Dynamic microsimulation models for health outcomes: a review

Discrete-Event Simulation Model for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline

Simulation models inform health policy decisions by integrating data from multiple sources and forecasting outcomes when there is a lack of comprehensive evidence from empirical studies. Such models have long supported health policy for cancer, the first or second leading cause of death in over 100 countries. Discrete-event simulation (DES) and Bayesian calibration have gained traction in the field of Decision Science because they enable efficient and flexible modeling of complex health conditions and produce estimates of model parameters that reflect real-world disease epidemiology and data uncertainty given model constraints. This uncertainty is then propagated to model-generated outputs, enabling decision makers to determine the optimal strategy to recommend, assess confidence in the recommendation, and estimate the value of collecting additional information. However, there is limited end-to-end guidance on structuring a DES model for cancer progression, estimating its parameters using Bayesian calibration, and applying the calibration outputs to policy evaluation and other downstream tasks. To fill this gap, we introduce the DES Model for Cancer Interventions and Population Health in R (DESCIPHR), an open-source framework and codebase integrating a flexible DES model for the natural history of cancer, Bayesian calibration for parameter estimation, and screening strategy evaluation. We also introduce an automated method to generate data-informed parameter prior distributions and enhance the accuracy and flexibility of a neural network emulator-based Bayesian calibration algorithm. We anticipate that the adaptable DESCIPHR modeling template will facilitate the construction of future decision models evaluating the risks and benefits of health interventions.

Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada

BackgroundEstimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040.MethodsPOSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation.ResultsThe number of children (aged 0-14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353-486) in 2020 to 561 (95% CrI: 481-653) in 2039. The 5-y overall survival rate for 2030-2034 is estimated to reach 90% (95% CrI: 88%-92%). By 2040, 24,088 (95% CrI: 22,764-25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated.ConclusionsThe rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs.HighlightsThis article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040.Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence.In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes.This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.

Simulation Models for Bladder Cancer: A Scoping Review

Objectives

The study identifies and summarizes information from manuscripts using simulation models for Bladder Cancer (BCA) research.

Methods

We conducted and presented results of a systematic literature search of Medline, Web of Science, and Google scholar, following the PRISMA guidelines for scoping reviews. We summarized extracted key components of the methodology, data sources, and software used for the development of simulation models and classify eligible articles in terms of the study objectives and conclusions.

Results

The 97 identified modeling studies simulating aspects of BCA included models that (1) describe the biological process of carcinogenesis and tumor progression (mostly compartmental models); (2) examine the impact of screening protocols and interventions on disease progression and prognosis (mostly microsimulation models); and (3) assess the cost-effectiveness of BCA treatment and control strategies (cohort-based simulation models or simpler decision tree structures). The scope, objectives, and conclusions of these studies varied substantially. Most focused on evaluating treatments, mostly for non-muscle invasive bladder cancer, with some examining BCA screening and surveillance. Their objectives, methods, and analyses were inconsistently and often incompletely reported.

Conclusions

Simulation models in bladder cancer examine questions that span the range from tumor kinetics to cost effectiveness of tumor management, but shortcomings in their reporting hinder assessments of their applicability and methodological rigor, severely limiting their practical usefulness.

Highlight statements

We assessed the available landscape of simulation modeling for health decision making in BCA research.Shortcomings in the reporting of this research severely limit their practical usefulness.Future population modeling should assess BCA screening and surveillance.

Strengths

This is the first, to our knowledge, systematic appraisal of simulation models in bladder cancer. Simulation modeling will be a key technology to assess the utility of highly promising novel diagnostics and treatments, while evidence accumulates.The described variation in the objectives, methodological rigor, and reporting of models' development, validation, and analysis likely generalize to other disease areas.

Limitations

This descriptive compendium does not explicitly compare the results of different models between them or with observed data.

Adopting a complex systems approach to functional ageing: bridging the gap between gerontological theory and empirical research

Functional ageing, related to but distinct from biological and environmental systems, is defined as the changes in physical, psychological, cognitive, and social functioning, as well as behavioural factors of individuals as they age. In this Personal View, we propose that a complex systems perspective to functional ageing can show how outcomes such as quality of life and longevity, and success in prevention and treatment, emerge from dynamic interactions among these domains, rather than from single causes. We support this view in three ways. First, we explain how three key principles of complex systems science-namely, resilience, non-linearity, and heterogeneity-apply to functional ageing. Second, we show how established gerontological theories and geriatric models align with these principles. Third, we illustrate the use of novel methodological tools available from complex systems science for studying functional ageing. Finally, we offer a glossary of key concepts and recommendations for researchers to adopt this perspective in future studies on functional ageing.

Time-varying cost-effectiveness analysis of sodium-glucose cotransporter-2 inhibitors in Chinese patients with heart failure and reduced ejection fraction: A microsimulation of the real-world population

Objective

Sodium-glucose cotransporter-2 (SGLT2) inhibitors showed time-varying effects in heart failure and reduced ejection fraction (HFrEF), but corresponding cost-effectiveness in different timeframes remained poorly understood. This study estimated the time-varying cost-effectiveness of SGLT2 inhibitors in HFrEF from the perspective of the Chinese healthcare system.

Methods

Based on real-world individual patient data, a 2-year microsimulation model was constructed to evaluate the cost-effectiveness of adding SGLT2 inhibitors to standard therapy compared with standard therapy alone among patients with HFrEF. A published prediction model informed transition probabilities for all-cause death and hospitalization for heart failure. The time-varying effects of SGLT2 inhibitors, medical costs, and utility values were derived from the published literature. Scenario analyses in different timeframes were conducted to assess the trend of cost-effectiveness over time.

Results

Compared with standard therapy alone, SGLT2 inhibitors plus standard therapy were found cost-effective at a willingness-to-pay (WTP) threshold of $12,741 per quality-adjusted life year (QALY) gained in 2 years. The incremental cost-effectiveness ratio (ICER) decreased from $12,346.07/QALY at 0.5 years to $9,355.66/QALY at 2 years. One-direction sensitivity analysis demonstrated that the cost-effectiveness of SGLT2 inhibitors was most sensitive to the cost of SGLT2 inhibitors, the cost of hospitalization for heart failure, the cost of standard therapy for heart failure, and the baseline risks of all-cause death and hospitalization for heart failure. Probabilistic sensitivity analysis proved the robustness of the results.

Conclusion

Adding SGLT2 inhibitors to standard therapy was found to be cost-effective in Chinese patients with HFrEF. Longer treatment appeared to be more economically favorable, but further explorations are warranted.

Evolution of digital twins in precision health applications: a scoping review study

An increasing amount of research is incorporating the concept of Digital twin (DT) in biomedical and health care applications. This scoping review aims to summarize existing research and identify gaps in the development and use of DTs in the health care domain. The focus of this study lies on summarizing: the different types of DTs, the techniques employed in DT development, the DT applications in health care, and the data resources used for creating DTs. We identified fifty studies, which mainly focused on creating organ- (n=15) and patient-specific twins (n=30). The research predominantly centers on cardiology, endocrinology, orthopedics, and infectious diseases. Only a few studies used real-world datasets for developing their DTs. However, there remain unresolved questions and promising directions that require further exploration. This review provides valuable reference material and insights for researchers on DTs in health care and highlights gaps and unmet needs in this field.

Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation

PURPOSE

Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner.

METHODS

Retrospective real-world data are leveraged to emulate a target trial for treatment timing using multistate modeling and microsimulation. This case study focuses on myelodysplastic syndromes, serving as a prototype for rare cancers characterized by a heterogeneous clinical course and complex genomic background. A cohort of 7,118 patients treated according to conventional available treatments/evidence across Europe and United States is analyzed. The primary clinical objective is to determine the ideal timing for HSCT, the only curative option for these patients.

RESULTS

This analysis enabled us to identify the most appropriate time frames for HSCT on the basis of each patient's unique profile, defined by a combination relevant patients' characteristics.

CONCLUSION

The developed methodology offers a structured framework to address a relevant clinical issue in the field of hematology. It makes several valuable contributions: (1) novel insights into how to develop decision models to identify the most favorable HSCT timing, (2) evidence to inform clinical decisions in a real-world context, and (3) the incorporation of complex information into decision making. This framework can be applied to provide medical insights for clinical issues that cannot be adequately addressed through randomized clinical trials.

The future burden of type 2 diabetes in Belgium: a microsimulation model

OBJECTIVE

To forecast the annual burden of type 2 diabetes and related socio-demographic disparities in Belgium until 2030.

METHODS

This study utilized a discrete-event transition microsimulation model. A synthetic population was created using 2018 national register data of the Belgian population aged 0-80 years, along with the national representative prevalence of diabetes risk factors obtained from the latest (2018) Belgian Health Interview and Examination Surveys using Multiple Imputation by Chained Equations (MICE) as inputs to the Simulation of Synthetic Complex Data (simPop) model. Mortality information was obtained from the Belgian vital statistics and used to calculate annual death probabilities. From 2018 to 2030, synthetic individuals transitioned annually from health to death, with or without developing type 2 diabetes, as predicted by the Finnish Diabetes Risk Score, and risk factors were updated via strata-specific transition probabilities.

RESULTS

A total of 6722 [95% UI 3421, 11,583] new cases of type 2 diabetes per 100,000 inhabitants are expected between 2018 and 2030 in Belgium, representing a 32.8% and 19.3% increase in T2D prevalence rate and DALYs rate, respectively. While T2D burden remained highest for lower-education subgroups across all three Belgian regions, the highest increases in incidence and prevalence rates by 2030 are observed for women in general, and particularly among Flemish women reporting higher-education levels with a 114.5% and 44.6% increase in prevalence and DALYs rates, respectively. Existing age- and education-related inequalities will remain apparent in 2030 across all three regions.

CONCLUSIONS

The projected increase in the burden of T2D in Belgium highlights the urgent need for primary and secondary preventive strategies. While emphasis should be placed on the lower-education groups, it is also crucial to reinforce strategies for people of higher socioeconomic status as the burden of T2D is expected to increase significantly in this population segment.

OLYMPUS-POPGEN: A synthetic population generation model to represent urban populations for assessing exposure to air quality

SCIENTIFIC QUESTION

With the new individual- and activity-based approaches to simulating exposure to air pollutants, exposure models must now provide synthetic populations that realistically reflect the demographic profiles of individuals in an urban territory. Demographic profiles condition the behavior of individuals in urban space (activities, mobility) and determine the resulting risks of exposure and environmental inequalities. In this context, there is a strong need to determine the relevance of the population modeling methods to reproduce the combinations of socio-demographic parameters in a population from the existing databases. The difficulty of accessing complete, high-resolution databases indeed proves to be very limiting for the ambitions of the different approaches.

OBJECTIVE

This work proposes to evaluate the potential of a statistical approach for the numerical modeling of synthetic populations, at the scale of dwellings and including the representation of coherent socio-demographic profiles. The approach is based on and validated against the existing open databases. The ambition is to be able to build upon such synthetic populations to produce a comprehensive assessment of the risk of environmental exposure that can be cross-referenced with lifestyles, indicators of social, professional or demographic category, and even health vulnerability data.

METHOD

The approach implemented here is based on the use of conditional probabilities to model the socio-demographic properties of individuals, via the deployment of a Monte Carlo Markov Chain (MCMC) simulation. Households are assigned to housing according to income and house price classes. The resulting population generation model was tested in the Paris region (Ile de France) for the year 2010, and applied to a population of almost 12 million individuals. The approach is based on the use of census and survey databases.

RESULTS

Validation, carried out by comparison with regional census data, shows that the model accurately reproduces the demographic attributes of individuals (age, gender, professional category, income) as well as their combination, at both regional and sub-municipal levels. Notably, population distribution at the scale of the model buildings remains consistent with observed data patterns.

CONCLUSIONS AND RELEVANCE

The outcomes of this work demonstrate the ability of our approach to create, from public data, a coherent synthetic population with broad socio-demographic profiles. They give confidence for the use of this approach in an activity-based air quality exposure study, and thus for exploring the interrelations between social determinants and environmental risks. The non-specific nature of this work allows us to consider its extension to broader demographic profiles, including health indicators, and to different study regions.

Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States

There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183-232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18-49 years, the model estimated that annual boosters would reduce this risk by 14 (10-19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.

Estimated reductions in type 2 diabetes burden through nutrition policies in AZAR cohort population: A PRIME microsimulation study for primary health care

Background

Given the impact of high intake of sugar-sweetened beverages on type 2 diabetes, intervention to reduce their consumption can be a top priority for any health system. Thus, the purpose of the present study is to simulate the impact of policy options related to reduce consumption of sugar-sweetened beverages (SSBs) on the prevalence and mortality of type 2 diabetes in Iranian men and women.

Methods

A discrete event simulation (DES) model was used to predict the effect of several policy options on the prevalence and death from type 2 diabetes in Azar Cohort Databases. Population age- and sex-specific prevalence and incidence rate of diagnosed diabetes were derived from the national health data. The Preventable Risk Integrated Model (PRIME) model was used for coding the input parameters of simulation using R and Python software.

Results

The prevalence and mortality rate of type 2 diabetes under the scenario of reduced consumption of SSBs indicated that the highest and the lowest prevalence and mortality rates of type 2 diabetes for men and women were related to no policy condition and replacing SSBs with healthy drinks, like water, respectively. Also, the maximum "number of deaths postponed/ prevented" from type 2 diabetes was related to replacing SSBs with water (n=2015), and an integration of reformulation and applying 10% tax on SSBs (n=1872), respectively.

Conclusion

Simulating the effect of different policy options on reducing the consumption of SSBs showed "replacing of SSBs with water" as the most effective policy option in Iranian setting.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

OBJECTIVE

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

DESIGN

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

SETTING AND PARTICIPANTS

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

RESULTS

5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states.

CONCLUSION

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BACKGROUND

The rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.

METHODS

We present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator's descriptive accuracy.

RESULTS

In this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8-22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65-5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.

CONCLUSIONS

The emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

Immunotherapy Guided by Immunohistochemistry PD-L1 Testing for Patients with NSCLC: A Microsimulation Model-Based Effectiveness and Cost-Effectiveness Analysis

BACKGROUND

On the basis of immunohistochemistry PD-L1 testing results, patients with advanced non-small cell lung cancer (NSCLC) are treated differently. Theoretically, patients with high PD-L1 expression (50% or 1%) should receive PD-1 monotherapy for fewer adverse reactions and cost savings from avoiding chemotherapy; however, there is controversy surrounding the cut-off criteria (1% or 50%) for immunohistochemistry testing and threshold for PD-1 monotherapy.

