title: Standardized data collection practices and the racial/ethnic distribution of hospitalized patients.
abstract: BACKGROUND
  Although frequently used to track health care disparities, patient race/ethnicity data collected by hospitals can be unreliable, particularly for smaller minority groups. We sought to determine whether the racial/ethnic distribution of hospitalized patients shifted after implementation of a statewide initiative to standardize data collection practices.
  
  
  METHODS
  We conducted a difference-in-differences analysis of the State Inpatient Databases to estimate changes in the proportion of patients identified as non-Hispanic white, non-Hispanic black, Hispanic, Asian/Pacific Islander, and "other," before (2005-2006) and after (2008-2009) standardized practices were implemented in New Jersey relative to New York, a state with similar demographics but no changes to data collection.
  
  
  RESULTS
  Among 12,552,702 hospital discharges, modest relative changes were noted in the proportion of patients identified as non-Hispanic white [+1.1%; 95% confidence interval (CI): +0.9 to +1.2] and non-Hispanic black (+1.6%; 95% CI: +1.1 to +2.1) in New Jersey that were attributed to its use of standardized data collection practices as compared with New York. Larger relative changes were noted in the proportion of patients identified as Hispanic (-7.1%; 95% CI: -7.8 to -6.4), Asian/Pacific Islander (+26.5%; 95% CI: +25.1 to +27.9), and "other" (-24.6%; 95% CI: -26.4 to -22.8). This pattern was largely consistent in analyses stratified by sex, age, and Major Diagnostic Category.
  
  
  CONCLUSIONS
  Measurement of health care disparities fundamentally depends on the racial/ethnic categorization of individuals. By redistributing substantial proportions of patients across smaller minority groups, standardized data collection could lead to shifts in estimates of health care disparities for these rapidly growing populations.
authors.affiliation: *Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT †Department of Health Management and Policy, Saint Louis University, St. Louis, MO ‡Center for Outcomes Research and Evaluation, Yale-New Haven Hospital §Department of Health Policy and Management, Yale School of Public Heatlh ∥Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT ¶Division of Cardiovascular Medicine and Center for Health Outcomes and Policy, University of Michigan Medical School, Ann Arbor, MI.
authors.forename: Rosette J
authors.initials: RJ
authors.lastname: Chakkalakal
authors.forename: Jeremy C
authors.initials: JC
authors.lastname: Green
authors.forename: Harlan M
authors.initials: HM
authors.lastname: Krumholz
authors.forename: Brahmajee K
authors.initials: BK
authors.lastname: Nallamothu
mesh_terms: D005006:Ethnicity / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D008913:Minority Groups / Q000706:statistics & numerical data; D009515:New Jersey / Q000453:epidemiology; D009518:New York / Q000453:epidemiology; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D044469:Racial Groups / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors