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疾病数字报告中的差异:人口统计学和社会经济学评估。

Disparities in digital reporting of illness: A demographic and socioeconomic assessment.

机构信息

Department of Economics, University of Washington, Seattle, WA, United States.

Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States.

出版信息

Prev Med. 2017 Aug;101:18-22. doi: 10.1016/j.ypmed.2017.05.009. Epub 2017 May 17.

Abstract

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.

摘要

虽然公共卫生官员目前正在使用数字疾病报告进行疾病监测和决策制定,但对于可能影响报告模式的环境因素和组成特征知之甚少。本研究的目的是量化气候、人口统计学和社会经济因素与美国县级数字疾病报告之间的关联。我们参考了 2004 年至 2014 年间大约 150 万份餐饮业务评论,并使用人口普查数据、机器学习方法和回归模型来评估疾病的数字报告是否与气候、社会经济和人口统计学因素有关。结果表明,餐饮业务评论和食源性疾病的数字报告都呈现出明显的季节性模式,夏季报告较多,大多数食源性疾病暴发都在夏季报告,冬季报告较少。此外,与富裕相关的因素(如较高的中位数收入和拥有学士学位的人口比例)与食源性疾病报告呈正相关。然而,人均餐馆数量和教育是美国县级疾病报告的最显著预测因素。这些结果表明,众所周知的健康差距也可能反映在网络环境中。尽管这是一项观察性研究,但它是了解在线公共卫生环境中差异的重要一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6669/5553633/5f888ca9d784/nihms880377f1.jpg

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