Medical Decision Making

PurposeWe introduce PRE-CISE, a pre-calibration workflow that integrates coverage analysis, local sensitivity, and collinearity diagnostics to streamline model calibration and transparently address nonidentifiability. We demonstrate the benefits of PRE-CISE using a four-state Sick-Sicker Markov testbed and a COVID-19 case study. MethodsPRE-CISE begins with a coverage analysis to verify that model outputs generated with parameter sets drawn from their prior distribution span calibration targets, followed by local sensitivities to quantify the influence of parameters on model outputs, guiding the resizing of the prior distribution bounds to improve coverage. Identifiability is then assessed via collinearity analysis; large indices indicate practical nonidentifiability. For the testbed model, we calibrated 3 parameters to survival, prevalence, and the proportion of Sick to Sicker at 10, 20, and 30 years. For the COVID-19 model, we calibrated 11 parameters to match daily confirmed incident cases. Bayesian calibration was conducted on both analyses. ResultsCoverage analyses flagged initial misfits; local sensitivities identified the Sick-to-Sicker transition probability has a greater effect on model outputs, and resizing its prior distribution bounds improved coverage. Collinearity analyses showed that combining multiple calibration targets across time points enabled recovery of all three parameters. In the COVID-19 model, local sensitivity analyses prioritized time-varying detection rates and contact-reduction effects, reducing the search space, thereby improving calibration efficiency. Daily incident case calibration targets yielded collinearity indices below practical thresholds (e.g., < 15) for all parameter combinations, whereas weekly calibration targets were larger and closer to the cutoff. ConclusionsPRE-CISE provides a practical, transparent pathway that helps modelers refine prior distribution bounds and calibration targets before intensive calibration, improving uncertainty reporting and strengthening the reliability of model-based health policy analyses.

IntroductionDiscrete-time Markov models are widely used within health economic modelling. Analyses usually associate costs and health outcomes with health states and calculate totals for each decision option over some timeframe. Frequently, a correction method (e.g. half-cycle correction) is applied to unadjusted model outputs to yield an approximation to an assumed underlying continuous-time Markov model. In this study, we introduce a novel approximation method based on Gaussian Quadrature (GQ). MethodsWe exploited analytical results for time-homogeneous Markov chains to derive a new GQ-based approximation, which is applied to an unadjusted discrete-time model output. The GQ method approximates a continuous-time Markov model result by approximating a correction matrix, formulated as an integral, using a weighted sum of integrand values at specified points. GQ approximations can be made arbitrarily accurate by increasing order of the approximation. We compared the first five orders of GQ approximation with four existing cycle correction methods (half-cycle correction, trapezoidal and Simpsons 1/3 and 3/8 rules) across 100,000 randomly generated input parameter-sets. ResultsWe show that first-order GQ method is identical to half-cycle correction method, which is itself equivalent to trapezoidal method. The second-order GQ is identical to Simpsons 1/3 method. The third, fourth and fifth order GQ methods are novel in this context and provide increasingly accurate approximations to the output of the continuoustime model. In our simulation study, fifth-order GQ method outperformed other existing methods in over 99.8% of simulations. Of the existing methods, Simpsons 1/3 rule performed the best. ConclusionOur novel GQ-based approximation outperforms other cycle correction methods for time-homogeneous models. The method is easy to implement, and R code and an Excel workbook are provided as supplementary materials.

PurposeHealth policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we developed a novel decision-analytic modeling framework to integrate social processes. MethodsWe evaluated a simplified decision problem using two models: a standard decision-analytic model and a model incorporating our social factors framework. The standard model simulated individuals transitioning through three disease natural history states-healthy, sick, and dead-without accounting for differential health system utilization. Our social factors framework incorporated heterogeneous health insurance coverage, which influenced disease progression and health system utilization. We assessed the impact of a new treatment on a hypothetical cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-year-old adults. Primary outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization throughout a persons lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios. ResultsIn the standard model, the new treatment increased life expectancy by 2.7 years for both non-Hispanic Black and non-Hispanic white adults, without affecting racial/ethnic gaps in life expectancy. However, incorporating known racial/ethnic disparities in health insurance coverage with the social factors framework led to smaller life expectancy gains for non-Hispanic Black adults (2.0 years) compared to non-Hispanic white adults (2.2 years), increasing racial/ethnic disparities in life expectancy. LimitationsThe availability of social factors data and complexity of causal pathways between factors may pose challenges in applying our social factors framework. ConclusionsExcluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating the social factors framework enhances simulation models effectiveness in evaluating interventions with health equity implications. HighlightsO_LIHealth policy simulation models that ignore social processes may be biased and lead to suboptimal policy recommendations. To address this, we proposed a novel social factors framework to integrate social factors into decision-analytic models for health policy. C_LIO_LIApplying our social factors framework to a simplified example highlighted the potential bias that results from ignoring social factors. In a standard model, a hypothetical new treatment appeared to have no effect on health disparities. However, incorporating our social factors framework demonstrated that this treatment would exacerbate disparities. C_LIO_LIIncorporating a social factors framework into health policy simulation models has particular relevance for evaluating health interventions with equity implications. C_LI

