Medical Decision Making

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.

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.

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

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.

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.

ImportancePrevention-focused health policy requires analytic frameworks capable of detecting changes in population health and associated costs within policy-relevant time horizons, particularly in managed care systems where premiums reflect actuarial risk rather than realized medical expenditures. ObjectiveTo evaluate a healthstate-based analytic framework (CareMaps) for measuring population health dynamics, disease progression, and associated costs using longitudinal Medicaid managed care claims data. Design, Setting, and ParticipantsRetrospective longitudinal analysis of deidentified Medicaid managed care claims in New Mexico from 2011 through 2014. The study included individuals aged 0 to 64 years enrolled in managed care plans. ExposuresChronic disease burden categorized into mutually exclusive, ordered healthstates based on the number of chronic conditions. Main Outcomes and MeasuresCounty- and managed care organization (MCO) level prevalence of healthstates, transition rates between healthstates, and healthstate-specific cost estimates derived from capitation premiums and medical loss ratio defined medical expenditures. ResultsThe CareMaps framework identified specific geographic and MCO level variation in chronic disease prevalence, healthstate transition rates, and per-member spending patterns that were not fully explained by actuarial risk adjustment. Transitions from nonchronic to chronic healthstates varied markedly across counties, indicating heterogeneity in disease progression and prevention related outcomes. Conclusions and RelevanceA healthstate based analytic framework applied to longitudinal Medicaid managed care data enables standardized measurement of population health dynamics and associated costs within policy relevant time horizons. Such approaches may support evaluation of preventive care performance, inform risk adjustment, and enhance public-sector oversight of managed care programs.

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.

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

BackgroundEvaluating long-term health workforce planning requires robust and globally valid indicators. While many existing metrics, such as patient satisfaction or healthcare utilization, are inherently uncertain over long horizons, mortality is an objective and stable metric. To analyze how physician supply impacts population health, we propose a model that links physician density to age-specific mortality rates and incorporate it to the age-sturctured dynamics. MethodsWe developed a unified model that links physician workforce density to age-structured mortality rates. Subsequently, we applied this model within a Lotka-McKendrick framework to simulate physician supply expansion scenarios in South Korea. The system simulates future age-specific mortality outcomes under varying physician supply scenarios, including a baseline intake of 3,058 physicians per year and an expanded intake of up to 7,058 physicians per year, projected through 2065. FindingsOur model was validated using WHO mortality data including Japan, the United States, and the United Kingdom. Its validity of our model holds across all age groups in each country, as confirmed by statistical analysis with false discovery rate correction (maximum adjusted p < 0.05). Using the age-structured dynamics with the model in South Korea, we confirm that future physician density increases even under the baseline scenario (3,058 physicians per year), and that projected population sizes under the baseline and the aggressive expansion scenario (7,058 physicians per year) are not statistically different (p > 0.07). Moreover, under the aggressive expansion scenario, the projected reductions in age-specific mortality rates by 2065 remain marginal: less than 0.27% for those under 65, less than 0.69% for ages 65-75, less than 2.75% for ages 75-84, less than 7.31% for ages 85-94, and less than 12.6% for ages 95-99. InterpretationIn well-resourced health systems facing aging populations and persistently low fertility rates, further expansion of physician supply alone offers limited mortality benefits. Our findings suggest a paradigm shift: from quantity-driven to efficiency-focused workforce strategies. The proposed method is readily adaptable to other contries, offering a policy-relevant and outcome-oriented tool for long-term health workforce planning. FundingNone. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Web of Science, and Google Scholar for studies published between 2000 and 2024 using terms such as "physician workforce," "healthcare workforce planning," "medical workforce density," and "age-structured dynamics." Most physician workforce planning models are either utilization-based (projected service demand) or needs-based (estimated population health requirements). These models often extrapolate current health service utilization rates or population health needs into the future, typically assuming that such values remain constant over time, despite the acknowledged difficulty of forecasting long-term demand dynamics. Additionally, methodologies differ widely across countries, which hampers direct comparisons of outcomes. Some metrics, such as patient needs or preferences, are also subjective and difficult to measure consistently. In contrast, more objective indicators like mortality rates are more readily available and comparable across settings. As far as we are aware, no published model has dynamically integrated physician density--endogenously derived from workforce supply--into an age-structured framework for projecting population mortality. Added value of this studyTo the best of our knowledge, this study is the first to integrate a model linking physician workforce density with age-structured mortality rates into the Lotka-McKendrick framework, capturing the interaction between physicians and populations across age groups. By modeling how changes in doctor-to-population ratios affect mortality, our approach moves beyond traditional models that treat health outcomes as exogenous. This allows novel analyses of policy scenarios, such as estimating life expectancy gains from increasing physician supply. Our model provides a unified framework to project both physician workforce dynamics and population health outcomes simultaneously. Implications of all available evidenceOur findings underscore the importance of explicitly linking age-structured population dynamics with physician density and mortality outcomes. Traditional models often overlook these interdependencies, risking inaccurate forecasts and limiting the relevance of cross-national comparisons. By capturing how physician supply influences mortality across age groups, our approach enables more precise, outcome-driven workforce planning.

