title: Effects of poverty and race on outcomes in acute myeloid leukemia.
abstract: OBJECTIVE
  To determine how patient race, ethnicity, and degree of poverty affect treatment and survival for acute myeloid leukemia (AML).
  
  
  METHODS
  A linked database of the Florida cancer registry and State inpatient and outpatient hospital data for 1998-2002 was queried. Effects of demographic and treatment characteristics on survival were explored using univariate and multivariate analyses methods.
  
  
  RESULTS
  A total of 4659 patients with AML were identified. Over 50% of patients with AML were 70 years of age or older. African American (AA) patients were diagnosed at significantly younger ages than were whites (P < 0.001). In multivariate analysis, independent predictors of worse survival in AML were aged over 50 (hazard ratios [HRs]: 1.60, 2.15, 3.04, and 3.62 over the decade-cohorts, all P < 0.001), AA race (HR: 1.27, P < 0.001), being a former or current user of tobacco (HR: 1.13, P = 0.004 and HR: 1.28, P < 0.001, respectively), residing in an area with the highest poverty level (HR: 1.15, P = 0.007), and being covered only by Medicaid (HR: 1.23, P = 0.014). No differences in outcomes were observed related to gender or ethnicity. Receipt of chemotherapy was strongly associated with improved survival (HR: 0.59, P < 0.001). When only those patients who received and appeared to respond to treatment are included, AAs continued to demonstrate a worse outcome than Whites.
  
  
  CONCLUSIONS
  AML disproportionately affects the elderly. AA patients and patients from poorer communities with AML have significantly worse survival. Interventions to provide earlier diagnosis in these patients as well as to improve overall outcomes are needed to address these disparities.
authors.affiliation: Department of Epidemiology and Public Health, University of Miami Miller School of Medicine, Miami, FL 33136, USA. mbyrne2@med.miami.edu
authors.forename: Margaret M
authors.initials: MM
authors.lastname: Byrne
authors.forename: L Jill
authors.initials: LJ
authors.lastname: Halman
authors.forename: Leonidas G
authors.initials: LG
authors.lastname: Koniaris
authors.forename: Peter A
authors.initials: PA
authors.lastname: Cassileth
authors.forename: Joseph D
authors.initials: JD
authors.lastname: Rosenblatt
authors.forename: Michael C
authors.initials: MC
authors.lastname: Cheung
mesh_terms: D000293:Adolescent; D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data*; D000367:Age Factors; D000704:Analysis of Variance; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D002648:Child; D015897:Comorbidity; D005260:Female; D005431:Florida / Q000453:epidemiology; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D053208:Kaplan-Meier Estimate; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis / Q000188:drug therapy* / Q000208:ethnology* / Q000401:mortality; D008297:Male; D008484:Medicaid; D008498:Medical Record Linkage; D006278:Medicare; D008875:Middle Aged; D016017:Odds Ratio; D011203:Poverty; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012042:Registries; D012307:Risk Factors; D016896:Treatment Outcome; D014481:United States; D044465:White People / Q000706:statistics & numerical data