Factors Associated With Abrupt Discontinuation of Long-Term High-Dose Opioid Treatment.

This cohort study investigates factors associated with abrupt discontinuation of long-term high-dose opioid treatment at the national level and across US states.

Assessing the Impact of Indiana Public Law 194 on Curbing the Concurrent Opioid Prescribing for Indiana Medicaid Enrollees.

Background: Several US states have introduced legislation to support the legitimate medical use of opioids while limiting misuse and diversion. One concern which has been addressed through legislation is preventing individuals from seeking opioid prescriptions concurrently from multiple providers. However, the impact of this legislation on the incidence of patients receiving concurrent prescriptions remains relatively unexplored. This study examines this phenomenon based on claims data from Medicaid enrollees and the enactment of legislation in Indiana. Methods: Indiana Medicaid claims data over the period of January 2014 to December 2019 were used to determine the changes in the percentage of individuals receiving opioid prescriptions from multiple providers within a 30-day period, that is, concurrent opioid prescription (COP) individuals. Indiana Medicaid enrollees with a diagnosis of opioid use disorder (OUD) receiving opioid prescriptions, that is, the OUD-group, were identified and separated from the enrollees without a diagnosis but receiving opioid prescriptions, that is, the non-OUD group. The mean percentages of COP individuals (with or without an OUD diagnosis) within the subset of individuals that received opioid prescriptions were compared before and after the passage of Indiana Public Law 194. Results: There were 5336 who met the criteria of COP individuals, and 2050 of those were in the OUD-group. In either group, there was a significant difference in the change in percentages (slope) before and after Indiana Public Law 194 passed. In addition, there was a significant decrease in the mean percentage of COP individuals in the non-OUD group, while the difference was not significant in the OUD group. Conclusion: Our study suggests that Indiana Public Law 194 had a positive impact on curbing COP. This study is limited by the level of details available from claims data and suggests additional studies to evaluate prescription use and prescribing practices are warranted.

Opioid-Related Treatment Disparities Among Medicaid Enrollees in Indiana.

Introduction: Health care disparities based on race/ethnicity and sex can be found in a variety of settings. Our aim is to determine if there are disparities in treatment provided to Indiana Medicaid enrollees who have medically documented opioid use. Study Data and Methods: We used Medicaid reimbursement claims data to extract patients who were diagnosed with opioid use disorder (OUD) or had other medical event related to opioid use between January 2018 and March 2019. We used a two-proportion Z-test to verify the difference in the proportion of treatment provided between population subgroups. The study was approved by the Purdue University Institutional Review Board (2019-118). Study Results: Over the study period, there were 52,994 Indiana Medicaid enrollees diagnosed with OUD or documentation of another opioid related event. Only 5.41% of them received at least one type of treatment service (detoxification, psychosocial, medication assisted treatment, or comprehensive). Discussion: Although Medicaid began covering treatment services for enrollees with an OUD in Indiana at the start of 2018, very few received evidence-based services. Men and White enrollees with an OUD were in general more likely to receive services compared to women and non-White enrollees.

A two-phase approach to re-calibrating expensive computer simulation for sex-specific colorectal neoplasia development modeling.

