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Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.
Shaw, Richard J; Harron, Katie L; Pescarini, Julia M; Pinto Junior, Elzo Pereira; Allik, Mirjam; Siroky, Andressa N; Campbell, Desmond; Dundas, Ruth; Ichihara, Maria Yury; Leyland, Alastair H; Barreto, Mauricio L; Katikireddi, Srinivasa Vittal.
Afiliação
  • Shaw RJ; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, UK. richard.shaw@glasgow.ac.uk.
  • Harron KL; UCL Great Ormond Street Institute of Child Health, UCL, London, UK.
  • Pescarini JM; Centro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz, Salvador, Brazil.
  • Pinto Junior EP; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
  • Allik M; Centro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz, Salvador, Brazil.
  • Siroky AN; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, UK.
  • Campbell D; Centro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz, Salvador, Brazil.
  • Dundas R; Departamento de Estatística, Universidade Federal do Rio Grande do Norte, Natal, Brazil.
  • Ichihara MY; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, UK.
  • Leyland AH; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, UK.
  • Barreto ML; Centro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz, Salvador, Brazil.
  • Katikireddi SV; MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, UK.
Eur J Epidemiol ; 37(12): 1215-1224, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36333542
Linked administrative data offer a rich source of information that can be harnessed to describe patterns of disease, understand their causes and evaluate interventions. However, administrative data are primarily collected for operational reasons such as recording vital events for legal purposes, and planning, provision and monitoring of services. The processes involved in generating and linking administrative datasets may generate sources of bias that are often not adequately considered by researchers. We provide a framework describing these biases, drawing on our experiences of using the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018. Datasets for epidemiological research were derived by linking the 100MCohort to health-related databases such as the Mortality Information System and the Hospital Information System. Using the framework, we demonstrate how selection and misclassification biases may be introduced in three different stages: registering and recording of people's life events and use of services, linkage across administrative databases, and cleaning and coding of variables from derived datasets. Finally, we suggest eight recommendations which may reduce biases when analysing data from administrative sources.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registro Médico Coordenado Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Eur J Epidemiol Assunto da revista: EPIDEMIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registro Médico Coordenado Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Eur J Epidemiol Assunto da revista: EPIDEMIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de publicação: Holanda