Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. “Nowcast” approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission-that future cases are intrinsically linked to past reported cases-and are optimized to one or two applications, which may limit generalizability. Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accurate nowcasts in multiple disease settings. We test NobBS on dengue in Puerto Rico and influenza-like illness (ILI) in the United States to examine performance and robustness across settings exhibiting a range of common reporting delay characteristics (from stable to time-varying), and compare this approach with a published nowcasting software package while investigating the features of each approach that contribute to good or poor performance. We show that introducing a temporal relationship between cases considerably improves performance when the reporting delay distribution is time-varying, and we identify trade-offs in the role of moving windows to accurately capture changes in the delay. We present software implementing this new approach (R package “NobBS”) for widespread application and provide practical guidance on implementation.

Investing efficiently in future research to improve policy decisions is an important goal. Expected value of sample information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Recently, several more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared, and therefore their comparative performance across different examples has not been explored. We compared 4 EVSI methods using 3 previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. As all the evaluated methods gave estimates similar to those given by traditional Monte Carlo, we suggest that EVSI can now be efficiently computed with confidence in realistic examples. No systematically superior EVSI computation method exists as the properties of the different methods depend on the underlying health economic model, data generation process, and user expertise.

Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk.

We estimate that there will be 13·7 million new cases of childhood cancer globally between 2020 and 2050. At current levels of health system performance (including access and referral), 6·1 million (44·9%) of these children will be undiagnosed. Between 2020 and 2050, 11·1 million children will die from cancer if no additional investments are made to improve access to health-care services or childhood cancer treatment. Of this total, 9·3 million children (84·1%) will be in low-income and lower-middle-income countries. This burden could be vastly reduced with new funding to scale up cost-effective interventions. Simultaneous comprehensive scale-up of interventions could avert 6·2 million deaths in children with cancer in this period, more than half (56·1%) of the total number of deaths otherwise projected. Taking excess mortality risk into consideration, this reduction in the number of deaths is projected to produce a gain of 318 million life-years. In addition, the global lifetime productivity gains of US$2580 billion in 2020-50 would be four times greater than the cumulative treatment costs of $594 billion, producing a net benefit of $1986 billion on the global investment: a net return of $3 for every $1 invested. In sum, the burden of childhood cancer, which has been grossly underestimated in the past, can be effectively diminished to realise massive health and economic benefits and to avert millions of needless deaths.

B cells in human food allergy have been studied predominantly in the blood. Little is known about IgE B cells or plasma cells in tissues exposed to dietary antigens. We characterized IgE clones in blood, stomach, duodenum, and esophagus of 19 peanut-allergic patients, using high-throughput DNA sequencing. IgE cells in allergic patients are enriched in stomach and duodenum, and have a plasma cell phenotype. Clonally related IgE and non-IgE-expressing cell frequencies in tissues suggest local isotype switching, including transitions between IgA and IgE isotypes. Highly similar antibody sequences specific for peanut allergen Ara h 2 are shared between patients, indicating that common immunoglobulin genetic rearrangements may contribute to pathogenesis. These data define the gastrointestinal tract as a reservoir of IgE B lineage cells in food allergy.