Epidemiological research on rare diseases using large-scale online search queries and reported case data

Abstract

Background: 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. Results: 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. Conclusion: 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.