Capturing sexual contact patterns in modelling the spread of sexually transmitted infections: Evidence using Natsal-3

• Published in: PloS one Vol. 13; no. 11; p. e0206501
• Main Authors: Datta, Samik, Mercer, Catherine H, Keeling, Matt J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2018; Public Library of Science (PLoS)
• Subjects: Acquired immune deficiency syndrome; AIDS; Attitude to Health; Biobehavioral Sciences; Biology and Life Sciences; Chlamydia; Contact Tracing - methods; Demographics; Demography; Epidemics; Female; Gays & lesbians; Health Risk Behaviors; HIV; Human immunodeficiency virus; Human papillomavirus; Humans; Infections; Life Style; Male; Mathematical models; Medicine and Health Sciences; Mens health; Models, Theoretical; Monogamy; Partnerships; People and Places; Prediction models; Research and Analysis Methods; Sex; Sexual and Gender Minorities - statistics & numerical data; Sexual behavior; Sexual Behavior - physiology; Sexual Partners; Sexually transmitted diseases; Sexually Transmitted Diseases - epidemiology; Sexually Transmitted Diseases - transmission; Social Sciences; STD; Stochastic Processes; Surveys and Questionnaires; Unsafe Sex - statistics & numerical data; Vaccination; United Kingdom > UK; England
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0206501

Understanding the spread of sexually transmitted infections (STIs) in a population is of great importance to the planning and delivery of health services globally. The worldwide rise of HIV since the 1980's, and the recent increase in common STIs (including HPV and Chlamydia) in many countries, means that there is an urgent need to understand transmission dynamics in order to better predict the spread of such infections in the population. Unlike many other infections which can be captured by assumptions of random mixing, STI transmission is intimately linked to the number and pattern of sexual contacts. In fact, it is the huge variation in the number of new sexual partners that gives rise to the extremes of risk within populations which need to be captured in predictive models of STI transmission. Such models are vital in providing the necessary scientific evidence to determine whether a range of controls (from education to screening to vaccination) are cost-effective. We use probability sample survey data from Britain's third National Survey of Sexual Attitudes and Lifestyles (Natsal-3) to determine robust distributions for the rate of new partnerships that involve condomless sex and can therefore facilitate the spread of STIs. Different distributions are defined depending on four individual-level characteristics: age, sex, sexual orientation, and previous sexual experience. As individual behaviour patterns can change (e.g. by remaining in a monogamous relationship for a long period) we allow risk-percentiles to be randomly redrawn, to capture longer term behaviour as measured by Natsal-3. We demonstrate how this model formulation interacts with the transmission of infection by constructing an individual-based SIS-P (Susceptible-Infected-Susceptible-Protected) transmission model for the spread of a generic STI, and observing overall population demographics when varying the transmission probability within a partnership, recovery rate and the level of population protection (e.g. from vaccination where applicable).