Friday, November 11, 2011

Unintended Consequences of Employer Carrot and Stick?

US Employer Health Insurance Hits New Low

Published: Friday, 11 Nov 2011 | 10:56 AM ET

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By: Reuters

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The percentage of Americans who have health insurance through their employer slipped to a new low of 44.5 percent in the third quarter, a drop of more than 5 percentage points in three years, according to a new poll released Friday.

Adam Gault | Getty Images

Pollsters at Gallup and Healthways [HWAY 6.91

0.04 (+0.58%)

] , who surveyed more than 90,000 U.S. adults, blamed the decline on high unemployment , underemployment and an increased number of employers who do not offer health insurance to their workers.

Employer-sponsored health insurance is one of the main pillars of the $2.6 trillion U.S. health-care industry. But companies have increasingly scaled back benefits and raised employee charges to cope with health-care costs that are rising sharply despite anemic economic growth.

The latest figure was 5.3 percentage points below a high of 49.8 percent in 2008, when the two companies began tracking trends in employer-sponsored health insurance.

"The health insurance system in the United States is experiencing numerous changes. Governments and businesses have and will continue to cut back and/or reform their health coverage offerings," the pollsters said in a statement.

Retailing giant Wal-Mart Stores [WMT 59.03

0.90 (+1.55%)

] announced in October that it would no longer offer health insurance to new part-time employees and slashed its contributions to employee health expense accounts.

Wednesday, November 9, 2011

The Right Incentives from Feds: Pay for Performance

March 2011 Reimbursement Corner: PQRS: CMS Pay for Performance Program

By: James M. Fox, M.D., M.B.A., F.A.C.E.P. and Sandra A. Steele, C.P.C., C.E.D.C.
Recent updates to the Physician Quality Reporting Initiative (PQRI) indicate that the shift from pay for reporting to pay for performance has begun. PQRI has been assigned a new name – Physician Quality Reporting System (PQRS) to reflect its permanence.
For those physicians that reported PQRI measures in 2010, a 2% bonus can be expected this fall.

For 2011, CMS has made the reporting process less cumbersome by reducing the number of claims with accurate reporting to 50% (down from 80 percent in 2010). Incentive payments have been extended through 2014; 1% incentive payment in 2011, and a 0.05 % incentive payment is available in 2012 to 2014. A negative payment adjustment of 1.5% will apply for noncompliance in 2015, increasing to 2% in 2016. PQRS incentive payments are based on the total allowed, reimbursed charge for services covered under the Medicare Part B fee schedule. As we found in the PQRI program, the amount of incentive payment, or future negative payment adjustment, can be significant.
Emergency physicians typically report on 7 measures (under PQRS you will need to successfully report at least 3 measures on 50% of your eligible claims). There have been important changes to the documentation requirements for some of the measures:

Vital Signs for CAP – All four vital signs must be documented and the provider must document all vital signs or document “vital signs reviewed”.
Oxygen Saturation for CAP – providers should document the oxygen saturation within their portion of the record “02 sat 96%”.
Mental Status and Empiric Antibiotic Therapy for CAP – the provider should continue to document the mental status and antibiotic therapy in their record.
Aspirin at Arrival for Acute Myocardial Infarction – providers should make sure all AMI patients have their aspirin status documented in the record.
12-Lead ECG performed for Non-Traumatic Chest Pain (age 40+) & 12-Lead ECG performed for Syncope (age 60+) – if these patients do not require an ECG, document the medical reason the ECG is not required.

CMS is developing a “Physician Compare” website which will publicize data on providers who satisfactorily participate in the 2011 PQRS program. Quality reporting can no longer be ignored; if you have not yet embraced the process, it is now time to start

Saturday, November 5, 2011

The Economics of Drug Shortages

The Shortage of Essential Chemotherapy Drugs in the United States

Mandy L. Gatesman, Pharm.D., and Thomas J. Smith, M.D.

N Engl J Med 2011; 365:1653-1655November 3, 2011

Comments open through November 9, 2011

Article
References
Comments (13): For the first time in the United States, some essential chemotherapy drugs are in short supply. Most are generic drugs that have been used for years in childhood leukemia and curable cancers — vincristine, methotrexate, leucovorin, cytarabine, doxorubicin, bleomycin, and paclitaxel.1 The shortages have caused serious concerns about safety, cost, and availability of lifesaving treatments. In a survey from the Institute for Safe Medication Practices, 25% of clinicians indicated that an error had occurred at their site because of drug shortages. Many of these errors were attributed to inexperience with alternative products — for instance, incorrect administration of levoleucovorin (Fusilev) when used as a substitute for leucovorin or use of a 1000-mg vial of cytarabine instead of the usual 500-mg one, resulting in an overdose. Most cancer centers quadruple-check drugs for accuracy, and we're unaware of any documented death of a patient with cancer such as the nine deaths in Alabama attributable to the use of locally compounded liquid nutrition because the sterile product was not available. However, it is only a matter of time.
These shortages have increased the already escalating costs of cancer care. Brand-name substitutes for generic drugs can add substantial cost. For instance, Abraxane, a protein-bound version of paclitaxel, costs 19 times as much as equally effective generic paclitaxel (see tableAverage Wholesale Prices (AWPs) of Selected Oncology Drugs in Short Supply and Their Potential Alternatives.). Since 2010, health care labor costs in the United States have increased by about $216 million because of the increased time and work required to manage drug shortages.2 A gray market for essential drugs — an unofficial alternative market of drugs obtained by vendors outside the usual distribution networks — has grown rapidly, with unregulated vendors charging markups of up to 3000% for cancer drugs.
The main cause of drug shortages is economic. If manufacturers don't make enough profit, they won't make generic drugs. There have been some manufacturing problems, but manufacturers are not required to report any reasons or timetable for discontinuing a product. Contamination and shortages of raw materials probably account for less than 10% of the shortages. In addition, if a brand-name drug with a higher profit margin is available, a manufacturer may stop producing its generic. For instance, leucovorin has been available from several manufacturers since 1952. In 2008, levoleucovorin, the active l-isomer of leucovorin, was approved by the Food and Drug Administration. It was reportedly no more effective than leucovorin and 58 times as expensive, but its use grew rapidly. Eight months later, a widespread shortage of leucovorin was reported.
The second economic cause of shortages is that oncologists have less incentive to administer generics than brand-name drugs. Unlike other drugs, chemotherapeutics are bought and sold in the doctor's office — a practice that originated 40 years ago, when only oncologists would handle such toxic substances and the drugs were relatively cheap. A business model evolved in which oncologists bought low and sold high to support their practice and maximize financial margins. Oncologists buy drugs from wholesalers, mark them up, and sell them to patients (or insurers) in the office. Since medical oncology is a cognitive specialty lacking associated procedures, without drug sales, oncologists' salaries would be lower than geriatricians'. In recent decades, oncology-drug prices have skyrocketed, and today more than half the revenue of an oncology office may come from chemotherapy sales, which boost oncologists' salaries and support expanding hospital cancer centers.
Before 2003, Medicare reimbursed 95% of the average wholesale price — an unregulated price set by manufacturers — whereas oncologists paid 66 to 88% of that price and thus received $1.6 billion annually in overpayments.3 To blunt unsustainable cost increases, the Medicare Modernization Act mandated that the Centers for Medicare and Medicaid Services (CMS) set reimbursement at the average sales price plus a 6% markup to cover practice costs. This policy has reduced not only drug payments but also demand for generics. In some cases, the reimbursement is less than the cost of administration. For instance, the price of a vial of carboplatin has fallen from $125 to $3.50, making the 6% payment trivial. So some oncologists switched to higher-margin brand-name drugs.4Why use paclitaxel (and receive 6% of $312) when you can use Abraxane (for 6% of $5,824)?
Now practices are struggling to treat their patients because of the unavailability of drugs. Short-term solutions include gray-market purchases, which more than half of surveyed hospitals say they've made, but that option introduces safety and quality-control issues. Pharmacists are intensively managing inventories and alerting prescribers to developing shortages and potential alternatives. Some centers now have a red–yellow–green system for quickly recognizing developing shortages and determining which patients get priority (usually those with curable cancers) when supply is limited.
Long-term, non–market-based solutions have been elusive. Proposed legislation would require manufacturers to give 3 to 6 months' notice before discontinuing a drug in order to allow others to pick up production. However, it is likely that gray-market vendors would buy the remaining inventory of such drugs and charge huge markups. Creating a national stockpile is impractical: Do we stockpile the drugs and then waste whatever is not used or stockpile the ingredients and make new batches as needed? A national health care plan with a single formulary and a central pharmacy stockpile is possible for Medicare or Veterans Affairs but unrealistic given oncologists' dependence on drug income and difficulties with timely, safe distribution.
Market solutions take one of two approaches: let the market work and accept short-term uncertainties or regulate the market more tightly. For instance, the CMS could reimburse at the average sales price plus 30%, but that wouldn't help if the drug price has fallen from $125 to $3.50 per vial. The government could set a floor for average sales prices to encourage the production of generic drugs, but that would increase the total cost of cancer drugs unless brand-name prices were reduced. Europe has fewer shortages for that reason: prices are set higher for generics so that companies will make them, but prices of brand-name drugs are often much lower than U.S. prices.
More far-reaching reforms of oncology practices and reimbursement are necessary if there is no national intervention or federal market regulation. One solution is adopting clinical pathways for which practices are paid disease-management fees that are not based on chemotherapy sales. For instance, one large oncology group has developed care pathways specifying preferred drug combinations and sequences — for example, allowing only a few first-line, mostly generic regimens for patients with non–small-cell lung cancer, as compared with the 16 possible drugs and many more combinations included in National Comprehensive Cancer Network pathways. This approach has been shown to result in equal or better survival, less use of chemotherapy near the end of life, and 35% lower costs than usual care.5 Another solution is to pay physicians salaries, as Kaiser Permanente, Veterans Affairs, and most academic centers do, but that would reduce oncologists' earnings at a time when a 40% workforce shortage is predicted, so the effect must be monitored.
To ensure a predictable supply of generic cancer drugs, manufacturers need reasonable markets and profits, and oncologists need incentives to use generics. Standardized clinical pathways with drug choices based only on effectiveness will enable the prediction of drug needs, practices for effective management of inventory, and planning by manufacturers for adequate production. Such pathways, disease-management fees, and physician salaries would dramatically change oncologic practice, but since drug costs will increase by 4 to 6% this year alone, they are necessary. The current system not only is unsustainable but also puts oncologists in potential ethical conflict with patients, since it hides revenue information that might influence drug choices and thus affects costs and patients' copayments.
The only good news is that the drug shortages may catalyze a shift from a mostly market-based system to one that rewards the provision of high-quality cancer care at an affordable cost.
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
This article (10.1056/NEJMp1109772) was published on October 31, 2011, and updated on November 2, 2011, at NEJM.org.
SOURCE INFORMATION
From the Virginia Commonwealth University Health System, Richmond (M.L.G.); and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore (T.J.S.).

Impact of Medicare Part D on Hospitalization Rates

RESEARCH ARTICLE
The Impact of Medicare Part D on
Hospitalization Rates
Christopher C. Afendulis, Yulei He, Alan M. Zaslavsky, and
Michael E. Chernew
Objective. To determine whether the change in prescription drug insurance coverage
associated with Medicare PartDreduced hospitalization rates for conditions sensitive to
drug adherence.
Data Sources/Study Setting. Hospital discharge data from 2005 to 2007 for 23
states, linked with state-level data on drug coverage.
Study Design. We use a difference-in-difference-in-differences approach, comparing
changes in the probability of hospitalization before and after the introduction of the PartD
benefit in 2006, for individuals aged 65 and older (versus individuals aged 60–64) in states
with low drug coverage in 2005 (versus those in states with high pre-PartDdrug coverage).
Data Collection/Extraction Methods. Hospitalization rates for selected ambulatory
care sensitive conditions in 23 states were computed using data from the Census
and Health Care Utilization Project. Drug coverage rates were computed using data
from several sources.
Principal Findings. For the conditions studied, our point estimates suggest that PartD
reduced the overall rate of hospitalization by 20.5 per 10,000 (4.1 percent), representing
approximately 42,000 admissions, about half of the reduction in admissions over our
study period.
Conclusions. The increase in drug coverage associated with Medicare Part D had
positive effects on the health of elderlyAmericans, which reduced use of nondrug health
care resources.
Key Words. Medicare, prescription drugs, hospitalization
The Medicare Part D program, launched in 2006, increased the share of
Medicare beneficiaries with prescription drug coverage from 59 to 89 percent
(authors’ calculation). This expansion of benefits recognizes that prescription
drugs are an indispensable component of care management, particularly for
chronic disease. The evidence suggests that, even in a narrow time window,
better management of certain conditions with prescription drugs can reduce
the likelihood of adverse events like hospitalizations and the costs associated
with them (Goldman, Joyce, and Zheng 2007; Stuart, Doshi, and Terza 2009).
r Health Research and Educational Trust
DOI: 10.1111/j.1475-6773.2011.01244.x
1022
Health Services Research
Thus, because use of prescription medications is related to the generosity of
coverage (Goldman et al. 2007), we would expect that the increase in drug
coverage resulting from Part D will increase adherence to important medications
and lead to improved health and fewer hospitalizations.1
Evidence from national data supports the first portion of this argument,
that Part D increased prescription drug use (Yin et al. 2008; Schneeweiss et al.
2009). Other research examining the experience of a single insurer suggests
that extra spending on medications was offset by reduced spending on other
medical services (Zhang et al. 2009). Presumably, a reduction in hospitalizations
was a significant component of this offset. The effects could be greatest on
admissions for ambulatory care sensitive conditions (Weissman, Gatsonis, and
Epstein 1992; Bindman et al. 1995).
Examination of hospitalization is also important because it can help
assess the clinical impact of Part D. Specifically, because Part D did not affect
incentives for hospitalization, any changes in hospitalizations related to Part
D-induced changes in drug coverage are likely due to changes in underlying
health status. By assessing the impact of PartDon hospitalizations, we can gain
insight about the effects of this policy change on health more generally.
Existing research has not examined the impact of Part D on hospitalization
(or any markers of health outcomes) directly. In part, this is because
linked data on drug coverage, drug utilization, and outcomes are not available.
We surmount this problem by conducting an area level study. Our analytic
strategy uses the fact that drug coverage was more prevalent in some geographic
areas than in others before Part D. Thus, some states were more
affected by Part D than others. We assess whether the states most affected by
Part D had greater changes in admissions for diagnoses potentially amenable
to drug coverage, compared with states less affected by Part D. We control for
unobserved state trends by examining admission rate changes in the same
states over the same study period for individuals aged 60–64, who for the most
part did not see their drug coverage change with the introduction of Part D.
This study design is analogous to an intention to treat analysis and therefore
addresses the issue of nonrandom selection into Part D plans by examining
market-level effects.
Address correspondence to Christopher C. Afendulis, Ph.D., Department of Health Care Policy,
Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115; e-mail:
afendulis@hcp.med.harvard.edu. Yulei He, Ph.D., Alan M. Zaslavsky, Ph.D., and Michael E.
Chernew, Ph.D., are with the Department of Health Care Policy, Harvard Medical School,
Boston, MA.
Impact of Medicare Part D 1023
METHODS
Data Sources
We use data from several sources. First, we calculate rates of hospitalizations
for our eight conditions using counts of admissions inpatient data and
estimates of the population fromthe census. The data on admissions are taken
from the Agency for Healthcare Research and Quality’s (AHRQ) Health Care
Utilization Project (HCUP). Among the HCUP databases is the Statewide
Inpatient Database (SID), which contains discharge data from nearly
all hospitalizations in several states. We utilize SID data from the 23 states
for which we were able to procure data from AHRQ using simplified request
procedures, and for which records were available for the period 2005–
2007: Arizona, Arkansas, California, Colorado, Florida, Hawaii, Iowa,
Kentucky, Maryland, Michigan, Nebraska, Nevada, New Jersey, New York,
North Carolina, Oregon, Rhode Island, South Carolina, Utah, Vermont,
Washington State, West Virginia, and Wisconsin. Of the approximately 37M
individuals aged 65 and older in 2005, more than half (20 M) resided in
these states.
Using the SID files for these states, we count hospitalizations separately
by state, year, and two age groups: 60–64 and 65-plus. We count the number
of hospitalizations for eight conditions that we expect to be sensitive to drug
adherence: short-term complications of diabetes, chronic obstructive pulmonary
disorder, congestive heart failure (CHF), angina, uncontrolled diabetes,
asthma, stroke, and acute myocardial infarction (AMI). A description of the
ICD-9-CMdiagnosis codes used to identify each hospitalization is available in
an appendix. As a summary measure, we count the number of hospitalizations
for any of these eight conditions.
We compute a denominator for each condition using data from the
United States Census and calculate the condition-specific hospitalization rate.
The denominator is comprised of the entire population for each state-andyear-
specific age group. Because the vast majority of individuals 65-plus are
covered by Medicare, these population estimates are a very close approximation
of the true denominator.
For the same time period, we generate state-level drug coverage estimates
for individuals aged 65 and older. We use data on nine types of drug
coverage among Medicare beneficiaries: stand-alone Medicare Part D plans,
Medicare Advantage Part D plans, beneficiaries dually enrolled in Medicaid,
employer-sponsored retiree coverage (ESR), Federal Employees Health Benefits
Plan and Tricare coverage, Veteran’s Administration coverage, Indian
1024 HSR: Health Services Research 46:4 (August 2011)
Health Service coverage, active workers, and state pharmaceutical assistance
programs (SPAP). In an appendix, we describe how we derive our estimates
for each of these coverage categories.
Wedo not have comparable drug coverage data for individuals aged 60–
64. We make the assumption that any change in the coverage rate for this
age group between 2005 and 2006 was not systematically related to Part Dinduced
state-level changes in coverage for Medicare beneficiaries. Estimates
from the Census Bureau’s Small Area Health Insurance Estimates (which
provide estimates at the state and county level) indicate that among individuals
aged 50–64, no state in our sample experienced a reduction or increase in
health insurance coverage of more than one percentage point between 2005
and 2006 or between 2005 and 2007 (United States Bureau of the Census
2005, 2006, 2007).
Analytic Strategy
For each of our hospitalization measures, we estimate the following regression
model:
PrðHast Þ ¼ L
a þ bagea þ gyeart þ dstatesþ zagea % yeart þ yagea % states þ kyeart % states þ lagea % covst
! "
ð1Þ
where Hast is an indicator variable coded as 1 for individuals from each
age–state–time period cluster who were hospitalized for the condition under
study and 0 otherwise, L( ) is the logit transformation, agea is an indicator
coded as 1 for individuals aged 65-plus and 0 for individuals aged 60–64, yeart
are year indicators for 2005–2007 (one omitted), states is a set of 23 indicator
variables (one omitted) for each of the states in our sample, and covst is the
Medicare drug coverage rate for each state and year. Each cell is weighted
by population size, as calculated from the Census. There are 276 observations
(23 states % 2 age groups % 3 years % 2 outcomes——hospitalization versus no
hospitalization). Weighting by population, the data represent 82,464,740
person-year observations. We are interested in the coefficient for the
age-coverage interaction term, l.
This specification is analogous to a difference-in-difference-indifferences
(DDD) model.2 It assesses whether hospitalization rates for Medicare
beneficiaries changed more in states that had big Part D-induced changes
in drug coverage relative to changes with smaller Part D-induced drug coverage
changes. The indicator variables and associated interactions allow us to
control for time-invariant differences in states and age groups within each
Impact of Medicare Part D 1025
state, as well as general time trends that vary by state and age. For example,
one concern in a state-level analysis of hospitalization rates is the substantial
geographic variation in hospitalizations, which may be due to market-level
provider characteristics rather than underlying differences in disease (Fisher et
al. 2003). The state–age interactions will control for any age group-specific
geographic differences in hospitalization rates across states, and our state–time
period interactions will control for any age group-specific geographic differences
in hospitalization trends.Ultimatelywemeasurewhether each state’s time
trend in hospitalizations forMedicare beneficiaries following the introduction of
Part D, relative to the trend for near-elderly individuals, was systematically
related to the impact of Part D on rates of drug coverage in the state.
A critical difference between the DDD model and the more common
difference-in-differences (DD) model is that our approach allows us to compare
the experiences of a group of patients whose coverage, adherence, and
hospitalization histories would have been influenced by the introduction of
Part D with another group who would not have been affected. A DD model
would exclude this additional comparison, which could lead to incorrect inferences
about the impact of the Part D policy change. For example, if a DD
analysis focused only on 65-plus patients and demonstrated that higher coverage
rates led to reductions in hospitalization rates, it could be the case that
other characteristics of states with large coverage changes were driving the
result. Adding the comparison with younger patients eliminates this concern,
as we would expect these other characteristics to influence hospitalization
rates for patients in both age groups.
To compute the estimated magnitude of the impact of Part D on hospitalizations
using the results from our logistic regression models, we calculate two
predicted hospitalization rates in 2007 for those aged 65-plus: a version using the
2007 average coverage rate, and a counterfactual version using the 2005 average
