Objective

Although lower respiratory infections (LRI) are among the leading causes of mortality in the United States, their association with underlying factors and geographic variation have not been adequately examined.

Methods

In this study, explanatory variables (n = 46) including climatic, topographic, socio-economic, and demographic factors were compiled at the county level across the continental US. Machine learning algorithms - logistic regression (LR), random forest (RF), gradient boosting decision trees (GBDT), k-nearest neighbors (KNN), and support vector machine (SVM) - were used to predict the presence/absence of hotspots (P < 0.05) for elevated age-adjusted LRI mortality rates in a geographic information system framework.

Results

Overall, there was a historical shift in hotspots away from the western United States into the southeastern parts of the country and they were highly localized in a few counties. The two decision tree methods (RF and GBDT) outperformed the other algorithms (accuracies: 0.92; F1-scores: 0.85 and 0.84; area under the precision-recall curve: 0.84 and 0.83, respectively). Moreover, the results of the RF and GBDT indicated that higher spring minimum temperature, increased winter precipitation, and higher annual median household income were among the most substantial factors in predicting the hotspots.

Conclusions

This study helps raise awareness of public health decision-makers to develop and target LRI prevention programs.

中文翻译：

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预测美国大陆下呼吸道感染的按年龄调整死亡率的热点：GIS，空间统计和机器学习算法的集成。

目的

尽管在美国，下呼吸道感染（LRI）是导致死亡的主要原因，但尚未充分检查它们与潜在因素和地理差异的关系。

方法

在这项研究中，解释变量（n = 46），包括气候，地形，社会经济和人口因素，在美国大陆的县级进行了汇总。机器学习算法-Logistic回归（LR），随机森林（RF），梯度提升决策树（GBDT），k近邻（KNN）和支持向量机（SVM）-用于预测热点的存在/不存在（P <0.05），以提高地理信息系统框架中经年龄调整的LRI死亡率。

结果

总体而言，热点地区从美国西部向美国东南部地区发生了历史性转变，并且热点高度集中在少数几个县。两种决策树方法（RF和GBDT）优于其他算法（准确性：0.92； F1分数：0.85和0.84；精确召回曲线下的面积：0.84和0.83）。此外，RF和GBDT的结果表明，较高的春季最低温度，较高的冬季降水以及较高的年家庭收入中位数是预测热点的最重要因素。

结论

这项研究有助于提高公众卫生决策者的意识，以制定和针对LRI预防计划。