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Sexually transmitted infections (STIs) impact the sexual and reproductive health of millions of people worldwide. According to the World Health Organisation, more than one million STIs are acquired each day. Due to the scale of the problem and the serious consequences these infections can inflict on a person’s health, there is much work on prevention, diagnostics, and treatment of STIs.  

For Sexual Health awareness week on 14^th to 20^th September, we discuss research on the variation in the prevalence of STIs by Chris Kenyon, University of Cape Town, South Africa, published on F1000Research, and how this could help in the prevention and management of STIs.

Knowing your sexual connectivity network

Globally, there is dramatic variation in the spread of STIs and many have tried to explain the difference, to understand why some STIs are more prevalent in some populations than others. In their Opinion Article, Chris Kenyon and Wim Delva, University of Stellenbosch, South Africa, argue that the high prevalence of traditional STIs, such as HIV and bacterial vaginosis (BV), is because these populations have more connected sexual networks. A sexual network is the sexual relations within a set group of individuals, where the characteristics of these determine the speed and extent of STI spread.

These characteristics include the number of partners at a given period, frequency of sexual intercourse, concurrency (having two or more partners at the same time), the size of the core group, type of sex and the length of gaps between partnerships. The more relationships occurring concurrently in a high connectivity sexual network reduces the time between STI transmission infections, leading to a greater spread of STIs.

There are many risks factors that play a role in the spread of STIs, including variation in male circumcision, effectiveness of STI treatment, socioeconomic inequality, hormonal contraception, and sexual behaviour. Kenyon and Delva propose that differences in sexual connectivity networks are the main driver that underpins the other risk factors mentioned and determines the role they play.

Ann Jolly, University of Ottawa, Canada, one of the reviewers of the article, rated it highly: “I think this article is thought provoking and even if one is not entirely convinced by it, it adds much deeper and more critical thought to assumptions of mathematical modellers and epidemiologists about the immediate causes of HIV than I have ever seen.”

Striking clustering

Kenyon and Delva examine mathematical models to trace the history of HIV transmission rates and refute the claim that STIs are a disease of poverty, finding that prevalence was higher among the more educated and those from wealthier households in 19 countries, including Cambodia, Cameroon, Ethiopia, Ghana and Guinea.

In 47 surveys from 27 African countries, the lifetime number of partners reported by men and women was positively correlated with HIV prevalence. Likewise, reporting sex with a non-marital, non-cohabiting partner by men and women was positively correlated with HIV prevalence. Meanwhile, reduction in the number of sex partners and concurrency saw impressive declines in the incidence of HIV in Uganda, Zimbabwe and other countries in Southern and Eastern Africa.

Modelling studies have found that a relatively small increase in sex network connectivity can lead to a significant increase in HIV and the spread of STIs, since the following STIs were found in HIV epidemics: syphilis, gonorrhoea, HSV2 and trichomoniasis. The same pattern is believed to apply for BV. BV is strongly associated with the number of sexual partners and concurrency, so a high sexual network connectivity is likely to result in a high prevalence of BV. This not only has its own clinical implications, but enhances susceptibility to chlamydia, gonorrhoea, and HIV. So, more connected sexual networks could facilitate the rapid spread of BV, increasing the susceptibility and transmission of other STIs.

Sex diaries and concurrency interventions

Despite successes in STI control interventions, none address the root cause of high STI prevalence, therefore will be unlikely to achieve prevention at the scale required. If Kenyon’s and Delva’s theory is supported by further experimental evidence and models, the approach could generate new opportunities for STI prevention interventions.

One such intervention, called ‘Know your Network’, was successfully piloted in Kenya and could provide guidance for future trials. This was a community level awareness intervention addressing concurrency and its role in HIV transmission, through presentations and group discussions. This helped participants understand the messaging, which resonated with them, so they shared what they’d learnt with friends and family, and shared data on their own sexual partnerships. A formal trial is needed to assess the efficacy of this intervention on STI incidence.

Higher risk sex 

Further exploring sexual networks and the propagation of STIs along such networks, Chris Kenyon carried out an ecological study on sexual risk taking. Behavioural risk factors were defined as: having sex with non-marital, non-cohabiting partners; having sexual intercourse with multiple partners, the number of lifetime partners; and median age of sexual intercourse. All risk factors, except debut of sexual intercourse, were positively associated with one another and HIV prevalence. 

This research by Kenyon quantifies the relationship between the prevalence of HIV and risky sex behaviour and how it varies among populations and regions within countries in sub-Saharan Africa. Despite research to identify sexual behaviour, such as this finding reported by Kenyon, it is complex and challenging work, with much variation between populations. We need to increase our efforts in understanding the differences and work with communities to contain the spread of STIs. 

If you’re a researcher working on sexual or reproductive health, consider submitting your next article to F1000Research. We publish a wide range of article types, including study protocols, opinion articles, data notes and case reports. You can find out more about our publishing model here, read through our article guidelines and submit.