title: Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race.
abstract: A large number of epidemiologic studies have focused on racial/ethnic differences, particularly between blacks and whites. Because health endpoints and racial categorizations are associated with socioeconomic status, investigators generally adjust for socioeconomic indicators. The intention is usually to control for confounding, thereby making groups comparable and excluding socioeconomic status as an alternative explanation to hypotheses of innate physiologic differences. A threat to the validity of these analyses is therefore the presence of residual confounding. We identify four potential sources of residual confounding in this analytical design: categorization of socioeconomic status variables, measurement error in socioeconomic indicators, use of aggregated socioeconomic status measures, and incommensurate socioeconomic indicators. Using simulations and examples from the literature, we demonstrate that the effect of residual confounding is to bias interpretation of data toward the conclusion of independent racial/ethnic group effects. Investigators often refer to possible "genetic" differences on the basis of models that control for socioeconomic status. We propose that such conclusions on the basis of this analytical strategy are generally unwarranted. Racial/ethnic differences in disease are a pressing public health concern, but the current approach does not often provide a basis for inference about putative biological factors in the etiology of this disparity.
authors.affiliation: Department of Preventive Medicine and Epidemiology, Loyola University Stritch School of Medicine, Maywood, IL, USA.
authors.forename: J S
authors.initials: JS
authors.lastname: Kaufman
authors.forename: R S
authors.initials: RS
authors.lastname: Cooper
authors.forename: D L
authors.initials: DL
authors.lastname: McGee
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data*; D015982:Bias; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003710:Demography; D003920:Diabetes Mellitus / Q000453:epidemiology; D004812:Epidemiologic Methods; D020022:Genetic Predisposition to Disease; D006304:Health Status; D006801:Humans; D007182:Income / Q000145:classification / Q000706:statistics & numerical data; D016015:Logistic Models; D016017:Odds Ratio; D012111:Residence Characteristics / Q000145:classification / Q000706:statistics & numerical data; D018570:Risk Assessment; D012923:Social Class; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data*