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.

Algorithmic decision systems mediate access to healthcare, credit, employment and housing, yet individuals who experience adverse decisions face multi-stage barriers when seeking recourse. We formalize these barriers as a series-structured system with 11 empirically parameterized stages across three layers (data integration, data accuracy and institutional access) and prove that single-barrier interventions are bounded by baseline system success. Under baseline parameterization derived from federal datasets and peer-reviewed algorithmic audit studies, end-to-end recourse probability is 0.0018%. Removing any single barrier yields negligible improvement (<0.02%). Factorial decomposition reveals that the three-way cross-layer interaction accounts for 87.6% of achievable improvement, confirmed by Shapley attribution, Sobol sensitivity analysis and bootstrap resampling (n = 1,000). These results provide a structural explanation for the limited impact of incremental reforms and support coordinated multi-layer intervention approaches for clinical AI governance and algorithmic fairness.

Importance: The 2026 public charge rule could discourage immigrants from accessing health coverage programs, creating a chilling effect that potentially leads to negative health outcomes; However, its long-term health impact is poorly understood. Objective: To model potential impacts of the 2026 public charge rule on primary care and premature mortality among immigrants in New York City (NYC). Design, Setting, and Participants: The simulation used a deterministic compartmental model with Ordinary Differential Equations (ODEs) using 2023 NYC Vital statistics data and American Community Survey, and estimates obtained from 2 previous studies about effects of healthcare access on primary care and Medicaid expansion on premature mortality. Main Outcomes and Measures: Rates of primary care outcomes (access, doctor's visits) in 5 years, and premature mortality in 5 and 20 years, projected by the model under conservative, moderate, and aggressive scenarios of avoidance/disenrollment due to the public charge rule, known as the 'chilling effect'. Effects of the avoidance/disenrollment on primary care outcomes and premature mortality were obtained from 2 previous studies. Projected rates of the outcomes under each scenario were compared with counterfactuals to estimate the health impacts of the chilling effect. Results: Implementation of the public charge rule was projected to decrease the primary care access rate by 4.1% (conservative) to 9.9% (aggressive) over 5 years, relative to the counterfactual scenario without the rule. The rate of doctors' visits was projected to decrease over 5 years by 5.1% (conservative) to 12.2% (aggressive). Premature mortality was projected to increase by 4.4% (conservative) to 10.6% (aggressive) in 5 years and 7.4% (conservative) to 17.4% (aggressive) in 20 years. Legal noncitizens and Black immigrant New Yorkers were predicted to experience higher burdens of premature mortality attributed to the chilling effect, compared with other immigrant groups and racial/ethnic groups, respectively. Conclusions and Relevance: This study demonstrates adverse health consequences of federal public charge rule changes among immigrants in NYC. The model projected a decrease in primary care visits and increase in premature mortality across various scenarios. These findings suggest urgent reconsideration of a regulatory change that disproportionately increases risk of premature mortality among immigrants in NYC.

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.

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.

Since December 2019, the world has been ravaged by the COVID-19 pandemic, with over 150 million confirmed cases and 3 million confirmed deaths worldwide. To combat the spread of COVID-19, governments have issued unprecedented non-pharmaceutical interventions (NPIs), ranging from mass gathering restrictions to complete lockdowns. Despite their proven effectiveness in reducing virus transmission, the policies often carry significant economic and humanitarian cost, ranging from unemployment to depression, PTSD, and anxiety. In this paper, we create a data-driven system dynamics framework, THEMIS, that allows us to compare the costs and benefits of a large class of NPIs in any geographical region across different cost dimensions. As a demonstration, we analyzed thousands of alternative policies across 5 countries (United States, Germany, Brazil, Singapore, Spain) and compared with the actual implemented policy. Our results show that moderate NPIs (such as restrictions on mass gatherings) usually produce the worst results, incurring significant cost while unable to sufficiently slow down the pandemic to prevent the virus from becoming endemic. Short but severe restrictions (complete lockdown for 4-5 weeks) generally produced the best results for developed countries, but only if the speed of reopening is slow enough to prevent a resurgence. Developing countries exhibited very different trade-off profiles from developed countries, and suggests that severe NPIs such as lockdowns might not be as suitable for developing countries in general.