Abstract Body

High antiretroviral therapy (ART) coverage and high rates of viral load suppression (VLS) should reduce transmission of HIV, and ultimately, HIV incidence and the number of new HIV diagnoses. We used 3 years of HIV program data in Kenya to assess whether trends in the number of new HIV diagnoses were associated to ART coverage and VLS rates and spatial-temporally auto correlated at county-level [sub-National unit (SNU)].


We analyzed routine program SNU-level aggregate ART coverage and VLS (proportion of persons on ART with VL<1000 copies/mL) data for 3 years (2015-2017). We examined the association between ART coverage and VLS rates to new HIV diagnoses by fitting spatial and spatial-temporal semi-parametric Poisson regression models using R-Integrated Nested Laplace Approximation (INLA) package. We used the extended Cochran-Mantel-Haenszel stratified test of association to test for trend across years for fitted rates of new HIV diagnosis and a structural equation model to assess direct effects between the two exogenous covariates to fitted newly HIV-diagnosed as the endogenous variable adjusting for clustering by 47 SNUs. Finally, we mapped fitted HIV positivity using QGIS version 3.2.


A spatial-temporal model with covariates was better in explaining geographical variation in HIV positivity (deviance information criterion (DIC) 381.2) than either a non-temporal spatial model (DIC 418.6) or temporal model without covariates (DIC 449.2). Overall, the fitted HIV positivity decreased over 3 years from median of 2.9% in 2015, [interquartile range (IQR): 1.9-3.4] to 1.5% in 2017, IQR(1.3-2.0), (Figure), stratified test of association p=0.032. VLS had a direct effect on HIV positivity rates p=0.014, but ART coverage did not, p=0.502.


In 3 years of widespread availability of ART, we have observed a general decline of rates of new HIV diagnoses associated with improved VLS rates. To assess the trends and impact of implementation of scaled-up care and treatment, spatial-temporal analyses help to identify geographic areas that need focused interventions.