Publications

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.

OBJECTIVE: Urban Medicaid enrollees with opioid use disorder often rely on public transit to reach buprenorphine prescribers. Research has not shown whether public transit provides this population with adequate geographic access to buprenorphine prescribers. We examined travel times to buprenorphine prescribers by car and public transit in urban areas, and determined whether car-based Medicaid regulatory standards produce their intended geographic coverage.

METHODS: We obtained data for this study from the Substance Abuse and Mental Health Services Administration's Buprenorphine Practitioner Locator, Microsoft Bing Maps, and the American Community Survey. We examined four urban counties at the centers of the metropolitan statistical areas with the highest 2017 accidental drug poisoning death rates: Kanawha, WV; Montgomery, OH; Philadelphia, PA; and St. Louis City, MO. These counties comprised 696 census tracts representing 1,038,564 households. We calculated travel times from each census tract center to the nearest buprenorphine prescribers by car and public transit, and compared that to 30-min regulatory standards and by whether census tracts had below median levels of car access. We calculated Global Moran's I statistics to determine whether spatial clustering was present among census tracts with limited access to buprenorphine prescribers.

RESULTS: Households in all but two census tracts could access a buprenorphine prescriber within 30 min by car. However, households in 12.1% (84) of census tracts could not do so by public transit. The correlation between car- and public transit-based travel times to the nearest buprenorphine prescriber was 0.11 (95% CI = 0.07-0.22). More than 15% (47,918) of households in the two less densely populated counties could not travel to the nearest prescriber in 30 min and resided in census tracts where access to cars was relatively low. There was no evidence of spatial clustering among census tracts with public transit travel times exceeding 30 min, or among census tracts with public transit travel times exceeding 30 min and below median values of access to cars.

CONCLUSIONS: Geographic access to buprenorphine prescribers is overestimated by regulatory standards that apply car-based travel time estimates, which are a weak proxy for public transit-based travel times. Since geographic areas with limited access to buprenorphine prescribers do not tend to cluster near one another, individually targeted interventions may be necessary to improve buprenorphine access and utilization.

OBJECTIVE: To examine whether low-income children's use of preventive dental services is linked to variation in state Medicaid policies that affect parents' access to dental care in Medicaid.

DATA SOURCES: Medical Expenditure Panel Survey (2011-2016), Area Health Resources File, and Medicaid adult dental coverage policies.

STUDY DESIGN: We conducted a quasi-experimental analysis using linked parent-child dyads in low-income families (≤125 percent of the Federal Poverty Level). We assessed whether expansions of Medicaid to low-income adults under the Affordable Care Act were associated with increases in the use of preventive dental services among low-income children when state Medicaid programs did vs did not cover these services for adults.

PRINCIPAL FINDINGS: Over the study period, 37.8 percent of low-income children received at least one annual preventive dental visit. We found no change in children's receipt of preventive dental care associated with Medicaid expansions in states that covered (1.26 percentage points; 95% CI: -3.74 to 6.27) vs did not cover preventive dental services for adults (3.03 percentage points; 95% CI: -2.76 to 8.81). (differential change: -1.76 percentage points; 95% CI: -8.09, 4.56). However, our estimates are imprecise, with wide confidential intervals that are unable to rule out sizable effects in either direction.

CONCLUSION: We did not find an association between Medicaid expansions with concurrent coverage of preventive dental services for adults and children's use of these services. Factors other than parental access to dental benefits through Medicaid may be more salient determinants of preventive dental care use among low-income children.

Two-photon time-frequency entanglement is a valuable resource in quantum information. Resolving the wavepacket of ultrashort pulsed single-photons, however, is a challenge. Here, we demonstrate remote spectral shaping of single photon states and probe the coherence properties of two-photon quantum correlations in the time-frequency domain, using engineered parametric down-conversion (PDC) and a quantum pulse gate (QPG) in nonlinear waveguides. Through tailoring the joint spectral amplitude function of our PDC source we control the temporal mode structure between the generated photon pairs and show remote state-projections over a range of time-frequency mode superpositions.

AIMS: We evaluated the relationship between the timing of insulin initiation and cardiovascular diseases (CVD) risk in Pennsylvania Medicaid enrollees with type 2 diabetes (T2D).

METHODS: We included 17,873 enrollees (age 47.4 ± 10.3 years; range 18-64 years) initially treated with non-insulin glucose-lowering agents (GLAs) in 2008-2016. Based on clinical guidelines, we identified early (N = 1,158; 6%; insulin initiation ≤ 6 months after first-line GLAs), in-time (N = 569; 3%; 6-12 months), delayed (N = 2,761; 15%; >12 months), and non-insulin users (N = 13,385; 75%). The Prentice-Williams-Peterson (PWP) models with inverse probability weighting estimated CVD risk across the four groups and the change in risk after insulin initiation.

RESULTS: Regardless of time to insulin initiation, insulin users had higher CVD risks after first-line GLAs than non-insulin users (aHR: early: 2.0 [1.5-2.5], in-time: 1.8 [1.2-2.6], delayed: 1.9 [1.6-2.3]). However, we found only a borderline increase in CVD risk after insulin initiation vs. before in early (aHR: 1.4 [1.1-1.8]) and delayed users (aHR: 1.3 [1.0-1.7]), and no increase in in-time users (aHR: 1.3 [0.9-2.0]).

CONCLUSIONS: We observed no gains in CVD benefits from insulin initiation in the early stages of pharmacotherapy possibly because CVD developed before insulin initiation. Additional management of hypertension and dyslipidemia may be important to reduce CVD risk in this young and middle-aged T2D cohort.