BACKGROUND: Medical evidence from more recent observational studies may significantly alter our understanding of disease incidence and progression, and would require recalibration of existing computational and predictive disease models. However, it is often challenging to perform recalibration when there are a large number of model parameters to be estimated. Moreover, comparing the fitting performances of candidate parameter designs can be difficult due to significant variation in simulated outcomes under limited computational budget and long runtime, even for one simulation replication. METHODS: We developed a two-phase recalibration procedure. As a proof-of-the-concept study, we verified the procedure in the context of sex-specific colorectal neoplasia development. We considered two individual-based state-transition stochastic simulation models, estimating model parameters that govern colorectal adenoma occurrence and its growth through three preclinical states: non-advanced precancerous polyp, advanced precancerous polyp, and cancerous polyp. For the calibration, we used a weighted-sum-squared error between three prevalence values reported in the literature and the corresponding simulation outcomes. In phase 1 of the calibration procedure, we first extracted the baseline parameter design from relevant studies on the same model. We then performed sampling-based searches within a proper range around the baseline design to identify the initial set of good candidate designs. In phase 2, we performed local search (e.g., the Nelder-Mead algorithm), starting from the candidate designs identified at the end of phase 1. Further, we investigated the efficiency of exploring dimensions of the parameter space sequentially based on our prior knowledge of the system dynamics. RESULTS: The efficiency of our two-phase re-calibration procedure was first investigated with CMOST, a relatively inexpensive computational model. It was then further verified with the V/NCS model, which is much more expensive. Overall, our two-phase procedure showed a better goodness-of-fit than the straightforward employment of the Nelder-Mead algorithm, when only a limited number of simulation replications were allowed. In addition, in phase 2, performing local search along parameter space dimensions sequentially was more efficient than performing the search over all dimensions concurrently. CONCLUSION: The proposed two-phase re-calibration procedure is efficient at estimating parameters of computationally expensive stochastic dynamic disease models.

Examining how physician factors influence patient satisfaction during clinical consultations about cancer prognosis and pain.

Objective: Patient-physician communication affects cancer patients' satisfaction, health outcomes, and reimbursement for physician services. Our objective is to use machine learning to comprehensively examine the association between patient satisfaction and physician factors in clinical consultations about cancer prognosis and pain. Methods: We used data from audio-recorded, transcribed communications between physicians and standardized patients (SPs). We analyzed the data using logistic regression (LR) and random forests (RF). Results: The LR models suggested that lower patient satisfaction was associated with more in-depth prognosis discussion; and higher patient satisfaction was associated with a greater extent of shared decision making, patient being black, and doctor being young. Conversely, the RF models suggested the opposite association with the same set of variables. Conclusion: Somewhat contradicting results from distinct machine learning models suggested possible confounding factors (hidden variables) in prognosis discussion, shared decision-making, and doctor age, on the modeling of patient satisfaction. Practitioners should not make inferences with one single data-modeling method and enlarge the study cohort to help deal with population heterogeneity. Innovation: Comparing diverse machine learning models (both parametric and non-parametric types) and carefully applying variable selection methods prior to regression modeling, can enrich the examination of physician factors in characterizing patient-physician communication outcomes.

Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes.

Background. Developing efficient procedures of model calibration, which entails matching model predictions to observed outcomes, has gained increasing attention. With faithful but complex simulation models established for cancer diseases, key parameters of cancer natural history can be investigated for possible fits, which can subsequently inform optimal prevention and treatment strategies. When multiple calibration targets exist, one approach to identifying optimal parameters relies on the Pareto frontier. However, computational burdens associated with higher-dimensional parameter spaces require a metamodeling approach. The goal of this work is to explore multiobjective calibration using Gaussian process regression (GPR) with an eye toward how multiple goodness-of-fit (GOF) criteria identify Pareto-optimal parameters. Methods. We applied GPR, a metamodeling technique, to estimate colorectal cancer (CRC)-related prevalence rates simulated from a microsimulation model of CRC natural history, known as the Colon Modeling Open Source Tool (CMOST). We embedded GPR metamodels within a Pareto optimization framework to identify best-fitting parameters for age-, adenoma-, and adenoma staging-dependent transition probabilities and risk factors. The Pareto frontier approach is demonstrated using genetic algorithms with both sum-of-squared errors (SSEs) and Poisson deviance GOF criteria. Results. The GPR metamodel is able to approximate CMOST outputs accurately on 2 separate parameter sets. Both GOF criteria are able to identify different best-fitting parameter sets on the Pareto frontier. The SSE criterion emphasizes the importance of age-specific adenoma progression parameters, while the Poisson criterion prioritizes adenoma-specific progression parameters. Conclusion. Different GOF criteria assert different components of the CRC natural history. The combination of multiobjective optimization and nonparametric regression, along with diverse GOF criteria, can advance the calibration process by identifying optimal regions of the underlying parameter landscape.