PharmacoEconomics最新文献

Objectives: Most medication errors occur in primary and long-term care, and a wide range of medication safety interventions have been implemented, but these are often expensive, with little evidence around cost-effectiveness. We report a systematic review of economic evaluations of these interventions within primary and long-term healthcare settings.

Methods: A comprehensive search was conducted in databases (Medline, Embase, Econlit and PsycINFO) for full economic evaluations of primary care interventions targeting all errors in the medication use process (January 2004 to September 2025). Methodological and reporting qualities were assessed using standard tools.

Results: From 8523 records, 44 studies evaluating interventions in general/family practice (22), community pharmacy (11) and nursing/care/residential homes (11) met the inclusion criteria, 24 of which were either pharmacy led (19) or multidisciplinary medication reviews (5). All but one study looked at prescribing or monitoring interventions only. A total of 12 studies included all patients, with 24 focusing on older adults (> 65 years) and 3 focusing on condition-specific groups. Most studies only included costs from a healthcare perspective (39). Outcomes ranged from prescribing errors (9), hospital utilisation (13) and health-related quality of life (15) to falls (6) and adverse drug events (6). In total, 21 studies carried out an incremental cost-effectiveness analysis (16 including the incremental cost per quality-adjusted life year gained), and 14 reported the intervention cost-effectiveness. Remaining studies were cost-consequence (18) and cost-benefit analyses (5). Study reporting quality varied considerably, with lack of transparency in the design of the decision-analytic model, varied reporting of costs, little consideration of indirect costs or the impact of loss of trust on future use of healthcare, limitations in handling of uncertainty or discounting and very little patient involvement around targeting patients or designing interventions. Of the ten studies using decision models, all scored poorly for model validation. The quality of studies has not improved over time.

Conclusions: While some interventions demonstrated cost-effectiveness, study quality was variable, with generally poorly validated models. Study heterogeneity precluded meaningful direct comparison between studies. Significant research gaps remain as studies focused mainly on prescribing and monitoring errors, there was little or no investigation of technology-based interventions and there was inadequate targeting of patients most vulnerable to harm.

This illustration uses the Scottish Cardiovascular Disease (CVD) Policy Model as a case study to provide a comprehensive, step-by-step guide to building a discrete event simulation (DES) model in R. It is specifically designed for practitioners who are familiar with constructing Markov models in R and wish to transition their theoretical knowledge of DES into practical implementation. The Scottish CVD Policy Model was originally developed as an Excel-based Markov model with a sophisticated structure: a primary Markov model for first events and nested sub-Markov models for subsequent events. Later replicated in R by Xin, Yiqiao et al., the model's source code was made publicly available on GitHub, underscoring its potential as a teaching tool. The intricate structure of this model presents several challenges in health economic modeling, making it an ideal candidate for demonstrating how DES techniques can address such complexities effectively. In this illustration, we deliberately avoid using R packages developed specifically for DES to enhance transparency. Instead, we rely on base R functions, and the tidyverse package for tidy data wrangling. This approach ensures that every step of the DES implementation is clear and reproducible. In addition to covering fundamental topics such as how to simulate a time to event according to an assumed distribution, and continuous discounting, the illustration also provides solutions to more advanced modeling challenges, such as handling piecewise-modeled cost and utility. By discussing both general principles and complex scenarios, this paper equips readers with the practical tools needed to transition from Markov to DES frameworks, enhancing the accuracy and flexibility of health economic evaluations.

