Publications

In the opioid research, predicting the risk of overdose or other adverse outcomes from opioid prescription patterns can help health professionals identify high-risk individuals. Challenges may arise in modeling the exposure-time-response association if the intensity, duration, and timing of exposure vary among subjects, and if exposures have a cumulative or latency effect on the risk. Further challenges may arise when the data involve competing risks, where subjects may fail from one of multiple events and failure from one precludes the risk of experiencing others. In this study, we proposed a competing risks regression model via subdistribution hazards to directly estimate the association between longitudinal patterns of opioid exposure and cumulative incidence of opioid overdose. The model incorporated weighted cumulative effects of the exposure and used penalized splines in the partial likelihood equation to estimate the weights flexibly. The proposed model is able to distinguish different opioid prescription patterns even though these patterns have the same overall intensity during the study period. Performance of the model was evaluated through simulation.

Among Medicaid-enrolled pregnant persons with opioid use disorder, one third are diagnosed with hepatitis C virus, but only 6% receive postpartum follow-up or medication treatment.

BACKGROUND AND AIMS: The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions.

DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%).

MEASUREMENTS: We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time.

FINDINGS: Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%).

CONCLUSIONS: More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).

METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.

FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.

INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.

FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

INTRODUCTION: Follow-up visits within 7 days of an emergency department (ED) visit related to opioid use disorder (OUD) is a key measure of treatment quality, but we know little about its protective effect on future opioid-related overdoses. The objective this paper is to examine the rate of 7-day follow-up after an OUD-related ED visit and the association with future overdoses.

METHODS: Retrospective analysis of Medicaid enrollees in 11 states that had an OUD-related ED visit from 2016 through 2018. Each state used Cox proportional hazard models to estimate the association between having a follow-up visit within 7 days of an OUD-related ED visit, and an overdose within 6 months of the ED visit. State analyses were pooled to generate global estimates using random effects meta-analysis.

RESULTS: Among 114,945 Medicaid enrollees with an OUD-related ED visit, 15.7% had a follow-up visit within 7 days. State-specific rates varied from 7.2% to 22.4% across the 11 states. Compared to those with no follow-up visit, enrollees with a follow-up visit were more likely to be female, non-Hispanic White, less likely to have had an overdose or other substance use disorder at the time of the ED visit, and much more likely to have been receiving MOUD treatment prior to the ED visit. Global estimates based on multivariate analysis showed that having a 7-day follow-up visit was associated with a lower likelihood of overdose within 6 months of the index ED visit (HR = 0.91, CI = 0.84, 0.99). However, states had considerable heterogeneity in this association, with only two states having statistically significant results.

CONCLUSIONS: Among Medicaid enrollees with OUD, having a follow-up visit 7 days after an ED visit is protective against fatal or nonfatal overdose within 6 months, although the association varies considerably across states. Although the association with future overdoses was relatively modest, both practitioners and policymakers should seek to increase the number of Medicaid enrollees with OUD who receive follow-up care within 7 days after an ED visit.

IMPORTANCE: Medicaid is the largest health insurance program by enrollment in the US and has an important role in financing care for eligible low-income adults, children, pregnant persons, older adults, people with disabilities, and people from racial and ethnic minority groups. Medicaid has evolved with policy reform and expansion under the Affordable Care Act and is at a crossroads in balancing its role in addressing health disparities and health inequities against fiscal and political pressures to limit spending.

OBJECTIVE: To describe Medicaid eligibility, enrollment, and spending and to examine areas of Medicaid policy, including managed care, payment, and delivery system reforms; Medicaid expansion; racial and ethnic health disparities; and the potential to achieve health equity.

EVIDENCE REVIEW: Analyses of publicly available data reported from 2010 to 2022 on Medicaid enrollment and program expenditures were performed to describe the structure and financing of Medicaid and characteristics of Medicaid enrollees. A search of PubMed for peer-reviewed literature and online reports from nonprofit and government organizations was conducted between August 1, 2021, and February 1, 2022, to review evidence on Medicaid managed care, delivery system reforms, expansion, and health disparities. Peer-reviewed articles and reports published between January 2003 and February 2022 were included.

FINDINGS: Medicaid covered approximately 80.6 million people (mean per month) in 2022 (24.2% of the US population) and accounted for an estimated $671.2 billion in health spending in 2020, representing 16.3% of US health spending. Medicaid accounted for an estimated 27.2% of total state spending and 7.6% of total federal expenditures in 2021. States enrolled 69.5% of Medicaid beneficiaries in managed care plans in 2019 and adopted 139 delivery system reforms from 2003 to 2019. The 38 states (and Washington, DC) that expanded Medicaid under the Affordable Care Act experienced gains in coverage, increased federal revenues, and improvements in health care access and some health outcomes. Approximately 56.4% of Medicaid beneficiaries were from racial and ethnic minority groups in 2019, and disparities in access, quality, and outcomes are common among these groups within Medicaid. Expanding Medicaid, addressing disparities within Medicaid, and having an explicit focus on equity in managed care and delivery system reforms may represent opportunities for Medicaid to advance health equity.

CONCLUSIONS AND RELEVANCE: Medicaid insures a substantial portion of the US population, accounts for a significant amount of total health spending and state expenditures, and has evolved with delivery system reforms, increased managed care enrollment, and state expansions. Additional Medicaid policy reforms are needed to reduce health disparities by race and ethnicity and to help achieve equity in access, quality, and outcomes.

BACKGROUND: In the US, Medicaid covers over 80 million Americans. Comparing access, quality, and costs across Medicaid programs can provide policymakers with much-needed information. As each Medicaid agency collects its member data, multiple barriers prevent sharing Medicaid data between states. To address this gap, the Medicaid Outcomes Distributed Research Network (MODRN) developed a research network of states to conduct rapid multi-state analyses without sharing individual-level data across states.

OBJECTIVE: To describe goals, design, implementation, and evolution of MODRN to inform other research networks.

METHODS: MODRN implemented a distributed research network using a common data model, with each state analyzing its own data; developed standardized measure specifications and statistical software code to conduct analyses; and disseminated findings to state and federal Medicaid policymakers. Based on feedback on Medicaid agency priorities, MODRN first sought to inform Medicaid policy to improve opioid use disorder treatment, particularly medication treatment.

RESULTS: Since its 2017 inception, MODRN created 21 opioid use disorder quality measures in 13 states. MODRN modified its common data model over time to include additional elements. Initial barriers included harmonizing utilization data from Medicaid billing codes across states and adapting statistical methods to combine state-level results. The network demonstrated its utility and addressed barriers to conducting multi-state analyses of Medicaid administrative data.

CONCLUSIONS: MODRN created a new, scalable, successful model for conducting policy research while complying with federal and state regulations to protect beneficiary health information. Platforms like MODRN may prove useful for emerging health challenges to facilitate evidence-based policymaking in Medicaid programs.

BACKGROUND: Follow-up after residential treatment is considered best practice in supporting patients with opioid use disorder (OUD) in their recovery. Yet, little is known about rates of follow-up after discharge. The objective of this analysis was to measure rates of follow-up and use of medications for OUD (MOUD) after residential treatment among Medicaid enrollees in 10 states, and to understand the enrollee and episode characteristics that are associated with both outcomes.

METHODS: Using a distributed research network to analyze Medicaid claims data, we estimated the likelihood of 4 outcomes occurring within 7 and 30 days post-discharge from residential treatment for OUD using multinomial logit regression: no follow-up or MOUD, follow-up visit only, MOUD only, or both follow-up and MOUD. We used meta-analysis techniques to pool state-specific estimates into global estimates.

RESULTS: We identified 90,639 episodes of residential treatment for OUD for 69,017 enrollees from 2018 to 2019. We found that 62.5% and 46.9% of episodes did not receive any follow-up or MOUD at 7 days and 30 days, respectively. In adjusted analyses, co-occurring mental health conditions, longer lengths of stay, prior receipt of MOUD or behavioral health counseling, and a recent ED visit for OUD were associated with a greater likelihood of receiving follow-up treatment including MOUD after discharge.

CONCLUSIONS: Forty-seven percent of residential treatment episodes for Medicaid enrollees are not followed by an outpatient visit or MOUD, and thus are not following best practices.