Treffer: Primary care research on hypertension: A bibliometric analysis using machine-learning.
Title:
Primary care research on hypertension: A bibliometric analysis using machine-learning.
Authors:
Yasli G; Department of Public Health, İzmir Health Directorate, İzmir, Turkey., Damar M; Information Center, Dokuz Eylul University, İzmir, Turkey.; Upstream Lab, MAP, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada., Özbiçakci Ş; Department of Public Health Nursing, Faculty of Nursing, Dokuz Eylul University, İzmir, Turkey., Alici S; Faculty of Economics and Administrative Sciences, Dokuz Eylul University, İzmir, Turkey., Pinto AD; Upstream Lab, MAP, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada.; Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Source:
Medicine [Medicine (Baltimore)] 2024 Nov 22; Vol. 103 (47), pp. e40482.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 2985248R Publication Model: Print Cited Medium: Internet ISSN: 1536-5964 (Electronic) Linking ISSN: 00257974 NLM ISO Abbreviation: Medicine (Baltimore) Subsets: MEDLINE
Imprint Name(s):
Original Publication: Hagerstown, Md : Lippincott Williams & Wilkins
MeSH Terms:
References:
Adeyeye E, Kapil V, Lobo MD. Hypertension. Medicine (Baltimore). 2022;50:399–407.
Kochanek KD, Murphy SL, Xu J, Arias E. Deaths: final data for 2017. Natl Vital Stat Rep. 2019;68:1–77.
. World Health Organization. Hypertension. Fact Sheets; 2024. https://www.who.int/news-room/fact-sheets/detail/hypertension . Accessed March 01, 2024.
Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37.
Pazoki R, Dehghan A, Evangelou E, et al. Genetic predisposition to high blood pressure and lifestyle factors: associations with midlife blood pressure levels and cardiovascular events. Circulation. 2018;137:653–61.
Yurekli AA, Bilir N, Husain MJ. Projecting burden of hypertension and its management in Turkey, 2015-2030. PLoS One. 2019;14:e0221556.
Bhagani S, Kapil V, Lobo MD. Hypertension. Medicine (Baltimore). 2018;46:509–15.
Arora A, Gola A, Loganathan V, Lakshminarayanan S, Kar SS. Hypertension control status among patients receiving treatment from selected primary health centres in puducherry: a cohort-analysis approach. Cureus. 2023;15:e45042.
Bilik O, Damar HT, Ozdagoglu G, Ozdagoglu A, Damar M. Identifying trends, patterns, and collaborations in nursing career research: a bibliometric snapshot (1980–2017). Collegian. 2020;27:40–8.
Moran AE, Gupta R; Global Hearts Initiative Collaborators. Implementation of global hearts hypertension control programs in 32 low-and middle-income countries: JACC international. J Am Coll Cardiol. 2023;82:1868–84.
Allen MP, Jacobs SK, Levy JR. Mapping the literature of nursing: 1996–2000. J Med Libr Assoc. 2006;94:206–20.
Damar HT, Bilik O, Ozdagoglu G, Ozdagoglu A, Damar M. Scientometric overview of nursing research on pain management. Rev Lat Am Enfermagem. 2018;26:e3051.
Karabaeva RZ, Vochshenkova TA, Zare A, et al. Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses. Front Mol Biosci. 2023;10:1221337.
Abba M, Nduka C, Anjorin S, Mohamed S, Agogo E, Uthman O. One hundred years of hypertension research: topic modeling study. JMIR Form Res. 2022;6:e31292.
Rezapour M, Yazdinejad M, Rajabi Kouchi F, et al. Text mining of hypertension researches in the west Asia region: a 12-year trend analysis. Ren Fail. 2024;46:2337285.
Lou Y, Sun N, Zhang M, Qiu Y, Wang J, Chen J. Trends in exercise for hypertension: a bibliometric analysis. Front Cardiovasc Med. 2023;10:1260569.
Yang W, Shen J, Shi G, et al. Bibliometric and visual analysis of hypoxic pulmonary hypertension from 2013 to 2022. Heliyon. 2023;9:e22044.
