Evolution of primary care databases in UK: a scientometric analysis of research output

Objective To identify publication and citation trends, most productive institutions and countries, top journals, most cited articles and authorship networks from articles that used and analysed data from primary care databases (CPRD, THIN, QResearch) of pseudonymised electronic health records (EHRs) in UK. Methods Descriptive statistics and scientometric tools were used to analyse a SCOPUS data set of 1891 articles. Open access software was used to extract networks from the data set (Table2Net), visualise and analyse coauthorship networks of scholars and countries (Gephi) and density maps (VOSviewer) of research topics co-occurrence and journal cocitation. Results Research output increased overall at a yearly rate of 18.65%. While medicine is the main field of research, studies in more specialised areas include biochemistry and pharmacology. Researchers from UK, USA and Spanish institutions have published the most papers. Most of the journals that publish this type of research and most cited papers come from UK and USA. Authorship varied between 3 and 6 authors. Keyword analyses show that smoking, diabetes, cardiovascular diseases and mental illnesses, as well as medication that can treat such medical conditions, such as non-steroid anti-inflammatory agents, insulin and antidepressants constitute the main topics of research. Coauthorship network analyses show that lead scientists, directors or founders of these databases are, to various degrees, at the centre of clusters in this scientific community. Conclusions There is a considerable increase of publications in primary care research from EHRs. The UK has been well placed at the centre of an expanding global scientific community, facilitating international collaborations and bringing together international expertise in medicine, biochemical and pharmaceutical research.

[1]  Ross J. Anderson,et al.  The collection, linking and use of data in biomedical research and health care: ethical issues , 2015 .

[2]  R. Tibshirani,et al.  Increasing value and reducing waste in research design, conduct, and analysis , 2014, The Lancet.

[3]  C. Schweiger [Statins and the risk of dementia]. , 2001, Italian heart journal. Supplement : official journal of the Italian Federation of Cardiology.

[4]  David Stables,et al.  QRESEARCH: a new general practice database for research. , 2004, Informatics in primary care.

[5]  Tao Zhang,et al.  International collaboration to assess the risk of Guillain Barré Syndrome following Influenza A (H1N1) 2009 monovalent vaccines. , 2013, Vaccine.

[6]  A. Barkun,et al.  Use of gastric acid-suppressive agents and the risk of community-acquired Clostridium difficile-associated disease. , 2005, JAMA.

[7]  Katie Brittain,et al.  The effectiveness of collaborative care for people with memory problems in primary care: results of the CAREDEM case management modelling and feasibility study. , 2014, Health technology assessment.

[8]  Daniel B. Shin,et al.  Prevalence of cardiovascular risk factors in patients with psoriasis. , 2022, Central European journal of public health.

[9]  F. Shahram,et al.  Scientometric analysis and mapping of scientific articles on Behcet's disease , 2013, International journal of rheumatic diseases.

[10]  Joy Adamson,et al.  The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. , 2014, Health technology assessment.

[11]  A. Bourke,et al.  Feasibility study and methodology to create a quality-evaluated database of primary care data. , 2004, Informatics in primary care.

[12]  J. Chisholm,et al.  The Read clinical classification. , 1990, BMJ.

[13]  D H Lawson,et al.  The General Practice Research Database. Scientific and Ethical Advisory Group. , 1998, QJM : monthly journal of the Association of Physicians.

[14]  R. Hubbard,et al.  Risk of myocardial infarction and stroke after acute infection or vaccination. , 2004, The New England journal of medicine.

[15]  Beth Simone Noveck,et al.  The Open Data Era in Health and Social Care , 2014 .

[16]  Daniel B. Shin,et al.  Risk of myocardial infarction in patients with psoriasis. , 2006, JAMA.

[17]  John P. A. Ioannidis,et al.  Research: increasing value, reducing waste 2 , 2014 .

[18]  Pierre Azoulay,et al.  Does Science Advance One Funeral at a Time? , 2015, The American economic review.

[19]  K. Bhaskaran,et al.  Data Resource Profile: Clinical Practice Research Datalink (CPRD) , 2015, International journal of epidemiology.

[20]  Liam Smeeth,et al.  MMR vaccination and pervasive developmental disorders: a case-control study , 2004, The Lancet.

[21]  N. Freemantle,et al.  Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research , 2013, BMJ.

[22]  L. Wood,et al.  The General Practice Research Database , 2004, Drug safety.

[23]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  D. D. Des Jarlais,et al.  HIV research productivity and structural factors associated with HIV research output in European Union countries: a bibliometric analysis , 2015, BMJ Open.

[25]  Dipak Kalra,et al.  Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER) , 2012, International journal of epidemiology.

[26]  A. Majeed,et al.  A user's guide to data collected in primary care in England , 2006 .

[27]  E. Speed,et al.  Politics, Policy and Privatisation in the Everyday Experience of Big Data in the NHS , 2014 .

[28]  H. Krumholz Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. , 2014, Health affairs.

[29]  L. Smeeth,et al.  Pragmatic randomised trials using routine electronic health records: putting them to the test , 2012, BMJ : British Medical Journal.

[30]  H. Jick,et al.  Risk of idiopathic cardiovascular death and rionfatal venous thromboembolism in women using oral contraceptives with differing progestagen components , 1995, The Lancet.

[31]  Ludo Waltman,et al.  Visualizing Bibliometric Networks , 2014 .

[32]  Brian Sauer,et al.  Guidelines for good database selection and use in pharmacoepidemiology research , 2012, Pharmacoepidemiology and drug safety.

[33]  Susan Clamp,et al.  Centralised Electronic Health Records Research Across Health Organisation Types , 2013, BIOSTEC.

[34]  C. Cooper,et al.  Use of Inhaled Corticosteroids and Risk of Fractures , 2001, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[35]  S. Hill,et al.  Variability in risk of gastrointestinal complications with individual non-steroidal anti-inflammatory drugs: results of a collaborative meta-analysis , 1996, BMJ.

[36]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[37]  Stefan Voß,et al.  A Scientometric Analysis of Cloud Computing Literature , 2014, IEEE Transactions on Cloud Computing.

[38]  T. V. van Staa,et al.  Recent advances in the utility and use of the General Practice Research Database as an example of a UK Primary Care Data resource , 2012, Therapeutic advances in drug safety.

[39]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel program: status and direction , 2012, Pharmacoepidemiology and drug safety.

[40]  R. Scoble,et al.  Assessment, evaluations, and definitions of research impact: A review , 2014 .

[41]  M. Jacomy,et al.  ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software , 2014, PloS one.

[42]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[43]  Martin Hand,et al.  Big Data? Qualitative Approaches to Digital Research , 2014 .

[44]  Michelle Dunn,et al.  The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data , 2014, J. Am. Medical Informatics Assoc..

[45]  Yu-Xiao Yang,et al.  Long-term proton pump inhibitor therapy and risk of hip fracture. , 2006, JAMA.

[46]  E. Gale,et al.  The influence of glucose-lowering therapies on cancer risk in type 2 diabetes , 2009, Diabetologia.

[47]  T. Walley,et al.  The UK General Practice Research Database , 1997, The Lancet.

[48]  C. Cooper,et al.  Use of Oral Corticosteroids and Risk of Fractures , 2005, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.