OBJECTIVE

This study aims to predict the effectiveness and cost-effectiveness of different immunotherapy strategies for patients with NSCLC in China from the healthcare system perspective.

PATIENTS AND METHODS

A microsimulation model was developed to evaluate the effectiveness and cost-effectiveness of three treatment strategies: PD-L1 testing (1%) (PD-1 monotherapy for those with PD-L1 expression at 1% threshold, and combination with chemotherapy for others with immunohistochemistry testing), PD-L1 testing (50%) (PD-1 monotherapy for those with PD-L1 expression at 50% threshold, and combination with chemotherapy for others with immunohistochemistry testing), and No PD-L1 testing (PD-1 combined with chemotherapy without immunohistochemistry testing). The model assumed 1000 patients per strategy, with each patient entering a unique clinical path prior to receiving treatment on the basis of PD-L1 test results. Clinical inputs were derived from clinical trials. Cost and utility parameters were obtained from the database and literature. One-way probabilistic sensitivity analyses (PSA) and six scenario analyses were used to test the model's robustness.

RESULTS

The study revealed a hierarchy of survival benefits across three strategies, with No PD-L1 testing demonstrating the most survival advantage, followed by PD-L1 testing (50%), and finally, PD-L1 testing (1%). The comparative analysis demonstrated that No PD-L1 testing significantly enhanced overall survival (OS) (HR 0.85, 95% CI 0.78-0.93), progression-free survival (HR 0.82, 95% CI 0.75-0.90), and progression-free2 survival (PFS2) (HR 0.91, 95% CI 0.83-0.99) when juxtaposed against PD-L1 testing (1%). However, these improvements were not as pronounced when compared with PD-L1 testing (50%), particularly in relation to PFS, PFS2, and OS. The cost-effectiveness analysis further unveiled incremental cost-utility ratios (ICUR), with No PD-L1 testing versus PD-L1 testing (50%) at $34,003 per quality-adjusted life year (QALY) and No PD-L1 testing versus PD-L1 testing (1%) at $34,804 per QALY. In parallel, the ICUR for PD-L1 testing (50%) versus PD-L1 testing (1%) stood at $35,713 per QALY. Remarkably, the PSA result under a willingness-to-pay (WTP) threshold of $10,144 per QALY, with a 100% probability, demonstrated PD-L1 testing (1%) as the most cost-effective option.

CONCLUSIONS

The survival benefits of PD-1 monotherapy for high expression with PD-L1 immunohistochemistry testing are inferior to those of PD-1 combined with chemotherapy without testing, but it is found to be more cost-effective at the WTP thresholds in China and holds great potential in increasing affordability and reducing the economic burden.

Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses

Background

Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening.

Purpose

To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening.

Materials and methods

The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed.

Results

Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes.

Conclusion

Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.

Engaging stakeholders in the use of an interactive simulation tool to support decision-making about the implementation of colorectal cancer screening interventions

PURPOSE

We aimed to understand how an interactive, web-based simulation tool can be optimized to support decision-making about the implementation of evidence-based interventions (EBIs) for improving colorectal cancer (CRC) screening.

METHODS

Interviews were conducted with decision-makers, including health administrators, advocates, and researchers, with a strong foundation in CRC prevention. Following a demonstration of the microsimulation modeling tool, participants reflected on the tool's potential impact for informing the selection and implementation of strategies for improving CRC screening and outcomes. The interviews assessed participants' preferences regarding the tool's design and content, comprehension of the model results, and recommendations for improving the tool.

RESULTS

Seventeen decision-makers completed interviews. Themes regarding the tool's utility included building a case for EBI implementation, selecting EBIs to adopt, setting implementation goals, and understanding the evidence base. Reported barriers to guiding EBI implementation included the tool being too research-focused, contextual differences between the simulated and local contexts, and lack of specificity regarding the design of simulated EBIs. Recommendations to address these challenges included making the data more actionable, allowing users to enter their own model inputs, and providing a how-to guide for implementing the simulated EBIs.

CONCLUSION

Diverse decision-makers found the simulation tool to be most useful for supporting early implementation phases, especially deciding which EBI(s) to implement. To increase the tool's utility, providing detailed guidance on how to implement the selected EBIs, and the extent to which users can expect similar CRC screening gains in their contexts, should be prioritized.

Reflecting Parameter Uncertainty in Addition to Variability in Constrained Healthcare Resource Discrete Event Simulations: Worth Going the Extra Mile or a Road to Nowhere?

OBJECTIVES

Probabilistic sensitivity analysis (PSA) has been shown to reduce bias in outcomes of health economic models. However, only 1 existing study has been identified that incorporates PSA within a resource-constrained discrete event simulation (DES) model. This article aims to assess whether it is feasible and appropriate to use PSA to characterize parameter uncertainty in DES models that are primarily constructed to explore the impact of constrained resources.

METHODS

PSA is incorporated into a new case study of an Emergency Department DES. Structured expert elicitation is used to derive the variability and uncertainty input distributions associated with length of time taken to complete key activities within the Emergency Department. Potential challenges of implementation and analysis are explored.

RESULTS

The results of a trial of the model, which used the best estimates of the elicited means and variability around the time taken to complete activities, provided a reasonable fit to the data for length of time within the Emergency Department. However, there was substantial and skewed uncertainty around the activity times estimated from the elicitation exercise. This led to patients taking almost 3 weeks to leave the Emergency Department in some PSA runs, which would not occur in practice.

CONCLUSIONS

Structured expert elicitation can be used to derive plausible estimates of activity times and their variability, but experts' uncertainty can be substantial. For parameters that have an impact on interactions within a resource-constrained simulation model, PSA can lead to implausible model outputs; hence, other methods may be needed.

Development and Validation of a Simulation Model for Treatment to Maintain Remission in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

OBJECTIVE

Fixed and tailored rituximab retreatment strategies to maintain remission in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) are associated with tradeoffs. The current study was undertaken to develop a simulation model (AAV-Sim) to project clinical outcomes with these strategies.

METHODS

We developed the AAV-Sim, a microsimulation model of clinical events among individuals with AAV initiating treatment to maintain remission. Individuals transition between health states of remission or relapse and are at risk for severe infection, end-stage renal disease, or death. We estimated transition rates from published literature, stratified by individual-level characteristics. We performed validation using the mean average percent error (MAPE) and the coefficient of variation of root mean square error (CV-RMSE). In internal validation, we compared model-projected outcomes over 28 months with outcomes observed in the Rituximab versus Azathioprine in ANCA-Associated Vasculitis 2 (MAINRITSAN2) trial, which compared fixed versus tailored retreatment. In external validation, we compared outcomes with fixed rituximab retreatment from the AAV-Sim to outcomes from the MAINRITSAN1 trial and an observational study.

RESULTS

The AAV-Sim projected outcomes similar to those in the MAINRITSAN2 trial, including minor (AAV-Sim 6.0% fixed versus 7.3% tailored; MAINRITSAN2 6.2% versus 8.6%; MAPE 3% and 15%) and major relapse (AAV-Sim 3.5% versus 5.5%; MAINRITSAN2 3.7% versus 7.4%; MAPE 5% and 26%), severe infection (AAV-Sim 19.4% versus 11.1%; MAINRITSAN2 19.8% versus 10.2%; MAPE 2% and 9%), and relapse-free survival (AAV-Sim 84.8% versus 82.3%; MAINRITSAN2 86% versus 84%; CV-RMSE 2.3% and 2.5%). Similar performance was observed in external validation.

CONCLUSION

The AAV-Sim projected a range of clinical outcomes for different treatment approaches that were validated against published data. The AAV-Sim has the potential to inform management guidelines and research priorities.

How Could Different Obesity Scenarios Alter the Burden of Type 2 Diabetes and Liver Disease in Saudi Arabia?

INTRODUCTION

Obesity is a major risk factor for type 2 diabetes (T2DM) and liver disease, and obesity-attributable liver disease is a common indication for liver transplant. Obesity prevalence in Saudi Arabia (SA) has increased in recent decades. SA has committed to the WHO "halt obesity" target to shift prevalence to 2010 levels by 2025. We estimated the future benefits of reducing obesity in SA on incidence and costs of T2DM and liver disease under two policy scenarios: (1) SA meets the "halt obesity" target; (2) population body mass index (BMI) is reduced by 1% annually from 2020 to 2040.

METHODS

We developed a dynamic microsimulation of working-age people (20-59 years) in SA between 2010 and 2040. Model inputs included population demographic, disease and healthcare cost data, and relative risks of diseases associated with obesity. In our two policy scenarios, we manipulated population BMI and compared predicted disease incidence and associated healthcare costs to a baseline "no change" scenario.

RESULTS

Adults <35 years are expected to meet the "halt obesity" target, but those ≥35 years are not. Obesity is set to decline for females, but to increase amongst males 35-59 years. If SA's working-age population achieved either scenario, >1.15 million combined cases of T2DM, liver disease, and liver cancer could be avoided by 2040. Healthcare cost savings for the "halt obesity" and 1% reduction scenarios are 46.7 and 32.8 billion USD, respectively.

CONCLUSION

SA's younger working-age population is set to meet the "halt obesity" target, but those aged 35-59 are off track. Even a modest annual 1% BMI reduction could result in substantial future health and economic benefits. Our findings strongly support universal initiatives to reduce population-level obesity, with targeted initiatives for working-age people ≥35 years of age.

Estimating quality of life: A spatial microsimulation model of well-being in Aotearoa New Zealand

Quality of life is a complex concept characterised by several dualities, it has many definitions depending on the field of research and an abundance of diverse objective and subjective measures. The latter often represents the extent of perceived (dis)satisfaction with various domains of life experienced by individuals or groups, and research is increasingly focusing on subjective measures of well-being to better understand personal drivers related to quality of life. A better understanding of these factors at a local level has potential to shed light on an often-overlooked aspect of the mental health landscape in Aotearoa New Zealand. Individual-level data on adults (15+ years) is sourced from the New Zealand Attitudes and Values Study 2018 (N = 47,949) and aggregate-level data from the Census 2018 (N = 3,775,854). Matching constraint variables include sex, age, ethnicity, highest qualification, and labour force status. Outcome variables include personal and national well-being scores from 0 to 10 (extremely dissatisfied-extremely satisfied). Spatial microsimulation is used to create a synthetic population based on the above data. Results show lower mean national well-being scores than personal well-being scores, with spatial variations that broadly reflect patterns of socioeconomic deprivation. Low mean values for both personal and national well-being scores are seen in rural areas of high socioeconomic deprivation, particularly those with large Māori populations. High mean values are associated with areas of low deprivation. Additionally, high national well-being scores are associated with areas of agricultural activity, particularly in the South Island. Consideration should be given to factors that influence responses in such topics however, including demographic profiles as well as economic and social conditions of individuals and their surrounding communities. This study demonstrates that spatial microsimulation can be used as a powerful tool to understand population well-being. It can help support future planning and resource allocation, aiding in achieving health equity.

A Simple Cost-Effectiveness Model of Screening: An Open-Source Teaching and Research Tool Coded in R

Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.

Opportunities for Achieving the Cancer Moonshot Goal of a 50% Reduction in Cancer Mortality by 2047

On February 2, 2022, President Biden and First Lady Dr. Biden reignited the Cancer Moonshot, setting a new goal to reduce age-standardized cancer mortality rates by at least 50% over the next 25 years in the United States. We estimated trends in U.S. cancer mortality during 2000 to 2019 for all cancers and the six leading types (lung, colorectum, pancreas, breast, prostate, liver). Cancer death rates overall declined by 1.4% per year from 2000 to 2015, accelerating to 2.3% per year during 2016 to 2019, driven by strong declines in lung cancer mortality (-4.7%/year, 2014 to 2019). Recent declines in colorectal (-2.0%/year, 2010-2019) and breast cancer death rates (-1.2%/year, 2013-2019) also contributed. However, trends for other cancer types were less promising. To achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. We reviewed opportunities to prevent, detect, and treat these common cancers that could further reduce population-level cancer death rates and also reduce disparities.

SIGNIFICANCE

We reviewed opportunities to prevent, detect, and treat common cancers, and show that to achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. See related commentary by Bertagnolli et al., p. 1049. This article is highlighted in the In This Issue feature, p. 1027.

Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review

BACKGROUND

Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice.

METHODS

Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths.

RESULTS

Seven relevant models for health impacts forecasting were identified, consisting of (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agent-based models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users.

CONCLUSION

The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.

Modeling the Natural History and Screening Effects of Colorectal Cancer Using Both Adenoma and Serrated Neoplasia Pathways: The Development, Calibration, and Validation of a Discrete Event Simulation Model

Background. Existing colorectal cancer (CRC) screening models mostly focus on the adenoma pathway of CRC development, overlooking the serrated neoplasia pathway, which might result in overly optimistic screening predictions. In addition, Bayesian inference methods have not been widely used for model calibration. We aimed to develop a CRC screening model accounting for both pathways, calibrate it with approximate Bayesian computation (ABC) methods, and validate it with large CRC screening trials. Methods. A discrete event simulation (DES) of the CRC natural history (DECAS) was constructed using the adenoma and serrated pathways in R software. The model simulates CRC-related events in a specific birth cohort through various natural history states. Calibration took advantage of 74 prevalence data points from the German screening colonoscopy program of 5.2 million average-risk participants using an ABC method. CRC incidence outputs from DECAS were validated with the German national cancer registry data; screening effects were validated using 17-y data from the UK Flexible Sigmoidoscopy Screening sigmoidoscopy trial and a German screening colonoscopy cohort study. Results. The Bayesian calibration rendered 1,000 sets of posterior parameter samples. With the calibrated parameters, the observed age- and sex-specific CRC prevalences from the German registries were within the 95% DECAS-predicted intervals. Regarding screening effects, DECAS predicted a 41% (95% intervals 30%-51%) and 62% (95% intervals 55%-68%) reduction in 17-y cumulative CRC mortality for a single screening sigmoidoscopy and colonoscopy, respectively, falling within 95% confidence intervals reported in the 2 clinical studies used for validation. Conclusions. We presented DECAS, the first Bayesian-calibrated DES model for CRC natural history and screening, accounting for 2 CRC tumorigenesis pathways. The validated model can serve as a valid tool to evaluate the (cost-)effectiveness of CRC screening strategies.

