Publications

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.

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.

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: The opioid epidemic has disproportionately affected rural areas, where a limited number of health care providers offer medication-assisted treatment (MAT), the mainstay of treatment for opioid use disorder (OUD). Rural residents with OUD may face multiple barriers to engagement in MAT including long travel distances.

OBJECTIVE: To examine the degree to which rural residents with OUD are engaged with primary care providers (PCPs), describe the role of rural PCPs in MAT delivery, and estimate the association between enrollee distance to MAT prescribers and MAT utilization.

DESIGN: Retrospective cohort study.

PARTICIPANTS: Medicaid-enrolled adults diagnosed with OUD in 23 rural Pennsylvania counties.

MAIN MEASURES: Primary care utilization, MAT utilization, distance to nearest possible MAT prescriber, mean distance traveled to actual MAT prescribers, and continuity of pharmacotherapy.

KEY RESULTS: Of the 7930 Medicaid enrollees with a diagnosis of OUD, a minority (18.6%) received their diagnosis during a PCP visit even though enrollees with OUD had 4.1 visits to PCPs per person-year in 2015. Among enrollees with an OUD diagnosis recorded during a PCP visit, about half (751, 50.8%) received MAT, most of whom (508, 67.6%) received MAT from a PCP. Enrollees with OUD with at least one PCP visit were more likely than those without a PCP visit to receive MAT (32.7% vs. 25%; p < 0.001), and filled more buprenorphine and naltrexone prescriptions (mean = 11.1 vs. 9.3; p < 0.001). The median of the distances traveled to actual MAT prescribers was 48.8 miles, compared to a median of 4.2 miles to the nearest available MAT prescriber. Enrollees traveling a mean distance greater than 45 miles to MAT prescribers were less likely to receive continuity of pharmacotherapy (OR = 0.71, 95% CI = 0.56-0.91, p = 0.007).

CONCLUSIONS: PCP utilization among rural Medicaid enrollees diagnosed with OUD is high, presenting a potential intervention point to treat OUD, particularly if the enrollee's PCP is located nearer than their MAT prescriber.

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.

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: Patterns of inpatient opioid use and their associations with postdischarge opioid use are poorly understood.

OBJECTIVE: To measure patterns in timing, duration, and setting of opioid administration in opioid-naive hospitalized patients and to examine associations with postdischarge use.

DESIGN: Retrospective cohort study using electronic health record data from 2010 to 2014.

SETTING: 12 community and academic hospitals in Pennsylvania.

PATIENTS: 148 068 opioid-naive patients (191 249 admissions) with at least 1 outpatient encounter within 12 months before and after admission.

MEASUREMENTS: Number of days and patterns of inpatient opioid use; any outpatient use (self-report and/or prescription orders) 90 and 365 days after discharge.

RESULTS: Opioids were administered in 48% of admissions. Patients were given opioids for a mean of 67.9% (SD, 25.0%) of their stay. Location of administration of first opioid on admission, timing of last opioid before discharge, and receipt of nonopioid analgesics varied substantially. After adjustment for potential confounders, 5.9% of inpatients receiving opioids had outpatient use at 90 days compared with 3.0% of those without inpatient use (difference, 3.0 percentage points [95% CI, 2.8 to 3.2 percentage points]). Opioid use at 90 days was higher in inpatients receiving opioids less than 12 hours before discharge than in those with at least 24 opioid-free hours before discharge (7.5% vs. 3.9%; difference, 3.6 percentage points [CI, 3.3 to 3.9 percentage points]). Differences based on proportion of the stay with opioid use were modest (opioid use at 90 days was 6.4% and 5.4%, respectively, for patients with opioid use for ≥75% vs. ≤25% of their stay; difference, 1.0 percentage point [CI, 0.4 to 1.5 percentage points]). Associations were similar for opioid use 365 days after discharge.

LIMITATION: Potential unmeasured confounders related to opioid use.

CONCLUSION: This study found high rates of opioid administration to opioid-naive inpatients and associations between specific patterns of inpatient use and risk for long-term use after discharge.

PRIMARY FUNDING SOURCE: UPMC Health System and University of Pittsburgh.

For some low-income Medicare beneficiaries, Medicaid provides financial protection against Medicare's out-of-pocket costs, but many Medicare beneficiaries who qualify for Medicaid are not continuously enrolled. We examined Medicaid disenrollment among fee-for-service Medicare beneficiaries and the relationship between disenrollment and state policies. In the period 2012-16, 18.2 percent of Medicare beneficiaries who received full or partial Medicaid disenrolled for reasons other than death. More than 50 percent of Medicare beneficiaries who remained without Medicaid one year after disenrolling continued to receive low-income subsidies for Medicare Part D coverage with eligibility requirements similar to those of Medicaid. Among Medicare beneficiaries with continuous Part D subsidies, the rate of Medicaid disenrollment was 24 percent lower in states that automatically enrolled recipients of the federal Supplemental Security Income program in full Medicaid, 33 percent lower in states with more generous provider payment policies, and 37 percent lower in states with less restrictive asset limits for partial Medicaid. Policies that make it easier for people to maintain Medicaid eligibility and that enhance access to care in Medicaid via higher provider reimbursements may reduce disenrollment.

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's 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 ≥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 < .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 < .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.