Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

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

PURPOSE: We aimed to understand how an interactive, web-based simulation tool can be optimized to support decision-making about the implementation of evidence-based interventions (EBIs) for improving colorectal cancer (CRC) screening. METHODS: Interviews were conducted with decision-makers, including health administrators, advocates, and researchers, with a strong foundation in CRC prevention. Following a demonstration of the microsimulation modeling tool, participants reflected on the tool's potential impact for informing the selection and implementation of strategies for improving CRC screening and outcomes. The interviews assessed participants' preferences regarding the tool's design and content, comprehension of the model results, and recommendations for improving the tool. RESULTS: Seventeen decision-makers completed interviews. Themes regarding the tool's utility included building a case for EBI implementation, selecting EBIs to adopt, setting implementation goals, and understanding the evidence base. Reported barriers to guiding EBI implementation included the tool being too research-focused, contextual differences between the simulated and local contexts, and lack of specificity regarding the design of simulated EBIs. Recommendations to address these challenges included making the data more actionable, allowing users to enter their own model inputs, and providing a how-to guide for implementing the simulated EBIs. CONCLUSION: Diverse decision-makers found the simulation tool to be most useful for supporting early implementation phases, especially deciding which EBI(s) to implement. To increase the tool's utility, providing detailed guidance on how to implement the selected EBIs, and the extent to which users can expect similar CRC screening gains in their contexts, should be prioritized.

Assessing the impact of multicomponent interventions on colorectal cancer screening through simulation: What would it take to reach national screening targets in North Carolina?

Healthy People 2020 and the National Colorectal Cancer Roundtable established colorectal cancer (CRC) screening targets of 70.5% and 80%, respectively. While evidence-based interventions (EBIs) have increased CRC screening, the ability to achieve these targets at the population level remains uncertain. We simulated the impact of multicomponent interventions in North Carolina over 5 years to assess the potential for meeting national screening targets. Each intervention scenario is described as a core EBI with additional components indicated by the "+" symbol: patient navigation for screening colonoscopy (PN-for-Col+), mailed fecal immunochemical testing (MailedFIT+), MailedFIT+ targeted to Medicaid enrollees (MailedFIT + forMd), and provider assessment and feedback (PAF+). Each intervention was simulated with and without Medicaid expansion and at different levels of exposure (i.e., reach) for targeted populations. Outcomes included the percent up-to-date overall and by sociodemographic subgroups and number of CRC cases and deaths averted. Each multicomponent intervention was associated with increased CRC screening and averted both CRC cases and deaths; three had the potential to reach screening targets. PN-for-Col + achieved the 70.5% target with 97% reach after 1 year, and the 80% target with 78% reach after 5 years. MailedFIT+ achieved the 70.5% target with 74% reach after 1 year and 5 years. In the Medicaid population, assuming Medicaid expansion, MailedFIT + forMd reached the 70.5% target after 5 years with 97% reach. This study clarifies the potential for states to reach national CRC screening targets using multicomponent EBIs, but decision-makers also should consider tradeoffs in cost, reach, and ability to reduce disparities when selecting interventions.

Estimating the impact of insurance expansion on colorectal cancer and related costs in North Carolina: A population-level simulation analysis.

Although screening is effective in reducing incidence, mortality, and costs of treating colorectal cancer (CRC), it remains underutilized, in part due to limited insurance access. We used microsimulation to estimate the health and financial effects of insurance expansion and reduction scenarios in North Carolina (NC). We simulated the full lifetime of a simulated population of 3,298,265 residents age-eligible for CRC screening (ages 50-75) during a 5-year period starting January 1, 2018, including polyp incidence and progression and CRC screening, diagnosis, treatment, and mortality. Insurance scenarios included: status quo, which in NC includes access to the Health Insurance Exchange (HIE) under the Affordable Care Act (ACA); no ACA; NC Medicaid expansion, and Medicare-for-all. The insurance expansion scenarios would increase percent up-to-date with screening by 0.3 and 7.1 percentage points for Medicaid expansion and Medicare-for-all, respectively, while insurance reduction would reduce percent up-to-date by 1.1 percentage points, compared to the status quo (51.7% up-to-date), at the end of the 5-year period. Throughout these individuals' lifetimes, this change in CRC screening/testing results in an estimated 498 CRC cases averted with Medicaid expansion and 6031 averted with Medicare-for-all, and an additional 1782 cases if health insurance gains associated with ACA are lost. Estimated cost savings - balancing increased CRC screening/testing costs against decreased cancer treatment costs - are approximately $30 M and $970 M for Medicaid expansion and Medicare-for-all scenarios, respectively, compared to status quo. Insurance expansion is likely to improve CRC screening both overall and in underserved populations while saving money, with the largest savings realized by Medicare.

Mailed FIT (fecal immunochemical test), navigation or patient reminders? Using microsimulation to inform selection of interventions to increase colorectal cancer screening in Medicaid enrollees.

Colorectal cancer (CRC) can be effectively prevented or detected with guideline concordant screening, yet Medicaid enrollees experience disparities. We used microsimulation to project CRC screening patterns, CRC cases averted, and life-years gained in the population of 68,077 Oregon Medicaid enrollees 50-64 over a five year period starting in January 2019. The simulation estimated the cost-effectiveness of five intervention scenarios - academic detailing plus provider audit and feedback (Detailing+), patient reminders (Reminders), mailing a Fecal Immunochemical Test (FIT) directly to the patient's home (Mailed FIT), patient navigation (Navigation), and mailed FIT with Navigation (Mailed FIT + Navigation) - compared to usual care. Each intervention scenario raised CRC screening rates compared to usual care, with improvements as high as 11.6 percentage points (Mailed FIT + Navigation) and as low as 2.5 percentage points (Reminders) after one year. Compared to usual care, Mailed FIT + Navigation would raise CRC screening rates 20.2 percentage points after five years - averting nearly 77 cancer cases (a reduction of 113 per 100,000) and exceeding national screening targets. Over a five year period, Reminders, Mailed FIT and Mailed FIT + Navigation were expected to be cost effective if stakeholders were willing to pay $230 or less per additional year up-to-date (at a cost of $22, $59, and $227 respectively), whereas Detailing+ and Navigation were more costly for the same benefits. To approach national CRC screening targets, health system stakeholders are encouraged to implement Mailed FIT with or without Navigation and Reminders.

Colorectal cancer screening in newly insured Medicaid members: a review of concurrent federal and state policies.

BACKGROUND: Colorectal cancer (CRC) screening is underutilized by Medicaid enrollees and the uninsured. Multiple national and state policies were enacted from 2010 to 2014 to increase access to Medicaid and to promote CRC screening among Medicaid enrollees. We aimed to determine the impact of these policies on screening initiation among newly enrolled Oregon Medicaid beneficiaries age-eligible for CRC screening. METHODS: We identified national and state policies affecting Medicaid coverage and preventive services in Oregon during 2010-2014. We used Oregon Medicaid claims data from 2010 to 2015 to conduct a cohort analysis of enrollees who turned 50 and became age-eligible for CRC screening (a prevention milestone, and an age at which guideline-concordant screening can be assessed within a single year) during each year from 2010 to 2014. We calculated risk ratios to assess whether first year of Medicaid enrollment and/or year turned 50 was associated with CRC screening initiation. RESULTS: We identified 14,576 Oregon Medicaid enrollees who turned 50 during 2010-2014; 2429 (17%) completed CRC screening within 12 months after turning 50. Individuals newly enrolled in Medicaid in 2013 or 2014 were 1.58 and 1.31 times more likely, respectively, to initiate CRC screening than those enrolled by 2010. A primary care visit in the calendar year, having one or more chronic conditions, and being Hispanic was also associated with CRC screening initiation. DISCUSSION: The increased uptake of CRC screening in 2013 and 2014 is associated with the timing of policies such as Medicaid expansion, enhanced federal matching for preventive services offered to Medicaid enrollees without cost sharing, and formation of Medicaid accountable care organizations, which included CRC screening as an incentivized quality metric.

Cost-Effectiveness Analysis of Four Simulated Colorectal Cancer Screening Interventions, North Carolina.

