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Sexually transmitted infection (STI) screening prevents complications and is cost-effective. Annual screening is recommended in sexually active women below 25 years and older women at increased risk. Cervical cancer (CC) screening guideline changes in 2009 and 2012 recommended less frequent screening, causing concern that STI screening would decrease. Pennsylvania Medicaid implemented a family planning program in 2007 which covered women's health services (including STI testing) for uninsured women. It is unclear how STI screening was affected by these countervailing forces.The main objective of this study was to assess STI screening before and after CC screening guideline changes and family planning program implementation, and to determine factors associated with STI screening.This study was an observational cross-sectional study of Pennsylvania Medicaid administrative claims from 2007 to 2013.Sixteen-year-old to 30-year-old women enrolled in Pennsylvania Medicaid.Annual STI screening, defined as receipt of ≥1 STI test in respective 1-year periods.Our population included 1,226,079 women-years for 467,143 women. STI screening increased by 48% between 2007 and 2011, and stabilized by 2013. Odds for STI screening were higher among black compared with white women [adjusted odds ratio (AOR), 2.56; 95% confidence interval (CI), 2.60-3.10]; Hispanic compared with non-Hispanic women (AOR, 1.42; 95% CI, 1.39-1.46); family planning program enrollees (AOR, 1.42; 95% CI, 1.40-1.45); and urban compared with rural residents (AOR, 1.05; 95% CI, 1.03-1.06).STI screening dramatically increased between 2007 and 2011. Potential reasons are family planning program implementation, increased urine/vaginal testing, and reporting improvements. It is reassuring that STI screening did not decrease despite CC screening guideline changes. Between 2011 and 2013, rates stabilized at 45% among all women and 60% among sexually active women below 25 years, suggesting opportunities for improvement.

People with mental illnesses (MI) receive suboptimal care for medical comorbidities and their high risk for readmission may be addressed by adequate medication management and follow-up care. We examined the association between MI, medication changes, and post-discharge outpatient visits with 30-day readmission in 40,048 Medicare beneficiaries hospitalized for acute myocardial infarction, heart failure or pneumonia. Beneficiaries with MI were more likely to be readmitted than those without MI (14 vs. 11%). Probability of readmission was 13 and 12% when medications were dropped or added, respectively, versus 11% when no change was made. Probability of readmission also increased with outpatient visits.

Objective: Patient refusal of and nonadherence to treatment with disease-modifying antirheumatic drugs (DMARDs) can adversely affect disease outcomes in rheumatoid arthritis (RA). This qualitative study describes how RA patients' feelings in response to experiences and information affected their decisions to accept (agree to adopt, initiate and implement) or resist (refuse, avoid and discontinue) DMARD treatment regimens. Methods: 48 RA patients were interviewed about their experiences making decisions about DMARDs. The interviews were transcribed, coded, and analyzed for themes related to their internal motivations for accepting or resisting treatment regimens, using a narrative analysis approach. Results: In addition to feelings about the necessity and dangers of medications, patients' feelings towards their identity as an ill person, the act of taking medication, and the decision process itself were important drivers of patient's decisions. For patients' motivations to accept treatment regimens, two themes emerged: 1) desire to return to a "normal" life and 2) fear of future disability due to RA. For motivations to resist treatment regimens, five themes emerged: 1) fear of medications, 2) maintaining control over health, 3) denial of sick identity, 4) disappointment with treatment, and 5) feeling overwhelmed by the cognitive burden of deciding. Conclusion: Feelings in response to experiences and information played a major role in how patients weighed the benefits and costs of treatment options, suggesting that addressing patients' feelings may be important when rheumatologists counsel about therapeutic options. Further research is needed to learn how best to address patients' feelings throughout the treatment decision making process. This article is protected by copyright. All rights reserved.

BACKGROUND: Little is known about physicians’ approaches to adopting new cardiovascular drugs and how adoption varies between drugs of differing novelty. METHODS: Using data on dispensed prescriptions from IMS Health’s Xponent™ database, we created a cohort of all primary care physicians (PCPs) and cardiologists in Pennsylvania who regularly prescribed anticoagulants, antihypertensives and statins from 2007 to 2011. We examined prescribing of three new cardiovascular drugs of differing novelty: dabigatran, aliskiren and pitavastatin. Outcomes were rapid adoption of each new drug, defined by early and sustained monthly prescribing detected by group-based trajectory models, by physicians within the first 15 months of marketplace introduction. RESULTS: 5,953 physicians regularly prescribed each drug class. The majority of physicians (63.8%) adopted zero new drugs in the first 15 months, 35.0% rapidly adopted one or two, and 1.2% rapidly adopted all three. Physicians were more likely to rapidly adopt the most novel drug, dabigatran (27.3%), than aliskiren (10.5%) or pitavastatin (8.0%). Physician specialty and sex were the most consistent predictors of adoption. Compared to PCPs, cardiologists were more likely to rapidly adopt dabigatran (Adjusted Odds Ratio 8.90, 95% confidence interval 7.42–10.67; P

Objectives The Alternative Quality Contract (AQC) implemented in 2009 by Blue Cross Blue Shield of Massachusetts (BCBSMA) is intended to improve quality and control costs by putting providers at risk for total medical spending and tying payment to performance on specified quality measures. We examined the AQC's early effects on use of and spending on medication treatment (MT) for addiction among individuals with alcohol use disorders (AUDs) and opioid use disorders (OUDs), conditions not subject to any performance measurement in the AQC. Methods Using data from 2006 to 2011, we use difference-in-difference estimation of the effect of the AQC on MT using a comparison group of enrollees in BCBSMA whose providers did not participate in the AQC. We compared AQC and non-AQC enrollees with AUDs (n = 37,113 person-years) and/or OUDs (n = 12,727 person-years) on any use of MT, number of prescriptions filled, and MT spending adjusting for demographic and health status characteristics. Results There was no difference in MT use among AQC enrollees with OUD (38.7%) relative to the comparison group (39.1%) (adjusted difference = -0.4%, 95% confidence interval -3.8% to 3.0%, P = 0.82). Likewise, there was no difference in MT use for AUD between the AQC (6.3%) and comparison group (6.5%) (P = 0.64). Similarly, we detected no differences in number of prescriptions or spending. Conclusions Despite incentives for improved integration and quality of care under a global payment contract, the initial 3 years of the AQC showed no impact on MT use for AUD or OUD among privately insured enrollees with behavioral health benefits.

This cross-sectional study identifies the number and class of drugs approved through the US Food and Drug Administration’s accelerated approval pathway and analyzes state Medicaid programs’ use and spending on these drugs from 2015 through 2019., Key Points Question How much do prescription drugs approved through the US Food and Drug Administration’s accelerated approval program contribute to state Medicaid program spending? Findings In this cross-sectional study of 216 drugs granted accelerated approval from 1992 through 2020, relative to all drugs paid for by Medicaid, products with accelerated approval comprised less than 1% of use. Despite their infrequent use, annual net spending on drugs with accelerated approval represented 6.4% to 9.1% of net spending on all drugs covered by Medicaid (in 2015 and 2018, respectively). Meaning Medicaid spending on drugs with accelerated approval represents an outsized amount of spending relative to their use., Importance State Medicaid programs have reported concerns about rising drug prices and spending, particularly regarding drugs entering the market through the accelerated approval program under the US Food and Drug Administration (FDA). The accelerated approval program enables the FDA to approve drugs on the basis of unverified surrogate end points, meaning that clinical benefits for these products are uncertain at the time of approval. However, state Medicaid programs are legally required to cover these drugs. Little is known about the set of products with accelerated approval over time, their use among Medicaid beneficiaries, or the magnitude of their financial influence on state Medicaid programs. Objective To identify the number and class of drugs approved through the FDA’s accelerated approval pathway and analyze state Medicaid programs’ use and spending on these drugs from 2015 through 2019. Design, Setting, and Participants In this cross-sectional study, biannual FDA reports were used to identify products granted accelerated approval and their associated indications approved between December 1992 and December 2020. State Medicaid Drug Utilization Data files available for 1992 through 2019 were used to estimate national totals for spending and use of outpatient drugs. Main Outcomes and Measures National Medicaid use and gross and net spending on drugs with accelerated approval from 2015 through 2019. Results Since the inception of the FDA’s accelerated approval pathway in 1992 through 2020, 216 product-indication pairs granted accelerated approval were identified, comprising 149 unique products. The composition of drugs approved through the pathway has changed over time, with 28 of 30 (93.3%) product-indication pairs receiving accelerated approval in 2020 being indicated for cancer. Relative to all outpatient prescription drugs paid for by Medicaid, products with accelerated approval ranged from 0.2% to 0.4% of use (1.3-2.4 million prescriptions annually). Despite their infrequent use, drugs with accelerated approval represented a minimum annual net spending on all drugs covered by Medicaid of 6.4% ($2.2 billion of $34.6 billion) in 2015 and a maximum of 9.1% ($2.5 billion of $27.6 billion) in 2018. Estimated annual gross spending on drugs with accelerated approval ranged from $4.2 billion to $4.9 billion over 2015 through 2019, and estimated net spending from $2.2 billion to $2.6 billion. Conclusions and Relevance In this cross-sectional study of 216 drugs granted accelerated approval, state spending on drugs approved through the FDA’s growing accelerated approval program represented an outsized amount of spending relative to use. Because drugs with accelerated approval have come to market on the basis of trials using surrogate end points, considerable amounts of this spending may have been attributable to products with unproven clinical benefits.

Background Despite evidence that individuals with opioid use disorder (OUD) have a lower risk of mortality when using evidence-based medications for OUD (MOUD), only 20 % of people with OUD receive MOUD. Black patients are significantly less likely than White patients to initiate MOUD. We measured the association between various facilitators and barriers to initiation, including criminal justice, human services, and health care factors, and variation in initiation of MOUD by race. Methods We used data from a comprehensive, linked data set of health care, human services, and criminal justice programs from Allegheny County in Western Pennsylvania to measure disparities in MOUD initiation by race in the first 180 days after an OUD diagnosis, as well as mediation by potential facilitators and barriers to treatment, among Medicaid enrollees. This is a cross-sectional analysis. Results Among 6374 Medicaid enrollees who met study criteria, Black enrollees were 18.2 percentage points less likely than White enrollees to start MOUD after controlling for gender, age, and Medicaid eligibility (95 % CI: -21.5 % - -14.8 %). Each day in the emergency department or county jail was associated with a decrease in the likelihood of initiation, as was the presence of a non-OUD substance use disorder diagnosis or participation in intensive non-MOUD treatment. Mediators accounted for approximately one-fifth of the variation in initiation related to race. Conclusions Acute care facilities and settings in which people with OUD are incarcerated may have an opportunity to increase the use of MOUD overall and close the racial gap in initiation.

Since the Supreme Court decided that the Affordable Care Act's (ACA) Medicaid expansion is optional for the states, several have obtained federal approval to use Section 1115 waivers to expand Medicaid while changing its coverage and benefits design. There has long been concern that policy making for Medicaid populations may lack meaningful engagement with low-income constituents, and therefore the ACA established a new process under which the public can submit comments on pending Medicaid waiver applications. We analyzed 291 comment letters submitted to federal regulators pertaining to Medicaid Section 1115 waiver applications in the first five states to seek such waivers: Arkansas, Indiana, Iowa, Michigan, and Pennsylvania. We found that individual citizens, including those who identified as Medicaid-eligible, submitted a sizable majority of the comment letters. Comment letters tended to mention controversial provisions of the waivers and reflected the competing political rhetoric of "personal responsibility" versus "vulnerable populations." Despite the fact that the federal government seemed likely to approve the waiver applications, we found robust public engagement, reflecting the salience of the issue of Medicaid expansion under the ACA. Our findings are consistent with the argument that Medicaid is a program of growing centrality in US health politics.

Underuse of clozapine and overuse of antipsychotic polypharmacy are both indicators of poor quality of care. This study examined variation in prescribing clozapine and antipsychotic polypharmacy across providers, as well as factors associated with these practices.Using 2010-2012 Pennsylvania Medicaid data, prescribers were identified if they wrote antipsychotic prescriptions for ten or more nonelderly adult patients with schizophrenia annually. Generalized linear mixed models with a binomial distribution and a logit link were used to examine prescriber-level annual percentages of patients with clozapine use and with long-term (≥90 days) antipsychotic polypharmacy and associated characteristics of prescribers' patient caseloads, prescriber characteristics, and Medicaid payer (fee-for-service versus managed care plans).The study cohort included 645 prescribers in 2010, 632 in 2011, and 650 in 2012. In 2012, the mean prescriber-level annual percentage of patients with any clozapine use was 7% (range 0%-89%), and the mean percentage of patients with any long-term antipsychotic polypharmacy was 7% (range 0%-45%) (similar rates were found during 2010-2012). Prescribers with high prescription volume, a smaller percentage of patients from racial or ethnic minority groups, and a larger percentage of patients eligible for Supplemental Security Income were more likely to use both clozapine and antipsychotic polypharmacy for treating schizophrenia. Prescriber specialty and Medicaid payer were also associated with prescribers' practices.Considerable variation was found in clozapine and antipsychotic polypharmacy practices across prescribers in their treatment of schizophrenia. Targeting efforts to selected prescribers holds promise as an approach to promote evidence-based antipsychotic prescribing.

BACKGROUND Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist. OBJECTIVE Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data. RESEARCH DESIGN A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use. SUBJECTS Adult Pennsylvania Medicaid program 2007-2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice. MEASURES Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes. RESULTS A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P

SummaryWhat is known and objective There are few studies examining both drug–drug and drug–disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug–drug and drug–disease interactions and associated factors in community-dwelling older adults. Methods This cross-sectional study included 3055 adults aged 70–79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug–drug and drug–disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use. Results Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug–drug interactions. Nearly 10·7% of the participants had a drug–drug interaction that involved a non-prescription medication. % The most common drug–drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug–disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug–disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35–40% [drug–drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27–1·42; drug–disease interaction AOR = 1·30; CI = 1·21–1·40; and both AOR = 1·45; CI = 1·34–1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49–84% compared with those not hospitalized (drug–drug interaction AOR = 1·49, 95% CI = 1·11–2·01; drug–disease interaction AOR = 1·69, CI = 1·15–2·49; and both AOR = 1·84, CI = 1·20–2·84). What is new and conclusion Drug interactions are common among community-dwelling older adults and are associated with the number of medications and hospitalization in the previous year. Longitudinal studies are needed to evaluate the impact of drug interactions on health-related outcomes.

Objective To examine associations between opioid prescriber specialty and patient likelihood of opioid use disorder (OUD), opioid misuse, and opioid overdose. Design Longitudinal retrospective study using Pennsylvania Medicaid data (2007–2015). Methods We constructed an incident cohort of 432,110 enrollees initiating prescription opioid use without a history of OUD or overdose six months before opioid initiation. We attributed patients to one of 10 specialties using the first opioid prescriber’s specialty or, alternatively, the specialty of the dominant prescriber writing the majority of the patient’s opioid prescriptions. We estimated adjusted rates for OUD, misuse, and overdose, adjusting for demographic variables and medical (including pain) and psychiatric comorbidities. Results The unadjusted incidence rates of OUD, misuse, and overdose were 7.13, 4.73, and 0.69 per 100,000 person-days, respectively. Patients initiating a new episode of opioid treatment with Pain Medicine/Anesthesiology (6.7 events, 95% confidence interval [CI] = 5.5 to 8.2) or Physical Medicine and Rehabilitation (PM&R; 6.1 events, 95% CI = 5.1 to 7.2) had higher adjusted rates for OUD per 100,000 person-days compared with Primary Care practitioners (PCPs; 4.4 events, 95% CI = 4.1 to 4.7). Patients with index prescriptions from Pain Medicine/Anesthesiology (15.9 events, 95% CI = 13.2 to 19.3) or PM&R (15.8 events, 95% CI = 13.5 to 18.4) had higher adjusted rates for misuse per 100,000 person-days compared with PCPs (9.6 events, 95% CI = 8.8 to 10.6). Findings were largely similar when patients were attributed to specialty based on dominant prescriber. Conclusions Differences in opioid-related risks by specialty of opioid prescriber may arise from differences in patient risk factors, provider behavior, or both. Our findings inform targeting of opioid risk mitigation strategies to specific practitioner specialties.

BACKGROUND: There is systemic undercoding of medical comorbidities within administrative claims in the Department of Veterans Affairs (VA). This leads to bias when applying claims-based risk adjustment indices to compare outcomes between VA and non-VA settings. Our objective was to compare the accuracy of a medication-based risk adjustment index (RxRisk-VM) to diagnostic claims-based indices for predicting mortality. METHODS: We modified the RxRisk-V index (RxRisk-VM) by incorporating VA and Medicare pharmacy and durable medical equipment claims in Veterans dually-enrolled in VA and Medicare in 2012. Using the concordance (C) statistic, we compared its accuracy in predicting 1 and 3-year all-cause mortality to the following models: demographics only, demographics plus prescription count, or demographics plus a diagnostic claims-based risk index (e.g., Charlson, Elixhauser, or Gagne). We also compared models containing demographics, RxRisk-VM, and a claims-based index. RESULTS: In our cohort of 271,184 dually-enrolled Veterans (mean age=70.5 years, 96.1% male, 81.7% non-Hispanic white), RxRisk-VM (C=0.773) exhibited greater accuracy in predicting 1-year mortality than demographics only (C=0.716) or prescription counts (C=0.744), but was less accurate than the Charlson (C=0.794), Elixhauser (C=0.80), or Gagne (C=0.810) indices (all P

Background The continued escalation of opioid use disorder (OUD) calls for heightened vigilance to implement evidence-based care across the US. Rural care providers and patients have limited resources, and a number of barriers exist that can impede necessary OUD treatment services. This paper reports the design and protocol of an implementation study seeking to advance availability of medication assisted treatment (MAT) for OUD in rural Pennsylvania counties for patients insured by Medicaid in primary care settings. Methods This project was a hybrid implementation study. Within a chronic care model paradigm, we employed the Framework for Systems Transformation to implement the American Society for Addiction Medicine care model for the use of medications in the treatment of OUD. In partnership with state leadership, Medicaid managed care organizations, local care management professionals, the Universities of Pittsburgh and Utah, primary care providers (PCP), and patients; the project team worked within 23 rural Pennsylvania counties to engage, recruit, train, and collaborate to implement the OUD service model in PCP practices from 2016 to 2019. Formative measures included practice-level metrics to monitor project implementation, and outcome measures involved employing Medicaid claims and encounter data to assess changes in provider/patient-level OUD-related metrics, such as MAT provider supply, prevalence of OUD, and MAT utilization. Descriptive statistics and repeated measures regression analyses were used to assess changes across the study period. Discussion There is an urgent need in the US to expand access to high quality, evidence-based OUD treatment—particularly in rural areas where capacity is limited for service delivery in order to improve patient health and protect lives. Importantly, this project leverages multiple partners to implement a theory- and practice-driven model of care for OUD. Results of this study will provide needed evidence in the field for appropriate methods for implementing MAT among a large number of rural primary care providers.

Background and aims Global payment and accountable care reform efforts in the United States may connect more individuals with substance use disorders (SUD) to treatment. We tested whether such changes instituted under an Alternative Quality Contract (AQC) model within the Blue Cross Blue Shield of Massachusetts' (BCBSMA) insurer increased care for individuals with SUD. Design Difference-in-differences design comparing enrollees in AQC organizations with a comparison group of enrollees in organizations not participating in the AQC. Setting Massachusetts, USA. Participants BCBSMA enrollees aged 13-64 years from 2006 to 2011 (3 years prior to and after implementation) representing 1 333 534 enrollees and 42 801 SUD service users. Measurements Outcomes were SUD service use and spending and SUD performance metrics. Primary exposures were enrollment into an AQC provider organization and whether the AQC organization did or did not face risk for behavioral health costs. Findings Enrollees in AQC organizations facing behavioral health risk experienced no change in the probability of using SUD services (1.64 versus 1.66%; P = 0.63), SUD spending ($2807 versus $2700; P = 0.34) or total spending ($12 631 versus $12 849; P = 0.53), or SUD performance metrics (identification: 1.73 versus 1.76%, P = 0.57; initiation: 27.86 versus 27.02%, P = 0.50; engagement: 11.19 versus 10.97%, P = 0.79). Enrollees in AQC organizations not at risk for behavioral health spending experienced a small increase in the probability of using SUD services (1.83 versus 1.66%; P = 0.003) and the identification performance metric (1.92 versus 1.76%; P = 0.007) and a reduction in SUD medication use (11.84 versus 14.03%; P = 0.03) and the initiation performance metric (23.76 versus 27.02%; P = 0.005). Conclusions A global payment and accountable care model introduced in Massachusetts, USA (in which a health insurer provided care providers with fixed prepayments to cover most or all of their patients' care during a specified time-period, incentivizing providers to keep their patients healthy and reduce costs) did not lead to sizable changes in substance use disorder service use during the first 3 years following its implementation.

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.Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant.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.Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran.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 for10% 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).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 Interferon-free hepatitis C treatment regimens are effective but very costly. The cost-effectiveness, budget, and public health impacts of current Medicaid treatment policies restricting treatment to patients with advanced disease remain unknown. Objectives To evaluate the cost-effectiveness of current Medicaid policies restricting hepatitis C treatment to patients with advanced disease compared with a strategy providing unrestricted access to hepatitis C treatment, assess the budget and public health impact of each strategy, and estimate the feasibility and long-term effects of increased access to treatment for patients with hepatitis C. Methods Using a Markov model, we compared two strategies for 45- to 55-year-old Medicaid beneficiaries: 1) Current Practice—only advanced disease is treated before Medicare eligibility and 2) Full Access—both early-stage and advanced disease are treated before Medicare eligibility. Patients could develop progressive fibrosis, cirrhosis, or hepatocellular carcinoma, undergo transplantation, or die each year. Morbidity was reduced after successful treatment. We calculated the incremental cost-effectiveness ratio and compared the costs and public health effects of each strategy from the perspective of Medicare alone as well as the Centers for Medicare & Medicaid Services perspective. We varied model inputs in one-way and probabilistic sensitivity analyses. Results Full Access was less costly and more effective than Current Practice for all cohorts and perspectives, with differences in cost ranging from $5,369 to $11,960 and in effectiveness from 0.82 to 3.01 quality-adjusted life-years. In a probabilistic sensitivity analysis, Full Access was cost saving in 93% of model iterations. Compared with Current Practice, Full Access averted 5,994 hepatocellular carcinoma cases and 121 liver transplants per 100,000 patients. Conclusions Current Medicaid policies restricting hepatitis C treatment to patients with advanced disease are more costly and less effective than unrestricted, full-access strategies. Collaboration between state and federal payers may be needed to realize the full public health impact of recent innovations in hepatitis C treatment.

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 and aims Uncertainty about optimal treatment duration for buprenorphine opioid agonist therapy may lead to substantial variation in provider and payer decision-making regarding treatment course. We aimed to identify distinct trajectories of buprenorphine use and examine outcomes associated with these trajectories to guide health system interventions regarding treatment length. Design Retrospective cohort study. Setting US Pennsylvania Medicaid. Patients A total of 10 945 enrollees aged 18–64 years initiating buprenorphine treatment between 2007 and 2012. Measurements Group-based trajectory models were used to identify trajectories based on monthly proportion of days covered with buprenorphine in the 12 months post-treatment initiation. We used separate multivariable Cox proportional hazard models to examine associations between trajectories and time to first all-cause hospitalization and emergency department (ED) visit within 12 months after the first-year treatment. Findings Six trajectories [Bayesian information criterion (BIC) = −86 246.70] were identified: 24.9% discontinued buprenorphine 8 months, 9.5% refilled intermittently and 21.2% refilled persistently for 12 months. Persistent refill trajectories were associated with an 18% lower risk of all-cause hospitalizations [hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70–0.95] and 14% lower risk of ED visits (HR = 0.86, 95% CI = 0.78–0.95) in the subsequent year, compared with those discontinuing between 3 and 5 months. Conclusions Six distinct buprenorphine treatment trajectories were identified in this population-based low-income Medicaid cohort in Pennsylvania, USA. There appears to be an association between persistent use of buprenorphine for 12 months and lower risk of all-cause hospitalizations/emergency department visits.

Objective Urban Medicaid enrollees with opioid use disorder often rely on public transit to reach buprenorphine prescribers. Research has not shown whether public transit provides this population with adequate geographic access to buprenorphine prescribers. We examined travel times to buprenorphine prescribers by car and public transit in urban areas, and determined whether car-based Medicaid regulatory standards produce their intended geographic coverage. Methods We obtained data for this study from the Substance Abuse and Mental Health Services Administration's Buprenorphine Practitioner Locator, Microsoft Bing Maps, and the American Community Survey. We examined four urban counties at the centers of the metropolitan statistical areas with the highest 2017 accidental drug poisoning death rates: Kanawha, WV; Montgomery, OH; Philadelphia, PA; and St. Louis City, MO. These counties comprised 696 census tracts representing 1,038,564 households. We calculated travel times from each census tract center to the nearest buprenorphine prescribers by car and public transit, and compared that to 30-min regulatory standards and by whether census tracts had below median levels of car access. We calculated Global Moran's I statistics to determine whether spatial clustering was present among census tracts with limited access to buprenorphine prescribers. Results Households in all but two census tracts could access a buprenorphine prescriber within 30 min by car. However, households in 12.1% (84) of census tracts could not do so by public transit. The correlation between car- and public transit-based travel times to the nearest buprenorphine prescriber was 0.11 (95% CI = 0.07–0.22). More than 15% (47,918) of households in the two less densely populated counties could not travel to the nearest prescriber in 30 min and resided in census tracts where access to cars was relatively low. There was no evidence of spatial clustering among census tracts with public transit travel times exceeding 30 min, or among census tracts with public transit travel times exceeding 30 min and below median values of access to cars. Conclusions Geographic access to buprenorphine prescribers is overestimated by regulatory standards that apply car-based travel time estimates, which are a weak proxy for public transit-based travel times. Since geographic areas with limited access to buprenorphine prescribers do not tend to cluster near one another, individually targeted interventions may be necessary to improve buprenorphine access and utilization.

Opioid prescribing safety among individuals with cancer is poorly understood. This study estimates the prevalence of Pharmacy Quality Alliance opioid measures among individuals with cancer undergoing or not undergoing active treatment versus those without cancer.Pennsylvania Medicaid data (2016) were analyzed in 2018 to identify adults aged 18-64 years with and without cancer diagnoses who had 2 or more opioid prescriptions. Active cancer treatment, defined as having chemotherapy, radiotherapy, cancer surgery, or hospitalization with a primary diagnosis of cancer, was evaluated from October 2015 to December 2016 allowing a ≥3-month look-back period for cancer diagnoses observed in the first quarter of 2016. Opioid dosages (120 morphine milligram equivalents for ≥90 consecutive days), multiple providers (4 or more prescribers and 4 or more pharmacies), and opioid and benzodiazepines overlapping ≥30 days were evaluated.The sample with opioid prescriptions included 111,491 enrollees without cancer diagnoses and 12,819 with cancer, 58.8% of whom were not in active cancer treatment. Among enrollees undergoing cancer treatment, with cancer but not in active treatment, and without cancer, the prevalence of high morphine milligram equivalents was 7.1%, 6.0%, and 4.7% (p0.001), respectively. The corresponding prevalence of multiple providers was 6.7%, 4.1%, and 3.4% (p0.001). Concurrent opioid and benzodiazepine prescriptions occurred in 28.6%, 30.5%, and 26.8% (p0.001), respectively.Individuals with cancer, regardless of treatment status, had higher-risk opioid use based on Pharmacy Quality Alliance measures versus those without cancer. Their systematic exclusion from opioid quality surveillance could create missed opportunities to identify patients at high risk of adverse opioid-related outcomes.

Background Overlapping use of opioids and benzodiazepines is associated with increased risk for overdose. Veterans receiving medications concurrently from the U.S. Department of Veterans Affairs (VA) and Medicare may be at higher risk for such overlap. Objective To assess the association between dual use of VA and Medicare drug benefits and receipt of overlapping opioid and benzodiazepine prescriptions. Design Cross-sectional. Setting VA and Medicare. Participants All veterans enrolled in VA and Medicare Part D who filled at least 2 opioid prescriptions in 2013 (n = 368 891). Measurements Outcomes were the proportion of patients with a Pharmacy Quality Alliance (PQA) measure of opioid-benzodiazepine overlap (≥2 filled prescriptions for benzodiazepines with ≥30 days of overlap with opioids) and the proportion of patients with high-dose opioid-benzodiazepine overlap (≥30 days of overlap with a daily opioid dose >120 morphine milligram equivalents). Augmented inverse probability weighting regression was used to compare these measures by prescription drug source: VA only, Medicare only, or VA and Medicare (dual use). Results Of 368 891 eligible veterans, 18.3% received prescriptions from the VA only, 30.3% from Medicare only, and 51.4% from both VA and Medicare. The proportion with PQA opioid-benzodiazepine overlap was larger for the dual-use group than the VA-only group (23.1% vs. 17.3%; adjusted risk ratio [aRR], 1.27 [95% CI, 1.24 to 1.30]) and Medicare-only group (23.1% vs. 16.5%; aRR, 1.12 [CI, 1.10 to 1.14]). The proportion with high-dose overlap was also larger for the dual-use group than the VA-only group (4.7% vs. 2.3%; aRR, 2.23 [CI, 2.10 to 2.36]) and Medicare-only group (4.7% vs. 2.9%; aRR, 1.06 [CI, 1.02 to 1.11]). Limitation Data are from 2013 and cannot capture medications purchased without insurance; unmeasured confounding may remain in this cross-sectional study. Conclusion Among a national cohort of veterans dually enrolled in VA and Medicare, receiving prescriptions from both sources was associated with greater risk for receiving potentially unsafe overlapping prescriptions for opioids and benzodiazepines. Primary Funding Source U.S. Department of Veterans Affairs.

