Publications

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.

This study uses the most recent national data available from Medicare and the Department of Veterans Affairs to quantify the savings Medicare Part D would achieve if it paid the same prices for prescription drugs currently paid by the Department of Veterans Affairs.

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 < 0.001). Combining RxRisk-VM with claims-based indices enhanced its accuracy over each index alone (all models C ≥ 0.81). Relative model performance was similar for 3-year mortality.

CONCLUSIONS: The RxRisk-VM index exhibited a high level of, but slightly less, accuracy in predicting mortality in comparison to claims-based risk indices.

IMPLICATIONS: Its application may enhance the accuracy of studies examining VA and non-VA care and enable risk adjustment when diagnostic claims are not available or biased.

LEVEL OF EVIDENCE: Level 3.

BACKGROUND: More than half of enrollees in the U.S. Department of Veterans Affairs (VA) are also covered by Medicare and can choose to receive their prescriptions from VA or from Medicare-participating providers. Such dual-system care may lead to unsafe opioid use if providers in these 2 systems do not coordinate care or if prescription use is not tracked between systems.

OBJECTIVE: To evaluate the association between dual-system opioid prescribing and death from prescription opioid overdose.

DESIGN: Nested case-control study.

SETTING: VA and Medicare Part D.

PARTICIPANTS: Case and control patients were identified from all veterans enrolled in both VA and Part D who filled at least 1 opioid prescription from either system. The 215 case patients who died of a prescription opioid overdose in 2012 or 2013 were matched (up to 1:4) with 833 living control patients on the basis of date of death (that is, index date), using age, sex, race/ethnicity, disability, enrollment in Medicaid or low-income subsidies, managed care enrollment, region and rurality of residence, and a medication-based measure of comorbid conditions.

MEASUREMENTS: The exposure was the source of opioid prescriptions within 6 months of the index date, categorized as VA only, Part D only, or VA and Part D (that is, dual use). The outcome was unintentional or undetermined-intent death from prescription opioid overdose, identified from the National Death Index. The association between this outcome and source of opioid prescriptions was estimated using conditional logistic regression with adjustment for age, marital status, prescription drug monitoring programs, and use of other medications.

RESULTS: Among case patients, the mean age was 57.3 years (SD, 9.1), 194 (90%) were male, and 181 (84%) were non-Hispanic white. Overall, 60 case patients (28%) and 117 control patients (14%) received dual opioid prescriptions. Dual users had significantly higher odds of death from prescription opioid overdose than those who received opioids from VA only (odds ratio [OR], 3.53 [95% CI, 2.17 to 5.75]; P < 0.001) or Part D only (OR, 1.83 [CI, 1.20 to 2.77]; P = 0.005).

LIMITATION: Data are from 2012 to 2013 and cannot capture prescriptions obtained outside the VA or Medicare Part D systems.

CONCLUSION: Among veterans enrolled in VA and Part D, dual use of opioid prescriptions was independently associated with death from prescription opioid overdose. This risk factor for fatal overdose among veterans underscores the importance of care coordination across health care systems to improve opioid prescribing safety.

PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.

The United States is facing an opioid crisis in which overdose is the leading cause of injury death-misuse of opioids constitutes the vast majority of those deaths. In 2016 alone, over 42,000 people died from opioid overdose, an increase of 27% from the prior year. Deployment of the Stratification Tool for Opioid Risk Mitigation (STORM), a clinical decision support tool to improve opioid safety, is one response by the Veterans Health Administration (VHA) to the opioid crisis. STORM identifies VHA patients at very high risk of opioid-related adverse events and lists potential risk mitigation strategies. Deployment of STORM also helps VHA meet certain requirements of the Comprehensive Addiction and Recovery Act of 2016. In alignment with the VHA's learning health care system initiative, a multidisciplinary team designed a randomized evaluation of a policy approach to mandating case reviews of very-high-risk patients identified by STORM and the impacts of patient inclusion versus exclusion in mandated STORM case reviews using a stepped-wedge design. The STORM evaluation involves drafting the policy notice, shepherding it through the VHA approval process, and implementing the cluster randomized design. This mixed-methods evaluation includes (1) a qualitative assessment of medical center implementation strategies with the aim of understanding of how STORM is incorporated into practice, and (2) quantitative analyses of the relations between policy mandates and STORM inclusion on opioid-related adverse events. The findings from this synergistic research design will yield critical insights for VHA leadership to refine opioid prescribing-related policy and practice.