Abstract
Objective
To evaluate Medicare Part D's impact on use of antihypertensive medications among seniors with hypertension.
Data Sources
Medicare-Advantage plan pharmacy data from January 1, 2004 to December 12, 2007 from three groups who before enrolling in Part D had no or limited drug benefits, and a comparison group with stable employer-based coverage.
Study Design
Pre–post intervention with a comparison group design was used to study likelihood of use, daily counts, and substitutions between angiotensin-converting enzyme inhibitors and angiotensin-II receptor blockers (ARBs).
Principal Findings
Antihypertensive use increased most among those without prior drug coverage: likelihood of use increased (odds ratio=1.40, 95 percent confidence interval [CI] 1.25–1.56), and daily counts increased 0.29 (95 percent CI 0.24–0.33). Proportion using ARBs increased from 40 to 46 percent.
Conclusions
Part D was associated with increased antihypertensive use and use of ARBs over less expensive alternatives.
Keywords: Medicare, pharmacy, access/demand/utilization, hypertension
Over half of those aged 60–69 years and three-quarters of those aged 70 and above have hypertension (Agency for Health Care Research and Quality 2007). While many antihypertensive medications effectively treat hypertension, <80 percent of Americans aged 60 years or older with hypertension receive treatment and only 40 percent of those have their blood pressure appropriately controlled (Egan, Zhao, and Axon 2010). Previous studies have shown that restrictions on drug coverage reduce the use of antihypertensive medications and increase the likelihood of emergency room and inpatient visits (Hsu et al. 2006). Furthermore, results from clinical trials have shown antihypertensive therapy reduced major cardiovascular events and mortality (SHEP Cooperative Research Group 1991).
The Medicare drug benefit (Part D), which provides outpatient prescription drug coverage for over 26 million Medicare beneficiaries, took effect January 1, 2006. One of Part D's major goals is to reduce cost-related underuse of medications, thereby increasing appropriate drug use and improving beneficiaries' health. No study has evaluated the effect of Part D on medication use among patients with hypertension.
Medicare Part D could affect not only the overall use of antihypertensives but also the types of medications used. Previous studies have shown that when drug coverage improved, individuals increased overall medication use (Zhang et al. 2009). Increases in out-of-pocket cost were also associated with substitution of cheaper generic medications for more expensive brand names (Huskamp et al. 2003). Hence, as drug coverage improves, patients might be more likely to initiate or switch to more expensive drug subclasses. Two groups of hypertension treatments that may be substitutes are angiotensin-converting enzyme (ACEs) inhibitors and angiotensin-II receptor blockers (ARBs). ACEs and ARBs have no significantly different blood pressure lowering or renal-protective effects (Kunz et al. 2008; Matchar et al. 2008;), yet they differ substantially in cost as ARBs are all brand name without any generic substitutes, whereas ACEs exist widely in generic form at one-eighth of the price (http://drugstore.com prices). While there are differences in the side effect profiles of the two subclasses, examining the substitution between ACEs and ARBs may be the best opportunity among hypertension medications to study whether insurance affects the use of branded versus generic medications that are equally effective but very different in price.
In this paper we address the following questions about the effect of Part D on drug use among beneficiaries age 65 and older with hypertension. (1) Did these beneficiaries increase use of antihypertensive medications overall? and (2) Did they increase use of some subclasses of antihypertensive medications more than others; in particular, did beneficiaries shift to newer, more expensive drugs (i.e., ARBs), and away from older, less expensive drugs (ACEs)? We further explore whether the shifting was due to switching from ACEs to ARBs or higher rates of initiating ARBs over ACEs.
Methods
Setting
We used a pre–post intervention with a comparison group design to evaluate the changes in medication use 2 years before and 2 years after the implementation of Part D in four groups of Medicare beneficiaries enrolled in Medicare-Advantage plans sold by a large insurance company in Pennsylvania. The three intervention groups who were automatically enrolled in the plan's Part D products in 2006 included those who had (1) no previous drug coverage (no coverage), (2) poor previous drug coverage (U.S.$150 quarterly cap of plan payment [U.S.$150 cap]), and (3) drug coverage that is comparable to that offered under Part D (U.S.$350 quarterly cap of plan payment [U.S.$350 cap]). Pre-Part D, the level of drug coverage in the latter two groups depended on members' county of residence (i.e., the insurer only offered either U.S.$150 cap or U.S.$350 cap in one county). After Part D, the three intervention groups faced monthly copayments of U.S.$8 (generic) and U.S.$20 (brand name) per month in the initial coverage period and a coverage gap between U.S.$2,250 (U.S.$2,400 in 2007) and U.S.$5,100 (U.S.$5,451 in 2007) of annual drug spending. Approximately 70 percent of members in the intervention groups had generic coverage in the donut hole. All beneficiaries had 95 percent coverage above the catastrophic limit (U.S.$5,451 in 2007).
The comparison group had generous employer-sponsored drug coverage that depended solely on whether members' former employers offered it. Because few people decline this coverage because it is more generous, bias due to beneficiaries' selecting into an intervention or comparison group is small. The comparison group faced copayments of U.S.$10–20 per monthly prescription and had no coverage gap or catastrophic limits during the entire study period. Its coverage did not change after Part D was implemented.
Data Source and Population
We obtained enrollment, benefits, and pharmacy claims on a 40 percent random sample of individuals enrolled in the plan between January 2003 and December 2007. We identified our study population as individuals who had at least two claims in 2003 with a diagnosis coded for hypertension (ICD-9 401, 402, 403, 404) and were continuously enrolled in the plan between 2004 and 2007, 2 years before and after Part D's implementation. (We also defined a subpopulation based on diagnosis and baseline use of antihypertensives. Because these results were quantitatively similar we only present results on the overall sample.)
Outcome Measures
We examined the proportion of members in each group who ever filled any antihypertensive medications as well as drugs in each subclass, including β-blockers, diuretics, ACEs, ARBs, and calcium channel blockers each year between 2004 and 2007. We also examined average daily counts of any antihypertensive filled each year between 2004 and 2007.
We calculated the proportion using ACE or ARB among those who used either in each year between 2004 and 2007 in each study group. In addition, we examined whether changes in use of ARBs relative to ACEs was due to switching or initiation. Thus, we measured (1) the proportion of individuals on an ACE but not on ARB in 2004 and 2005 who switched to an ARB in 2006 and 2007; and (2) the proportion of patients not on an ACE or ARB in 2003 who initiated an ACE versus ARB in the pre- and post-Part D periods.
Statistical Analyses
We created an indicator variable for each intervention group relative to the comparison group. We created a post-Part D indicator variable, which took the value 1 after January 1, 2006. The “Part D Effect” is measured by the interaction terms between the post-Part D and the three Prior Coverage indicator variables; this captures the changes in outcome before and after Part D in each intervention group relative to those in the comparison group.
Our pre–post intervention with a comparison group design guards against selection bias, which is likely small as discussed previously. We further used propensity score weighting to enhance the comparability between each intervention group and the comparison group in two steps (Hirano and Imbens 2001). First, we estimated the probability of being in each intervention group relative to the comparison group using three logistic regressions, controlling for zip-code level income and race, residence in an urban area, and individual-level variables such as age, sex, and 2004 and 2005 risk scores. Risk scores were calculated using the Risk Grouper software from DxCG to adjust prior-year medical diagnoses and spending. The risk scores are similar to the CMS-HCC weights used to adjust Medicare-Advantage plan payments, with higher scores indicating worse health status and greater expected future medical spending (Pope et al. 2004).
Second, we applied the probability of being in the other group as weights in general estimating equations (GEEs). This essentially assigned a higher weight to those individuals in the comparison group with more similarity to individuals in the intervention group. GEE adjusted for correlations across 4 years of repeated measures within individuals. To test robustness of results, we also used traditional multivariable regression models with adjustments for the same covariates used in the logistic regressions described above.
Results
Background Characteristics of Study Population
Table 1 shows the baseline characteristics of each group. The comparison group was younger, although prospective risk scores were similar across the groups. Members in the U.S.$150-cap group were more likely to live in the suburbs and in zip-code areas with higher proportions of whites. Members in the no-coverage group were more likely to have emergency department visits but had fewer number of outpatient visits per year. Medical spending was similar across groups.
Table 1.