Although physicians learn about new medical technologies from their peers, the magnitude and source of peer influence is unknown. We estimate the effect of peer adoption of three first-in-class medications (dabigatran, sitigliptin, and aliskiren) on physicians' own adoption of those medications. We included 11,958 physicians in Pennsylvania prescribing anticoagulant, antidiabetic, and antihypertensive medications. We constructed 4 types of peer networks based on shared Medicare and Medicaid patients, medical group affiliation, hospital affiliation, and medical school/residency training. Instrumental variables analysis was used to estimate the causal effect of peer adoption (fraction of peers in each network adopting the new drug) on physician adoption (prescribing at least the median number prescriptions within 15 months of the new drug's introduction). We illustrate how physician network position can inform targeting of interventions to physicians by computing a social multiplier. Dabigatran was adopted by 25.2%, sitagliptin by 24.5% and aliskiren by 8.3% of physicians. A 10-percentage point increase in peer adoption in the patient-sharing network led to a 5.90% (SE = 1.50%, p

Purpose Little is known about the longitudinal patterns of buprenorphine adherence among pregnant women with opioid use disorder, especially when late initiation, nonadherence, or early discontinuation of buprenorphine during pregnancy may increase the risk of adverse outcomes. We aimed to identify distinct trajectories of buprenorphine use during pregnancy, and factors associated with these trajectories in Medicaid-enrolled pregnant women. Methods A retrospective cohort study included 2361 Pennsylvania Medicaid enrollees aged 15 to 46 having buprenorphine therapy during pregnancy and a live birth between 2008 and 2015. We used group-based trajectory models to identify buprenorphine use patterns in the 40 weeks prior to delivery and 12 weeks postdelivery. Multivariable multinomial logistic regression models were used to identify factors associated with specific trajectories. Results Six distinct trajectories were identified. Four groups initiated buprenorphine during the first trimester of the pregnancy (early initiators): 31.6% with persistently high adherence, 15.1% with moderate-to-high adherence, 10.5% with declining adherence, and 16.7% with early discontinuation. Two groups did not initiate buprenorphine until midsecond or third trimester (late initiators): 13.5% had moderate-to-high adherence and 12.6% had low-to-moderate adherence. Factors significantly associated with late initiation and discontinuation were younger age, non-white race, residents of rural counties, fewer outpatient visits, more frequent emergency department visits and hospitalizations, and lower buprenorphine daily dose. Conclusions Six buprenorphine treatment trajectories during pregnancy were identified in this population-based Medicaid cohort, with 25% of women initiating buprenorphine late during pregnancy. Understanding trajectories of buprenorphine use and factors associated with discontinuation/nonadherence may guide integration of behavioral treatment with obstetrical/gynecological care to improve buprenorphine treatment during pregnancy.

Background In the United States, there is well-documented regional variation in prescription drug spending. However, the specific role of physician adoption of brand name drugs on the variation in patient-level prescription drug spending is still being investigated across a multitude of drug classes. Our study aims to add to the literature by determining the association between physician adoption of a first-in-class anti-diabetic (AD) drug, sitagliptin, and AD drug spending in the Medicare and Medicaid populations in Pennsylvania. Methods We obtained physician-level data from QuintilesIMS Xponent™ database for Pennsylvania and constructed county-level measures of time to adoption and share of physicians adopting sitagliptin in its first year post-introduction. We additionally measured total AD drug spending for all Medicare fee-for-service and Part D enrollees (N = 125,264) and all Medicaid (N = 50,836) enrollees with type II diabetes in Pennsylvania for 2011. Finite mixture model regression, adjusting for patient socio-demographic/clinical characteristics, was used to examine the association between physician adoption of sitagliptin and AD drug spending. Results Physician adoption of sitagliptin varied from 44 to 99% across the state’s 67 counties. Average per capita AD spending was $1340 (SD $1764) in Medicare and $1291 (SD $1881) in Medicaid. A 10% increase in the share of physicians adopting sitagliptin in a county was associated with a 3.5% (95% CI: 2.0–4.9) and 5.3% (95% CI: 0.3–10.3) increase in drug spending for the Medicare and Medicaid populations, respectively. Conclusions In a medication market with many choices, county-level adoption of sitagliptin was positively associated with AD spending in Medicare and Medicaid, two programs with different approaches to formulary management.

Doughnuts and Discounts The Bipartisan Budget Act of 2018 accelerates changes to Medicare Part D that were begun by the Affordable Care Act. But though the changes reduce beneficiaries’ out-of-pocket burden in the short run, their effects on drug prices in the long run are less clear.

Objective Guidelines recommend that rheumatoid arthritis (RA) patients with moderate to high disease activity (MHDAS) adjust disease-modifying antirheumatic drug (DMARD) therapy at least every 3 months until reaching low disease activity or remission (LDAS). We examined how quickly RA patients with MHDAS adjust DMARD therapy in clinical practice, and whether those who adjust DMARDs within 90 days in response to MHDAS reach LDAS sooner. Methods We identified RA patients with MHDAS in the University of Pittsburgh Rheumatoid Arthritis Comparative Effectiveness Research (RACER) registry, and conducted a competing risks regression on time to DMARD therapy adjustment and a Cox regression on time to LDAS. Results We identified 538 eligible subjects with 943.5 patient-years of follow-up. Sixty percent of patients with persistent MHDAS adjusted DMARDs within 90 days. Among all subjects, median times to DMARD adjustment and LDAS were 154 [IQR: 1-706] and 301 [140-706] days, respectively. Being elderly (subdistribution hazard ratio (SHR)=0.61, p=0.02), lower baseline disease activity (SHR=0.72, p

Objectives. To estimate the prevalence and consequences of receiving prescription opioids from both the Department of Veterans Affairs (VA) and Medicare Part D. Methods. Among US veterans enrolled in both VA and Part D filling 1 or more opioid prescriptions in 2012 (n = 539 473), we calculated 3 opioid safety measures using morphine milligram equivalents (MME): (1) proportion receiving greater than 100 MME for 1 or more days, (2) mean days receiving greater than 100 MME, and (3) proportion receiving greater than 120 MME for 90 consecutive days. We compared these measures by opioid source. Results. Overall, 135 643 (25.1%) veterans received opioids from VA only, 332 630 (61.7%) from Part D only, and 71 200 (13.2%) from both. The dual-use group was more likely than the VA-only group to receive greater than 100 MME for 1 or more days (34.3% vs 10.9%; adjusted risk ratio [ARR] = 3.0; 95% confidence interval [CI] = 2.9, 3.1), have more days with greater than 100 MME (42.5 vs 16.9 days; adjusted difference = 16.4 days; 95% CI = 15.7, 17.2), and to receive greater than 120 MME for 90 consecutive days (7.8% vs 3.1%; ARR = 2.2; 95% CI = 2.1, 2.3). Conclusions. Among veterans dually enrolled in VA and Medicare Part D, dual use of opioids was associated with more than 2 to 3 times the risk of high-dose opioid exposure.

Expanding Medicaid coverage to low-income adults may have increased smoking cessation through improved access to evidence-based treatments. Our study sought to determine if states' decisions to expand Medicaid increased recent smoking cessation.Using pooled cross-sectional data from the Behavioral Risk Factor Surveillance Survey for the years 2011-2015, we examined the association between state Medicaid coverage and the probability of recent smoking cessation among low-income adults without dependent children who were current or former smokers (n=36,083). We used difference-in-differences estimation to examine the effects of Medicaid coverage on smoking cessation, comparing low-income adult smokers in states with Medicaid coverage to comparable adults in states without Medicaid coverage, with ages 18-64 years to those ages 65 years and above. Analyses were conducted for the full sample and stratified by sex.Residence in a state with Medicaid coverage among low-income adult smokers ages 18-64 years was associated with an increase in recent smoking cessation of 2.1 percentage points (95% confidence interval, 0.25-3.9). In the comparison group of individuals ages 65 years and above, residence in a state with Medicaid coverage expansion was not associated with a change in recent smoking cessation (-0.1 percentage point, 95% confidence interval, -2.1 to 1.8). Similar increases in smoking cessation among those ages 18-64 years were estimated for females and males (1.9 and 2.2 percentage point, respectively).Findings are consistent with the hypothesis that Medicaid coverage expansions may have increased smoking cessation among low-income adults without dependent children via greater access to preventive health care services, including evidence-based smoking cessation services.

Objectives Use of buprenorphine - an effective treatment for opioid use disorders (OUDs) - has increased rapidly in recent years and is often financed by Medicaid. We investigated predictors of buprenorphine treatment, patterns of care, and quality of care in a large state Medicaid program. Methods Data from Pennsylvania Medicaid from 2007 to 2012 provided information regarding diagnoses, demographic characteristics, enrollment, and use of inpatient and outpatient services, and prescription drugs. We identified adult enrollees using buprenorphine, and examined prevalence of OUD diagnosis and patterns of use (duration and dose) and quality of care (physician visits, receipt of behavioral health counseling, urine drug screens, and other prescription drug use). We use a mixed logistic regression model to examine enrollee characteristics associated with buprenorphine use. Results The share of enrollees with OUD filling prescriptions for buprenorphine increased from 2985 (9.8%) to 12,691 (25.2%) from 2007 to 2012. Between 26.2 and 32.0% of enrollees using buprenorphine had no diagnosis of OUD, depending on the year. Only 60.1% of enrollees with buprenorphine use received at least one urine drug screen, 41.0% had behavioral health counseling services, and 34.7 and 38.0% had other opioid and benzodiazepine claims, respectively, concomitant with buprenorphine use. Quality of care was lower among those with no OUD diagnosis recorded. The mean daily doses of buprenorphine decreased over time. We found wide variation in likelihood of buprenorphine use among those with OUD based upon age, sex, and race. Conclusions Increases in buprenorphine treatment in a Medicaid population were observed across time; however, increases varied by age, sex, and rate, and the quality of care received seemed to be generally poor. The quality of the provision of buprenorphine treatment occurring in Medicaid populations should be further explored.

Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes.To apply machine learning to examine how adherence to oral hypoglycemic medications is associated with avoidance of hospitalizations, and to identify adherence thresholds for optimal discrimination of hospitalization risk.A retrospective cohort study of 33,130 non-dual-eligible Medicaid enrollees with type 2 diabetes. We randomly selected 90% of the cohort (training sample) to develop the prediction algorithm and used the remaining (testing sample) for validation. We applied random survival forests to identify predictors for hospitalization and fit survival trees to empirically derive adherence thresholds that best discriminate hospitalization risk, using the proportion of days covered (PDC).Time to first all-cause and diabetes-related hospitalization.The training and testing samples had similar characteristics (mean age, 48 y; 67% female; mean PDC=0.65). We identified 8 important predictors of all-cause hospitalizations (rank in order): prior hospitalizations/emergency department visit, number of prescriptions, diabetes complications, insulin use, PDC, number of prescribers, Elixhauser index, and eligibility category. The adherence thresholds most discriminating for risk of all-cause hospitalization varied from 46% to 94% according to patient health and medication complexity. PDC was not predictive of hospitalizations in the healthiest or most complex patient subgroups.Adherence thresholds most discriminating of hospitalization risk were not uniformly 80%. Machine-learning approaches may be valuable to identify appropriate patient-specific adherence thresholds for measuring quality of care and targeting nonadherent patients for intervention.

Background Health insurance claims data may play an important role for health care systems and payers in monitoring the nonmedical use of prescription opioids (NMPO) among patients. However, these systems require valid methods for identifying NMPO if they are to target individuals for intervention. Limited efforts have been made to define NMPO using administrative data available to health systems and payers. We conducted a systematic review of publications that defined and measured NMPO within health insurance claims databases in order to describe definitions of NMPO and identify areas for improvement. Methods We searched 8 electronic databases for articles that included terms related to NMPO and health insurance claims. A total of 2613 articles were identified in our search. Titles, abstracts, and article full texts were assessed according to predetermined inclusion/exclusion criteria. Following article selection, we extracted general information, conceptual and operational definitions of NMPO, methods used to validate operational definitions of NMPO, and rates of NMPO. Results A total of 7 studies met all inclusion criteria. A range of conceptual NMPO definitions emerged, from concrete concepts of abuse to qualified definitions of probable misuse. Operational definitions also varied, ranging from variables that rely on diagnostic codes to those that rely on opioid dosage and/or filling patterns. Quantitative validation of NMPO definitions was reported in 3 studies (e.g., receiver operating curves or logistic regression), with each study indicating adequate validity. Three studies reported qualitative validation, using face and content validity. One study reported no validation efforts. Rates of NMPO among the studies’ populations ranged from 0.75% to 10.32%. Conclusions Disparate definitions of NMPO emerged from the literature, with little uniformity in conceptualization and operationalization. Validation approaches were also limited, and rates of NMPO varied across studies. Future research should prospectively test and validate a construct of NMPO to disseminate to payers and health officials.

The study examined changes in French general practitioners' (GPs) antipsychotic preferences between 2003 and 2010, a period when evidence challenging the superiority and safety of second-generation antipsychotics was introduced.Data from the IMS Health Disease Analyzer database for a cohort of 347 GPs (with 12 or more antipsychotic prescriptions in 2003 and in 2010) were used. For each year and GP, preferred antipsychotic was defined as the drug most frequently prescribed at the patient level. Trends in mean number of prescriptions, preferred drug, and changes in preferred antipsychotic class were documented.The mean annual number of antipsychotic prescriptions increased over the period (p.001). The percentage of GPs who preferred a second-generation antipsychotic tripled, from 16% in 2003 to 50% in 2010. In 2010, 42% of GPs who preferred first-generation antipsychotics in 2003 had switched their preference to second-generation antipsychotics.GPs' preferences for antipsychotics changed dramatically between 2003 and 2010.

Background: When incretin mimetic (IM) medications were introduced in 2005, their effectiveness compared with other less-expensive second-line diabetes therapies was unknown, especially for older adults. Physicians likely had some uncertainty about the role of IMs in the diabetes treatment armamentarium. Regional variation in uptake of IMs may be a marker of such uncertainty. Objective: To investigate the extent of regional variation in the use of IMs among beneficiaries and estimate the cost implications for Medicare. Methods: This was a cross-sectional analysis of 2009-2010 claims data from a nationally representative sample of 238 499 Medicare Part D beneficiaries aged ≥65 years, who were continuously enrolled in fee-for-service Medicare and Part D and filled ≥1 antidiabetic prescription. Beneficiaries were assigned to 1 of 306 hospital-referral regions (HRRs) using ZIP codes. The main outcome was adjusted proportion of antidiabetic users in an HRR receiving an IM. Results: Overall, 29 933 beneficiaries (12.6%) filled an IM prescription, including 26 939 (11.3%) for sitagliptin or saxagliptin and 3718 (1.6%) for exenatide or liraglutide. The adjusted proportion of beneficiaries using IMs varied more than 3-fold across HRRs, from 5th and 95th percentiles of 5.2% to 17.0%. Compared with non-IM users, IM users faced a 155% higher annual Part D plan ($1067 vs $418) and 144% higher patient ($369 vs $151) costs for antidiabetic prescriptions. Conclusion: Among older Part D beneficiaries using antidiabetic drugs, substantial regional variation exists in the use of IMs, not accounted for by sociodemographics and health status. IM use was associated with substantially greater costs for Part D plans and beneficiaries.

