Luisa Salazar-Vizcaya1, Katharina Kusejko2,3, Axel J Schmidt4,5, Germán Carrillo-Montoya6, Dunja Nicca7, Gilles Wandeler1, Dominique L Braun2,3, Jan Fehr2, Katharine E A Darling8, Enos Bernasconi9, Patrick Schmid4, Huldrych F Günthard2,3, Roger D Kouyos2,3, Andri Rauch1. 1. Department of Infectious Diseases, Bern University Hospital Inselspital, University of Bern, Switzerland. 2. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Switzerland. 3. Institute of Medical Virology, University of 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. 7. Institute of Nursing Science, University of Basel, Switzerland. 8. Infectious Diseases Service, Department of Medicine, University Hospital of Lausanne (CHUV), Switzerland. 9. Division of Infectious Diseases, Lugano Regional Hospital, Switzerland.
Abstract
BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Daphne A van Wees; Janneke C M Heijne; Maartje Basten; Titia Heijman; John de Wit; Mirjam E E Kretzschmar; Chantal den Daas Journal: Sex Transm Dis Date: 2020-03 Impact factor: 2.830
Authors: Luisa Salazar-Vizcaya; Katharina Kusejko; Huldrych F Günthard; Jürg Böni; Karin J Metzner; Dominique L Braun; Dunja Nicca; Enos Bernasconi; Alexandra Calmy; Katharine E A Darling; Gilles Wandeler; Roger D Kouyos; Andri Rauch Journal: Viruses Date: 2022-04-10 Impact factor: 5.818