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Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized., Methods: We compare iSTM-produced estimates of decision uncertainty and VOI to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. We use numerical simulations to illustrate our findings and explore the impact of relaxing normality assumptions or having >2 decision alternatives., Results: iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias approaches 0 as number of microsimulated individuals approaches infinity). Decision uncertainty depends on 1 tail of the INMB distribution (e.g., P[INMB <0]), which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated since this increases INMB variance., Conclusions: iSTMs are useful for probabilistic economic evaluations. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results., Highlights: Individual-level state-transition microsimulation models (iSTMs) produce biased but consistent estimates of the probability that interventions are cost-effective.iSTMs also produce biased but consistent estimates of the expected value of perfect information.The biases in these decision uncertainty and value-of-information measures are not reduced by more parameter sets being sampled from their population-level uncertainty distribution but rather by more individuals being microsimulated for each parameter set sampled.Analysts using iSTMs to quantify decision uncertainty and value of information should account for these biases when allocating their computational budgets and, at minimum, characterize such bias in their reported results., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr. Alarid Escudero is supported by grant U01CA253913 from the National Cancer Institute (NCI) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET), Dr. Jalal is supported by a Canada Research Chair, and Drs. Alarid Escudero and Jalal are supported by grant U01CA265750 from NCI as part of CISNET. The funders had no role in designing the study, interpreting the data, writing, or publishing the report.

Highlights: The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.We provide a multivariate categorical sampling function for R and Python programming languages to sample times to events from processes with different hazards simultaneously., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided in part by grants from the National Cancer Institute as part of the Cancer Intervention and Surveillance Modeling Network. Dr. Alarid-Escudero is supported by grant U01CA253913, Dr. Jalal is supported by a Canada Research Chair, and Drs. Alarid-Escudero and Jalal are supported by grant U01CA265750. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Objectives: Gastric cancer (GC) imposes a significant burden of disease globally. Multiple treatments are available but are associated with high costs and potentially detrimental effects on quality of life. The utility values of health status are measures of patient preference over quality of life, which are increasingly used for health and economic decision-making. Currently, there is little systematized information on the utility values for different stages of GC. This systematic review synthesizes and meta-analyses the literature on GC utilities., Methods: A search was conducted in PubMed, Embase, MEDLINE, and Cochrane Library for studies reporting utility values calculated using direct and indirect methods. Information from the selected studies was extracted and appraised, and meta-analyses of utility values based on GC health states were performed., Results: Twelve studies involving 4585 patients were included. Random-effects meta-analysis estimates showed a mean utility of 0.77 (95% CI 0.7-0.85) for stage I, 0.75 (95% CI 0.65-0.85) for stage II, 0.70 (95% CI 0.63-0.96) for stage III, and 0.64 (95% CI 0.56-0.32) for stage IV. All estimates showed considerable heterogeneity., Conclusions: Our study provides an updated overview of the literature on utility values in GC and presents a discussion of the relevance of GC stages for its analysis. Decision-makers should consider patients' preferences in the proposal of policies and clinical decisions., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Describe the survival, sociodemographic and clinical characteristics of subjects with disorders of consciousness in a reference rehabilitation center, in a developing country., Methods: Patients with disorders of consciousness (DoC) caused by acquired neurological injuries, admitted between the years 2002-2018 in a neurorehabilitation center. Extracted data covered demographics, clinical details, survival time, and discharge information. Cox proportional hazard model and Kaplan-Meier analysis were used to reveal, associations with survival., Result: Out of 5064 neurological cases, 159 patients were diagnosed with DoC. The demographic data showed a male dominance (65%), with an average injury age of 42 years. The most common causes were traumatic (41%), anoxic (36%), and vascular (10%), with traffic accidents accounting for 71% of traumatic injuries. The study found that 75% of patients remained in a vegetative state (VS), and 25% in a minimally conscious state (MCS), with an average survival of 2110 days., Conclusion: There were no significant differences in survival days between patients in MCS and VS. Patients with traumatic injuries showed a higher survival rate than those with non-traumatic injuries. Age and etiology were identified as factors associated with a higher risk of death.

Background: Rehabilitation is a critical process for enhancing functionality, independence, and quality of life in individuals with disabilities. Grounded in the biopsychosocial model, it addresses physical, emotional, and social dimensions through personalized, evidence-based interventions. By integrating standardized assessments and continuous evaluation, rehabilitation has the potential to promote recovery and support active participation in society., Objectives: This study evaluated the impact of a long-term, multidisciplinary, home-based rehabilitation program on quality of life, balance, and functional autonomy in adults with neuromusculoskeletal disabilities., Methods: A total of 559 participants received individualized interventions from a team of physical therapists, occupational therapists, psychologists, and other health professionals. Functional independence, balance, depressive symptoms, and quality of life were assessed using the Barthel Index, Berg Balance Scale, Beck Depression Inventory, and SF-36 questionnaire, respectively., Results: A longitudinal analysis comparing pre- and post-intervention outcomes revealed statistically significant improvements ( p < 0.001) across all metrics. The Barthel Index median increased from 85 to 90 points, indicating greater functional independence, while the Berg Balance Scale improved from 39 to 47 points, reflecting reduced fall risk. Depressive symptoms decreased, with Beck Depression Inventory scores dropping from 12 to 9, and both physical and mental health components of the SF-36 showed marked enhancements., Conclusions: These findings demonstrate the program's effectiveness in addressing both physical and emotional needs, emphasizing the value of extended, personalized, home-based care in improving health, autonomy, and overall quality of life for individuals with disabilities. This study underscores the potential of multidisciplinary approaches to support long-term rehabilitation in diverse populations.