David Hutchins

Trouble Getting Started: Predictors of Primary Medication Nonadherence

BACKGROUND Patient nonadherence to prescribed medication is common and limits the effectiveness of treatment for many conditions. Most adherence studies evaluate behavior only among patients who have filled a first prescription. The advent of electronic prescribing (e-prescribing) systems provides the opportunity to track initial prescriptions and identify nonadherence that may have previously been undetected. METHODS We analyzed e-prescribing data and filled claims for all patients with CVS Caremark (Woonsocket, RI) drug coverage who received e-prescriptions from the iScribe e-prescribing system in calendar 2008. We matched e-prescriptions with filled claims by using data on the drug name, date of e-prescription, and date of filled claims, allowing up to 180 days for patients to fill e-prescriptions. We evaluated the rate of primary nonadherence to newly prescribed medications across multiple characteristics of patients, prescribers, and prescriptions and developed multivariable models to identify predictors of nonadherence. RESULTS We identified 423,616 e-prescriptions for new medications, with 3634 prescribers and 280,081 patients. The primary nonadherence rate was 24.0%. Several factors were associated with nonadherence to e-prescriptions, including nonformulary status of medications (odds ratio [OR] 1.31 compared with preferred medications; 95% confidence interval [CI], 1.26-1.36; P<.001) and residence in a low-income ZIP code (OR 1.23 compared with high-income ZIP code; 95% CI, 1.17-1.30; P<.001) Nonadherence occurred less often when e-prescriptions were transmitted directly to the pharmacy rather than printed to give to patients (OR 0.54; 95% CI, 0.52-0.57; P<.001). CONCLUSION 24% of e-prescriptions for new medications were not filled. Our results suggest that interventions to address economic barriers and increase electronic integration in the healthcare system may be promising approaches to improve medication adherence.

Comparative Effectiveness of Generic and Brand-Name Statins on Patient Outcomes: A Cohort Study

Context Some patients do not adhere to their prescribed statins and thus do not fully benefit from the decreases in blood lipid levels that these medications provide. Contribution This study found that, compared with those who initiated a brand-name statin, patients who initiated a generic statin had better adherence and fewer occurrences of a composite outcome that included death from any cause plus hospitalization for an acute coronary syndrome or stroke. Caution All patients were Medicare beneficiaries aged 65 years or older with prescription drug coverage. Implication The lower cost of generic statins allowed patients to adhere to the medication better. The Editors Statins are the most frequently prescribed drugs in the United States (1) and are effective in reducing low-density lipoprotein (LDL) cholesterol levels and cardiovascular events (24). Randomized, controlled trials have found that statins reduce the relative risk for major vascular events by 21% for each 1.0-mmol/L (39-mg/dL) reduction in LDL cholesterol level in patients at low risk for vascular disease (3). Patients assigned to statin therapy in trials tended to achieve reductions in LDL cholesterol level of 1.8 mmol/L (70 mg/dL) with doses used regularly in practice (2). However, a large body of evidence suggests that, in routine practice, patients do not fully adhere to statins and therefore may not receive their full benefit (5, 6). Approximately half of patients in ambulatory care settings discontinue statin therapy within 1 year of initiation (68). Medication nonadherence is a complex multifactorial process (9). Among its many determinants, drug cost may be one of the most easily modifiable (10). Reducing patient spending for prescription drugs can improve adherence (11, 12) and, in some cases, clinical outcomes (13). Generic drugs have been shown in small, short, randomized trials(14, 15) to be clinically equivalent to their brand-name counterparts, as required for approval by the U.S. Food and Drug Administration (FDA). They are usually less expensive than brand-name products and have been associated with better adherence (12). However, no study has investigated whether use of generic versus brand-name statins also leads to improved health outcomes (16). We sought to determine whether patients in a large cohort of Medicare beneficiaries were more adherent to therapy after initiating a generic statin versus a brand-name statin and whether this resulted in differences in health outcomes. Methods The study was designed by the authors and approved by the Institutional Review Board at Brigham and Womens Hospital. Study Cohort The study cohort comprised Medicare beneficiaries (aged 65 years) who had prescription drug coverage through either a stand-alone Medicare Part D plan or a retiree drug plan administered by CVS Caremark, a large national pharmacy benefits manager. For each patient, we linked claims for filled prescriptions to diagnostic, health care utilization, and demographic data from Medicare Parts A and B files and enrollment files. The cohort included patients who initiated a statin (lovastatin, pravastatin, or simvastatin) between 2006 and 2008 and had continuous Medicare and CVS Caremark eligibility in the 6 months before initiation. We restricted the cohort to patients initiating these drugs because they were the only statins for which generic versions were available in the United States during the study. Initiation was defined as a new (index) prescription for a study drug with no prescription for any single statin or statin combination product in the preceding 180 days. To maximize the generalizability of this comparative effectiveness study, we did not impose any other exclusion criteria. We classified patients as exposed to a generic or brand-name statin on the basis of the National Drug Code associated with the index prescription claim. We used the FDAs National Drug Code Directory (17) to determine the manufacturer of each drug and the FDAs Approved Drug Products with Therapeutic Equivalence Evaluations publication (18) to determine whether each manufacturers products were approved via a new drug application (brand-name) or abbreviated new drug application (generic). Outcomes and Follow-up The primary outcomes were adherence to the index statin and a composite cardiovascular outcome. Adherence was measured as the proportion of days covered (PDC) by the index statin up to 1 year after the index prescription date. The PDC is calculated by dividing the number of days of medication supplied by the number of days in a given interval (19). For each patient, the denominator interval began on the index date and ended at death, hospitalization, prescription for any other lipid-lowering drug (for example, a different statin or another lipid-lowering agent, such as a fibrate or bile acid sequestrant, although switches between brand-name and generic versions of the index statin were allowed), the end of the study (31 December 2008), or 365 days after the index prescription date, whichever occurred first. The numerator was the sum of the number of days in the interval for which medication was available based on the days supplied by each prescription. The primary clinical outcome comprised hospitalization for an acute coronary syndrome or stroke and all-cause mortality. We also examined each of these outcomes separately. We used a validated claims-based definition for each outcome, with positive predictive values ranging from 86% to 96% (2022). In the primary analysis, we followed patients from the day after index drug initiation until an occurrence of an event of interest, the end of the study (31 December 2008), or 365 days after initiation, whichever came first. Covariates We measured potential confounders in the 180-day baseline period preceding each patients index date. Demographic variables included age, sex, and race. Health service utilization variables included the number of unique drugs dispensed, number of hospitalizations, number of cardiovascular diagnoses, number of days in the hospital, number of physician office visits, and number of physician office visits with cardiovascular diagnoses. In addition to a comorbidity score that captured patients general health status (23), we determined whether patients had health care encounters with diagnoses for specific cardiovascular conditions (such as atrial fibrillation, congestive heart failure, or peripheral vascular disease) and other disorders (such as chronic obstructive pulmonary disease, diabetes, musculoskeletal conditions, and endocrine disease). Furthermore, we determined whether patients initiated statin treatment for primary or secondary prevention, with the latter defined as having been hospitalized for an acute coronary syndrome in the baseline period. We also measured use of preventive services, including screening mammography and vaccinations, to account for healthy-user effects (24, 25) and ascertained proxies of frailty, such as use of supplemental oxygen, to account for the propensity to stop preventive medications in patients who are very ill (26). Finally, we geocoded patients street addresses and linked them to U.S. census data at the block group level, which is the lowest level for which data are publicly available. We identified the unemployment rate and the median household income in each patients census block group as proxies for socioeconomic status (SES). Statistical Analysis We used propensity score matching (27) to mitigate confounding due to different characteristics between the brand-name and generic groups. The propensity score, which was estimated with a logistic regression model, was defined as a patients probability of receiving a generic statin versus a brand-name statin and was conditional on measured baseline covariates. We matched generic and brand-name drug recipients by using a nearest-neighbor algorithm and within calipers of 0.05 units on the propensity score scale in the primary analysis. Because the cohort included many more generic than brand-name drug recipients, we matched each patient in the brand-name group to as many patients as possible in the generic group with similar propensity scores within the specified caliper. We matched brand-name drug recipients only to recipients of the generic version of the same product (for example, brand-name and generic simvastatin) to compare patients who had initiated molecularly identical drugs. This ensured that differences in outcome rates between treatment groups could be attributed to the generic versus brand-name status rather than to differences among the 3 statins. To assess the performance of the propensity score matching process, we evaluated balance in each baseline covariate and overlap in propensity score distributions between treatment groups before and after matching. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs. To account for the variable ratio matching, the Cox models were stratified by matching set. We also estimated rate differences. We performed several prespecified secondary, sensitivity, and subgroup analyses to assess the validity of our study assumptions. We altered our primary analysis by shortening (to 90 days) and lengthening (to 720 days) the maximum follow-up time, excluding events that occurred within 30 and 60 days after index drug initiation, and performing 1:1 fixed-ratio matching on the propensity score. Furthermore, outcome event rates were compared between recipients of the generic and brand-name versions of each drug separately. The primary analysis was also repeated separately for primary prevention and secondary prevention patients. We conducted an on treatment analysis in which we censored patients when they discontinued statin therapy, defined as a gap of more than 30 days without filling a statin prescription beyond the number of days supplied by the last prescription or switching to another lipid-lowering

Comparative Effectiveness of Generic and Brand-Name Statins on Patient Outcomes

Context Some patients do not adhere to their prescribed statins and thus do not fully benefit from the decreases in blood lipid levels that these medications provide. Contribution This study found that, compared with those who initiated a brand-name statin, patients who initiated a generic statin had better adherence and fewer occurrences of a composite outcome that included death from any cause plus hospitalization for an acute coronary syndrome or stroke. Caution All patients were Medicare beneficiaries aged 65 years or older with prescription drug coverage. Implication The lower cost of generic statins allowed patients to adhere to the medication better. The Editors Statins are the most frequently prescribed drugs in the United States (1) and are effective in reducing low-density lipoprotein (LDL) cholesterol levels and cardiovascular events (24). Randomized, controlled trials have found that statins reduce the relative risk for major vascular events by 21% for each 1.0-mmol/L (39-mg/dL) reduction in LDL cholesterol level in patients at low risk for vascular disease (3). Patients assigned to statin therapy in trials tended to achieve reductions in LDL cholesterol level of 1.8 mmol/L (70 mg/dL) with doses used regularly in practice (2). However, a large body of evidence suggests that, in routine practice, patients do not fully adhere to statins and therefore may not receive their full benefit (5, 6). Approximately half of patients in ambulatory care settings discontinue statin therapy within 1 year of initiation (68). Medication nonadherence is a complex multifactorial process (9). Among its many determinants, drug cost may be one of the most easily modifiable (10). Reducing patient spending for prescription drugs can improve adherence (11, 12) and, in some cases, clinical outcomes (13). Generic drugs have been shown in small, short, randomized trials(14, 15) to be clinically equivalent to their brand-name counterparts, as required for approval by the U.S. Food and Drug Administration (FDA). They are usually less expensive than brand-name products and have been associated with better adherence (12). However, no study has investigated whether use of generic versus brand-name statins also leads to improved health outcomes (16). We sought to determine whether patients in a large cohort of Medicare beneficiaries were more adherent to therapy after initiating a generic statin versus a brand-name statin and whether this resulted in differences in health outcomes. Methods The study was designed by the authors and approved by the Institutional Review Board at Brigham and Womens Hospital. Study Cohort The study cohort comprised Medicare beneficiaries (aged 65 years) who had prescription drug coverage through either a stand-alone Medicare Part D plan or a retiree drug plan administered by CVS Caremark, a large national pharmacy benefits manager. For each patient, we linked claims for filled prescriptions to diagnostic, health care utilization, and demographic data from Medicare Parts A and B files and enrollment files. The cohort included patients who initiated a statin (lovastatin, pravastatin, or simvastatin) between 2006 and 2008 and had continuous Medicare and CVS Caremark eligibility in the 6 months before initiation. We restricted the cohort to patients initiating these drugs because they were the only statins for which generic versions were available in the United States during the study. Initiation was defined as a new (index) prescription for a study drug with no prescription for any single statin or statin combination product in the preceding 180 days. To maximize the generalizability of this comparative effectiveness study, we did not impose any other exclusion criteria. We classified patients as exposed to a generic or brand-name statin on the basis of the National Drug Code associated with the index prescription claim. We used the FDAs National Drug Code Directory (17) to determine the manufacturer of each drug and the FDAs Approved Drug Products with Therapeutic Equivalence Evaluations publication (18) to determine whether each manufacturers products were approved via a new drug application (brand-name) or abbreviated new drug application (generic). Outcomes and Follow-up The primary outcomes were adherence to the index statin and a composite cardiovascular outcome. Adherence was measured as the proportion of days covered (PDC) by the index statin up to 1 year after the index prescription date. The PDC is calculated by dividing the number of days of medication supplied by the number of days in a given interval (19). For each patient, the denominator interval began on the index date and ended at death, hospitalization, prescription for any other lipid-lowering drug (for example, a different statin or another lipid-lowering agent, such as a fibrate or bile acid sequestrant, although switches between brand-name and generic versions of the index statin were allowed), the end of the study (31 December 2008), or 365 days after the index prescription date, whichever occurred first. The numerator was the sum of the number of days in the interval for which medication was available based on the days supplied by each prescription. The primary clinical outcome comprised hospitalization for an acute coronary syndrome or stroke and all-cause mortality. We also examined each of these outcomes separately. We used a validated claims-based definition for each outcome, with positive predictive values ranging from 86% to 96% (2022). In the primary analysis, we followed patients from the day after index drug initiation until an occurrence of an event of interest, the end of the study (31 December 2008), or 365 days after initiation, whichever came first. Covariates We measured potential confounders in the 180-day baseline period preceding each patients index date. Demographic variables included age, sex, and race. Health service utilization variables included the number of unique drugs dispensed, number of hospitalizations, number of cardiovascular diagnoses, number of days in the hospital, number of physician office visits, and number of physician office visits with cardiovascular diagnoses. In addition to a comorbidity score that captured patients general health status (23), we determined whether patients had health care encounters with diagnoses for specific cardiovascular conditions (such as atrial fibrillation, congestive heart failure, or peripheral vascular disease) and other disorders (such as chronic obstructive pulmonary disease, diabetes, musculoskeletal conditions, and endocrine disease). Furthermore, we determined whether patients initiated statin treatment for primary or secondary prevention, with the latter defined as having been hospitalized for an acute coronary syndrome in the baseline period. We also measured use of preventive services, including screening mammography and vaccinations, to account for healthy-user effects (24, 25) and ascertained proxies of frailty, such as use of supplemental oxygen, to account for the propensity to stop preventive medications in patients who are very ill (26). Finally, we geocoded patients street addresses and linked them to U.S. census data at the block group level, which is the lowest level for which data are publicly available. We identified the unemployment rate and the median household income in each patients census block group as proxies for socioeconomic status (SES). Statistical Analysis We used propensity score matching (27) to mitigate confounding due to different characteristics between the brand-name and generic groups. The propensity score, which was estimated with a logistic regression model, was defined as a patients probability of receiving a generic statin versus a brand-name statin and was conditional on measured baseline covariates. We matched generic and brand-name drug recipients by using a nearest-neighbor algorithm and within calipers of 0.05 units on the propensity score scale in the primary analysis. Because the cohort included many more generic than brand-name drug recipients, we matched each patient in the brand-name group to as many patients as possible in the generic group with similar propensity scores within the specified caliper. We matched brand-name drug recipients only to recipients of the generic version of the same product (for example, brand-name and generic simvastatin) to compare patients who had initiated molecularly identical drugs. This ensured that differences in outcome rates between treatment groups could be attributed to the generic versus brand-name status rather than to differences among the 3 statins. To assess the performance of the propensity score matching process, we evaluated balance in each baseline covariate and overlap in propensity score distributions between treatment groups before and after matching. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs. To account for the variable ratio matching, the Cox models were stratified by matching set. We also estimated rate differences. We performed several prespecified secondary, sensitivity, and subgroup analyses to assess the validity of our study assumptions. We altered our primary analysis by shortening (to 90 days) and lengthening (to 720 days) the maximum follow-up time, excluding events that occurred within 30 and 60 days after index drug initiation, and performing 1:1 fixed-ratio matching on the propensity score. Furthermore, outcome event rates were compared between recipients of the generic and brand-name versions of each drug separately. The primary analysis was also repeated separately for primary prevention and secondary prevention patients. We conducted an on treatment analysis in which we censored patients when they discontinued statin therapy, defined as a gap of more than 30 days without filling a statin prescription beyond the number of days supplied by the last prescription or switching to another lipid-lowering

Comparative effectiveness of generic versus brand-name antiepileptic medications

OBJECTIVE The objective of this study was to compare treatment persistence and rates of seizure-related events in patients who initiate antiepileptic drug (AED) therapy with a generic versus a brand-name product. METHODS We used linked electronic medical and pharmacy claims data to identify Medicare beneficiaries who initiated one of five AEDs (clonazepam, gabapentin, oxcarbazepine, phenytoin, zonisamide). We matched initiators of generic versus brand-name versions of these drugs using a propensity score that accounted for demographic, clinical, and health service utilization variables. We used a Cox proportional hazards model to compare rates of seizure-related emergency room (ER) visit or hospitalization (primary outcome) and ER visit for bone fracture or head injury (secondary outcome) between the matched generic and brand-name initiators. We also compared treatment persistence, measured as time to first 14-day treatment gap, between generic and brand-name initiators. RESULTS We identified 19,760 AED initiators who met study eligibility criteria; 18,306 (93%) initiated a generic AED. In the matched cohort, we observed 47 seizure-related hospitalizations and ER visits among brand-name initiators and 31 events among generic initiators, corresponding to a hazard ratio of 0.53 (95% confidence interval, 0.30 to 0.96). Similar results were observed for the secondary clinical endpoint and across sensitivity analyses. Mean time to first treatment gap was 124.2 days (standard deviation [sd], 125.8) for brand-name initiators and 137.9 (sd, 148.6) for generic initiators. SIGNIFICANCE Patients who initiated generic AEDs had fewer adverse seizure-related clinical outcomes and longer continuous treatment periods before experiencing a gap than those who initiated brand-name versions.

Association Between Academic Medical Center Pharmaceutical Detailing Policies and Physician Prescribing

