A Systematic Review of Methodologies Used in Models of the Treatment of Diabetes Mellitus

BACKGROUND

Diabetes mellitus is a chronic and complex disease, increasing in prevalence and consequent health expenditure. Cost-effectiveness models with long time horizons are commonly used to perform economic evaluations of diabetes' treatments. As such, prediction accuracy and structural uncertainty are important features in cost-effectiveness models of chronic conditions.

OBJECTIVES

The aim of this systematic review is to identify and review published cost-effectiveness models of diabetes treatments developed between 2011 and 2022 regarding their methodological characteristics. Further, it also appraises the quality of the methods used, and discusses opportunities for further methodological research.

METHODS

A systematic literature review was conducted in MEDLINE and Embase to identify peer-reviewed papers reporting cost-effectiveness models of diabetes treatments, with time horizons of more than 5 years, published in English between 1 January 2011 and 31 of December 2022. Screening, full-text inclusion, data extraction, quality assessment and data synthesis using narrative synthesis were performed. The Philips checklist was used for quality assessment of the included studies. The study was registered in PROSPERO (CRD42021248999).

RESULTS

The literature search identified 30 studies presenting 29 unique cost-effectiveness models of type 1 and/or type 2 diabetes treatments. The review identified 26 type 2 diabetes mellitus (T2DM) models, 3 type 1 DM (T1DM) models and one model for both types of diabetes. Fifteen models were patient-level models, whereas 14 were at cohort level. Parameter uncertainty was assessed thoroughly in most of the models, whereas structural uncertainty was seldom addressed. All the models where validation was conducted performed well. The methodological quality of the models with respect to structure was high, whereas with respect to data modelling it was moderate.

CONCLUSIONS

Models developed in the past 12 years for health economic evaluations of diabetes treatments are of high-quality and make use of advanced methods. However, further developments are needed to improve the statistical modelling component of cost-effectiveness models and to provide better assessment of structural uncertainty.

A systematic review of whole disease models for informing healthcare resource allocation decisions

BACKGROUND

Whole disease models (WDM) are large-scale, system-level models which can evaluate multiple decision questions across an entire care pathway. Whilst this type of model can offer several advantages as a platform for undertaking economic analyses, the availability and quality of existing WDMs is unknown.

OBJECTIVES

This systematic review aimed to identify existing WDMs to explore which disease areas they cover, to critically assess the quality of these models and provide recommendations for future research.

METHODS

An electronic search was performed on multiple databases (MEDLINE, EMBASE, the NHS Economic Evaluation Database and the Health Technology Assessment database) on 23rd July 2023. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) appraisal checklist for economic evaluations. Model characteristics were descriptively summarised.

RESULTS

Forty-four WDMs were identified, of which thirty-two were developed after 2010. The main disease areas covered by existing WDMs are heart disease, cancer, acquired immune deficiency syndrome and metabolic disease. The quality of included WDMs is generally low. Common limitations included failure to consider the harms and costs of adverse events (AEs) of interventions, lack of probabilistic sensitivity analysis (PSA) and poor reporting.

CONCLUSIONS

There has been an increase in the number of WDMs since 2010. However, their quality is generally low which means they may require significant modification before they could be re-used, such as modelling AEs of interventions and incorporation of PSA. Sufficient details of the WDMs need to be reported to allow future reuse/adaptation.

Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge

BACKGROUND

Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models.

METHODS

Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences.

RESULTS

Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049).

CONCLUSIONS

Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Systematic review of the methods of health economic models assessing antipsychotic medication for schizophrenia

BACKGROUND

Numerous economic models have assessed the cost-effectiveness of antipsychotic medications in schizophrenia. It is important to understand what key impacts of antipsychotic medications were considered in the existing models and limitations of existing models in order to inform the development of future models.

OBJECTIVES

This systematic review aims to identify which clinical benefits, clinical harms, costs and cost savings of antipsychotic medication have been considered by existing models, to assess quality of existing models and to suggest good practice recommendations for future economic models of antipsychotic medications.

METHODS

An electronic search was performed on multiple databases (MEDLINE, EMBASE, PsycInfo, Cochrane database of systematic reviews, The NHS Economic Evaluation Database and Health Technology Assessment database) to identify economic models of schizophrenia published between 2005-2020. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) checklist and the Cooper hierarchy. Key impacts of antipsychotic medications considered by exiting models were descriptively summarised.

