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Aim Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. Methods PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. Results The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the ‘sunflower method’ (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. Conclusions The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. RESULTS Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups. RESULTS Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Background/Objectives Smoking is the leading risk factor for many chronic diseases. The quantitative analysis of potential health gains from reduced smoking is important for establishing priorities in Mongolia’s health policy. This study quantifies the effect of tobacco-tax increases on future smoking prevalence and the associated smoking-related burden of disease in Mongolia. Methods The dynamic model for health impact assessment (DYNAMO-HIA) tool was used. The most recent data were used as input for evaluating tobacco-taxation scenarios. Demographic data were taken from the Mongolian Statistical Information Services. Smoking data came from a representative population-based STEPS survey, and smoking-related disease data were obtained from the health-information database of Mongolia’s National Health Center. Simulation was used to evaluate various levels of one-time price increases on tobacco products (25% and 75%) in Mongolia. Conservative interpretation suggests that the population will eventually adjust to the higher tobacco price and return to baseline smoking behaviors. Results Over a three-year period, smoking prevalence would be reduced by 1.2% points, corresponding to almost 40 thousand smokers at the population level for a price increase of 75%, compared to the baseline scenario. Projected health benefits of this scenario suggest that more than 137 thousand quality adjusted of life years would be gained by avoiding smoking-related diseases within a population of three million over a 30-year period. Discussion Prevention through effective tobacco-control policy could yield considerable gains in population health in Mongolia. Compared to current policy, tax increases must be higher to have a significant effect on population health. Implications Tobacco taxation is an effective policy for reducing the harm of tobacco smoking, while benefiting population health in countries where the tobacco epidemic is still in an early stage. Smoking prevalence and smoking behaviors in these countries differ from those in Western countries. Reducing the uptake of smoking among young people could be a particularly worthwhile benefit of tobacco-tax increases.

Aims/hypothesis Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10–20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort. Methods A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots). Results We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75). Conclusions/interpretation Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice. Graphical abstract

BACKGROUND: A very common side effect of non-vitamin K antagonist oral anticoagulant (NOAC) is (minor) bleeding. Data about impact and costs of minor bleeds in NOAC therapy is still limited or not present in current literature. In this patient orientated study, we aim to provide an estimate of the costs of minor bleeds in patients with atrial fibrillation (AF) treated with a NOAC.METHODS: A retrospective observational cohort study was conducted. Patients with AF and on NOAC therapy were included. Data was obtained by questionnaires and information from electronic patient records. Reference prices were used to calculate the costs per patient. Furthermore, cost of minor bleeds per patient is compared with literature-based costs of minor and major bleeding.RESULTS: 139 patients were included. A total of 94 minor bleed were reported by 71 patients. The sum of minor bleeding costs from societal perspective were €9,851.49, or on average €70,87 (95% CI €54,37 - €85,68) per patient with AF. The biggest cost drivers were rectal and vaginal bleeds, epistaxis was most commonly reported.CONCLUSION: Total costs of minor bleeds from a societal perspective, in AF patients using NOACs, are non trivial and exceed the costs presented in existing literature.

Aims/hypothesis In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade). Methods For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998–2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies. Results For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy. Conclusions/interpretation The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months. Graphical abstract

Improvements in medical treatment have contributed to rising health spending. Yet there is relatively little evidence on whether the spending increase is “worth it” in the sense of producing better health outcomes of commensurate value—a critical question for understanding productivity in the health sector and, as that sector grows, for deriving an accurate quality-adjusted price index for an entire economy. We analyze individual-level panel data on medical spending and health outcomes for 123,548 patients with type 2 diabetes in four health systems: Japan, The Netherlands, Hong Kong and Taiwan. Using a “cost-of-living” method that measures value based on improved survival, we find a positive net value of diabetes care: the value of improved survival outweighs the added costs of care in each of the four health systems. This finding is robust to accounting for selective survival, end-of-life spending, and a range of values for a life-year or fraction of benefits attributable to medical care. Since the estimates do not include the value from improved quality of life, they are conservative. We, therefore, conclude that the increase in medical spending for management of diabetes is offset by an increase in quality.

BACKGROUND: This study aims to report on the design of a model to determine the cost-effectiveness of prevention and treatment of early psychosis (PsyMod) and to estimate ten-year cost-effectiveness and budget impact of interventions targeting individuals with ultra-high risk (UHR) of developing psychosis or with first episode psychosis (FEP).METHODS: PsyMod was built in parallel with the development of a new standard of care for treatment of early psychosis in the Netherlands. PsyMod is a state-transition cohort simulation model and considers six health states, namely ultra-high risk of psychosis (UHR), FEP, post-FEP, no-UHR, recovery/remission, and death. Results are expressed as total healthcare costs, QALYs, incremental cost-effectiveness ratio (ICER), and budget impact.RESULTS: PsyMod was used to extrapolate budget impact and cost-effectiveness of cognitive behavioural therapy for preventing FEP for individuals at UHR of psychosis (CBTuhr) compared to care as usual. CBTuhr resulted in a per-patient increase of 0.06 QALYs and a per patient cost reduction of €654 (dominant ICER) with a reduction in 5-year healthcare costs of €1,002,166.CONCLUSIONS: PsyMod can be used to examine cost-effectiveness and budget impact of interventions targeting prevention and treatment of FEP and is freely available for academic purposes upon request by the authors.

Aims Valid health economic models are essential to inform the adoption and reimbursement of therapies for diabetes mellitus. Often existing health economic models are applied in other countries and settings than those where they were developed. This practice requires assessing the transferability of a model developed from one setting to another. We evaluate the transferability of the MICADO model, developed for the Dutch 2007 setting, in two different settings using a range of adjustment steps. MICADO predicts micro- and macrovascular events at the population level. Methods MICADO simulation results were compared to observed events in an Italian 2000–2015 cohort (Casale Monferrato Survey [CMS]) and in a Dutch 2008–2019 (Hoorn Diabetes Care Center [DCS]) cohort after adjusting the demographic characteristics. Additional adjustments were performed to: (1) risk factors prevalence at baseline, (2) prevalence of complications, and (3) all-cause mortality risks by age and sex. Model validity was assessed by mean average percentage error (MAPE) of cumulative incidences over 10 years of follow-up, where lower values mean better accuracy. Results For mortality, MAPE was lower for CMS compared to DCS (0.38 vs. 0.70 following demographic adjustment) and adjustment step 3 improved it to 0.20 in CMS, whereas step 2 showed best results in DCS (0.65). MAPE for heart failure and stroke in DCS were 0.11 and 0.22, respectively, while for CMS was 0.42 and 0.41. Conclusions The transferability of the MICADO model varied by event and per cohort. Additional adjustments improved prediction of events for MICADO. To ensure a valid model in a new setting it is imperative to assess the impact of adjustments in terms of model accuracy, even when this involves the same country, but a new time period.

Valid health economic models are essential to inform the adoption and reimbursement of therapies for diabetes mellitus. Often existing health economic models are applied in other countries and settings than those where they were developed. This practice requires assessing the transferability of a model developed from one setting to another. We evaluate the transferability of the MICADO model, developed for the Dutch 2007 setting, in two different settings using a range of adjustment steps. MICADO predicts micro- and macrovascular events at the population level.MICADO simulation results were compared to observed events in an Italian 2000-2015 cohort (Casale Monferrato Survey [CMS]) and in a Dutch 2008-2019 (Hoorn Diabetes Care Center [DCS]) cohort after adjusting the demographic characteristics. Additional adjustments were performed to: (1) risk factors prevalence at baseline, (2) prevalence of complications, and (3) all-cause mortality risks by age and sex. Model validity was assessed by mean average percentage error (MAPE) of cumulative incidences over 10 years of follow-up, where lower values mean better accuracy.For mortality, MAPE was lower for CMS compared to DCS (0.38 vs. 0.70 following demographic adjustment) and adjustment step 3 improved it to 0.20 in CMS, whereas step 2 showed best results in DCS (0.65). MAPE for heart failure and stroke in DCS were 0.11 and 0.22, respectively, while for CMS was 0.42 and 0.41.The transferability of the MICADO model varied by event and per cohort. Additional adjustments improved prediction of events for MICADO. To ensure a valid model in a new setting it is imperative to assess the impact of adjustments in terms of model accuracy, even when this involves the same country, but a new time period.

Background Non-communicable diseases (NCDs) consistently pose a huge economic burden to health systems and countries in general. The aim of this study was to quantify inpatient costs associated with chronic obstructive pulmonary disease, stroke and ischemic heart disease stratified by type of referral pathway, and to investigate key factors that drive these costs. Methods A registry-based data analysis was performed using national public hospital inpatient records from 2016 to 2018 for 117,600 unique patients and linking patient-level inpatient health care use with hospital-specific unit cost per bed-day. These were combined to calculate the annual inpatient costs for each of the three disorders per person and per year. Generalized linear modeling was used to assess the association of inpatient costs with age, gender, location, comorbidity, treatment referral pathways and years. Results Across three diagnoses, the majority of patients were female. Most were over 50–60 years old, with more than half being a pensioner, typically with at least one comorbidity. About 25% of patients followed what might be considered inappropriate (unofficial) inpatient referral pathways. Mean annual inpatient costs were int$ 721. These costs rose to int$ 849 for unofficial pathways and dropped to int$677 for official pathways. Further covariates significantly associated with high inpatient costs were location, age, gender, and comorbidity. Conclusion Our findings provide background information essential to develop evidence-based and cost-effective interventions aimed at health promotion, prevention and service delivery. Reducing the unofficial use of inpatient care can improve efficient resource allocation in health care and prevent further escalation of inpatient costs in the future.

Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. Design Systematic review and external validation. Data sources PubMed and Embase. Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic). Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. Systematic review registration PROSPERO CRD42020192831.

Transparency in health economic decision modelling is important for engendering confidence in the models and in the reliability of model-based cost-effectiveness analyses. The Mount Hood Diabetes Challenge Network has taken a lead in promoting transparency through validation with biennial conferences in which diabetes modelling groups meet to compare simulated outcomes of pre-specified scenarios often based on the results of pivotal clinical trials. Model registration is a potential method for promoting transparency, while also reducing the duplication of effort. An important network initiative is the ongoing construction of a diabetes model registry (https://www.mthooddiabeteschallenge.com). Following the 2012 International Society for Pharmacoeconomics and Outcomes Research and the Society of Medical Decision Making (ISPOR-SMDM) guidelines, we recommend that modelling groups provide technical and non-technical documentation sufficient to enable model reproduction, but not necessarily provide the model code. We also request that modelling groups upload documentation on the methods and outcomes of validation efforts, and run reference case simulations so that model outcomes can be compared. In this paper, we discuss conflicting definitions of transparency in health economic modelling, and describe the ongoing development of a registry of economic models for diabetes through the Mount Hood Diabetes Challenge Network, its objectives and potential further developments, and highlight the challenges in its construction and maintenance. The support of key stakeholders such as decision-making bodies and journals is key to ensuring the success of this and other registries. In the absence of public funding, the development of a network of modellers is of huge value in enhancing transparency, whether through registries or other means.

Objective To estimate the impact of glycaemic control and time since diabetes diagnosis on care costs incurred by people with type 2 diabetes mellitus (T2DM). Research design and methods Random-effects linear regression models were run to test the impact of average glucose control (HbA1c) and time since diabetes diagnosis on total care spending in people with T2DM, adjusting for year of onset and other covariates. Two datasets were linked, Vektis (healthcare costs reimbursed by the Dutch mandatory health insurance) and Zodiac (clinical and sociodemographic data). The sample includes 22,612 observations, grouped in 5653 individuals from the Northern part of the Netherlands, covering 4 years (2008-2011). Results A 1% point increase in HbA1c is associated with a 2.2% higher total care costs. However, when treatment modality is included, the results are modified. A 1% point increase (11 mol/mol) in HbA1c is significantly associated with 3.4% higher total care costs for individuals without glucose-lowering treatment. Being treated with insulin is significantly associated with an increase in costs of 30-38% for every additional percentage point of HbA1c, depending on the covariates included. Without controlling for year of onset, an additional year of diabetes duration relates to 2.6% higher care costs, while this is 4.9% controlling for year of onset. The effect of HbA1c and diabetes duration differs between types of costs. Conclusion HbA1c, insulin treatment and diabetes duration are the main drivers of increasing care costs. The results signal the relevance of controlling for HbA1c together with treatment modality, diabetes duration and year of diagnosis effects.

