背景:性传播疾病(STDs)是世界范围内的严重问题。随着互联网的普及,在线健康信息寻求行为(OHISB)已被广泛采用,以改善健康和预防疾病。
目的:本研究旨在探讨不同类型的OHISBs对性病的短期和长期影响,包括梅毒,淋病,和艾滋病由于艾滋病毒,基于百度指数。
方法:收集多源大数据,包括性病的病例数,基于百度指数的搜索查询,省总人口,男女比例,65岁以上的人口比例,地区国内生产总值(GRDP),和2011-2018年中国大陆医疗机构数量数据。我们将OHISB分为4种类型:概念,症状,治疗,和预防。在控制社会经济和医疗状况之前和之后,我们应用多元线性回归分析了百度搜索指数(BSI)与百度搜索率(BSR)和性病病例数之间的关联.此外,我们比较了4种OHISB的效应,并进行了时滞交叉相关分析,以研究OHISB的长期效应.
结果:STD病例数和OHISB的分布呈现变异性。对于案例编号,梅毒,和淋病,病例主要分布在中国东南和西北地区,而艾滋病毒/艾滋病病例大多分布在西南地区。对于搜索查询,东部地区的BSI和BSR最高,而西部地区是最低的。对于4种OHISB的3种疾病,BSI与病例数呈正相关,而BSR与病例数呈显著负相关(P<0.05)。不同类别的OHISB对性病病例数的影响不同。寻找预防往往会产生更大的影响,而寻求治疗的影响往往较小。此外,由于时滞效应,这些影响会随着时间的推移而增加。
结论:我们的研究验证了4种OHISB类型与性病病例数之间的显著关联,随着时间的推移,OHISBs对性病的影响越来越强。它可以提供有关如何使用互联网大数据来更好地实现疾病监测和预防目标的见解。
Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease.
This
study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index.
Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB.
The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time.
Our
study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals.