Morgan State University Effects of Medicare on Mortality Outcomes Paper

Question Description

I’m working on a economics question and need an explanation and answer to help me learn.

1. Card et al. (2008) uses a natural experiment that uses eligibility criteria (age of 65) as an identification
strategy to identify the effects of Medicare on mortality outcomes.a. Explain in detail how the eligibility criteria set for Medicare is used as a quasi-natural experiment.
Talk about the approach (method) they use. b. The analysis of Card et al.’s study is based on hospital level data such that people in the sample
are those who are admitted to the hospital. This creates a selection problem if we compare people
who are right above the threshold vs. those who are right below the threshold. This is because
if a 64 year old plays a waiting game and goes to the doctor when she is 65, she would have
waited longer compared to a 64 year old with similar illness but who does not play the waiting
game (i.e., goes to the doctor when 64). Hence, comparing the health outcomes of 64 year old
individuals (without Medicare) vs. 65 year old individuls (with Medicare) might mean that we are
comparing two groups that are systematically different in health stock to begin with. Describe
how the researchers get around this selection problem. (hint: hospital admissions are higher in
week days compared to weekends.)
c. By using a figure that corresponds to the eligibility criteria (age), briefly describe the findings of
their study on: i) insurance coverage, ii) quality of care, and iii) mortality outcomes.
d. What are some potential drawbacks of their study?

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THE
QUARTERLY JOURNAL
OF ECONOMICS
Vol. 127
August 2012
Issue 3
Amy Finkelstein
Sarah Taubman
Bill Wright
Mira Bernstein
Jonathan Gruber
Joseph P. Newhouse
Heidi Allen
Katherine Baicker
Oregon Health Study Group
In 2008, a group of uninsured low-income adults in Oregon was selected by
lottery to be given the chance to apply for Medicaid. This lottery provides an
opportunity to gauge the effects of expanding access to public health insurance
on the health care use, financial strain, and health of low-income adults using a
randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely
to have insurance than the control group that was not selected. We find that in
this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as
well as hospitalizations), lower out-of-pocket medical expenditures and medical
debt (including fewer bills sent to collection), and better self-reported physical
and mental health than the control group. JEL Codes: H51, H75, I1.
*We are grateful to Josh Angrist, Robert Avery, David Autor, Ethan
Cohen-Cole, Carlos Dobkin, Esther Duflo, Jack Fowler, Guido Imbens, Larry
Katz, Jeff Kling, John McConnell, Jon Levin, Richard Levin, Ben Olken, Alan
Zaslavsky, three anonymous referees, and numerous seminar participants for
helpful comments and advice; to Brandi Coates, Michael Gelman, John Graves,
Ahmed Jaber, Andrew Lai, Conrad Miller, Iuliana Pascu, Adam Sacarny,
Nivedhitha Subramanian, Zirui Song, James Wang, and Annetta Zhou for
expert research assistance; and to numerous Oregon state employees for help
acquiring the necessary data and for answering our many questions about the
! The Author(s) 2012. Published by Oxford University Press, on behalf of President and
Fellows of Harvard College. All rights reserved. For Permissions, please email: journals
.permissions@oup.com
The Quarterly Journal of Economics (2012), 1057–1106. doi:10.1093/qje/qjs020.
Advance Access publication on May 3, 2012.
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THE OREGON HEALTH INSURANCE EXPERIMENT:
EVIDENCE FROM THE FIRST YEAR*
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QUARTERLY JOURNAL OF ECONOMICS
I. INTRODUCTION
administration of state programs. We gratefully acknowledge funding from the
Assistant Secretary for Planning and Evaluation in the Department of Health
and Human Services, the California HealthCare Foundation, the John D. and
Catherine T. MacArthur Foundation, the National Institute on Aging
(P30AG012810, RC2AGO36631, and R01AG0345151), the Robert Wood Johnson
Foundation, the Sloan Foundation, the Smith Richardson Foundation, and the U.S.
