Publications

BACKGROUND: Physicians face the choice of multiple ingredients when prescribing drugs in many therapeutic categories. For conditions with considerable patient heterogeneity in treatment response, customizing treatment to individual patient needs and preferences may improve outcomes.

AIMS OF THE STUDY: To assess variation in the diversity of antipsychotic prescribing for mental health conditions, a necessary although not sufficient condition for personalizing treatment. To identify patient caseload, physician, and organizational factors associated with the diversity of antipsychotic prescribing.

METHODS: Using 2011 data from Pennsylvania's Medicaid program, IMS Health's HCOSTM database, and the AMA Masterfile, we identified 764 psychiatrists who prescribed antipsychotics to 10 patients. We constructed three physician-level measures of diversity/concentration of antipsychotic prescribing: number of ingredients prescribed, share of prescriptions for most preferred ingredient, and Herfindahl-Hirschman index (HHI). We used multiple membership linear mixed models to examine patient caseload, physician, and healthcare organizational predictors of physician concentration of antipsychotic prescribing.

RESULTS: There was substantial variability in antipsychotic prescribing concentration among psychiatrists, with number of ingredients ranging from 2-17, share for most preferred ingredient from 16%-85%, and HHI from 1,088-7,270. On average, psychiatrist prescribing behavior was relatively diversified; however, 11% of psychiatrists wrote an average of 55% of their prescriptions for their most preferred ingredient. Female prescribers and those with smaller shares of disabled or serious mental illness patients had more concentrated prescribing behavior on average.

DISCUSSION: Antipsychotic prescribing by individual psychiatrists in a large state Medicaid program varied substantially across psychiatrists. Our findings illustrate the importance of understanding physicians' prescribing behavior and indicate that even among specialties regularly prescribing a therapeutic category, some physicians rely heavily on a small number of agents.

IMPLICATIONS FOR HEALTH POLICIES, HEALTH CARE PROVISION AND USE: Health systems may need to offer educational interventions to clinicians in order to improve their ability to tailor treatment decisions to the needs of individual patients.

IMPLICATIONS FOR FUTURE RESEARCH: Future studies should examine the impact of the diversity of antipsychotic prescribing to determine whether more diversified prescribing improves patient adherence and outcomes.

BACKGROUND: Medications to treat and prevent chronic disease have substantially reduced morbidity and mortality; however, their diffusion has been uneven. Little is known about prescribing of chronic disease medications by nurse practitioners (NPs) and physician assistants (PAs), despite their increasingly important role as primary care providers. Thus, we sought to conduct an exploratory analysis to examine prescribing of new chronic disease medications by NPs and PAs compared to primary care physicians (PCPs).

METHODS: We obtained prescribing data from IMS Health's Xponent™ on all NPs, PAs, and PCPs in Pennsylvania regularly prescribing anticoagulants, antihypertensives, oral hypoglycemics, and/or HMG-Co-A reductase inhibitors pre- and post-introduction of five new drugs in these classes that varied in novelty (i.e., dabigatran, aliskiren, sitagliptin or saxagliptin, and pitavastatin). We constructed three measures of prescriber adoption during the 15-month post-FDA approval period: 1) any prescription of the medication, 2) proportion of prescriptions in the class for the medication, and 3) time to adoption (first prescription) of the medication.

RESULTS: From 2007 to 2011, the proportion of antihypertensive prescriptions prescribed by NPs and PAs approximately doubled from 2.0 to 4.2 % and 2.2 to 4.9 %, respectively. Similar trends were found for anticoagulants, oral hypoglycemics, and HMG-Co-A reductase inhibitors. By 2011, more PCPs had prescribed each of the newly approved medications than NPs and PAs (e.g., 44.3 % vs. 18.5 % vs. 20 % for dabigatran among PCPs, NPs, and PAs). Across all medication classes, the newly approved drugs accounted for a larger share of prescriptions in the class for PCPs followed by PAs, followed by NPs (e.g., dabigatran: 4.9 % vs. 3.2 % vs. 2.8 %, respectively). Mean time-to-adoption for the newly approved medications was shorter for PCPs compared to NPs and PAs (e.g., dabigatran, 7.3 vs. 8.2 vs. 8.5 months; P all medications <0.001).

CONCLUSIONS: PCPs were more likely to prescribe each of the newly approved medications per each measure of drug adoption, regardless of drug novelty. Differences in the rate and speed of drug adoption between PCPs, NPs, and PAs may have important implications for care and overall costs at the population level as NPs and PAs continue taking on a larger role in prescribing.

BACKGROUND: Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption.

OBJECTIVE: Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant.

METHODS: A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions.

OUTCOMES: Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran.

RESULTS: We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ≤3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (≤3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6-10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt [odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27-16.1] as were younger prescribers (age 36-45 y: OR=1.49, 95% CI, 1.13-1.95; age 46-55: OR=1.34, 95% CI, 1.07-1.69 vs. >55 y).

CONCLUSIONS: Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.

BACKGROUND: Regional variation in US Medicare prescription drug spending is driven by higher prescribing of costly brand-name drugs in some regions. This variation likely arises from differences in the speed of diffusion of newly-approved medications. Second-generation antipsychotics were widely adopted for treatment of severe mental illness and for several off-label uses. Rapid diffusion of new psychiatric drugs likely increases drug spending but its relationship to non-drug spending is unclear. The impact of antipsychotic diffusion on drug and medical spending is of great interest to public payers like Medicare, which finance a majority of mental health spending in the US.

AIMS: We examine the association between physician adoption of new antipsychotics and antipsychotic spending and non-drug medical spending among disabled and elderly Medicare enrollees.

METHODS: We linked physician-level data on antipsychotic prescribing from an all-payer dataset (IMS Health's XponentTM) to patient-level data from Medicare. Our physician sample included 16,932 US. psychiatrists and primary care providers with > 10 antipsychotic prescriptions per year from 1997-2011. We constructed a measure of physician adoption of 3 antipsychotics introduced during this period (quetiapine, ziprasidone and aripiprazole) by estimating a shared frailty model of the time to first prescription for each drug. We then assigned physicians to one of 306 U.S. hospital referral regions (HRRs) and measured the average propensity to adopt per region. Using 2010 data for a random sample of 1.6 million Medicare beneficiaries, we identified 138,680 antipsychotic users. A generalized linear model with gamma distribution and log link was used to estimate the effect of region-level adoption propensity on beneficiary-level antipsychotic spending and non-drug medical spending adjusting for patient demographic and socioeconomic characteristics, health status, eligibility category, and whether the antipsychotic was for an on- vs. off-label use.

RESULTS: In our sample, mean patient age was 62 years, 42% were male, and 86% had low-income. Half of antipsychotic users in Medicare had an on-label indication. The weighted average propensity to adopt the three new antipsychotics varied four-fold across HRRs. For every one standard deviation increase in the propensity to adopt there was a 5% increase in antipsychotic spending after adjusting for covariates (adjusted ratio of spending 1.05, 95% CI 1.01-1.08, p = 0.005). Physician propensity to adopt new antipsychotics was not associated with non-drug medical spending (adjusted ratio 0.96, 95% CI 0.91-1.01, p < 0.117).

DISCUSSION: These findings suggest wide regional variation in physicians' propensity to adopt new antipsychotic medications. While physician adoption of new antipsychotics was positively associated with antipsychotic expenditures, it was not associated with non-drug spending. Our analysis is limited to Medicare and may not generalize to other payers. Also, claims data do not allow for the measurement of health outcomes, which would be important to evaluate when calculating the value of rapid vs. slow technology adoption.

UNLABELLED: Oral direct-acting antivirals (DAAs) represent a major advance in hepatitis C virus (HCV) treatment. Along with recent updates in HCV screening policy and expansions in insurance coverage, treatment demand in the United States is changing rapidly. Our objective was to project the characteristics and number of people needing antiviral treatment and HCV-associated disease burden in the era of oral DAAs. We used a previously developed and validated Hepatitis C Disease Burden Simulation model (HEP-SIM). HEP-SIM simulated the actual clinical management of HCV from 2001 onward, which included antiviral treatment with pegylated interferon (Peg-IFN)-based therapies as well as the recent oral DAAs, risk-based and birth-cohort HCV screening, and the impact of the Affordable Care Act. We also simulated two hypothetical scenarios-no treatment and treatment with Peg-IFN-based therapies only. We estimated that in 2010, 2.5 (95% confidence interval [CI], 1.9-3.1) million noninstitutionalized people were viremic, which dropped to 1.9 (95% CI, 1.4-2.6) million in 2015, and projected to drop below 1 million by 2020. A total of 1.8 million HCV patients will receive HCV treatment from the launch of oral DAAs in 2014 until 2030. Based on current HCV management practices, it will take 4-6 years to treat the majority of patients aware of their disease. However, 560,000 patients would still remain unaware by 2020. Even in the oral DAA era, 320,000 patients will die, 157,000 will develop hepatocellular carcinoma, and 203,000 will develop decompensated cirrhosis in the next 35 years.

