Health Economic Decision Models Research Articles

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

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 concerningwhich diseases and policy decisions they will be helpful for.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

EE427 Health Economic Decision Models Used in the Cost-Effectiveness Analyses of Innovative Products Submitted to the French National Authority for Health (HAS)

EE427 Health Economic Decision Models Used in the Cost-Effectiveness Analyses of Innovative Products Submitted to the French National Authority for Health (HAS)

Cost-Effectiveness Analysis of Personalized Hypertension Prevention.

While a population-wide strategy involving lifestyle changes and a high-risk strategy involving pharmacological interventions have been described, the recently proposed personalized medicine approach combining both strategies for the prevention of hypertension has increasingly gained attention. However, a cost-effectiveness analysis has been hardly addressed. This study was set out to build a Markov analytical decision model with a variety of prevention strategies in order to conduct an economic analysis for tailored preventative methods. The Markov decision model was used to perform an economic analysis of four preventative strategies: usual care, a population-based universal approach, a population-based high-risk approach, and a personalized strategy. In all decisions, the cohort in each prevention method was tracked throughout time to clarify the four-state model-based natural history of hypertension. Utilizing the Monte Carlo simulation, a probabilistic cost-effectiveness analysis was carried out. The incremental cost-effectiveness ratio was calculated to estimate the additional cost to save an additional life year. The incremental cost-effectiveness ratios (ICER) for the personalized preventive strategy versus those for standard care were -USD 3317 per QALY gained, whereas they were, respectively, USD 120,781 and USD 53,223 per Quality-Adjusted Life Year (QALY) gained for the population-wide universal approach and the population-based high-risk approach. When the ceiling ratio of willingness to pay was USD 300,000, the probability of being cost-effective reached 74% for the universal approach and was almost certain for the personalized preventive strategy. The equivalent analysis for the personalized strategy against a general plan showed that the former was still cost-effective. To support a health economic decision model for the financial evaluation of hypertension preventative measures, a personalized four-state natural history of hypertension model was created. The personalized preventive treatment appeared more cost-effective than population-based conventional care. These findings are extremely valuable for making hypertension-based health decisions based on precise preventive medication.

Extensions of Health Economic Evaluations in R for Microsoft Excel Users: A Tutorial for Incorporating Heterogeneity and Conducting Value of Information Analyses.

Advanced health economic analysis techniques currently performed in Microsoft Excel, such as incorporating heterogeneity, time-dependent transitions and a value of information analysis, can be easily transferred to R. Often the outputs of survival analyses (such as Weibull regression models) will estimate the impacts of correlated patient characteristics on patient outcomes, and are utilised directly as inputs for health economic decision models. This tutorial provides a step-by-step guide of how to conduct such analyses with a Markov model developed in R, and offers a comparison with established analyses performed in Microsoft Excel. This is done through the conversion of a previously published Microsoft Excel case study of a hip replacement surgery cost-effectiveness model. We hope that this paper can act as a facilitator in switching decision models from Microsoft Excel to R for complex health economic analyses, providing open-access code and data, suitable for future adaptation.

Cost-analysis comparison of clinical risk assessment with and without ROMA for the management of women with pelvic masses

Objective: Pelvic masses can be classified as low risk (likely benign) and high risk (likely malignant) based on an initial clinical risk assessment, which involves a detailed history, physical exam, basic laboratory tests, and imaging. In recent years, the Risk of Ovarian Malignancy Algorithm (ROMA), which combines CA125, HE4 and menopausal status, has emerged as a powerful tool in the classification of pelvic masses and triage of patients to either a generalist gynecologist or a gynecologic oncologist for management. The objective of this study was to evaluate whether the use of ROMA, alone or in combination with Initial Clinical Risk Assessment (ICRA), provides cost savings compared to triage based on ICRA alone. Methods: A health-economic decision model was developed to assess clinical and cost differences associated with three different clinical pathways of risk assessment for a pelvic mass: ICRA alone, ROMA alone, or ICRA + ROMA in combination. Using previously reported accuracy rates and patient characteristics from a prospective, multicenter, blinded clinical trial, total healthcare costs were modeled for each clinical pathway using the Medicare 2020 reimbursement rates. Results: A total of 461 patients with pelvic masses were included with 10.4% ultimately diagnosed with epithelial ovarian cancer. Total healthcare costs for patients with benign disease, EOC, or low malignant potential tumors (LMP) (n = 441) triaged using ROMA alone were 3.3% lower than when triaged using ICRA alone. While lab costs increased 55% using ROMA, the use of ROMA alone resulted in a 4% decrease in laparoscopy costs and a 3.1% decrease in laparotomy costs compared with ICRA alone. Similarly, total costs associated with a combination of ICRA + ROMA were 3.9% lower than total costs associated with ICRA alone. The model also predicted a 63% reduction in repeat surgeries resulting from false negative ICRA when using ROMA to triage patients. Conclusion: Triage of women with pelvic masses using the more sensitive ROMA score lowers overall healthcare costs compared to ICRA alone. With fewer false negative results than ICRA alone, the ROMA score improves initial detection of malignancy and reduces second surgical treatments in women with pelvic masses.

Four Aspects Affecting Health Economic Decision Models and Their Validation.

Health care decision makers in many jurisdictions use cost-effectiveness analysis based on health economic decision models for policy decisions regarding coverage and price negotiation for medicines and medical devices. While validation of health economic decision models has always been considered important, many reviews of model-based cost-effectiveness studies report limitations regarding their validation. The current opinion paper discusses four aspects of current health economic decision modeling with relevance for future directions in model validation: increased use of complex models, international cooperation, open-source modeling, and stakeholder involvement. First, new, more complex clinical study designs and treatment strategies may require relatively complex model structures and/or input data analyses. Simultaneously, more widespread technical knowledge along with wider data availability have led to a broader range of model types. This puts extra requirements on model validation and transparency. Second, increased international cooperation of policy makers and, in particular, health technology assessment (HTA) authorities in performing model assessments is discussed in relation to the repeated use of health economic models (multi-use disease models). We argue such coordinated efforts may benefit model validity. Third, open-source modeling is discussed as one possible answer to increased transparency requirements. Finally, involvement of all relevant stakeholders throughout the whole decision process is an ongoing development that necessarily also includes health economic modeling. We argue this implies that model validity should be considered in a broader perspective, with more focus on conceptual modeling, model transparency, accuracy requirements, and choice of relevant model outcomes than previously.

Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity

IntroductionOur aim was to describe the development and validation of an obesity model representing the cardiovascular risks associated with different body mass index (BMI) categories, through simulation, designed to evaluate the epidemiological and economic impact of population policies for obesity.MethodsA discrete event simulation model was built in R considering the risk of cardiovascular events (heart failure, stroke, coronary heart disease, and diabetes) associated with BMI categories in the Spanish population. The main parameters included in the model were estimated from Spanish hospital discharge records and the Spanish Health Survey and allowed both first-order and second-order (probabilistic sensitivity analysis) uncertainty to be programmed into the model. The simulation yielded the incidence and prevalence of cardiovascular events as validation outputs. To illustrate the capacity of the model, we estimated the reduction in cardiovascular events and cost-utility (incremental cost/incremental quality-adjusted life-years [QALYs]) of a hypothetical intervention that fully eliminated the cardiovascular risks associated with obesity and overweight.ResultsThe Validation Status of Health-Economic decision models (AdViSHE) tool was applied. Internal validation plots showed adequate goodness of fit for the Spanish population. External validation was achieved by comparing the simulated and real incidence by age group for stroke, acute myocardial infarction, and heart failure. The intervention reduced the population hazard ratios of stroke, acute myocardial infarction, and heart failure to 0.81, 0.74, and 0.78, respectively, and added 0.74 QALYs to the whole population.ConclusionsThis obesity simulation model evidenced good properties for estimating the long-term epidemiological and economic impact of policies to tackle obesity in Spain. The conceptual model could be implemented for other counties using country-specific input data.

Cost-analysis comparison of initial clinical risk assessment and ROMA score for the triage of women with pelvic masses

<h3>Objectives:</h3> In recent years, the Risk of Ovarian Malignancy Algorithm (ROMA) score has emerged as a useful tool in the triage of women with pelvic masses. Previously, triage of women with pelvic masses to management by either benign gynecology or gynecologic oncology was performed with an initial clinical risk assessment (ICRA) alone, involving a detailed history, exam, basic laboratory tests, and imaging. We sought to determine whether use of ROMA, which is calculated using CA125, HE4, and menopausal status for triage of women with pelvic masses, provides cost savings compared to triage based on ICRA alone. <h3>Methods:</h3> A health-economic decision model was developed to assess clinical and cost differences associated with referrals for patients presenting to their gynecologist or primary care physician with an adnexal mass. In our study, we modeled the differences of two clinical pathways that included 1) use of ICRA alone and 2) use of ROMA as a determining factor in referral to a gynecologic oncologist. Using previously reported accuracy rates and patient characteristics from a prospective, multicenter, blinded clinical trial (ClinicalTrial. gov identifier NCT00987649), we assessed triage of women with a pelvic mass using ICRA alone or ROMA and modeled the related lab testing, imaging, surgical procedures, and associated costs. Costs were estimated using current Medicare reimbursement rates. <h3>Results:</h3> A total of 423 patients with pelvic masses were included. Total healthcare costs for patients triaged for epithelial ovarian cancer (EOC) using ICRA alone were higher than when triaged by ROMA ($2,461,301 vs $2,428,628), a cost savings of 1.3%. While lab costs increased 55% when using ROMA ($16,006 vs $24,809), all other costs decreased, including total laparoscopy ($1,931,568 vs $1,896,314, -1.8% difference), total laparotomy ($436,910 vs $430,689, -1.4% difference), and total surgery ($2,368,479 vs $2,327,003, -1.8% difference) (Table 1). Repeat surgeries resulting from false negative ICRA were also reduced when using ROMA (8 vs 3), a 64% decrease. Notably, total healthcare costs were further reduced when including patients with low malignant potential (LMP) tumors ($2,646,738 vs $2,558,834, -3.3% difference) and analysis of all cancers ($2,847,829 vs $2,728,247, -4.2% difference). A similar reduction in repeat surgeries was noted in patients with LMP tumors (19 for ICRA, 7 for ROMA, -61% difference) and in all cancers (29 for ICRA, 14 for ROMA, -53% difference). <h3>Conclusions:</h3> Triage of women with a pelvic mass using the more sensitive ROMA score compared to ICRA alone lowers overall healthcare costs. With fewer false negatives than ICRA alone, the ROMA score improves initial detection of malignancy and reduces second surgical treatments in women with pelvic masses.

Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure

ObjectivesDeveloping and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes. MethodsThe model was developed using patient-level data from the Trans-European Network – Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation. ResultsPatients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data. ConclusionsThis study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.

Prediction of mortality and major cardiovascular complications in type 2 diabetes: External validation of UK Prospective Diabetes Study outcomes model version 2 in two European observational cohorts.

To externally validate the UK Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) by comparing the predicted and observed outcomes in two European population-based cohorts of people with type 2 diabetes. We used data from the Casale Monferrato Survey (CMS; n = 1931) and a subgroup of the Hoorn Diabetes Care System (DCS) cohort (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. All-cause mortality was overestimated by the UKPDS-OM2 in both the cohorts, with a bias of 0.05 in the CMS and 0.12 in the 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. CHF incidence was predicted well for the DCS (5 years bias -0.001), but underestimated for the CMS cohort. The 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.

Cost-effectiveness of Screening Program for Chronic Q Fever, the Netherlands.

In the aftermath of a large Q fever (QF) epidemic in the Netherlands during 2007–2010, new chronic QF (CQF) patients continue to be detected. We developed a health-economic decision model to evaluate the cost-effectiveness of a 1-time screening program for CQF 7 years after the epidemic. The model was parameterized with spatial data on QF notifications for the Netherlands, prevalence data from targeted screening studies, and clinical data from the national QF database. The cost-effectiveness of screening varied substantially among subpopulations and geographic areas. Screening that focused on cardiovascular risk patients in areas with high QF incidence during the epidemic ranged from cost-saving to €31,373 per quality-adjusted life year gained, depending on the method to estimate the prevalence of CQF. The cost per quality-adjusted life year of mass screening of all older adults was €70,000 in the most optimistic scenario.

Enhanced recovery following hip and knee arthroplasty: a systematic review of cost-effectiveness evidence

ObjectivesTo assess cost-effectiveness of enhanced recovery pathways following total hip and knee arthroplasties. Secondary objectives were to report on quality of studies and identify research gaps for future work.DesignSystematic review...

Development and Validation of the TRansparent Uncertainty ASsessmenT (TRUST) Tool for Assessing Uncertainties in Health Economic Decision Models.

