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A Direct Comparison of Two Densely Sampled HIV Epidemics: The UK and Switzerland.

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Ragonnet-Cronin Manon L, Shilaih Mohaned, Günthard Huldrych F, Hodcroft Emma B, Böni Jürg, Fearnhill Esther, Dunn David, Yerly Sabine, Klimkait Thomas, Aubert Vincent, Yang Wan-Lin, Brown Alison E, Lycett Samantha J, Kouyos Roger, Brown Andrew J Leigh,
Project Swiss HIV Cohort Study (SHCS)
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Original article (peer-reviewed)

Journal Scientific reports
Volume (Issue) 6
Page(s) 32251 - 32251
Title of proceedings Scientific reports
DOI 10.1038/srep32251

Open Access

URL http://doi.org/10.1038/srep32251
Type of Open Access Publisher (Gold Open Access)

Abstract

Phylogenetic clustering approaches can elucidate HIV transmission dynamics. Comparisons across countries are essential for evaluating public health policies. