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Abstract Background Decision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting. Methods This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2. Results First, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models’ parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed. Conclusion This conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.

BackgroundIn countries where malaria is endemic, the use of rapid diagnostic tests(RDTs) has become routine, especially in rural settings. Such regions are characterised by often having other co-endemic infectious diseases, at high levels of prevalence.AimTo illustrate the potential added-value of "sentinel" screening for patients presenting for a routine diagnostic test for malaria, at healthcare facilities in Uganda.MethodsWe developed an economic model by combining two decision trees, one for malaria and a second for the co-endemic disease schistosomiasis. The integrated model was designed to inform policy strategies for the co-endemic disease in addition to malaria (i.e., whether to test opportunistically for schistosomiasis or use mass drug administration(MDA) as per usual practice).We performed the analysis on three comparators varying testing accuracy and costs.ResultsSentinel screening can provide added value to the testing of patients compared with the status quo: when schistosomiasis prevalence is high then MDA is preferential; if low prevalence, treating no one is preferred. If the disease has average levels of prevalence, then a strategy involving testing is preferred. Prevalence thresholds driving the dominant strategy are dependent upon the model parameters, which are highly context specific. At average levels of prevalence for schistosomiasis and malaria for Uganda, adding a sentinel screening was cost-effective when the accuracy of test was higher than current diagnostics and when economies of scope were generated(Expected value clinical Information = 0.65$ per DALY averted, 137.91$ per correct diagnoses).Protocols using diagnostics with current accuracy levels were preferred only for levels of MDA coverage below 75%.ConclusionThe importance of the epidemiological setting is crucial in determining the best cost-effective strategy for detecting endemic disease. Economies of scope can make sentinel screenings cost-effective strategies in specific contexts. Blanket thresholds recommended for MDA may not always be the preferred option for endemic diseases.

Objective To explore methodological challenges when using real-world evidence (RWE) to estimate comparative-effectiveness in the context of Health Technology Assessment of direct oral anticoagulants (DOACs) in Scotland. Methods We used linkage data from the Prescribing Information System (PIS), Scottish Morbidity Records (SMR) and mortality records for newly anticoagulated patients to explore methodological challenges in the use of Propensity score (PS) matching, Inverse Probability Weighting (IPW) and covariate adjustment with PS. Model performance was assessed by standardised difference. Clinical outcomes (stroke and major bleeding) and mortality were compared for all DOACs (including apixaban, dabigatran and rivaroxaban) versus warfarin. Patients were followed for 2 years from first oral anticoagulant prescription to first clinical event or death. Censoring was applied for treatment switching or discontinuation. Results Overall, a good balance of patients’ covariates was obtained with every PS model tested. IPW was found to be the best performing method in assessing covariate balance when applied to subgroups with relatively large sample sizes (combined-DOACs versus warfarin). With the IPTW-IPCW approach, the treatment effect tends to be larger, but still in line with the treatment effect estimated using other PS methods. Covariate adjustment with PS in the outcome model performed well when applied to subgroups with smaller sample sizes (dabigatran versus warfarin), as this method does not require further reduction of sample size, and trimming or truncation of extreme weights. Conclusion The choice of adequate PS methods may vary according to the characteristics of the data. If assumptions of unobserved confounding hold, multiple approaches should be identified and tested. PS based methods can be implemented using routinely collected linked data, thus supporting Health Technology decision-making.

ObjectiveTo explore methodological challenges when using real-world evidence (RWE) to estimate comparative-effectiveness in the context of Health Technology Assessment of direct oral anticoagulants (DOACs) in Scotland.MethodsWe used linkage data from the Prescribing Information System (PIS), Scottish Morbidity Records (SMR) and mortality records for newly anticoagulated patients to explore methodological challenges in the use of Propensity score (PS) matching, Inverse Probability Weighting (IPW) and covariate adjustment with PS. Model performance was assessed by standardised difference. Clinical outcomes (stroke and major bleeding) and mortality were compared for all DOACs (including apixaban, dabigatran and rivaroxaban) versus warfarin. Patients were followed for 2 years from first oral anticoagulant prescription to first clinical event or death. Censoring was applied for treatment switching or discontinuation.ResultsOverall, a good balance of patients' covariates was obtained with every PS model tested. IPW was found to be the best performing method in assessing covariate balance when applied to subgroups with relatively large sample sizes (combined-DOACs versus warfarin). With the IPTW-IPCW approach, the treatment effect tends to be larger, but still in line with the treatment effect estimated using other PS methods. Covariate adjustment with PS in the outcome model performed well when applied to subgroups with smaller sample sizes (dabigatran versus warfarin), as this method does not require further reduction of sample size, and trimming or truncation of extreme weights.ConclusionThe choice of adequate PS methods may vary according to the characteristics of the data. If assumptions of unobserved confounding hold, multiple approaches should be identified and tested. PS based methods can be implemented using routinely collected linked data, thus supporting Health Technology decision-making.

ABSTRACT Background Atrial Fibrillation (AF) is a highly debilitating condition with significant economic burden. Previous studies have estimated the cost of hospitalisations associated with AF in Scotland. However, patients with AF are often elderly with co-morbidities requiring substantial outpatient and social care. Objectives This study seeks to estimate inpatient, outpatient and social care costs associated with AF in a Scottish cohort, by using individual-level linked data. Methods The AF cohort of 50 years and older patients, hospitalised with a known diagnosis of AF or atrial flutter between 1997 and 2014, was followed up for five years following the first AF event. Individual-level data on hospitalisation and discharge to social care home were obtained from the Scottish Morbidity Records (SMR01); whereas data on outpatient attendance were obtained from (SMR00). Death records for the same time period were extracted from National Records of Scotland (NRS). Hospital and outpatient costs associated with the corresponding data were estimated utilising the Scottish National Tariff (SNT) based on Healthcare Resource Groups (HRGs), and the Scottish Health Service Costs report, respectively. Social care costs were identified from the Care Home Census. Following data linkage, the econometric analysis was carried out using a two-part model where, the first part estimates through a probit model the probability of using a healthcare service, and the second part estimates costs conditional on having incurred positive costs. The regression model was adjusted for demographic characteristics, socio-economic status, year of admission and location. Results Overall, a cohort of 253,963 AF patients accounted for 2,988,607 hospital admissions and 4,452,476 outpatient attendances. The mean cost per patient was estimated to be £3,071 (95% CI 3,033-3,109). Overall, hospital admissions and outpatient visits accounted for 71.7% and 3.7% of the total cost, respectively; social care accounted for 24.6% of the total costs. The cost increased with age and females incurred higher costs than males. Significant differences were observed among the urban/rural classifications, individual health boards and the socio-economic status. Conclusions This study has shown the importance of taking into account healthcare resource use incurred beyond hospitalisation. In addition to inpatient costs, outpatient and social care costs contribute considerably to the overall economic burden.