BackgroundWomen with HIV face elevated cervical cancer risks, compounded by social conditions that influence both disease outcomes. Current models fail to adequately capture the complex interactions between diseases and social determinants. MethodsWe enhanced a mixed agent and compartment model for HIV and cervical cancer (MAC-HIV-CC) to model disparities by social conditions. We analyzed the impact of hypothetical 100% efficacious interventions over 30 years (2018-2048): (1) an HIV care intervention that eliminates disparities in viral load suppression between social groups, (2) a sexual behavior intervention aligning behaviors of women who exchange sex with those who do not, and (3) a combination of both interventions. ResultsThe HIV care intervention reduced HIV incidence by 26.9% and cervical cancer cases by 14.5% among HIV-positive women. The sexual behavior intervention decreased HIV prevalence by 8.1% and HPV prevalence by 36.1% among HIV-positive women engaged in exchange sex. The combination intervention reduced HIV prevalence by 25.3%, HIV incidence by 34.3%, and cervical cancer cases by 37.5% in the target population. ConclusionsThe proposed framework provides a novel approach for health equity analyses by modeling social determinants that are common pathways to interrelated diseases and health disparities. Such a model is of significance for cost-effectiveness intervention analyses of interrelated diseases.

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 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 assess confidence in recommendations 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. To fill this gap, we introduce the DES Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR), an open-source codebase integrating a flexible DES model for the natural history of cancer, Bayesian calibration for parameter estimation, and an example application of screening strategy evaluation. To illustrate the framework, we apply DESCIPHR to calibrate bladder and colorectal cancer models to real-world cancer registry targets. We also introduce an automated method for generating data-informed parameter prior distributions and increase the functionality 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. Key points for decision makersO_LIFor simulation models to be useful for decision-making, they should accurately reproduce real-world outcomes and their uncertainty. C_LIO_LIThe DESCIPHR framework and code repository address a gap in open-source resources to fit an individual-level model for cancer progression to real-world data and forecast the impact of cancer screening interventions while accounting for data uncertainty. C_LIO_LIThe codebase is designed to be highly adaptable for researchers who wish to apply DESCIPHR for economic evaluation or for studying methodological questions. C_LI

Colorectal Cancer (CRC) is a leading cause of cancer deaths in the United States. Despite significant overall declines in CRC incidence and mortality, there has been an alarming increase in CRC among people younger than 50. This study uses an established microsimulation model, CRC-SPIN, to perform a stress test of colonoscopy screening strategies. First, we expand CRC-SPIN to include birth-cohort effects. Second, we estimate natural history model parameters via Incremental Mixture Approximate Bayesian Computation (IMABC) for two model versions to characterize uncertainty while accounting for increased early CRC onset. Third, we simulate 26 colonoscopy screening strategies across the posterior distribution of estimated model parameters, assuming four different colonoscopy sensitivities (104 total scenarios). We find that model projections of screening benefit are highly dependent on natural history and test sensitivity assumptions, but in this stress test, the policy recommendations are robust to the uncertainties considered.