BackgroundAn increasing number of technologies are obtaining marketing authorisation based on sparse evidence, which causes growing uncertainty and risk within health technology reimbursement decision making. To ensure that uncertainty is considered and addressed within health technology assessment (HTA) recommendations, uncertainties need to be identified, included in health economic models, and reported.ObjectiveOur objective was to develop the TRansparent Uncertainty ASsessmenT (TRUST) tool for systematically identifying, assessing, and reporting uncertainties in decision models, with the aim of making uncertainties and their impact on cost effectiveness more explicit and transparent.MethodsTRUST was developed by drawing on the uncertainty and risk assessment literature. To develop and validate this tool, we conducted HTA stakeholder discussion meetings and interviews and applied it in six real-world HTA case studies in the Netherlands and the UK.ResultsThe TRUST tool enables the identification and categorisation of uncertainty according to its source (transparency issues, methodology issues, and issues with evidence: imprecision, bias and indirectness, and unavailability) in each model aspect. The source of uncertainty determines the appropriate analysis. The impact of uncertainties on cost effectiveness is also assessed. Stakeholders found using the tool to be feasible and of value for transparent uncertainty assessment. TRUST can be used during model development and/or model review.ConclusionThe TRUST tool enables systematic identification, assessment, and reporting of uncertainties in health economic models and may contribute to more informed and transparent decision making in the face of uncertainty.Electronic supplementary materialThe online version of this article (10.1007/s40273-019-00855-9) contains supplementary material, which is available to authorized users.

Estimating the cost-effectiveness of salt reformulation and increasing access to leisure centres in England, with PRIMEtime CE model validation using the AdViSHE tool

BackgroundPRIMEtime CE is a multistate life table model that can directly compare the cost effectiveness of public health interventions affecting diet and physical activity levels, helping to inform decisions about how to spend finite resources. This paper estimates the costs and health outcomes in England of two scenarios: reformulating salt and expanding subsidised access to leisure centres. The results are used to help validate PRIMEtime CE, following the steps outlined in the Assessment of the Validation Status of Health-Economic decision models (AdViSHE) tool.MethodsThe PRIMEtime CE model estimates the difference in quality adjusted life years (QALYs) and difference in NHS and social care costs of modelled interventions compared with doing nothing. The salt reformulation scenario models how salt consumption would change if food producers met the 2017 UK Food Standards Agency salt reformulation targets. The leisure centre scenario models change in physical activity levels if the Birmingham Be Active scheme (where swimming pools and gym access is free to residents during defined periods) was rolled out across England.The AdViSHE tool was developed by health economic modellers and divides model validation into five parts: validation of the conceptual model, input data validation, validation of computerised model, operational validation, and other validation techniques. PRIMEtime CE is discussed in relation to each part.ResultsSalt reformulation was dominant compared with doing nothing, and had a 10-year return on investment of £1.44 (£0.50 to £2.94) for every £1 spent. By contrast, over 10 years the Be Active expansion would cost £727,000 (£514,000 to £1,064,000) per QALY.PRIMEtime CE has good face validity of its conceptual model and has robust input data. Cross-validation produces mixed results and shows the impact of model scope, input parameters, and model structure on cost-per-QALY estimates.ConclusionsThis paper illustrates how PRIMEtime CE can be used to compare the cost-effectiveness of two different public health measures affecting diet and physical activity levels. The AdViSHE tool helps to validate PRIMEtime CE, identifies some of the key drivers of model estimates, and highlights the challenges of externally validating public health economic models against independent data.

An introduction to the methods of decision‐analytic modelling used in economic evaluations for Dermatologists

Economic evaluations are used to identify which health treatments or preventions offer the most effective use of resources, or value for money. This is achieved by identifying, measuring and valuing the inputs and outcomes of alternative interventions. These evaluations are often conducted alongside clinical trials; however, these trials may end before the outcomes of economic interest have been observed and measured. An alternative to within trial economic evaluation is to use decision modelling, which can model the cost-effectiveness of interventions over an extended time period. This paper aims to provide an overview for clinicians of the different modelling techniques used within health economic evaluations and to introduce methods for critical appraisal. The most common modelling approaches, and their associated strengths and weaknesses, were discussed. Alongside this, practical examples specific to dermatology were given. These examples include studies where the model chosen or the methods used may not have been the most appropriate. Methods for critical appraisal were also highlighted. Common modelling approaches include Decision Trees, Markov Cohort, extensions to the Markov model (Monte Carlo Simulation) and Discrete Event Simulation models. Items of the Philips Checklist were discussed in the context of performing critical appraisal. Health economic decision models are multi-faceted and can often be complex. Full critical appraisal requires clinicians' unique knowledge, which is complementary to the knowledge of health economists.

The cost impact of PCT-guided antibiotic stewardship versus usual care for hospitalised patients with suspected sepsis or lower respiratory tract infections in the US: A health economic model analysis.

BackgroundProcalcitonin is a biomarker that supports clinical decision-making on when to initiate and discontinue antibiotic therapy. Several cost (-effectiveness) analyses have been conducted on Procalcitonin-guided antibiotic stewardship, but none mainly based on US originated data.ObjectiveTo compare effectiveness and costs of a Procalcitonin-algorithm versus standard care to guide antibiotic prescription for patients hospitalized with a diagnosis of suspected sepsis or lower respiratory tract infection in the US.MethodsA previously published health economic decision model was used to compare the costs and effects of Procalcitonin-guided care. The analysis considered the societal and hospital perspective with a time horizon covering the length of hospital stay. The main outcomes were total costs per patient, including treatment costs and productivity losses, the number of patients with antibiotic resistance or C.difficile infections, and costs per antibiotic day avoided.ResultsProcalcitonin -guided care for hospitalized patients with suspected sepsis and lower respiratory tract infection is associated with a reduction in antibiotic days, a shorter length of stay on the regular ward and the intensive care unit, shorter duration of mechanical ventilation, and fewer patients at risk for antibiotic resistant or C.difficile infection. Total costs in the Procalcitonin-group compared to standard care were reduced by 26.0% in sepsis and 17.7% in lower respiratory tract infection (total incremental costs of −$11,311 per patient and −$2,867 per patient respectively).ConclusionsUsing a Procalcitonin-algorithm to guide antibiotic use in sepsis and hospitalised lower respiratory tract infection patients is expected to generate cost-savings to the hospital and lower rates of antibiotic resistance and C.difficile infections.

Long-Term Evaluation of Outcomes for Abdominal Ultrasound Screening for Hepatocellular Carcinoma

Background: Mass screening for hepatocellular carcinoma (HCC) with abdominal ultrasound (AUS) has been demonstrated in short-term follow-up in Taiwan, but long-term effectiveness and cost-effectiveness of AUS have not been well studied particularly when the recently emerging antiviral therapy has been introduced. The aim of this study is therefore to evaluate long-term outcomes of population-based organized service screening program without control group. Methods: AUS screening based on a risk score–guided invitation has been launched since October 2008 in Changhua. A total of 85,147 subjects aged 40-74 years attended the Changhua community-based integrated screening (CHCIS) program were targeted. Those with high score based on hepatitis virus infection, ALT, AST, type 2 diabetes, platelet count were invited to receive ultrasonography screening performed by board-certified gastroenterologists in town-based health center to identify liver cirrhosis, and suspected HCC cases. We developed a pseudo-control group by building up the disease natural history for HCC embedded with the risk prediction model and the survival by detection modes. The pseudo-control model was further used to the development of health economic decision model for cost-effectiveness of various preventive strategies. Results: We observed a 61% (RR = 0.39, 95% CI: 0.32-0.46) HCC mortality reduction with AUS by risk groups together with ∼30% coverage rate of antiviral therapy in an 8-year period. The simulated results indicates the additional contribution of 30% compliance rate of antiviral therapy (empirical estimate) to HCC mortality reduction was ∼22%. The corresponding figures were raised to 30% and 35% when the compliance rate of antiviral therapy was enhanced to 50% and 70%, respectively. The results of cost-effectiveness analysis shows the incremental cost-effectiveness ratio was $22,849 for the administration of AUS, and $101,849 for the administration of AUS plus 30% compliance to antiviral therapy. The corresponding figures for 50% and 70% compliance rate to antiviral therapy were $141,805 and $181,919, respectively. Conclusion: The population-based screening program for HCC together with antiviral therapy achieved HCC mortality reduction by 60%. The computer study design with a pseudo-control group on the basis of disease natural history of HCC embedded with the predictive model, and the survival part of HCC enables us to quantify the attributable proportion from different preventive regimes, and to estimate long-term effectiveness of the overall and marginal effectiveness and cost-effectiveness of AUS and antiviral therapy under different scenarios. Such approach provides the health policy-makers a reference for resource allocation.