Background Aggregated claims data on medication are often used as a proxy for the prevalence of diseases, especially chronic diseases. However, linkage between medication and diagnosis tend to be theory based and not very precise. Modelling disease probability at an individual level using individual level data may yield more accurate results. Methods Individual probabilities of having a certain chronic disease were estimated using the Random Forest (RF) algorithm. A training set was created from a general practitioners database of 276 723 cases that included diagnosis and claims data on medication. Model performance for 29 chronic diseases was evaluated using Receiver-Operator Curves, by measuring the Area Under the Curve (AUC). Results The diseases for which model performance was best were Parkinson’s disease (AUC = .89, 95% CI = .77–1.00), diabetes (AUC = .87, 95% CI = .85–.90), osteoporosis (AUC = .87, 95% CI = .81–.92) and heart failure (AUC = .81, 95% CI = .74–.88). Five other diseases had an AUC >.75: asthma, chronic enteritis, COPD, epilepsy and HIV/AIDS. For 16 of 17 diseases tested, the medication categories used in theory-based algorithms were also identified by our method, however the RF models included a broader range of medications as important predictors. Conclusion Data on medication use can be a useful predictor when estimating the prevalence of several chronic diseases. To improve the estimates, for a broader range of chronic diseases, research should use better training data, include more details concerning dosages and duration of prescriptions, and add related predictors like hospitalizations.

OBJECTIVES: This study aimed to investigate the cost-effectiveness of intensified breast cancer (BC) screening for women with a BRCA1/2 mutation aged 60-74. Simulated strategies were: (0) annual mammography as reference, (1) alternating annual mammography and MRI for women with dense breasts only; (2) addition of annual MRI for women with dense breasts only; (3) addition of annual MRI for all women.MATERIALS AND METHODS: A validated micro-simulation model of invasive BC was updated and validated for interval BC rates and tumor size distribution. Incremental cost-effectiveness ratios (ICER) of all three intensified strategies were compared to the next best strategy and stratified for BRCA1 and BRCA2. Discount rates for costs and life years gained (LYG) were 1.5% and 4% for the Dutch situation; 3% and 3% for international comparison. A threshold of €20,000 per LYG was applied.RESULTS: All intensified strategies showed more detected BCs and LYG, reduced BC deaths, and increased false positives. The Dutch discounted ICER of intensified strategy 1 compared to annual mammography was €38,000 per LYG in BRCA1 mutation carriers and €18,000 per LYG in BRCA2 mutation carriers. Further intensified strategies showed an ICER above the threshold when compared to this strategy. With international discount rate, the ICERs of all intensified strategies were above the threshold.CONCLUSION: Of the three alternative strategies, only alternating annual MRI and mammography for BRCA2 mutation carriers and dense breasts aged 60-75 is cost-effective compared to annual mammography. For BRCA1 mutation carriers, none of the alternative strategies is cost-effective compared to the next best strategy.

AIMS: The United Kingdom Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) is a health economic decision model supporting long-term evaluation of treatment options in diabetes. The aim of the study was to externally validate the model by comparing the predicted and observed outcomes in two European population-based cohorts of people with type 2 diabetes. MATERIALS AND METHODS: We used data from the Casale Monferrato Survey (CMS) [n=1931] and a subgroup of the Hoorn Diabetes Care System (DCS) cohorts [N=5188]. The following outcomes were analysed: all-cause mortality, myocardial infarction (MI), ischaemic heart disease (IHD), stroke, and congestive heart failure (CHF). Model performance was assessed by comparing predictions with observed cumulative incidences in each cohort during follow-up. RESULTS: All-cause mortality was overestimated by the UKPDS-OM2 in both the cohorts, with a bias of 0.05 in CMS and 0.12 in DCS at 10 years of follow-up. For MI, predictions were consistently higher than observed incidence over the entire follow-up in both cohorts (10 years bias 0.07 for CMS and 0.10 for DCS). The model performed well for stroke and IHD outcomes in both cohorts. Heart failure incidence was predicted well for DCS (5 years bias -0.001) but underestimated for CMS. CONCLUSIONS: UKPDS-OM2 consistently overpredicted the risk of mortality and MI in both cohorts during follow-up. Period effects may partially explain the differences. Results indicate that transferability is not satisfactory for all outcomes and new or adjusted risk equations may be needed before applying the model to the Italian or Dutch settings. This article is protected by copyright. All rights reserved.

Background Economic evaluations of tobacco control interventions support decisions regarding resource allocation in public health policy. Our systematic review was aimed at identifying potential bias in decision models used to estimate the long-term costs and effects of population-based tobacco control interventions in Asia. Methods We included studies conducted in Asian countries and using a modelling technique to evaluate the economic impacts of one or more population-based tobacco interventions in line with the Framework Convention on Tobacco Control (FCTC). We assessed the structure, input parameters, and risk of bias for each model, and performed a narrative synthesis of the included studies. Results Nine model-based economic evaluation studies of population-based tobacco interventions were identified. About 60% of the criteria for reporting quality were met in all studies, indicating that reporting generally lacked transparency. The studies were highly heterogeneous in terms of the scope, types, and structures of their models and the quality of input parameters. One-third of the models applied in the studies scored a high risk of bias, with problems mostly falling into the following categories: model type, time horizons, and smoking transition probabilities. Conclusions More data are needed to provide high-quality evidence regarding the cost-effectiveness of tobacco control policies in Asia. Strong evidence at the country level hinges on the availability of accurate estimates of the effects of the interventions, the relative risks of smoking, and the price elasticity of the demand for tobacco. Simple transfers of models built in Western populations do not suffice. Protocol registration PROSPERO CRD 42019141679.

Background: Schizophrenia Spectrum Disorder (SSD) is characterized by its chronic, episodic nature. The clear definition of such episodes is essential for various clinical and research purposes. Most current definitions of episodes in SSD are based on either hospitalizations or on symptom scales. Both have drawbacks; symptom scales are measured infrequently, while hospitalization rates are often affected by policy. This study presents an approach for defining episodes in healthcare data that does not suffer such drawbacks.Methods: Healthcare use of 13,155 SSD patients in the Northern Netherlands with up to 12 years of follow-up was available. Patient-level structural changes in the trend of healthcare use costs were determined using Exponentially Weighted Moving Average (EWMA) control charts. Control charts restart with updated parameters after a detected structural change. Episodes were defined using these structural changes. The resulting episodes were validated by investigating their association with the Global Assessment of Functioning (GAF) scale.Results: The mean number of episodes was 0.61 (sd: 0.60) per patient per year. For the sub-group without hospitalizations this was 0.51 (sd: 0.71). Average episode duration of the sub-group (147 days, sd: 309.4) was similar to that of the full sample (150 days, sd: 305.5). A significant inverse association was identified between GAF scores and the episode-state indicator.Conclusions: The repeated application of EWMA control charts based on healthcare-intensity is a feasible and promising tool for quantifying patient-level healthcare episodes. The validation using GAF scores indicates that our episode indicator is associated with lower levels of global functioning. Results for individuals without hospitalizations indicate that the method is robust with regard to changes in healthcare policy.

Objectives: The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. Methods: Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. Results: Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME–calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. Conclusions: The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed.

Background: Doubts exist on whether effects found in randomized controlled trials (RCTs) are directly generalizable to daily clinical practice. This study aimed (a) to investigate the effectiveness of treatment options within an algorithm-guided treatment (AGT) program for depression and compare their effectiveness with outcomes of efficacy trials and (b) to assess the relation between treatment continuity and outcomes.Methods: This naturalistic study linked treatment data from January 2012 to November 2014 from a Dutch mental healthcare provider, to routine outcome monitoring (ROM) data (N = 351). Effectiveness of the treatment options (pharmacotherapy, psychotherapy and their combination) was compared to the efficacy reported in the meta-analyses. We included treatment continuity as binary variable "early terminators versus completers of the recommended number of treatment sessions".Results: Remission rates for psychotherapy (38% [95% CI: 32-45]), pharmacotherapy (31% [95% CI: 22-42]) and combination therapy (46% [95% CI: 19-75]) were respectively lower, comparable, and comparable to those reported in the meta-analyses. Similarly, response rates were respectively lower (24% [95% CI: 19-30]), lower (21% [95% CI: 13-31]), and comparable (46% [95% CI: 19-75]) to meta-analyses results. A similar share of early terminators and completers achieved remission and response.Limitations: A substantial proportion of patients had incomplete ROM data after data linkage. Limited set of patient characteristics to check for selection bias.Conclusions: Despite the more heterogeneous patient population in clinical practice, the effectiveness of an AGT program, emphasizing strict guideline adherence, approached that found in RCTs. A fixed number of treatment sessions may not suit all individual patients.

Background: Scientific studies on cardiovascular disease (CVD) burden and risk factors are predominantly based on short-term risk in Westerner populations, and such information may not be applicable to Asian populations, especially over the longer term. This review aims to estimate the long-term (>10 years) CVD burden, including coronary heart disease (CHD) and stroke, as well as associated risk factors in Asian populations. Methods: PubMed, Embase and Web of Science were systematically searched, and hits screened on: Asian adults, free of CVD at baseline; cohort study design (follow-up >10 years). Primary outcomes were fatal and non-fatal CVD events. Pooled estimates and between-study heterogeneity were calculated using random effects models, Q and I2 statistics. Results: Overall, 32 studies were eligible for inclusion (follow-up: 11–29 years). The average long-term rate of fatal CVD is 3.68 per 1000 person-years (95% CI 2.84–4.53), the long-term cumulative risk 6.35% (95% CI 4.69%–8.01%, mean 20.13 years) and the cumulative fatal stroke/CHD risk ratio 1.5:1. Important risk factors for long-term fatal CVD (RR, 95% CI) were male gender (1.49, 1.36–1.64), age over 60/65 years (7.55, 5.59–10.19) and current smoking (1.68, 1.26–2.24). High non-HDL-c, and β- and γ-tocopherol serum were associated only with CHD (HR 2.46 [95% CI 1.29–4.71] and 2.47 [1.10–5.61] respectively), while stage 1 and 2 hypertensions were associated only with fatal stroke (2.02 [1.19–3.44] and 2.89 [1.68–4.96] respectively). Conclusions: Over a 10 year + follow-up period Asian subjects had a higher risk of stroke than CHD. Contrary to CVD prevention in Western countries, strategies should also consider stroke instead of CHD only.

Objectives To evaluate at which sensitivity digital breast tomosynthesis (DBT) would become cost-effective compared to digital mammography (DM) in a population breast cancer screening program, given a constant estimate of specificity. Methods In a microsimulation model, the cost-effectiveness of biennial screening for women aged 50–75 was simulated for three scenarios: DBT for women with dense breasts and DM for women with fatty breasts (scenario 1), DBT for the whole population (scenario 2) or maintaining DM screening (reference). For DM, sensitivity was varied depending on breast density from 65 to 87%, and for DBT from 65 to 100%. The specificity was set at 96.5% for both DM and DBT. Direct medical costs were considered, including screening, biopsy and treatment costs. Scenarios were considered to be cost-effective if the incremental cost-effectiveness ratio (ICER) was below €20,000 per life year gain (LYG). Results For both scenarios, the ICER was more favourable at increasing DBT sensitivity. Compared with DM screening, 0.8–10.2% more LYGs were found when DBT sensitivity was at least 75% for scenario 1, and 4.7–18.7% when DBT sensitivity was at least 80% for scenario 2. At €96 per DBT, scenario 1 was cost-effective at a DBT sensitivity of at least 90%, and at least 95% for scenario 2. At €80 per DBT, these values decreased to 80% and 90%, respectively. Conclusion DBT is more likely to be a cost-effective alternative to mammography in women with dense breasts. Whether DBT could be cost-effective in a general population highly depends on DBT costs. Key Points • DBT could be a cost-effective screening modality for women with dense breasts when its sensitivity is at least 90% at a maximum cost per screen of €96. • DBT has the potential to be cost-effective for screening all women when sensitivity is at least 90% at a maximum cost per screen of €80. • Whether DBT could be used as an alternative to mammography for screening all women is highly dependent on the cost of DBT per screen.

Aims To assess the impact of diabetes and diabetes-related complications on two measures of productivity for people in the labour force and out of it, namely “being afraid health limits ability to work before retirement” and “volunteering”. Methods and data Logistic regressions were run to test the impact of diabetes and its complications on the probability of being afraid health limits work and being a formal volunteer. The longitudinal sample for the former outcome includes 53,631 observations, clustered in 34,393 individuals, aged 50–65 years old whereas the latter consists of 45,384 observations, grouped in 29,104 individuals aged 65 and above across twelve European countries taken from the Survey of Health, Ageing and Retirement in Europe, from 2006 to 2013. Results Diabetes increased the probability of being afraid health limited work by nearly 11% points, adjusted by clinical complications, and reduced the likelihood of being a formal volunteer by 2.7% points, additionally adjusted by mobility problems. We also found that both the probability of being afraid health limits work and the probability of being a formal volunteer increased during and after the crisis. Moreover, having diabetes had a larger effect on being afraid health limits work during the year 2010, possibly related to the financial crisis. Conclusions Our findings show that diabetes significantly affects the perception of people regarding the effects of their condition on work, increasing the fear that health limits their ability to work, especially during the crisis year 2010, as well as the participation in volunteering work among retired people. Electronic supplementary material The online version of this article (doi:10.1007/s10198-017-0911-9) contains supplementary material, which is available to authorized users.

Objectives\ud The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.\ud \ud Methods\ud Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.\ud \ud Results\ud Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.\ud \ud Conclusions\ud Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Our aim was to evaluate the potential cost-effectiveness of PET/CT with FES and 89Zr-trastuzumab compared to pathology to select first-line targeted treatment in metastatic breast cancer (MBC) patients with non-rapidly progressive disease. A previously published and validated model was extended and adapted for this analysis. Two alternative scenarios were compared. In the care as usual pathway first-line targeted treatment of MBC patients was assigned on the basis of pathology results, while in the intervention pathway treatment selection was based on the results from the PET/CT imaging. Costs, life years gained (LYG) and incremental cost-effectiveness ratios (ICER) were calculated. More MBC lesions were detected in the intervention pathway than in the care as usual pathway. The diagnostic costs to evaluate the receptor status and the treatment costs were higher in the intervention strategy, as were total costs and total LYG. The ICER for replacing biopsies with PET/CT imaging with FES and 89Zr-trastuzumab, assuming sensitivity of 77.1% and specificity of 80%, ranged from €71,000 to €77,000 per LYG. When assuming sensitivity of 80% and specificity of 76.7%, the ICER for replacing biopsies with PET/CT imaging with FES and 89Zr-trastuzumab ranged from to €74,000 to €80,000 per LYG. The application of PET/CT with FES and 89Zr-trastuzumab in first-line treatment selection for MBC patients has the potential to be a cost-effective intervention. Our analysis demonstrated that even a small increase in the sensitivity and the specificity of PET/CT can have a large impact on its potential cost-effectiveness.

Background The costs of performing research are an important input in value of information (VOI) analyses but are difficult to assess. Objective The aim of this study was to investigate the costs of research, serving two purposes: (1) estimating research costs for use in VOI analyses; and (2) developing a costing tool to support reviewers of grant proposals in assessing whether the proposed budget is realistic. Methods For granted study proposals from the Netherlands Organization for Health Research and Development (ZonMw), type of study, potential cost drivers, proposed budget, and general characteristics were extracted. Regression analysis was conducted in an attempt to generate a ‘predicted budget’ for certain combinations of cost drivers, for implementation in the costing tool. Results Of 133 drug-related research grant proposals, 74 were included for complete data extraction. Because an association between cost drivers and budgets was not confirmed, we could not generate a predicted budget based on regression analysis, but only historic reference budgets given certain study characteristics. The costing tool was designed accordingly, i.e. with given selection criteria the tool returns the range of budgets in comparable studies. This range can be used in VOI analysis to estimate whether the expected net benefit of sampling will be positive to decide upon the net value of future research. Conclusion The absence of association between study characteristics and budgets may indicate inconsistencies in the budgeting or granting process. Nonetheless, the tool generates useful information on historical budgets, and the option to formally relate VOI to budgets. To our knowledge, this is the first attempt at creating such a tool, which can be complemented with new studies being granted, enlarging the underlying database and keeping estimates up to date. Electronic supplementary material The online version of this article (doi:10.1007/s40273-017-0572-7) contains supplementary material, which is available to authorized users.

Toxoplasma gondii infections cause a large disease burden in the Netherlands, with an estimated health loss of 1,900 Disability Adjusted Life Years and a cost-of-illness estimated at Euro44 million annually. Infections in humans occur via exposure to oocysts in the environment and after eating undercooked meat containing tissue cysts, leading to asymptomatic or mild symptoms, but potentially leading to the development of ocular toxoplasmosis. Infection in pregnant women can lead to stillbirth and disorders in newborns. At present, prevention is only targeted at pregnant women. Cat vaccination, freezing of meat destined for undercooked consumption and enhancing biosecurity in pig husbandries are possible interventions to prevent toxoplasmosis. As these interventions bear costs for sectors in society that differ from those profiting from the benefits, we perform a social cost-benefit analysis (SCBA). In an SCBA, costs and benefits of societal domains affected by the interventions are identified, making explicit which stakeholder pays and who benefits. Using an epidemiological model, we consider transmission of T.gondii after vaccination of all owned cats or cats at livestock farms. To identify relevant high-risk meat products that will be eaten undercooked, a quantitative microbial risk assessment model developed to attribute predicted T.gondii infections to specific meat products will be used. In addition, we evaluate serological monitoring of pigs at slaughter followed by an audit and tailor made advice for farmers in case positive results were found. The benefits will be modelled stochastically as reduction in DALYs and monetized in Euro's following reference prices for DALYs. If the balance of total costs and benefits is positive, this will lend support to implementation of these preventive interventions at the societal level. Ultimately, the SCBA will provide guidance to policy makers on the most optimal intervention measures to reduce the disease burden of T.gondii in the Netherlands.

