State-level disparities in cervical cancer prevention and impact on outcomes in the U.S.: A modeling study.

Background: Despite the availability of HPV vaccines for over a decade, coverage across the United States (US) is varied. While some states have made concerted efforts to increase HPV vaccination coverage, most model-based analyses have estimated vaccine impact on the US. We estimated the impact of hypothetical changes in HPV vaccination coverage at the state level for three states with varying levels of HPV vaccination coverage and cervical cancer incidence (California, New York, Texas) using a mathematical model. Methods: We developed a new mathematical model of HPV transmission and cervical cancer tailored to state-level cancer incidence and mortality. We quantified the public health impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 and the effect on time to elimination in the three states. Results: Increasing vaccination coverage to 80% in Texas in 10 years could reduce cervical cancer incidence by 50.9% (95%-CrI: 46.6-56.1%) by 2100. In New York and California, achieving the same coverage could reduce incidence by 27.3% (95%-CrI: 23.9-31.5%) and 24.4% (95%-CrI: 20.0-30.0%), respectively. Achieving 80% coverage in 5 years will slightly increase the reduction. If 2019 vaccination coverage continues, cervical cancer elimination would be reached in the US by 2051 (95%-Crl: 2034-2064). However, the timeline by which individual states reach elimination could vary by decades. Conclusion: Achieving an HPV vaccination coverage target of 80% by 2030 will benefit states with low vaccination coverage and high cervical cancer incidence the most. Our results highlight the value of more geographically focused analyses to inform priorities.

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.

PURPOSE: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. METHODS: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. RESULTS: The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. CONCLUSIONS: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach. HIGHLIGHTS: We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.

Characteristics of a cost-effective blood test for colorectal cancer screening.

BACKGROUND: Blood-based biomarker tests can potentially change the landscape of colorectal cancer (CRC) screening. We characterize the conditions under which blood test screening would be as effective and cost-effective as annual fecal immunochemical testing (FIT) or decennial colonoscopy. METHODS: We used the three CISNET-Colon models to compare scenarios of no screening, annual FIT, decennial colonoscopy, and a blood test meeting CMS coverage criteria (74% CRC sensitivity and 90% specificity). We varied the sensitivity to detect CRC (74%-92%), advanced adenomas (AAs, 10%-50%), screening interval (1-3 years), and test cost ($25-$500). Primary outcomes included quality-adjusted life-years gained (QALYG) from screening and costs for an US average-risk 45-year-old cohort. RESULTS: Annual FIT yielded 125-163 QALYG per 1,000 at a cost of $3,811-5,384 per person, whereas colonoscopy yielded 132-177 QALYG at a cost of $5,375-7,031 per person. A blood test with 92% CRC sensitivity and 50% AA sensitivity yielded 117-162 QALYG if used every three years and 133-173 QALYG if used every year but would not be cost-effective if priced above $125 per test. If used every three years, a $500 blood test only meeting CMS coverage criteria yielded 83-116 QALYG, at a cost of $8,559-9,413 per person. CONCLUSION: Blood tests that only meet CMS coverage requirements should not be recommended to patients who would otherwise undergo screening by colonoscopy or FIT due to lower benefit. Blood tests need higher AA sensitivity (above 40%) and lower costs (below $125) to be cost-effective.

A computationally efficient nonparametric sampling (NPS) method of time to event for individual-level models.

Purpose: Individual-level simulation models often require sampling times to events, however efficient parametric distributions for many processes may often not exist. For example, time to death from life tables cannot be accurately sampled from existing parametric distributions. We propose an efficient nonparametric method to sample times to events that does not require any parametric assumption on the hazards. Methods: We developed a nonparametric sampling (NPS) approach that simultaneously draws multiple time-to-event samples from a categorical distribution. This approach can be applied to univariate and multivariate processes. The probabilities for each time interval are derived from the time interval-specific constant hazards. The times to events can then be used directly in individual-level simulation models. We compared the accuracy of our approach in sampling time-to-events from common parametric distributions, including exponential, Gamma, and Gompertz. In addition, we evaluated the method's performance in sampling age to death from US life tables and sampling times to events from parametric baseline hazards with time-dependent covariates. Results: The NPS method estimated similar expected times to events from 1 million draws for the three parametric distributions, 100,000 draws for the homogenous cohort, 200,000 draws from the heterogeneous cohort, and 1 million draws for the parametric distributions with time-varying covariates, all in less than a second. Conclusion: Our method produces accurate and computationally efficient samples for time-to-events from hazards without requiring parametric assumptions. This approach can substantially reduce the computation time required to simulate individual-level models.

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.

PURPOSE: To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. METHODS: Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. RESULTS: The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. CONCLUSIONS: Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. HIGHLIGHTS: We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

Purpose: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. Methods: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. Results: The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Conclusions: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.

Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics.

BACKGROUND: Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. DESIGN: We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. RESULTS: Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. CONCLUSIONS: ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. HIGHLIGHTS: Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).

Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.

BACKGROUND: We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism. METHODS: Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population. DISCUSSION: The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.

Breastfeeding is associated with the intelligence of school-age children in Mexico.

Breastfeeding has been consistently associated with higher intelligence since childhood. However, this relation could be confounded due to maternal selection bias. We estimated the association between predominant breastfeeding and intelligence in school-age children considering potential selection bias and we simulated the intelligence gap reduction between low versus higher socioeconomic status children by increasing breastfeeding. We analysed predominant breastfeeding practices (breastmilk and water-based liquids) of children 0-3 years included in the Mexican Family Life Survey (MxFLS-1). Intelligence was estimated as the z-score of the abbreviated Raven score, measured at 6-12 years in the MxFLS-2 or MxFLS-3. We predicted breastfeeding duration among children with censored data with a Poisson model. We used the Heckman selection model to assess the association between breastfeeding and intelligence, correcting for selection bias and stratified by socioeconomic status. Results show after controlling for selection bias, a 1-month increase in predominant breastfeeding duration was associated with a 0.02 SD increase in the Raven z-score (p < 0.05). The children who were predominantly breastfed for 4-6 months versus <1 month had 0.16 SD higher Raven z-score (p < 0.05). No associations were found using multiple linear regression models. Among low socioeconomic status children, increasing predominantly breastfeeding duration to 6 months would increase their mean Raven z-score from -0.14 to -0.07 SD and reduce by 12.5% the intelligence gap with high socioeconomic status children. In conclusion, predominant breastfeeding duration was significantly associated with childhood intelligence after controlling for maternal selection bias. Increased breastfeeding duration may reduce poverty-driven intelligence inequities.

Cost effectiveness of non-drug interventions that reduce nursing home admissions for people living with dementia.

INTRODUCTION: Six million Americans live with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD), a major health-care cost driver. We evaluated the cost effectiveness of non-pharmacologic interventions that reduce nursing home admissions for people living with AD/ADRD. METHODS: We used a person-level microsimulation to model the hazard ratios (HR) on nursing home admission for four evidence-based interventions compared to usual care: Maximizing Independence at Home (MIND), NYU Caregiver (NYU); Alzheimer's and Dementia Care (ADC); and Adult Day Service Plus (ADS Plus). We evaluated societal costs, quality-adjusted life years and incremental cost-effectiveness ratios. RESULTS: All four interventions cost less and are more effective (i.e., cost savings) than usual care from a societal perspective. Results did not materially change in 1-way, 2-way, structural, and probabilistic sensitivity analyses. CONCLUSION: Dementia-care interventions that reduce nursing home admissions save societal costs compared to usual care. Policies should incentivize providers and health systems to implement non-pharmacologic interventions.

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.

A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.

Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.

BACKGROUND: Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern. METHODS: We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics. RESULTS: We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite. CONCLUSIONS: We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly. HIGHLIGHTS: We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.

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.

Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison.

