Reforming Reimbursement for the US Food and Drug Administration’s Accelerated Approval Program to Support State Medicaid Programs

Importance The US Food and Drug Administration (FDA) has an accelerated approval program that has become the subject of scholarly attention and criticism, not only for the FDA’s oversight of the program but also for its implications for payers. Observations State Medicaid programs’ legal obligations to provide reimbursement for accelerated approval products have created fiscal challenges for Medicaid that have been exacerbated by industry’s changing use of the accelerated approval program over time. Although strategies for accelerated approval reforms have been proposed, most focus on reforming the FDA’s accelerated approval pathway and product regulation without taking into account the implications of this pathway for state Medicaid programs. There is a need for policy reforms that balance the goal of speeding approval of important medicines with states’ real concerns regarding spending on medications with little evidence of clinical benefits. Areas of potential reform include formulary exclusion, Medicaid rebates, value-based pricing, and consolidated purchasing or carve outs. Conclusions and Relevance Policy makers may wish to consider options for reforming reimbursement for accelerated approval products in addition to reforms to the FDA’s operation of the pathway. Policy reform proposals can provide a range of options to evaluate trade-offs of access and pricing

Recent Trends in Medicaid Spending and Use of Drugs with US Food and Drug Administration Accelerated Approval

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

Trends in Cancer-Center Spending on Advertising in the United States, 2005 to 2014

In the United States, cancer centers commonly advertise clinical services directly to the public. Potential benefits of such advertising include informing patients about available treatments and reducing the stigma of cancer.1, 2 Potential risks include misleading vulnerable patients and creating false hopes, increasing demand for unnecessary tests and treatments, adversely affecting existing clinician-patient relationships, and increasing healthcare costs.3, 4 Understanding trends in the advertising spending of cancer centers and the characteristics of the centers that spend the most can inform the debate about the impact of these advertisements. Our hypothesis was that advertising spending has increased and that spending is concentrated among for-profit cancer centers

How Quickly Do Physicians Adopt New Drugs? The Case of Second-Generation Antipsychotics

Objective The authors examined physician adoption of second-generation antipsychotic medications and identified physician-level factors associated with early adoption. Methods The authors estimated Cox proportional-hazards models of time to adoption of nine second-generation antipsychotics by 30,369 physicians who prescribed antipsychotics between 1996 and 2008, when the drugs were first introduced, and analyzed the total number of agents prescribed during that time. The models were adjusted for physicians’ specialty, demographic characteristics, education and training, practice setting, and prescribing volume. Data were from IMS Xponent, which captures over 70% of all prescriptions filled in the United States, and the American Medical Association Physician Masterfile. Results On average, physicians waited two or more years before prescribing new second-generation antipsychotics, but there was substantial heterogeneity across products in time to adoption. General practitioners were much slower than psychiatrists to adopt second-generation antipsychotics (hazard ratios (HRs) range .10−.35), and solo practitioners were slower than group practitioners to adopt most products (HR range .77−.89). Physicians with the highest antipsychotic-prescribing volume adopted second-generation antipsychotics much faster than physicians with the lowest volume (HR range .15−.39). Psychiatrists tended to prescribe a broader set of antipsychotics (median=6) than general practitioners and neurologists (median=2) and pediatricians (median=1). Conclusions As policy makers search for ways to control rapid health spending growth, understanding the factors that influence physician adoption of new medications will be crucial in the efforts to maximize the value of care received by individuals with mental disorders as well as to improve medication safety.National Institute of Mental Health (U.S.) (R01 MH093359)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research)Agency for Healthcare Research and Quality (R01HS017695)National Institute of Mental Health (U.S.) ((NIMH) R34 MH082682)National Institute of Mental Health (U.S.) ((NIMH) P30 MH090333)National Institute of Mental Health (U.S.) ((NIMH) R01 MH087488)National Science Foundation (U.S.) (0915674

Regional Variation in Physician Adoption of Antipsychotics: Impact on US Medicare expenditures

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 U.S.National Institute of Mental Health (U.S.) (R01 MH093359

Persistent Polypharmacy and Fall Injury Risk: The Health, Aging and Body Composition Study

Background Older adults receive treatment for fall injuries in both inpatient and outpatient settings. The effect of persistent polypharmacy (i.e. using multiple medications over a long period) on fall injuries is understudied, particularly for outpatient injuries. We examined the association between persistent polypharmacy and treated fall injury risk from inpatient and outpatient settings in community-dwelling older adults. Methods The Health, Aging and Body Composition Study included 1764 community-dwelling adults (age 73.6 ± 2.9 years; 52% women; 38% black) with Medicare Fee-For-Service (FFS) claims at or within 6 months after 1998/99 clinic visit. Incident fall injuries (N = 545 in 4.6 ± 2.9 years) were defined as the initial claim with an ICD-9 fall E-code and non-fracture injury, or fracture code with/without a fall code from 1998/99 clinic visit to 12/31/08. Those without fall injury (N = 1219) were followed for 8.1 ± 2.6 years. Stepwise Cox models of fall injury risk with a time-varying variable for persistent polypharmacy (defined as ≥6 prescription medications at the two most recent consecutive clinic visits) were adjusted for demographics, lifestyle characteristics, chronic conditions, and functional ability. Sensitivity analyses explored if persistent polypharmacy both with and without fall risk increasing drugs (FRID) use were similarly associated with fall injury risk. Results Among 1764 participants, 636 (36%) had persistent polypharmacy over the follow-up period, and 1128 (64%) did not. Fall injury incidence was 38 per 1000 person-years. Persistent polypharmacy increased fall injury risk (hazard ratio [HR]: 1.31 [1.06, 1.63]) after adjusting for covariates. Persistent polypharmacy with FRID use was associated with a 48% increase in fall injury risk (95%CI: 1.10, 2.00) vs. those who had non-persistent polypharmacy without FRID use. Risks for persistent polypharmacy without FRID use (HR: 1.22 [0.93, 1.60]) and non-persistent polypharmacy with FRID use (HR: 1.08 [0.77, 1.51]) did not significantly increase compared to non-persistent polypharmacy without FRID use. Conclusions Persistent polypharmacy, particularly combined with FRID use, was associated with increased risk for treated fall injuries from inpatient and outpatient settings. Clinicians may need to consider medication management for FRID and other fall prevention strategies in community-dwelling older adults with persistent polypharmacy to reduce fall injury risk

Optimism may moderate screening mammogram frequency in Medicare: A longitudinal study

Higher trait optimism and/or lower cynical hostility are associated with healthier behaviors and lower risk of morbidity and mortality, yet their association with health care utilization has been understudied. Whether these psychological attitudes are associated with breast cancer screening behavior is unknown. To assess the association of optimism and cynical hostility with screening mammography in older women and whether sociodemographic factors acted as mediators of these relationships, we used Women\u27s Health Initiative (WHI) observational cohort survey data linked to Medicare claims. The sample includes WHI participants without history of breast cancer who were enrolled in Medicare Parts A and B for \u3e /=2 years from 2005-2010, and who completed WHI baseline attitudinal questionnaires (n = 48,291). We used survival modeling to examine whether screening frequency varied by psychological attitudes (measured at study baseline) after adjusting for sociodemographic characteristics, health conditions, and healthcare-related variables. Psychological attitudes included trait optimism (Life Orientation Test-Revised) and cynical hostility (Cook Medley subscale), which were self-reported at study baseline. Sociodemographic, health conditions, and healthcare variables were self-reported at baseline and updated through 2005 as available. Contrary to our hypotheses, repeated events survival models showed that women with the lowest optimism scores (i.e., more pessimistic tendencies) received 5% more frequent screenings after complete covariate adjustment (p \u3c .01) compared to the most optimistic group, and showed no association between cynical hostility and frequency of screening mammograms. Sociodemographic factors did not appear to mediate the relationship between optimism and screenings. However, higher levels of education and higher levels of income were associated with more frequent screenings (both p \u3c .01). We also found that results for optimism were primarily driven by women who were aged 75 or older after January 2009, when changes to clinical guidelines lead to uncertainty about risks and benefits of screening in this age group. The study demonstrated that lower optimism, higher education, and higher income were all associated with more frequent screening mammograms in this sample after repeated events survival modeling and covariate adjustment

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

IMPORTANCE Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. OBJECTIVE To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. DESIGN, SETTING, AND PARTICIPANTS A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. EXPOSURES Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. MAIN OUTCOMES AND MEASURES Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. RESULTS Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2%[142 180] of the cohort), medium-risk (18.6%[34 579] of the cohort), and high-risk (5.2%[9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. CONCLUSIONS AND RELEVANCE Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.NIH/National Institute on Drug Abuse [R01DA044985]; Pharmaceutical Research and Manufacturers of America Foundation Research Starter AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]