Social Security Administration (through grant 5 RRC 08098400-03-00 to the
National Bureau of Economic Research as part of the SSA Retirement Research
Consortium). We also gratefully acknowledge Centers for Medicare and Medicaid
Services’ matching funds for this evaluation. The findings and conclusions expressed are solely those of the author(s) and do not represent the views of SSA,
the National Institute on Aging, the National Institutes of Health, any agency of the
federal government, any of our funders, or the NBER. In addition to the individuals
named as coauthors for this article, the Oregon Health Study Group includes Matt
Carlson (Portland State University), Tina Edlund (Deputy Directory, Oregon
Health Authority), Charles Gallia (Oregon DHS), Eric Schneider (RAND), and
Jeanene Smith (Office for Oregon Health Policy and Research). Author disclosures:
Finkelstein served on the CBO’s Panel of Health Advisers through 2011. Wright is
employed by Providence Health & Services, a nonprofit integrated health care delivery system. Gruber was a paid technical consultant to the Obama administration
during the development of the Affordable Care Act and a paid consultant to the state
of Oregon for modeling health insurance expansion options, and serves on the
CBO’s Panel of Health Advisers. Newhouse is a director of and holds equity in
Aetna, which sells Medicaid policies, and serves on the CBO’s Panel of Health
Advisers. Allen is employed by Providence Health and Services, a nonprofit integrated health care delivery system. She formerly served as director of the Medicaid
Advisory Committee and as staff to the Oregon Health Fund Board at the Office for
Oregon Health Policy and Research. Baicker is a MedPAC commissioner, serves on
the CBO’s Panel of Health Advisers, is a director of Eli Lilly, has received honoraria
from several physician groups for speaking engagements, and previously served on
the Bush administration’s Council of Economic Advisers.
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In early 2008, Oregon opened a waiting list for a limited
number of spots in its Medicaid program for low-income adults,
which had previously been closed to new enrollment. The state
drew names by lottery from the 90,000 people who signed up. This
lottery presents an opportunity to study the effects of access to
public insurance using the framework of a randomized controlled
design.
Although the effects of health insurance on health and health
care use may seem intuitive, and there have been hundreds of
studies on the topic, research in this area has often been hampered by the difficulty of controlling for unobserved differences
between the insured and uninsured (Levy and Meltzer 2008).
Random assignment of health insurance to some but not others
OREGON HEALTH INSURANCE EXPERIMENT
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1. We know of only two other randomized health insurance experiments in the
United States. The RAND Health Insurance Experiment from the 1970s was designed to investigate the marginal impact of varying insurance cost-sharing features among approximately 6,000 insured individuals, not the effect of insurance
coverage itself (Manning et al. 1987; Newhouse and the Insurance Experiment
Group 1993). The more recent Accelerated Benefits Demonstration project was
designed to investigate the impact of health insurance for uninsured disabled
adults receiving Social Security Disability Insurance during the two-year waiting
period for Medicare (Michalopoulos et al. 2011).
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would avoid such confounding, but such opportunities are rarely
available.1
In this article we examine the effects of the Oregon Medicaid
lottery after approximately one year of insurance coverage. We
present comparisons of outcomes between the treatment group
(those selected by the lottery who had an opportunity to apply for
Medicaid) and the control group (those not selected and thus not
able to apply for Medicaid). We also present estimates of the
impact of insurance coverage, using the lottery as an instrument
for insurance coverage.
We organize our analysis around the potential costs and
benefits of health insurance. On the cost side, we examine the
impact of health insurance on increased health care utilization.
On the benefit side, we examine the impact of health insurance on
self-reported health, financial strain, and overall well-being. By
lowering the price of health care, health insurance is expected to
increase health care utilization. Ultimately additional health
care utilization may translate into improved health, although a
one-year window might be too short a time to observe health
improvements. Much less attention has been given in the literature to other potential benefits of health insurance. Because
risk-spreading is arguably the primary purpose of health insurance (e.g., Zeckhauser 1970), we try to examine the impact of
health insurance on consumption smoothing, which we proxy
for with measures of financial strain. We also examine the
impact of health insurance on overall well-being, specifically
self-reported happiness; this may capture, among other things,
any benefits of health insurance from reductions in stress or
stigma.
The impact of Medicaid among a low-income population may
be lower than that of private insurance or insurance among
higher income individuals. The impact of Medicaid may be attenuated (or potentially nonexistent) if public health clinics and
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QUARTERLY JOURNAL OF ECONOMICS
2. Our analysis plan was archived on December 3, 2010, at http://www.nber
.org/sap/20101203/. Some of those analyses yielded little of interest, and therefore
we describe them briefly, presenting the full results only in appendixes. In the few
instances in which the results suggested the performance of additional analyses
that had not originally been planned, we have indicated this in the text and tables.