PURPOSE: Analyzing and communicating uncertainty is essential in medical decision making. To judge whether risks are acceptable, policy makers require information on the expected outcomes but also on the uncertainty and potential losses related to the chosen strategy. We aimed to compare methods used to represent the impact of uncertainty in decision problems involving many strategies, enhance existing methods, and provide an open-source and easy-to-use tool. METHODS: We conducted a systematic literature search to identify methods used to represent the impact of uncertainty in cost-effectiveness analyses comparing multiple strategies. We applied the identified methods to probabilistic sensitivity analysis outputs of 3 published decision-analytic models comparing multiple strategies. Subsequently, we compared the following characteristics: type of information conveyed, use of a fixed or flexible willingness-to-pay threshold, output interpretability, and the graphical discriminatory ability. We further proposed adjustments and integration of methods to overcome identified limitations of existing methods. RESULTS: The literature search resulted in the selection of 9 methods. The 3 methods with the most favorable characteristics to compare many strategies were 1) the cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF), 2) the expected loss curve (ELC), and 3) the incremental benefit curve (IBC). The information required to assess confidence in a decision often includes the average loss and the probability of cost-effectiveness associated with each strategy. Therefore, we proposed the integration of information presented in an ELC and CEAC into a single heat map. CONCLUSIONS: This article presents an overview of methods presenting uncertainty in multiple-strategy cost-effectiveness analyses, with their strengths and shortcomings. We proposed a heat map as an alternative method that integrates all relevant information required for health policy and medical decision making. HIGHLIGHTS: To assess confidence in a chosen course of action, decision makers require information on both the probability and the consequences of making a wrong decision.This article contains an overview of methods for presenting uncertainty in multiple-strategy cost-effectiveness analyses.We propose a heat map that combines the probability of cost-effectiveness from the cost-effectiveness acceptability curve (CEAC) with the consequences of a wrong decision from the expected loss curve.Collapsing of the CEAC can be reduced by relaxing the CEAC, as proposed in this article.Code in Microsoft Excel and R is provided to easily analyze data using the methods discussed in this article.

CDX2 Biomarker Testing and Adjuvant Therapy for Stage II Colon Cancer: An Exploratory Cost-Effectiveness Analysis.

OBJECTIVES: Adjuvant chemotherapy is not recommended for patients with average-risk stage II (T3N0) colon cancer. Nevertheless, a subgroup of these patients who are CDX2-negative might benefit from adjuvant chemotherapy. We evaluated the cost-effectiveness of testing for the absence of CDX2 expression followed by adjuvant chemotherapy (fluorouracil combined with oxaliplatin [FOLFOX]) for patients with stage II colon cancer. METHODS: We developed a decision model to simulate a hypothetical cohort of 65-year-old patients with average-risk stage II colon cancer with 7.2% of these patients being CDX2-negative under 2 different interventions: (1) test for the absence of CDX2 expression followed by adjuvant chemotherapy for CDX2-negative patients and (2) no CDX2 testing and no adjuvant chemotherapy for any patient. We derived disease progression parameters, adjuvant chemotherapy effectiveness and utilities from published analyses, and cancer care costs from the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Sensitivity analyses were conducted. RESULTS: Testing for CDX2 followed by FOLFOX for CDX2-negative patients had an incremental cost-effectiveness ratio of $5500/quality-adjusted life-years (QALYs) compared with no CDX2 testing and no FOLFOX (6.874 vs 6.838 discounted QALYs and $89 991 vs $89 797 discounted US dollar lifetime costs). In sensitivity analyses, considering a cost-effectiveness threshold of $100 000/QALY, testing for CDX2 followed by FOLFOX on CDX2-negative patients remains cost-effective for hazard ratios of <0.975 of the effectiveness of FOLFOX in CDX2-negative patients in reducing the rate of developing a metastatic recurrence. CONCLUSIONS: Testing tumors of patients with stage II colon cancer for CDX2 and administration of adjuvant treatment to the subgroup found CDX2-negative is a cost-effective and high-value management strategy across a broad range of plausible assumptions.

