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- Behind the Cascade: Analyzing Spatial Patterns Along the HIV Care Continuum
- Eberhart MG, Yehia BR, Hillier A, Voytek CD, Blank MB, Frank I, Metzger DS, Brady KA
Successful HIV treatment as prevention requires individuals to be tested, aware of their status, linked to and retained in care, and virally suppressed. Spatial analysis may be useful for monitoring HIV care by identifying geographic areas with poor outcomes. Retrospective cohort of 1704 people newly diagnosed with HIV identified from Philadelphia's Enhanced HIV/AIDS Reporting System in 2008-2009, with follow-up to 2011. Outcomes of interest were not linked to care, not linked to care within 90 days, not retained in care, and not virally suppressed. Spatial patterns were analyzed using K-functions to identify "hot spots" for targeted intervention. Geographic components were included in regression analyses along with demographic factors to determine their impact on each outcome. Overall, 1404 persons (82%) linked to care; 75% (1059/1404) linked within 90 days; 37% (526/1059) were retained in care; and 72% (379/526) achieved viral suppression. Fifty-nine census tracts were in hot spots, with no overlap between outcomes. Persons residing in geographic areas identified by the local K-function analyses were more likely to not link to care [adjusted odds ratio 1.76 (95% confidence interval: 1.30 to 2.40)], not link to care within 90 days (1.49, 1.12-1.99), not be retained in care (1.84, 1.39-2.43), and not be virally suppressed (3.23, 1.87-5.59) than persons not residing in the identified areas. This study is the first to identify spatial patterns as a strong independent predictor of linkage to care, retention in care, and viral suppression. Spatial analyses are a valuable tool for characterizing the HIV epidemic and treatment cascade.
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- Travel Distance to HIV Medical Care
Eberhart MG, Voytek CD, Hillier A, Metzger DS, Blank MB, Brady KA
Decisions regarding where patients access HIV care are not well understood. The purpose of this analysis was to examine differences in travel distance to care among persons receiving care in Philadelphia. A multi-stage sampling design was utilized to identify 400 potential participants. 65 % (260/400) agreed to be interviewed. Participants were asked questions about medical care, supportive services, and geographic location. Distances were calculated between residence and care location. 46.3 % travelled more than three miles beyond the nearest facility. Uninsured travelled further (6.9 miles, 95 % CI 3.9-9.8) than persons with public insurance (3.3 miles, 2.9-3.6). In multivariate analyses, no insurance (20/260) was associated with increased distance (p = 0.0005) and Hispanic ethnicity was associated with decreased distance (p = 0.0462). Persons without insurance travel further but insurance status alone does not explain the variability in distance travelled to care. In Philadelphia, Hispanic populations, and providers that may be most accessible to them, are spatially contained.
Developed by Dr. Amy Hiller of the Penn School of Design for training undergraduate and graduate students in introductory Arc GIS, this manual is meant to be a complement, rather than substitute, for ArcView software manuals, ESRI training products, or the ArcView help options. Particularly helpful to people new to GIS who may be intimidated by conventional software manuals, this manual focuses on the basic tools and functions that users new to GIS should konw how to use. Those who master these basic functions shoulud have the skills to learn about additional tools, using the ArcView help menus, or just exploring additional menu options, toolbars and buttons. This manual is used for teaching people in the social sciences and public health including Robert Wood Johnson clinical scholars and for Penn's GIS and Public Health Institute that is offered ever summer. Click more details below to download for free.
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