The role of viral introductions in sustaining community-based HIV epidemics in rural Uganda: evidence from spatial clustering, phylogenetics, and egocentric transmission models

Abstract

Background: It is often assumed that local sexual networks play a dominant role in HIV spread in sub-Saharan Africa. The aim of this study was to determine the extent to which continued HIV transmission in rural communities--home to two-thirds of the African population--is driven by intra-community sexual networks versus viral introductions from outside of communities.

Methods and findings: We analyzed the spatial dynamics of HIV transmission in rural Rakai District, Uganda, using data from a cohort of 14,594 individuals within 46 communities. We applied spatial clustering statistics, viral phylogenetics, and probabilistic transmission models to quantify the relative contribution of viral introductions into communities versus community- and household-based transmission to HIV incidence. Individuals living in households with HIV-incident (n = 189) or HIV-prevalent (n = 1,597) persons were 3.2 (95% CI: 2.7-3.7) times more likely to be HIV infected themselves compared to the population in general, but spatial clustering outside of households was relatively weak and was confined to distances <500 m. Phylogenetic analyses of gag and env genes suggest that chains of transmission frequently cross community boundaries. A total of 95 phylogenetic clusters were identified, of which 44% (42/95) were two individuals sharing a household. Among the remaining clusters, 72% (38/53) crossed community boundaries. Using the locations of self-reported sexual partners, we estimate that 39% (95% CI: 34%-42%) of new viral transmissions occur within stable household partnerships, and that among those infected by extra-household sexual partners, 62% (95% CI: 55%-70%) are infected by sexual partners from outside their community. These results rely on the representativeness of the sample and the quality of self-reported partnership data and may not reflect HIV transmission patterns outside of Rakai.

Conclusions: Our findings suggest that HIV introductions into communities are common and account for a significant proportion of new HIV infections acquired outside of households in rural Uganda, though the extent to which this is true elsewhere in Africa remains unknown. Our results also suggest that HIV prevention efforts should be implemented at spatial scales broader than the community and should target key populations likely responsible for introductions into communities.

Figure 1. Rakai District, Uganda.

(A) Rakai (∼2,200 km²), a rural district in southwest Uganda, with population ∼450,000 (∼700 communities). RCCS R13 study participants (n = 1,085) reported 1,169 sexual partners with primary residence outside the Rakai District, but within Uganda (where disclosed, residential locations of sexual partners are indicated with red dots on the map). Only three sexual partners were reported to be living outside Uganda (two in Tanzania and one in the United Kingdom, not shown). (B) The Rakai district at a higher resolution, with the 11 geographic regions surveyed in RCCS R13 indicated in color. There are two primary highways (Masaka Road to Tanzania and the Trans-African National Highway to Rwanda and the Democratic Republic of the Congo [DR of Congo]) and numerous secondary roads that extend throughout the district.

Figure 2. Spatial clustering of HIV-seropositive persons within households (0 km) and in geographic windows of 250 m up to 10 km (the first window is 10–250 m, and windows are centered every 50 m starting at 125 m).

Spatial clustering analyses show whether HIV prevalence or incidence is elevated within certain distances of other HIV-seropositive persons. We define the spatial clustering of HIV-seropositive individuals as τ(d ₁,d ₂), the relative probability that an HIV-seropositive person resides within a distance window, d ₁ to d ₂, from another HIV-seropositive person compared to the probability that any individual is HIV seropositive in the entire study population. Where spatial clustering exists, values of τ(d ₁,d ₂) exceed one. Shaded areas show the 95% bootstrapped confidence intervals for spatial clustering estimates. (A) The spatial clustering between HIV-seropositive persons (prevalent or incident cases with other prevalent or incident cases; red). (B) The spatial clustering of HIV-seroincident cases with ART-naïve HIV-seroprevalent persons (yellow). (C) The spatial clustering of HIV-seroincident cases with other HIV-seroincident cases (blue). (D) A blowup of the area where significant extra-household spatial clustering (<500 m) was identified among all HIV-seropositive persons (marked with black box in [A–C]). Data are shown only up to 10 km (no significant spatial clustering was observed beyond this distance).

Figure 3. Maximum likelihood phylogenetic analyses of the HIV-1 gag gene.

(A) Boxplots of the intra-subtype gag genetic pairwise distances for epidemiologically linked (Epi linked) incident couples (i.e., at least one member of the couple was an incident case) and for all epidemiologically unlinked incident pairs of individuals in RCCS R13. (B) Boxplots of intra-subtype gag genetic pairwise distances by the geographic distance between the incident pair. (C) A ML phylogenetic tree (radial) of HIV-1 subtype A gag sequences from HIV-seroprevalent (n = 245) and HIV-incident (n = 55) cases, where taxa are colored by the geographic region from which they were isolated. Reference strains (n = 87) are in black. Grey circles indicate nodes with bootstrap support of ≥70%; black circles indicate intra-household clusters; † indicates an intra-household virus also sharing a cluster with at least one other household. Additional radial and rectangular phylogenetic trees for HIV-1 subtypes A, D, and C for gag and env genes are included in Figures S5, S6, S7, S8, S9, S10, S11, S12, S13.

Figure 4. Summary of inferential methods and study results and conclusions.

The dotted blue line represents the border of a hypothetical community.