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Background: Forecasts of future demand is foundational for effective resource allocation in emergency departments (EDs). As ED demand is inherently variable, it is important for forecasts to characterize the range of possible future demand. However, extant research focuses primarily on producing point forecasts using a wide variety of prediction algorithms. In this study, our objective is to generate point and interval predictions that accurately characterize the variability in ED demand using ensemble methods that combine predictions from multiple base algorithms based on their empirical performance., Methods: Data consisted in daily arrivals and subsequent hospitalizations at 72 emergency departments in Île-de-France from 2014-2018. Additional explanatory variables were collected including public and school holidays, meteorological variables, and public health trends. One-day ahead point and 80% interval predictions of arrivals and hospitalizations were produced by predicting the 10%, 50%, and 90% quantiles of the forecast distribution. Quantile prediction algorithms included methods such as ARIMAX, variations of random forests, and generalized additive models. Ensemble predictions were then formed using Exponentially Weighted Averaging, Bernstein Online Aggregation, and Super Learning. Prediction intervals were post-processed using Adaptive Conformal Inference techniques. Point predictions were evaluated by their Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), and 80% interval predictions by their empirical coverage and mean interval width., Results: For point forecasts, ensemble methods achieved lower average MAE and MAPE than any of the base algorithms. All of the base algorithms and ensemble methods yielded prediction intervals with near optimal empirical coverage after conformalization. For hospitalizations, the shortest mean interval widths were achieved by the ensemble methods., Conclusions: Ensemble methods yield joint point and prediction intervals that adapt to individual EDs and achieve better performance than individual algorithms. Conformal inference techniques improve the performance of the prediction intervals., Competing Interests: Declaration of Competing Interest Declarations of interest: none., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Family planning measures for unmarried women are based on contraceptive demand and use among sexually active women. Sexual activity status is commonly defined based on comparing reported time-since-last-sex to a cutoff time, with women defined to be sexually active if their most recent sex was within the last four weeks. While easy to understand and compute, this approach to constructing family planning measures results in a limited understanding of family planning and exposure to unintended pregnancy because it cannot comprehensively capture the frequency of sex at the population level. We propose a new statistical approach to quantify sexual activity, using reported time-since-last-sex data. Based on estimated frequencies of sex among users and nonusers in need of family planning, we propose new family planning measures, including the ratio of protected exposure over all women's exposure to risk of unintended pregnancy., (© 2023 The Authors. Studies in Family Planning published by Wiley Periodicals LLC on behalf of Population Council.)

There is growing interest in producing estimates of demographic and global health indicators in populations with limited data. Statistical models are needed to combine data from multiple data sources into estimates and projections with uncertainty. Diverse modelling approaches have been applied to this problem, making comparisons between models difficult. We propose a model class, Temporal Models for Multiple Populations (TMMPs), to facilitate both documentation of model assumptions in a standardised way and comparison across models. The class makes a distinction between the process model, which describes latent trends in the indicator interest, and the data model, which describes the data generating process of the observed data. We provide a general notation for the process model that encompasses many popular temporal modelling techniques, and we show how existing models for a variety of indicators can be written using this notation. We end with a discussion of outstanding questions and future directions., (© 2022 The Authors. International Statistical Review published by John Wiley & Sons Ltd on behalf of International Statistical Institute.)

Nearly all Americans have detectable concentrations of endocrine disrupting chemicals from consumer products in their bodies, and expert panels recommend reducing exposures. To inform exposure reduction, we investigated whether consumers who are trying to avoid certain chemicals in consumer products have lower exposures than those who are not. We also aimed to make exposure biomonitoring more widely available. We enrolled 726 participants in a crowdsourced biomonitoring study. We targeted phenolic compounds-specifically parabens, bisphenol A (BPA) and analogs bisphenol F (BPF) and bisphenol S (BPS), the UV filter benzophenone-3, the anti-microbial triclosan, 2,4-dichlorophenol, and 2,5-dichlorophenol-and collected survey data on consumer products, cleaning habits, and efforts to avoid related chemicals. We investigated associations between 68 self-reported exposure behaviors and urine concentrations of ten chemicals, and evaluated whether associations were modified by intention to avoid exposures. A large majority (87%) of participants reported taking steps to limit exposure to specific chemicals, and, overall, participants achieved lower concentrations than the general U.S. population for parabens, BPA, triclosan, and benzophenone-3 but not BPF and BPS. Participants who reported avoiding all four ingredient groups-parabens, triclosan, bisphenols, and fragrances-were twice as likely as others to be in the lowest quartile of cumulative exposure. Avoiding certain products and reading ingredient labels to avoid chemicals was most effective for parabens, triclosan, and benzophenone-3. Avoiding BPA was not effective for reducing bisphenol exposures. Avoiding certain chemicals in products was generally associated with reduced exposure for chemicals listed on labels. Greater ingredient transparency will help consumers who read labels to reduce their exposure to a wider range of potentially harmful chemicals. In order to more equitably address public health, labeling policies should be complemented by regulations that exclude harmful chemicals from consumer products., (Copyright © 2020 The Author(s). Published by Elsevier GmbH.. All rights reserved.)