RESULTS

Sixty models were included. Existing models varied greatly in key impacts of antipsychotic medication included in the model, especially in clinical outcomes used for assessing reduction in psychotic symptoms and types of adverse events considered in the model. Quality of existing models was generally low due to failure to capture the health and cost impact of adverse events of antipsychotic medications and input data not obtained from best available source. Good practices for modelling antipsychotic medications are suggested.

DISCUSSIONS

This review highlights inconsistency in key impacts considered by different models, and limitations of the existing models. Recommendations on future research are provided.

The Diffusion of Discrete Event Simulation Approaches in Health Care Management in the Past Four Decades: A Comprehensive Review

This study systematically examines the diffusion of the discrete event simulation (DES) approach in health services and health care management by examining relevant factors such as research areas, channels with the objective of promoting the application of DES in the health field. We examined 483 journal papers referencing this approach that were published in 230 journals during 1981 to 2014. The application of DES has extended from health service operational research evaluation to the assessment of interventions in diverse health arenas. The increase in the number of adopters (paper authors) of DES and the increase in number of related channels (journals publishing DES-related articles) are highly correlated, which suggests an increase of DES-related publications in health research. The same conclusion is reached, that is, an increased diffusion of DES in health research, when we focus on the temporal trends of the channels and adopters. The applications of DES in health research cover 22 major areas based on our categorization. The expansion in the health areas also suggests to a certain extent the rapid diffusion of DES in health research.

A Systematic Review of Economic Models Across the Entire Schizophrenia Pathway

BACKGROUND

Schizophrenia is associated with a high economic burden. Economic models can help to inform resource allocation decisions to maximise benefits to patients.

OBJECTIVES

This systematic review aims to assess the availability, quality and consistency of conclusions of health economic models evaluating the cost effectiveness of interventions for schizophrenia.

METHODS

An electronic search was performed on multiple databases (MEDLINE, EMBASE, PsycINFO, Cochrane database of systematic reviews, NHS Economic Evaluation Database and Health Technology Assessment database) to identify economic models of interventions for schizophrenia published between 2005 and 2020. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) checklist and the Cooper hierarchy. Model characteristics and conclusions were descriptively summarised.

RESULTS

Seventy-three models met inclusion criteria. Seventy-eight percent of existing models assessed antipsychotics; however, due to inconsistent conclusions reported by different studies, no antipsychotic can be considered clearly cost effective compared with the others. A very limited number of models suggest that the following non-pharmacological interventions might be cost effective: psychosocial interventions, stratified tests, employment intervention and intensive intervention to improve liaison between primary and secondary care. The quality of included models is generally low due to use of a short time horizon, omission of adverse events of interventions, poor data quality and potential conflicts of interest.

CONCLUSIONS

This review highlights a lack of models for non-pharmacological interventions, and limitations of the existing models, including low quality and inconsistency in conclusions. Recommendations on future modelling approaches for schizophrenia are provided.

Systematic review of utility values used in the pharmacoeconomic evaluations for schizophrenia: implications on cost-effectiveness results

Background and Objectives: Utility elicitation studies for schizophrenia generate different utility values for the same health states. We reviewed utility values used in schizophrenia pharmacoeconomic evaluations and evaluated the impact of their selection on the incremental cost-effectiveness ratio (ICER). Methods: A systematic search was performed in Medline and Embase. Health state definitions, associated utility values, elicitation studies, and value selection processes were extracted. Sets of utility values for all schizophrenia health states were used in a cost-effectiveness model to evaluate the ICER. Results: Thirty-five cost-utility analyses (CUAs) referring to 11 utility elicitation studies were included. The most frequent health states were 'stable' (28 CUAs, 7 utility elicitation studies, 10 values, value range 0.650-0.919), 'relapse requiring hospitalisation' (18, 5, 7, 0.270-0.604), 'relapse not requiring hospitalisation' (18, 5, 10, 0.460-0.762), and 'relapse only' (10, 5, 6, 0.498-0.700). Seventeen sets of utility values were identified with difference in utility values between relapse and stable ranging from -0.358 to -0.050, resulting in ICERs ranging from -56.2% to +222.6% from average. Conclusion: The use of utility values for schizophrenia health states differs among CUAs and impacts on the ICER. More rigorous and transparent use of utility values and sensitivity analysis with different sets of utility values are suggested for future CUAs.

Multivariable and Structural Uncertainty Analyses for Cost-Effectiveness Estimates: Back to the Future

BACKGROUND

In this commentary, celebrating the 20th anniversary of the journal Value in Health, I present a brief overview and illustration of the evolution over the past 20 years of the methodological literature providing guidelines for multivariable and structural uncertainty analysis for cost-effectiveness estimates.

METHODS

To illustrate the impact of the guidelines for uncertainty analyses, I show how the inclusion of multivariable and structural uncertainty analyses in cost-effectiveness analyses published in Value in Health changed over the past 20 years using publications from 1999/2000, 2007 and 2017.

RESULTS

The commentary is organized in three sections: past, focusing on the development and use of methods for multivariable uncertainty analysis; present, focusing on the growing awareness of the need for structural uncertainty analysis, suggested frameworks for structural uncertainty analysis and how it is currently implemented; and future, considering different methods for combining multivariable and structural uncertainty analyses over the next decades.

CONCLUSIONS

I conclude by suggesting how the continued evolution of uncertainty analyses in published studies and health technology assessment submissions can best take into account an important goal of cost-effectiveness analyses: to provide useful information to decision makers.

Systematic review of pharmacoeconomic models for schizophrenia

Background: Economic models are broadly used in the economic evaluation of antipsychotics in schizophrenia. Our objective was to summarize the structure of these models. Methods: Model-based economic evaluations of antipsychotics in schizophrenia were identified through Medline and Embase. General information was extracted including analysis type, model type, perspective, population, comparator, outcome, and timeframe. Model-specific structures for decision tree (DT), cohort- and patient-level Markov model (CLMM, PLMM), and discrete-event simulation (DES) models were extracted. Results: A screen of 1870 records identified 79 studies. These were mostly cost-utility analyses (n = 48) with CLMM (n = 32) or DT models (n = 29). They mostly applied payer perspective (n = 68), focused on general schizophrenia for relapse prevention (n = 73), compared pharmacotherapies as first-line (n = 71), and evaluated incremental cost per quality-adjusted life year (QALY) gained (n = 40) with a 1-year (n = 32) or 5-year (n = 26) projection. DT models progressed with the branching points of response, relapse, discontinuation, and adherence. CLMM models transitioned between disease states, whereas PLMM models transitioned between adverse event states with/without disease state. DES models moved forward with times to remission, relapse, psychiatrist visit, and death. Conclusions: A pattern of pharmacoeconomic models for schizophrenia was identified. More subtle structures and patient-level models are suggested for a future modelling exercise.

Comparative cost-effectiveness of 11 oral antipsychotics for relapse prevention in schizophrenia within Singapore using effectiveness estimates from a network meta-analysis

This study modelled the cost-effectiveness of 11 oral antipsychotics for relapse prevention among patients with remitted schizophrenia in Singapore. A network meta-analysis determined the relative efficacy and tolerability of 11 oral antipsychotics (amisulpride, aripiprazole, chlorpromazine, haloperidol, olanzapine, paliperidone, quetiapine, risperidone, sulpiride, trifluoperazine and ziprasidone). The clinical estimates were applied in a Markov model to estimate lifetime costs and quality-adjusted life-years gained. Quality-of-life data were obtained from published literature. Resource utilization and cost data were retrieved from local hospital databases. The annual direct cost of healthcare services for a patient experiencing a relapse episode was three-fold that of a patient not in relapse of schizophrenia. The most favourable pharmacological treatment for relapse prevention was olanzapine with an annual probability of relapse of 0.24 (0.13-0.38) with placebo as a reference of 0.75 (0.73-0.78). Olanzapine emerged as the dominant treatment with the highest quality-adjusted life-years gained and lowest lifetime costs. Ziprasidone, aripiprazole and paliperidone incurred higher lifetime costs compared with no treatment. Probability and cost of relapse were key drivers of cost-effectiveness in sensitivity analyses. The data can help prescribers in choosing appropriate treatment and payers in allocating resources for the clinical management of this serious psychiatric disorder.