ObjectivesThe aim of this study was to conduct a cost-effectiveness analysis comparing conventional removable partial dentures (RPDs) and implant-supported RPDs (ISRPDs) treatment in patients with an edentulous maxilla and a bilateral free-ending situation in the mandible.Material and methodsThirty subjects were included. A new RPD was made and implant support was provided 3months later. Treatment costs (opportunity costs and costs based on tariffs) were calculated. Treatment effect was expressed by means of the Dutch Oral Health Impact Profile questionnaire (OHIP-NL49), a chewing ability test (Mixing Ability Index, MAI) and a short-form health survey measuring perceived general health (SF-36), which was subsequently converted into quality-adjusted-life-years (QALYs). The incremental cost-effectiveness ratio (ICER) was the primary outcome measure of cost-effectiveness, comparing both treatment strategies.ResultsThe mean total opportunity costs were Euro981 (95% CI Euro971-Euro991) for the RPD treatment and Euro2.480 (95% CI Euro2.461-Euro2.500) for the ISRPD treatment. The total costs derived from the national tariff structure were Euro850 for the RPD treatment and Euro2.610 for the ISRPD treatment. The ICER for OHIP-NL49 and MAI using the opportunity costs was Euro80 and Euro786, respectively. When using the tariff structure, corresponding ICERs were Euro94 and Euro921. The effect of supporting an RPD with implants when expressed in QALYs was negligible; hence an ICER was not determined.ConclusionsIt is concluded that depending on the choice of outcome measure and monetary threshold, supporting an RPD with implants is cost-effective when payers are willing to pay more than Euro80 per OHIP point gained. Per MAI point gained, an additional Euro786 has to be invested.

Objectives To validate outcomes of presently available chronic obstructive pulmonary disease (COPD) cost-effectiveness models against results of two large COPD trials—the 3-year TOwards a Revolution in COPD Health (TORCH) trial and the 4-year Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT) trial. Methods Participating COPD modeling groups simulated the outcomes for the placebo-treated groups of the TORCH and UPLIFT trials using baseline characteristics of the trial populations as input. Groups then simulated treatment effectiveness by using relative reductions in annual decline in lung function and exacerbation frequency observed in the most intensively treated group compared with placebo as input for the models. Main outcomes were (change in) total/severe exacerbations and mortality. Furthermore, the absolute differences in total exacerbations and quality-adjusted life-years (QALYs) were used to approximate the cost per exacerbation avoided and the cost per QALY gained. Result Of the six participating models, three models reported higher total exacerbation rates than observed in the TORCH trial (1.13/patient-year) (models: 1.22–1.48). Four models reported higher rates than observed in the UPLIFT trial (0.85/patient-year) (models: 1.13–1.52). Two models reported higher mortality rates than in the TORCH trial (15.2%) (models: 20.0% and 30.6%) and the UPLIFT trial (16.3%) (models: 24.8% and 36.0%), whereas one model reported lower rates (9.8% and 12.1%, respectively). Simulation of treatment effectiveness showed that the absolute reduction in total exacerbations, the gain in QALYs, and the cost-effectiveness ratios did not differ from the trials, except for one model. Conclusions Although most of the participating COPD cost-effectiveness models reported higher total exacerbation rates than observed in the trials, estimates of the absolute treatment effect and cost-effectiveness ratios do not seem different from the trials in most models.

BACKGROUND: Smoking is widely recognized as one of the most prevalent and preventable causes of many cancer types. This study aimed to quantify the population attributable fraction (PAF) of the lung cancer burden for smoking in Mongolia.METHODS: Lung cancer incidence and lung cancer-related death data came from the population-based national registry covering the period 2007-2016. Smoking prevalence data came from the STEPwise approach (STEP) national survey. The lung cancer-related disease burden was calculated and expressed in Disability Adjusted Life Years (DALYs) lost by gender and by year. This was combined with current smoking and former smoking prevalence data, and relative risks (RR) of lung cancer-related deaths for current smokers and former smokers versus never smokers from region-specific cohort studies to estimate the PAF of lung cancer attributable to "ever-smoking" in Mongolia.RESULTS: Between 2007 and 2016, lung cancer accounted for the loss of over 63,000 DALYs in Mongolia. The PAF of lung cancer-related deaths attributable to current and former smoking combined was 58.1% (95% IR = 43.1%-72.2%) for men and 8.9% (95% IR = 4.1% -13.5%) for women. Smoking-attributable DALYs loss amounted to 2589 years (95% IR = 1907-3226) in 2016.CONCLUSIONS: A considerable health loss may be prevented with an effective anti-smoking policy. In Mongolia, more than one third of lung cancer-related DALY loss is attributable to active smoking, and thus is potentially preventable. Furthermore, a gender-specific tobacco control policy may be worthwhile because of the large gender difference in smoking exposure in Mongolia. Next to this, age specific policy, including a smoke-free generation policy for adolescents, with targeted education, and mass media campaigns is needed.

Objectives Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to use a LASSO (Least Absolute Shrinkage and Selection) model on a wide set of variables including medication use to obtain small geographic area prevalence estimates for four common chronic diseases and investigate regional patterns of disease. Methods Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n=707021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP and hospital admission was available. A LASSO operator regression model for binary outcomes was used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on the average of individual predicted probabilities. Results Adding medication use data as a predictor substantially improves model performance. Estimates at the municipality level are best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst WPE for COPD, with 14.5%. Disease prevalence has clear regional patterns, also after standardization for age. Conclusion Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities can be aggregated into any desired regional level and provide a useful tool to identify regional patterns and subsequently inform local policy.

Introduction: This study assessed the cost-effectiveness and budget impact of a lifestyle intervention to improve cardiometabolic health in severe mentally ill (SMI) patients in the LION trial.Methods: Patients (n = 244) were randomized to receive either care-as-usual or a lifestyle intervention in which mental health nurses coached patients in changing their lifestyle by using a web tool. Costs and quality of life were assessed at baseline and at 6 and 12 months. Incremental costs per centimeter waist circumference (WC) lost and per Quality-Adjusted Life Year (QALY) gained were assessed. Budget impact was estimated based on three intervention-uptake scenarios using a societal and a third-party payer perspective.Results: Costs and reduction in WC were higher in the intervention (n = 114) than in the control (n = 94) group after 12 months, although not statistically significant, resulting in (sic)1,370 per cm WC lost. QALYs did not differ between the groups, resulting in a low probability of the intervention being cost-effective in cost/QALY gained. The budget impact analysis showed that for a reasonable participation of 43%, total costs were around (sic)81 million over 5 years, or on average (sic)16 million annually (societal perspective).Conclusions: The intervention is not cost-effective at 12 months and the budget impact over 5 years is substantial. Possibly, 12 months was too short to implement the intervention, improve cardiometabolic health, and reduce care costs. Therefore, the incentive for this intervention cannot be found in short-term financial advantages. However, there may be benefits associated with lifestyle interventions in the long term that remain unclear.

