Analysis of research hotspots based on the International Conference on Pharmacoepidemiology and Therapeutic Risk Management from 2021 to 2022

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Author: Zi-Ming YANG ^1, ² Xiang-Long MENG ^1, ² Hao-Liang CUI ¹  Sheng-Feng WANG ^1, ² Si-Yan ZHAN ^1, ^2, ^3, ⁴

Affiliation: 1. School of Public Health, Peking University, Beijing 100191, China 2. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China 3. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China 4. Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100871, China

Keywords: International Conference on Pharmacoepidemiology and Therapeutic Risk Management Pharmacoepidemiology Bibliometrics Social network analysis

DOI: 10.19960/j.issn.1005-0698.202312011

Reference: Zi-Ming YANG, Xiang-Long MENG, Hao-Liang CUI, Sheng-Feng WANG,Si-Yan ZHAN.Analysis of research hotspots based on the International Conference on Pharmacoepidemiology and Therapeutic Risk Management from 2021 to 2022[J].Yaowu Liuxingbingxue Zazhi,2023, 32(12):1408-1416.DOI: 10.19960/j.issn.1005-0698.202312011.[Article in Chinese]  Copied

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Abstract

Objective To describe the research hotspots and authors’ cooperative relationships in the field of pharmacoepidemiology mentioned in the 2022 International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), and compare with those result of ICPE 2021.

Methods Taking the 2021—2022 ICPE abstracts as the data source, we used Python to extract literature information. R software networkD3 package was used to draw network diagrams of cooperative relationships among authors and organizations. Term frequency - inverse document frequency technology in tidytext package was used to conduct weighted statistics on the frequency of tokens to identify hotspot diseases, drugs, databases, etc.

Results 2 086 ICPE abstracts were included. Between 2021 and 2022, the number of abstracts, authors, and organizations increased by 58.8%, 42.3%, and 24.9% respectively. In 2022, there were a total of 387 core authors (including 14 from China, all from Hong Kong and Taiwan district), forming 26 close cooperation groups (including 2 Chinese groups). 161 core organizations (including 8 in China), constituted 18 close cooperation groups (including 1 Chinese-foreign group and 1 Chinese domestic group). The topic of COVID-19, type 2 diabetes, and rheumatoid arthritis have always been deeply discussed, meanwhile, the discussion popularity of breast cancer, Parkinson's disease and dementia have increased significantly. Opioids, sodium-glucose transporters 2 inhibitors, and antipsychotics have always been popular drugs, and the popularity of antidepressants, COVID-19 vaccines and antihypertensive drugs have increased markedly. The hotspot databases included Clinical Practice Research Datalink, IBM MarketScan Commercial Claims and Encounters, and Food and Drug Administration Adverse Event Reporting System, etc.

Conclusion From 2021 to 2022, the scale of ICPE has expanded, and academic cooperation has become more extensive and in-depth. However, there is still a big gap between the participation and cooperation scale of scholars from Chinese mainland and foreign countries. Breast cancer, Parkinson's disease, dementia, antidepressants, and COVID-19 vaccines are new hot topics at ICPE 2022. The research hotspots and cooperation networks displayed in this article have certain reference values for Chinese pharmacoepidemiologists in selecting future research topics and partners.

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References

1.ICPE 2022: Advancing Pharmacoepidemiology and Real-World Evidence for the Global Community[EB/OL]. (2022-11-10) [2022-03-21]. https://www.eventscribe.net/2022/ICPE/.

2.ABSTRACTS of ICPE 2022, the 38th International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), Copenhagen, Denmark, 26-28 August, 2022[J]. Pharmacoepidemiol Drug Saf, 2022, 31(S2): 3-628. DOI: 10.1002/pds.5518.

3.杨子铭, 王胜锋, 詹思延. 基于第37届国际药物流行病学与治疗风险管理会议摘要的文献计量学分析 [J]. 医药导报, 2023, 42(1): 31-38. [Yang ZM, Wang SF, Zhan SY. Bibliometric analysis based on abstracts of the 37th International Conference on Pharmacoepidemiology & Therapeutic Risk Management[J]. Herald of Medicine, 2023, 42(1): 31-38.] DOI: 10.3870/j.issn.1004-0781. 2023.01.006.

4.Abstracts of the 37th International Conference on Pharmacoepidemiology & Therapeutic Risk Management, Virtual, August 23, 2021[J]. Pharmacoepidemiol Drug Saf, 2021, 30(S1): 3-400. DOI: 10.1002/pds.5305.

5.孙鸿飞, 侯伟, 于淼. 我国情报学研究方法应用领域作者合作关系研究 [J]. 情报科学, 2015, 33(4): 69-74. [Sun HF, Hou W, Yu M. Study of the author cooperation relationship of application of research methods of information science in our Country[J]. Information Science, 2015, 33(4): 69-74.] DOI: 10.13833/j.cnki.is.2015.04.015.

6.Silge J, Robinson D. Tidytext: Text mining and analysis using tidy data principles in R[J]. J Open Source Software, 2016, 1(3): 37. DOI: 10.21105/joss. 00037.

7.Bégaud B. A history of pharmacoepidemiology[J]. Therapie, 2019, 74(2): 175-179. DOI: 10.1016/j.therap.2018.10.003.

8.吴明智, 史国兵, 徐莉莉, 等. 药物流行病学研究热点的可视化分析——基于文献计量和内容分析[J]. 医药导报, 2021, 40(4): 461-466. [Wu MZ, Shi GB, Xu LL, et al. Visualization analysis of research hotspots on pharmacoepidemiology: Based on bibliometrics and content analysis[J]. Herald of Medicine, 2021, 40(4): 461-466.] DOI: 10.3870/j.issn.1004-0781.2021.04.005.

9.宫建, 李翠英, 毕开顺, 等. 药物流行病学在临床药学研究领域的可视化引文分析[J]. 药物流行病学杂志, 2015, 24(5): 302-305. [Gong J, Li CY, Bi KS, et al. Visualized citation analysis on research fields of pharmacoepidemiology in clinical pharmacy[J]. Chinese Journal of Pharmacoepidemiology, 2015, 24(5): 302-305.]DOI: 10.19960/j.cnki.issn1005-0698.2015.05.014.

10.ICPE 2021. Real evidence for the real world[EB/OL]. (2021-10-24) [2023-03-21]. https://icpemeeting.secure-platform.com/a/.

11.章雨帆, 张平, 戴静, 等. 绝经后乳腺癌患者辅助AIs治疗骨折及骨关节症状危险因素分析[J]. 中华肿瘤防治杂志, 2020, 27(15): 1230-1235. [Zhang Y, Zhang P, Dai J, et al. Analysis of corelative factors about osteoporotic fractures and musculoskeletal symptoms in 191 postmenopausal breast cancer patients treated with aromatase inhibitors[J]. Chinese Journal of Cancer Prevention and Treatment, 2020, 27(15): 1230-1235.] DOI: 10.16073/j.cnki.cjcpt.2020.15.08.

12.李娟娟, 赵树勇, 杜媛泽,等. 中国居民1990—2019年心理性疾病负担及其危险因素变化趋势[J]. 中国公共卫生, 2022, 38(5): 518-522. [Li JJ，Zhao SY，Du YZ，et al.Changing trend in disease burden of mental illness and its risk factors in China, 1990—2019[J].Chinese Journal of Public Health，2022, 38(5): 518-522.] DOI: 10.11847/zgggws1136676.

13.Sturkenboom M, Schink T. Databases for pharmacoepidemiological research[M]. Cham, Switzerland: Springer International Publishing, 2021: 236-237.

14.United States National Cancer Institute. Overview of the SEER Program[EB/OL]. (2023-03-14) [2023-03-21]. https://seer.cancer.gov/about/overview.html.

15.Johnston EE, Martinez I, Davis ES, et al. SARS-CoV-2 in childhood cancer in 2020: A disease of disparities[J]. J Clin Oncol, 2021, 39(34): 3778-3788. DOI: 10.1200/jco.21.00702.

16.Karim MA, Ramezani M, Leroux T, et al. Healthcare costs for medicare patients with hepatocellular carcinoma in the United States[J]. Clin Gastroenterol Hepatol, 2023 , 21(9): 2327-2337. e9. DOI: 10.1016/j.cgh.2022.11.015.

17.Zou Z, Chang D, Liu H, et al. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?[J]. Insights Imaging, 2021, 12(1): 31. DOI: 10.1186/s13244-021-00977-9.

18.Shameer K, Johnson KW, Glicksberg BS, et al. Machine learning in cardiovascular medicine: are we there yet?[J]. Heart, 2018, 104(14): 1156-1164. DOI: 10.1136/heartjnl-2017-311198.