Characteristics of the Study Population in 2005†
| Intervention Groups | Comparison Group | |||
|---|---|---|---|---|
| N=16,002 | No Coverage (N=1,478) | U.S.$150 Cap (N=1,326) | U.S.$350 Cap (N=8,945) | No Cap (N=4,253) |
| Female sex (%) | 56.6 | 64.1 | 63.1 | 53.0* |
| Age (%) | ||||
| 65–74 years | 41.3 | 46.3 | 48.4 | 55.8* |
| 75–84 years | 48.0 | 43.9 | 42.6 | 38.0* |
| ≥85 years | 10.7 | 9.8 | 9.0 | 6.2* |
| Median income | ||||
| Among 65–74 years (U.S.$) | 27,273 ± 156 | 25,736 ± 105 | 28,486 ± 69 | 28,717 ± 100 |
| Among ≥75 years (U.S.$) | 19,925 ± 105 | 19,109 ± 86 | 20,583 ± 46 | 20,855 ± 66 |
| Proportion of whites | 91.6 | 96.0* | 91.6 | 91.7 |
| Proportion of living in urban areas | 75.4 | 57.0* | 79.7 | 80.3 |
| Diagnosed chronic conditions (%) | ||||
| Hyperlipidemia | 59.8 | 64 | 64.9 | 70.0* |
| Diabetes | 27.5 | 27.5 | 28.8 | 30.9* |
| Prospective risk score, mean (SE)‡ | ||||
| 2004 | 0.97 ± 0.020 | 0.94 ± 0.022 | 0.97 ± 0.008 | 0.99 ± 0.013 |
| 2005 | 1.07 ± 0.022 | 1.04 ± 0.025 | 1.05 ± 0.009 | 1.07 ± 0.015 |
| Use of medical services in 2005 | ||||
| Emergency department visit (%) | 31.3* | 25.3 | 28.7 | 26.9 |
| Proportion of hospitalization (%) | 21.5 | 18.3 | 20.5 | 19.3 |
| Outpatient visit (no.) | 26 ± 1* | 27 ± 1* | 28 ± 0 | 30 ± 0 |
| Outpatient cost (U.S.$) | 4,054 ± 174 | 3,836 ± 201 | 4,147 ± 72 | 4,473 ± 113 |
| Nondrug medical cost (U.S.$) | 6,720 ± 303 | 6,404 ± 352 | 6,932 ± 134 | 7,300 ± 218 |
p<.05. If * is indicated for the comparison group, it means the variable is statistically significantly different between each intervention group and the comparison group. If * is indicated for an intervention group, it means the difference between that particular intervention group and the comparison group is statistically significant. We used χ2-tests for categorical variables and one-way analysis of variance (ANOVA) test for continuous variables. Some percentages do not sum up to one because of rounding effects.
These numbers are unweighted raw data. ±, values are means ± SE.
Prospective risk scores were calculated with the use of an algorithm that is described in the text, with higher scores indicating greater expected future medical spending.
Likelihood of Use of Antihypertensive Medications
Table 2 panel A presents the likelihood of use of any antihypertensive and each subclass before and after Part D. Before Part D, members in the no-coverage group were less likely to use any antihypertensive than those in the other three groups (p-value <.05). The likelihood of use of any antihypertensive in the comparison group did not change (88.1 percent pre to 89.1 percent post). Relative to the comparison group, the likelihood of any antihypertensive use did not change in the U.S.$150-cap and U.S.$350-cap groups, but the proportion of individuals with hypertension in the no-coverage group who used at least one antihypertensive medication increased from 59.8 to 69.7 percent (odds ratio [OR]=1.40, 95 percent confidence interval [CI] 1.25–1.56).
Table 2.
The Impact of Medicare Part D on Likelihood of Use among Those with Hypertension Diagnosis in 2003
| 2-Year Part D Effect† | ||||||
|---|---|---|---|---|---|---|
| Unadjusted Likelihood of Use* | Propensity Score Weighting | Multivariate Regression | ||||
| Pre | Post | Odds Ratio | (95% CI) | Odds Ratio | (95% CI) | |
| Panel A: Likelihood of use | ||||||
| Any antihypertensive medications | ||||||
| Comparison | ||||||
| No cap | 88.1 | 89.1 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 59.8 | 69.7 | 1.40 | (1.25,1.56) | 1.43 | (1.27,1.6) |
| U.S.$150 cap | 82.2 | 84.6 | 1.08 | (0.95,1.23) | 1.07 | (0.94,1.23) |
| U.S.$350 cap | 86.1 | 86.9 | 0.97 | (0.89,1.05) | 0.97 | (0.9,1.05) |
| ACE | ||||||
| Comparison | ||||||
| No cap | 29.5 | 28.5 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 16.7 | 20.3 | 1.34 | (1.20,1.49) | 1.33 | (1.2,1.48) |
| U.S.$150 cap | 25.9 | 26.4 | 1.08 | (0.98,1.18) | 1.09 | (0.99,1.19) |
| U.S.$350 cap | 29.1 | 29.1 | 1.05 | (1.00,1.10) | 1.05 | (1,1.11) |
| ARB | ||||||
| Comparison | ||||||
| No cap | 27.7 | 29.8 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 10.9 | 17.3 | 1.53 | (1.35,1.75) | 1.52 | (1.34,1.72) |
| U.S.$150 cap | 19.4 | 22.0 | 1.05 | (0.96,1.15) | 1.06 | (0.96,1.16) |
| U.S.$350 cap | 24.7 | 26.6 | 0.99 | (0.95,1.04) | 1 | (0.95,1.04) |
| CCB | ||||||
| Comparison | ||||||
| No cap | 31.9 | 32.9 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 18.7 | 22.6 | 1.21 | (1.10,1.33) | 1.19 | (1.09,1.31) |
| U.S.$150 cap | 28.4 | 29.7 | 1.02 | (0.94,1.11) | 1.03 | (0.95,1.12) |
| U.S.$350 cap | 29.8 | 31.5 | 1.03 | (0.98,1.08) | 1.03 | (0.98,1.08) |
| β-blockers | ||||||
| Comparison | ||||||
| No cap | 41.5 | 45.5 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 23.6 | 34.3 | 1.44 | (1.30,1.59) | 1.45 | (1.32,1.6) |
| U.S.$150 cap | 34.9 | 41.5 | 1.13 | (1.04,1.22) | 1.14 | (1.05,1.23) |
| U.S.$350 cap | 38.1 | 43.6 | 1.07 | (1.02,1.12) | 1.08 | (1.03,1.13) |
| Diuretics | ||||||
| Comparison | ||||||
| No cap | 37.8 | 39.8 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 26.0 | 33.8 | 1.34 | (1.21,1.47) | 1.38 | (1.25,1.53) |
| U.S.$150 cap | 36.1 | 39.7 | 1.07 | (0.98,1.17) | 1.08 | (0.98,1.18) |
| U.S.$350 cap | 36.4 | 38.4 | 1.00 | (0.95,1.06) | 1.01 | (0.96,1.06) |
| 2-year Part D Effect† | ||||||
|---|---|---|---|---|---|---|
| Unadjusted* | Propensity Score Weighting | Multivariate Regression | ||||
| Pre | Post | Estimate | (95% CI) | Estimate | (95% CI) | |
| Panel B: Average daily counts | ||||||
| Any antihypertensive medications | ||||||
| Comparison | ||||||
| No cap | 1.53 | 1.66 | Reference | Reference | ||
| Intervention groups | ||||||
| No coverage | 0.75 | 1.18 | 0.29 | (0.24,0.33) | 0.30 | (0.26,0.34) |
| U.S.$150 cap | 1.27 | 1.46 | 0.05 | (0.01,0.08) | 0.06 | (0.02,0.09) |
| U.S.$350 cap | 1.38 | 1.51 | 0.00 | (−0.02,0.02) | 0.00 | (−0.02,0.02) |
Pre- and postcomparisons are unadjusted raw numbers.
“2-year Part D Effects” are adjusted difference-in-difference estimates from GEE regression models, which measured changes in likelihood of use 2 years pre- and 2 years post-Part D in each intervention group, relative to the changes in outcomes in the comparison group. One set of results were models with propensity score weighting, and the other set of results were from multivariate regressions without propensity score weighting. In calculating propensity score, the following variables were included in the logistic regression: zip-code level of income, race, residing in the urban areas, and individual-level variables such as age categories, sex, and 2004 and 2005 risk scores. Then an inverse weight was applied for each individual in the analytic GEE models. In the multivariate regression, shown for comparison, the above variables used to calculate propensity score were included in the model as covariates.
ACE, angiotensin-converting enzyme; ARB, angiotensin-II receptor blocker; CCB, calcium channel blocker; CI, confidence interval; GEE, general estimating equations.
Among members in the no-coverage group, the increase in the likelihood of antihypertensive use is largest for ARBs (OR=1.53, 95 percent CI 1.35–1.75), followed by β-blockers (OR=1.44, 95 percent CI 1.30–1.59), and then by ACE (OR=1.34, 95 percent CI 1.20–1.49) and diuretics (OR=1.34, 95 percent CI 1.21–1.47). The likelihood of β-blocker use increased slightly in the U.S.$150-cap and U.S.$350-cap groups as well. Results from multivariate regressions are quantitatively similar.
Average Daily Counts of Antihypertensive Medications
Table 2 panel B shows the average daily counts of antihypertensive medications before and after Part D. After adjusting for propensity score weights and secular trends in the comparison group, the no-coverage group increased the number of daily antihypertensive medications from 0.75 pre-Part D to 1.18 (raw data) post-Part D, an increase of 0.29 pills per day (95 percent CI 0.24–0.33). The number of daily counts increased slightly in the U.S.$150-cap group from 1.27 pre-Part D to 1.46 post-Part D, an increase of 0.05 (95 percent CI 0.01–0.08). No effect was observed in the U.S.$350-cap group. Results from multivariate regressions are quantitatively similar.
Substitution Effects of ACE and ARB
Figure 1 shows the yearly proportion of members using ARB or ACE among those who used either. The proportion of ARB use among patients who used either appears to increase with the generosity of prescription coverage. In the no-coverage group, the proportion of beneficiaries using an ARB increased from 40 percent pre-Part D to 46 percent post-Part D, an increase that was significantly higher than the secular trend observed in the comparison group (p-value <.001).
Figure 1.
Proportion of Members Filling an Angiotensin-Converting Enzyme (ACE) or Angiotensin-II Receptor Blockers (ARB) Each Year among Those Who Filled Either
| Data Table A. Number of Users in Each Group and Every Year Reported in the Figure | |||||||
|---|---|---|---|---|---|---|---|
| Study Sample | Number of Members Filling an ACE, ARB or Either | % of Members Filling an ACE or ARB among Those Who Filled Either | |||||
| Study Groups | Year | N | ACE | ARB | ACE or ARB | ACE | ARB |
| No Coverage | 2004 | 1,478 | 241 | 158 | 399 | 60% | 40% |
| 2005 | 1,478 | 251 | 165 | 416 | 60% | 40% | |
| 2006 | 1,478 | 312 | 261 | 572 | 54% | 46% | |
| 2007 | 1,478 | 289 | 249 | 538 | 54% | 46% | |
| $150 Cap | 2004 | 1,326 | 342 | 255 | 597 | 57% | 43% |
| 2005 | 1,326 | 344 | 260 | 604 | 57% | 43% | |
| 2006 | 1,326 | 354 | 282 | 636 | 56% | 44% | |
| 2007 | 1,326 | 345 | 301 | 646 | 53% | 47% | |
| $350 Cap | 2004 | 8,945 | 2,617 | 2,167 | 4,784 | 55% | 45% |
| 2005 | 8,945 | 2,586 | 2,252 | 4,838 | 53% | 47% | |
| 2006 | 8,945 | 2,641 | 2,404 | 5,045 | 52% | 48% | |
| 2007 | 8,945 | 2,559 | 2,358 | 4,917 | 52% | 48% | |
| No Cap | 2004 | 4,253 | 1,263 | 1,150 | 2,413 | 52% | 48% |
| 2005 | 4,253 | 1,243 | 1,203 | 2,447 | 51% | 49% | |
| 2006 | 4,253 | 1,250 | 1,245 | 2,495 | 50% | 50% | |
| 2007 | 4,253 | 1,173 | 1,291 | 2,464 | 48% | 52% | |
| Data Table B. Reasons Explaining Substitution Effects of ACE and ARB Comparing No-Coverage and No-Cap Groups | |||
|---|---|---|---|
| No Coverage | No Cap | ||
| Switch from ACE or ARB | Among those on ACE but not ARB in 2004–2005 | 283 | 1,252 |
| Switching to ARB in 2006–2007, N | 22 | 87 | |
| % Switching to ARB in 2006–2007 | 8% | 7% | |
| Proportion initiating ARB among those who initiated either ACE or ARB | Among those not on ACE or ARB in 2003 | 1,096 | 1,978 |
| Initiated ACE or ARB in 2004–2005, N | 202 | 414 | |
| % Initiating ARB among those initiating ACE or ARB | 39% | 44% | |
| Initiated ACE or ARB in 2006–2007, N | 245 | 322 | |
| % Initiating ARB among those initiating ACE or ARB | 47% | 47% | |
The increase in the ratio of ARB to ACE use does not appear to be due to switching from ACE to ARB post-Part D but rather to different treatment choices among those initiating treatment (data table B of Figure 1). Among those who initiated either an ACE or ARB, a higher proportion used ARB after gaining drug coverage (39 percent pre versus 47 percent post). This increase in initiating ARBs in the no-coverage group is significantly higher (p-value <.05) than the small increase in trend observed in the comparison group (44 percent pre versus 47 percent post).
Discussion
Medicare beneficiaries with hypertension in our sample who transitioned from no drug coverage to Part D plans had increased odds of using any antihypertensive medication as well as increased counts of antihypertensive medications post-Part D. These findings point to an important public health benefit from Part D and likely translate into improvements in blood pressure control and overall health.
We are not aware of prior studies that have specifically studied the effect of Medicare Part D on prescription medication use by patients with hypertension during the first 2 years of the program. Previous studies found Part D increased overall drug use 6–22 percent and reduced out-of-pocket spending 13–23 percent (Ketcham and Simon 2008; Yin et al. 2008;). The effect is consistently higher among those with no prior prescription coverage; researchers have found that this group increased its overall medication use by 11–37 percent, and its overall pharmacy spending by as much as 74 percent (Schneeweiss et al. 2009; Zhang et al. 2009;).
We find less of an impact of Part D for those with limited prior drug coverage. There were no improvements in the likelihood of using any antihypertensive post-Part D among the sample with prior prescription coverage, and only minimal improvements in annual counts of medications. This lack of improvement may represent a ceiling effect, as the U.S.$150-cap and U.S.$350-cap groups had relatively high usage to begin with.
Our findings also suggest that Part D may encourage the use of more costly agents that, on average, may not improve health outcomes. While Part D is associated with an increased likelihood of patients' without prior coverage using an antihypertensive, it is also associated with these patients being more likely to initiate treatment with more expensive ARBs over ACEs. It is unlikely that these differences can be accounted for by differences among the study groups in rates of ACE-related side effects (the main indication for using ARB over ACE). Because ARBs are available as brand-name drugs only, while ACEs are primarily lower priced generics, these use patterns have important cost implications. This outcome is consistent with the finding of a recent study that noted Part D was associated with less use of generic drugs (Zhang et al. 2008).
There are some potential limitations to our study. First, our results are based on drugs purchased at network pharmacies, but we believe any bias from missing claims is negligible, for several reasons. Our study design, with longitudinal data and a control group, should guard against this bias. Members who filled prescriptions in the network received a 15 percent discount from the plan's negotiated prices that are already lower than retail price. In addition, network pharmacies were numerous (around 58,000 nationwide) and included almost all local pharmacies. It is possible that we might not have observed all prescriptions filled by members through a U.S.$4 program introduced by Wal-Mart in November 2006; however, the use of these programs was limited in 2007, and any decrease in filled hypertension claims post-Part D would, if anything, underestimate the Part D effects. Second, our results might not be generalizable because our study members were continuously enrolled in MA-PD products offered by a single insurer in western Pennsylvania. In addition, our samples were more likely to be white with relatively high median income compared with national averages, and their higher incomes may make them less sensitive to changes in out-of-pocket-costs than other Medicare beneficiaries. Finally, although we used pre/post-design and propensity scores to control for health status differences between comparison groups, we acknowledge the potential for unobserved differences in the health status of our subjects as an explanation for our results. In addition, some of the measures used for deriving the propensity score are measured at the community level and therefore there is the potential for substantial error in deriving a propensity score with those measures.
In conclusion, Part D increased medication use among seniors with hypertension who previously lacked drug coverage. In addition, Part D appears to have increased the use of ARBs more than less expensive alternative medications. If these results are reproducible, a more careful design of drug coverage advocating for use of the most cost-effective drugs may improve the efficiency of the U.S. health care system. This is especially important with the passage of national health care reform (Patient Protection and Affordable Care Act), in which the Part D benefit will become more generous as the coverage gap is eliminated.
Acknowledgments
Joint Acknowledgment/Disclosure Statement: This publication was supported by the National Center for Research Resources, National Institutes of Health, NIH Roadmap for Medical Research (grant no. KL2-RR024154-01), and the University of Pittsburgh's Graduate School of Public Health Computational and Systems Models in Public Health Pilot Program.
During the study period, Dr. Zhang was also supported by NIMH RC1MH088510 and AHRQ R01HS018657.
Conflict of interests: None.
Disclosure: Drs. Zhang and Lave investigated a study evaluating the impact of high-deductible health care on medical spending in part supported by Highmark Inc., which offers Part D products.
Disclaimers: None.
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