Publications

BACKGROUND: Mitigating the risks of adverse outcomes from opioids is critical. Thus, the Veterans Affairs (VA) Healthcare System developed the Stratification Tool for Opioid Risk Management (STORM), a dashboard to assist clinicians with opioid risk evaluation and mitigation. Updated daily, STORM calculates a "risk score" of adverse outcomes (e.g., suicide-related events, overdoses, overdose death) from variables in the VA medical record for all patients with an opioid prescription and displays this information along with documentation of recommended risk mitigation strategies and non-opioid pain treatments. In March 2018, the VA issued a policy notice requiring VA Medical Centers (VAMCs) to complete case reviews for patients whom STORM identifies as very high-risk (i.e., top 1% of STORM risk scores). Half of VAMCs were randomly assigned notices that also stated that additional support and oversight would be required for VAMCs that failed to meet an established percentage of case reviews. Using a stepped-wedge cluster randomized design, VAMCs will be further randomized to conduct case reviews for an expanded pool of patients (top 5% of STORM risk scores vs. 1%) starting either 9 or 15 months after the notice was released, creating four natural arms. VA commissioned an evaluation to understand the implementation strategies and factors associated with case review completion rates, whose protocol is described in this report.

METHODS: This mixed-method study will include an online survey of all VAMCs to identify implementation strategies and interviews at a subset of facilities to identify implementation barriers and facilitators. The survey is based on the Expert Recommendations for Implementing Change (ERIC) project, which engaged experts to create consensus on 73 implementation strategies. We will use regression models to compare the number and types of implementation strategies across arms and their association with case review completion rates. Using questions from the Consolidated Framework for Implementation Research, we will interview stakeholders at 40 VAMCs with the highest and lowest adherence to opioid therapy guidelines.

DISCUSSION: By identifying which implementation strategies, barriers, and facilitators influence case reviews to reduce opioid-related adverse outcomes, this unique implementation evaluation will enable the VA to improve the design of future opioid safety initiatives.

TRIAL REGISTRATION: This project is registered at the ISRCTN Registry with number ISRCTN16012111 . The trial was first registered on 5/3/2017.

OBJECTIVES: To evaluate how changes in generic drug prices and the incidence of abrupt price increases varied with the number of manufacturers supplying each drug.

STUDY DESIGN: Analysis of 2005 to 2016 monthly wholesale acquisition costs (WACs) and University of Pittsburgh Medical Center Health Plan counts of pharmacy claims for National Drug Codes (NDCs) for generic drugs.

METHODS: Each year, NDCs were categorized according to the number of manufacturers offering each combination of active ingredient and dosage form: 1 to 3, 4 to 7, and more than 7. For every month from January 2006 to January 2017, we estimated the 12-month change in WAC (eg, 12-month change in January 2006 was calculated as the difference in WAC between January 2006 and January 2005, divided by the WAC in January 2005), before and after weighting each NDC by counts of pharmacy claims. We evaluated the proportion of NDCs that had large price increases, greater than 20%, 50%, 100%, and 500% within a year.

RESULTS: Before 2010, price changes were higher for drugs supplied by a lower number of manufacturers; however, after 2010, prices increased sharply, and drugs supplied by 4 to 7 manufacturers showed increases similar to or higher than those supplied by 1 to 3. In 2013, prices increased by an average of 29% for drugs supplied by 1 to 3 and 4 to 7 manufacturers, and 10% for more than 7. Price changes increased after weighting by counts of pharmacy claims, demonstrating that price increases disproportionately affected widely used drugs. The proportion of NDCs from drugs supplied by 1 to 3 manufacturers that doubled in price within a year was 3.6 times higher in 2012 to 2015 than in 2005 to 2009 (4.6% vs 1.3%, respectively).

CONCLUSIONS: Increases in generic drug prices are concerning because they affected widely used drugs and suggest that generic drug prices may be increasingly insensitive to competition.

OBJECTIVE: To characterize primary care physicians' (PCPs') perceptions of the reasons patients receive opioid medications from both VA and non-VA healthcare systems.

DESIGN: Qualitative.

SETTING: Department of Veterans Affairs (VA).

PARTICIPANTS: Forty-two VA PCPs who prescribed opioids to at least 15 patients and who practiced in Massachusetts, Illinois, or Pennsylvania.

METHODS: Thirty-minute, semistructured telephone interviews were conducted in 2016, addressing topics regarding PCPs' experiences and perspectives on patients who use both VA and non-VA healthcare systems to obtain prescription opioids. The analysis focused on two questions: attributes that PCPs believe characterize dual-use patients and reasons that PCPs believe patients obtain opioids from both VA and non-VA sources.

RESULTS: PCPs identified multiple attributes of, and reasons for, patients obtaining opioid medications from both VA and non-VA healthcare systems, including pain issues, opioid misuse, having healthcare managed through multiple healthcare systems, and transferring care between systems. More than half of the PCPs identified addiction and diversion as key attributes and reasons why patients obtain prescription opioids from multiple sources. PCPs also identified several behavioral and psychological factors as attributes of these patients.

CONCLUSIONS: PCPs within the VA have varying perceptions of patients obtaining opioid medications from multiple healthcare systems, with pain complaints and opioid misuse as the primary themes. This knowledge about PCPs' perceptions can be incorporated into interventions to better manage pain and prescription opioid use by VA patients.

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.

BACKGROUND: In 2012, Medicare incorporated medication adherence targeting oral antidiabetic medications, renin-angiotensin system (RAS) antagonists, and statins as highly weighted components in its Star Ratings Program. In the same year, health plans began receiving quality bonus payments for higher star ratings.

OBJECTIVE: We aimed to assess how these policy changes affected adherence to targeted and other chronic disease medications in the United States.

METHODS: We performed interrupted time series analyses to assess monthly changes in medication adherence from 2010 to 2016 using health plans' Medicare claims submitted to a large pharmacy benefits manager. We conducted 2 sets of analyses. The first examined whether policy changes affected adherence to the 3 targeted therapy classes, and the second assessed the association between policy changes and adherence to 5 chronic disease classes not targeted by star ratings. For the second analysis, we further compared adherence between members who concomitantly used and did not use targeted medications.

RESULTS: For star-ratings analyses, we studied 240 811 members on oral antidiabetic medications, 500 958 on RAS antagonists, and 471 135 on statins. Adherence for all star rating-targeted and nontargeted medications increased after 2012 (P < .001). Oral antidiabetic, statin, and RAS antagonist adherence was, respectively, 11.2%, 3.7%, and 8.1% higher than adherence without policy changes (P < .001). Nontargeted antihypertensive and antihyperlipidemic adherence trends were higher among those concomitantly on star rating-targeted medications compared with those who were not (P < .001).

CONCLUSIONS: As policy makers strive to identify optimal quality measures for improving healthcare delivery, it is important to consider that incentives can promote improved performance in both targeted measures and related outcomes.

BACKGROUND: Few studies have assessed prescription opioid supply preceding death in individuals dying from unintentional prescription opioid overdoses, or described the characteristics of these individuals, particularly among Veterans.

OBJECTIVES: To describe the history of prescription opioid supply preceding prescription opioid overdose death among Veterans.

METHODS: In a national cohort of Veterans who filled ≥1 opioid prescriptions from the Veterans Health Administration (VA) or Medicare Part D during 2008-2013, we identified deaths from unintentional or undetermined-intent prescription opioid overdoses in 2012-2013. We captured opioid prescriptions using both linked VA and Part D data, and VA data only.

RESULTS: Among 1181 decedents, 643 (54.4%) had prescription opioid supply on the day of death, and 735 (62.2%) within 30 days based on linked data, compared to 40.1% and 46.7%, respectively, using VA data alone. Decedents with prescription opioid supply were significantly older and less likely to have alcohol or illicit drugs as co-occurring substances involved in the overdose. Using linked data, 241 (20.4%) decedents lacked prescription opioid supply within a year of death.

CONCLUSIONS: Many VA patients who die from prescription opioid overdose receive opioid prescriptions outside VA or not at all. It is important to supplement VA with non-VA data to more accurately measure prescription opioid exposure and improve opioid medication safety.

Background Only 50% of atrial fibrillation ( AF ) patients recommended for oral anticoagulation ( OAC ) use these medications, and less than half of them adhere to OAC . In a cohort of Medicare beneficiaries newly diagnosed with AF , we identified groups of patients with similar trajectories of OAC use and adherence, and evaluated patient characteristics affecting group membership. Methods and Results We selected continuously enrolled Medicare Part D beneficiaries with first AF diagnosis in 2014 to 2015 (n=34 898). We calculated the proportion of days covered with OAC over the first 12 months after diagnosis and identified OAC adherence trajectories using group-based trajectory models. We constructed multinomial logistic regression models to evaluate how demographics, system-level factors, and clinical characteristics were associated with group membership. We identified 4 trajectories of OAC adherence: patients who never used OAC (43.8%), late OAC initiators (7.6%), early OAC discontinuers (8.9%), and continuously adherent patients (40.1%). Predictors such as sex, black race, residence in the South, or HAS - BLED score were associated with not only OAC use, but also the timing of initiation and the likelihood of discontinuation. For example, HAS - BLED score ≥4 was associated with a higher likelihood of not using OAC (odds ratio 1.35; 95% CI , 1.14-1.62), of late initiation (1.55; 95% CI , 1.11-2.05), and of early discontinuation (odds ratio 1.35; 95% CI , 1.01-1.84). Conclusions We identified 4 distinct trajectories of OAC adherence after first AF diagnosis, with <45% of newly diagnosed AF patients belonging to the trajectory group characterized by continuous OAC adherence. Trajectories were associated not only with demographic and clinical characteristics but also with regional factors.

