Evidence map›Paper›PMID 21836744›Full record

ArticlePublic health reports (Washington, D.C. : 1974)

Spatial visualization of multivariate datasets: an analysis of STD and HIV/AIDS diagnosis rates and socioeconomic context using ring maps.

Ana Lòpez-De Fede, John E Stewart, James W Hardin, Kathy Mayfield-Smith, Dawn Sudduth

Open access · bronzeAbstract read
In one paragraph

Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Ana Lòpez-De FedeUniversity of South Carolina, Institute for Families in Society, Policy and Research Unit on Medicaid and Medicare, Columbia, SC 29208, USA. adefede@mailbox.sc.edu
John E Stewart
James W Hardin
Kathy Mayfield-Smith
Dawn Sudduth
University of South Carolina · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe 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.

methodsUsing 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.

resultsRing 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).

conclusionsRing 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.

Indexed as

Acquired Immunodeficiency SyndromeBlack or African AmericanFemaleHIV InfectionsHumansMaleSex FactorsSexually Transmitted DiseasesSocioeconomic FactorsSociology, MedicalSouth Carolina

Identifiers

PMID21836744
PMCPMC3150136
OpenAlexW2110343367

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.