An Improved Statistical Modeling Approach to Individual Anticholinergic Drug Use Trend Analysis.

Anticholinergic (AC) drugs are commonly prescribed to older adults for treating diseases and chronic conditions, such as chronic obstructive pulmonary disease, urinary incontinence, gastrointestinal disorder, or simply pain and allergy. The high prevalence of AC drug use can have a detrimental effect on the mental health of older adults. We aim to improve the prediction of future trends of AC drug use at the individual level, with pharmacy refill data. The individual drug use data presents challenges in the modeling, such as data being discrete-valued with excess zeros and having significant unobserved heterogeneity in the trend pattern. To address these challenges, we propose a statistical model of hierarchical structure and an EM scheme for the model parameter estimation. We evaluate the proposed modeling approach through a numerical study with synthetic data and a case study with real-world pharmacy refill data. The simulation study show that our analysis method outperforms the existing ones (e.g., reducing MSE significantly), particularly in terms of accurately predicting the trend pattern. The real-world case study further verifies the out-performance and demonstrate the advantageous features of our method. We expect the prediction tool developed based on our study can assist pharmacists' decision on initiating or strengthening behavioral interventions with the hope of discontinuing AC drug misuse.

The Health and Economic Impact of Using a Sugar Sweetened Beverage Tax to Fund Fruit and Vegetable Subsidies in New York City: A Modeling Study.

Low fruit and vegetable (FV) intake and high sugar-sweetened beverage (SSB) consumption are independently associated with an increased risk of developing cardiovascular disease (CVD). Many people in New York City (NYC) have low FV intake and high SSB consumption, partly due to high cost of fresh FVs and low cost of and easy access to SSBs. A potential implementation of an SSB tax and an FV subsidy program could result in substantial public health and economic benefits. We used a validated microsimulation model for predicting CVD events to estimate the health impact and cost-effectiveness of SSB taxes, FV subsidies, and funding FV subsidies with an SSB tax in NYC. Population demographics and health profiles were estimated using data from the NYC Health and Nutrition Examination Survey. Policy effects and price elasticity were derived from recent meta-analyses. We found that funding FV subsidies with an SSB tax was projected to be the most cost-effective policy from the healthcare sector perspective. From the societal perspective, the most cost-effective policy was SSB taxes. All policy scenarios could prevent more CVD events and save more healthcare costs among men compared to women, and among Black vs. White adults. Public health practitioners and policymakers may want to consider adopting this combination of policy actions, while weighing feasibility considerations and other unintended consequences.

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.

Cost Effectiveness of Different Strategies for Detecting Cirrhosis in Patients With Nonalcoholic Fatty Liver Disease Based on United States Health Care System.

BACKGROUND & AIMS: Several strategies are available for detecting cirrhosis in patients with nonalcoholic fatty liver disease (NAFLD), but their cost effectiveness is not clear. We developed a decision model to quantify the accuracy and costs of 9 single or combination strategies, including 3 noninvasive tests (fibrosis-4 [FIB-4], vibration-controlled transient elastography [VCTE], and magnetic resonance elastography [MRE]) and liver biopsy, for the detection of cirrhosis in patients with NAFLD. METHODS: Data on the diagnostic accuracy, costs, adverse events, and cirrhosis outcomes over a 5-year period were obtained from publications. The diagnostic accuracy, per-patient cost per correct diagnosis of cirrhosis, and incremental cost-effectiveness ratios (ICERs) were calculated for each strategy for base cirrhosis prevalence values of 0.27%, 2%, and 4%. RESULTS: The combination of the FIB-4 and VCTE identified patients with cirrhosis in NAFLD populations with a 0.27%, 2%, and 4% prevalence of cirrhosis with the lowest cost per person ($401, $690, and $1024, respectively) and highest diagnostic accuracy (89.3%, 88.5%, and 87.5% respectively). The combination of FIB-4 and MRE ranked second in cost per person ($491, $781, and $1114, respectively) and diagnostic accuracy (92.4%, 91.6%, 90.6%, respectively). Compared with the combination of FIB-4 and VCTE (least costly), the ICERs were lower for the combination of FIB-4 and MRE ($2864, $2918, and $2921) than the combination of FIB-4 and liver biopsy ($4454, $5156, and $5956) at the cirrhosis prevalence values tested. When the goal was to avoid liver biopsy, FIB-4 + VCTE and FIB-4 + MRE had similar diagnostic accuracies, ranging from 87.5% to 89.3% and 90.6% to 92.4% for a cirrhosis diagnosis, respectively, although FIB-4 + MRE had a slightly higher cost. CONCLUSIONS: In our cost-effectiveness analysis based on the US health care system, we found that results from FIB-4, followed by either VCTE, MRE, or liver biopsy, detect cirrhosis in patients with NAFLD with a high level of accuracy and low cost. Compared with FIB-4 + VCTE, which was the least costly strategy, FIB-4 + MRE had a lower ICER than FIB-4 + LB.