Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Department of Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Orphanet J Rare Dis. 2023 Aug 9;18(1):236. doi: 10.1186/s13023-023-02839-7.
Rare diseases have become a major public health concern worldwide. However, detailed epidemiological data are lacking. With the development of the Internet, search queries have played an important role in disease surveillance. In this study, we explored a new method for the epidemiological research on rare diseases, using large-scale online search queries and reported case data. We distilled search logs related to rare diseases nationwide from 2016 to 2019. The case data were obtained from China's national database of rare diseases during the same period.
A total of 120 rare diseases were included in this study. From 2016 to 2019, the number of patients with rare diseases estimated using search data and those obtained from the case database showed an increasing trend. Rare diseases can be ranked by the number of search estimated patients and reported patients, and the rankings of each disease in both search and reported case data were generally stable. Furthermore, the disease rankings in the search data were relatively consistent with the reported case data in each year, with more than 50% of rare diseases having a ranking difference of -20 to 20 between the two systems. In addition, the relationship between the disease rankings in the two systems was generally stable over time. Based on the relationship between the disease rankings in the search and reported case data, rare diseases can be classified into two categories.
Online search queries may provide an important new resource for detecting rare diseases. Rare diseases can be classified into two categories to guide different epidemiological research strategies.
罕见病已成为全球主要的公共卫生关注点。然而，详细的流行病学数据仍十分缺乏。随着互联网的发展，搜索查询在疾病监测中发挥了重要作用。在本研究中，我们探索了一种利用大规模在线搜索查询和已报告病例数据进行罕见病流行病学研究的新方法。我们从 2016 年至 2019 年全国范围内提取了与罕见病相关的搜索日志。同期，病例数据来自中国国家罕见病数据库。
本研究共纳入 120 种罕见病。2016 年至 2019 年，使用搜索数据和病例数据库估计的罕见病患者数量呈上升趋势。可以根据搜索估计患者和报告病例患者的数量对罕见病进行排名，并且每种疾病在搜索和报告病例数据中的排名通常都很稳定。此外，搜索数据中的疾病排名与每年的报告病例数据基本一致，超过 50%的罕见病在两个系统中的排名差异在-20 到 20 之间。此外，两个系统中疾病排名之间的关系随时间基本保持稳定。基于搜索和报告病例数据中疾病排名的关系，罕见病可分为两类。
在线搜索查询可能为发现罕见病提供了一个重要的新资源。罕见病可以分为两类，以指导不同的流行病学研究策略。
