Follow the Sex: Influence of Network Structure on the Effectiveness and Cost-Effectiveness of Partner Management Strategies for Sexually Transmitted Infection Control

Abstract

Background: It is well established that network structure strongly influences infectious disease dynamics. However, little is known about how the network structure impacts the cost-effectiveness of disease control strategies. We evaluated partner management strategies to address bacterial sexually transmitted infections (STIs) as a case study to explore the influence of the network structure on the optimal disease management strategy.

Methods: We simulated a hypothetical bacterial STI spread through 4 representative network structures: random, community-structured, scale-free, and empirical. We simulated disease outcomes (prevalence, incidence, total infected person-months) and cost-effectiveness of 4 partner management strategies in each network structure: routine STI screening alone (no partner management), partner notification, expedited partner therapy, and contact tracing. We determined the optimal partner management strategy following a cost-effectiveness framework and varied key compliance parameters of partner management in sensitivity analysis.

Results: For the same average number of contacts and disease parameters in our setting, community-structured networks had the lowest incidence, prevalence, and total infected person-months, whereas scale-free networks had the highest without partner management. The highly connected individuals were more likely to be reinfected in scale-free networks than in the other network structures. The cost-effective partner management strategy depended on the network structures, the compliance in partner management, the willingness-to-pay threshold, and the rate of external force of infection.

Conclusions: Our findings suggest that contact network structure matters in determining the optimal disease control strategy in infectious diseases. Information on a population's contact network structure may be valuable for informing optimal investment of limited resources.

Figure 1.

The optimal partner management strategy with varying levels of partner compliance using a willingness-to-pay (WTP) threshold = $200 in (A) random, (B) community, (C) scale-free, and (D) empirical networks in the high external force of infection (EFOI) scenario. For a given partner compliance level, the optimal strategy was the one that averted the greatest number of infected person-months at a cost per infected person-month averted less than the WTP. PN = partner notification; EPT = expedited partner therapy; Tracing = contact tracing. These partner management strategies were implemented in addition to routine screening alone. The base case is indicated with a red star. Partner compliance under contact tracing is always assumed to be the maximum of partner compliance under PN or EPT.

Figure 2.

The optimal partner management strategy with varying levels of partner compliance using a willingness-to-pay (WTP) threshold = $500 in (A) random, (B) community, (C) scale-free, and (D) empirical networks in the high external force of infection (EFOI) scenario. For a given partner compliance level, the optimal strategy was the one that averted the greatest number of infected person-months at a cost per infected person-month averted less than the WTP. PN = partner notification; EPT = expedited partner therapy; Tracing = contact tracing. These partner management strategies were implemented in addition to routine screening alone. The base case is indicated with a red star. Partner compliance under contact tracing is always assumed to be the maximum of partner compliance under PN or EPT.