz P. Stryjewski MD, MPP,1,2,4
Fang Zhang PhD1,3, Dean Eliott MD,1,2,4
and J. Frank Wharam MB, BCh, BAO, MPH1,3,5
Affiliations:
1: Harvard Medical School 2: Massachusetts General Hospital
3: Department of Population Medicine, Harvard Pilgrim Health Care Institute
4: Massachusetts Eye and Ear Infirmary 5: Brigham and Women's Hospital
• No studies have examined the impact of Massachusetts Health
Reform (MHR) on disease outcomes.
• Prior attempts to study the impact of MHR have used:
• surveys to assess changes in self-reported health status1, or
• billing data to measure change in access/utilization2.
• We analyzed whether MHR affected chronic disease outcomes
in the five years after the passage of MHR.
• hyperlipidemia (total cholesterol mg/dl)
• diabetes (HbA1c %)
• hypertension (systolic blood pressure mmHg)
• Interrupted time series analysis of longitudinal patient data from
Partners HealthCare from October 1 2003 to December 31 2011.3
• Cases: uninsured patients prior to MHR
• Controls: insured patients
• Cases matched to controls (1:2) based on 7 demographic & clinical
variables using propensity score matching4:
1) Age
2) Race
3) Sex
4) Income
5) Comorbidity
6) Date of first
presentation
7) Disease severity at first
presentation
= 100 patients
1,463 uninsured cases
3,448 insured, matched
controls (1:2 match)
67,588 insured controls
• Primary analysis (all uninsured cases with matched controls)
• Subgroup analyses:
• Patients with poorly controlled disease during baseline
•
Uninsured patients who gained insurance in the first followup year
•
Uninsured patients with no evidence of established primary
care in the baseline
• Sensitivity Analyses
• 1:1 and 1:3 propensity score matching with insured controls
Hyperlipidemia
Estimate p
Baseline
0.32
0.31
Level Δ
-0.32
0.89
Trend Δ
-0.39
0.29
Diabetes
Estimate
Baseline 0.03
Level Δ
-0.20
Trend Δ
-0.02
p
0.16
0.17
0.48
Hypertension
Estimate
Baseline 0.09
Level Δ
-1.24
Trend Δ
-0.06
p
0.40
0.14
0.64
• No evidence of improved hyperlipidemia, diabetes, or
hypertension after passage of MHR.
• Similar findings in subgroups:
• Patients with poorly controlled disease during baseline
•
Uninsured patients who gained insurance in the first followup year
•
Uninsured patients with no evidence of established primary
care in the baseline
• The association between uninsured status and poorer health is
well-documented.5,6
• Reasons for lack of improvement of chronic disease control after
MHR may be multifactorial.
•
•
•
•
fragmented delivery system
an inadequate focus on preventive health
suboptimal patient lifestyles
generous compensation pool for uninsured7
• Study limitations
• Limited to uninsured who had contact with healthcare system8
• Use of intermediate health outcomes (vs. MI, mortality, etc.)
1. Pande AH, Ross-Degnan D, Zaslavsky AM, Salomon JA. Effects of healthcare reforms on coverage,
access, and disparities: quasi-experimental analysis of evidence from Massachusetts. Am J Prev Med
2011;41(1):1–8.
2. Capoccia VA, Grazier KL, Toal C, Ford JH, Gustafson DH. Massachusetts's Experience Suggests
Coverage Alone Is Insufficient To Increase Addiction Disorders Treatment. Health Aff (Millwood)
2012;31(5):1000–8.
3. Iseline, S. Uncompensated Care Pool PFY06 Annual Report. Massachusetts Division of Health Care
Finance and Policy. July 2007.
4. D'Agostino RB, Jr. Propensity score methods for bias reduction in the comparison of a treatment to a
non-randomized control group. Stat Med. Oct 15 1998;17(19):2265-2281. 2013;
5. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Health of previously uninsured adults after
acquiring Medicare coverage. JAMA 2007;298(24):2886–94.
6. Baicker K, Taubman SL, Allen HL, et al. The Oregon experiment--effects of Medicaid on clinical
outcomes. N Engl J Med 2013;368(18):1713–22.
7. Galbraith AA, Sinaiko AD, Soumerai SB, Ross-Degnan D, Dutta-Linn MM, Lieu TA. Some families who
purchased health coverage through the Massachusetts connector wound up with high financial burdens.
Health Aff (Millwood) 2013;32(5):974–83.
8. Davidoff A., Kenney GM. Uninsured Americans with Chronic Conditions: Key Findings from the
National Health Interview Survey. Princeton, N.J. Robert Wood Johnson Foundation, 2005.
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