Land-use regression models and remote sensing data have been widely employed to forecast atmospheric aerosol levels. Recently, these methodologies have been combined to predict the influence of this pollutant on human health. However, traditional land-use regression models do not often consider the complex interactions between predictors, and most of these do not include socioeconomic variables. Thus, in the present study, we aimed to estimate suspended particle-related hospital admissions by employing remote sensing, meteorological, environmental, and demographic parameters. In this cohort study, we analyzed 1,612,049 hospital admissions from Córdoba city, Argentina, from 2005 to 2011, and developed several regression and machine learning land-use models to compare their predictive powers. We found that childhood was the age group with the highest number of hospital admissions related with upper respiratory tract diseases. When predicting population-normalized hospital admissions, the machine learning models, in particular the generalized boosted machine, revealed a better performance than regression models, exhibiting the lowest root mean square error (0.4264) in the test data set. This model also achieved the best R²_adj (0.6088) when plotting predicted vs. reported normalized cases. The most important predictors were the meteorological variables, followed by the aerosol optical depth and the planet boundary layer height. Some other predictors, such as educational level, land value, and unsatisfied basic needs, showed less relevance but enhanced the model's prediction power. Furthermore, the predictive power increased after a 1-day lag in hospital admissions (RMSE = 0.4121), highlighting the importance of meteorological and environmental variables in the onset of respiratory diseases.

土地利用回归模型和遥感数据已被广泛用于预测大气气溶胶水平。最近，这些方法被结合起来预测这种污染物对人类健康的影响。然而，传统的土地利用回归模型通常不考虑预测变量之间复杂的相互作用，其中大部分不包括社会经济变量。因此，在本研究中，我们旨在通过使用遥感、气象、环境和人口统计参数来估计与悬浮颗粒相关的住院人数。在这项队列研究中，我们分析了 2005 年至 2011 年阿根廷科尔多瓦市的 1,612,049 例住院患者，并开发了几种回归和机器学习土地利用模型来比较它们的预测能力。我们发现儿童是与上呼吸道疾病相关的住院人数最多的年龄组。在预测人口归一化入院人数时，机器学习模型，特别是广义增强机器，显示出比回归模型更好的性能，在测试数据集中表现出最低的均方根误差 (0.4264)。这个模型也取得了最好的R² _adj (0.6088) 在绘制预测与报告的标准化病例时。最重要的预测因子是气象变量，其次是气溶胶光学深度和行星边界层高度。其他一些预测因素，如教育水平、土地价值和未满足的基本需求，显示出较少的相关性，但增强了模型的预测能力。此外，在入院延迟 1 天后预测能力增加（RMSE = 0.4121），突出了气象和环境变量在呼吸系统疾病发病中的重要性。