IMPORTANCE: The United States consumes most of the opioids worldwide despite representing a small portion of the world's population. Dentists are one of the most frequent US prescribers of opioids despite data suggesting that nonopioid analgesics are similarly effective for oral pain. While oral health and dentist use are generally similar between the United States and England, it is unclear how opioid prescribing by dentists varies between the 2 countries.

OBJECTIVE: To compare opioid prescribing by dentists in the United States and England.

DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional study of prescriptions for opioids dispensed from outpatient pharmacies and health care settings between January 1 and December 31, 2016, by dentists in the United States and England. Data were analyzed from October 2018 to January 2019.

EXPOSURES: Opioids prescribed by dentists.

MAIN OUTCOMES AND MEASURES: Proportion and prescribing rates of opioid prescriptions.

RESULTS: In 2016, the proportion of prescriptions written by US dentists that were for opioids was 37 times greater than the proportion written by English dentists. In all, 22.3% of US dental prescriptions were opioids (11.4 million prescriptions) compared with 0.6% of English dental prescriptions (28 082 prescriptions) (difference, 21.7%; 95% CI, 13.8%-32.1%; P < .001). Dentists in the United States also had a higher number of opioid prescriptions per 1000 population (35.4 per 1000 US population [95% CI, 25.2-48.7 per 1000 population] vs 0.5 per 1000 England population [95% CI, 0.03-3.7 per 1000 population]) and number of opioid prescriptions per dentist (58.2 prescriptions per dentist [95% CI, 44.9-75.0 prescriptions per dentist] vs 1.2 prescriptions per dentist [95% CI, 0.2-5.6 prescriptions per dentist]). While the codeine derivative dihydrocodeine was the sole opioid prescribed by English dentists, US dentists prescribed a range of opioids containing hydrocodone (62.3%), codeine (23.2%), oxycodone (9.1%), and tramadol (4.8%). Dentists in the United States also prescribed long-acting opioids (0.06% of opioids prescribed by US dentists [6425 prescriptions]). Long-acting opioids were not prescribed by English dentists.

CONCLUSIONS AND RELEVANCE: This study found that in 2016, dentists in the United States prescribed opioids with significantly greater frequency than their English counterparts. Opioids with a high potential for abuse, such as oxycodone, were frequently prescribed by US dentists but not prescribed in England. These results illustrate how 1 source of opioids differs substantially in the United States vs England. To reduce dental opioid prescribing in the United States, dentists could adopt measures similar to those used in England, including national guidelines for treating dental pain that emphasize prescribing opioids conservatively.

BACKGROUND/OBJECTIVES: Prostate-specific antigen (PSA) screening can be of low value in older adults. Our objective was to quantify the prevalence and variation of low-value PSA screening across the Veterans Health Administration (VA), which has instituted programs to reduce low-value care.

DESIGN: Retrospective cohort.

SETTING: VA administrative data, 2014 to 2015.

PARTICIPANTS: National random sample (N = 214 480) of male veterans, aged 75 years or older.

MEASUREMENTS: We defined PSA screening in men aged 75 years or older without a history of prostate cancer as low value, per established definitions in Medicare. We calculated screening rates overall and by VA Medical Center (VAMC), adjusting for patient and VAMC-level factors. We characterized variation across VAMCs using the adjusted median odds ratio (OR) and compared the adjusted OR of screening between VAMCs in different deciles of low-value screening rates. In separate sensitivity analyses, we assessed screening in veterans at greatest risk of 1-year mortality and among veterans after excluding those who underwent prostatectomy, had a prior PSA elevation, or had a clinical indication for testing.

RESULTS: Overall, 37 867 (17.7%) of veterans underwent low-value PSA screening (VAMC range = 3.3%-38.2%). The adjusted median OR was 1.88, meaning the median odds of screening would increase by 88% were a veteran to transfer his care to a VAMC with higher screening rates. Veterans at VAMCs in the top decile had an adjusted OR of 12.9 (95% confidence interval = 11.0-15.2) compared to those veterans in the lowest decile. Among veterans with the greatest mortality risk (n = 23 377), 3496 (15.0%) underwent screening (VAMC range = 1.7%-46.3%). After excluding veterans with a prior prostatectomy, PSA elevation, or a potential clinical indication, 31 556 (14.7%) underwent screening (VAMC range = 2.0%-49.9%).

CONCLUSIONS: In a national cohort of older veterans, more than one in six received low-value PSA screening, with greater than 10-fold variation across VAMCs and high rates of screening among those with the greatest mortality risk. J Am Geriatr Soc 67:1922-1927, 2019.