ABSTRACT Incorporating patient preferences into drug development is crucial, particularly, for rare diseases with significant unmet needs. This study used Best‐Worst Scaling type 2 (BWS‐2) to explore benefit–risk trade‐offs for patients and caregivers in two rare neuromuscular diseases (NMDs), myotonic dystrophy type 1 (DM1), and mitochondrial myopathy (MM). Patients with DM1 and MM, along with caregivers, completed a BWS‐2 survey assessing four treatment benefits (muscle strength, energy and endurance, balance, cognition) and two risks (permanent liver damage, temporary blurring of vision). Participants were stratified by disease group and age of onset (< 20, ≥ 20 years). A latent class analysis was used to calculate the relative importance of each treatment attribute. Sociodemographic and disease‐related data were also collected. A total of 270 participants (DM1 n = 143, MM n = 127, including 37 caregivers) were included. BWS‐2 results revealed a priority for improvements in muscle strength (24%), and energy and endurance (23%) across all groups, with caregivers placing a higher priority on cognition improvements (17%) compared to patients. There were no significant differences between disease groups or by age of onset. This study underscores the importance of patient preferences in drug development for rare NMDs. The consensus on treatment priorities across both diseases suggests that overlapping clinical features can inform and expedite future NMD or rare disease drug development.

Background:National dietary guidelines can help in promoting healthier and more sustainable dietary patterns. Although the potential benefits of adherence are well documented, observed adherence in the Netherlands remains suboptimal. Objective:This study estimated the annual hidden health and environmental costs associated with suboptimal dietary patterns, defined here as low adherence to the Dutch dietary guidelines. Methods:Adherence to the Dutch dietary guidelines was assessed using the Dutch Healthy Diet index 2015 (DHD15). Individuals in the lowest (Q1) and highest (Q5) quintiles of adherence were compared based on food consumption data from the Dutch National Food Consumption Survey (DNFCS) 2019-2021 among adults aged 20-79 years. Hidden health costs were estimated as productivity losses using the Human Capital Approach (HCA) for diet-related non-communicable diseases (NCDs), along with the economic valuation of disability-adjusted life years (DALYs). Environmental costs were calculated across six indicators, including greenhouse gas (GHG) emissions, terrestrial acidification, freshwater eutrophication, marine eutrophication, land use, and blue water consumption, and were monetised using environmental shadow prices. Results:Low adherence to dietary guidelines is associated with EUR 410 million in productivity losses, with DALY-related costs ranging from EUR 910 million to 1.8 billion. Low adherence imposes EUR 3.0 billion more in annual environmental costs compared to high adherence. Discussion:These results reinforce calls for policies and interventions that promote healthier eating patterns as a cost-effective means of reducing the hidden costs embedded in current dietary habits and transforming the agrifood system toward health and sustainability goals.

Background: Approximately 20% of the total healthcare expenditure in high-income countries is spent on low-value care, ie, care that is unnecessary, potentially harmful, or provides marginal health benefits to patients. Demand for low-value care is considered a multifactorial, complex problem, as a multitude of factors have been associated with low-value care use. However, there is limited knowledge on how these factors interrelate and lead to demand for low-value care. Therefore, the aim of this study was to explore and map the factors and their relations contributing to patients’ demand for low-value care using a complex systems approach. Methods: Two group model building (GMB) sessions on the topic of low-value care were organised with experts from the Netherlands. Each session’s transcript was thematically analysed, resulting in one causal loop diagram (CLD) per session. These CLDs included determinants, relations between these determinants and feedback loops. Finally, the CLDs were synthesised into one CLD combining the insights from both sessions. Results: The final CLD consisted of 42 factors influencing demand for low-value care. It includes biomedical factors, cognitive biases, socio-cultural factors, economic factors, emotions, knowledge-related factors, factors related to the interaction with the provider, and preferences and expectations. By mapping the relations between these factors, we identified 59 connections and nine reinforcing feedback loops potentially influencing demand for low-value care. Conclusion: The CLD provides insight into factors, mechanisms and feedback loops influencing patients’ demand for low-value care. It highlights perceived insecurity as a central driver that influences multiple other factors and eventually affects patients’ demand for low-value care. These central factors influencing multiple other factors may be potential leverage points for policies aiming to reduce demand for low-value care. Further research is required to clarify the relative importance of the identified factors, relationships, and feedback loops to determine effective leverage points.

ABSTRACT Background The involvement of stakeholders in computational model development is an emerging and promising practice that can enhance the understanding of the model context and improve its relevance for supporting decision‐making processes. However, little is known about stakeholders’ experiences in these engagement processes. This study explored the experiences of stakeholders on their involvement in the participatory development of a youth mental health model. Methods Focus groups were conducted with four stakeholder groups: (1) experts by experience who have experienced mental health problems, (2) young adults aged 16–25 interested in mental health, (3) professionals working in (preventive) mental health services for youth and (4) youth mental health researchers. Fourteen focus groups and one individual interview were conducted across three distinct rounds, in which a total of 43 stakeholders participated. Additionally, questionnaires were administered to evaluate the focus group sessions. The data were analysed using thematic content analysis. Results During the focus groups, participants valued comparing ideas across stakeholder groups and incorporating lived experiences into model development, but expressed doubts about the validity of their contributions and noted insufficient transparency in the modelling process. Participants also encountered complexities related to specific requirements of modelling, including the need to simplify the concept of mental health and to address interactions among mental health factors. Conclusion Tailored information provision per stakeholder group and transparency of the modelling process can help address challenges in participatory modelling. This study offers practical guidance to improve stakeholder contributions in computational model development, ultimately improving model quality and supporting policy‐making in complex domains such as mental health. Patient or Public Contribution A participatory research approach was used to develop the computational model, thereby ensuring that it incorporated lived experiences and priorities of the stakeholders. Additionally, the involvement of two researchers with lived experience and expertise in patient participation in mental health research, together with an advisory board of advocates with lived experience, facilitated the continuous integration of these stakeholder group perspectives throughout all phases of the research process.

Fiscal policies to curb sugar-sweetened beverages (SSBs) consumption are often recommended to address the associated health risks and societal burden. The differential impact of these policies across socioeconomic position (SEP) is important for promoting equity in public health outcomes. This study aimed to estimate the impact of a flat-rate tax (€26.13 per 100 L) and tiered tax based on the sugar content ceiling limits of the Dutch National Approach to Product Improvement (NAPV) on sugar and energy intake from SSBs by total population, SEP, age, and sex in the Netherlands. The impact of price changes on daily SSB consumption was modelled by applying a price elasticity of -1.59 on nationally-representative food consumption data from the Dutch National Food Consumption Survey 2019–2021. We assumed a pass-through rate of 82

High-flux hemodialysis (HD) and high-dose hemodiafiltration (HDF) are established treatments for patients with kidney failure. Since HDF has been associated with improved survival rates compared to HD, we evaluated the cost-effectiveness of HDF compared to HD. Cost-utility analyses were performed from a societal perspective alongside the multinational randomized controlled CONVINCE trial. A Markov cohort model was used to extrapolate results to a lifetime time horizon. Costs of dialysis sessions were based on published data, with two scenarios reflecting different estimates for costs of dialysis staff. Other healthcare resource use, productivity losses and quality of life were collected in the electronic case report form or by country-adapted, self-reported questionnaires. Scenario and probabilistic sensitivity analyses were performed. In the two-year trial-based analysis, HDF was associated with higher quality-adjusted life years (QALYs) and higher costs, with incremental costs per QALY (ICER) of €31,898 and €37,344, depending on dialysis staff costs. The lifetime Markov cohort model resulted in ICERs of €27,068 and €36,751. Compared to HD, HDF resulted in an additional year in perfect health at increased costs. Sensitivity analyses of the lifetime analyses showed the probability of cost-effectiveness was more than 90% at willingness-to-pay threshold of €50,000/QALY. The ICER was €13,231 when excluding all costs in additional life years. The probability of cost-effectiveness was mainly driven by costs due to additional dialysis sessions in life years gained, and not due to additional costs per dialysis session. As costs may differ between countries and centers, we recommend translating our results to local settings.

Urban planning can help tackle environmental health issues. We demonstrate that agent-based simulation can discover unintended as well as intended environmental health effects and social inequalities of intervention scenarios. We developed, calibrated and validated UrbHealth-ABM, an empirically grounded agent-based model of Amsterdam The Netherlands, integrating data and models of individual mobility choices, traffic, air pollution, physical activity and personal exposure. We used the 2019 parking price increase as a natural experiment, confirming the models’ accuracy in predicting traffic reduction. Projections for the planned 2030 no emission zone show a significant reduction of nitrogen dioxide exposure for everyone and an increase in transport-related physical activity, especially for less affluent outer-city residents. However, disproportionate increases in their travel times raise equity concerns. Several 15-minutes city scenarios reveal that although driving may increase to distant destinations, nitrogen dioxide exposure decreases overall. Moreover, transport-related physical activity might decrease in the Amsterdam context due to shorter active travel distances, but travel time savings could be used for mitigation strategies.

The market price of fatty fish does not currently reflect their full societal impact, as it excludes both the health benefits of consumption and the environmental costs associated with its production. This study offers a unique application of true pricing by internalising these hidden costs in the Dutch context. Data on the consumption of 21 generic wild and cultivated fatty fish products were obtained from the Dutch National Food Consumption Survey (DNFCS) 2019-2021. Health costs of coronary heart disease (CHD) and stroke were estimated using the cost-of-illness (COI) approach, and the Population Attributable Fraction (PAF) was applied to determine the proportion of disease incidence attributable to insufficient fatty fish consumption. Environmental impacts, including global warming, terrestrial acidification, freshwater eutrophication, marine eutrophication, land use, and water use were assessed using the Dutch Life Cycle Assessment Food Database and monetised with Dutch environmental unit prices. The true price was estimated by adjusting retail prices to account for health and environmental impacts. The estimated health-related discount was EUR 1.50 per kilogram of fatty fish. Environmental premiums vary by species and fishery type (EUR 0.22-3.76), with the highest for cultivated salmon and the lowest for mackerel. The difference between true prices and market prices ranges from -18.1 % to +12.4 %. The results suggest that eel and cultivated salmon are priced below their true societal costs, whereas small pelagic species are priced above them. True pricing can support sustainable food systems by making lowimpact seafood more accessible and discouraging overconsumption of high-impact species.

The involvement of stakeholders in the development of computational models to support public health policy decisions has gained popularity, as it can enhance the quality of these models. This scoping review aims to synthesise the current body of literature on how stakeholders have been involved in the development of computational models in public health. A literature search was performed in MEDLINE, APA PsycInfo and Scopus, from inception to October 2023. In addition, supplementary articles were identified through snowballing and a Google Scholar search. We searched for studies that developed computational models, involved stakeholders in the model development process, and focused on public health domains. Two reviewers independently screened abstracts and full-text articles. A total of 3438 titles and abstracts were screened, 40 articles underwent full-text review, and 17 articles were included in this review, presenting information on 11 unique computational modelling studies. The studies spanned a diverse range of nine public health domains, including areas such as alcohol use and suicide prevention, with most studies developing system dynamics models. A key rationale for adopting a participatory approach was to ensure that the model accurately reflected the context-specific characteristics of the setting being modelled. Various stakeholder groups were involved, predominantly policy-makers, researchers and community representatives. During the development phases of problem mapping, model conceptualisation, and model validation, stakeholders were mostly engaged through participatory workshops. Studies reported that a major strength of the participatory approach was to facilitate transparent consensus-building processes during model development, while the management of stakeholder engagement timelines was experienced a major challenge. Involving stakeholders in the development of computational models can improve the accurate representation of the model’s context, thereby enhancing the quality and applicability of these models to support decision-making processes. However, there are limited computational modelling studies available in the literature that provide detailed elaboration on their methods of stakeholder engagement. There is a need for improved reporting and evaluation of the impact of participatory modelling processes, as well as more guidance on effective stakeholder involvement, to fully realise the potential of participatory modelling in public health.

BACKGROUND:The long-term impact of preventive policies in the Netherlands on the mental health of young adults remains unclear. Therefore, this paper describes the development of a conceptual model of youth mental health that serves as the foundation of a future decision-analytic model. RESEARCH DESIGN AND METHODS:Stakeholders were engaged through three rounds of focus group discussions to indicate the factors of youth mental health that affect the likelihood of developing mental disorders later in life and the relationships among them. Findings were discussed with stakeholders and in a study team that included members with diverse backgrounds. Literature was used as an additional information source for the relationships among the selected factors. RESULTS:In total, 43 stakeholders participated in the focus group discussions. Eleven factors of youth mental health were regarded as most influential, with 13 relationships among them. The final conceptual model was approved by the stakeholders and the study team. CONCLUSIONS:Through integrating stakeholder perspectives and published literature, a conceptual model was created that captures essential factors and relationships affecting (long-term) mental health. Although stakeholder engagement requires extensive planning, it enhanced the model's credibility and validity, and could therefore serve as a complement to other conceptual modeling approaches.

AIMS:Recent trials have shown that low-dose colchicine (0.5 mg once daily) reduces major cardiovascular events in patients with acute and chronic coronary syndromes. We aimed to estimate the cost-effectiveness of low-dose colchicine therapy in patients with chronic coronary disease when added to standard background therapy. METHODS AND RESULTS:This Markov cohort cost-effectiveness model used estimates of therapy effectiveness, transition probabilities, costs, and quality of life obtained from the Low-Dose Colchicine 2 trial, as well as meta-analyses and public sources. In this trial, low-dose colchicine was added to standard of care and compared with placebo. The main outcomes were cardiovascular events, including myocardial infarction, stroke, and coronary revascularization, quality-adjusted life year (QALY), the cost per QALY gained (incremental cost-effectiveness ratio), and net monetary benefit. In the model, low-dose colchicine therapy yielded 0.04 additional QALYs compared with standard of care at an incremental cost of €455 from a societal perspective and €729 from a healthcare perspective, resulting in a cost per QALY gained of €12 176/QALY from a societal perspective and €19 499/QALY from a healthcare perspective. Net monetary benefit was €1414 from a societal perspective and €1140 from a healthcare perspective. Low-dose colchicine has a 96 and 94% chance of being cost-effective, from a societal and a healthcare perspective, respectively, when using a willingness to pay of €50 000/QALY. Net monetary benefit would decrease below zero when annual low-dose colchicine costs would exceed an annual cost of €221 per patient. CONCLUSION:Adding low-dose colchicine to standard of care in patients with chronic coronary disease is cost-effective according to commonly accepted thresholds in Europe and Australia and compares favourably in cost-effectiveness to other drugs used in chronic coronary disease.

Patients on kidney replacement therapy (KRT) face high symptom and treatment burden, especially at end of life. Yet insights into end-of-life healthcare utilisation and costs remain limited. We aimed to describe healthcare utilisation and costs during the last year of life across KRT modalities. We used Dutch health insurance claims data to identify incident and prevalent patients aged ≥ 65 years, treated with KRT during the year preceding death, and who deceased between June 2016 and December 2021. Healthcare utilisation and costs in the last year of life were analysed for different KRT modalities and compared with controls without kidney disease-related claims, with controls being matched on sex, age, socio-economic status, and year of death. We identified 7279 patients on KRT (4614 haemodialysis [HD] patients, 766 peritoneal dialysis [PD] patients and 1899 kidney transplant [KTx] recipients) and 14,558 controls. During their last year of life, 85