coverage level. The second of these predicted rates is meant to reflect what the
hospitalization ratewould have been if there had been no change in coverage due
to Part D. We hypothesize that the difference between these two predicted
probabilities of hospitalization will be negative, reflecting the beneficial effects of
drug coverage on adherence and adherence on avoiding hospitalization.
We estimate the standard errors for the model parameters clustering on
observations from the same state (White 1980; Bertrand, Duflo, and Mullainathan
2004). Standard errors for other quantities of interest (e.g., the predicted
hospitalization rates) are calculated using the delta method (Ai and
Norton 2003). We perform all of our analyses using Stata statistical software,
version 9.2 (StataCorp 2007).
1026 HSR: Health Services Research 46:4 (August 2011)
RESULTS
Table 1 presents means for each of our analysis variables by age group and
time period, weighted by population. The unadjusted hospitalization rate for
any of our conditions among the elderly declined by 9.0 percent, from 501.3
per 10,000 in 2005 to 456.2 in 2007. For our nonelderly group, the rate of
decline for the same measure was even larger, 11.8 percent, from 200.8 in
2005 to 177.1 in 2007. Moreover, the hospitalization rates vary significantly
across conditions. For example, in 2005, 188.5 out of every 10,000 individuals
aged 65-plus were hospitalized for CHF, while the rate for uncontrolled
diabetes was 3.6 per 10,000.
Table 1 also describes the increase in drug coverage among the elderly
in our 23-state sample, from 61 percent in 2005 to 88 percent in 2007. While
there was considerable variation in drug coverage before Part D, it shrunk
dramatically after the introduction of the program in 2006 (Figure 1; the
coverage rates for 2006 and 2007 are very similar). In 2006, no state had a
Medicare prescription drug coverage rate lower than 81 percent. Medicare
beneficiaries in every state experienced an increase in coverage, but the magnitude
of the change varied significantly across states. For example, Iowa’s
coverage rate increased 43 percentage points (from 45 to 88 percent), while
Table 1: Condition-Specific Hospitalization Rates (per 10,000) and Coverage
Rate by Year and Age Group
2005 2006 2007
60–64 65-Plus 60–64 65-Plus 60–64 65-Plus
Any condition 200.8 501.3 190.2 478.6 177.1 456.2
Diabetes short term 3.9 3.6 3.8 3.3 3.7 3.2
COPD 38.9 76.4 35.3 70.1 33.2 68.3
CHF 54.4 188.5 51.2 183.4 46.4 171.6
Angina 7.1 9.4 6.2 8.4 5.4 7.4
Uncontrolled diabetes 2.9 3.6 2.9 3.6 2.8 3.7
Asthma 17.5 23.6 15.8 22.0 14.9 20.9
Stroke 37.5 112.6 37.4 110.0 36.1 106.8
AMI 38.7 83.6 37.5 77.8 34.7 74.4
Coverage – 0.61 – 0.88 – 0.88
N 46 46 46 46 46 46
Weighted N 6,965,242 19,851,319 7,179,769 20,160,375 7,773,124 20,534,911
AMI, acute myocardial infarction; CHF, congestive heart failure; COPD, chronic obstructive
pulmonary disorder.
Impact of Medicare Part D 1027
South Carolina’s coverage rate changed by only 19 percentage points (from 68
to 87 percent). South Carolina had a much higher ESR coverage rate at baseline
(35 versus 18 percent in Iowa), a higher dual eligible rate (19 versus 13
percent), and a higher rate of coverage through SPAP (8 percent; Iowa had no
SPAP in 2005). Much of the 24 percentage point difference in the coverage
change between these two states can be explained by Part D coverage: 48
percent of beneficiaries in Iowa enrolled in a Part D plan, compared with 29
percent in South Carolina. Because SPAP enrollees in South Carolina were
moved to Part D, on net only 21 percent of the state’s Medicare beneficiaries
gained prescription drug coverage from the new Medicare benefit.
The regression results in Table 2 indicate that in aggregate, the Part D
coverage change reduced hospitalizations for these conditions by 20.5 per
10,000. This is 4.1 percent of baseline admissions and about half of the total
decline in admissions (Figure 2). This reduction is significant at the 0.01 level,
as is the coefficient estimate on the age–coverage interaction. If we drop our
control group of individuals aged 60–64 and run DD models (regressing
40%
50%
60%
70%
80%
90%
100%
40% 50% 60% 70% 80% 90% 100%
2006 coverage rate .
2005 coverage rate
Figure 1: Prescription Drug Coverage Rates among Medicare Beneficiaries
2005 and 2006
The figure depicts the 2005 prescription drug coverage rate along the x-axis, and the 2006
coverage rate along the y-axis, for elderly individuals in each of the 23 states in our analysis
sample.
1028 HSR: Health Services Research 46:4 (August 2011)
Table 2: Results
Any Condition
Diabetes
Short Term COPD CHF Angina
Uncontrolled
Diabetes Asthma Stroke AMI
Regression coefficients
Age 65-plus 0.9428nnn 0.1728 0.6924nnn 1.3509nnn 0.2231 0.3736nn 0.5324nnn 1.0202nnn 0.6503
&(0.0339) &(0.1506) &(0.0787) &(0.0453) &(0.1377) &(0.1619) &(0.0815) &(0.0523) &(0.0656)
Year52006 &0.0728nnn &0.0122 &0.1461nnn &0.0645nnn &0.0187 0.0687n &0.1557nnn &0.0500nnn &0.0214
&(0.0081) &(0.0372) &(0.0191) &(0.0132) &(0.0376) &(0.0387) &(0.0236) &(0.0139) &(0.0145)
Age 65-plus 2006n 0.0524nnn 0.0581 0.0631n 0.0985nnn 0.0537 0.1419nn 0.1527nnn &0.0293 &0.0326
&(0.0165) &(0.0662) &(0.0372) &(0.027) &(0.0686) &(0.0714) &(0.0341) &(0.0262) &(0.0405)
Year52007 &0.1479nnn &0.0050 &0.2298nnn &0.1603nnn &0.2628nnn 0.2366nnn &0.1888nnn &0.1048nnn &0.0985
&(0.012) &(0.0332) &(0.0244) &(0.0137) &(0.0413) &(0.0435) &(0.0224) &(0.016) &(0.0128)
Age 65-plus 2007n 0.0737nnn 0.0496 0.0985nnn 0.1275nnn 0.0603 0.2088nnn 0.1634nnn &0.0230 0.0014
&(0.0124) &(0.072) &(0.0346) &(0.0224) &(0.0482) &(0.067) &(0.0369) &(0.0229) &(0.0391)
Age 65-plus coveragen &0.1686nnn &0.4292n &0.1899 &0.2434nnn &0.1358 &0.6081nn &0.4671nnn 0.0257 &0.0245
&(0.0573) &(0.2587) &(0.1359) &(0.0806) &(0.2322) &(0.27) &(0.1386) &(0.0903) &(0.1216)
Constant &4.0678nnn &8.0689nnn &5.6614nnn &5.6029nnn &7.8096nnn &8.6911nnn &6.5402nnn &5.5925nnn &5.6124
&(0.0064) &(0.023) &(0.0134) &(0.008) &(0.0232) &(0.028) &(0.014) &(0.0096) &(0.0089)
N 276 276 276 276 276 276 276 276 276
Weighted N 82,464,740 82,464,740 82,464,740 82,464,740 82,464,740 82,464,740 82,464,740 82,464,740 82,464,740
