Conceived and designed the experiments: JMP WHS HAH RJG SS. Performed the experiments: JMP. Analyzed the data: JMP WHS HAH RJG JNL SS. Contributed reagents/materials/analysis tools: JMP WHS JNL. Wrote the paper: JMP WHS HAH RJG JNL SS.
JMP is a consultant to Buccaneer Computer Systems and Service, Inc, a contractor for the Centers for Medicare and Medicaid Services. Within the past 5 years, JMP's spouse was employed as an engineer by DePuy Orthopaedics, a subsidiary of Johnson & Johnson, and had Johnson & Johnson stock totaling < US$3100 in value. WHS is a consultant to United Healthcare, which has a Part D business, but his consulting is unrelated. WHS has received research funding from CVS Caremark, Aetna, and Express Scripts, which all have Part D business. RJG has worked on grants to the Brigham & Women's Hospital, his employer, from Astra Zeneca and Novartis related to the design, statistical monitoring, and analysis of clinical trials in the setting of cardiovascular drugs. RJG also signed a consulting agreement to give a one-time Grand Rounds talk on comparative effectiveness research methods at Merck. JNL was an employee of CVS Caremark at the time of the study. SS is a paid member of the Scientific Advisory Board of HealthCore and a consultant to World Health Information Science Consultants, LLC. SS is Principal Investigator of the Brigham and Women's Hospital DEcIDE Center on Comparative Effectiveness Research funded by AHRQ and the DEcIDE Methods Center. Within the past 5 years, SS was funded by an investigator-initiated grant from Pfizer, which has ended. Opinions expressed here are only those of the authors and not necessarily those of the agencies.
Jennifer Polinski and colleagues estimated the effect of the "coverage gap" during which US Medicare beneficiaries are fully responsible for drug costs and found that the gap was associated with a doubling in discontinuing essential medications.
Nations are struggling to expand access to essential medications while curbing rising health and drug spending. While the US government's Medicare Part D drug insurance benefit expanded elderly citizens' access to drugs, it also includes a controversial period called the “coverage gap” during which beneficiaries are fully responsible for drug costs. We examined the impact of entering the coverage gap on drug discontinuation, switching to another drug for the same indication, and drug adherence. While increased discontinuation of and adherence to essential medications is a regrettable response, increased switching to less expensive but therapeutically interchangeable medications is a positive response to minimize costs.
We followed 663,850 Medicare beneficiaries enrolled in Part D or retiree drug plans with prescription and health claims in 2006 and/or 2007 to determine who reached the gap spending threshold,
A lack of financial assistance after reaching the gap spending threshold was associated with a doubling in discontinuing essential medications but not switching drugs in 2006 and 2007. Blunt cost-containment features such as the coverage gap have an adverse impact on drug utilization that may conceivably affect health outcomes.
Every year, more effective drugs for more diseases become available. But the availability of so many drugs poses a problem. How can governments provide their citizens with access to essential medications but control drug costs? Many different approaches have been tried, among them the “coverage gap” or “donut hole” approach that the US government has incorporated into its Medicare program. Medicare is the US government's health insurance program for people aged 65 or older and for younger people with specific conditions. Nearly 50 million US citizens are enrolled in Medicare. In 2006, the government introduced a prescription drug insurance benefit called Medicare Part D to help patients pay for their drugs. Until recently, beneficiaries of this scheme had to pay all their drug costs after their drug spending reached an initial threshold in any calendar year ($2,830 in 2010). Beneficiaries remained in this coverage gap (although people on low incomes received subsidies to help them pay for their drugs) until their out-of-pocket spending reached a catastrophic coverage spending threshold ($4,550 in 2010) or a new year started, after which the Part D benefit paid for most drug costs. Importantly, the 2010 US health reforms have mandated a gradual reduction in the amount that Medicare Part D enrollees have to pay for their prescriptions when they reach the coverage gap.
Three to four million Medicare Part D beneficiaries reach the coverage gap every year (nearly 15% of all Part D beneficiaries). Supporters of the coverage gap concept argue that withdrawal of benefits increases beneficiaries' awareness of medication costs and encourages switching to cost-effective therapeutic options. However, critics argue that the coverage gap is likely to lead to decreased drug utilization, increased use of health services, and adverse outcomes. In this study, the researchers examine the impact of entering the coverage gap on drug discontinuation, switching to another drug for the same indication, and drug adherence (whether patients take their prescribed drugs regularly).
The researchers studied 663,850 Medicare beneficiaries enrolled in Part D or in retiree drug plans (which provide coverage under a employer's group health plan after retirement; the retiree drug plans included in this study did not have coverage gaps) who made prescription claims in 2006 and/or 2007. A third of these individuals reached the gap spending threshold. The researchers used detailed statistical analyses to compare the drug discontinuation, switching, and adherence rates of 1,993 beneficiaries who had no financial assistance during the coverage gap (exposed beneficiaries) with those of 9,965 matched beneficiaries who had financial assistance during the coverage gap (unexposed). On average, beneficiaries reached the gap spending threshold 222 days into the year (mid August). In a drug-level analysis, exposed beneficiaries were twice as likely to discontinue a drug and slightly more likely to have reduced drug adherence than unexposed beneficiaries but 40% less likely to switch a drug after reaching the threshold. Similar results were obtained in a beneficiary-level analysis in which discontinuation, switching, and adherence rates were considered in terms of the complete drug regimen of individual beneficiaries.
These findings show that, among the Medicare beneficiaries investigated, a lack of financial assistance to pay for drugs after reaching the coverage gap spending threshold led to a doubling in the rate of drug discontinuation and a slight reduction in drug adherence. Surprisingly, lack of financial assistance resulted in a decrease in drug switching even though the Centers for Medicare and Medicaid Services advise patients to consider switching to generic or low-cost drugs. Importantly, the researchers estimate that, for the whole Medicare population, the lack of financial assistance to pay for drugs could result in an additional 18,000 patients discontinuing one or more prescription drug per year. Although this study did not directly investigate the effect of the coverage gap on patient outcomes, these findings suggest that this and other blunt cost-containment approaches could adversely affect health outcomes through their effects on drug utilization. Thus, insurance strategies that specifically promote the use of drugs with high benefit but low cost might be a better approach for governments seeking to improve the health of their citizens while reining in drug costs.
Please access these websites via the online version of this summary at
The US Department of Health and Human Services
An information sheet from the Kaiser Family Foundation explains the
MedlinePlus provides links to further information about
Internationally, governments are wrestling with the seemingly contradictory goals of expanding citizens' access to essential medications while at the same time controlling rising drug costs. Multiple benefit designs have been proposed and implemented by diverse countries such as Canada, China, Australia, Germany, and the United Kingdom, including reference pricing
After drug spending reaches an initial threshold ($2,830 in 2010) in a calendar year, beneficiaries enter the coverage gap, a period during which they are responsible for 100% of drug costs. Beneficiaries remain in the coverage gap period until out-of-pocket drug spending reaches a catastrophic coverage spending threshold ($4,550 in 2010) at which time cost-sharing is dramatically reduced, or until the benefit resets at the next calendar year
Between 2.9–3.8 million (11%–14%) Medicare Part D beneficiaries reach the coverage gap each year and receive no financial assistance to help pay for drugs during this period
To date, researchers have observed reduced drug utilization and adherence among beneficiaries enrolled in Medicare Advantage plans who reach the coverage gap spending threshold and had no financial assistance to pay for drugs
In this study, we used nationally representative cohorts of Medicare beneficiaries who enrolled in one of 182 stand-alone Part D plans or in retiree plans with drug coverage. We assessed the characteristics of beneficiaries who reached the coverage gap spending threshold and determined their time to reach the threshold. Among those who reached the threshold, we compared rates of drug discontinuation and switching and the odds of reduced drug adherence between those who were 100% responsible for their drug costs during the coverage gap and those who received financial assistance to pay for drugs during this time. We hypothesized that compared to beneficiaries who received financial assistance, beneficiaries who were fully responsible for their drug costs during the coverage gap would be more likely to discontinue medications but less likely to switch from one medication to a second, potentially less costly medication with the same indication for use. We also hypothesized that beneficiaries would be less adherent to their medications if they had no financial assistance during the gap. Our study aimed to provide information about the coverage gap's influence on beneficiaries' drug utilization behaviors and to evaluate the applicability of the coverage gap design to other insurance settings.
The Human Subjects Committee at Brigham and Women's Hospital approved this study. Because the study was a secondary analysis of previously collected data, both written and oral consent requirements were waived. Data use agreements were in place with all data providers.
We studied community-dwelling, fee-for-service Medicare beneficiaries with prescription drug coverage through either a stand-alone Part D plan or a retiree drug plan in 2006 or 2007 that was administered by CVS Caremark, a pharmacy benefits management company that adjudicates approximately 660 million prescriptions per year
We established two cohorts of beneficiaries age 65 or older. Because Part D did not begin until 2006, beneficiaries in the “Early Part D
We used plan enrollment and beneficiaries' out-of-pocket drug spending in the study year to categorize beneficiaries into four groups. Of the three Part D groups, two received subsidies to defray cost-sharing. Full subsidy beneficiaries had incomes ≤$7,500 in 2006 or ≤$7,620 in 2007 and per prescription cost-sharing that did not exceed $5 in 2006 or $5.35 in 2007, even when in the coverage gap. Partial subsidy beneficiaries had higher incomes ($7,501–$11,500 in 2006, $7,620–$11,710 in 2007) and cost-sharing ≤15% for each prescription in both the initial coverage and coverage gap periods. In contrast, the third Part D group, nonsubsidy enrollees, exceeded these income limits and was responsible for 100% of drug costs in the coverage gap. Retirees enrolled in retiree plans, none of which had a coverage gap design or benefit cap, comprised the final group and thus always had financial assistance to pay for drugs. Assignment algorithm details are in
We hypothesized that a beneficiary's plan enrollment and subsequent drug utilization were good predictors of whether he would reach the coverage gap spending threshold; however, baseline year drug use was not available for the Early Part D cohort. To ensure comparable drug data from both cohorts, we limited our cohorts to beneficiaries who reached the threshold ≥60 d after plan enrollment.
In total, 663,850 beneficiaries met inclusion and exclusion criteria. Using beneficiaries' and plans' drug spending in study years 2006 and 2007, we further limited our primary study cohort to the 217,131 (33%) beneficiaries who reached the coverage gap spending threshold in each year (cumulative spending of $2,250 in 2006; $2,400 in 2007).
To assess drug utilization changes after reaching the coverage gap spending threshold, we conducted two prospective open cohort studies (
We used two steps to balance measured covariate distributions in the exposed and unexposed groups. First, we constructed a propensity score (PS) that assessed each beneficiary's propensity to receive financial assistance to pay for drug costs upon reaching the coverage gap spending threshold. PS models included age, gender, race, region of the US, rural/urban residence, median household income, Charlson comorbidity score
Second, we measured additional covariates in the postbaseline-precoverage gap period. Beneficiaries' utilization during this period was likely a function of their health status, their drug plan's features, and their intuition as to whether they would reach the coverage gap spending threshold. In the 6 mo prior to reaching the threshold, we assessed the number of physician visits and hospitalizations, the Charlson comorbidity score, and days to reach the coverage gap spending threshold. In the 2 mo prior, we determined the number of unique drugs used and total drug spending. Follow-up for the adjusted analyses also began after beneficiaries reached the coverage gap spending threshold.
We considered three outcomes, drug discontinuation, switching, and adherence, after a beneficiary reached the coverage gap spending threshold. We included any drug with available days' supply on the exposure date (date beneficiary reached the coverage gap spending threshold) for analysis, and only the first outcome observed on or after cohort entry was considered. In a first set of analyses, the unit of analysis was at the drug level. Drug X was discontinued if >30 d elapsed during the coverage gap when no drug X was available and no further fills of drug X were made during the coverage gap period. Drug X was switched if a beneficiary entered the coverage gap and switched from the generic to the brand version of drug X or vice versa, or stopped filling prescriptions for drug X but filled a new prescription for a drug with the same indication as drug X within 30 d after the days' supply of drug X was exhausted.