Salmani D, Purushothaman S, Somashekara SC, et al. Study of structural changes in placenta in pregnancy-induced hypertension. J Nat Sci Biol Med. 2014;5:352–5.
Alam MS, Islam MM. Bibliometric study of placenta in pregnancy-induced hypertension. J Sci Soc. 2022;49:298–303.
Man TM, Wu L, Zhang JY, Dong YT, Sun YT, Luo L. Research trends of acupuncture therapy for hypertension over the past two decades: a bibliometric analysis. Cardiovasc Diagn Ther. 2023;13:67–82.
Liu N, Xia L. Investigation of epigenetics insights of hypertension: a bibliometric analysis. Medicine (Baltim). 2023;102:e35125.
Devos P, Ménard J. Trends in worldwide research in hypertension over the period 1999–2018: a bibliometric study. Hypertension. 2020;76:1649–55.
Kaplan K, Solak Y. Portal hypertension research activity: a bibliometric analysis. Cukurova Med J. 2022;47:1709–21.
Niu Y, Cai H, Zhou W, et al. Research trends in hypertension associated with obstructive sleep apnea: a bibliometric analysis. Sleep Breath. 2023;27:411–9.
Visser M, Van Eck NJ, Waltman L. Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. Quant Sci Stud. 2021;2:20–41.
van Weel C. The web of science subject category “primary health care.”. Fam Pract. 2011;28:351–351.
Lim S, Tucker CS, Kumara S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. J Biomed Inform. 2017;66:82–94.
Choi S, Seo J. An exploratory study of the research on caregiver depression: using bibliometrics and LDA topic modeling. Issues Ment Health Nurs. 2020;41:592–601.
Jelodar H, Wang Y, Yuan C, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78:15169–211.
Inoue M, Fukahori H, Matsubara M, Yoshinaga N, Tohira H. Latent Dirichl et al location topic modeling of free-text responses exploring the negative impact of the early COVID-19 pandemic on research in nursing. Jpn J Nurs Sci. 2023;20:e12520.
Ossai CI, Wickramasinghe N. Sentiments prediction and thematic analysis for diabetes mobile apps using embedded deep neural networks and latent Dirichlet allocation. Artif Intell Med. 2023;138:102509.
Li Y, Rapkin B, Atkinson TM, Schofield E, Bochner BH. Leveraging latent Dirichlet allocation in processing free-text personal goals among patients undergoing bladder cancer surgery. Qual Life Res. 2019;28:1441–55.
Oh YW, Kim J. Insights into Korean public perspectives on urology: online news data analytics through latent Dirichlet allocation topic modeling. Int Neurourol J. 2023;27(Suppl 2):S91–98.
Tran BX, Nghiem S, Sahin O, et al. Modeling research topics for artificial intelligence applications in medicine: latent Dirichlet allocation application study. J Med Internet Res. 2019;21:e15511.
Pérez J, Pérez A, Casillas A, Gojenola K. Cardiology record multi-label classification using latent Dirichlet allocation. Comput Methods Programs Biomed. 2018;164:111–9.
Lee MS, Lee S. Identifying latent topics and trends in premature infant-related nursing studies using a latent Dirichlet allocation method. Comput Inform Nurs. 2023;41:957–67.
Geletta S, Follett L, Laugerman M. Latent Dirichlet allocation in predicting clinical trial terminations. BMC Med Inform Decis Mak. 2019;19:242.
Blei DM. Surveying a suite of algorithms that offer a solution to managing large document archives. Probabilistic topic models. Commun ACM. 2012;55:77–84.
Churchill R, Singh L. The evolution of topic modeling. ACM Comput Surv. 2022;54:1–35.
Chung Y. Bradford distribution and core authors in classification systems literature. Scientometrics. 1994;29:253–69.
Xu F. A standard procedure for Bradford analysis and its application to the periodical literature in systems librarianship. Libr Hi Tech. 2011;29:751–63.
