Environmental exposures are recognized as determinants of health, particularly in vulnerable populations such as hemodialysis patients, whose impaired kidney function may heighten susceptibility. This study assesses the association between meteorological and air-pollution factors and the risk of hospital admission and mortality in end-stage kidney disease patients, considering both acute peaks and sustained exposure through weekly and monthly averages. A retrospective cohort of 336 hemodialysis patients treated between 2016 and 2024 at Hospital Universitario Príncipe de Asturias (Madrid, Spain) was analyzed, including 563 admissions and 90 deaths. Time-to-event analyses were conducted separately for mortality and admissions. Cox proportional hazards and parametric survival models (Weibull, log-normal, log-logistic) were applied for mortality, while recurrent-event models (Andersen–Gill, Prentice–Williams–Peterson, frailty) used for admissions. Sulfur dioxide (SO ₂ ) was consistently associated with increased risk in both acute and cumulative exposure models for mortality and admissions. Nitrogen dioxide (NO ₂ ) predicted mortality only in long-term analyses, suggesting a cumulative effect. Solar radiation was linked to accelerated mortality in non-linear models, whereas atmospheric pressure appeared protective for admissions. These findings highlight the influence of environmental exposures, particularly SO ₂ , on adverse outcomes in hemodialysis patients and support incorporating environmental indicators into clinical risk prediction and public health strategies.

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities.