title: Spatial visualization of multivariate datasets: an analysis of STD and HIV/AIDS diagnosis rates and socioeconomic context using ring maps.
abstract: OBJECTIVE
  We used existing data systems to examine sexually transmitted disease (STD) and HIV/AIDS diagnosis rates and explore potential county-level associations between HIV/AIDS diagnosis rates and socioeconomic disadvantage.
  
  
  METHODS
  Using South Carolina county data, we constructed multivariate ring maps to spatially visualize syphilis, gonorrhea, chlamydia, and HIV/AIDS diagnosis rates; gender- and race-specific HIV/AIDS diagnosis rates; and three measures of socioeconomic disadvantage-an unemployment index, a poverty index, and the Townsend index of social deprivation. Statistical analyses were performed to quantitatively assess potential county-level associations between HIV/AIDS diagnosis rates and each of the three indexes of socioeconomic disadvantage.
  
  
  RESULTS
  Ring maps revealed substantial spatial association in STD and HIV/AIDS diagnosis rates and highlighted large gender and racial disparities in HIV/AIDS across the state. The mean county-level HIV/AIDS diagnosis rate (per 100,000 population) was 24.2 for males vs. 11.2 for females, and 34.8 for African Americans vs. 5.2 for white people. In addition, ring map visualization suggested a county-level association between HIV/AIDS diagnosis rates and socioeconomic disadvantage. Significant positive bivariate relationships were found between HIV/AIDS rate categories and each increase in poverty index category (odds ratio [OR] = 2.03; p=0.006), as well as each increase in Townsend index of social deprivation category (OR=4.98; p<0.001). A multivariate ordered logistic regression model in which all three socioeconomic disadvantage indexes were included showed a significant positive association between HIV/AIDS and Townsend index categories (adjusted OR=6.10; p<0.001).
  
  
  CONCLUSIONS
  Ring maps graphically depicted the spatial coincidence of STD and HIV/AIDS and revealed large gender and racial disparities in HIV/AIDS across South Carolina counties. This spatial visualization method used existing data systems to highlight the importance of social determinants of health in program planning and decision-making processes.
authors.affiliation: University of South Carolina, Institute for Families in Society, Policy and Research Unit on Medicaid and Medicare, Columbia, SC 29208, USA. adefede@mailbox.sc.edu
authors.forename: Ana
authors.initials: A
authors.lastname: Lòpez-De Fede
authors.forename: John E
authors.initials: JE
authors.lastname: Stewart
authors.forename: James W
authors.initials: JW
authors.lastname: Hardin
authors.forename: Kathy
authors.initials: K
authors.lastname: Mayfield-Smith
authors.forename: Dawn
authors.initials: D
authors.lastname: Sudduth
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000175:diagnosis / Q000453:epidemiology; D001741:Black or African American; D005260:Female; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D008297:Male; D012737:Sex Factors; D012749:Sexually Transmitted Diseases / Q000175:diagnosis / Q000453:epidemiology*; D012959:Socioeconomic Factors; D012962:Sociology, Medical; D013022:South Carolina