Publications

Background Persons experiencing homelessness bear high rates of morbidity, injury, and mortality. Medicaid offers an opportunity to provide support, but barriers persist to enrolling and maintaining enrollment among this vulnerable population. The objective of this study was to determine the initial and longer-term effects of the Affordable Care Act (ACA) Medicaid eligibility expansion on Medicaid enrollment among persons observed to be unhoused or housing insecure through recorded housing services utilization. Methods We applied interrupted time-series analyses of linked administrative data from two expansion states - New Jersey (NJ) and Pennsylvania (PA); Homeless Management Information System data were linked to monthly Medicaid enrollment files of non-elderly adults (aged 18-64) utilizing shelter, street outreach and other housing assistance from January 2011 to December 2016. The study outcome was a binary measure of Medicaid enrollment status in month of Homelessness Management Information System service exit overall and stratified by race/ethnicity. Results ACA Medicaid eligibility expansion was associated with a level change in the likelihood of enrollment of 7.5 percentage points (pp) in NJ and 8.5 pp in PA. The trend in enrollment post-expansion also increased by 0.6 pp/month in NJ. Compared to no homeless-related service use in the year, being recorded with one month with a shelter stay or other homelessness assistance services in the year was associated with a higher likelihood of Medicaid enrollment (14.8 pp higher in NJ and 6.9 pp higher in PA), and likelihood of enrollment was highest when two or more months with homelessness assistance services were used in the year (18.6 pp higher in NJ and 12.8 pp higher in PA). However, the effect of the policy change attenuated back to the pre-ACA trend in both states by the end of 2016. Results were similar across race/ethnicity stratifications. Conclusions We found significant increases in the likelihood of Medicaid enrollment after the ACA Medicaid expansions in the months immediately following the expansion. Additional months with homeless shelter stay or other housing services for unhoused persons were associated with a higher likelihood of Medicaid enrollment; this suggests the need for further investigation into the potential of leveraging staff-client relationships at homelessness assistance programs during future health policy initiatives.

BACKGROUND: Low-value health services adversely affect outcomes and unnecessarily increase the cost of care. Approximately 10% of Veterans receive at least one of 29 low-value services delivered or paid for by the Veterans Health Administration (VA) annually. However, determinants of and potential solutions to reduce low-value service delivery are poorly understood.

OBJECTIVE: To characterize the drivers of and approaches to reduce low-value service delivery across VA Medical Centers (VAMCs) from the perspective of VA clinicians.

DESIGN: Qualitative study using semi-structured interviews conducted from October 2022 to November 2023.

PARTICIPANTS: 65 VA clinicians, including 32 generalists and 33 medical and surgical specialists, at 46 VAMCs.

APPROACH: We used deductive analysis based on a priori categories and definitions structured by the Theoretical Domains Framework to identify predominant themes related to drivers of low-value service delivery. We used inductive analysis to identify clinician-suggested approaches to reduce low-value services.

KEY RESULTS: We identified three overarching domains as drivers of low-value service delivery at VA: 1) environmental context and resources; 2) social influence; and 3) belief about consequences. Regarding key subthemes, social pressure from Veterans emerged among generalists and specialists. Generalists were more likely to identify referral parameters or requirements compared to specialists, while specialists were more like to identify negative consequences compared to generalists. We identified four overarching domains as approaches to reduce low-value service delivery at VA, which were consistently identified by both generalists and specialists: 1) improving quality and access to VA health care; 2) dissemination of best practices; 3) optimizing use of the electronic health record; and 4) instilling an organizational culture on value.

CONCLUSIONS: We identified the most salient drivers of and approaches to reduce low-value services from the perspective of VA clinicians. These findings may inform the design of future de-implementation interventions and policy to reduce VA-delivered low-value services.

BACKGROUND: Opioid exposure during cancer therapy may increase long-term unsafe opioid prescribing. This study sought to determine the rates of coprescription of benzodiazepine and opioid medications and new persistent opioid use after surgical treatment of early-stage cancer.

METHODS: A retrospective cohort study was conducted among a US veteran population via the Veterans Affairs Corporate Data Warehouse database. Participants were opioid-naive persons aged ≥21 years with a new diagnosis of stage 0-III cancer between January 1, 2015, and December 31, 2016. Outcomes were days of coprescription of benzodiazepines and opioids in the 13 months posttreatment and new persistent opioid use. The exposure was total morphine milligram equivalents (MMEs) attributed to treatment and prescribed from 30 days before through 14 days after the index surgical procedure.

RESULTS: Among 9213 veterans, coprescription of benzodiazepines and opioids occurred in 366 patients (4.0%) and new persistent opioid use in 981 patients (10.6%). In a linear model adjusting for patient, clinical, and geographic factors, persons in the highest quartile compared to no opioid exposure had increased days with coprescription of benzodiazepines and opioids (mean difference, 1.0; 95% CI, 0.3-1.7). In a discrete time survival analysis, persons in the highest quartile of MME exposure compared to none had a greater risk of new persistent opioid use (hazard ratio, 1.6; 95% CI, 1.3-1.9).

CONCLUSIONS: More than one of 10 opioid-naive veterans undergoing curative-intent surgical treatment for cancer developed new persistent opioid use. Optimizing cancer treatment pain management strategies to mitigate long-term opioid-related health risks is crucial.

BACKGROUND: Twelve state Medicaid programs limit the monthly number of covered prescriptions. Such cap policies may force enrollees to forego essential medications with important health consequences. We aimed to determine the impact of cap policies on acute care use and all-cause mortality among enrollees with opioid use disorder (OUD).

METHODS: Using 2016-2019 T-MSIS Analytical Files, we propensity-score matched enrollees with OUD in 12 states with cap policies and 26 states without cap policies. Outcomes measured over 12 months included emergency department (ED) visits, hospitalization, and all-cause mortality and were analyzed via generalized linear regression models. We conducted subgroup analyses by use of medications for OUD (MOUD) and comorbidity level and sensitivity analyses to examine the role of cap policy characteristics.

RESULTS: Unadjusted risks were 64.0 % vs. 62.5 % for ED visits, 27.6 % vs. 27.5 % for hospitalizations, and 3.2 % vs. 2.7 % for mortality in cap states and non-cap states, respectively. After adjustment, hospitalization risk was higher (RR=1.89, 99.5 %CI:1.13,3.16) in cap states than non-cap states whereas ED visits and mortality did not differ. There were largely no outcome differences by cap status in subgroups. Strict prescription limits allowing 3-4 prescriptions monthly (RR=1.90, 95 %CI:1.09,3.30) and lack of MOUD exemptions (RR=2.23, 95 %CI:1.32,3.78) were associated with increased hospitalization risk relative to non-cap states.

CONCLUSIONS: Medicaid prescription cap policies were associated with increased hospitalization risk, but there were no differences in ED use or all-cause mortality. Cap policies may undermine the health of individuals with OUD and could be counterproductive to state efforts to curb Medicaid spending.

State regulations governing opioid treatment programs (OTPs) vary widely in their restrictiveness, but how they affect geographic access to methadone maintenance treatment for opioid use disorder remains poorly understood. In this study of comprehensive data on methadone shipments to OTPs in 2019, we found that ZIP codes just across the border in states with low restrictiveness in OTP regulations had more than twice the OTP density and methadone shipments compared with nearby and otherwise similar ZIP codes in states with high restrictiveness. These findings suggest that restrictive state OTP regulations constrain geographic access to methadone maintenance treatment and that easing such restrictions is a viable strategy to expand access.

Private equity (PE) acquisitions of opioid treatment programs (OTPs) are growing, with the potential to expand access to methadone, a critical yet underused medication that can cut the risk for overdose deaths by more than half. At the same time, PE's emphasis on short-term profitability has raised concerns from policy makers that PE acquisitions can consolidate ownership of OTPs among financial firms without expanding access to treatment. Using a difference-in-differences design with novel data on PE acquisitions of OTPs and methadone shipments to all OTPs during the period 2006-19, this study examined the effects of PE acquisitions on methadone supply. PE firms acquired 67 percent of the OTP market in the median county with any acquisition, often through multiple acquisitions within the same county. After acquisition, methadone shipments to PE-acquired OTPs increased by 13 percent relative to matched controls, but this was not statistically significant after adjustment for differential preacquisition trends, indicating that the increase was not driven by the acquisition itself. County-level methadone shipments and opioid mortality remained unchanged. Findings suggest that PE acquisitions of OTPs may consolidate ownership of OTPs among financial investors without changing methadone supply. Given policy makers' widespread call for increased supply, additional scrutiny of the impact of PE investment on patient access to methadone might be warranted.

BACKGROUND: Chronic diseases such as diabetes are a major burden to the US health care system. High medication adherence helps improve diabetes outcomes and reduce cost. Cost of medications can contribute to nonadherence. Use of a formulary decision support system with e-prescribing may be associated with greater use of generic medications, leading to lower costs and better adherence. A real-time prescription benefit (RTPB) solution provides patient-specific drug pricing, benefit information, and therapeutic options to choose the most cost-effective and clinically appropriate treatment.

OBJECTIVES: To examine whether RTPB is associated with increased adherence measured by proportion of days covered, higher utilization of generics, and generic dispensing rate? Is RTPB associated with lower plan and patient out-of-pocket (OOP) per-user per-month costs?

METHODS: This study used a retrospective, matched intervention-control analysis of commercial health plan members from a large pharmacy benefits manager. Members were eligible for inclusion if they initiated therapy between January and August 2021. Members were excluded if they were not continuously eligible for coverage over the study period. Members who initiated diabetes therapy with a prescriber using RTPB (intervention) were compared with those new to therapy with a prescriber not using RTPB (control). Index date for both samples was the first medication prescription in the index period. Members were matched on age and sex demographics. The evaluation period lasted 12 months after index date. Multivariable linear regression models were used to assess the impact of an RTPB program on adherence and proportion of prescriptions filled with a generic. A generalized linear model (gamma distribution, log link) estimated plan and OOP patient costs, whereas a generalized linear model model with the Poisson distribution was used to estimate the number of controlling for patient age, sex, social determinants of health score, and other patient- and plan-level covariates.

RESULTS: 1,302 matched pairs were included in the analysis. Findings show the proportion of days covered was 68.7% for control and 71.4% for RTPB members (P < 0.05). The average number of generic prescriptions for control and RTPB samples were 4.06 and 5.66, respectively (P < 0.05) and the generic dispensing rates were 44.9% and 60.1%, respectively (P < 0.05). The mean plan cost per member per month for diabetes medications, for the non-RTPB group, was 32.3% higher than the RTPB sample (a difference of $81.69, P < 0.0001) and the mean patient cost per month was 88.8% higher than the RTPB sample (a difference of $9.71, P < 0.0001).

CONCLUSIONS: Access to RTPB tools provides prescribers with formulary benefit and therapeutic options that allow them to provide the lowest-cost clinical treatment, thus improving adherence, increasing use of generic medications, and lowering plan and patient OOP costs.

BACKGROUND: Amid the opioid epidemic in the United States, hepatitis C virus (HCV) infections are rising, with one-third of individuals with infection unaware due to the asymptomatic nature. This study aimed to develop and validate a machine learning (ML)-based algorithm to screen individuals at high risk of HCV infection.

METHODS: We conducted prognostic modeling using the 2016-2023 OneFlorida+ database of all-payer electronic health records. The study included individuals aged ≥18 years who were tested for HCV antibodies, RNA, or genotype. We identified 275 features of HCV, including sociodemographic and clinical characteristics, during a 6-month period before the test result date. Four ML algorithms-elastic net (EN), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)-were developed and validated to predict HCV infection. We stratified patients into deciles based on predicted risk.

RESULTS: Among 445 624 individuals, 11 823 (2.65%) tested positive for HCV. Training (75%) and validation (25%) samples had similar characteristics (mean, standard deviation age, 45 [16] years; 62.86% female; 54.43% White). The GBM model (C statistic, 0.916 [95% confidence interval = .911-.921]) outperformed the EN (0.885 [.879-.891]), RF (0.854 [.847-.861]), and DNN (0.908 [.903-.913]) models (P < .0001). Using the Youden index, GBM achieved 79.39% sensitivity and 89.08% specificity, identifying 1 positive HCV case per 6 tests. Among patients with HCV, 75.63% and 90.25% were captured in the top first and first to third risk deciles, respectively.

CONCLUSIONS: ML algorithms effectively predicted and stratified HCV infection risk, offering a promising targeted screening tool for clinical settings.