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Clusters of Sexual Behavior in Human Immunodeficiency Virus-positive Men Who Have Sex With Men Reveal Highly Dissimilar Time Trends.

Authors
  • Salazar-Vizcaya, Luisa1
  • Kusejko, Katharina2, 3
  • Schmidt, Axel J4, 5
  • Carrillo-Montoya, Germán6
  • Nicca, Dunja7
  • Wandeler, Gilles1
  • Braun, Dominique L2, 3
  • Fehr, Jan2
  • Darling, Katharine E A8
  • Bernasconi, Enos9
  • Schmid, Patrick4
  • Günthard, Huldrych F2, 3
  • Kouyos, Roger D2, 3
  • Rauch, Andri1
  • 1 Department of Infectious Diseases, Bern University Hospital Inselspital, University of Bern, Switzerland. , (Switzerland)
  • 2 Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Switzerland. , (Switzerland)
  • 3 Institute of Medical Virology, University of Zurich, Switzerland. , (Switzerland)
  • 4 Division of Infectious Diseases and Infection Control, Cantonal Hospital St. Gallen, Switzerland. , (Switzerland)
  • 5 Sigma Research, London School of Hygiene and Tropical Medicine, United Kingdom. , (United Kingdom)
  • 6 Alpiq Energy AI, Olten, Solothurn.
  • 7 Institute of Nursing Science, University of Basel, Switzerland. , (Switzerland)
  • 8 Infectious Diseases Service, Department of Medicine, University Hospital of Lausanne (CHUV), Switzerland. , (Switzerland)
  • 9 Division of Infectious Diseases, Lugano Regional Hospital, Switzerland. , (Switzerland)
Type
Published Article
Journal
Clinical Infectious Diseases
Publisher
Oxford University Press
Publication Date
Jan 16, 2020
Volume
70
Issue
3
Pages
416–424
Identifiers
DOI: 10.1093/cid/ciz208
PMID: 30874293
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Separately addressing specific groups of people who share patterns of behavioral change might increase the impact of behavioral 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). By means of unsupervised learning, we inferred "behavioral clusters" based on the recognition of similarities and differences in longitudinal patterns of condomless anal intercourse with nonsteady partners (nsCAI) in the HIV Cohort Study over the last 18 years. We then used supervised learning to investigate whether sociodemographic variables could predict cluster membership. We identified 4 behavioral clusters. The largest behavioral cluster (cluster 1) contained 53% of the study population and displayed the most stable behavior. Cluster 3 (17% of the study population) displayed consistently increasing nsCAI. Sociodemographic variables were predictive for both of these clusters. The other 2 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. We identified highly dissimilar behavioral patterns across behavioral clusters, including drastic, atypical changes. The patterns suggest that the overall increase in the frequency of nsCAI is largely attributable to 2 clusters, accounting for a third of the population. © The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.

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