Mao X, Chen C, Wang B, Hou J, Xiang C. A global bibliometric and visualized analysis in the status and trends of subchondral bone research. Medicine (Baltim). 2020;99:e20406.
Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci U S A. 2005;102:16569–72.
Chobufo MD, Gayam V, Soluny J, et al. Prevalence and control rates of hypertension in the USA: 2017-2018. Int J Cardiol Hypertens. 2020;6:100044.
Finley CR, Chan DS, Garrison S, et al. What are the most common conditions in primary care? Systematic review. Can Fam Physician. 2018;64:832–40.
Salter AJ, Martin BR. The economic benefits of publicly funded basic research: a critical review. Res Pol. 2001;30:509–32.
Salager-Meyer F. Scientific publishing in developing countries: challenges for the future. J Engl Acad Purp. 2008;7:121–32.
. Times Higher Education. The times higher education world university rankings 2023. https://www.timeshighereducation.com/world-university-rankings/2023/world-ranking . Accessed February 20, 2024.
. Shanghai Ranking. Academic ranking of world universities 2023. https://www.shanghairanking.com/institution . Accessed February 20, 2024.
NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants [published correction appears in Lancet. 2022 Feb 5;399(10324):520]. Lancet. 2021;398:957–80.
Koya SF, Pilakkadavath Z, Chandran P, et al. Hypertension control rate in India: systematic review and meta-analysis of population-level non-interventional studies, 2001-2022. Lancet Reg Health Southeast Asia. 2022;9:100113.
Richardson G, Godfrey L, Gravelle H, Watt I. Cost-effectiveness of implementing new guidelines for treatment of hypertension in general practice. Br J Gen Pract. 2004;54:765–71.
Mancia G, Laurent S, Agabiti-Rosei E, et al. Reappraisal of European guidelines on hypertension management: a European Society of Hypertension Task Force document. Blood Press. 2009;18:308–47.
Pallarés-Carratalá V, Divisón Garrote JA, Prieto Díaz MA, et al. Posicionamiento para el manejo de la hipertensión arterial en atención primaria a partir del análisis crítico de las guías americana (2017) y europea (2018). Sociedad Española de Médicos de Atención Primaria (SEMERGEN) [Positioning for the management of arterial hypertension in Primary Care from the critical analysis of the American (2017) and European (2018) guidelines. Spanish Society of Primary Care Physicians (SEMERGEN)]. Semergen. 2019;45:251–72.
Ekesbo R, Midlöv P, Gerward S, Persson K, Nerbrand C, Johansson L. Lack of adherence to hypertension treatment guidelines among GPs in southern Sweden—a case report-based survey. BMC Fam Pract. 2012;13:34.
Ijäs J, Alanen S, Kaila M, et al. Primary care guidelines: senior executives’ views on changing health centre practices in hypertension treatment. Scand J Prim Health Care. 2009;27:202–7.
Gelfer M, Dawes M, Kaczorowski J, Padwal R, Cloutier L. Diagnosing arterial hypertension. Evidence supporting the 2015 recommendations of the Canadian Education Program on Hypertension. Can Fam Physician. 2015;61:1050–1050.
Fahey T, Peters TJ. Clinical guidelines and the management of hypertension: a between-practice and guideline comparison. Br J Gen Pract. 1997;47:729–30.
Comin E, Catalan-Ramos A, Iglesias-Rodal M, et al. Impact of implementing electronic clinical practice guidelines for the diagnosis, control and treatment of cardiovascular risk factors: a pre-post controlled study. Aten Primaria. 2017;49:389–98.
Gupta A, Ellis SD, Burkhardt C, et al. Implementing a home-based virtual hypertension programme—a pilot feasibility study. Fam Pract. 2023;40:414–22.
Herbert CP, Wright JM, Maclure M, et al. Better Prescribing Project: a randomized controlled trial of the impact of case-based educational modules and personal prescribing feedback on prescribing for hypertension in primary care. Fam Pract. 2004;21:575–81.
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Kochanek KD, Murphy SL, Xu J, Arias E. Deaths: final data for 2017. Natl Vital Stat Rep. 2019;68:1–77.
. World Health Organization. Hypertension. Fact Sheets; 2024. https://www.who.int/news-room/fact-sheets/detail/hypertension . Accessed March 01, 2024.
Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37.
Pazoki R, Dehghan A, Evangelou E, et al. Genetic predisposition to high blood pressure and lifestyle factors: associations with midlife blood pressure levels and cardiovascular events. Circulation. 2018;137:653–61.
Yurekli AA, Bilir N, Husain MJ. Projecting burden of hypertension and its management in Turkey, 2015-2030. PLoS One. 2019;14:e0221556.
Bhagani S, Kapil V, Lobo MD. Hypertension. Medicine (Baltimore). 2018;46:509–15.
Arora A, Gola A, Loganathan V, Lakshminarayanan S, Kar SS. Hypertension control status among patients receiving treatment from selected primary health centres in puducherry: a cohort-analysis approach. Cureus. 2023;15:e45042.
Bilik O, Damar HT, Ozdagoglu G, Ozdagoglu A, Damar M. Identifying trends, patterns, and collaborations in nursing career research: a bibliometric snapshot (1980–2017). Collegian. 2020;27:40–8.
Moran AE, Gupta R; Global Hearts Initiative Collaborators. Implementation of global hearts hypertension control programs in 32 low-and middle-income countries: JACC international. J Am Coll Cardiol. 2023;82:1868–84.
Allen MP, Jacobs SK, Levy JR. Mapping the literature of nursing: 1996–2000. J Med Libr Assoc. 2006;94:206–20.
Damar HT, Bilik O, Ozdagoglu G, Ozdagoglu A, Damar M. Scientometric overview of nursing research on pain management. Rev Lat Am Enfermagem. 2018;26:e3051.
Karabaeva RZ, Vochshenkova TA, Zare A, et al. Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses. Front Mol Biosci. 2023;10:1221337.
Abba M, Nduka C, Anjorin S, Mohamed S, Agogo E, Uthman O. One hundred years of hypertension research: topic modeling study. JMIR Form Res. 2022;6:e31292.
Rezapour M, Yazdinejad M, Rajabi Kouchi F, et al. Text mining of hypertension researches in the west Asia region: a 12-year trend analysis. Ren Fail. 2024;46:2337285.
Lou Y, Sun N, Zhang M, Qiu Y, Wang J, Chen J. Trends in exercise for hypertension: a bibliometric analysis. Front Cardiovasc Med. 2023;10:1260569.
Yang W, Shen J, Shi G, et al. Bibliometric and visual analysis of hypoxic pulmonary hypertension from 2013 to 2022. Heliyon. 2023;9:e22044.
Salmani D, Purushothaman S, Somashekara SC, et al. Study of structural changes in placenta in pregnancy-induced hypertension. J Nat Sci Biol Med. 2014;5:352–5.
Alam MS, Islam MM. Bibliometric study of placenta in pregnancy-induced hypertension. J Sci Soc. 2022;49:298–303.
Man TM, Wu L, Zhang JY, Dong YT, Sun YT, Luo L. Research trends of acupuncture therapy for hypertension over the past two decades: a bibliometric analysis. Cardiovasc Diagn Ther. 2023;13:67–82.
Liu N, Xia L. Investigation of epigenetics insights of hypertension: a bibliometric analysis. Medicine (Baltim). 2023;102:e35125.
Devos P, Ménard J. Trends in worldwide research in hypertension over the period 1999–2018: a bibliometric study. Hypertension. 2020;76:1649–55.
Kaplan K, Solak Y. Portal hypertension research activity: a bibliometric analysis. Cukurova Med J. 2022;47:1709–21.
Niu Y, Cai H, Zhou W, et al. Research trends in hypertension associated with obstructive sleep apnea: a bibliometric analysis. Sleep Breath. 2023;27:411–9.
Visser M, Van Eck NJ, Waltman L. Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. Quant Sci Stud. 2021;2:20–41.
van Weel C. The web of science subject category “primary health care.”. Fam Pract. 2011;28:351–351.
Lim S, Tucker CS, Kumara S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. J Biomed Inform. 2017;66:82–94.
Choi S, Seo J. An exploratory study of the research on caregiver depression: using bibliometrics and LDA topic modeling. Issues Ment Health Nurs. 2020;41:592–601.
Jelodar H, Wang Y, Yuan C, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78:15169–211.
Inoue M, Fukahori H, Matsubara M, Yoshinaga N, Tohira H. Latent Dirichl et al location topic modeling of free-text responses exploring the negative impact of the early COVID-19 pandemic on research in nursing. Jpn J Nurs Sci. 2023;20:e12520.
Ossai CI, Wickramasinghe N. Sentiments prediction and thematic analysis for diabetes mobile apps using embedded deep neural networks and latent Dirichlet allocation. Artif Intell Med. 2023;138:102509.
Li Y, Rapkin B, Atkinson TM, Schofield E, Bochner BH. Leveraging latent Dirichlet allocation in processing free-text personal goals among patients undergoing bladder cancer surgery. Qual Life Res. 2019;28:1441–55.
Oh YW, Kim J. Insights into Korean public perspectives on urology: online news data analytics through latent Dirichlet allocation topic modeling. Int Neurourol J. 2023;27(Suppl 2):S91–98.
Tran BX, Nghiem S, Sahin O, et al. Modeling research topics for artificial intelligence applications in medicine: latent Dirichlet allocation application study. J Med Internet Res. 2019;21:e15511.
Pérez J, Pérez A, Casillas A, Gojenola K. Cardiology record multi-label classification using latent Dirichlet allocation. Comput Methods Programs Biomed. 2018;164:111–9.
Lee MS, Lee S. Identifying latent topics and trends in premature infant-related nursing studies using a latent Dirichlet allocation method. Comput Inform Nurs. 2023;41:957–67.
Geletta S, Follett L, Laugerman M. Latent Dirichlet allocation in predicting clinical trial terminations. BMC Med Inform Decis Mak. 2019;19:242.
Blei DM. Surveying a suite of algorithms that offer a solution to managing large document archives. Probabilistic topic models. Commun ACM. 2012;55:77–84.
Churchill R, Singh L. The evolution of topic modeling. ACM Comput Surv. 2022;54:1–35.
Chung Y. Bradford distribution and core authors in classification systems literature. Scientometrics. 1994;29:253–69.
Xu F. A standard procedure for Bradford analysis and its application to the periodical literature in systems librarianship. Libr Hi Tech. 2011;29:751–63.
Mao X, Chen C, Wang B, Hou J, Xiang C. A global bibliometric and visualized analysis in the status and trends of subchondral bone research. Medicine (Baltim). 2020;99:e20406.
Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci U S A. 2005;102:16569–72.
Chobufo MD, Gayam V, Soluny J, et al. Prevalence and control rates of hypertension in the USA: 2017-2018. Int J Cardiol Hypertens. 2020;6:100044.
Finley CR, Chan DS, Garrison S, et al. What are the most common conditions in primary care? Systematic review. Can Fam Physician. 2018;64:832–40.
Salter AJ, Martin BR. The economic benefits of publicly funded basic research: a critical review. Res Pol. 2001;30:509–32.
Salager-Meyer F. Scientific publishing in developing countries: challenges for the future. J Engl Acad Purp. 2008;7:121–32.
. Times Higher Education. The times higher education world university rankings 2023. https://www.timeshighereducation.com/world-university-rankings/2023/world-ranking . Accessed February 20, 2024.
. Shanghai Ranking. Academic ranking of world universities 2023. https://www.shanghairanking.com/institution . Accessed February 20, 2024.
NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants [published correction appears in Lancet. 2022 Feb 5;399(10324):520]. Lancet. 2021;398:957–80.
Koya SF, Pilakkadavath Z, Chandran P, et al. Hypertension control rate in India: systematic review and meta-analysis of population-level non-interventional studies, 2001-2022. Lancet Reg Health Southeast Asia. 2022;9:100113.
Richardson G, Godfrey L, Gravelle H, Watt I. Cost-effectiveness of implementing new guidelines for treatment of hypertension in general practice. Br J Gen Pract. 2004;54:765–71.
Mancia G, Laurent S, Agabiti-Rosei E, et al. Reappraisal of European guidelines on hypertension management: a European Society of Hypertension Task Force document. Blood Press. 2009;18:308–47.
Pallarés-Carratalá V, Divisón Garrote JA, Prieto Díaz MA, et al. Posicionamiento para el manejo de la hipertensión arterial en atención primaria a partir del análisis crítico de las guías americana (2017) y europea (2018). Sociedad Española de Médicos de Atención Primaria (SEMERGEN) [Positioning for the management of arterial hypertension in Primary Care from the critical analysis of the American (2017) and European (2018) guidelines. Spanish Society of Primary Care Physicians (SEMERGEN)]. Semergen. 2019;45:251–72.
Ekesbo R, Midlöv P, Gerward S, Persson K, Nerbrand C, Johansson L. Lack of adherence to hypertension treatment guidelines among GPs in southern Sweden—a case report-based survey. BMC Fam Pract. 2012;13:34.
Ijäs J, Alanen S, Kaila M, et al. Primary care guidelines: senior executives’ views on changing health centre practices in hypertension treatment. Scand J Prim Health Care. 2009;27:202–7.
Gelfer M, Dawes M, Kaczorowski J, Padwal R, Cloutier L. Diagnosing arterial hypertension. Evidence supporting the 2015 recommendations of the Canadian Education Program on Hypertension. Can Fam Physician. 2015;61:1050–1050.
Fahey T, Peters TJ. Clinical guidelines and the management of hypertension: a between-practice and guideline comparison. Br J Gen Pract. 1997;47:729–30.
Comin E, Catalan-Ramos A, Iglesias-Rodal M, et al. Impact of implementing electronic clinical practice guidelines for the diagnosis, control and treatment of cardiovascular risk factors: a pre-post controlled study. Aten Primaria. 2017;49:389–98.
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Entry Date(s):
Date Created: 20250114 Date Completed: 20250501 Latest Revision: 20250503
Update Code:
20250505
PubMed Central ID:
PMC11596423
DOI:
10.1097/MD.0000000000040482
PMID:
39809211
Database:
MEDLINE
Weitere Informationen
Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, forming a significant area of service in Primary Health Care (PHC), which healthcare professionals often confront. It serves as a precursor to many critical illnesses and can lead to fatalities if not addressed promptly. Our study underscores the importance of this critical issue by analyzing articles related to hypertension in the PHC research area from the Web of Science Core Collection using bibliometric methods and machine learning techniques, specifically topic analyses using the latent Dirichlet allocation method. The analysis was conducted using Python Scikit-learn, Gensim, and Wordcloud Libraries, the VosViewer program, and the Bibliometrix R Biblioshiny library. Our findings revealed a steady increase in publication output in hypertension-related research. Analysis shows that hypertension-related research in the PHC research area is clustered into 8 groups: (1) management of hypertension in PHC, risk factors, and complications; (2) psychiatric disorders and hypertension; (3) pediatric and pregnancy hypertension; (4) environmental factors and living conditions; (5) sex and age effects on hypertension; (6) COVID-19 and hypertension; (7) behavioral risk factors, quality of life, and awareness; and (8) current treatment methods and guidelines. Research on hypertension has focused intensively on kidney disease, obesity, pregnancy, cardiovascular risk, heart disease, calcium channel blockers, body mass index, amlodipine, mortality, risk factors, hyperlipidemia, depression, and resistant hypertension. This study represents the first and comprehensive bibliometric analysis of hypertension in the PHC research area. Annual publication volumes have steadily increased over the years. In recent years, topics such as social determinants, patient attendance, self-management, diabetes mellitus, COVID-19, telemedicine, type 2 diabetes, and noncommunicable diseases have garnered significant interest in the field of PHC services.
(Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)
The authors have no funding and conflicts of interest to disclose.