Spatiotemporal distribution of migraine in China: analyses based on baidu index

Liling Lin; Mengyi Zhu; Junxiong Qiu; Qiang Li; Junmeng Zheng; Yanni Fu; Jianwei Lin

doi:10.1186/s12889-023-16909-9

Spatiotemporal distribution of migraine in China: analyses based on baidu index

BMC Public Health. 2023 Oct 10;23(1):1958. doi: 10.1186/s12889-023-16909-9.

Authors

Liling Lin^#¹, Mengyi Zhu^#², Junxiong Qiu³, Qiang Li⁴, Junmeng Zheng³, Yanni Fu⁵, Jianwei Lin^{6

7}

Affiliations

¹ Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. linll3@mail.sysu.edu.cn.
² Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
³ Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
⁴ Department of Anesthesiology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China.
⁵ Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. fuyanni@mail.sysu.edu.cn.
⁶ Big Data Laboratory, Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China. ljw@jsiec.org.
⁷ Big Data AI Laboratory, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong, China. ljw@jsiec.org.

^# Contributed equally.

Abstract

Background: In recent years, innovative approaches utilizing Internet data have emerged in the field of syndromic surveillance. These novel methods aim to aid in the early prediction of epidemics across various scenarios and diseases. It has been observed that these systems demonstrate remarkable accuracy in monitoring outbreaks even before they become apparent in the general population. Therefore, they serve as valuable complementary tools to augment existing methodologies. In this study, we aimed to investigate the spatiotemporal distribution of migraine in China by leveraging Baidu Index (BI) data.

Methods: Migraine-related BI data from January 2014 to December 2022 were leveraged, covering 301 city-level areas from 31 provincial-level regions by using the keyword "migraine ()". Prevalence data from the Global Burden of Disease study (GBD) were attracted to ensure the reliability of utilizing migraine-related BI data for research. Comprehensive analytical methods were then followed to investigate migraine's spatiotemporal distribution. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal distribution. Spatial distribution was explored using the Getis-Ord Gi^* statistic, standard deviation ellipse analysis, Moran's Index, and Ordinary Kriging. The top eight migraine-related search terms were analyzed through the Demand Graph feature in the Baidu Index platform to understand the public's concerns related to migraine.

Results: A strong association was observed between migraine-related BI and the prevalence data of migraine from GBD with a Spearman correlation coefficient of 0.983 (P = 4.96 × 10^{- 5}). The overall trend of migraine-related BI showed a gradual upward trend over the years with a sharp increase from 2017 to 2019. Seasonality was observed and the peak period occurred in spring nationwide. The middle-lower reaches of the Yangtze River were found to be hotspots, while the eastern coastal areas had the highest concentration of migraine-related BI, with a gradual decrease towards the west. The most common search term related to migraine was "How to treat migraine quickly and effectively ()".

Conclusions: This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies.

Keywords: Baidu index; Infodemiology; Migraine; Prevalence; Spatiotemporal distribution.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China / epidemiology
Disease Outbreaks*
Epidemics*
Humans
Reproducibility of Results
Seasons
Spatial Analysis
Spatio-Temporal Analysis