Optimal primary care management of clinical osteoarthritis and joint pain in older people: a mixed-methods programme of systematic reviews, observational and qualitative studies, and randomised controlled trials

BackgroundOsteoarthritis (OA) is the most common long-term condition managed in UK general practice. However, care is suboptimal despite evidence that primary care and community-based interventions can reduce OA pain and disability.ObjectivesThe overall aim was to improve primary care management of OA and the health of patients with OA. Four parallel linked workstreams aimed to (1) develop a health economic decision model for estimating the potential for cost-effective delivery of primary care OA interventions to improve population health, (2) develop and evaluate new health-care models for delivery of core treatments and support for self-management among primary care consulters with OA, and to investigate prioritisation and implementation of OA care among the public, patients, doctors, health-care professionals and NHS trusts, (3) determine the effectiveness of strategies to optimise specific components of core OA treatment using the example of exercise and (4) investigate the effect of interventions to tackle barriers to core OA treatment, using the example of comorbid anxiety and depression in persons with OA.Data sourcesThe North Staffordshire Osteoarthritis Project database, held by Keele University, was the source of data for secondary analyses in workstream 1.MethodsWorkstream 1 used meta-analysis and synthesis of published evidence about effectiveness of primary care treatments, combined with secondary analysis of existing longitudinal population-based cohort data, to identify predictors of poor long-term outcome (prognostic factors) and design a health economic decision model to estimate cost-effectiveness of different hypothetical strategies for implementing optimal primary care for patients with OA. Workstream 2 used mixed methods to (1) develop and test a ‘model OA consultation’ for primary care health-care professionals (qualitative interviews, consensus, training and evaluation) and (2) evaluate the combined effect of a computerised ‘pop-up’ guideline for general practitioners (GPs) in the consultation and implementing the model OA consultation on practice and patient outcomes (parallel group intervention study). Workstream 3 developed and investigated in a randomised controlled trial (RCT) how to optimise the effect of exercise in persons with knee OA by tailoring it to the individual and improving adherence. Workstream 4 developed and investigated in a cluster RCT the extent to which screening patients for comorbid anxiety and depression can improve OA outcomes. Public and patient involvement included proposal development, project steering and analysis. An OA forum involved public, patient, health professional, social care and researcher representatives to debate the results and formulate proposals for wider implementation and dissemination.ResultsThis programme provides evidence (1) that economic modelling can be used in OA to extrapolate findings of cost-effectiveness beyond the short-term outcomes of clinical trials, (2) about ways of implementing support for self-management and models of optimal primary care informed by National Institute for Health and Care Excellence recommendations, including the beneficial effects of training in a model OA consultation on GP behaviour and of pop-up screens in GP consultations on the quality of prescribing, (3) against adding enhanced interventions to current effective physiotherapy-led exercise for knee OA and (4) against screening for anxiety and depression in patients with musculoskeletal pain as an addition to current best practice for OA.ConclusionsImplementation of evidence-based care for patients with OA is feasible in general practice and has an immediate impact on improving the quality of care delivered to patients. However, improved levels of quality of care, changes to current best practice physiotherapy and successful introduction of psychological screening, as achieved by this programme, did not substantially reduce patients’ pain and disability. This poses important challenges for clinical practice and OA research.LimitationsThe key limitation in this work is the lack of improvement in patient-reported pain and disability despite clear evidence of enhanced delivery of evidence-based care.Future work recommendations(1) New thinking and research is needed into the achievable and desirable long-term goals of care for people with OA, (2) continuing investigation into the resources needed to properly implement clinical guidelines for management of OA as a long-term condition, such as regular monitoring to maintain exercise and physical activity and (3) new research to identify subgroups of patients with OA as a basis for stratified primary care including (i) those with good prognosis who can self-manage with minimal investigation or specialist treatment, (ii) those who will respond to, and benefit from, specific interventions in primary care, such as physiotherapy-led exercise, and (iii) develop research into effective identification and treatment of clinically important anxiety and depression in patients with OA and into the effects of pain management on psychological outcomes in patients with OA.Trial registrationCurrent Controlled Trials ISRCTN06984617, ISRCTN93634563 and ISRCTN40721988.FundingThis project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research Programme and will be published in full inProgramme Grants for Applied Research Programme; Vol. 6, No. 4. See the NIHR Journals Library website for further project information.

Methods for Handling Unobserved Covariates in a Bayesian Update of a Cost-effectiveness Model.

Health economic decision models often involve a wide-ranging and complicated synthesis of evidence from a number of sources, making design and implementation of such models resource-heavy. When new data become available and reassessment of treatment recommendations is warranted, it may be more efficient to perform a Bayesian update of an existing model than to construct a new model. If the existing model depends on many, possibly correlated, covariates, then an update may produce biased estimates of model parameters if some of these covariates are completely absent from the new data. Motivated by the need to update a cost-effectiveness analysis comparing diagnostic strategies for coronary heart disease, this study develops methods to overcome this obstacle by either introducing additional data or using results from previous studies. We outline a framework to handle unobserved covariates, and use our motivating example to illustrate both the flexibility of the proposed methods and some potential difficulties in applying them.