Remoteness influences access to sexual partners and drives patterns of viral sexually transmitted infection prevalence among nomadic pastoralists

Abstract

Sexually transmitted infections (STIs) comprise a significant portion of the infectious-disease burden among rural people in the Global South. Particular characteristics of ruralness-low-density settlements and poor infrastructure-make healthcare provision difficult, and remoteness, typically a characteristic of ruralness, often compounds the difficultly. Remoteness may also accelerate STI transmission, particularly that of viral STIs, through formation of small, highly connected sexual networks through which pathogens can spread rapidly, especially when partner concurrency is broadly accepted. Herein, we explored the effect of remoteness on herpes simplex virus type-2 (HSV-2) epidemiology among semi-nomadic pastoralists in northwestern (Kaokoveld) Namibia, where, in 2009 we collected HSV-2-specific antibody status, demographic, sexual network, and travel data from 446 subjects (women = 213, men = 233) in a cross-sectional study design. HSV-2 prevalence was high overall in Kaokoveld (>35%), but was heterogeneously distributed across locally defined residential regions: some regions had significantly higher HSV-2 prevalence (39-48%) than others (21-33%). Using log-linear models, we asked the following questions: 1) Are sexual contacts among people in high HSV-2-prevalence regions more likely to be homophilous (i.e., from the same region) than those among people from low-prevalence regions? 2) Are high-prevalence regions more "functionally" remote, in that people from those regions are more likely to travel within their own region than outside, compared to people from other regions? We found that high-prevalence regions were more sexually homophilous than low-prevalence regions and that those regions also had higher rates of within-region travel than the other regions. These findings indicate that remoteness can create contact structures for accelerated STI transmission among people who are already disproportionately vulnerable to consequences of untreated STIs.

Fig 1. Map of Kaokoveld, Namibia: Black dots indicate location of 28 data-collection sites.

Grey dots indicate locations of the four urban settlements in Kaokoveld. (Reprinted from Hazel, Foxman, & Low, 2015).

Fig 2. Herpes-simplex virus type-2 (HSV-2) prevalence by region.

Black bars indicate regions with significantly higher prevalence than the reference category (Omaanda). MF = The Marienfluss; OZO = Ozosemo; OMAA = Omaanda; EHA = Ehama; OMUN = Omunjandu; OUTER = other regions and villages.

Fig 3. Mixing between regions: Region names are sized according to degree of homophily (bigger name indicates higher homophily) and the edges between the regions are sized according to amount of mixing between the regions (thicker edges indicate greater contact).

Fig 4

a-c. Network graphs of three hypothetical models: 4a) Hypothetical network constructed from structural elements of Kaokoveld network representing the five main regions and their mixing dynamics. Structures were simplified so that no regional subcomponents had any ties between them at T₀. 4b) Hypothetical network from (4a) with 3% more ties between the subcomponents, all added at random. 4c) Hypothetical network from (4b) with 2% more ties (5% more than (4a)) between the subcomponents, all added at random.

Fig 5

a-c. Network graphs of simulations on graphs from Fig 4a–4c after transmission of an HSV-2-like virus at T₂₅₀ (~8 months), blue nodes = susceptible; red nodes = infectious: 5a) Simulation on network with zero ties at T₀ (Fig 4a); 5b) Simulation on network with 3% more ties between subcomponents at T₀ (Fig 4b); Simulation on network with 5% more ties between subcomponents at T₀ (Fig 4c).

Fig 6

a-c. Infection dynamics for three hypothetical epidemic simulations (Fig 5a–5c): 6a) Number of infected individuals increases but the infection rate slowed after ~T₁₅₀. Susceptibles still dominate the population. 6b) Number of individuals increases and, in contrast to (6a), the infection rate increases after T₁₅₀. 6c) The infection rate increases similarly but at a faster rate than (6b) and by T₂₅₀, the number of infected individuals has surpassed susceptibles.