title: Accounting for context in studies of health inequalities: a review and comparison of analytic approaches.
abstract: BACKGROUND
  A common epidemiologic objective is to evaluate the contribution of residential context to individual-level disparities by race or socioeconomic position.
  
  
  OBJECTIVE
  We reviewed analytic strategies to account for the total (observed and unobserved factors) contribution of environmental context to health inequalities, including conventional fixed effects (FE) and hybrid FE implemented within a random effects (RE) or a marginal model.
  
  
  METHODS
  To illustrate results and limitations of the various analytic approaches of accounting for the total contextual component of health disparities, we used data on births nested within neighborhoods as an applied example of evaluating neighborhood confounding of racial disparities in gestational age at birth, including both a continuous and a binary outcome.
  
  
  RESULTS
  Ordinary and RE models provided disparity estimates that can be substantially biased in the presence of neighborhood confounding. Both FE and hybrid FE models can account for cluster level confounding and provide disparity estimates unconfounded by neighborhood, with the latter having greater flexibility in allowing estimation of neighborhood-level effects and intercept/slope variability when implemented in a RE specification.
  
  
  CONCLUSIONS
  Given the range of models that can be implemented in a hybrid approach and the frequent goal of accounting for contextual confounding, this approach should be used more often.
authors.affiliation: Office of Epidemiology, Policy & Evaluation, Maternal and Child Health Bureau, Health Resources and Services Administration, Rockville, MD 20857, USA. aschempf@hrsa.gov
authors.forename: Ashley H
authors.initials: AH
authors.lastname: Schempf
authors.forename: Jay S
authors.initials: JS
authors.lastname: Kaufman
mesh_terms: D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D005865:Gestational Age; D054624:Health Status Disparities; D006801:Humans; D054525:Minority Health; D015233:Models, Statistical; D011247:Pregnancy; D047928:Premature Birth; D012044:Regression Analysis; D012107:Research Design; D012111:Residence Characteristics; D012959:Socioeconomic Factors