Predicted probabilities
Hospitalization rate per 10,000,
2007 coverage rates
449.6 3.1 65.6 167.7 7.1 3.2 20.1 106.0 73.8
Hospitalization rate per 10,000,
2005 coverage rates
470.1 3.5 69.2 179.2 7.3 3.8 22.8 105.3 74.3
Impact of coverage change &20.5nnn &0.4 &3.5 &11.5nnn &0.3 &0.6nn &2.8nnn 0.7 &0.5
(7.1) (0.3) (2.6) (3.9) (0.5) (0.3) (0.9) (2.6) (2.5)
Relative impact of
coverage change &4.4%nnn &11.2% &5.1% &6.4%nnn &3.7% &15.5%nn &12.1%nnn 0.7% &0.7%
(1.5%) (7.2%) (3.7%) (2.2%) (6.4%) (7.5%) (3.8%) (2.5%) (3.3%)
Notes. Coefficients for the state indicator variables, state–year interactions, and state interactions with age have been omitted. Standard errors in
parentheses. Standard errors allow for clustering at the state level.
The first predicted probability describes the probability of hospitalization for each measure for an individual age 65-plus in 2007 using 2007 coverage
rates and the second describes the probability of hospitalization for the same individual using 2005 coverage rates. For both predictions, all other
variables have their values set to their mean. The relative impact of the coverage change is the absolute difference between the two predicted
probabilities, divided by the probability of hospitalization using the 2005 coverage rates.
np-valueo.10; nnp-valueo.05; nnnp-valueo.01.
AMI, acute myocardial infarction; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disorder.
Impact of Medicare Part D 1029
hospitalization on age group, year and state indicators, and coverage), our
estimate of the aggregate decline in hospitalizations is larger.
The results for specific conditions vary (Table 2). For example, the coefficient
estimates for CHF and asthma are both significant at the 0.01 level,
while the estimates for short-term complications of diabetes and uncontrolled
diabetes are significant at the 0.10 and 0.05 levels, respectively. The estimates
also indicate that Part D reduced hospitalizations for three of the other four
conditions (stroke being the exception), but the effect is not statistically significant
in any of these analyses. Because of multiple comparisons, we would
expect some variation in the condition specific results do to pure randomness.
Thus, we emphasize the findings from our aggregate measure. It is also worth
noting that the percentage reduction in our aggregate measure lies within the
95 percent confidence interval for each of the eight specific conditions, as
depicted in Figure 2.
–35%
–30%
–25%
–20%
–15%
–10%
–5%
0%
5%
10%
15%
Percentage reduction
Figure 2: Percentage Reduction in Hospitalization Rates Due to Coverage
Change for Individuals Aged 65 and Older, 2007
The figure depicts the percentage reduction in the hospitalization rate, for individuals aged 65
and older in 2007. The percentage reduction is calculated as the difference in the predicted
probability of hospitalization in 2007 assuming 2005 and 2007 coverage rates, respectively, divided
by the predicted probability assuming 2005 coverage rates. The box for each condition
represents the predicted percentage reduction, and the error bars represent the 95 percent confidence
interval for each prediction.
1030 HSR: Health Services Research 46:4 (August 2011)
Compared with estimates of what hospitalization rates would have been
in the absence of increased coverage, Part D has had a significant impact on
hospitalization for the conditions studied; among the 20M elderly Medicare
beneficiaries each year represented in our sample, the results suggest that Part
D led to approximately 42,000 fewer hospitalizations each year after its introduction.
If we were to apply this result to the entire 65 and older Medicare
population, it would represent about 77,000 annual hospitalizations.
Results for other covariates were consistent with expectations. Older
individuals have significantly higher hospitalization rates. Many of the state–
time period interactions and age–state interactions are significant in each regression,
which indicates that including state-specific time and age trends was
an appropriate modeling choice (results not shown). Focusing on the aggregate
hospitalization measure, the general trend (holding coverage constant) for
all patients was a reduction in the hospitalization rate over time, although the
rate of decline was lower for older individuals. (Full results from each regression
are available from the authors upon request.)
We perform some additional analyses to assess the sensitivity of the results
to ourmodeling assumptions. First,we assign the 2006 coverage estimates to the
pooled 2006 and 2007 hospitalization data. Second, we pair the 2007 coverage
estimates with the pooled 2006 and 2007 hospitalization data. In both analyses,
the magnitude and statistical significance of our main results are essentially the
same. Third, we run the regression on the aggregate hospitalization measure 23
additional ways, each time dropping a single state from the analysis (along with
the state’s indicator variable and its interactionswith age and year). In all but one
of these regression runs, the sign, magnitude, and statistical significance from
these runs are very similar to the main results. Only the estimate for the regression
that omits Florida leads to a sizable change: the coefficient estimate on
the age–coverage interaction is lower, at &0.108 (compared with &0.169 in
our main analysis), and the p-value of the estimate is .032 (compared with a pvalue
of .003). This reduces our estimates of the absolute and percentage reduction
in admissions due to Part D to about 12.3 per 10,000 and 2.7 percent,
respectively. Yet even this most conservative estimate is statistically significant
and is consistent with our primary conclusion that PartD reduced the aggregate
hospitalization rates for the conditions under study.
To assess the reasonableness of our findings, we compare our results to
those one would expect based on estimates from our data and the literature of
the parameters that determine the impact of Part D on coverage. Specifically,
the impact of Part D on hospitalizations will depend on the extent to which
