title: Optimizing primary care research participation: a comparison of three recruitment methods in data-sharing studies.
abstract: BACKGROUND
  Recruitment of representative samples in primary care research is essential to ensure high-quality, generalizable results. This is particularly important for research using routinely recorded patient data to examine the delivery of care. Yet little is known about how different recruitment strategies influence the characteristics of the practices included in research.
  
  
  OBJECTIVE
  We describe three approaches for recruiting practices to data-sharing studies, examining differences in recruitment levels and practice representativeness.
  
  
  METHODS
  We examined three studies that included varying populations of practices from West Yorkshire, UK. All used anonymized patient data to explore aspects of clinical practice. Recruitment strategies were 'opt-in', 'mixed opt-in and opt-out' and 'opt-out'. We compared aggregated practice data between recruited and not-recruited practices for practice list size, deprivation, chronic disease management, patient experience and rates of unplanned hospital admission.
  
  
  RESULTS
  The opt-out strategy had the highest recruitment (80%), followed by mixed (70%) and opt-in (58%). Practices opting-in were larger (median 7153 versus 4722 patients, P = 0.03) than practices that declined to opt-in. Practices recruited by mixed approach were larger (median 7091 versus 5857 patients, P = 0.04) and had differences in the clinical quality measure (58.4% versus 53.9% of diabetic patients with HbA1c ≤ 59 mmol/mol, P < 0.01). We found no differences between practices recruited and not recruited using the opt-out strategy for any demographic or quality of care measures.
  
  
  CONCLUSIONS
  Opt-out recruitment appears to be a relatively efficient approach to ensuring participation of typical general practices. Researchers should, with appropriate ethical safeguards, consider opt-out recruitment of practices for studies involving anonymized patient data sharing.
authors.affiliation: Leeds Institute of Health Sciences, University of Leeds, Leeds and p.lord@leeds.ac.uk.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Lord
authors.affiliation: Leeds Institute of Health Sciences, University of Leeds, Leeds and.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Willis
authors.affiliation: NHS Yorkshire and Humber Commissioning Support, Bradford, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Carder
authors.affiliation: Leeds Institute of Health Sciences, University of Leeds, Leeds and.
authors.forename: Robert M
authors.initials: RM
authors.lastname: West
authors.affiliation: Leeds Institute of Health Sciences, University of Leeds, Leeds and.
authors.forename: Robbie
authors.initials: R
authors.lastname: Foy
mesh_terms: D000068456:Clinical Studies as Topic / Q000379:methods*; D057286:Electronic Health Records; D005260:Female; D058006:General Practice; D006801:Humans; D033181:Information Dissemination / Q000379:methods; D007258:Informed Consent; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D011320:Primary Health Care; D012107:Research Design; D006113:United Kingdom
keywords: Electronic health records; family practice; primary health care; quality improvement; research design; research subject recruitment.