Effectiveness of Coronavirus Disease 2019 (COVID-19) Vaccines Among Incarcerated People in California State Prisons: Retrospective Cohort Study.

BACKGROUND: Prisons and jails are high-risk settings for coronavirus disease 2019 (COVID-19). Vaccines may substantially reduce these risks, but evidence is needed on COVID-19 vaccine effectiveness for incarcerated people, who are confined in large, risky congregate settings. METHODS: We conducted a retrospective cohort study to estimate effectiveness of messenger RNA (mRNA) vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among incarcerated people in California prisons from 22 December 2020 through 1 March 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates, adjusted for resident characteristics and infection rates across prisons. RESULTS: Among 60 707 cohort members, 49% received at least 1 BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64%-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88%-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable: 74% (95% CI, 62%-82%) and 92% (95% CI, 74%-98%) from 14 days after first and second doses, respectively. CONCLUSIONS: Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination, and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population.

Retention in Care, Mortality, Loss-to-Follow-Up, and Viral Suppression among Antiretroviral Treatment-Naïve and Experienced Persons Participating in a Nationally Representative HIV Pre-Treatment Drug Resistance Survey in Mexico.

We describe associations of pretreatment drug resistance (PDR) with clinical outcomes such as remaining in care, loss to follow-up (LTFU), viral suppression, and death in Mexico, in real-life clinical settings. We analyzed clinical outcomes after a two-year follow up period in participants of a large 2017-2018 nationally representative PDR survey cross-referenced with information of the national ministry of health HIV database. Participants were stratified according to prior ART exposure and presence of efavirenz/nevirapine PDR. Using a Fine-Gray model, we evaluated virological suppression among resistant patients, in a context of competing risk with lost to follow-up and death. A total of 1823 participants were followed-up by a median of 1.88 years (Interquartile Range (IQR): 1.59-2.02): 20 (1%) were classified as experienced + resistant; 165 (9%) naïve + resistant; 211 (11%) experienced + non-resistant; and 1427 (78%) as naïve + non-resistant. Being ART-experienced was associated with a lower probability of remaining in care (adjusted Hazard Ratio(aHR) = 0.68, 0.53-0.86, for the non-resistant group and aHR = 0.37, 0.17-0.84, for the resistant group, compared to the naïve + non-resistant group). Heterosexual cisgender women compared to men who have sex with men [MSM], had a lower viral suppression (aHR = 0.84, 0.70-1.01, p = 0.06) ART-experienced persons with NNRTI-PDR showed the worst clinical outcomes. This group was enriched with women and persons with lower education and unemployed, which suggests higher levels of social vulnerability.

Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study.

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

Effectiveness of COVID-19 Vaccines among Incarcerated People in California State Prisons: A Retrospective Cohort Study.

BACKGROUND: Prisons and jails are high-risk settings for COVID-19 transmission, morbidity, and mortality. COVID-19 vaccines may substantially reduce these risks, but evidence is needed of their effectiveness for incarcerated people, who are confined in large, risky congregate settings. METHODS: We conducted a retrospective cohort study to estimate effectiveness of mRNA vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed SARS-CoV-2 infections among incarcerated people in California prisons from December 22, 2020 through March 1, 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination status, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates that adjusted for resident characteristics and infection rates across prisons. FINDINGS: Among 60,707 residents in the cohort, 49% received at least one BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable (74% [95% CI, 62-82%] and 92% [95% CI, 74-98%] from 14 days after first and second doses, respectively), as well as among the subset of residents who received the mRNA-1273 vaccine (71% [95% CI, 58-80%] and 96% [95% CI, 67-99%]). CONCLUSIONS: Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population. FUNDING: Horowitz Family Foundation, National Institute on Drug Abuse, Centers for Disease Control and Prevention, National Science Foundation, Open Society Foundation, Advanced Micro Devices.