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Early HIV diagnosis enables prompt treatment initiation, thereby contributing to decreased morbidity, mortality, and transmission. We aimed to describe the association between distance from residence to testing sites and HIV disease stage at diagnosis. Using HIV surveillance data, we identified all new HIV diagnoses made at publicly funded testing sites in central North Carolina during 2005–2013. Early-stage HIV was defined as acute HIV (antibody-negative test with a positive HIV RNA) or recent HIV (normalized optical density <0.8 on the BED assay for non-AIDS cases); remaining diagnoses were considered post-early-stage HIV. Street distance between residence at diagnosis and (1) the closest testing site and (2) the diagnosis site was dichotomized at 5 miles. We fit log-binomial models using generalized estimating equations to estimate prevalence ratios (PR) and robust 95% confidence intervals (CI) for post-early-stage diagnoses by distance. Models were adjusted for race/ethnicity and testing period. Most of the 3028 new diagnoses were black (N = 2144; 70.8%), men who have sex with men (N = 1685; 55.7%), and post-early-stage HIV diagnoses (N = 2010; 66.4%). Overall, 1145 (37.8%) cases traveled <5 miles for a diagnosis. Among cases traveling ≥5 miles for a diagnosis, 1273 (67.6%) lived <5 miles from a different site. Residing ≥5 miles from a testing site was not associated with post-early-stage HIV (adjusted PR, 95% CI: 0.98, 0.92–1.04), but traveling ≥5 miles for a diagnosis was associated with higher post-early HIV prevalence (1.07, 1.02–1.13). Most of the elevated prevalence observed in cases traveling ≥5 miles for a diagnosis occurred among those living <5 miles from a different site (1.09, 1.03–1.16). Modest increases in post-early-stage HIV diagnosis were apparent among persons living near a site, but choosing to travel longer distances to test. Understanding reasons for increased travel distances could improve accessibility and acceptability of HIV services and increase early diagnosis rates. [ABSTRACT FROM AUTHOR]

Background: Identifying geographical clusters of sexually transmitted infections can aid in targeting prevention and control efforts. However, detectable clusters can vary between detection methods because of different underlying assumptions. Furthermore, because disease burden is not geographically homogenous, the reference population is sensitive to the study area scale, affecting cluster outcomes. We investigated the influence of cluster detection method and geographical scale on syphilis cluster detection in Mecklenburg County, North Carolina.Methods: We analyzed primary and secondary syphilis cases reported in North Carolina (2003-2010). Primary and secondary syphilis incidence rates were estimated using census tract-level population estimates. We used 2 cluster detection methods: local Moran's I using an areal adjacency matrix and Kulldorff's spatial scan statistic using a variable size moving circular window. We evaluated 3 study area scales: North Carolina, Piedmont region, and Mecklenburg County. We focused our investigation on Mecklenburg, an urban county with historically high syphilis rates.Results: Syphilis clusters detected using local Moran's I and Kulldorff's scan statistic overlapped but varied in size and composition. Because we reduced the scale to a high-incidence urban area, the reference syphilis rate increased, leading to the identification of smaller clusters with higher incidence. Cluster demographic characteristics differed when the study area was reduced to a high-incidence urban county.Conclusions: Our results underscore the importance of selecting the correct scale for analysis to more precisely identify areas with high disease burden. A more complete understanding of high-burden cluster location can inform resource allocation for geographically targeted sexually transmitted infection interventions. [ABSTRACT FROM AUTHOR]