Patient and public involvement (PPI) in health economics modelling is increasingly recommended, yet formal guidance for how to structure or evaluate it remains limited. The Values in Modelling (VIM) framework was developed to address this gap by helping teams identify and deliberate on value-laden decisions in modelling. Drawing on philosophical theory, the framework defines five steps to guide collaboration between modellers and transdisciplinary participators and to document their influence on decision making: (1) identify ethical issues and perspectives; (2) characterize modelling decisions; (3) select decision-making strategies; (4) deliberate 'open' decisions; and (5) report and evaluate. We applied the VIM framework in the Lifetime Exposures and Asthma Outcomes Projection (LEAP) model project, which models the cost effectiveness of high-efficiency particulate air (HEPA) filters for asthma prevention and management. In this application, the framework helped prioritize modelling decisions for PPI, supported transparent deliberation about uncertainty, and led to concrete methodological changes-including new sensitivity analyses and revised outcome measures. These results demonstrate how a theory-informed process can enhance PPI in modelling, improving transparency, justification, and adequacy-for-purpose in health economics research.

Objectives: Obstructive hypertrophic cardiomyopathy (oHCM) is a myocardial disease, characterised by left ventricular hypertrophy, hampering the ventricular blood outflow. Standard of care (SoC) includes medications such as beta-blockers (BB) and calcium channel blockers (CCB) and septal reduction therapies. Recently, mavacamten, a first-in-class myosin inhibitor, became available to oHCM patients. The objective was to develop a decision analytic model to evaluate the cost effectiveness of mavacamten compared with SoC in oHCM patients from a Dutch societal perspective.

Methods: A Markov model was developed in R based on the Decision Analysis in R for Technologies in Health framework with data from the EXPLORER-HCM trial. This trial compared mavacamten in combination with background therapy (BB and CCB) versus placebo, including oHCM patients (n = 251; mean age 59 years) in New York Heart Association (NYHA) functional classes II (72.9%) and III (27.1%). For the model, four health states were defined based on the NYHA classes, including NYHA I-NYHA III/IV and death. The model evaluated mavacamten with SoC versus SoC alone over a lifetime horizon with a cycle length of 4 weeks, following the most recent Dutch guidelines. Health state utilities and societal costs were derived from the AFFECT-HCM study, with utilities measured using the EQ-5D-5L. Outcomes included (incremental) societal costs, life years (LYs), quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER). The Dutch willingness-to-pay thresholds of €50,000 and €80,000 per QALY were applied. Uncertainty of parameters was assessed in deterministic and probabilistic sensitivity and scenario analyses.

Results: Results indicate mavacamten being more effective (Δ4.75 LYs; Δ3.36 QALYs) and more costly (Δ€235,951) compared with SoC with an ICER of €70,223 per QALY gained. Varying parameters by 20% showed that the utility value of patients in NYHA class I (ICER: €57,199; €111,506 per QALY) and drug costs (ICER: €53,985; €86,555 per QALY) were most sensitive. Mavacamten accumulated most LYs, QALYs and costs by patients improving to NYHA class I, compared with SoC, and patients remained longer in that state throughout the model. For men, incremental QALYs (Δ 3.36) and costs (Δ €239,743) were slightly higher compared with women. The probability of the intervention being cost effective at the willingness-to-pay thresholds €50,000 and €80,000 per QALY was 1.3% and 87.4%, respectively. Conclusion The results show that mavacamten increased LYs and QALYs compared with SoC, however, at substantial additional costs. The probability of mavacamten being cost effective depends on the selected willingness-to-pay threshold.

Background: Post-traumatic stress disorder (PTSD) is a debilitating condition that arises after exposure to a traumatic event and leads to significant impairment in daily functioning if left untreated. Economic evaluations are essential for understanding the comparative value of PTSD treatments and ultimately supporting their implementation. Several model-based economic evaluations exist in this area; however, these can differ in their methodological approaches and parameter inputs, which can influence conclusions drawn.

Objective: This systematic review aimed to explore model structures and parameter inputs employed in model-based economic evaluations of PTSD treatment.

Methods: A literature search was carried out in the following databases: MEDLINE, PsycINFO, SCOPUS, Econlit, CINAHL, Web of Science Core Collection, and Cochrane Collaboration Library between 1 January 2000 and 1 May 2025. Studies were eligible if they presented a full economic evaluation of a treatment for PTSD using a decision-analytic model. Data relating to the model structure and parameter inputs were extracted and quality assessment was conducted.

Results: This review identified 14 model-based studies, of which two used decision trees, six used a Markov model, four used a combined decision tree and Markov model, and two used an agent-based model. There was significant variation across model parameters, including in disease conceptualisation and progression, data sources utilised, assumptions reported, and costs included. The quality assessment revealed the following key areas of concern: insufficient consideration of methodological uncertainty and heterogeneity, internal consistency, and incorporation of relevant disease and intervention characteristics.

Conclusions: This paper highlights important variations in current model-based economic evaluations of PTSD treatment. Future work should seek to generate evidence to support consistency in future economic evaluations of PTSD treatment options.

Background: Children with multidrug-resistant (MDR)/rifampicin-resistant (RR) tuberculosis (TB) are an important but neglected group in cost-effectiveness research. Digital health information systems enable new approaches to health-service cost analysis. The Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, collates disparate health system data including hospital inpatient and outpatient data, medications, laboratory tests, and primary health care utilisation.

Methods: A health-service cost analysis used anonymised, integrated PHDC data for children treated for MDR/RR-TB between 2018 and 2021. Health-service utilisation was costed using local unit prices, and total per-patient costs were summarised by key patient and disease characteristics (age, sex, resistance profile, site of disease, and HIV status) and reported in 2021 USD. A log-linear regression model identified cost drivers, and alternative parametric distributions were fitted to total costs to assess distributional fit.

Results: There was significant total cost variation across the 271 children in the data sample (median US$7576; interquartile range 2725-22,986). Regression analysis indicates younger age, extrapulmonary disease site, living with HIV, and treatment duration had significant impact on costs; impact of resistance profile was significant but subject to modelling assumptions. The distribution of total per-patient costs fitted a gamma distribution (α = 0.93, β = 14,496).

Conclusion: Treatment for MDR/RR-TB in children remains costly for health systems. Utilising routinely collected, real-world data from an established health information system enables accurate and representative insights to overall costs and major cost drivers. Costs were highly skewed, with a small proportion of patients incurring very high costs. This cost analysis can assist in decision making and programme development at local and international levels and as an input to secondary analysis.

Background and objective: Collaborative engagement with individuals invested in or affected by health research, beyond researchers themselves, is advantageous and encouraged by major funding bodies. However, the degree of collaborative engagement in health state valuation is unclear. A scoping review was conducted to (i) identify recommendations on best practice in collaborative engagement in health economics and related literature; (ii) identify examples of collaborative engagement in valuation studies; and (iii) map (ii) onto (i) to identify current practice and future recommendations.

Methods: Eight databases were searched in March-May 2024, with grey literature searches in August-September 2024. For objective (i), reports or manuscripts in health economics or patient-reported outcome measure development/evaluation of any date providing recommendations for collaborative engagement were included. For objective (ii), articles published since 2019 featuring health state valuation and collaborative engagement were included. Best practice recommendations were extracted and thematically synthesised. Examples of collaborative engagement were extracted and mapped against recommendations.

Results: Twenty-two records featuring recommendations and 15 valuation studies were included. A 15-item framework of emerging best practice recommendations for collaborative engagement was synthesised. Most examples of collaborative engagement involved patients and/or experts helping inform health states for valuation. There was no evidence for 9 out of 15 synthesised recommendations having been applied in any of the valuation studies and only minimal evidence was extracted for the remaining six.

Conclusions: Collaborative engagement in health state valuation is underdeveloped and unaligned with literature recommendations. A 15-point framework has been developed as a strategic starting point for developing guidance to improve practice in the field.

Background and objective: In cost-effectiveness analysis, treatment decisions are analysed at the population level. Combinations of treatment strategies that account for the heterogeneity of costs and effects across patients can be more cost-effective than a "one size fits all" approach. Individualized treatment rules (ITRs) assign a specific treatment to every patient based on their relevant characteristics, such that overall cost-effectiveness is optimized, but do not include feasibility or ethical considerations. We propose an approach for the design of ITRs based on simulated patient data from microsimulation models using statistical learning techniques.

