Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya

Journal of Acquired Immune Deficiency Syndromes : JAIDS
Anthony WaruruThorkild Tylleskär

Abstract

In a spatially well known and dispersed HIV epidemic, identifying geographic clusters with significantly higher HIV prevalence is important for focusing interventions for people living with HIV (PLHIV). We used Kulldorff spatial-scan Poisson model to identify clusters with high numbers of HIV-infected persons 15-64 years old. We classified PLHIV as belonging to either higher prevalence or lower prevalence (HP/LP) clusters, then assessed distributions of sociodemographic and biobehavioral HIV risk factors and associations with clustering. About half of survey locations, 112/238 (47%) had high rates of HIV (HP clusters), with 1.1-4.6 times greater PLHIV adults observed than expected. Richer persons compared with respondents in lowest wealth index had higher odds of belonging to a HP cluster, adjusted odds ratio (aOR) 1.61 [95% confidence interval (CI): 1.13 to 2.3], aOR 1.66 (95% CI: 1.09 to 2.53), aOR 3.2 (95% CI: 1.82 to 5.65), and aOR 2.28 (95% CI: 1.09 to 4.78) in second, middle, fourth, and highest quintiles, respectively. Respondents who perceived themselves to have greater HIV risk or were already HIV-infected had higher odds of belonging to a HP cluster, aOR 1.96 (95% CI: 1.13 to 3.4) and aOR 5.51 (95% CI: 2.42 to 12.55),...Continue Reading

References

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Citations

Mar 25, 2020·Frontiers of Medicine·Xiaoxu HanHong Shang
Feb 15, 2020·Journal of Acquired Immune Deficiency Syndromes : JAIDS·Susie WeltyGeorge W Rutherford

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Methods Mentioned

BETA
circumcision

Software Mentioned

Statistical Analysis System ( SAS )
Open Map
Quantum GIS ( QGIS )
SAS
KAIS
Global Information System ( GIS
SaTScan

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