title: Why poor quality of ethnicity data should not preclude its use for identifying disparities in health and healthcare.
abstract: BACKGROUND
  Data of quality are needed to identify ethnic disparities in health and healthcare and to meet the challenges in governance of race relations. Yet concerns over completeness, accuracy and timeliness have been long-standing and inhibitive with respect to the analytical use of the data.
  
  
  OBJECTIVE
  To identify incompleteness of ethnicity data across routine health and healthcare datasets and to investigate the utility of analytical strategies for using data that is of suboptimal quality.
  
  
  METHODS
  An analysis by government office regions of ethnicity data incompleteness in routine datasets and a comprehensive review and evaluation of the literature on appropriate analytical strategies to address the use of such data.
  
  
  RESULTS
  There is only limited availability of ethnically coded routine datasets on health and healthcare, with substantial variability in valid ethnic coding: although a few have high levels of completeness, the majority are poor (notably hospital episode statistics, drug treatment data and non-medical workforce). In addition, there is also a more than twofold regional difference in quality. Organisational factors seem to be the main contributor to the differentials, and these are amenable-yet, in practice, difficult-to change. This article discusses the strengths and limitations of a variety of analytical strategies for using data of suboptimal quality and explores how they may answer important unresolved questions in relation to ethnic inequalities.
  
  
  CONCLUSIONS
  Only by using the data, even when of suboptimal quality, and remaining close to it can healthcare organisations drive up quality.
authors.affiliation: Centre for Health Services Studies, University of Kent, Canterbury, Kent, UK. p.j.aspinall@kent.ac.uk
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Aspinall
authors.forename: Bobbie
authors.initials: B
authors.lastname: Jacobson
mesh_terms: D000465:Algorithms; D015982:Bias; D003625:Data Collection / Q000592:standards*; D016208:Databases, Factual / Q000592:standards*; D005006:Ethnicity / Q000145:classification* / Q000706:statistics & numerical data; D019538:Health Care Surveys / Q000379:methods* / Q000592:standards; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006779:Hospitals, Public / Q000706:statistics & numerical data*; D006801:Humans; D008498:Medical Record Linkage; D008499:Medical Records / Q000145:classification*; D015201:Meta-Analysis as Topic; D009275:Names; D011822:Race Relations; D012107:Research Design; D012959:Socioeconomic Factors; D013222:State Medicine / Q000706:statistics & numerical data*; D006113:United Kingdom