3. In economics, within the last few years, prespecification of hypotheses has
started to become more common in analyses of randomized experiments in developing countries (e.g., Alatas et al. 2010; Olken, Onishi, and Wong 2010; Schaner 2010;
Casey, Glennerster, and Miguel 2011).
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uncompensated care allow low-income individuals to consume de
facto free medical care similar to that of the insured. Medicaid’s
impact would also be attenuated if—as is often claimed—
Medicaid itself is not particularly ‘‘good’’ insurance in terms of
being able to access health care providers (e.g., Medicaid Access
Study Group 1994; GAO 2011; Rosenbaum 2011).
Our analysis draws on administrative data from hospital
discharge, credit report, and mortality records, as well as on responses to a large mail survey we conducted. The administrative
data are objectively measured and should not be biased by the
treatment and control groups differentially reporting outcomes,
but they only cover a relatively narrow set of outcomes. The
survey data allow examination of a much richer set of outcomes
than is feasible with administrative data alone, but with a 50%
effective response rate, are subject to potential nonresponse bias.
Our available evidence on this issue is limited but reasonably
reassuring.
Prior to looking at the data on outcomes for the treatment
group, virtually all of the analysis presented here was prespecified and publicly archived in a detailed analysis plan.2 Although
prespecification of hypotheses is the norm for randomized controlled medical trials, is it rare in evaluation of social policy experiments.3 Our prespecification was designed to minimize issues
of data and specification mining and to provide a record of the full
set of planned analyses.
About one year after enrollment, we find that those selected
by the lottery have substantial and statistically significantly
higher health care utilization, lower out-of-pocket medical
expenditures and medical debt, and better self-reported health
than the control group that was not given the opportunity to
apply for Medicaid. Being selected through the lottery is associated with a 25 percentage point increase in the probability of
having insurance during our study period. This net increase in
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insurance appears to come entirely through a gross increase in
Medicaid coverage, with little evidence of crowd-out of private
insurance. Using lottery selection as an instrument for insurance
coverage, we find that insurance coverage is associated with a 2.1
percentage point (30%) increase in the probability of having a
hospital admission, an 8.8 percentage point (15%) increase in
the probability of taking any prescription drugs, and a 21 percentage point (35%) increase in the probability of having an outpatient visit. We are unable to reject the null of no change in
emergency room utilization, although the confidence intervals
do not allow us to rule out substantial effects in either direction.
In addition, insurance is associated with 0.3 standard deviation increase in reported compliance with recommended preventive care such as mammograms and cholesterol monitoring.
Insurance also results in decreased exposure to medical liabilities
and out-of-pocket medical expenses, including a 6.4 percentage
point (25%) decline in the probability of having an unpaid medical
bill sent to a collections agency and a 20 percentage point (35%)
decline in having any out-of-pocket medical expenditures.
Because much medical debt is never paid, the financial incidence
of expanded coverage thus appears to be not only on the newly
insured but also on their medical providers (or whomever they
pass the costs on to).
Finally, we find that insurance is associated with improvements across the board in measures of self-reported physical and
mental health, averaging 0.2 standard deviation improvement.
Two pieces of evidence suggest that the improvements in selfreported health that we detect may at least partly reflect a general sense of improved well-being. First, evidence from a separate
survey we conducted very shortly after random assignment
shows no impact of lottery selection on health care utilization
but improvements in self-reported health that are about twothirds the magnitude of our main survey results more than
a year later. Second, we find that one year later, Medicaid is
associated with about a 32% increase in self-reported overall
happiness, albeit reported in the context of a survey primarily
about health. Whether there are also improvements in objective,
physical health is more difficult to determine with the data
we now have available. More data on physical health, including
biometric measures such as blood pressure and blood sugar, will
be available from the in-person interviews and health exams
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II. OREGON’S MEDICAID LOTTERY
The Oregon Health Plan (OHP)—created by one of the first
federal waivers of traditional Medicaid rules—currently consists
of two distinct programs: OHP Standard and OHP Plus. OHP
Plus serves the categorically eligible Medicaid population,
which includes (up to specific income thresholds) children and
pregnant women, the disabled, and families enrolled in
Temporary Assistance to Needy Families (TANF). OHP
Standard, which is the program that was lotteried, is a
Medicaid expansion program to cover low-income adults who
are not categorically eligible for OHP Plus. Specifically, it
covers adults ages 19–64 not otherwise eligible for public insurance who are Oregon residents, are U.S. citizens or legal immigrants, have been without health insurance for six months, have
income below the federal poverty level (FPL), and have assets
below $2,000 (Office for Oregon Health Policy and Research
2009).
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that we conducted about six months after the time frame in this
article.
Our estimates of the impact of public health insurance apply
to able-bodied uninsured adults below 100% of poverty who express interest in insurance coverage, a population of considerable
policy interest. In 2011, fewer than half of the states offered
Medicaid coverage to able-bodied adults with income up to
100% of poverty absent specific categorical requirements
(Kaiser Family Foundation 2011). As part of the 2010 Patient
Protection and Affordable Care Act, starting in 2014 all states
will be required to extend Medicaid eligibility to all adults up to
133% of the federal poverty level, with no financial penalties for
many individuals in this income range who do not take up coverage (Kaiser Family Foundation 2010a, 2010b; U.S. GPO 2010).
The rest of the article is structured as follows. Section II provides background on the Oregon Medicaid program and the lottery design. Section III describes the primary data sources, and
Section IV presents our empirical framework. Section V presents
our main results. Section VI discusses interpretation and extrapolation of our estimates. The Online Appendix provides additional details.
OREGON HEALTH INSURANCE EXPERIMENT
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OHP Standard provides relatively comprehensive benefits
with no consumer cost sharing. It covers physician services, prescription drugs, all major hospital benefits, mental health and
chemical dependency services (including outpatient services),
hospice care, and some durable medical equipment. Vision is
not covered, nor are nonemergency dental services. Wallace
et al. (2008) estimate that in 2001–2004, average annual
Medicaid expenditures for an individual on OHP Standard were
about $3,000. Most care is provided through managed care organizations. Monthly enrollee premiums range from $0 to $20 depending on income, with those below 10% of the FPL paying $0.
At its peak in early 2002, about 110,000 people were enrolled
in OHP Standard, about one-third the size of OHP Plus enrollment at that time. Due to budgetary shortfalls, OHP Standard
was closed to new enrollment in 2004. By early 2008, attrition had
reduced enrollment to about 19,000 and the state determined it
had the budget to enroll an additional 10,000 adults. Therefore, in
January 2008 the state reopened OHP Standard to new
enrollment.
Because the state (correctly) anticipated that the demand for
the program among eligible individuals would far exceed the
10,000 available slots, it applied for and received permission
from the Centers for Medicare and Medicaid Services to add the
new members through random lottery draws from a new reservation list. From January 28 to February 29, 2008, anyone could
be added to the lottery list by telephone, by fax, in person sign-up,
by mail, or online. The state conducted an extensive public awareness campaign about the lottery opportunity. To keep barriers to
sign-up low, the sign-up form (shown in Online Appendix Figure
A2) requested limited demographic information on the individual
and any interested household member, and no attempt was made
to verify the information or screen for program eligibility at
sign-up for the lottery. A total of 89,824 individuals were placed
on the list during the five-week window it was open.
The state conducted eight lottery drawings from the list with
roughly equal numbers selected from each drawing; the drawings
were fairly evenly spaced from March through September 2008.
Selected individuals won the opportunity—for themselves and
any household member (whether listed or not)—to apply for
OHP Standard coverage. Treatment thus occurred at the household level. In total, 35,169 individuals—representing 29,664
households—were selected by lottery. If individuals in a selected
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QUARTERLY JOURNAL OF ECONOMICS
III. DATA
We briefly describe each data source here. Additional details
can be found in Online Appendix 1.
