Publications

OBJECTIVE: To identify sociodemographic profiles of patients prescribed high-dose opioids.

DESIGN: Cross-sectional cohort study.

SETTING/PATIENTS: Veterans dually-enrolled in Veterans Health Administration and Medicare Part D, with ≥1 opioid pre-scription in 2012.

MAIN OUTCOME MEASURES: We identified five patient-level demographic characteristics and 12 community variables re-flective of region, socioeconomic deprivation, safety, and internet connectivity. Our outcome was the proportion of vet-erans receiving >120 morphine milligram equivalents (MME) for ≥90 consecutive days, a Pharmacy Quality Alliance measure of chronic high-dose opioid prescribing. We used classification and regression tree (CART) methods to identify risk of chronic high-dose opioid prescribing for sociodemographic subgroups.

RESULTS: Overall, 17,271 (3.3 percent) of 525,716 dually enrolled veterans were prescribed chronic high-dose opioids. CART analyses identified 35 subgroups using four sociodemographic and five community-level measures, with high-dose opioid prescribing ranging from 0.28 percent to 12.1 percent. The subgroup (n = 16,302) with highest frequency of the outcome included veterans who were with disability, age 18-64 years, white or other race, and lived in the Western Census region. The subgroup (n = 14,835) with the lowest frequency of the outcome included veterans who were with-out disability, did not receive Medicare Part D Low Income Subsidy, were >85 years old, and lived in communities within the second and sixth to tenth deciles of community public assistance.

CONCLUSIONS: Using CART analyses with sociodemographic and community-level variables only, we identified sub-groups of veterans with a 43-fold difference in chronic high-dose opioid prescriptions. Interactions among disability, age, race/ethnicity, and region should be considered when identifying high-risk subgroups in large populations.

IMPORTANCE: Low-value care is associated with harm among patients and with wasteful health care spending but has not been well characterized in the Veterans Health Administration.

OBJECTIVES: To characterize the frequency of and variation in low-value diagnostic testing for 4 common conditions at Veterans Affairs medical centers (VAMCs) and to examine the correlation between receipt of low-value testing for each condition.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used Veterans Health Administration data from 127 VAMCs from fiscal years 2014 to 2015. Data were analyzed from April 2018 to March 2020.

EXPOSURES: Continuous enrollment in Veterans Health Administration during fiscal year 2015.

MAIN OUTCOMES AND MEASURES: Receipt of low-value testing for low back pain, headache, syncope, and sinusitis. For each condition, sensitive and specific criteria were used to evaluate the overall frequency and range of low-value testing, adjusting for sociodemographic and VAMC characteristics. VAMC-level variation was calculated using median adjusted odds ratios. The Pearson correlation coefficient was used to evaluate the degree of correlation between low-value testing for each condition at the VAMC level.

RESULTS: Among 1 022 987 veterans, the mean (SD) age was 60 (16) years, 1 008 336 (92.4%) were male, and 761 485 (69.8%) were non-Hispanic White. A total of 343 024 veterans (31.4%) were diagnosed with low back pain, 79 176 (7.3%) with headache, 23 776 (2.2%) with syncope, and 52 889 (4.8%) with sinusitis. With the sensitive criteria, overall and VAMC-level low-value testing frequency varied substantially across conditions: 4.6% (range, 2.7%-10.1%) for sinusitis, 12.8% (range, 8.6%-22.6%) for headache, 18.2% (range, 10.9%-24.6%) for low back pain, and 20.1% (range, 16.3%-27.7%) for syncope. With the specific criteria, the overall frequency of low-value testing across VAMCs was 2.4% (range, 1.3%-5.1%) for sinusitis, 8.6% (range, 6.2%-14.6%) for headache, 5.6% (range, 3.6%-7.7%) for low back pain, and 13.3% (range, 11.3%-16.8%) for syncope. The median adjusted odds ratio ranged from 1.21 for low back pain to 1.40 for sinusitis. At the VAMC level, low-value testing was most strongly correlated for syncope and headache (ρ = 0.56; P < .001) and low back pain and headache (ρ = 0.48; P < .001).

CONCLUSIONS AND RELEVANCE: In this cohort study, low-value diagnostic testing was common, varied substantially across VAMCs, and was correlated between veterans' receipt of different low-value tests at the VAMC level. The findings suggest a need to address low-value diagnostic testing, even in integrated health systems, with robust utilization management practices.

Despite implementation of the Affordable Care Act (ACA), many Americans remain uninsured and receive care in free clinics. It is unknown what free clinic attendees in Pennsylvania know about health insurance expansion or what they perceive as barriers in enrolling in health insurance. The objective of this study was to assess the perceptions and experiences of free clinic patients from southwestern Pennsylvania when applying for health insurance after implementation of the ACA. We designed and implemented a survey of patients at three free clinics within Allegheny County, Pennsylvania from September 2016 to February 2017. Our survey included 22-items, 7 sociodemographic questions and 15 questions regarding the patient's health status and their perspectives related to obtaining health insurance. Data was obtained from 203 patient surveys; 110 (55.3%) of the respondents were men and 99 (48.8%) were African American. There were 48 respondents (24.1%) who did not report any income at the time of the study, and of those that did report an income, 92 (46.2%) respondents reported an income below 133% of the federal poverty level. The main barriers patients faced when applying for health insurance were: (1) lack of knowledge about health insurance (n = 127, 58.1%), (2) cost of health coverage (n = 85, 41.9%), (3) lack of resources (n = 83, 40.4%), and (4) lack of enrollment documentation (n = 43, 23.8%). Significant work is needed to better educate patients about their eligibility and options for health insurance. Free clinics can play a key role in eliminating barriers to health insurance enrollment.

The health of women and children affected by opioid use disorder is a priority for state Medicaid programs. Little is known about longer-term outcomes among Medicaid-enrolled children exposed to opioids in utero. We examined well-child visit use and diagnoses of pediatric complex chronic conditions in the first five years of life among children with opioid exposure, tobacco exposure, or neither exposure in utero. The sample consisted of 82,329 maternal-child dyads in the Pennsylvania Medicaid program in which the children were born in the period 2008-11 and followed up for five years. Children with in utero opioid exposure had a lower predicted probability of recommended well-child visit use at age fifteen months (42.1 percent) compared to those with tobacco exposure (54.1 percent) and those with neither exposure (55.7 percent). Children with in utero opioid exposure had a predicted probability of being diagnosed with a pediatric complex chronic condition similar to that among children with tobacco exposure and those with neither exposure (20.4 percent, 18.7 percent, and 20.2 percent, respectively). Our findings were consistent when we examined a subgroup of opioid-exposed children identified as having neonatal opioid withdrawal symptoms.

OBJECTIVE: To examine associations between opioid prescriber specialty and patient likelihood of opioid use disorder (OUD), opioid misuse, and opioid overdose.

DESIGN: Longitudinal retrospective study using Pennsylvania Medicaid data (2007-2015).

METHODS: We constructed an incident cohort of 432,110 enrollees initiating prescription opioid use without a history of OUD or overdose six months before opioid initiation. We attributed patients to one of 10 specialties using the first opioid prescriber's specialty or, alternatively, the specialty of the dominant prescriber writing the majority of the patient's opioid prescriptions. We estimated adjusted rates for OUD, misuse, and overdose, adjusting for demographic variables and medical (including pain) and psychiatric comorbidities.

RESULTS: The unadjusted incidence rates of OUD, misuse, and overdose were 7.13, 4.73, and 0.69 per 100,000 person-days, respectively. Patients initiating a new episode of opioid treatment with Pain Medicine/Anesthesiology (6.7 events, 95% confidence interval [CI] = 5.5 to 8.2) or Physical Medicine and Rehabilitation (PM&R; 6.1 events, 95% CI = 5.1 to 7.2) had higher adjusted rates for OUD per 100,000 person-days compared with Primary Care practitioners (PCPs; 4.4 events, 95% CI = 4.1 to 4.7). Patients with index prescriptions from Pain Medicine/Anesthesiology (15.9 events, 95% CI = 13.2 to 19.3) or PM&R (15.8 events, 95% CI = 13.5 to 18.4) had higher adjusted rates for misuse per 100,000 person-days compared with PCPs (9.6 events, 95% CI = 8.8 to 10.6). Findings were largely similar when patients were attributed to specialty based on dominant prescriber.

CONCLUSIONS: Differences in opioid-related risks by specialty of opioid prescriber may arise from differences in patient risk factors, provider behavior, or both. Our findings inform targeting of opioid risk mitigation strategies to specific practitioner specialties.

BACKGROUND: The association between pharmaceutical industry promotion and physician opioid prescribing is poorly understood. Whether the influence of industry gifts on prescribing varies by specialty is unknown.

OBJECTIVE: To examine the relationship between opioid-related gifts to physicians and opioid prescribing in the subsequent year across 7 physician specialties.

DESIGN: Panel study using data from 2014 to 2016.

PARTICIPANTS: 236,103 unique Medicare Part D physicians (389,622 physician-years) who received any gifts from pharmaceutical companies measured using Open Payments and prescribed opioids in the subsequent year.

MAIN MEASURES: Amounts paid by pharmaceutical companies for opioid-related gifts including meals and lodging; quartile of opioid prescribing as a percent of total prescribing compared with other same-specialty physicians.

KEY RESULTS: In 2014-2015, 14.1% of physician received opioid-related gifts from the industry with 2.6% receiving > $100. Gifts varied by specialty and were concentrated among two pharmaceutical companies responsible for 60% of the value of opioid-related gifts. Receiving opioid-related gifts was associated with greater prescribing of opioids compared with same-specialty physicians in the next year. Primary care physicians are nearly 3.5 times as likely to be in the highest quartile of prescribing versus the lower quartiles if they were paid ≥ $100. Psychiatrists and neurologists were 7 to 13 times as likely to be in a higher quartile of opioid prescribing compared with colleagues who were paid $0 in the preceding year.

CONCLUSIONS: The value of opioid-related gifts given to physicians varies substantially by provider specialty, as does the relationship between payment amounts and prescriber behavior in the following year.

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.