BackgroundDecision makers may use social distancing to reduce transmission between risk groups in a pandemic scenario like Covid-19. However, it may result in both financial, mental, and social costs. Given these tradeoffs, it is unclear when and who needs to social distance over the course of a pandemic when policies are allowed to change dynamically over time and vary across different risk groups (e.g., older versus younger individuals face different Covid-19 risks). In this study, we examine the optimal time to implement social distancing to optimize social utility, using Covid-19 as an example. MethodologyWe propose using a Markov decision process (MDP) model that incorporates transmission dynamics of an age-stratified SEIR compartmental model to identify the optimal social distancing policy for each risk group over time. We parameterize the model using population-based tracking data on Covid-19 within the US. We compare results of two cases: allowing the social distancing policy to vary only over time, or over both time and population (by risk group). To examine the robustness of our results, we perform sensitivity analysis on patient costs, transmission rates, clearance rates, mortality rates. ResultsOur model framework can be used to effectively evaluate dynamic policies while disease transmission and progression occurs. When the policy cannot vary by subpopulation, the optimal policy is to implement social distancing for a limited duration at the beginning of the epidemic; when the policy can vary by subpopulation, our results suggest that some subgroups (older adults) may never need to socially distance. This result may occur because older adults occupy a relatively small proportion of the total population and have less contact with others even without social distancing. ConclusionOur results show that the additional flexibility of allowing social distancing policies to vary over time and across the population can generate substantial utility gain even when only two patient risk groups are considered. MDP frameworks may help generate helpful insights for policymakers. Our results suggest that social distancing for high-contact but low-risk individuals (e.g., such as younger adults) may be more beneficial in some settings than doing so for low-contact but high-risk individuals (e.g., older adults).

Recent U.S. legislative proposals include sweeping Medicaid retractions and the expiration of enhanced ACA Premium Tax Credits, threatening health insurance coverage for millions of Americans. Using a validated simulation model, we estimate that 7.7 million individuals becoming uninsured due to the proposed Medicaid changes would lead to a median of 11,308 excess deaths annually. When combined with the 5 million projected to lose coverage due to ACA policy expirations, over 20,000 additional deaths may occur each year. In addition to mortality, coverage loss is projected to result in substantial increases in uncontrolled chronic conditions, including 138,851 additional cases of uncontrolled diabetes, 165,165 cases of uncontrolled hypertension, and 46,200 cases of uncontrolled high cholesterol annually. These projections underscore the wide-reaching public health consequences of limiting access to healthcare.

Cohort-level models in Microsoft Excel(R) remain the standard for cost-effectiveness modelling to inform health technology assessment (HTA), despite calls and rationale for more flexible approaches. Their limited ability to capture patient-level characteristics can, in the presence of patient heterogeneity or the need to track patient characteristics to accurately capture a technologys implications, introduce bias. Their continued prevalence is explained by key stakeholders familiarity with spreadsheet software, and the lower computational burden of cohort-level versus patient-level models. However, contemporary Excel functions have opened up possibilities for efficient calculations within native Excel that enable more flexible, patient-level approaches to be implemented in familiar spreadsheet-based software. Therefore, this tutorial aims to provide step-by-step guidance on how to implement a previously published and freely available individual-level discrete event simulation (DES) in Excel, using contemporary Excel functions and without any Visual Basic for Applications (VBA) code. Key Points for Decision-MakersO_LIPerceived and real requirements for cost-effectiveness models for HTA to be built in Excel may have led to overuse of cohort-level approaches, with probable bias implications for HTA decision-making. C_LIO_LIContemporary Excel functions now allow the efficient implementation and execution of patient-level model calculations within native Excel, without any VBA code. Such capabilities may reduce technical barriers across key stakeholders, enhance transparency, and ultimately lead to improvements in HTA decision-making. C_LIO_LIThis tutorial demonstrates provides step-by-step guidance on how to implement an efficient patient-level cost-effectiveness model in Excel without any VBA, with an executable model example included as supplementary material. C_LI

BackgroundThis study addresses the critical implementation science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We develop and validate models demonstrating how SDoH predicts mammogram screening behavior within a rural population. Our work provides healthcare systems with an evidence-based framework for translating SDoH data into effective interventions. MethodsWe model the relationship between SDoH and breast cancer screening adherence using data from over 63,000 patients with established primary care relationships within the Dartmouth Health System. Our analytical framework integrates multiple machine learning techniques including light gradient boosting machine, random forest, elastic-net logistic regression, Bayesian regression, and decision tree classifier with SDoH questionnaire responses, demographic information, geographic indicators, insurance status, and clinical measures to quantify and characterize the influence of SDoH on mammogram scheduling and attendance. ResultsOur models achieve moderate discriminative performance in predicting screening behaviors, with an average area under the receiver operating characteristic curve (ROC AUC) of 71% for scheduling and 70% for attendance in validation datasets. Key social factors influencing screening behaviors include geographic accessibility measured by the rural-urban commuting area, neighborhood socioeconomic status captured by the area deprivation index, and healthcare access factors related to clinical sites. Additional influential variables include months since the last mammogram, current age, and the Charlson comorbidity score, which intersect with social factors influencing healthcare utilization. By systematically modeling these SDoH and related factors, we identify opportunities for healthcare organizations to transform SDoH data into targeted, facility-level intervention strategies while adapting to payer incentives and addressing screening disparities. ConclusionsOur model provides healthcare systems with a data-driven approach to understanding and addressing how SDoH shape mammogram screening behaviors, particularly among rural populations. While initially focused on breast cancer screening, this systematic framework lays the groundwork for analyzing SDoHs influence on other preventive health behaviors, demonstrating the potential for broader applications in improving routine preventive care utilization.

The outbreak of COVID-19 has spurred extensive research worldwide to develop a vaccine. However, when a vaccine becomes available, limited production and distribution capabilities will likely lead to another challenge: who to prioritize for vaccination to mitigate the near-end impact of the pandemic? To tackle that question, this paper first expands a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across subpopulations. It then integrates this predictive model into a prescriptive model to optimize vaccine allocation, formulated as a bilinear, non-convex optimization model. To solve it, this paper proposes a coordinate descent algorithm that iterates between optimizing vaccine allocations and simulating the dynamics of the pandemic. We implement the model and algorithm using real-world data in the United States. All else equal, the optimized vaccine allocation prioritizes states with a large number of projected cases and sub-populations facing higher risks (e.g., older ones). Ultimately, the optimized vaccine allocation can reduce the death toll of the pandemic by an estimated 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. Highlights- This paper formulates an optimization model for vaccine allocation in response to the COVID-19 pandemic. This model, referred to as DELPHI-V-OPT, integrates a predictive epidemiological model into a prescriptive model to support the allocation of vaccines across geographic regions (e.g., US states) and across risk classes (e.g., age groups). - This paper develops a scalable coordinate descent algorithm to solve the DELPHI-V-OPT model. The proposed algorithm converges effectively and in short computational times. Therefore, the proposed approach can be implemented efficiently, and allows extensive sensitivity analyses for scenario planning and policy analysis. - Computational results demonstrate that optimized vaccine allocation strategies can curb the death toll of the COVID-19 pandemic by an estimated at 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. These results highlight the critical role of vaccine allocation to combat the COVID-19 pandemic, in addition to vaccine design and vaccine production.

The sustainable delivery of Home Health Care (HHC) is increasingly important as ageing populations and rising demands place pressure on health and social care systems. HHC involves complex decision-making across strategic, tactical, and operational levels, often constrained by limited resources such as workforce availability. While Operational Research (OR) has been widely applied to support operational decisions in HHC, recent reviews highlight a lack of focus on strategic and tactical planning, and limited recognition of the hierarchical structure linking decisions across levels. This undermines the potential of OR to inform real-world planning effectively. To address these gaps, we propose a modelling approach enabling consistent analysis of decisions across planning levels. We defined a modular approach for analysing hierarchies of decisions and accounting for cascade effects. Then, we applied those principles by developing a configurable tool in R, comprising a synthetic data generator and a suite of optimisation and heuristic routines. We illustrate the benefits of this approach through a case study. Our results demonstrate the value of this structured modelling approach for informing decisions in HHC. The emphasis we gave on modularity facilitated the development of an analysis tool that can be easily adapted to different hierarchies of decisions and settings.

ABSTRACTPopulation-scale agent-based simulations of the opioid epidemic help evaluate intervention strategies and overdose outcomes in heterogeneous communities and provide estimates of localized treatment effects, which support the design of locally-tailored policies for precision public health. However, it is prohibitively costly to run simulations of all treatment conditions in all communities because the number of possible treatments grows exponentially with the number of interventions and levels at which they are applied. To address this need efficiently, we develop a metamodel framework, whereby treatment outcomes are modeled using a response function whose coefficients are learned through Gaussian process regression (GPR) on locally-contextualized covariates. We apply this framework to efficiently estimate treatment effects on overdose deaths in Pennsylvania counties. In contrast to classical designs such as fractional factorial design or Latin hypercube sampling, our approach leverages spatial correlations and posterior uncertainty to sequentially sample the most informative counties and treatment conditions. Using a calibrated agent-based opioid epidemic model, informed by county-level overdose mortality and baseline dispensing rate data for different treatments, we obtained county-level estimates of treatment effects on overdose deaths per 100,000 population for all treatment conditions in Pennsylvania, achieving approximately 5% average relative error using one-tenth the number of simulation runs required for exhaustive evaluation. Our bi-level framework provides a computationally efficient approach to decision support for policy makers, enabling rapid evaluation of alternative resource-allocation strategies to mitigate the opioid epidemic in local communities. The same analytical framework can be applied to guide precision public health interventions in other epidemic settings.