Methods: We mathematically define the optimal ITR and how to measure the value of an ITR in a cost-effectiveness context. We explore least absolute shrinkage and selection operator (LASSO) regression, classification trees, and policy trees to illustrate how standard statistical learning techniques can be used to derive ITRs. We compare the strengths and limitations of these three approaches in terms of three criteria: the incremental value of the ITRs compared to optimal treatment assignment in terms of net monetary benefit (NMB), computational speed, and the interpretability of the ITRs. We propose methods to describe the impact of parameter uncertainty on the ITRs. We also explore how stochastic uncertainty can impact the ITR incremental value. We illustrate the methods by applying them to a microsimulation model for haemophilia B comparing four treatment strategies as a case study. The relevant patient characteristics in this model are the annualized bleeding rate, age, and sex.

Results: In our case study, a simple two-layer-deep classification tree is best suited based on the three criteria. This classification tree allocates treatments depending on whether the annualized bleeding rate of a patient is above or below 30 and whether their age is above or below 51. The optimal threshold values are uncertain based on the 95% credible ranges from the probabilistic analysis: 21-46 for annualized bleeding rate and 42-56 for age. Scenarios show that stochastic uncertainty has an impact on the incremental value of the ITR.

Discussion: Based on methodological considerations and the empirical findings in our case study, we expect the superiority of classification trees for the derivation of ITRs to be generalizable to other microsimulation models. This finding needs to be confirmed in future applications. Stochastic uncertainty has significant impacts on the ITRs, such that accurate representations of individual patient pathways are particularly crucial when designing ITRs. Future research could explore further empirical models and analytical approaches for ITRs or consider the translation of ITRs into the real-world decision-making context.

Background and objective: The reliability of a decision model to guide decision making depends on its ability to accurately predict patient outcomes. We present results of an external validation of the MicroSimulation Core Obesity Model (MS-COM) that was developed to compare the cost effectiveness of obesity management interventions in adults.

Methods: We updated a 2018 systematic literature review of economic models in overweight and obesity and conducted additional targeted searches to identify suitable sources and outcomes to validate against MS-COM in people with overweight or obesity with or without type 2 diabetes. We extracted baseline characteristics and cardiovascular and mortality outcomes, where these were closely matched with MS-COM, and incidence of type 2 diabetes. We performed external-dependent (sources used in MS-COM) and external-independent (sources not used in MS-COM) validation. The extent of concordance between predicted and observed outcomes was assessed using the coefficient of determination (R²), ordinary least-squares linear regression line (OLS LRL), mean absolute percentage error, root mean square percentage error and mean squared log of accuracy ratio.

Results: Ninety-nine potential independent validation sources were identified from 6381 screened records, of which nine studies reported cardiovascular and mortality outcomes that were closely matched with MS-COM, along with two studies that reported type 2 diabetes incidence (number of endpoints = 106). The dependent validation of cardiovascular and mortality outcomes (N = 18), based on the QRisk3 risk equation (normoglycaemia/prediabetes population) and UKPDS 82 (type 2 diabetes population), showed a good linear correlation with observed outcomes (R² = 0.99 and 0.98, respectively). There was some slight overprediction of QRisk3 (OLS LRL slope = 1.11) and underprediction of UKPDS 82 (OLS LRL slope = 0.97). The independent validation of cardiovascular and mortality outcomes also showed a good linear correlation with observed outcomes, particularly in adults with normoglycaemia/prediabetes (R² = 0.90; OLS LRL slope = 0.86); however, an independent validation of type 2 diabetes incidence showed a poorer fit with some degree of underprediction (R² = 0.74; OLS LRL slope = 0.66). Mean error estimates were lower in the dependent validation, showing good concordance between predicted and observed values.

Conclusions: External validation of MS-COM showed good concordance with dependent and independent sources, suggesting the model accurately predicts obesity-related complications in an overweight/obese population with normoglycaemia/prediabetes and type 2 diabetes.