1. Patterns of HIV in the Lake Victoria Region, a Spatiotemporal Analysis
Nathan Heard, Gina Sarfaty, Christine Fellenz, Irum Zaidi | July 24, 2018
Good afternoon. I’d like to start by acknowledging my coauthors, Gina Sarfaty, Christine Fellenz, and Irum Zaidi.

2. Background and methods
Question: Can we use routine program indicator data to detect spatial clustering of HIV positivity over time?
Study area: PEPFAR- supported health facilities within 50 kilometers of Lake Victoria
Data: quarterly testing from 2,122 PEPFAR-supported health facilities, FY
Methods: Hexagon aggregation and Getis-Ord Gi* statistic
Over the past few years PEPFAR has made intensive use of subnational indicator data to better focus the programming it supports, typically analyzing data at the district level. It was great to see Anthony’s analysis of facility-level data. One of the questions we were interested in is how stable are high and low HIV positivity where we observe it? Now that we have a growing time series of quarterly indicator data, we’re eager to explore how spatial patterns in the data change over time. We’re also interested in cross border dynamics that wouldn’t be apparent in the study of country-specific data in isolation.
So our study seeks to characterize the stability of identified clusters of HIV positivity in the Lake Victoria Basin. We selected this area because the counties and districts that border the lake are among those with the highest HIV prevalence of their respective countries.
We selected PEPFAR-supported health facilities within 50 kilometers of Lake Victoria and aggregated facility-level testing data to a mesh of hexagons in order to calculate HIV positivity. We repeated this process for eight quarters and tested the data for clustering.

3. HIV positivity and spatial clustering
In this slide we’re showing hexagons, which is a great way of displaying large amounts data from thousands of health facilities while also obscuring the location of individual facilities. On the left you see HIV positivity and on the right you see clustering. The reds are statistically significant hot spots which means that there is high positivity surrounded by more high positivity. Similarly blues are significant clusters of relatively low HIV positivity.
I should point out that an area may be a hotspot within a country, but cold when compared to the Lake as a whole.
We detected what seem to be at least four stable clusters of high HIV positivity as well as two unstable clusters, appearing significant in at least three but not all of the quarters. A stable cold spot crosses the border of Kenya and Uganda.
HIV positivity
Spatial clustering
(significance)
0% - 0.9%
1% - 1.9%
2% - 3.9%
4% - 36%
Cold Spot 99%
Cold Spot 95%
Cold Spot 90%
Not Significant
Hot Spot 90%
Hot Spot 95%
Hot Spot 99%

4. Conclusions Limitations Takeaways Future
Health facility locations are not spatially representative of the underlying epidemic
Quality of program data
Program indicator definition reflects number of tests, not people
Takeaways
Time series indicates programmatically useful insights into areas of consistently high and consistently low HIV positivity
Consistent patterns detected in fixed places, despite labor-related population mobility in the region
Regional analytic approaches warranted based on cross border epidemic spatial structure
For a statistical analysis, this is a quick and dirty approach. We’re using program data. This is not a proper surveillance study. But there are programmatically useful insights here. Even though there’s labor-related mobility in the region, HIV testing shows detectable and persistent patterns in both space and time. High population mobility complicates linkage to care and follow up. However, consistently high testing positivity in fixed places suggests that geographic focus is an important part of local responses in the region. I’m hopeful that future studies consider taking a spatiotemporal approach and consider multiple geographic scales to assess both cross-border and internal disease dynamics.
Future
Program data should be leveraged for future analysis
Inclusion of community testing data
More rigorous statistical testing

5. THANK YOU!
The content in this presentation is that of the presenters and does not necessarily reflect the view of the U.S. President's Emergency Plan for AIDS Relief, the U.S. Agency for International Development or the U.S. Government.