Part D increased coverage, the extent to which coverage increases use of
Impact of Medicare Part D 1031
prescription drugs, and the extent to which use of prescription drugs reduces
hospitalizations. (The precise relationship between these parameters and the
impact of part D on coverage is derived in an appendix.) We assume that Part
D increased coverage by 28 percentage points (based on our data) and that
drug coverage increased adherence by 21 percentage points (based on a report
by Zhang et al. 2009), and that the average probability of adherence (combining
those with and without coverage) is 0.60.
The most difficult parameter to estimate from the literature is the percent
reduction in the likelihood of hospitalization due to adherence. Estimates in the
literature will be sensitive to the population studied and do not span all of our
conditions. Sokol et al. (2005) estimate that adherence reduces the likelihood of
hospitalization by as much as 58 percent. We consider this our high-end estimate.
Evidence from randomized clinical trials examining the impact of medications
on outcomes report reductions in adverse events as high as 45 percent,
and as low as 19.5 percent, depending on the medications being tested and the
patient population under observation (Beta-Blocker Heart Attack Study Group
1983; The SOLVD Investigators 1991, 1992; Sacks et al. 1996; Long Term
Intervention with Pravastatin in Ischemic Disease [LIPID] Study Group 1998;
TheHeartOutcomes Prevention Evaluation Study Investigators 2000; Brophy,
Joseph, and Rouleau 2001; Heart Protection Study Collaborative Group 2002;
The EURopean Trial on Reduction of Cardiac Events with Perindopril in
Stable Coronary Artery Disease Investigators 2003).
These parameters suggest a range of Part D effect on the percentage
reduction of the hospitalization rate of between 1.2 and 4.6 percent. The range
would be higher if we were to assume Part D increased the generosity of
coverage for those that had some coverage before Part D (as observed in
Zhang et al. 2009). Our baseline point estimate of 4.1 percent is within that
range, albeit at the high end. Our point estimate when Florida is excluded is
2.7 percent and that is well within the range. Thus, we believe it is reasonable
to conclude, as we do, that Part D has a significant effect on hospitalizations.
DISCUSSION
Our analysis demonstrates that increased drug utilization induced by the introduction
of Medicare Part D had measurable clinical benefits. Specifically,
the change in drug coverage due to the passage of Part D——from 61 percent in
2005 to 88 percent in 2006 and 2007 in our analysis sample——led to a reduction
of about 42,000 admissions from any of the conditions we studied, a 4.1
1032 HSR: Health Services Research 46:4 (August 2011)
percent decline from 2005. This estimate reflects the actual change in drug
coverage in our sample due to the introduction of Part D; a larger coverage
increase would have increased the estimate of prevented hospitalizations.
Because our analysis is limited to data from the first 2 years of the Part D
program, which may not be long enough to identify changes in hospitalization
rates, our estimates may understate the impact of Part D. 2006 was a transition
year for the Part D program. A number of individuals who would eventually
become adherent due to their Part D coverage were likely in transition during
2006. Many waited until the May 15, 2006 deadline to enroll in a plan; Part D
enrollment jumped fromroughly 16–22 million between February and June of
2006 (Centers for Medicare and Medicaid Services [CMS] 2009). Anecdotal
evidence suggests that others may have experienced delays utilizing their
policies as plans and pharmacies worked to implement and submit claims
under the new program, and still others may have delayed utilizing their
benefit before discussing the appropriate drug regimen with a physician (Pear
2006). Thus, many Part D enrollees would have gained coverage for only a
part of 2006. If the impact of adherence on health, and hence hospitalizations,
may not be immediate, a longer time window might allow identification of
larger effects. For example, cholesterol-lowering drugs have been documented
to reduce AMI and stroke rates, but over a period of multiple years
(LaRosa et al. 2005). Even with these data limitations, we have demonstrated
that the change in coverage associated with Part D led to reduced hospitalization
rates for four conditions that are plausibly adherence sensitive and for
our aggregate measure.
There are other limitations to our study. First, ourmeasures of prescription
drug coverage are based on surveys, administrative records, and other imperfect
data sources. This introduces imprecision into these coverage estimates, which
may bias our findings toward zero. Moreover, our area-level analysis does not
use individual-level characteristics such as medical history and prescription drug
utilization. This will not bias our results unless these traits were changing systematically
at the area level for the elderly relative to the nonelderly.
Second, our coverage data pertain to the entire Medicare population,
but our analysis is confined to beneficiaries 65 and older. We expect that the
coverage rates for the over 65 population are quite similar to the aggregate
rates because the over 65 population comprises more than 80 percent of the
total Medicare population. Data support this assumption; for example, data
from the 2006 Medical Expenditure Panel Survey indicate that Medicare Part
D coverage was 47 percent among all Medicare beneficiaries, and 47 percent
among those 65 and older. Also, as described above, our statistical analysis
Impact of Medicare Part D 1033
controls for any state-specific or time-period-specific difference in coverage
between these two groups.
Third, a natural question is whether our analysis could have used individual-
level data. While the CMS provides individual-level enrollment and
claims data for beneficiaries enrolled in Part D, it is not possible using these data
to ascertain each beneficiary’s drug coverage status at baseline (i.e., in 2005), for
example, through a former employer or Medicaid (CMS 2010). More important,
nonrandom selection into Part D plans would complicate causal inference in an
individual-level analysis; beneficiaries whowere unobservably less healthymay
have been more likely to enroll in the new program. Our area-level study design
is analogous to an intention to treat analysis and therefore avoids the issue of
nonrandom selection that would arise with an individual-level approach.
Fourth, one might argue that our state-level hospitalization rates should
have been calculated with a different denominator: the number of individuals
in each state with each of the conditions we studied. Under this approach one
would assess the hospitalization risk of these patients alone. However, measuring
disease prevalence is difficult and coding may be sensitive to changes in
treatment patterns. Our analysis assumes that Part D did not alter the underlying
prevalence of disease.
Fifth, the Part D ‘‘donut hole,’’ which requires that beneficiaries cover
the full cost of drugs beyond an initial coverage limit (in 2010, U.S.$2,830 in
total drug expenses) and up a catastrophic coverage threshold (U.S.$6,440 in
expenses), may affect adherence. Because we do not have individual-level
drug claims, we cannot track drug spending throughout the year to assess the
impact of this coverage gap. However, one might expect that individuals who
reach the donut hole would reduce their adherence due to higher out-ofpocket
costs. Recent work has demonstrated that compared with those who
had coverage in the donut hole, beneficiaries who fell into the gap reduced
adherence levels (Fung et al. 2010). Nevertheless, our results should reflect the
total effect of Part D: the initial cost sharing, the lack of cost sharing in the
coverage gap, and the cost sharing beyond the catastrophic coverage limit.
Changes in the 2010 health care reformlaw eliminating the donut hole should
have the effect of further improving adherence and reducing hospitalization
rates for the conditions we have studied here.
A related concern is that we lack data on the generosity of benefits of
different plans. In essence, we are measuring a combined effect of coverage
expansion and generosity changes. For example, if generosity changes were
greater in states with big coverage changes, then our estimates include the
effect of not only gaining coverage but also of generosity increases. While our
1034 HSR: Health Services Research 46:4 (August 2011)
data do not permit us to assess the impact of Part D holding generosity constant,
we do provide reasonable estimates of the total Part D effect.
Finally, our findings do not address the impact of expanded prescription
coverage on total program costs. A more comprehensive evaluation of this
expansion of Medicare benefits would attempt to balance the benefits of the
program (including those we identified) with the overall cost, recognizing that
some of the Part D cost will be offset by reduced hospitalization costs.
Despite these limitations, our analysis confirms the positive clinical
benefits derived from Part D. We estimate roughly 42,000 hospitalizations
were avoided in our sample states fromconditions amenable to drug coverage.
The estimate for the entire Medicare population would be about 77,000. This
is within the range of what one might predict based on estimates of the impact
of Part D on drug use and the impact of drug use on hospitalizations.
Most directly, this finding bolsters the case for Part D and improved
access to prescription drugs. While we did not conduct a full cost-benefit
analysis, the findings illustrate that the benefits of expanded drug coverage
extended beyond better financial protection. This is consistent with other
findings based on analyses of individual insurers, suggesting that those results
likely generalize.
More broadly, these results highlight the importance of recognizing the
connections between different types of care. The design of Medicare tends to
lead analysts to think about the programin silos, such as PartA(largely inpatient
care), Part B (largely physician and outpatient services), and Part D (drugs). Yet
this division is artificial. Beneficiaries require care to be coordinated across
programs. Current efforts to bundle payments and improve care coordination
are a step toward recognizing these connections andmay provide incentives for
providers to manage across the spectrum of care needs. Cross-program effects,
such as those we investigate here, lend support for such efforts.
ACKNOWLEDGMENTS
Joint Acknowledgment/Disclosure Statement: This study was supported by a grant
from the Pharmaceutical Research and Manufacturers of America, and by
funds from the Marshall J. Seidman Program in Health Economics in the Department
of Health Care Policy at Harvard Medical School. The study funders
did not require review of the manuscript before submission for publication.
Disclosures: None.
Disclaimers: None.
Impact of Medicare Part D 1035
NOTES
1. We use the term adherence to describe not only the maintenance of a prescribed
course of drug therapy but also the initiation of drug therapy and compliance with
the fully prescribed dose.
2. While DD and DDD models typically exploit a dichotomous ‘‘treatment’’ (e.g., the
introduction of a new policy by a specific state at a point in time), our model uses a
continuous treatment, the level of drug coverage for the elderly in each state and year.
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SUPPORTING INFORMATION
Additional supporting information may be found in the online version of this
article:
Appendix SA1: Author Matrix.
Appendix SA2: ICD-9-CM Codes for Ambulatory Care Sensitive
Conditions.
Appendix SA3: Derivation of Drug Coverage Estimates.
Appendix SA4: Decomposition of the Impact of Coverage on
Hospitalization.
Please note: Wiley-Blackwell is not responsible for the content or functionality
of any supporting materials supplied by the authors. Any queries
(other than missing material) should be directed to the corresponding author
for the article.
1038 HSR: Health Services Research 46:4 (August 2011)

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