Background In the Netherlands, toxoplasmosis ranks third in disease burden among foodborne pathogens, with an estimated health loss of 1,900 Disability Adjusted Life Years and a cost-of-illness estimated at €44 million annually. We performed a Social Cost-Benefit Analysis (SCBA) to evaluate the net value of two potential interventions, freezing meat and improving biosecurity in pig farms, for the Dutch society. Methods We assessed the costs and benefits of the two interventions and compared them with the current practice of education, especially during pregnancy. A ‘minimum scenario’ and a ‘maximum scenario’ was assumed, using input parameters with least benefits to society and input parameters with most benefits to society, respectively. Results The freezing meat intervention was far more effective than the biosecurity intervention. Despite high freezing costs, freezing two meat products: steak tartare and mutton leg yielded net social benefits in both the minimum and maximum scenario, ranging from €10.6 million to €31 million for steak tartare and €0.6 million to €1.5 million for mutton leg. The biosecurity intervention would result in net costs in all scenarios ranging from €1 million to €2.5 millions. Conclusions From a public health perspective (i.e. reducing the burden of toxoplasmosis) freezing steak tartare and leg of mutton is to be considered.

Background: Exacerbations are important outcomes in COPD both from a clinical and economic perspective. This study aimed to estimate prediction models for exacerbations to compare predictors between populations that differ in COPD severity. Methods: Members of an existing COPD health economic modelling network who had access to a large database (> 500 patients) with longitudinal data on exacerbations were asked to use two-third of the dataset to estimate several prediction models for total and severe exacerbations. The models differed in terms of predictors (depending on availability) and type of model. One-third of the dataset was used for validation. Results: Five COPD databases participated: two population-based studies (COPDGene and OLIN studies), one study in primary care patients (RECODE) and two clinical trials (ECLIPSE and UPLIFT). FEV 1 % predicted and previous exacerbations were significant predictors for total exacerbations in all five databases. Disease-specific quality of life and gender were predictors in 4 out of 4 and 3 out of 5 databases, respectively. Age was significant only in the two trials including severe COPD patients. Other significant predictors available in one database were cough, wheeze, pack-years and 6 MWD. Predictors for severe exacerbations were about the same, but in addition presence of cardiovascular disease and emphysema were found to be predictors in severe patients. Conclusion: FEV 1 % predicted, previous exacerbations and disease-specific quality of life were identified as predictors of exacerbations in COPD patients regardless of COPD severity, while age, cardiovascular disease and emphysema seemed to be predictors in severe patients only.

Background A trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool providing model users with a structured view into the validation status of HE decision models, to address this trade-off. Methods A Delphi panel was organized, and was completed by a workshop during an international conference. The proposed tool was constructed iteratively based on comments from, and the discussion amongst, panellists. During the Delphi process, comments were solicited on the importance and feasibility of possible validation techniques for modellers, their relevance for decision makers, and the overall structure and formulation in the tool. Results The panel consisted of 47 experts in HE modelling and HE decision making from various professional and international backgrounds. In addition, 50 discussants actively engaged in the discussion at the conference workshop and returned 19 questionnaires with additional comments. The final version consists of 13 items covering all relevant aspects of HE decision models: the conceptual model, the input data, the implemented software program, and the model outcomes. Conclusions Assessment of the Validation Status of Health-Economic decision models (AdViSHE) is a validation-assessment tool in which model developers report in a systematic way both on validation efforts performed and on their outcomes. Subsequently, model users can establish whether confidence in the model is justified or whether additional validation efforts should be undertaken. In this way, AdViSHE enhances transparency of the validation status of HE models and supports efficient model validation. Electronic supplementary material The online version of this article (doi:10.1007/s40273-015-0327-2) contains supplementary material, which is available to authorized users.

Contains fulltext : 195171.pdf (Publisher’s version ) (Closed access) Background: Evidence-based clinical guidelines for major depressive disorder (MDD) recommend stepped-care strategies for sequencing evidence-based treatments conditional on treatment outcomes. This study aims to evaluate the cost-effectiveness of stepped care as recommended by the multidisciplinary clinical guideline vis-à-vis usual care in the Netherlands. Methods: Guideline-congruent care as described in stepped-care algorithms for either mild MDD or moderate and severe MDD was compared with usual care in a health-economic state-transition simulation model. Incremental costs per QALY gained were estimated over five years from a healthcare perspective. Results: For mild MDD, the cost-utility analysis showed a 67% likelihood of better health outcomes against lower costs, and 33% likelihood of better outcomes against higher costs, implying dominance of guideline-congruent stepped care. For moderate and severe MDD, the cost-utility analysis indicated a 67% likelihood of health gains at higher costs following the stepped-care approach and 33% likelihood of health gains at lower costs, with a mean ICER of about €3,200 per QALY gained. At a willingness to pay threshold of €20,000 per QALY, the stepped-care algorithms for both mild MDD and moderate or severe MDD is deemed cost-effective compared to usual care with a greater than 95% probability. Limitations: The findings of our decision-analytic modelling are limited by the accuracy and availability of the underlying evidence. This hampers taking into account all individual differences relevant to optimise treatment to individual needs. Conclusions: It is highly likely that guideline-congruent stepped care for MDD is cost-effective compared to usual care. Our findings support current guideline recommendations. 13 p.

AIMS: With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarize and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, to evaluate their potential use for the assessment of novel prevention strategies and to discuss the knowledge gaps, challenges and opportunities.MATERIALS AND METHODS: We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or we used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardized form and double extraction, each of four reviewers extracted 50% of the identified studies.RESULTS: A total of 29 published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n = 8) and type 2 diabetes (n = 17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n = 7) and accounted for heterogeneity among individuals with prediabetes (n = 7).CONCLUSIONS: Current prediabetes decision models have considerable limitations in terms of their quality and validity and do not allow evaluation of stratified strategies using novel biomarkers, highlighting a clear need for more comprehensive prediabetes decision models.

