Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the "First 90"

Abstract

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status - the "first 90." In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the "first 90" targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.

Keywords: HIV testing; Kenya; UNAIDS 90-90-90 Fast-Track targets; country operational plans; hotspots; spatial auto-correlation.

Figure 1

County classification in the country operational plan (A) and reporting sites (B), Kenya 2016. (A) of the figure shows the country operational plan county-classification. (B) shows the sites reporting HIV testing by the yield of HIV-infected persons.

Figure 2

New HIV diagnoses in relation to country operational plan (A) and by HIV burden percentiles (B), Kenya 2016. (A) of the figure shows median number of annual site-level new HIV diagnoses per county classification. (B) shows percent distribution significant 123 clusters by county and HIV burden classes standardized per 100,000 population and classified using percentiles.

Figure 3

Spatial clustering of newly diagnosed HIV-infected persons in five high HIV-burden counties and low-burden region, Kenya 2016. (A–C,E,F) show sites within the five high HIV burden counties according to Moran's I local auto-correlation clustering classes. (D,G,H) provide a contrast of sites distributed according to clustering but for low HIV burden regions spanning across multiple counties. Hotspots are represented by (H) and low spots by an (L) neighboring each other in these combinations HH, HL, LH, and LL.

Figure 4

Local Moran's I clustering analyses and Kulldorff spatial-scan analyses of HTS yield taking into account the number tested, Kenya 2016. (A) of the figure shows site level auto-correlation and (B) shows significant and non-significant clusters identified. Hotspots are represented by (H) and low spots by an (L) neighboring each other in these combinations HH, HL, LH, and LL.