AO_SCPLOWBSTRACTC_SCPLOWThe proliferation of algorithm-assisted decision making has prompted calls for careful assessment of algorithm fairness. One popular fairness metric, equal opportunity, demands parity in true positive rates (TPRs) across different population subgroups. However, we highlight a critical but overlooked weakness in this measure: at a given decision threshold, TPRs vary when the underlying risk distribution varies across subgroups, even if the model equally captures the underlying risks. Failure to account for variations in risk distributions may lead to misleading conclusions on performance disparity. To address this issue, we introduce a novel metric called adjusted TPR (aTPR), which modifies subgroup-specific TPRs to reflect performance relative to the risk distribution in a common reference subgroup. Evaluating fairness using aTPRs promotes equal treatment for equal risk by reflecting whether individuals with similar underlying risks have similar opportunities of being identified as high risk by the model, regardless of subgroup membership. We demonstrate our method through numerical experiments that explore a range of differential calibration relationships and in a real-world data set that predicts 6-month mortality risk in an in-patient sample in order to increase timely referrals for palliative care consultations.

Discrete-time Markov cohort-state transition models are now well-established as the preferred choice of analysts across application areas including health technology assessment. This preference arises out of its relative intuition and its capability to strike a fine balance between complex disease pathways, statistical precision, and parsimony although being criticized by a wide variety of stakeholders. Transition probability matrices (TPMs) are the "heart and soul" of such models responsible for estimating patient dispositions. However, estimating such TPMs comes with its own set of challenges. In some situations, the transition data may be censored such that the health state of a patient is unknown for multiple time steps before the next observation or data immaturity especially in rare diseases. Craig and Sendi proposed the expectation-maximization (EM) algorithm using uniform weights as a solution for unequal estimation intervals for partially observed data. However, this typically comes at the cost of increased within-state output variations with no optimization technique available in the literature. The objective of this paper is to explore an optimized weighted version of the original EM algorithm, that aims to estimate the set of weights which minimizes the uncertainty of the estimated TPM against a target objective function. The weighting reduces the uncertainty of the estimate by considering the difference in temporal sparsity of the data when there are missing time steps. Further, we demonstrate the applicability of this weighting method using a fictitious cost-effectiveness model with our approach, showing a fine but definitive change over the original approach.

Testing people without symptoms for SARS-CoV-2 followed by isolation of those who test positive could mitigate the covid-19 epidemic pending arrival of an effective vaccine. Key questions for such programs are who should be tested, how often, and when should such testing stop. Answers to these questions depend on test and population characteristics. A cost-effectiveness model that provides answers depending on user-adjustable parameter values is described. Key parameters are the value ascribed to preventing a death and the reproduction number (roughly, rate of spread) at the time surveillance testing is initiated. For current rates of spread, cost-effectiveness usually requires a value per life saved greater than $100,000 and depends critically on the extent and frequency of testing.

ObjectiveCapital budgeting in intensive care units (ICUs) demands rapid, high-stakes investment decisions. We developed an open-source ICU Capital Budgeting Workbench that merges a deterministic finance engine with GPT-based natural-language processing (NLP) to streamline planning. Materials and MethodsThe web application couples classic valuation metrics (e.g., net present value, internal rate of return, and payback) with an NLP module that converts multi-year free-text scenarios into structured projections. Users describe projects in everyday language; GPT parses these narratives into year-by-year cash flows and discount rates, after which the engine computes financial metrics and produces interactive sensitivity, scenario, and Monte Carlo analyses. ResultsIn illustrative cases the workbench translated narrative ICU expansion proposals into five and ten-year cash-flow tables and NPV calculations within seconds, eliminating manual spreadsheet construction. Interactive dashboards let users test key assumptions, instantly revealing how occupancy, reimbursement, or inflation shifts influence returns. Compared with traditional ad-hoc spreadsheets, the tool demonstrated marked time savings and consistent analytic structure. DiscussionThis proof-of-concept shows how large language models can reduce transcription errors, standardize methodology, and embed uncertainty modeling in routine capital planning. Limitations include dependence on GPTs parsing accuracy and the need for real-world validation with authentic hospital data. ConclusionThe ICU Capital Budgeting Workbench exemplifies practical AI integration for finance and operations leaders, offering transparent, reproducible, and scalable decision support for ICU equipment and facility investments. By replacing bespoke spreadsheets with a governed, open-source platform, it may improve efficiency and support better-informed, data-driven investment strategy.

Closed-form minimum sample size criteria for developing logistic prediction models, such as the Riley framework implemented in pmsampsize, are widely used but may become optimistic when anticipated discrimination is high. We conducted a Monte Carlo simulation study to compare the formula-based recommended development sample size, nRiley, with an empirical required sample size, nreq, defined by out-of-sample calibration-slope stability under repeated development sampling. Scenarios fixed the candidate parameter dimension at p = 10 and crossed predictor distribution (normal, standardized skewed continuous, binary), signal density (dense versus sparse), prevalence ({phi} [isin] {0.05, 0.10, 0.20}), and target discrimination (AUCtarget [isin] {0.70, 0.75, 0.80, 0.85, 0.90}), with intercept and signal strength calibrated to match targets. We defined nreq as the smallest n such that [E] (bn) [&ge;] 0.90 and Pr(bn < 0.80) [&le;] 0.20, where bn is the truth-based logit-scale calibration slope evaluated on a large fixed validation covariate set. At moderate discrimination, nRiley approximated nreq, but as discrimination increased the formula increasingly underestimated the sample size required for calibration stability, with large deficits at AUCtarget = 0.90. Separation-like behavior (extreme fitted risks and linear predictors) at n = nRiley became common in high-discrimination settings despite nominal convergence, providing a plausible mechanism for formula optimism. These findings support augmenting formula-based planning with targeted simulation stress tests and instability diagnostics when high discrimination is anticipated.

BackgroundPhysical activity levels worldwide have declined over recent decades, with the average number of daily steps decreasing steadily since 1995. Given that physical inactivity is a major modifiable risk factor for chronic disease and mortality, increasing the level of physical activity is a clear opportunity to improve population health on a broad scale. The current study aims to assess the cost-effectiveness and budget impact of a Fitbit-based intervention among healthy, but insufficiently active, adults to quantify the potential clinical and economic value for a commercially insured population in the U.S. MethodsAn economic model was developed to compare physical activity levels, health outcomes, costs, and quality-adjusted life-years (QALYs) associated with usual care and a Fitbit-based inter-vention that consists of a consumer wearable device alongside goal setting and feedback features provided in a companion software application. Improvement in physical activity was measured in terms of mean daily step count. The effects of increased daily step count were characterized as reduced short-term healthcare costs and decreased incidence of chronic diseases with corresponding improvement in health utility and reduced disease costs. Published literature, standardized costing resources, and data from a National Institutes of Health-funded research program were utilized. Cost-effectiveness and budget impact analyses were performed for a hypothetical cohort of middle-aged adults. ResultsThe base case cost-effectiveness results found the Fitbit intervention to be dominant (less costly and more effective) compared to usual care. Discounted 15-year incremental costs and QALYs were -$1,257 and 0.011, respectively. In probabilistic analyses, the Fitbit intervention was dominant in 93% of simulations and either dominant or cost-effective (defined as less than $150,000/QALY gained) in 99.4% of simulations. For budget impact analyses conducted from the perspective of a U.S. Commercial payer, the Fitbit intervention was estimated to save approximately $6.5 million dollars over 2 years and $8.5 million dollars over 5 years for a cohort of 8,000 participants. Although the economic analysis results were very robust, the short-term healthcare cost savings were the most uncertain in this population and warrant further research. ConclusionsThere is abundant evidence documenting the benefits of wearable activity trackers when used to increase physical activity as measured by daily step counts. Our research provides additional health economic evidence supporting implementation of wearable-based interventions to improve population health, and offers compelling support for payers to consider including wearable-based physical activity interventions as part of a comprehensive portfolio of preventive health offerings for their insured populations.

COVID-19 created a global public health and economic emergency. Policymakers acted quickly and decisively to contain the spread of disease through physical distancing measures. However, these measures also impact physical, mental and economic well-being, creating difficult trade-offs. Here we use a simple mathematical model to explore the balance between public health measures and their associated social and economic costs. Across a range of cost-functions and model structures, commitment to intermittent and strict social distancing measures leads to better overall outcomes than temporally consistent implementation of moderate physical distancing measures. With regard to the trade-offs that policymakers may soon face, our results emphasize that economic and health outcomes do not exist in full competition. Compared to consistent moderation, intermittently strict policies can better mitigate the impact of the pandemic on both of these priorities for a range of plausible utility functions.