INTRODUCTION: Colorectal cancer (CRC) screening rates are suboptimal, particularly among the uninsured and the under-insured and among rural and African American populations. Little guidance is available for state-level decision makers to use to prioritize investment in evidence-based interventions to improve their population's health. The objective of this study was to demonstrate use of a simulation model that incorporates synthetic census data and claims-based statistical models to project screening behavior in North Carolina. METHODS: We used individual-based modeling to simulate and compare intervention costs and results under 4 evidence-based and stakeholder-informed intervention scenarios for a 10-year intervention window, from January 1, 2014, through December 31, 2023. We compared the proportion of people living in North Carolina who were aged 50 to 75 years at some point during the window (that is, age-eligible for screening) who were up to date with CRC screening recommendations across intervention scenarios, both overall and among groups with documented disparities in receipt of screening. RESULTS: We estimated that the costs of the 4 intervention scenarios considered would range from $1.6 million to $3.75 million. Our model showed that mailed reminders for Medicaid enrollees, mass media campaigns targeting African Americans, and colonoscopy vouchers for the uninsured reduced disparities in receipt of screening by 2023, but produced only small increases in overall screening rates (0.2-0.5 percentage-point increases in the percentage of age-eligible adults who were up to date with CRC screening recommendations). Increased screenings ranged from 41,709 additional life-years up to date with screening for the voucher intervention to 145,821 for the mass media intervention. Reminders mailed to Medicaid enrollees and the mass media campaign for African Americans were the most cost-effective interventions, with costs per additional life-year up to date with screening of $25 or less. The intervention expanding the number of endoscopy facilities cost more than the other 3 interventions and was less effective in increasing CRC screening. CONCLUSION: Cost-effective CRC screening interventions targeting observed disparities are available, but substantial investment (more than $3.75 million) and additional approaches beyond those considered here are required to realize greater increases population-wide.

Declining liver graft quality threatens the future of liver transplantation in the United States.

National liver transplantation (LT) volume has declined since 2006, in part because of worsening donor organ quality. Trends that degrade organ quality are expected to continue over the next 2 decades. We used the United Network for Organ Sharing (UNOS) database to inform a 20-year discrete event simulation estimating LT volume from 2010 to 2030. Data to inform the model were obtained from deceased organ donors between 2000 and 2009. If donor liver utilization practices remain constant, utilization will fall from 78% to 44% by 2030, resulting in 2230 fewer LTs. If transplant centers increase their risk tolerance for marginal grafts, utilization would decrease to 48%. The institution of "opt-out" organ donation policies to increase the donor pool would still result in 1380 to 1866 fewer transplants. Ex vivo perfusion techniques that increase the use of marginal donor livers may stabilize LT volume. Otherwise, the number of LTs in the United States will decrease substantially over the next 15 years. In conclusion, the transplant community will need to accept inferior grafts and potentially worse posttransplant outcomes and/or develop new strategies for increasing organ donation and utilization in order to maintain the number of LTs at the current level.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina.

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Evaluating the potential impact of rubella-containing vaccine introduction on congenital rubella syndrome in Afghanistan, Dem. Republic of Congo, Ethiopia, Nigeria, and Pakistan: A mathematical modeling study.

We assessed the potential impact of introducing rubella-containing vaccine (RCV) on congenital rubella syndrome (CRS) incidence in Afghanistan (AFG), Democratic Republic of Congo (COD), Ethiopia (ETH), Nigeria (NGA), and Pakistan (PAK). We simulated several RCV introduction scenarios over 30 years using a validated mathematical model. Our findings indicate that RCV introduction could avert between 86,000 and 535,000 CRS births, preventing 2.5 to 15.8 million disability-adjusted life years. AFG and PAK could reduce about 90% of CRS births by introducing RCV with current measles routine coverage and executing supplemental immunization activities (SIAs). However, COD, NGA, and ETH must increase their current routine vaccination coverage to reduce CRS incidence significantly. This study showcases the potential benefits of RCV introduction and reinforces the need for global action to strengthen immunization programs.

Estimated number of preterm births and low birth weight children born in the United States due to maternal binge drinking.

The objective of this study was to estimate the aggregate burden of maternal binge drinking on preterm birth (PTB) and low birth weight (LBW) across American sociodemographic groups in 2008. To estimate the aggregate burden of maternal binge drinking on preterm birth (PTB) and low birth weight (LBW) across American sociodemographic groups in 2008. A simulation model was developed to estimate the number of PTB and LBW cases due to maternal binge drinking. Data inputs for the model included number of births and rates of preterm and LBW from the National Center for Health Statistics; female population by childbearing age groups from the U.S. Census; increased relative risks of preterm and LBW deliveries due to maternal binge drinking extracted from the literature; and adjusted prevalence of binge drinking among pregnant women estimated in a multivariate logistic regression model using Behavioral Risk Factor Surveillance System survey. The most conservative estimates attributed maternal binge drinking to 8,701 (95% CI: 7,804-9,598) PTBs (1.75% of all PTBs) and 5,627 (95% CI 5,121-6,133) LBW deliveries in 2008, with 3,708 (95% CI: 3,375-4,041) cases of both PTB and LBW. The estimated rate of PTB due to maternal binge drinking was 1.57% among all PTBs to White women, 0.69% among Black women, 3.31% among Hispanic women, and 2.35% among other races. Compared to other age groups, women ages 40-44 had the highest adjusted binge drinking rate and highest PTB rate due to maternal binge drinking (4.33%). Maternal binge drinking contributed significantly to PTB and LBW differentially across sociodemographic groups.

Maternal pre-pregnancy weight and gestational weight gain and their association with birthweight with a focus on racial differences.

Our objectives were to examine the interaction between maternal pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) and their association with birthweight, with a focus on racial differences. We used birth certificate data from live singleton births of South Carolina resident mothers, who self-reported their race as non-Hispanic white (NHW, n = 140, 128) or non-Hispanic black (NHB, n = 82,492) and who delivered at 34-44 weeks of gestation between 2004 and 2008 to conduct a cross-sectional study. Linear regression was used to examine the relationship between our exposures (i.e., race, BMI and GWG) and our outcome birthweight. Based on 2009 Institute of Medicine guidelines, the prevalence of adequate, inadequate and excessive GWG was 27.1, 24.2 and 48.7%, respectively, in NHW women and 24.2, 34.8 and 41.0%, respectively, in NHB women. Adjusting for infant sex, gestational age, maternal age, tobacco use, education, prenatal care, and Medicaid, the difference in birthweight between excessive and adequate GWG at a maternal BMI of 30 kg/m(2) was 118 g (95% CI: 109, 127) in NHW women and 101 g (95% CI: 91, 111) in NHB women. Moreover, excessive versus adequate GWG conveyed similar protection from having a small for gestational age infant in NHW [OR = 0.64 (95% CI 0.61, 0.67)] and NHB women [OR = 0.68 (95% CI: 0.65, 0.72)]. In conclusion, we report a strong association between excessive GWG and higher infant birthweight across maternal BMI classes in NHW and NHB women. Given the high prevalence of excessive GWG even a small increase in birthweight may have considerable implications at the population level.

Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records.

BACKGROUND: Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications. METHODS: We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression. RESULTS: Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31]). The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications. CONCLUSIONS: The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.

Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub.

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting: The entire United States. Participants: None. Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions-A Case Study on the Microvascular Complications of Diabetes.

Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the 'best' prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies. To support this decision process, we developed the Selection of Machine-learning Algorithms with ReplicaTions (SMART) Framework that integrates building and selecting ML models with decision theory. We build ML models and estimate performance for multiple plausible future populations with a replicated nested cross-validation technique. We rank ML models by simulating decision-maker priorities, using a range of accuracy measures (e.g., AUC) and robustness metrics from decision theory (e.g., minimax Regret). We present the SMART Framework through a case study on the microvascular complications of diabetes using data from the ACCORD clinical trial. We compare selections made by risk-averse, -neutral, and -seeking decision-makers, finding agreement in 80% of the risk-averse and risk-neutral selections, with the risk-averse selections showing consistency for a given complication. We also found that the models that best predicted outcomes in the validation set were those with low performance variance on the testing set, indicating a risk-averse approach in model selection is ideal when there is a potential for high population feature variability. The SMART Framework is a powerful, interactive tool that incorporates various ML algorithms and stakeholder preferences, generalizable to new data and technological advancements.

Modeling the Impact of Nonpharmaceutical Interventions on COVID-19 Transmission in K-12 Schools.

