Publications

BACKGROUND: After non-fatal opioid overdoses, opioid prescribing patterns are often unchanged and the use of medications for opioid use disorder (MOUDs) remains low. Whether such prescribing differs by race/ethnicity remains unknown.

OBJECTIVE: To assess the association of race/ethnicity with the prescribing of opioids and MOUDs after a non-fatal opioid overdose.

DESIGN: Retrospective cohort study.

PARTICIPANTS: Patients prescribed ≥ 1 opioid from July 1, 2010, to September 30, 2015, with a non-fatal opioid overdose in the Veterans Health Administration (VA).

MAIN MEASURES: Primary outcomes were the proportion of patients prescribed: (1) any opioid during the 30 days before and after overdose and (2) MOUDs within 30 days after overdose by race and ethnicity. We conducted difference-in-difference analyses using multivariable regression to assess whether the change in opioid prescribing from before to after overdose differed by race/ethnicity. We also used multivariable regression to test whether MOUD prescribing after overdose differed by race/ethnicity.

KEY RESULTS: Among 16,210 patients with a non-fatal opioid overdose (81.2% were white, 14.3% black, and 4.5% Hispanic), 10,745 (66.3%) patients received an opioid prescription (67.1% white, 61.7% black, and 65.9% Hispanic; p < 0.01) before overdose. After overdose, the frequency of receiving opioids was reduced by 18.3, 16.4, and 20.6 percentage points in whites, blacks, and Hispanics, respectively, with no significant difference-in-difference in opioid prescribing by race/ethnicity (p = 0.23). After overdose, 526 (3.2%) patients received MOUDs (2.9% white, 4.6% black, and 5.5% Hispanic; p < 0.01). Blacks (adjusted OR (aOR) 1.6; 95% CI 1.2, 1.9) and Hispanics (aOR 1.8; 95% CI 1.2, 2.6) had significantly larger odds of receiving MOUDs than white patients.

CONCLUSIONS: In a national cohort of patients with non-fatal opioid overdose in VA, there were no racial/ethnic differences in changes in opioid prescribing after overdose. Although blacks and Hispanics were more likely than white patients to receive MOUDs in the 30 days after overdose, less than 4% of all groups received such therapy.

OBJECTIVES: Continuation of aspirin for secondary prevention in persons with limited life expectancy (LLE) is controversial. We sought to determine the incidence and predictors of aspirin discontinuation in veterans with LLE and/or advanced dementia (LLE/AD) who were taking aspirin for secondary prevention at nursing home admission, stratified by whether their limited prognosis (LP) was explicitly documented at admission.

DESIGN: Retrospective cohort study using linked Veterans Affairs (VA) and Medicare clinical/administrative data and Minimum Data Set resident assessments.

SETTING: All VA nursing homes (referred to as community living centers [CLCs]) in the United States.

PARTICIPANTS: Older (≥65 y) CLC residents with LLE/AD, admitted for 7 days or longer in fiscal years 2009 to 2015, who had a history of coronary artery disease and/or stroke/transient ischemic attack, and used aspirin within the first week of CLC admission (n = 13 844).

MEASUREMENTS: The primary dependent variable was aspirin discontinuation within the first 90 days after CLC admission, defined as 14 consecutive days of no aspirin receipt. Independent variables included an indicator for explicit documentation of LP, sociodemographics, environment of care characteristics, cardiovascular risk factors, bleeding risk factors, individual markers of poor prognosis (eg, cancer, weight loss), and facility characteristics. Fine and Gray subdistribution hazard models with death as a competing risk were used to assess predictors of discontinuation.

RESULTS: Cumulative incidence of aspirin discontinuation was 27% (95% confidence interval [CI] = 26%-28%) in the full sample, 34% (95% CI = 33%-36%) in residents with explicit documentation of LP, and 24% (95% CI = 23%-25%) in residents with no such documentation. The associations of independent variables with aspirin discontinuation differed in residents with vs without explicit LP documentation at admission.

CONCLUSION: Just over one-quarter of patients discontinued aspirin, possibly reflecting the unclear role of aspirin in end of life among prescribers. Future research should compare outcomes of aspirin deprescribing in this population. J Am Geriatr Soc 68:725-735, 2020.

We aimed to assess the association between urban/rural residence and the risk of ischemic stroke in individuals with newly diagnosed atrial fibrillation (AF), and to quantify the role of oral anticoagulation (OAC) initiation in the variation in stroke risk between urban and rural residents with AF. Using 5% random samples of Medicare claims, we identified fee-for-service beneficiaries who were diagnosed with AF between January 2014 and December 2015. Beneficiaries were followed for 1 year since their AF diagnosis, and were categorized according to their initiation of OAC within the year. We used the Rural-Urban Continuum Codes to define urban (levels 1 to 3) and rural (levels 4 to 9) areas. We applied marginal structural models to examine to what extent the difference in stroke risk between rural and urban areas were attributable to the difference in OAC initiation. In the year of AF diagnosis, 52% of those residing in urban areas and 56% residing in rural areas initiated an OAC (p <0.001). Urban residence, compared with rural residence, was associated with a 22% (hazard ratio and 95% confidence interval: 1.22 [1.13, 1.31]) increased risk of stroke. The hazard ratio attributed to urban residence decreased to 1.14 (1.01, 1.30) after accounting for the mediating role of lack of OAC initiation. Lack of OAC initiation explained 34% of the increased stroke risk observed in urban areas. In conclusion, urban residents with newly diagnosed AF were less likely to initiate OAC than rural counterparts, which explained one third of the increased risk of stroke observed in urban areas.

BACKGROUND: Opioid prescribing by dentists for older patients receiving medications with potential contraindications and the subsequent impact on acute care outcomes is not well described.

OBJECTIVES: Our objective of this paper was to evaluate the use of potentially inappropriate medication combinations (PIMCs) involving opioids prescribed by dentists according to the Beers Criteria and risks of 30-day emergency department (ED) visits and all-cause hospitalization among commercially insured dental patients ages 65 years and older.

METHODS: We conducted a retrospective cohort study of 40,800 older dental patient visits in which opioids were prescribed between 2011 and 2015 using the IBM MarketScan databases. Data collection from dental, medical, and pharmacy claims included information on the concurrent use of PIMCs and outcomes of all-cause acute care utilization over the 30-day period after dental encounters.

RESULTS: For the overall cohort, the median age was 69 years, and 45% were women. The prevalence of PIMCs per Beers Criteria was 10.4%. A total of 947 all-cause acute care events were observed in the 30 days post-dental visit. Patients with PIMCs involving opioids prescribed by dentists according to the Beers Criteria had higher rates of acute care use (3.3% vs 2.2%, p<0.001), which were associated with an increased risk of all-cause acute care utilization (adjusted risk ratio [RR] 1.23, 95% confidence interval [CI] 1.02-1.48). A dose-response relationship was seen with increasing oral morphine equivalents prescribed and increased acute care utilization (p<0.001).

CONCLUSION: A significant proportion of older patients receiving opioids at dental visits use psychotropic medications that in combination should be avoided according to the American Geriatric Society Beers Criteria.

OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.

METHODS: This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile.

RESULTS: The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000).

CONCLUSIONS: Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

INTRODUCTION: The objective of this study was to compare the risk of stroke in atrial fibrillation (AF) with adherent use of oral anticoagulation (OAC), non-adherent use, and non-use of OAC.

METHODS: Using 2013-2016 Medicare claims data, we identified patients newly diagnosed with AF in 2014-2015 and collected prescriptions filled for OAC in the 12 months after AF diagnosis (n = 39,272). We categorized participants each day into three time-dependent exposures: adherent use (≥ 80% of the previous 30 days covered with OAC), non-adherent use (0-80% covered with OAC), and non-use (0%). We constructed Cox proportional hazards models to estimate the association between time-dependent exposures and time to stroke, adjusting for demographics and clinical characteristics.

RESULTS: The sample included 39,272 patients. Study participants spent 35.0% of the follow-up period in the adherent use exposure category, 10.9% in the non-adherent category, and 54.0% in the non-use category. OAC adherent use [hazard ratio (HR) 0.62; 95% confidence interval (CI) 0.52-0.74] and non-adherent use (HR 0.74; 95% CI 0.57-0.95) were associated with lower hazards of stroke than non-use. Adherent use of DOAC (HR 0.54; 95% CI 0.42-0.69) and warfarin (HR 0.70; 95% CI 0.56-0.89) was associated with lower risk of stroke than non-use, but the risk of stroke did not statistically differ between DOAC and warfarin adherent use (HR 0.77; 95% CI 0.56-1.04).

DISCUSSION: Although adherence to OAC reduces stroke risk by nearly 40%, newly diagnosed AF patients in Medicare adhere to OAC on average only one third of the first year after AF diagnosis.

BACKGROUND: In 2018, the Department of Veterans Affairs (VA) issued Notice 2018-08 requiring facilities to complete "case reviews" for Veterans identified in the Stratification Tool for Opioid Risk Mitigation (STORM) dashboard as high risk for adverse outcomes among patients prescribed opioids. Half of the facilities were randomly assigned to a Notice version including additional oversight. We evaluated implementation strategies used, whether strategies differed by randomization arm, and which strategies were associated with case review completion rates.

METHODS: Facility points of contact completed a survey assessing their facility's use of 68 implementation strategies based on the Expert Recommendations for Implementing Change taxonomy. We collected respondent demographic information, facility-level characteristics, and case review completion rates (percentage of high-risk patients who received a case review). We used Kruskal-Wallis tests and negative binomial regression to assess strategy use and factors associated with case reviews.

RESULTS: Contacts at 89 of 140 facilities completed the survey (64%) and reported using a median of 23 (IQR 16-31) strategies. The median case review completion rate was 71% (IQR 48-95%). Neither the number or types of strategies nor completion rates differed by randomization arm. The most common strategies were using the STORM dashboard (97%), working with local opinion leaders (80%), and recruiting local partners (80%). Characteristics associated with case review completion rates included respondents being ≤ 35 years old (incidence rate ratio, IRR 1.35, 95% CI 1.09-1.67) and having < 5 years in their primary role (IRR 1.23; 95% CI 1.01-1.51), and facilities having more prior academic detailing around pain and opioid safety (IRR 1.40, 95% CI 1.12-1.75). Controlling for these characteristics, implementation strategies associated with higher completion rates included (1) monitoring and adjusting practices (adjusted IRR (AIRR) 1.40, 95% CI 1.11-1.77), (2) identifying adaptations while maintaining core components (AIRR 1.28, 95% CI 1.03-1.60), (3) conducting initial training (AIRR 1.16, 95% CI 1.02-1.50), and (4) regularly sharing lessons learned (AIRR 1.32, 95% CI 1.09-1.59).

CONCLUSIONS: In this national evaluation of strategies used to implement case reviews of patients at high risk of opioid-related adverse events, point of contact age and tenure in the current role, prior pain-related academic detailing at the facility, and four specific implementation strategies were associated with case review completion rates, while randomization to additional centralized oversight was not.

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

This cross-sectional study assesses drug coverage, formulary restrictions, median retail prices, and annual out-of-pocket costs associated with commonly used insulin products across Medicare Part D plans.