Acceptability of Traditional Chinese Medicine in Chinese People Based on 10-Year's Real World Study With Mutiple Big Data Mining.

China TCM big data data mining social review traditional Chinese medicine

Journal

Frontiers in public health
ISSN: 2296-2565
Titre abrégé: Front Public Health
Pays: Switzerland
ID NLM: 101616579

Informations de publication

Date de publication:
2021
Historique:
received: 09 11 2021
accepted: 30 11 2021
entrez: 3 2 2022
pubmed: 4 2 2022
medline: 28 4 2022
Statut: epublish

Résumé

In the past decades, numerous clinical researches have been conducted to illuminate the effects of traditional Chinese medicine for better inheritance and promotion of it, which are mostly clinical trials designed from the doctor's point of view. This large-scale data mining study was conducted from real-world point of view in up to 10 years' big data sets of Traditional Chinese Medicine (TCM) in China, including both medical visits to hospital and cyberspace and contemporaneous social survey data. Finally, some important and interesting findings appear: (1) More Criticisms vs. More Visits. The intensity of criticism increased by 2.33 times over the past 10 years, while the actual number of visits increased by 2.41 times. (2) The people of younger age, highly educated and from economically developed areas have become the primary population for utilizing TCM, which is contrary to common opinions on the characteristics of TCM users. The discovery of this phenomenon indicates that TCM deserves further study on how it treats illness and maintains health.

Identifiants

pubmed: 35111723
doi: 10.3389/fpubh.2021.811730
pmc: PMC8802718
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

811730

Informations de copyright

Copyright © 2022 Guo, Wang, Chen, Kaptchuk, Li, Gao, Yao, Tang and Xu.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

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Auteurs

Yan Guo (Y)

Xiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.

Tengjiao Wang (T)

Big Data Center for Arts and Sciences, Peking University, Beijing, China.
Key Laboratory of High Confidence Software Technologies, Ministry of Education and School of Electronics Engineering and Computer Science (EECS), Peking University, Beijing, China.

Wei Chen (W)

Big Data Center for Arts and Sciences, Peking University, Beijing, China.
Key Laboratory of High Confidence Software Technologies, Ministry of Education and School of Electronics Engineering and Computer Science (EECS), Peking University, Beijing, China.

Ted J Kaptchuk (TJ)

Global Health and Social Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, United States.

Xilian Li (X)

Big Data Center for Arts and Sciences, Peking University, Beijing, China.
Key Laboratory of High Confidence Software Technologies, Ministry of Education and School of Electronics Engineering and Computer Science (EECS), Peking University, Beijing, China.

Xiang Gao (X)

Big Data Center for Arts and Sciences, Peking University, Beijing, China.
Key Laboratory of High Confidence Software Technologies, Ministry of Education and School of Electronics Engineering and Computer Science (EECS), Peking University, Beijing, China.

Jiahui Yao (J)

Institute of Social Science Survey, Peking University, Beijing, China.

Xudong Tang (X)

Xiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.

Ziming Xu (Z)

Xiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.

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Classifications MeSH