title: Racial/ethnic differences in obesity and comorbidities between safety-net- and non safety-net integrated health systems.
abstract: Previous research shows that patients in integrated health systems experience fewer racial disparities compared with more traditional healthcare systems. Little is known about patterns of racial/ethnic disparities between safety-net and non safety-net integrated health systems.We evaluated racial/ethnic differences in body mass index (BMI) and the Charlson comorbidity index from 3 non safety-net- and 1 safety-net integrated health systems in a cross-sectional study. Multinomial logistic regression modeled comorbidity and BMI on race/ethnicity and health care system type adjusting for age, sex, insurance, and zip-code-level incomeThe study included 1.38 million patients. Higher proportions of safety-net versus non safety-net patients had comorbidity score of 3+ (11.1% vs. 5.0%) and BMI ≥35 (27.7% vs. 15.8%). In both types of systems, blacks and Hispanics were more likely than whites to have higher BMIs. Whites were more likely than blacks or Hispanics to have higher comorbidity scores in a safety net system, but less likely to have higher scores in the non safety-nets. The odds of comorbidity score 3+ and BMI 35+ in blacks relative to whites were significantly lower in safety-net than in non safety-net settings.Racial/ethnic differences were present within both safety-net and non safety-net integrated health systems, but patterns differed. Understanding patterns of racial/ethnic differences in health outcomes in safety-net and non safety-net integrated health systems is important to tailor interventions to eliminate racial/ethnic disparities in health and health care.
authors.affiliation: Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA Division of Research, Kaiser Permanente Northern California, Oakland, CA Department of Family Medicine and Community Health, and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA Group Health Research Institute, Seattle, WA Research & Evaluation Department, Kaiser Permanente Southern California, Pasadena, CA Department of Biostatistics and Biomathematics, Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA Department of Clinical Sciences and Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical School, Dallas, TX.
authors.forename: Bijal A
authors.initials: BA
authors.lastname: Balasubramanian
authors.forename: Michael P
authors.initials: MP
authors.lastname: Garcia
authors.forename: Douglas A
authors.initials: DA
authors.lastname: Corley
authors.forename: Chyke A
authors.initials: CA
authors.lastname: Doubeni
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: Haas
authors.forename: Aruna
authors.initials: A
authors.lastname: Kamineni
authors.forename: Virginia P
authors.initials: VP
authors.lastname: Quinn
authors.forename: Karen
authors.initials: K
authors.lastname: Wernli
authors.forename: Yingye
authors.initials: Y
authors.lastname: Zheng
authors.forename: Celette Sugg
authors.initials: CS
authors.lastname: Skinner
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data; D000367:Age Factors; D015992:Body Mass Index; D015897:Comorbidity; D003430:Cross-Sectional Studies; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D054625:Healthcare Disparities / Q000208:ethnology*; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000208:ethnology*; D044469:Racial Groups / Q000706:statistics & numerical data; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012307:Risk Factors; D064876:Safety-net Providers / Q000706:statistics & numerical data*; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000706:statistics & numerical data