Background: As human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. Objective: We aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. Methods: We tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. Results: Incidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80%, 88%, 96%, 80%, and 78%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82%, 62%, 94%, 78%, and 62%, respectively. Conclusions: An advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention.

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中文翻译：

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利用互联网搜索数据改善非传染性疾病的预测和预防：回顾性观察研究

背景：随着人类社会进入一个庞大且易于访问的社交媒体时代，越来越多的人正在利用互联网搜索和交换医学信息。由于互联网搜索数据可以反映出人们对特定健康主题的兴趣，因此它们提供了一种新的方式来理解有关非传染性疾病（NCD）的健康问题及其在预防中的作用。目的：我们旨在探讨非传染性疾病的互联网搜索数据与美国已公布的疾病发病率和死亡率的关联，并了解非传染性疾病的健康问题。方法：我们通过检查2004年至2017年美国的发病率，死亡率和互联网搜索之间的相关性来跟踪NCD，我们根据疾病发生率与互联网搜索之间的关系建立了预测模型。结果：美国29种疾病的发病率和死亡率与它们的搜索词的相对搜索量（RSV）在统计学上显着相关（P <.05）。从多元回归预测模型的拟合优度来看，糖尿病，中风，房颤和扑动，霍奇金淋巴瘤和睾丸癌的结果最接近1；他们用于预测发病率的线性回归模型的确定系数分别为80％，88％，96％，80％和78％。同时，他们用于预测死亡率的线性回归模型的确定系数分别为82％，62％，94％，78％和62％。结论：

这仅仅是抽象的。阅读JMIR网站上的全文。JMIR是互联网时代电子健康和医疗保健领域领先的开放获取期刊。