Importance In an effort to regulate physician conflicts of interest, some US academic medical centers (AMCs) enacted policies restricting pharmaceutical representative sales visits to physicians (known as detailing) between 2006 and 2012. Little is known about the effect of these policies on physician prescribing. Objective To analyze the association between detailing policies enacted at AMCs and physician prescribing of actively detailed and not detailed drugs. Design, Setting, and Participants The study used a difference-in-differences multivariable regression analysis to compare changes in prescribing by physicians before and after implementation of detailing policies at AMCs in 5 states (California, Illinois, Massachusetts, Pennsylvania, and New York) that made up the intervention group with changes in prescribing by a matched control group of similar physicians not subject to a detailing policy. Exposures Academic medical center implementation of policies regulating pharmaceutical salesperson visits to attending physicians. Main Outcomes and Measures The monthly within-drug class market share of prescriptions written by an individual physician for detailed and nondetailed drugs in 8 drug classes (lipid-lowering drugs, gastroesophageal reflux disease drugs, diabetes drugs, antihypertensive drugs, hypnotic drugs approved for the treatment of insomnia [sleep aids], attention-deficit/hyperactivity disorder drugs, antidepressant drugs, and antipsychotic drugs) comparing the 10- to 36-month period before implementation of the detailing policies with the 12- to 36-month period after implementation, depending on data availability. Results The analysis included 16 121 483 prescriptions written between January 2006 and June 2012 by 2126 attending physicians at the 19 intervention group AMCs and by 24 593 matched control group physicians. The sample mean market share at the physician-drug-month level for detailed and nondetailed drugs prior to enactment of policies was 19.3% and 14.2%, respectively. Exposure to an AMC detailing policy was associated with a decrease in the market share of detailed drugs of 1.67 percentage points (95% CI, −2.18 to −1.18 percentage points; P < .001) and an increase in the market share of nondetailed drugs of 0.84 percentage points (95% CI, 0.54 to 1.14 percentage points; P < .001). Associations were statistically significant for 6 of 8 study drug classes for detailed drugs (lipid-lowering drugs, gastroesophageal reflux disease drugs, antihypertensive drugs, sleep aids, attention-deficit/hyperactivity disorder drugs, and antidepressant drugs) and for 9 of the 19 AMCs that implemented policies. Eleven of the 19 AMCs regulated salesperson gifts to physicians, restricted salesperson access to facilities, and incorporated explicit enforcement mechanisms. For 8 of these 11 AMCs, there was a significant change in prescribing. In contrast, there was a significant change at only 1 of 8 AMCs that did not enact policies in all 3 areas. Conclusions and Relevance Implementation of policies at AMCs that restricted pharmaceutical detailing between 2006 and 2012 was associated with modest but significant reductions in prescribing of detailed drugs across 6 of 8 major drug classes; however, changes were not seen in all of the AMCs that enacted policies.

Patterns and Predictors of Generic Narrow Therapeutic Index Drug Use Among Older Adults

To ascertain predictors of initiation of brand‐name versus generic narrow therapeutic index (NTI) drugs.

Revealed preference for community and mail service pharmacy.

OBJECTIVE To determine revealed pharmacy preference and predictors among patients enrolled in a pharmacy benefit that offered a 90-day supply of prescriptions via mail service and community pharmacy channels, with no differences in out-of-pocket costs. DESIGN Retrospective cohort study. SETTING United States in 2008-09. PATIENTS 324,968 commercially insured participants enrolled in plans that required use of mail service pharmacy for maintenance medications. INTERVENTION Implementation of a pharmacy benefit design with optional use of either mail service or community pharmacy for 90-day supply prescriptions. MAIN OUTCOME MEASURES Selection rates of mail service and community pharmacy and adjusted odds ratios for predicting community pharmacy for selected characteristics. RESULTS In the first 4 months of the benefit design, 31.8% of participants previously mandated to use mail service pharmacy elected to fill 90-day prescriptions at community pharmacies. Selection of community pharmacy ranged from a low of 23.7% (previous mail service pharmacy users) to 66.3% (previous community pharmacy users). Among those initiating therapy, 44.3% selected community pharmacy for their new prescriptions, and among those with no previous mail use, 68% selected community pharmacy for new prescriptions. Preference for community/mail service pharmacy was dependent on numerous characteristics, including age, gender, household income, region, driving distance (time), and concomitant medication use. CONCLUSION Patient behavior indicates that certain patients prefer to access prescription medications via mail service and others through community pharmacy channels. Restrictive benefit designs that incentivize patients to use less preferable pharmacy channels may adversely affect patient convenience, which could have the unintended consequence of reducing medication use and adherence.

Prescriber and pharmacy variation in patient adherence to five medication classes measured using implementation during persistent episodes

The objective of this study was to determine the fraction of variance in patient‐level medication adherence accounted for by prescribers and pharmacies.