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Clin Infect Dis. 2019 Mar 15. pii: ciz208. doi: 10.1093/cid/ciz208. [Epub ahead of print]

Clusters of sexual behaviour in HIV-positive men who have sex with men reveal highly dissimilar time trends.

Author information

1
Department of Infectious Diseases, Bern University Hospital Inselspital, University of Bern, Bern, Switzerland.
2
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
3
Institute of Medical Virology, University of Zurich, Zurich, Switzerland.
4
Division of Infectious Diseases and Infection Control, Cantonal Hospital St. Gallen, Switzerland.
5
Sigma Research, London School of Hygiene and Tropical Medicine, United Kingdom.
6
Alpiq Energy AI, Olten, Solothurn, Switzerland.
7
Institute of Nursing Science, University of Basel, Basel, Switzerland.
8
Infectious Diseases Service, Department of Medicine, University Hospital of Lausanne (CHUV), Switzerland.
9
Division of Infectious Diseases, Lugano Regional Hospital, Lugano, Switzerland.

Abstract

BACKGROUND:

Separately addressing specific groups of people who share patterns of behavioural change might increase the impact of behavioural interventions to prevent transmission of sexually transmitted infections. We propose a method based on machine learning to assist the identification of such groups among men who have sex with men (MSM).

METHODS:

By means of unsupervised learning, we inferred "behavioural clusters" based on the recognition of similarities and differences in longitudinal patterns of condomless anal intercourse with non-steady partners (nsCAI) in the Swiss HIV cohort study over the last 18 years. We then used supervised learning to investigate whether sociodemographic variables could predict cluster membership.

RESULTS:

We identified four behavioural clusters. The largest behavioural cluster (Cluster 1) contained 53% of the study population and displayed the most stable behaviour. Cluster 3 (17% of the study population) displayed consistently increasing nsCAI. Sociodemographic variables were predictive for both of these clusters. The other two clusters displayed more drastic changes: nsCAI frequency in Cluster 2 (20% of the study population) was initially similar to that in Cluster 3, but accelerated in 2010. Cluster 4 (10% of the study population) had significantly lower estimates of nsCAI than all other clusters until 2017, when it increased drastically, reaching 85% by the end of the study period.

CONCLUSIONS:

We identified highly dissimilar behavioural patterns across behavioural clusters, including drastic, atypical changes. These patterns suggest that the overall increase in the frequency of nsCAI is largely attributable to two clusters, accounting for a third of the population.

KEYWORDS:

HIV; STI; clusters; condom; men who have sex with men; sexual behaviour

PMID:
30874293
DOI:
10.1093/cid/ciz208
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