Abstract
Background: Linkage to care is a crucial early step in successful HIV treatment. This study sought to identify the spatial patterning characteristics of HIV positive clients that are not linked to care in the Kisumu West HIV program using a geographic information system. Methods: The geocodes of HIV positive, non-linked clients’ residences were exported to ArcGIS software. The spatial patterning characteristics of HIV clients that are testing positive and not linked to care was described using Global Moran’s I statistic, which is a measure of spatial autocorrelation. Results: A total of 14,077 clients were tested for HIV. Of clients testing positive for HIV, 10% (n=34) were not yet linked to care two weeks after the diagnosis of HIV. Of the HIV positive non-linked clients, most (65%; n= 32) had spatially identifiable data about where they resided. Regarding the spatial patterning characteristics of the clients who tested HIV positive but were not linked to care and with spatially identifiable residence information, the Global Moran I statistic for autocorrelation was 0.435 (z score 1.383, p- value 0.167). Conclusion: By using age as an attribute value, the spatial distribution of clients testing HIV positive and not being linked to care is random. Geographical information systems can be used to identify the spatial patterning characteristics of HIV positive clients that are not linked to care. A key requirement to achieving this would require the collection of precise and accurate spatially identifiable locator information but without compromising patient confidentiality.
Highlights
For many years, the analysis and use of spatial data has guided public health efforts, and Dr John Snow, who is often credited as the founder of epidemiology, is a classic example of using this type of data[1]
This study aimed to identify the spatial patterning characteristics of clients testing HIV positive who are not linked to care in the Kisumu West HIV program
A total of 14, 077 clients were tested for HIV during the rapid results initiative (RRI) period
Summary
The analysis and use of spatial data has guided public health efforts, and Dr John Snow, who is often credited as the founder of epidemiology, is a classic example of using this type of data[1]. This study sought to identify the spatial patterning characteristics of HIV positive clients that are not linked to care in the Kisumu West HIV program using a geographic information system. The spatial patterning characteristics of HIV clients that are testing positive and not linked to care was described using Global Moran’s I statistic, which is a measure of spatial autocorrelation. Of the HIV positive non-linked clients, most (65%; n= 32) had spatially identifiable data about where they resided. Regarding the spatial patterning characteristics of the clients who tested HIV positive but were not linked to care and with spatially identifiable residence information, the Global Moran I statistic for autocorrelation was 0.435 (z score 1.383, p- value 0.167). Geographical information systems can be used to identify the spatial patterning characteristics of HIV positive clients that are not linked to care. A key requirement to achieving this would require the collection of precise and accurate spatially identifiable locator information but without compromising patient confidentiality
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