In 2009, Blue Cross-Blue Shield of Massachusetts (BCBSMA) implemented the alternative quality contract (AQC), which pays provider organizations a global payment for all services used by enrollees. BCBSMA claims for 2006-2011 were used to compare youths enrolled in provider organizations participating in the AQC (7,407 person-years [PYs]) with those not participating (45,398 PYs). Difference-in-differences models estimated changes in mental health and substance abuse treatment service utilization and spending attributable to the AQC. The AQC was associated with small increases in the probability of any outpatient visits and in the probability and number of medication management visits among children with attention-deficit hyperactivity disorder (ADHD). Spending did not change, and there was no evidence of reductions in service utilization or spending for children with ADHD in the first three years of AQC implementation.

Second-generation antipsychotics increase the risk of diabetes and other metabolic conditions among individuals with schizophrenia. Although metabolic testing is recommended to reduce this risk, low testing rates have prompted concerns about negative health consequences and downstream medical costs. This study simulated the effect of increasing metabolic testing rates on ten-year prevalence rates of prediabetes and diabetes (diabetes conditions) and their associated health care costs.A microsimulation model (N=21,491 beneficiaries) with a ten-year time horizon was used to quantify the impacts of policies that increased annual testing rates in a Medicaid population with schizophrenia. Data sources included California Medicaid data, National Health and Nutrition Examination Survey data, and the literature. In the model, metabolic testing increased diagnosis of diabetes conditions and diagnosis prompted prescribers to switch patients to lower-risk antipsychotics. Key inputs included observed diagnoses, prescribing rates, annual testing rates, imputed rates of undiagnosed diabetes conditions, and literature-based estimates of policy effectiveness.Compared with 2009 annual testing rates, ten-year outcomes for policies that achieved universal testing reduced exposure to higher-risk antipsychotics by 14%, time to diabetes diagnosis by 57%, and diabetes prevalence by .6%. These policies were associated with higher spending because of testing and earlier treatment.The model showed that policies promoting metabolic testing provided an effective approach to improve the safety of second-generation antipsychotic prescribing in a Medicaid population with schizophrenia; however, the policies led to additional costs at ten years. Simulation studies are a useful source of information on the potential impacts of these policies.

Fall injuries are prevalent in older adults. Whether higher costs occur after both non-fracture and fracture fall injuries is unknown. Medicare payments for 1,790 Health ABC participants (53% women, 37% black; 76.5±2.9 years), were examined 12 months before and after incident fall injuries over 8 years. Non-fall injury participants were matched on utilization in fall injury claim month, with payments/month calculated for 12 months prior vs. after index date. Increased payments/month occurred after vs. prior to total (N=473, 26.4%; $2,602 vs. $1,530), non-fracture (N=102, 5.7%; $2,520 vs. $1,523), and fracture (N=371, 20.7%; $2,624 vs. $1,532) fall injuries (all p

Optimism and cynical hostility are associated with health behaviors and health outcomes, including morbidity and mortality. This analysis assesses their association with longitudinal vigorous physical activity (PA) in postmenopausal women of the Women's Health Initiative (WHI). Subjects include 73,485 women nationwide without history of cancer or cardiovascular disease (CVD), and no missing baseline optimism, cynical hostility, or PA data. The Life Orientation Test-Revised Scale measured optimism. A Cook Medley questionnaire subscale measured cynical hostility. Scale scores were divided into quartiles. Vigorous PA three times or more per week was assessed via self-report at study baseline (1994–1998) and through follow-up year 6. Descriptive analysis mapped lifetime trajectories of vigorous PA (recalled at ages 18, 25, 50; prospectively assessed at baseline, and 3 and 6years later). Hierarchical generalized linear mixed models examined the prospective association between optimism, cynical hostility, and vigorous PA over 6years. Models adjusted for baseline sociodemographic variables, psychosocial characteristics, and health conditions and behaviors. Vigorous PA rates were highest for most optimistic women, but fell for all women by approximately 60% between age 50 and study baseline. In adjusted models from baseline through year 6, most vs. least optimistic women were 15% more likely to exercise vigorously (p

The Affordable Care Act is a substantial reform of the U.S. health care insurance system. Using the RAND COMPARE model, researchers assessed the act's potential economic effects on Pennsylvania, factoring in an optional expansion of Medicaid, and found the state would enjoy significant net benefits. With or without the expansion of Medicaid, the act will increase insurance coverage to hundreds of thousands of Pennsylvanians, but the COMPARE model estimates that the expansion of Medicaid eligibility would cover an additional 350,000 people and bring more than $2 billion in federal spending into the state annually than if the state did not expand. Should the state expand Medicaid, the additional spending will add more than $3 billion a year to the state's GDP and support 35,000 jobs. But Medicaid expansion is not without cost for the state; the estimated cumulative effect on Pennsylvania's Medicaid spending will be $180 million higher with the expansion than without between 2014 and 2020. Substantial reductions in uncompensated care costs for hospitals are possible even without expansion, but savings to hospitals for uncompensated care funding are even larger with the Medicaid expansion, amounting to $550 million or more each year.

Objective To determine the 12-month cost-effectiveness of a collaborative care (CC) program for treating depression following coronary artery bypass graft (CABG) surgery versus physicians' usual care (UC). Methods We obtained 12 continuous months of Medicare and private medical insurance claims data on 189 patients who screened positive for depression following CABG surgery, met criteria for depression when reassessed by telephone 2 weeks following hospitalization (nine-item Patient Health Questionnaire ≥10) and were randomized to either an 8-month centralized, nurse-provided and telephone-delivered CC intervention for depression or to their physicians' UC. Results At 12 months following randomization, CC patients had $2068 lower but statistically similar estimated median costs compared to UC ( P =.30) and a variety of sensitivity analyses produced no significant changes. The incremental cost-effectiveness ratio of CC was −$9889 (−$11,940 to −$7838) per additional quality-adjusted life-year (QALY), and there was 90% probability it would be cost-effective at the willingness to pay threshold of $20,000 per additional QALY. A bootstrapped cost-effectiveness plane also demonstrated a 68% probability of CC "dominating" UC (more QALYs at lower cost). Conclusions Centralized, nurse-provided and telephone-delivered CC for post-CABG depression is a quality-improving and cost-effective treatment that meets generally accepted criteria for high-value care.

