Spatiotemporal distribution of migraine in China: analyses based on baidu index
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 remar...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-023-16909-9 |
| _version_ | 1827707852939067392 |
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| author | Liling Lin Mengyi Zhu Junxiong Qiu Qiang Li Junmeng Zheng Yanni Fu Jianwei Lin |
| author_facet | Liling Lin Mengyi Zhu Junxiong Qiu Qiang Li Junmeng Zheng Yanni Fu Jianwei Lin |
| author_sort | Liling Lin |
| collection | DOAJ |
| description | 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 (偏头痛)”. 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 (偏头痛怎么办最快最有效的方法)”. Conclusions This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies. |
| first_indexed | 2024-03-10T16:54:31Z |
| format | Article |
| id | doaj.art-fdf38299fa6d4aebac36e2cb6a93814b |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-10T16:54:31Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
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| series | BMC Public Health |
| spelling | doaj.art-fdf38299fa6d4aebac36e2cb6a93814b2023-11-20T11:11:45ZengBMCBMC Public Health1471-24582023-10-0123111010.1186/s12889-023-16909-9Spatiotemporal distribution of migraine in China: analyses based on baidu indexLiling Lin0Mengyi Zhu1Junxiong Qiu2Qiang Li3Junmeng Zheng4Yanni Fu5Jianwei Lin6Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityZhongshan School of Medicine, Sun Yat-sen UniversityDepartment of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityDepartment of Anesthesiology, Sun Yat-sen University Cancer Center, Sun Yat-sen UniversityDepartment of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityBig Data Laboratory, Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong KongAbstract 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 (偏头痛)”. 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 (偏头痛怎么办最快最有效的方法)”. Conclusions This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies.https://doi.org/10.1186/s12889-023-16909-9MigraineBaidu indexPrevalenceSpatiotemporal distributionInfodemiology |
| spellingShingle | Liling Lin Mengyi Zhu Junxiong Qiu Qiang Li Junmeng Zheng Yanni Fu Jianwei Lin Spatiotemporal distribution of migraine in China: analyses based on baidu index BMC Public Health Migraine Baidu index Prevalence Spatiotemporal distribution Infodemiology |
| title | Spatiotemporal distribution of migraine in China: analyses based on baidu index |
| title_full | Spatiotemporal distribution of migraine in China: analyses based on baidu index |
| title_fullStr | Spatiotemporal distribution of migraine in China: analyses based on baidu index |
| title_full_unstemmed | Spatiotemporal distribution of migraine in China: analyses based on baidu index |
| title_short | Spatiotemporal distribution of migraine in China: analyses based on baidu index |
| title_sort | spatiotemporal distribution of migraine in china analyses based on baidu index |
| topic | Migraine Baidu index Prevalence Spatiotemporal distribution Infodemiology |
| url | https://doi.org/10.1186/s12889-023-16909-9 |
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