For a second set of analyses at the beneficiary level, where a beneficiary might be taking one or more drugs, a beneficiary discontinued drugs if at least one of his available drugs was discontinued as described above. The beneficiary's date of discontinuation was the first date after reaching the coverage gap spending threshold on which there was no days' supply of the discontinued drug +30-d grace period. A beneficiary switched drugs if at least one of his available drugs was switched according to the definition above, with the switching date defined as the date of the first switch after cohort entry. Drug adherence was defined as a PDC ≥80% for all drugs a beneficiary was taking
We focused on drugs used to treat one of five diseases of interest: RA, cardiovascular conditions, diabetes, depression, or dementia, each described in
Among our primary cohort who reached the coverage gap spending threshold, we cross-tabulated beneficiaries' characteristics at baseline by benefit group (full, partial, and nonsubsidy enrollees, retirees) and exposure status. We calculated the average time to reach the threshold among all beneficiaries and by beneficiary group, the proportion of beneficiaries who reached the threshold each month, and their top ten diagnoses.
Among exposed beneficiaries and multivariate PS-matched unexposed beneficiaries and with additional adjustment for postbaseline-precoverage gap covariates, we modeled the hazards of drug discontinuation and drug switching for each drug (drug-level analyses) using Cox proportional hazards models
Among the 121,760 Early Part D and 95,371 Established Part D cohort beneficiaries who reached the coverage gap spending threshold, there were covariate imbalances across beneficiary groups, for example, female gender in the Early Part D cohort (76% of full subsidy versus 68% of partial subsidy, 64% of nonsubsidy enrollees, and 58% of retirees) and white race in 2006 (72% of full subsidy versus 93% of partial subsidy, 96% nonsubsidy enrollees, and 94% of retirees) (
Characteristics | Early Part D Cohort, 2005–2006, |
Established Part D Cohort, 2006–2007, |
||||||
Exposed | Unexposed | Exposed | Unexposed | |||||
Nonsubsidy | Full Subsidy | Partial Subsidy | Retirees | Nonsubsidy | Full Subsidy | Partial Subsidy | Retirees | |
|
1,084 | 19,255 | 1,699 | 99,722 | 909 | 15,120 | 1,751 | 77,951 |
Female gender | 689 (64) | 14,634 (76) | 1,153 (68) | 56,754 (57) | 603 (66) | 11,464 (76) | 1,153 (66) | 43,959 (56) |
Age (y) as of January 1, 2006 | 77±7 | 77±8 | 76±7 | 76±7 | 77±7 | 76±7 | 77±7 | 76±7 |
65–74 | 485 (45) | 8,538 (44) | 793 (47) | 47,478 (48) | 354 (39) | 7,048 (47) | 772 (44) | 36,024 (46) |
75–84 | 433 (40) | 7,406 (38) | 659 (39) | 41,904 (42) | 395 (43) | 5,618 (37) | 679 (39) | 33,179 (43) |
85+ | 166 (15) | 3,311 (17) | 247 (15) | 10,340 (10) | 160 (18) | 2,454 (16) | 300 (17) | 8748 (11) |
Race | ||||||||
White | 1,041 (96) | 13,805 (72) | 1,584 (93) | 93,907 (94) | 878 (97) | 11,049 (73) | 1,619 (92) | 73,908 (95) |
Black | 30 (3) | 3676 (19) | 65 (4) | 4,472 (4) | 20 (2) | 2,655 (18) | 97 (6) | 2,950 (4) |
Other | 13 (1) | 1,774 (9) | 50 (3) | 1,343 (1) | 11 (1) | 1,416 (9) | 35 (2) | 1,093 (1) |
Region | ||||||||
Northeast | 513 (47) | 6,670 (35) | 774 (46) | 18,771 (19) | 399 (44) | 4,976 (33) | 963 (55) | 16,416 (21) |
Central | 210 (19) | 5727 (30) | 341 (20) | 28,783 (29) | 132 (15) | 4,689 (31) | 257 (15) | 22,820 (29) |
South | 273 (25) | 5,800 (30) | 452 (27) | 41,847 (42) | 287 (32) | 4,463 (30) | 443 (25) | 30,644 (39) |
West | 88 (8) | 1,058 (5) | 132 (8) | 10,321 (10) | 91 (10) | 992 (7) | 88 (5) | 8,071 (10) |
Urban residence | 871 (80) | 13,313 (69) | 1,361 (80) | 73,558 (74) | 682 (75) | 10,251 (68) | 1,452 (83) | 57,434 (74) |
Median household income (US$) | 50,708± | 38,848± | 48,724± | 45,583± | 49,558± | 39,432± | 51,759± | 45,377± |
20,978 | 16,077 | 20,527 | 17,981 | 20,527 | 16,073 | 22,634 | 18,252 | |
Total Medicare Parts A, B spending in the baseline year (US$) | 4,606 | 5,844 | 6,000 | 3,452 | 4,465 | 5,704 | 6,882 | 3,565 |
(Median; IQR) | (1,959; 11,035) | (2,190; 15,040) | (2,479; 15,599) | (1,466; 9,286) | (2,012; 13,109) | (2,187; 14,654) | (2,877; 17,209) | (1521; 9,647) |
Charlson comorbidity score | 2±2 | 2±2 | 2±2 | 2±2 | 2±2 | 2±2 | 2±2 | 2±2 |
13±10 | 14±12 | 14±12 | 10±9 | 12±10 | 13±11 | 15±12 | 10±9 | |
0.3±1 | 0.4±1 | 0.4±1 | 0.2±1 | 0.3±1 | 0.4±1 | 0.4±1 | 0.2±1 | |
0.1±1 | 0.1±1 | 0.1±1 | 0.1±1 | 0.1±1 | 0.1±1 | 0.1±1 | 0.1±1 | |
— | — | — | — | 5±1 | 6±3 | 6±3 | 9±4 | |
Out-of-pocket drug spending (median; IQR) | — | — | — | — | 794 | 51 | 946 | 561 |
(445; 1,336) | (12; 81) | (285; 1,645) | (320; 973) | |||||
Plan drug spending (median; IQR) | — | — | — | — | 902 | 2,604 | 1,094 | 3,055 |
(635; 1,160) | (1,714; 3712) | (698; 1,865) | (2101; 4,429) | |||||
Diagnosis of cancer | 223 (21) | 2,470 (13) | 303 (18) | 17,929 (18) | 175 (19) | 1,946 (13) | 342 (20) | 14,153 (18) |
Diagnosis of rheumatoid arthritis | 42 (4) | 814 (4) | 80 (5) | 3,419 (3) | 40 (4) | 671 (4) | 104 (6) | 2,960 (3) |
Diagnosis of cardiovascular condition | 1,014 (94) | 18,089 (94) | 1,589 (94) | 90,452 (91) | 844 (93) | 14,230 (94) | 1,665 (95) | 70,892 (91) |
Diagnosis of depression | 123 (11) | 5,204 (27) | 337 (20) | 10,193 (10) | 98 (11) | 3,861 (26) | 321 (18) | 8,279 (11) |
Diagnosis of diabetes | 436 (40) | 10,729 (56) | 832 (49) | 36,394 (37) | 349 (38) | 8,501 (56) | 859 (49) | 29,736 (38) |
Diagnosis of dementia | 105 (10) | 4,838 (25) | 337 (20) | 7,874 (8) | 93 (10) | 3,504 (23) | 343 (20) | 6,293 (8) |
IQR, interquartile range; SD, standard deviation.
The top inpatient or outpatient diagnoses among beneficiaries who reached the coverage gap spending threshold in each 30-d period were remarkably consistent: anemia, chest pain, coronary atherosclerosis, uncontrolled diabetes, hypertension, hyperlipidemia, hypercholesterolemia, musculoskeletal pain, shortness of breath, and other malaise and fatigue (unpublished data).
In both 2006 and 2007, retirees reached the coverage gap spending threshold most quickly (
(B) Proportion of beneficiaries who reach the coverage gap spending threshold in each month in 2007, by beneficiary group.
After PS matching, the measured covariate distributions were largely balanced between exposed and unexposed beneficiaries, with few residual differences (
Characteristics | Early Part D Cohort, 2006 |
Established Part D Cohort, 2007 |
||||
Exposed (No Financial Assistance) |
Unexposed (Receive Financial Assistance) |
Delta | Exposed (No Financial Assistance) |
Unexposed (Receive Financial Assistance) |
Delta | |
|
||||||
Female gender | 689 (64) | 3,439 (63) | −1% | 603 (66) | 2,996 (66) | 0% |
Age (y) as of January 1 of study year | ||||||
65–74 | 485 (45) | 2,312 (43) | −2% | 354 (39) | 1,753 (39) | 0% |
75–84 | 433 (40) | 2,253 (42) | +2% | 395 (43) | 1,984 (44) | +1% |
85+ | 166 (15) | 855 (16) | +1% | 160 (18) | 808 (18) | 0% |
Race | ||||||
White | 1,041 (96) | 5,198 (96) | 0% | 878 (97) | 4,401 (97) | 0% |
Black | 30 (3) | 156 (3) | 0% | 20 (2) | 100 (2) | 0% |
Other | 13 (1) | 66 (1) | 0% | 11 (1) | 44 (1) | 0% |
Region | ||||||
Northeast | 513 (47) | 2,526 (47) | 0% | 399 (44) | 1,998 (44) | 0% |
Midwest | 210 (19) | 1,085 (20) | +1% | 132 (15) | 723 (16) | +1% |
South | 273 (25) | 1,415 (26) | +1% | 287 (32) | 1,373 (30) | −2% |
West | 88 (8) | 394 (7) | −1% | 91 (10) | 451 (10) | 0% |
Charlson comorbidity score | 2±2 | 2±2 | 0 points | 2±2 | 2±2 | 0 points |
13±10 | 13±12 | 0 visits | 12±10 | 12±11 | 0 visits | |
0.3±1 | 0.3±1 | 0 hospitalizations | 0.3±1 | 0.3±1 | 0 hospitalizations | |
0.1±1 | 0.1±1 | 0 infusions | 0.1±1 | 0.1±1 | 0 infusions | |
Diagnosis of cancer | 223 (21) | 1,111 (21) | 0% | 175 (19) | 838 (18) | −1% |
Diagnosis of rheumatoid arthritis | 42 (4) | 205 (4) | 0% | 40 (4) | 235 (5) | +1% |
Diagnosis of cardiovascular condition | 1,014 (94) | 5,062 (93) | −1% | 844 (93) | 4,253 (94) | +1% |
Diagnosis of depression | 123 (11) | 570 (11) | 0% | 98 (11) | 468 (10) | −1% |
Diagnosis of diabetes | 436 (40) | 2,181 (40) | 0% | 349 (38) | 1,750 (39) | +1% |
Diagnosis of dementia | 105 (10) | 527 (10) | 0% | 93 (10) | 457 (10) | 0% |
SD, standard deviation.
In drug-level PS-matched analyses additionally adjusted for postbaseline–precoverage gap covariates, exposed beneficiaries were 2.00 (95% confidence interval [CI] 1.64–2.43) times more likely to discontinue a drug after reaching the coverage gap spending threshold than were unexposed beneficiaries (pooled cohort analyses,
Drug Changes |
|
|||||
Early Part DCohort, 2006 | Established Part D Cohort, 2007 | Pooled Cohorts | ||||
Exposed (2,336) |
Unexposed (15,521) |
Exposed (1,841) |
Unexposed (13,037) |
Exposed (4,177) |
Unexposed (28,558) |
|
|
|
|
|
|||
Discontinue a cardiovascular drug | 1.94 (1.47–2.58) | 2.20 (1.63–2.97) | 2.06 (1.68–2.53) | |||
Discontinue a branded cardiovascular drug | 1.81 (1.18–2.77) | 4.48 (2.82–7.13) | 2.63 (1.93–3.58) | |||
Discontinue a generic cardiovascular drug | 2.02 (1.44–2.85) | 1.51 (1.04–2.20) | 1.79 (1.38–2.32) | |||
Discontinue an oral hypoglycemic drug | 0.60 (0.17–2.08) | 4.51 (1.97–10.35) | 1.86 (0.95–3.62) | |||
|
|
|
|
|||
Switch a cardiovascular drug | 0.69 (0.47–1.02) | 0.40 (0.23–0.70) | 0.57 (0.41–0.79) | |||
Switch from a generic cardiovascular drug to a branded cardiovascular drug | 0.90 (0.36–2.23) | 0.38 (0.05–2.91) | 0.72 (0.31–1.63) | |||
Switch from a branded cardiovascular drug to a generic cardiovascular drug | 0.50 (0.23–1.08) | 0.25 (0.06–1.05) | 0.43 (0.22–0.84) | |||
Switch an oral hypoglycemic drug | 1.08 (0.46–2.54) | 0.32 (0.10–1.04) | 0.59 (0.30–1.15) | |||
|
|
|
||||
Reduced adherence to a cardiovascular drug | 1.01 (0.87–1.17) | 1.11 (0.94–1.30) | 1.05 (0.94–1.17) | |||
Reduced adherence to an oral hypoglycemic drug | 1.01 (0.68–1.50) | 1.13 (0.72–1.78) | 1.05 (0.78–1.42) |
Covariate-adjusted hazards of changes in drug discontinuation, switching, and covariate-adjusted odds of reduced drug adherence after reaching the coverage gap spending threshold among propensity score matched beneficiaries. Adjusted for the number of physician visits and hospitalizations, drugs used, drug spending, and Charlson comorbidity score in the postbaseline, predoughnut hole period after propensity score matching for baseline characteristics, which included: age, gender, race, region of the US, rural/urban residence, median household income, Charlson comorbidity score, number of office-based drug infusions, physician visits and hospitalizations, Medicare Parts A and B spending, and diagnosis of cancer, RA, cardiovascular conditions (atrial or ventricular fibrillation, hypertension, hyperlipidemia, hypercholesterolemia, myocardial infarction, angina, atherosclerosis, or congestive heart failure), depression, dementia, and/or diabetes.
Reduced adherence is defined as PDC <80%.
Although they discontinued drugs more often, exposed beneficiaries were less likely to switch a drug after reaching the coverage gap spending threshold than were unexposed beneficiaries, HR = 0.60 (0.46–0.78). This decreased hazard of switching was consistent for cardiovascular drugs, HR = 0.57 (0.41–0.79) but inconclusive for the oral hypoglycemic drugs, HR = 0.59 (0.30–1.15). Exposed beneficiaries were 57% less likely to switch from a branded cardiovascular drug to a generic cardiovascular drug (0.22–0.84) than were unexposed beneficiaries. In the sensitivity analysis that accounted for the competing risk of drug discontinuation, exposed beneficiaries were also less likely to switch a drug after reaching the threshold than were unexposed beneficiaries, risk ratio = 0.51 (unpublished data). Exposed beneficiaries showed increased odds of nonadherence to a drug after reaching the coverage gap as compared to unexposed beneficiaries, OR = 1.07 (0.98–1.18), but these results were not significant. Sensitivity analyses with 15- and 45-d grace periods did not change discontinuation or switching results.
In beneficiary-level analyses (
Drug Changes | Early Part DCohort, 2006 | Established Part D Cohort, 2007 | Pooled Cohorts | |||
Exposed (897) |
Unexposed (4,769) |
Exposed (721) |
Unexposed (3,994) |
Exposed (1,618) |
Unexposed (8,763) |
|
|
||||||
Discontinue ≥1 drug | 1.63 (1.20–2.22) | 1.79 (1.27–2.53) | 1.72 (1.36–2.16) | |||
Switch ≥1 drug | 0.74 (0.51–1.07) | 0.40 (0.22–0.74) | 0.60 (0.44–0.83) | |||
|
||||||
Reduced adherence: adherence <80% for at least one drug | 1.16 (0.99–1.35) | 1.21 (1.02–1.44) | 1.18 (1.05–1.32) |
Covariate-adjusted hazards of changes in drug discontinuation, switching, and covariate-adjusted odds of reduced drug adherence after reaching the coverage gap spending threshold among propensity score matched beneficiaries. Adjusted for the number of physician visits and hospitalizations, drugs used, drug spending, and Charlson comorbidity score in the postbaseline, predoughnut hole period after PS matching for baseline characteristics, which included: age, gender, race, region of the US, rural/urban residence, median household income, Charlson comorbidity score, number of office-based drug infusions, physician visits and hospitalizations, Medicare Parts A and B spending, and diagnosis of cancer, RA, cardiovascular conditions (atrial or ventricular fibrillation, hypertension, hyperlipidemia, hypercholesterolemia, myocardial infarction, angina, atherosclerosis, or congestive heart failure), depression, dementia, and/or diabetes.
In this paper we have shown that one-third of Medicare beneficiaries reached the coverage gap spending threshold in an average of 7 mo after enrollment. Beneficiaries who received no financial assistance to help pay drug costs after reaching the threshold were two times more likely to discontinue a drug but were 40% less likely to switch a drug compared to beneficiaries who did receive financial assistance. After accounting for a beneficiary's complete drug regimen, beneficiaries who received no financial assistance were 18% more likely to reduce their drug adherence. These surprising findings mean that when faced with the responsibility of paying 100% of their drug costs, beneficiaries discontinued therapy frequently or reduced adherence but were less likely to switch to less expensive or generic drugs. Among the cardiovascular drugs, there was a 2.6-fold increased likelihood of discontinuing a branded cardiovascular drug and a 1.8-fold increased likelihood of discontinuing a generic cardiovascular drug but no effect modification by brand/generic status. These results strongly suggest that increased discontinuation rates among the exposed were not driven by drug price alone.
Recent trends in drug insurance design have focused on making consumers more sensitive to drug costs. Our results demonstrate that while a blunt cost-sharing mechanism like the coverage gap does raise consumer sensitivity, it produces surprising consequences. Instead of incentivizing beneficiaries to switch to lower-priced or generic drugs, entry into the coverage gap resulted in an abrupt discontinuation of or reduced adherence to drugs among elderly Medicare beneficiaries. These results echo those of other studies that demonstrated that blunt measures had adverse effects on drug utilization and adherence
An alternative strategy that may help beneficiaries forestall entry into the coverage gap is the initial prescription of generic or preferred medications, which has been associated with lower costs and better adherence over time
Our study has several strengths that enhance the validity of findings. Unlike previous studies
To assess whether any potential interdependence between the discontinuation and switching outcomes was indeed responsible for the opposite results of increased discontinuation but decreased switching among the exposed compared to the unexposed, we conducted a competing risks analysis. The competing risks analysis confirmed our findings. Based on each outcome's defined period of follow-up, this is not surprising. Study follow-up for the discontinuation outcome began 30 d after reaching the coverage gap spending threshold in order to allow for each drug's days supply to run out, thus avoiding immortal person-time bias
In examining drug discontinuations and switches, other modeling approaches, such as a multistate model, in which beneficiaries could switch among the outcomes over time, are possible
The adverse clinical consequences of stopping or reducing adherence to essential medications can be both severe and costly. Our results indicate that beneficiaries faced with increased out-of-pocket cost burdens during the Part D coverage gap are twice as likely to discontinue and more likely to reduce adherence to their medications but not to switch medications. At the population level, an estimated 18,000 additional patients discontinued ≥one medication because of an absence of financial assistance in the coverage gap period. Given the potential adverse health consequences of such discontinuations, changes to the coverage gap's structure are needed. The 2010 US Patient Protection and Affordable Care Act's Part D provisions will eliminate the coverage gap period incrementally by 2020, but beneficiaries may still be at risk of decreased drug utilization and adverse clinical consequences during that time. In contrast to blunt cost-sharing approaches such as the coverage gap feature, more nuanced, clinically informed insurance strategies that specifically promote the use of drugs with high benefit and low cost may hold the most promise for governments and insurers seeking to improve the health of their citizens while reigning in drug costs.
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The authors wish to thank Joyce Lii for exceptional programming assistance.
confidence interval
proportion of days covered
propensity score
rheumatoid arthritis