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Objective Noroviruses are the leading cause of acute gastroenteritis in the United States and outbreaks frequently occur in daycare settings. Results of norovirus vaccine trials have been promising, however there are open questions as to whether vaccination of daycare children would be cost-effective. We investigated the incremental cost-effectiveness of a hypothetical norovirus vaccination for children in daycare settings compared to no vaccination. Methods We conducted a model-based cost-effectiveness analysis using a disease transmission model of children attending daycare. Vaccination with a 90% coverage rate in addition to the observed standard of care (exclusion of symptomatic children from daycare) was compared to the observed standard of care. The main outcomes measures were infections and deaths averted, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER). Cost-effectiveness was analyzed from a societal perspective, including medical costs to children as well as productivity losses of parents, over a two-year time horizon. Data sources included outbreak surveillance data and published literature. Results A 50% efficacious norovirus vaccine averts 571.83 norovirus cases and 0.003 norovirus-related deaths per 10,000 children compared to the observed standard of care. A $200 norovirus vaccine that is 50% efficacious has a net cost increase of $178.10 per child and 0.025 more QALYs, resulting in an ICER of $7,028/QALY. Based on the probabilistic sensitivity analysis, we estimated that a $200 vaccination with 50% efficacy was 94.0% likely to be cost-effective at a willingness-to-pay of $100,000/QALY threshold and 95.3% likely at a $150,000/QALY threshold. Conclusion Due to the large disease burden associated with norovirus, it is likely that vaccinating children in daycares could be cost-effective, even with modest vaccine efficacy and a high per-child cost of vaccination. Norovirus vaccination of children in daycare has a cost-effectiveness ratio similar to other commonly recommended childhood vaccines.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility —- a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Background Intensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach. Methods and findings Cox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure < 120 mm Hg) versus standard (, Using data from two large clinical trials that showed heterogeneity in blood pressure treatment effects, Sanjay Basu and colleagues investigate how risks of treatment benefit and harm vary across individuals., Author summary Why was this study done? It is known that elevated blood pressure is a major risk factor for cardiovascular and related diseases. Intensive treatment of elevated blood pressure (aimed at keeping systolic blood pressures less than or equal to 120 mm Hg) may avert cardiovascular disease events, but may also pose the risk of some serious adverse events. We sought to create risk calculators to estimate individual patients’ chances of benefit and harm from intensive treatment. We additionally sought to test whether the statistical method known as elastic net regularization, which aims to reduce overfitting and improve external validity, would improve the estimation of risk models for absolute risk reduction or increase. What did the researchers do and find? We developed statistical models of cardiovascular events and serious adverse events from individual participant data from the SPRINT trial of intensive blood pressure treatment (N = 9,069 with complete covariate data) and validated them on individual participant data from the ACCORD-BP trial of intensive blood pressure treatment (N = 4,498 with complete covariate data). We used the models to calculate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for individuals from intensive BP treatment. We found that the models could identify groups with high and with low absolute risk reduction in cardiovascular events and, similarly, identify groups with high and with low absolute risk increase in serious adverse events. Some participants in both the SPRINT and ACCORD studies were in groups with high predicted absolute risk reduction and low predicted absolute risk increase, and vice versa. We additionally found that using the statistical method of elastic net regularization improved the ability to identify groups with high versus low absolute risk increase in serious adverse events, compared to traditional backwards variable selection. We made an online risk calculator available, along with statistical code to apply the method to other trial datasets. What do these findings mean? The models derived in this study helped identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher absolute risk decreases in CVD events, and participants who had lower versus higher absolute risk increases in serious adverse events. In the future, as individual participant data become increasingly available from randomized controlled trials, benefit and harm risk calculators for personalizing therapy may become more common. The study revealed that such risk calculations for serious adverse events were improved by using an elastic net regularization approach that involves rigorous cross-validation and improves model stability when risk factors for an outcome are correlated, as with cardiovascular disease risk factors. The limitations of the study include having a limited sample size in the intensive blood pressure treatment trial that enrolled people with type 2 diabetes (ACCORD-BP) and being a secondary data analysis that cannot provide mechanistic explanations for the observed heterogeneities in treatment effect.