Little is known about how Medicare Part D plan features influence choice of generic versus brand drugs.To examine the association between Part D plan features and generic medication use.Data from a 2009 random sample of 1.6 million fee-for-service, Part D enrollees aged 65 years and above, who were not dually eligible or receiving low-income subsidies, were used to examine the association between plan features (generic cost-sharing, difference in brand and generic copay, prior authorization, step therapy) and choice of generic antidepressants, antidiabetics, and statins. Logistic regression models accounting for plan-level clustering were adjusted for sociodemographic and health status.Generic cost-sharing ranged from $0 to $9 for antidepressants and statins, and from $0 to $8 for antidiabetics (across 5th-95th percentiles). Brand-generic cost-sharing differences were smallest for statins (5th-95th percentiles: $16-$37) and largest for antidepressants ($16-$64) across plans. Beneficiaries with higher generic cost-sharing had lower generic use [adjusted odds ratio (OR)=0.97, 95% confidence interval (CI), 0.95-0.98 for antidepressants; OR=0.97, 95% CI, 0.96-0.98 for antidiabetics; OR=0.94, 95% CI, 0.92-0.95 for statins]. Larger brand-generic cost-sharing differences and prior authorization were significantly associated with greater generic use in all categories. Plans could increase generic use by 5-12 percentage points by reducing generic cost-sharing from the 75th ($7) to 25th percentiles ($4-$5), increasing brand-generic cost-sharing differences from the 25th ($25-$26) to 75th ($32-$33) percentiles, and using prior authorization and step therapy.Cost-sharing features and utilization management tools were significantly associated with generic use in 3 commonly used medication categories.

It has been a decade since the Institute of Medicine published its report Unequal Treatment on racial/ethnic disparities in health in the United States (Smedley, Stith, and Nelson 2003). Substantial effort has been devoted to documenting racial/ethnic disparities and understanding their causes. Disparities among people with schizophrenia further burden a severely ill and disabled population whose quality of care is undermined by underuse of evidence-based practices and overuse of low-value or unsupported practices (U.S. Department of Health and Human Services 2003; Horvitz-Lennon et al. 2009a). Racial/ethnic disparities in schizophrenia care have been documented, but little research exists on Latino-white disparities (Vega et al. 2007). Furthermore, most studies have focused on one or a small number of interventions (Barrio et al. 2003; Kreyenbuhl et al. 2003; Leslie and Rosenheck 2004), and although some evidence exists on longitudinal trends (e.g., Busch et al. 2009) it pertains to earlier time periods. Last, although there is evidence of variations in racial/ethnic disparities in health care across regions (Fisher, Goodman, and Chandra 2008), we are not aware of studies that have investigated geographic variations in disparities in schizophrenia care. These knowledge gaps limit our ability to guide policy and improve clinical practice and outcomes (Zaslavsky and Ayanian 2005). We examined whether a comprehensive measure of quality of mental health care differed for black, Latino, and white Medicaid beneficiaries in four large states that contribute about one-third of all Medicaid beneficiaries. We also examined whether overall quality and disparities in quality changed over time, and whether disparities in quality of care differed among the states. We focused on Medicaid beneficiaries because of the dominant role played by Medicaid as health care payer for people with schizophrenia in the United States (Frank and Glied 2006). We hypothesized that (1) racial/ethnic disparities in quality of care exist in all study states; (2) the size of the disparity varies across the study states; and (3) disparities have not improved meaningfully over time.

Objective Physician antipsychotic prescribing behavior may be influenced by comparative effectiveness evidence, regulatory warnings, and formulary and other restrictions on these drugs. This study measured changes in the degree to which physicians are able to customize treatment choices and changes in physician preferences for specific agents after these events. Methods The study used 2002–2007 prescribing data from the IMS Health Xponent database and data on physician characteristics from the American Medical Association for a longitudinal cohort of 7,399 physicians. Descriptive and multivariable regression analyses were conducted of the concentration of prescribing (physician-level Herfindahl index) and preferences for and likelihood of prescribing two first-generation antipsychotics and six second-generation antipsychotics. Analyses adjusted for prescribing volume, specialty, demographic characteristics, practice setting, and education. Results Antipsychotic prescribing was highly concentrated at the physician level, with a mean unadjusted Herfindahl index of .33 in 2002 and .29 in 2007. Psychiatrists reduced the concentration of their prescribing more over time than did other physicians. High-volume psychiatrists had a Herfindahl index that was half that of low-volume physicians in other specialties (.18 versus .36), a difference that remained significant (p, National Institute of Mental Health (U.S.) (Grant R01MH093359), National Institute of Mental Health (U.S.) (Grant P30 MH090333), National Institute of Mental Health (U.S.) (Grant R01MH087488), Agency for Healthcare Research and Quality (Grant R01HS017695), Robert Wood Johnson Foundation (Investigator Award in Health Policy Research)

OBJECTIVE: To assess changes in cervical cancer screening after the 2009 American College of Obstetricians and Gynecologists' guideline change and to determine predictors associated with underscreening and overscreening among Medicaid-enrolled women. METHODS: We performed an observational cohort study of Pennsylvania Medicaid claims from 2007 to 2013. We evaluated guideline adherence of 18- to 64-year-old continuously enrolled women before and after the 2009 guideline change. To define adherence, we categorized intervals between Pap tests as longer than (underscreening), within (appropriate screening), or shorter than (overscreening) guideline-recommended intervals (±6-month). We stratified results by age and assessed predictors of underscreening and overscreening through logistic regression. RESULTS: Among 29,650 women, appropriate cervical cancer screening significantly decreased after the guideline change (from 45% [95% confidence interval (CI) 44–46%] to 11% [95% CI 11–12%] among 17,360 younger than 30 year olds and from 27% [95% CI 26–28%] to 6% [95% CI 6–7%] among 12,290 women 30 years old or older). Overscreening significantly increased (from 6% [95% CI 5–6%] to 67% [95% CI 66–68%] in those younger than 30 years old and from 54% [95% CI 52–55%] to 65% [95% CI 64–67%] in those 30 years old or older), whereas underscreening significantly increased only in those 30 years old or older (from 20% [95% CI 19–21%] to 29% [95% CI 27–30%]). Pap tests after guideline change, pregnancy, Managed Care enrollment (in those younger than 30 years old), and black race (in those younger than 30 years old) were associated with underscreening. Pap tests after guideline change, more visits, more sexually transmitted infection testing, and white race (in those 30 years old or older) were associated with overscreening. CONCLUSION: We observed high rates of cervical cancer overscreening and underscreening and low rates of appropriate screening after the guideline change. Interventions should target both underscreening and overscreening to address these separate yet significant issues.

Multiple medication use within one year is associated with increased fall injury risk in older adults. However, chronically using multiple medications and treated fall injury have rarely been explored, particularly in cohort studies linked with claims data. We examined using >5 medications in 2 or more consecutive years (chronic medication use) as a risk factor for treated fall injury in 1,898 community-dwelling adults (age 73.6±2.9 years; 53% women; 37% black) with linked Medicare Fee-For-Service (FFS) claims from the Health, Aging and Body Composition Study since 1997/98 clinic visit. Incident fall injury (N=546) was the first claim from 1998/99 clinic visit to 12/31/08 with an ICD-9 fall code and non-fracture injury code, or fracture code with/without a fall code. Stepwise Cox models with a time-varying predictor of chronic medication use before fall injury or censoring (N=414) vs. not using >5 medications at the same time (N=1008) were adjusted for baseline demographics, lifestyle factors, fall history, quadriceps strength, cardiovascular disease (CVD), diabetes, sensory nerve impairment, and kidney function. Fall injury risk increased for chronic medication users (37%) vs. non-users (29%) (HR=1.25[1.00-1.57]), though was attenuated after adjustment for CVD and diabetes (HR=1.18[0.93-1.51]). Sensitivity analyses excluding fall-risk-increasing drugs (FRIDs) from medication counts (HR=1.32[0.54-3.20]), or including those using >5 medications non-chronically (N=365) in referent groups (HR=1.22[0.96-1.55]) had consistent findings. Unmeasured comorbidity differences may confound associations of chronic medication use and treated fall injury risk in older adults with Medicare FFS. Considering both chronic diseases and medication use in fall risk assessments is needed.