Background. The novel coronavirus SARS-CoV-2 spread across the world causing many waves of COVID-19. Children were at high risk of being exposed to the disease because they were not eligible for vaccination during the first 20 mo of the pandemic in the United States. While children 5 y and older are now eligible to receive a COVID-19 vaccine in the United States, vaccination rates remain low despite most schools returning to in-person instruction. Nonpharmaceutical interventions (NPIs) are important for controlling the spread of COVID-19 in K-12 schools. US school districts used varied and layered mitigation strategies during the pandemic. The goal of this article is to analyze the impact of different NPIs on COVID-19 transmission within K-12 schools. Methods. We developed a deterministic stratified SEIR model that captures the role of social contacts between cohorts in disease transmission to estimate COVID-19 incidence under different NPIs including masks, random screening, contact reduction, school closures, and test-to-stay. We designed contact matrices to simulate the contact patterns between students and teachers within schools. We estimated the proportion of susceptible infected associated with each intervention over 1 semester under the Omicron variant. Results. We find that masks and reducing contacts can greatly reduce new infections among students. Weekly screening tests also have a positive impact on disease mitigation. While self-quarantining symptomatic infections and school closures are effective measures for decreasing semester-end infections, they increase absenteeism. Conclusion. The model provides a useful tool for evaluating the impact of a variety of NPIs on disease transmission in K-12 schools. While the model is tested under Omicron variant parameters in US K-12 schools, it can be adapted to study other populations under different disease settings. Highlights: A stratified SEIR model was developed that captures the role of social contacts in K-12 schools to estimate COVID-19 transmission under different nonpharmaceutical interventions.While masks, random screening, contact reduction, school closures, and test-to-stay are all beneficial interventions, masks and contact reduction resulted in the greatest reduction in new infections among students from the tested scenarios.Layered interventions provide more benefits than implementing interventions independently.

When History and Heterogeneity Matter: A Tutorial on the Impact of Markov Model Specifications in the Context of Colorectal Cancer Screening.

BACKGROUND: Markov models are used in health research to simulate health care utilization and disease states over time. Health phenomena, however, are complex, and the memoryless assumption of Markov models may not appropriately represent reality. This tutorial provides guidance on the use of Markov models of different orders and stratification levels in health decision-analytic modeling. Colorectal cancer (CRC) screening is used as a case example to examine the impact of using different Markov modeling approaches on CRC outcomes. METHODS: This study used insurance claims data from commercially insured individuals in Oregon to estimate transition probabilities between CRC screening states (no screen, colonoscopy, fecal immunochemical test or fecal occult blood test). First-order, first-order stratified by sex and geography, and third-order Markov models were compared. Screening trajectories produced from the different Markov models were incorporated into a microsimulation model that simulated the natural history of CRC disease progression. Simulation outcomes (e.g., future screening choices, CRC incidence, deaths due to CRC) were compared across models. RESULTS: Simulated CRC screening trajectories and resulting CRC outcomes varied depending on the Markov modeling approach used. For example, when using the first-order, first-order stratified, and third-order Markov models, 30%, 31%, and 44% of individuals used colonoscopy as their only screening modality, respectively. Screening trajectories based on the third-order Markov model predicted that a higher percentage of individuals were up-to-date with CRC screening as compared with the other Markov models. LIMITATIONS: The study was limited to insurance claims data spanning 5 y. It was not possible to validate which Markov model better predicts long-term screening behavior and outcomes. CONCLUSIONS: Findings demonstrate the impact that different order and stratification assumptions can have in decision-analytic models. HIGHLIGHTS: This tutorial uses colorectal cancer screening as a case example to provide guidance on the use of Markov models of different orders and stratification levels in health decision-analytic models.Colorectal cancer screening trajectories and projected health outcomes were sensitive to the use of alternate Markov model specifications.Although data limitations precluded the assessment of model accuracy beyond a 5-y period, within the 5-y period, the third-order Markov model was slightly more accurate in predicting the fifth colorectal cancer screening action than the first-order Markov model.Findings from this tutorial demonstrate the importance of examining the memoryless assumption of the first-order Markov model when simulating health care utilization over time.

Vaccinating children against COVID-19 is crucial to protect schools and communities.

To evaluate the joint impact of childhood vaccination rates and school masking policies on community transmission and severe outcomes due to COVID-19, we utilized a stochastic, agent-based simulation of North Carolina to test 24 health policy scenarios. In these scenarios, we varied the childhood (ages 5 to 19) vaccination rate relative to the adult's (ages 20 to 64) vaccination rate and the masking relaxation policies in schools. We measured the overall incidence of disease, COVID-19-related hospitalization, and mortality from 2021 July 1 to 2023 July 1. Our simulation estimates that removing all masks in schools in January 2022 could lead to a 31% to 45%, 23% to 35%, and 13% to 19% increase in cumulative infections for ages 5 to 9, 10 to 19, and the total population, respectively, depending on the childhood vaccination rate. Additionally, achieving a childhood vaccine uptake rate of 50% of adults could lead to a 31% to 39% reduction in peak hospitalizations overall masking scenarios compared with not vaccinating this group. Finally, our simulation estimates that increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 83% and 87% across all masking scenarios compared to the scenarios where no children are vaccinated. Our simulation suggests that high vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.