III.A. Administrative Data on Outcomes: Hospital Discharges,
Credit Reports, and Mortality
We obtained standard individual-level hospital discharge
data for the entire state of Oregon from January 2008 through
September 2009 and probabilistically matched them to the lottery
list based on information provided at the time of lottery sign-up
on full name, ZIP code, and date of birth. The data include a
hospital identifier, dates of admission and discharge, source of
admission, detail on diagnoses and procedures, and discharge
destination. Similar discharge data have been used to study the
impact of health insurance in other contexts (see, e.g., Doyle
2005; Card, Dobkin, and Maestas 2008, 2009; Anderson,
Dobkin, and Gross 2010). Although inpatient admissions are relatively rare (the annual admission rate for our controls is only
about 5%), they are expensive, accounting for about one-quarter
4. The state reviewed applications, first examining eligibility for OHP Plus
and then, if not eligible for Plus, examining eligibility for OHP Standard. Those who
did not apply during this window could not apply later (so unlike those categorically
eligible for Medicaid/OHP Plus, did not have ‘‘conditional coverage’’ if unenrolled).
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household submitted the appropriate paperwork within 45 days
after the state mailed them an application and demonstrated that
they met the eligibility requirements, they were enrolled in OHP
Standard.4 About 30% of selected individuals successfully enrolled. There were two main sources of slippage: only about 60%
of those selected sent back applications, and about half of those
who sent back applications were deemed ineligible, primarily due
to failure to meet the requirement of income in the last quarter
corresponding to annual income below the poverty level, which in
2008 was $10,400 for a single person and $21,200 for a family of
four (Allen et al. 2010). If they did successfully enroll in OHP
Standard, individuals could remain enrolled indefinitely, provided that they recertified their eligibility status every six
months.
OREGON HEALTH INSURANCE EXPERIMENT
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III.B. Survey Data on Outcomes
We supplement the outcome measures available in the administrative data with a mail survey that was sent out in seven
waves over July and August 2009 to virtually all individuals selected by the lottery and a roughly equal number of unselected
individuals.7 The complete survey instrument is shown in Online
Appendix Figure A4. The basic protocol involved three mail
attempts. In addition, we designed a more intensive protocol,
which we conducted on approximately 30% of nonrespondents.
It included additional tracking efforts, mailings, and phone
5. Author calculations based on publicly available tables from the 2008
Medical Expenditure Panel Survey.
6. Avery, Calem and Canner (2003) provide an excellent, detailed discussion of
credit bureau data; much of our discussion of the data and our choice of analysis
variables is guided by their work.
7. The seven survey waves sent out do not map directly to lottery drawings (of
which there were eight). See Online Appendix Table A2 (on eight lottery drawings)
and Table A9 (on the seven survey waves) for more detail.
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of total medical expenditures for 18–64-year-olds.5 We observe,
on average, about five months of prerandomization data.
We obtained detailed credit records from TransUnion’s
Consumer Credit Database. Credit bureaus like TransUnion
collect rich and detailed information on virtually all formal consumer borrowing gleaned from public records, collections agencies, and ‘‘trade lines’’ such as credit card balances (but do not
capture informal borrowing such as through relatives or pawnbrokers). The analysis of such data is still relatively rare in the
economics literature and, to our knowledge, has never been done
before in a health insurance context.6 TransUnion used the full
name, full address, and date of birth reported at sign-up to match
68.5% of lottery participants to their prerandomization credit
report in February 2008. The credit bureau was able to track
over 97% of those found in the February 2008 file to their
September 2009 file. Our primary outcomes of financial strain
are measured in this 2009 file, which thus has an effective postrandomization ‘‘attrition rate’’ of 3%. We also observe prerandomization outcomes measured in February 2008.
We obtained mortality data from Oregon’s Center of Health
Statistics for all deaths occurring in Oregon from January 1,
2008, through September 30, 2009, and probabilistically matched
our sample using full name, ZIP code, and date of birth.
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QUARTERLY JOURNAL OF ECONOMICS
III.C. Other Data
We obtained prerandomization demographic information
that the participants provided at the time of lottery sign-up.
Online Appendix Figure A2 shows the sign-up form. We use
these data primarily to construct nine ‘‘lottery list variables’’
that we use to examine treatment and control balance on prerandomization demographics.8 We also obtained state administrative records on the complete Medicaid enrollment history of
lottery list participants from prior to the lottery through
September 2009. We use these data as our primary measure of
the first-stage outcome (i.e., insurance coverage). Finally we obtained state administrative records on the Food Stamp and TANF
benefit history of lottery list participants from prior to the lottery
through September 2009.
8. These nine lottery list variables are year of birth; sex; whether English is the
preferred language for receiving materials; whether the individuals signed themselves up for the lottery or were signed up by a household member; whether they
provided a phone number on sign-up; whether the individuals gave their address as
a PO box; whether they signed up the first day the lottery list was open; the median
household income in the 2000 census from their ZIP code; and whether the ZIP code
they gave is within a census-defined metropolitan statistical area.
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contacts. The response rate to the basic protocol was 36%; about
22% of those who did not respond to the basic protocol and who
received the intensive protocol responded. We calculate an effective response rate of 50%, with individuals who responded to the
intensive follow-up weighted by the inverse probability of being
included in the intensive follow-up subsample.
In Section V.C, we also briefly compare some of our estimates
from this main survey to those from two earlier, virtually identical surveys of the same population: an ‘‘initial survey’’ conducted
approximately one year earlier (i.e., shortly after random assignment), and a ‘‘six-month’’ survey conducted about midway between the initial and main survey. The six-month survey was
conducted on a 20% subsample of the sample used in the other
two surveys. The earlier surveys used similar protocols but did
not have an intensive follow-up arm; the initial and six-month
surveys achieved response rates of 45% and 42%, respectively.
OREGON HEALTH INSURANCE EXPERIMENT
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III.D. Time Frame of the Study
III.E. Sample Definition and Descriptive Statistics
Of the 89,824 individuals who were on the lottery list, we
used prerandomization data to exclude individuals who were
not eligible for OHP Standard (because they gave an address outside of Oregon, were not in the right age range, or died prior to the
lottery), had institutional addresses, were signed up by third
parties, would have been eligible for Medicare by the end of our
study period, or were inadvertently included on the original list
multiple times by the state. These exclusions brought our study
9. We randomly assigned lottery draws to the control individuals as discussed
in more detail in Section IV.
10. We suspect, and focus group interviews with selected individuals suggest,
that selected individuals would have been unlikely to change their behavior while
their applications were being processed; however, the retroactive insurance coverage may affect the financial burden associated with health care utilization during
that time period.
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In the administrative data we measure outcomes from the
date individuals were notified that they were selected (i.e., their
‘‘notification date’’) through the end of September 2009.9 This
observation period represents, on average, 16 months (std.
dev. = 2 months) after individuals are notified of their selection
and, on average 14 months (std. dev. = 3 months) after insurance
coverage is approved for those selected by the lottery who successfully enrolled in OHP Standard. If an individual successfully
obtained insurance through the lottery, coverage was applied
retroactively to only a few days after the state mailed the application to the individual, which was on average about one month
after the notification date and one month prior to the approval
date.10
In our survey most outcomes were asked with a six-month
look-back period (e.g., number of doctor visits in the last six
months) or based on ‘‘current’’ conditions (e.g., self-reported
health). There is variation across individuals in when surveys
were mailed and how long they took to respond, as well as their
lottery draw (and hence notification date). Our average survey
response occurs 15.3 months after notification date (std.
dev. = 2.7) months or 13.1 months after insurance approval (std.
dev. = 2.9 months).
Ethnicity
% Spanish/Hispanic/Latino
0.123
0.820
0.038
0.316
0.684
0.591
0.267
0.733
0.557
Control mean
OF
0.175
0.276
0.399
0.129
0.557
Health status
Ever diagnosed with:
Diabetes
Asthma
High blood pressure
Emphysema or chronic bronchitis
Depression (screen positive)
(continued)
0.751
$39,225
0.917
0.773
$39,265
0.922
Control mean
Language
% English preferred
ZIP code–level variables
% MSA
ZIP code median household income
Language
% English preferred
ZIP code–level variables
% MSA
ZIP code median household income
Variable
STUDY POPULATION (CONTROL GROUP)
TABLE I
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Panel A: Full sample
Sex
% Female
Age
% 50–64
% 20–50
Panel B: Survey responders only
Lottery list variables
Sex
% Female
Age
% 50–64
% 20–50
12-month mail survey variables
Race
% White
% Black
Variable
DEMOGRAPHIC CHARACTERISTICS
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QUARTERLY JOURNAL OF ECONOMICS
0.551
0.090
0.099
0.259
13,035
Employment
% don’t currently work
% work
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Explanation & Answer:
4 Questions

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mortality outcomes

Effects of Medicare

quasi natural experiment

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