CONCLUSIONS: HCV-associated disease burden will still remain substantial in the era of oral DAAs. Increasing HCV screening and treatment capacity is essential to further decreasing HCV burden in the United States. (Hepatology 2016;64:1442-1450).

BACKGROUND: Tobacco use is the leading cause of preventable death and disability. New payment and delivery system models including global payment and accountable care have the potential to increase use of cost-effective tobacco cessation services.

OBJECTIVE: To examine how the Alternative Quality Contract (AQC) established in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA) has affected tobacco cessation service use.

DESIGN: We used 2006-2011 BCBSMA claims and enrollment data to compare adults 18-64 years in AQC provider organizations to adults in non-AQC provider organizations. We examined the AQC's effects on all enrollees; a subset at high risk of tobacco-related complications due to certain medical conditions; and behavioral health service users.

MAIN MEASURES: We examined use of: (1) any cessation treatment (pharmacotherapy or counseling); (2) varenicline or bupropion; (3) nicotine replacement therapies (NRTs); (4) cessation counseling; and (4) combination therapy (pharmacotherapy plus counseling). We also examined duration of pharmacotherapy use and number of counseling visits among users.

KEY RESULTS: Rates of tobacco cessation treatment use were higher following implementation of the AQC relative to the comparison group overall (2.02 vs. 1.87 %, p < 0.0001), among enrollees at risk for tobacco-related complications (4.97 vs. 4.66 %, p < 0.0001), and among behavioral health service users (3.67 vs. 3.25 %, p < 0.0001). Statistically significant increases were found for use of varenicline or bupropion alone, counseling alone, and combination therapy, but not for NRT use, pharmacotherapy duration, or number of counseling visits among users.

CONCLUSIONS: In its initial three years, the AQC was associated with increases in use of tobacco cessation services.

BACKGROUND: Numerous interventions are available to boost medication adherence, but the targeting of these interventions often relies on crude measures of poor adherence. Group-based trajectory models identify individuals with similar longitudinal prescription filling patterns. Identifying distinct adherence trajectories may be more useful for targeting interventions, although the association between adherence trajectories and clinical outcomes is unknown.

OBJECTIVE: To examine the association between adherence trajectories for oral hypoglycemics and subsequent hospitalizations among diabetes patients.

DESIGN: Retrospective cohort study.

PATIENTS: A total of 16,256 Pennsylvania Medicaid enrollees, non-dually eligible for Medicare, initiating oral hypoglycemics between 2007 and 2009.

MAIN MEASURES: We used group-based trajectory models to identify trajectories of oral hypoglycemics in the 12 months post-treatment initiation, using monthly proportion of days covered (PDC) as the adherence measure. Multivariable Cox proportional hazard models were used to examine the association between trajectories and time to first diabetes-related hospitalization/emergency department (ED) visits in the following year. We used the C-index to compare prediction performance between adherence trajectories and dichotomous cutpoints (annual PDC <80 vs. ≥80 %).

RESULTS: The mean annual PDC was 0.58 (SD 0.32). Seven trajectories were identified: perfect adherers (9 % of the cohort), nearly perfect adherers (31.4 %), moderate adherers (21.0 %), low adherers (11.0 %), late discontinuers (6.8 %), early discontinuers (9.7 %), and non-adherers with only one fill (11.1 %). Compared to perfect adherers, trajectories of moderate adherers (HR = 1.48, 95 % CI 1.25, 1.75), low adherers (HR = 1.51, 95 % CI 1.25, 1.83), and non-adherers with only one fill (HR = 1.35, 95 % CI 1.09, 1.67) had greater risk of diabetes-related hospitalizations/ED visits. Predictive accuracy was improved using trajectories compared to dichotomized cutpoints (C-index = 0.714 vs. 0.652).

CONCLUSIONS: Oral hypoglycemic treatment trajectories were highly variable in this large Medicaid cohort. Low and moderate adherers and those filling only one prescription had a modestly higher risk of hospitalizations/ED visits compared to perfect adherers. Trajectory models may be valuable in identifying specific non-adherence patterns for targeting interventions.

A time lens, which can be used to reshape the spectral and temporal properties of light, requires the ultrafast manipulation of optical signals and presents a significant challenge for single-photon application. In this work, we construct a time lens based on dispersion and sum-frequency generation to spectrally engineer single photons from an entangled pair. The strong frequency anticorrelations between photons produced from spontaneous parametric down-conversion are converted to positive correlations after the time lens, consistent with a negative-magnification system. The temporal imaging of single photons enables new techniques for time-frequency quantum state engineering.