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Use of Primary Care Data in Research and Pharmacovigilance: Eight Scenarios Where Prescription Data are Absent.
Okoli, Grace N; Myles, Puja; Murray-Thomas, Tarita; Shepherd, Hilary; Wong, Ian C K; Edwards, Duncan.
Afiliação
  • Okoli GN; Centre for Primary Care and Mental Health, Institute of Population Health Sciences, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London, E1 2AB, UK. g.okoli@qmul.ac.uk.
  • Myles P; Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
  • Murray-Thomas T; Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
  • Shepherd H; Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
  • Wong ICK; Research Department of Practice and Policy, Centre for Medicines Optimisation Research and Education (CMORE), School of Pharmacy, University College London, London, UK.
  • Edwards D; Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, London, UK.
Drug Saf ; 44(10): 1033-1040, 2021 10.
Article em En | MEDLINE | ID: mdl-34296384
ABSTRACT
The use of primary care databases has been integral in pharmacoepidemiological studies and pharmacovigilance. Primary care databases derive from electronic health records and offer a comprehensive description of aggregate patient data, from demography to medication history, and good sample sizes. Studies using these databases improve our understanding of prescribing characteristics and associated risk factors to facilitate better patient care, but there are limitations. We describe eight key scenarios where study data outcomes can be affected by absent prescriptions in UK primary care databases (1) out-of-hours, urgent care and acute care prescriptions; (2) specialist-only prescriptions; (3) alternative community prescribing, such as pharmacy, family planning clinic or sexual health clinic medication prescriptions; (4) newly licensed medication prescriptions; (5) medications that do not require prescriptions; (6) hospital inpatient and outpatient prescriptions; (7) handwritten prescriptions; and (8) private pharmacy and private doctor prescriptions. The significance of each scenario is dependent on the type of medication under investigation, nature of the study and expected outcome measures. We recommend that all researchers using primary care databases be aware of the potential for missing prescribing data and be sensitive to how this can vary substantially between items, drug classes, patient groups and over time. Close liaison with practising primary care clinicians in the UK is often essential to ensure awareness of nuances in clinical practice.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Prescrição Eletrônica / Farmacovigilância Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Revista: Drug Saf Assunto da revista: TERAPIA POR MEDICAMENTOS / TOXICOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Prescrição Eletrônica / Farmacovigilância Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Revista: Drug Saf Assunto da revista: TERAPIA POR MEDICAMENTOS / TOXICOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Reino Unido