Background: Despite programmatic protocolised care and structured support, considerable variation is observed in completeness of registration and achieving targets of cardiovascular risk management (CVRM) between individual GPs in the Netherlands. Aim: To determine whether completeness of registration and achieved targets of cardiovascular risk factors improves with practice visitation. Design & setting: Observational study utilising the care group's database (2016-2019), comparing changes in registration and achieved targets in non- visited practices and visited practices. Method: We compared completeness scores of registration and scores of targets achieved before visitation and 1 year after visitation. Data were analysed on patient level and GP level. Separate analyses were performed among GPs who were ranked in the lower 25% of score distributions. Results: We observed no clinically relevant improvements in completeness of registration and targets achieved in 2017, 2018, and 2019 that could be attributed to visitations in the previous year, both on individual patient level and on aggregated level per general practice. In practices ranked in the lower 25% of the distribution, improvements over time were clinically relevant and larger than the overall changes. Yet, these findings were irrespective of the number of practice visitations. Conclusion: Practice visitations in our setting did not seem to lead to improvements in practice performance, nor in completeness of registration of risk factors or in reaching predefined target goals for cardiovascular risk factors.

Abstract Background During the COVID-19 pandemic, provision of non-COVID healthcare was recurrently severely disrupted. The objective was to determine whether disruption of non-COVID hospital use, either due to cancelled, postponed, or forgone care, during the first pandemic year of COVID-19 impacted socioeconomic groups differently compared with pre-pandemic use. Methods National population registry data, individually linked with data of non-COVID hospital use in the Netherlands (2017–2020). in non-institutionalised population of 25–79 years, in standardised household income deciles (1 = low, 10 = high) as proxy for socioeconomic status. Generic outcome measures included patients who received hospital care (dichotomous): outpatient contact, day treatment, inpatient clinic, and surgery. Specific procedures were included as examples of frequently performed elective and acute procedures, e.g.: elective knee/hip replacement and cataract surgery, and acute percutaneous coronary interventions (PCI). Relative risks (RR) for hospital use were reported as outcomes from generalised linear regression models (binomial) with log-link. An interaction term was included to assess whether income differences in hospital use during the pandemic deviated from pre-pandemic use. Results Hospital use rates declined in 2020 across all income groups. With baseline (2019) higher hospital use rates among lower than higher income groups, relatively stronger declines were found for lower income groups. The lowest income groups experienced a 10% larger decline in surgery received than the highest income group (RR 0.90, 95% CI 0.87 – 0.93). Patterns were similar for inpatient clinic, elective knee/hip replacement and cataract surgery. We found small or no significant income differences for outpatient clinic, day treatment, and acute PCI. Conclusions Disruption of non-COVID hospital use in 2020 was substantial across all income groups during the acute phases of the pandemic, but relatively stronger for lower income groups than could be expected compared with pre-pandemic hospital use. Although the pandemic’s impact on the health system was unprecedented, healthcare service shortages are here to stay. It is therefore pivotal to realise that lower income groups may be at risk for underuse in times of scarcity.

The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. In total, 54 respondents contributed to the panel results. The term ‘multi-use disease models’ was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders’ roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes.

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs). A total of 730 eligible patients with 2722 lesions were included. The DLM reached an area under the receiver operating characteristic (AUROC) of 0.607 [95%CI 0.565-0.648]. In comparison, a model of clinical predictors reached an AUROC of 0.635 [95%CI 0.59 -0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595-0.676]. Differences in AUROC were not statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model. The DLM reached a statistically significant discriminative value, but was unable to improve over known clinical predictors. The present work shows that the assessment over known clinical predictors is an essential step for imaging-based prediction and brings important nuance to the almost exclusively positive findings in this field.

Introduction Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. Methods A total of 307 patients with non–small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen’s weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. Results Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC ( P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR ( P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53–0.55). Conclusion Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. Highlights Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability. Most respondents found the DCE and SW tasks very easy or easy to understand and answer. A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.