Background: Increased breast cancer incidence between 45 and 50 years of age requires reconsideration of the current starting age of routine mammography. Our aim was to evaluate the quantitative benefits, harms, and cost-effectiveness of lowering the starting age of breast cancer screening in the Dutch general population. Methods: Economic modelling with a lifelong perspective compared biennial screening 48-74 and 46-74 to the current Dutch screening program (50-74). Tumour deaths prevented, years of life saved (YOLS), false positives, radiation-induced tumours, costs and incremental costseffectiveness ratios (ICERs) were evaluated. Results: Starting the screening at 48 vs. 50 led to an increase in: the number of small screen detected tumours (4.0%), tumour deaths prevented (5.6%), false positives (9.2%), YOLS (5.6%), radiation-induced tumours (14.7%), and costs (4.1%). Starting the screening at 46 vs. 48 increased the number of small screen detected tumours (3.3%), tumour deaths prevented (4.2%), false positives (8.8%), YOLS (3.7%), radiation-induced tumours (15.2%), and costs (4.0%). The ICER was €5,600/YOLS for 48-74 scenario and €5,600/YOLS for 46-74. Conclusions: Women could benefit from lowering the starting age of screening as more breast cancer deaths will be averted. Starting regular breast cancer screening earlier is also cost-effective. As the number of additional expected harms is relatively small in both alternative scenarios and the difference in ICERs is not large, introducing two additional screening rounds is justifiable.

BackgroundBecause the incidence of breast cancer increases between 45 and 50 years of age, a reconsideration is required of the current starting age (typically 50 years) for routine mammography. Our aim was to evaluate the quantitative benefits, harms, and cost-effectiveness of lowering the starting age of breast cancer screening in the Dutch general population.MethodsEconomic modelling with a lifelong perspective compared biennial screening for women aged 48–74 years and for women aged 46–74 years with the current Dutch screening programme, which screen women between the ages of 50 and 74 years. Tumour deaths prevented, years of life saved (YOLS), false-positive rates, radiation-induced tumours, costs and incremental cost-effectiveness ratios (ICERs) were evaluated.ResultsStarting the screening at 48 instead of 50 years of age led to increases in: the number of small tumours detected (4.0%), tumour deaths prevented (5.6%), false positives (9.2%), YOLS (5.6%), radiation-induced tumours (14.7%), and costs (4.1%). Starting the screening at 46 instead of 48 years of age increased the number of small tumours detected (3.3%), tumour deaths prevented (4.2%), false positives (8.8%), YOLS (3.7%), radiation-induced tumours (15.2%), and costs (4.0%). The ICER was €5,600/YOLS for the 48–74 scenario and €5,600/YOLS for the 46–74 scenario.ConclusionsWomen could benefit from lowering the starting age of screening as more breast cancer deaths would be averted. Starting regular breast cancer screening earlier is also cost-effective. As the number of additional expected harms is relatively small in both the scenarios examined, and the difference in ICERs is not large, introducing two additional screening rounds is justifiable.

Background: The aim of this study was to evaluate the effect on the number of performed biopsies and costs associated with implementing positron emission tomography (PET) and computed tomography (PET/CT) with 16 alpha-[F-18]fluoro-17 beta-oestradiol (FES) or 2-[F-18] fluoro-2-deoxy-D-glucose (FDG) as an upfront imaging test for diagnosing metastatic breast cancer (MBC) in comparison with the standard work-up in oestrogen receptor-positive women with symptoms.Methods: A published computer simulation model was adapted and validated. Three follow-up strategies were evaluated in a simulated cohort of women with primary breast cancer over a 5-year-time horizon: (1) the standard work-up, (2) upfront FES-PET/CT and (3) upfront FDG-PET/CT. The main outcome was the number of avoided biopsies to assess MBC. The costs for all three strategies were calculated based on the number of imaging tests and biopsies. The incremental cost-effectiveness ratio (ICER) to avoid a biopsy was calculated only based on the costs of initial imaging and staging tests.Results: The FES-PET/CT strategy decreased the number of biopsies by 39 +/- 9%, while upfront FDG-PET/CT increased the number of biopsies by 38 +/- 15% when compared with the standard work-up. Both PET/CT strategies reduced the number of imaging tests and false positives when compared with the standard work-up. The number of false negatives decreased only in the FES-PET/CT strategy. The ICER in the FES-PET/CT strategy per avoided biopsy was 12.1 +/- 3.4 thousand Euro. In the FDG-PET/CT strategy, the costs were higher and there were no avoided biopsies as compared with the standard work-up, hence this was an inferior strategy in terms of cost effectiveness.Conclusions: The number of performed biopsies was lower in the FES-PET/CT strategy at an ICER of 12.1 +/- 3.4 thousand Euro per biopsy avoided, whereas the application of the FDG-PET/CT did not reduce the number of biopsies and was more expensive. Whether the FES-PET/CT strategy has additional benefits for patients in terms of therapy management has to be evaluated in clinical studies.

Aims To test a simulation model, the MICADO model, for estimating the long-term effects of interventions in people with and without diabetes.Methods The MICADO model includes micro-and macrovascular diseases in relation to their risk factors. The strengths of this model are its population scope and the possibility to assess parameter uncertainty using probabilistic sensitivity analyses. Outcomes include incidence and prevalence of complications, quality of life, costs and cost-effectiveness. We externally validated MICADO's estimates of micro-and macrovascular complications in a Dutch cohort with diabetes (n = 498 400) by comparing these estimates with national and international empirical data.Results For the annual number of people undergoing amputations, MICADO's estimate was 592 (95% interquantile range 291-842), which compared well with the registered number of people with diabetes-related amputations in the Netherlands (728). The incidence of end-stage renal disease estimated using the MICADO model was 247 people (95% interquartile range 120-363), which was also similar to the registered incidence in the Netherlands (277 people). MICADO performed well in the validation of macrovascular outcomes of population-based cohorts, while it had more difficulty in reflecting a highly selected trial population.Conclusions Validation by comparison with independent empirical data showed that the MICADO model simulates the natural course of diabetes and its micro-and macrovascular complications well. As a population-based model, MICADO can be applied for projections as well as scenario analyses to evaluate the long-term (cost-) effectiveness of population-level interventions targeting diabetes and its complications in the Netherlands or similar countries.