Highlights

This article presents a new discrete event simulation model, DECAS, which models both adenoma-carcinoma and serrated neoplasia pathways for colorectal cancer (CRC) development and CRC screening effects.DECAS is calibrated based on a Bayesian inference method using the data from German screening colonoscopy program, which consists of more than 5 million first-time average-risk participants aged 55 years and older in 2003 to 2014.DECAS is flexible for evaluating various CRC screening strategies and can differentiate screening effects in different parts of the colon.DECAS is validated with large screening sigmoidoscopy and colonoscopy clinical study data and can be further used to evaluate the (cost-)effectiveness of German colorectal cancer screening strategies.

Projecting the Epidemiological and Economic Impact of Chronic Kidney Disease Using Patient-Level Microsimulation Modelling: Rationale and Methods of Inside CKD

INTRODUCTION

Chronic kidney disease (CKD) is a serious condition associated with significant morbidity and healthcare costs. Despite this, early-stage CKD is often undiagnosed, and globally there is substantial variation in the effectiveness of screening and subsequent management. Microsimulations can estimate future epidemiological costs, providing useful insights for clinicians, policymakers and researchers. Inside CKD is a programme designed to analyse the projected prevalence and burden of CKD for countries across the world, and to simulate hypothetical intervention strategies that can then be assessed for potential impact on health and economic outcomes at a national and a global level.

METHODS

Inside CKD uses a population-based approach that creates virtual individuals for a given country, with this simulated population progressing through a microsimulation in 1-year increments. A series of data inputs derived from national statistics and key literature defined the likelihood of a change in health state for each individual. Input modules allow for the input of nationally specific demographic and CKD status (including prevalence, diagnosis rates, disease stage and likelihood of renal replacement therapy), disease progression, critical comorbidities, and mortality. Health economics are reflected in cost data and a flexible intervention module allows for the testing of hypothetical policies-such as screening strategies-that may alter disease progression and outcomes.

RESULTS

Using input data from the UK as a case study and a 6-year simulation period, Inside CKD estimated a prevalence of 9.2 million individuals (both diagnosed and estimated undiagnosed) with CKD by 2027 and a 5.0% increase in costs for diagnosed CKD and renal replacement therapy. External validation and sensitivity analyses confirmed the observed trends, substantiating the robustness of the microsimulation.

CONCLUSIONS

Using a microsimulation approach, Inside CKD extends the reach of current CKD policy analyses by factoring in multiple inputs that reflect national healthcare systems and enable analysis of the effect of multiple hypothetical screening scenarios on disease progression and costs.

Behavioral stability of alcohol consumption and socio-demographic correlates of change among a nationally representative cohort of US adults

AIMS

To estimate the probability of transitioning between different categories of alcohol use (drinking states) among a nationally representative cohort of United States (US) adults and to identify the effects of socio-demographic characteristics on those transitions.

DESIGN, SETTING AND PARTICIPANTS

Secondary analysis of data from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), a prospective cohort study conducted in 2001-02 and 2004-05; a US nation-wide, population-based study. Participants included 34 165 adults (mean age = 45.1 years, standard deviation = 17.3; 52% women).

MEASUREMENTS

Alcohol use was self-reported and categorized based on the grams consumed per day: (1) non-drinker (no drinks in past 12 months), (2) category I (women = ≤ 20; men = ≤ 40), (3) category II (women = 21-40; men = 41-60) and (4) category III (women = ≥ 41; men = ≥ 61). Multi-state Markov models estimated the probability of transitioning between drinking states, conditioned on age, sex, race/ethnicity and educational attainment. Analyses were repeated with alcohol use categorized based on the frequency of heavy episodic drinking.

FINDINGS

The highest transition probabilities were observed for staying in the same state; after 1 year, the probability of remaining in the same state was 90.1% [95% confidence interval (CI) = 89.7%, 90.5%] for non-drinkers, 90.2% (95% CI = 89.9%, 90.5%) for category I, 31.8% (95% CI = 29.7, 33.9%) category II and 52.2% (95% CI = 46.0, 58.5%) for category III. Women, older adults, and non-Hispanic Other adults were less likely to transition between drinking states, including transitions to lower use. Adults with lower educational attainment were more likely to transition between drinking states; however, they were also less likely to transition out of the 'weekly HED' category. Black adults were more likely to transition into or stay in higher use categories, whereas Hispanic/Latinx adults were largely similar to White adults.

CONCLUSIONS

In this study of alcohol transition probabilities, some demographic subgroups appeared more likely to transition into or persist in higher alcohol consumption states.

The health and economic impact of the Tobacco 21 Law in El Paso County, Texas: A modeling study

In December 2019, the US federal Tobacco 21 (T21) law passed to raise the minimum legal purchase age for tobacco products from 18 to 21 years. Preliminary evidence suggests that the T21 law will restrict youth access to tobacco products, leading to decreases in tobacco use over their lifetime. This study expands the science through the use of systems modeling by linking decreases in youth tobacco use in El Paso County, Texas, due to the T21 law implementation, to potential cardiovascular health (CVH) benefits and health care cost reductions. Using a smoking behavior and cardiovascular disease agent-based model, we projected the T21 law's long-term effects on smoking prevalence and CVH outcomes in El Paso County, Texas. The estimated smoking prevalence in El Paso County, Texas, decreased by 2.7% among 18-24 year olds and by 5.2% among 25-44 year olds in 20 years with T21 law implementation (p < 0.01 for both population groups). By reducing tobacco use, the T21 law could prevent 5.4 coronary heart disease events per 1,000 adults and 6.1 S events per 1,000 adults over a lifetime. The model estimated a reduction in lifetime health care costs from $42,929 per person without T21 law to $41,985 per person with the policy. This study provides further evidence for policymakers and communities to understand the potential health and economic impacts of the federal T21 law at the local level. Results emphasize the need for comprehensive policy implementation and enforcement to produce its intended impact on health outcomes.

The future health and economic burden of obesity-attributable type 2 diabetes and liver disease among the working-age population in Saudi Arabia

Background

Obesity and type 2 diabetes (T2DM) are increasing in Saudi Arabia (SA). Among other conditions, these risk factors increase the likelihood of non-alcoholic fatty liver disease (NAFLD), which in turn increases risks for advanced liver diseases, such as non-alcoholic steatohepatitis (NASH), cirrhosis and cancer. The goal of this study was to quantify the health and economic burden of obesity-attributable T2DM and liver disease in SA.

Methods

We developed a microsimulation of the SA population to quantify the future incidence and direct health care costs of obesity-attributable T2DM and liver disease, including liver cancer. Model inputs included population demographics, body mass index, incidence, mortality and direct health care costs of T2DM and liver disease and relative risks of each condition as a function of BMI category. Model outputs included age- and sex-disaggregated incidence of obesity-attributable T2DM and liver disease and their direct health care costs for SA’s working-age population (20–59 years) between 2020 and 2040.

Results

Between 2020 and 2040, the available data predicts 1,976,593 [± 1834] new cases of T2DM, 285,346 [±874] new cases of chronic liver diseases, and 2,101 [± 150] new cases of liver cancer attributable to obesity, amongst working-age people. By 2040, the direct health care costs of these obesity-attributable diseases are predicted to be 127,956,508,540 [± 51,882,446] USD.

Conclusions

The increase in obesity-associated T2DM and liver disease emphasises the urgent need for obesity interventions and strategies to meaningfully reduce the future health and economic burden of T2DM, chronic liver diseases and liver cancer in SA.

Using Microsimulation Modeling to Inform EHE Implementation Strategies in Los Angeles County

BACKGROUND

Pre-exposure prophylaxis (PrEP) is essential to ending HIV. Yet, uptake remains uneven across racial and ethnic groups. We aimed to estimate the impacts of alternative PrEP implementation strategies in Los Angeles County.

SETTING

Men who have sex with men, residing in Los Angeles County.

METHODS

We developed a microsimulation model of HIV transmission, with inputs from key local stakeholders. With this model, we estimated the 15-year (2021-2035) health and racial and ethnic equity impacts of 3 PrEP implementation strategies involving coverage with 9000 additional PrEP units annually, above the Status-quo coverage level. Strategies included PrEP allocation equally (strategy 1), proportionally to HIV prevalence (strategy 2), and proportionally to HIV diagnosis rates (strategy 3), across racial and ethnic groups. We measured the degree of relative equalities in the distribution of the health impacts using the Gini index (G) which ranges from 0 (perfect equality, with all individuals across all groups receiving equal health benefits) to 1 (total inequality).

RESULTS

HIV prevalence was 21.3% in 2021 [Black (BMSM), 31.1%; Latino (LMSM), 18.3%, and White (WMSM), 20.7%] with relatively equal to reasonable distribution across groups (G, 0.28; 95% confidence interval [CI], 0.26 to 0.34). During 2021-2035, cumulative incident infections were highest under Status-quo (n = 24,584) and lowest under strategy 3 (n = 22,080). Status-quo infection risk declined over time among all groups but remained higher in 2035 for BMSM (incidence rate ratio, 4.76; 95% CI: 4.58 to 4.95), and LMSM (incidence rate ratio, 1.74; 95% CI: 1.69 to 1.80), with the health benefits equally to reasonably distributed across groups (G, 0.32; 95% CI: 0.28 to 0.35). Relative to Status-quo, all other strategies reduced BMSM-WMSM and BMSM-LMSM disparities, but none reduced LMSM-WMSM disparities by 2035. Compared to Status-quo, strategy 3 reduced the most both incident infections (% infections averted: overall, 10.2%; BMSM, 32.4%; LMSM, 3.8%; WMSM, 3.5%) and HIV racial inequalities (G reduction, 0.08; 95% CI: 0.02 to 0.14).

CONCLUSIONS

Microsimulation models developed with early, continuous stakeholder engagement and inputs yield powerful tools to guide policy implementation.

Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial

Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models

Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters' posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold. Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians

BACKGROUND

Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world's largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS).

OBJECTIVE

We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations.

METHODS AND ANALYSIS

We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the "100 Million Brazilian Cohort", over a 19-year period (2000-2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios.

CONCLUSION

The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.

Microsimulation Modeling in Food Policy: A Scoping Review of Methodological Aspects

Food policies for the prevention and management of diet-related noncommunicable diseases (NCDs) have been increasingly relying on microsimulation models (MSMs) to assess effectiveness. Given the increased uptake of MSMs, this review aims to provide an overview of the characteristics of MSMs that link diets with NCDs. A comprehensive review was conducted in PubMed and Web of Knowledge. Inclusion criteria were: 1) findings from an MSM; 2) diets, foods, or nutrients as the main exposure of interest; and 3) NCDs, such as overweight/obesity, type 2 diabetes, coronary heart disease, stroke, or cancer, as the disease outcome for impact assessment. This review included information from 33 studies using MSM in analyzing diet and diverse food policies on NCDs. Hereby, most models employed stochastic, discrete-time, dynamic microsimulation techniques to calculate anticipated (cost-)effectiveness of strategies based on food pricing, food reformulation, or dietary (lifestyle) interventions. Currently available models differ in the methodology used for quantifying the effect of the dietary changes on disease, and in the method for modeling the disease incidence and mortality. However, all studies provided evidence that the models were sufficiently capturing the close-to-reality situation by justifying their choice of model parameters and validating externally their modeled disease incidence and mortality with observed or predicted event data. With the increasing use of various MSMs, between-model comparisons, facilitated by open access models and good reporting practices, would be important for judging a model's accuracy, leading to continued improvement in the methodologies for developing and applying MSMs and, subsequently, a better understanding of the results by policymakers.

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

BACKGROUND

Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive.

METHODS

This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low.

RESULTS

The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity.

CONCLUSIONS

A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

A Simple Method for Simulating Dementia Onset and Death within an Existing Demographic Model

BACKGROUND

Dementia is a common disease that has an impact on both the affected individual and family members who provide caregiving. Simulation models can assist in setting policy that anticipates public health needs by predicting the demand for and availability of care.

OBJECTIVE

We developed a relatively simple method for simulating the onset of dementia that can be used in combination with an existing microsimulation model.

METHODS

We started with Socsim, a demographic microsimulation model that simulates a population with family kinship networks. We simulated dementia in the Socsim population by simulating the number of individuals diagnosed with dementia in their lifetime and the ages of onset and death from dementia for each of these dementia cases. We then matched dementia cases to the simulated population based on age at death, so for each individual, we simulate whether they develop dementia and, if so, their age at onset. This approach simulates dementia onset but does not alter the demographic model's simulated age of death.

RESULTS

We selected model dementia parameters so that the combined Socsim-Dementia model reproduces published dementia prevalence rates and survival times after diagnosis.

CONCLUSIONS

Adding simulation of dementia to a kinship network model enables prediction of the availability of family caregivers for people with dementia under a range of different assumptions about future fertility, mortality, and dementia risk. We demonstrated how to add simulation of dementia onset and death to an existing microsimulation model to obtain a method for predicting dementia prevalence in the context of another more detailed model. The approach we developed can be generalized to simulate other progressive health conditions that affect mortality.

Microsimulation projections of obesity interventions on cardiometabolic health disparities in the United States

OBJECTIVE

The aim of this study was to estimate long-term impacts of health education interventions on cardiometabolic health disparities.

METHODS

The model simulates how health education implemented in the United States throughout 2019 to 2049 would lead to changes in adult BMI and consequent hypertension and type 2 diabetes. Health outcome changes by sex, racial/ethnic (non-Hispanic White, non-Hispanic Black, and Hispanic), and weight status (normal: 18.5 ≤ BMI < 25; overweight: 25 ≤ BMI < 30; and obesity: 30 ≤ BMI) subpopulations were compared under a scenario with and one without health education.

RESULTS

By 2049, the intervention would reduce average BMI of women with obesity to 27.7 kg/m2 (CI: 27.4-27.9), which would be 2.9 kg/m2 lower than the expected average BMI without an intervention. Education campaigns would reduce type 2 diabetes prevalence, but it would remain highest among women with obesity at 27.7% (CI: 26.2%-29.2%). The intervention would reduce hypertension prevalence among White women by 4.7 percentage points to 38.0% (CI: 36.4%-39.7%). For Black women in the intervention, the 2049 hypertension prevalence would be 52.6% (CI: 50.7%-54.5%). Results for men and women were similar.

CONCLUSIONS

Long-term health education campaigns can reduce obesity-related disease. All population groups benefit, but they would not substantially narrow cardiometabolic health disparities.

Assessing the Health and Economic Impact of a Potential Menthol Cigarette Ban in New York City: a Modeling Study

Menthol in cigarettes increases nicotine dependence and decreases the chances of successful smoking cessation. In New York City (NYC), nearly half of current smokers usually smoke menthol cigarettes. Female and non-Latino Black individuals were more likely to smoke menthol-flavored cigarettes compared to males and other races and ethnicities. Although the US Food and Drug Administration recently announced that it will ban menthol cigarettes, it is unclear how the policy would affect population health and health disparities in NYC. To inform potential policymaking, we used a microsimulation model of cardiovascular disease (CVD) to project the long-term health and economic impact of a potential menthol ban in NYC. Our model projected that there could be 57,232 (95% CI: 51,967-62,497) myocardial infarction (MI) cases and 52,195 (95% CI: 47,446-56,945) stroke cases per 1 million adult smokers in NYC over a 20-year period without the menthol ban policy. With the menthol ban policy, 2,862 MI cases and 1,983 stroke cases per 1 million adults could be averted over a 20-year period. The model also projected that an average of $1,836 in healthcare costs per person, or $1.62 billion among all adult smokers, could be saved over a 20-year period due to the implementation of a menthol ban policy. Results from subgroup analyses showed that women, particularly Black women, would have more reductions in adverse CVD outcomes from the potential implementation of the menthol ban policy compared to males and other racial and ethnic subgroups, which implies that the policy could reduce sex and racial and ethnic CVD disparities. Findings from our study provide policymakers with evidence to support policies that limit access to menthol cigarettes and potentially address racial and ethnic disparities in smoking-related disease burden.

Projecting the Influence of Sugar-Sweetened Beverage Warning Labels and Restaurant Menu Labeling Regulations on Energy Intake, Weight Status, and Health Care Expenditures in US Adults: A Microsimulation

BACKGROUND

Accurate, readily accessible, and easy-to-understand nutrition labeling is a promising policy strategy to address poor diet quality and prevent obesity.

OBJECTIVE

This study projected the influence of nationwide implementation of sugar-sweetened beverage (SSB) warning labels and restaurant menu labeling regulations.

DESIGN

A stochastic microsimulation model was built to estimate the influences of SSB warning labels and menu labeling regulations on daily energy intake, body weight, body mass index, and health care expenditures among US adults.

PARTICIPANTS/SETTING

The model used individual-level data from the National Health and Nutrition Examination Survey, Medical Expenditure Panel Survey, and other validated sources.

STATISTICAL ANALYSES PERFORMED

The model was simulated using the bootstrapped samples, and the means and associated 95% CIs of the policy effects were estimated.

RESULTS

SSB warning labels and restaurant menu labeling regulations were estimated to reduce daily energy intake by 19.13 kcal (95% CI 18.83 to 19.43 kcal) and 33.09 kcal (95% CI 32.39 to 33.80 kcal), body weight by 0.92 kg (95% CI 0.90 to 0.93 kg) and 1.57 kg (95% CI 1.54 to 1.60 kg), body mass index by 0.32 (95% CI 0.31 to 0.33) and 0.55 (95% CI =0.54 to 0.56), and per-capita health care expenditures by $26.97 (95% CI $26.56 to $27.38) and $45.47 (95% CI $44.54 to $46.40) over 10 years, respectively. The reduced per-capita health care expenditures translated into an annual total medical cost saving of $0.69 billion for SSB warning labels and $1.16 billion for menu labeling regulations. No discernable policy effect on all-cause mortality was identified. The policy effects could be heterogeneous across population subgroups, with larger effects in men, non-Hispanic Black adults, and younger adults.

CONCLUSIONS

SSB warning labels and menu labeling regulations could be effective policy leverage to prevent weight gains and reduce medical expenses attributable to adiposity.

Comparison of smoking reduction with improvement of social conditions in early life: simulation in a British cohort

BACKGROUND

Health care evaluation models can be useful to assign different levels of priority to interventions or policies targeting different age groups or different determinants of health. We aimed to assess early mortality in counterfactual scenarios implying reduced adverse childhood experience (ACE) and/or improved educational attainment (childhood and early life characteristics), compared with a counterfactual scenario implying reduced smoking in adulthood.

METHODS

We used data from the 1958 National Child Development Study British birth cohort, which initially included 18 558 subjects. Applying a potential outcome approach, scenarios were simulated to estimate the expected mortality between ages 16 and 55 under a counterfactual decrease by half of the observed level of exposure to (i) ACE, (ii) low educational attainment (at age 22), (iii) ACE and low educational attainment (a combined exposure) and (iv) smoking at age 33. Estimations were obtained using g-computation, separately for men and women. Analyses were further stratified according to the parental level of education, to assess social inequalities.

RESULTS

The study population included 12 164 members. The estimated decrease in mortality in the counterfactual scenarios with reduced ACE and improved educational attainment was close to the decreased mortality in the counterfactual scenario with reduced smoking, showing a relative difference in mortality of respectively -7.2% [95% CI (confidence interval) = (-12.2% to 1.2%)] versus -7.0% (-13.1% to +1.2%) for women, and -9.9% (-15.6% to -6.2%) versus -12.3% (-17.0% to -5.9%) for men.

CONCLUSIONS

Our results highlight the potential value of targeting early social characteristics such as ACE and education, compared with well-recognized interventions on smoking.

Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis

Calibration of a microsimulation model (MSM) is a challenging but crucial step for the development of a valid model. Numerous calibration methods for MSMs have been suggested in the literature, most of which are usually adjusted to the specific needs of the model and based on subjective criteria for the selection of optimal parameter values. This article compares 2 general approaches for calibrating MSMs used in medical decision making, a Bayesian and an empirical approach. We use as a tool the MIcrosimulation Lung Cancer (MILC) model, a streamlined, continuous-time, dynamic MSM that describes the natural history of lung cancer and predicts individual trajectories accounting for age, sex, and smoking habits. We apply both methods to calibrate MILC to observed lung cancer incidence rates from the Surveillance, Epidemiology and End Results (SEER) database. We compare the results from the 2 methods in terms of the resulting parameter distributions, model predictions, and efficiency. Although the empirical method proves more practical, producing similar results with smaller computational effort, the Bayesian method resulted in a calibrated model that produced more accurate outputs for rare events and is based on a well-defined theoretical framework for the evaluation and interpretation of the calibration outcomes. A combination of the 2 approaches is an alternative worth considering for calibrating complex predictive models, such as microsimulation models.

Differentiated Thyroid Cancer: A Health Economic Review

Simple Summary

This review reflects on health economic considerations associated with the increasing diagnosis and treatment of differentiated thyroid cancer. Analysis of different relevant health economic topics, such as overdiagnosis, overtreatment, surgical costs, and costs of follow-up are being addressed. Several unanswered research questions such as optimising molecular markers for diagnosis, active surveillance of primary tumours, and improved risk stratification and survivorship care all influence future healthcare expenditures.

Abstract

The incidence of differentiated thyroid cancer (DTC) is rising, mainly because of an increased detection of asymptomatic thyroid nodularity revealed by the liberal use of thyroid ultrasound. This review aims to reflect on the health economic considerations associated with the increasing diagnosis and treatment of DTC. Overdiagnosis and the resulting overtreatment have led to more surgical procedures, increasing health care and patients’ costs, and a large pool of community-dwelling thyroid cancer follow-up patients. Additionally, the cost of thyroid surgery seems to increase year on year even when inflation is taken into account. The increased healthcare costs and spending have placed significant pressure to identify potential factors associated with these increased costs. Some truly ground-breaking work in health economics has been undertaken, but more cost-effectiveness studies and micro-cost analyses are required to evaluate expenses and guide future solutions.

Prediction of Microscopic Metastases in Patients with Metachronous Oligo-Metastases after Curative Treatment of Non-Small Cell Lung Cancer: A Microsimulation Study

Simple Summary

Many patients with metachronous oligo-metastases in non-small cell lung cancer have their recurrences surgically removed, although the 5-year recurrence-free survival of this group is 16%. This does not provide any benefit for patients with additional undetected metastases. Therefore, we aim to find patient characteristics that are predictive for having additional undetected microscopic metastases. Based on a theoretical approach, we identified the size and number of detected oligo-metastases, as well as the presence of symptoms that are the most important risk predictors.

Abstract

Metachronous oligo-metastatic disease is variably defined as one to five metastases detected after a disease-free interval and treatment of the primary tumour with curative intent. Oligo-metastases in non-small cell lung cancer (NSCLC) are often treated with curative intent. However additional metastases are often detected later in time, and the 5-year survival is low. Burdensome surgical treatment in patients with undetected metastases may be avoided if patients with a high versus low risk of undetected metastases can be separated. Because there is no clinical data on undetected metastases available, a microsimulation model of the development and detection of metastases in 100,000 hypothetical stage I NSCLC patients with a controlled primary tumour was constructed. The model uses data from the literature as well as patient-level data. Calibration was used for the unobservable model parameters. Metastases can be detected by a scheduled scan, or an unplanned scan when the patient develops symptoms. The observable information at time of detection is used to identify subgroups of patients with a different risk of undetectable metastases. We identified the size and number of detected oligo-metastases, as well as the presence of symptoms that are the most important risk predictors. Based on these predictors, patients could be divided into a low-risk and a high-risk group, having a model-based predicted probability of 8.1% and 89.3% to have undetected metastases, respectively. Currently, the model is based on a synthesis of the literature data and individual patient-level data that were not collected for the purpose of this study. Optimization and validation of the model is necessary to allow clinical usability. We describe the type of data that needs to be collected to update our model, as well as the design of such a validation study.

Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

A proposed new generation of evidence-based microsimulation models to inform global control of cervical cancer

Health decision models are the only available tools designed to consider the lifetime natural history of human papillomavirus (HPV) infection and pathogenesis of cervical cancer, and the estimated long-term impact of preventive interventions. Yet health decision modeling results are often considered a lesser form of scientific evidence due to the inherent needs to rely on imperfect data and make numerous assumptions and extrapolations regarding complex processes. We propose a new health decision modeling framework that de-emphasizes cytologic-colposcopic-histologic diagnoses due to their subjectivity and lack of reproducibility, relying instead on HPV type and duration of infection as the major determinants of subsequent transition probabilities. We posit that the new model health states (normal, carcinogenic HPV infection, precancer, cancer) and corollary transitions are universal, but that the probabilities of transitioning between states may vary by population. Evidence for this variability in host response to HPV infections can be inferred from HPV prevalence patterns in different regions across the lifespan, and might be linked to different average population levels of immunologic control of HPV infections. By prioritizing direct estimation of model transition probabilities from longitudinal data (and limiting reliance on model-fitting techniques that may propagate error when applied to multiple transitions), we aim to reduce the number of assumptions for greater transparency and reliability. We propose this new microsimulation model for critique and discussion, hoping to contribute to models that maximally inform efficient strategies towards global cervical cancer elimination.

Projecting health-ageing trajectories in Europe using a dynamic microsimulation model

The extent of the challenges and opportunities that population ageing presents depends heavily on the population's health. Hence, for the development of appropriate strategies that enable countries to adopt the emerging demographic and epidemiological realities, information on future health trajectories of elderly population is a natural requirement. This study presents an innovative methodological framework for projecting the health of individuals using a dynamic microsimulation model that considers interactions between sociodemographic characteristics, health, mortality, bio-medical and behavioral risk factors. The model developed, called ATHLOS-Mic, is used to project the health of cohorts born before 1960 for the period 2015-2060 for selected European Countries using SHARE data to illustrate the possible effects of some selected risk factors and education on future health trajectories. Results show that, driven by a better educational attainment, each generation will be healthier than the previous one at same age. Also, we see that, on average, an individual of our base population will live about 18 more years since the start of the projection period, but only 5 years in good health. Finally, we find that a scenario that removes the effect of having a low level of education on individual health has the largest impact on the projected average health, the average number of years lived per person, and the average number of years lived in good health.

Simulating the impact of health behavior interventions in the SNAP-Ed population

In 2020, the US invested $441 million dollars in the Supplement Nutrition Assistance Program Education (SNAP-Ed), a program that encourages a healthy diet and promotes physical activity. Understanding the long-term health outcomes associated with promoting physical activity versus weight loss among the low-income population it serves could help guide the direction of future program efforts. We used the Future Americans Model (FAM), a microsimulation, to model over 10 years the impacts of changes in Body Mass Index (BMI) and exercise interventions on future health outcomes among adults aged 25 and older that could potentially accrue from SNAP-Ed interventions. We applied data from the Panel Study of Income Dynamics and data collected from 2,323 SNAP-Ed eligible adults in Los Angeles County in 2019. By 2029 interventions that increased vigorous physical activity by 20% would reduce the prevalence of difficulties with activities of daily living (ADL) by 4.72%. Interventions that would reduce BMI by 0.5 could decrease the prevalence of diabetes and heart disease by 5.34% and 0.66%, respectively. Helping people maintain weight loss, even as little as 3-4 lb, results in significant future health benefits. Given continued increases in weight at the population level, weight maintenance should be a focus of future interventions.

Provision of contraceptive implants in school-based health centers: A cost-effectiveness analysis

OBJECTIVE

To evaluate the cost-effectiveness of providing contraceptive implants in school-based health centers (SBHCs) compared to the practice of referring adolescents to non-SBHCs in New York City.

STUDY DESIGN

We developed a microsimulation model of teen pregnancy to estimate the cost-effectiveness of immediate provision of contraceptive implants at SBHCs over a 3-year time horizon. Model parameters were derived from both a retrospective chart review of patient data and published literature. The model projected the number of pregnancies as well as the total costs for each intervention scenario. The incremental cost-effectiveness ratio was calculated using the public payer perspective, using direct costs only.

RESULTS

The health care cost of immediate provision of contraceptive implants at SBHCs was projected to be $13,719 per person compared to $13,567 per person for delayed provision at the referral appointment over 3 years. However, immediate provision would prevent 78 more pregnancies per 1000 adolescents over 3 years. The incremental cost-effectiveness ratio for implementing in-school provision was $1940 per additional pregnancy prevented, which was less than the $4206.41 willingness-to-pay threshold. Sensitivity analyses showed that the cost-effectiveness conclusion was robust over a wide range of key model inputs.

CONCLUSION

Provision of contraceptive implants in SBHCs compared to non-SBHCs is cost-effective for preventing unintended teen pregnancy. Health care providers and policymakers should consider expanding this model of patient-centered health care delivery to other locations.