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We appreciate the comments by Stafoggia et al. (1) on our article. (2) We would like to clarify that our article aimed to introduce a novel methodology, deep ensemble machine [...]

BACKGROUND: Accurate estimation of historical P[M.sub.2.5] (particle matter with an aerodynamic diameter of less than 2.5 [micro]m) is critical and essential for environmental health risk assessment. OBJECTIVES: The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level P[M.sub.2.5] concentrations. METHODS: An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily P[M.sub.2.5] concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of P[M.sub.2.5] concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate P[M.sub.2.5] concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for P[M.sub.2.5] estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily P[M.sub.2.5] at each 1 km x 1 km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)]. RESULTS: The results revealed that the P[M.sub.2.5] prediction performance of DEML [coefficients of determination ([R.sup.2]) = 0.87 and root mean square error (RMSE) =5.38 [micro]g/[m.sup.3]] was superior to any benchmark models (with [R.sup.2] of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of P[M.sub.2.5] in Italy. DISCUSSION: The proposed DEML framework achieved an outstanding performance in P[M.sub.2.5] estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752, Introduction Both short-term and long-term exposure to ambient fine particulate matter (PM) with an aerodynamic diameter of 2.5 pm or less (P[M.sub.2.5]) are related to a broad range of adverse [...]

Background: Exposure to excessive heat, which will continue to increase with climate change, is associated with increased morbidity due to a range of noncommunicable diseases (NCDs). Whether this is true for diabetes is unknown. Objectives: We aimed to quantify the relationship between heat exposure and risk of hospitalization due to diabetes in Brazil. Methods: Data on hospitalizations and weather conditions were collected from 1,814 cities during the hot seasons from 2000 to 2015. A time-stratified case-crossover design was used to quantify the association between hospitalization for diabetes and heat exposure. Region-specific odds ratios (ORs) were used to calculate the attributable fractions (AFs). Results: A total of 553,351 hospitalizations associated with diabetes were recorded during 2000-2015. Every 5[degrees]C increase in daily mean temperature was associated with 6% [OR=1.06; 95% confidence interval (CI): 1.04, 1.07] increase in hospitalization due to diabetes with lag 0-3 d. The association was greatest (OR = 1.18; 95% CI: 1.13, 1.23) in those [greater than or equal to]80 y of age, but did not vary by sex, and was generally consistent by region and type of diabetes. Assuming a causal association, we estimated that 7.3% (95% CI: 3.5, 10.9) of all hospitalizations due to diabetes in the hot season could be attributed to heat exposure during the study period. Discussion: Short-term heat exposure may increase the burden of diabetes-related hospitalization, especially among the very elderly. As global temperatures continue to rise, this burden is likely to increase. https://doi.org/10.1289/EHP5688, Introduction Diabetes mellitus is an important public health concern affecting an estimated 422 million (including both diagnosed and undiagnosed) people worldwide (Zhou et al. 2016). It is a heterogeneous condition [...]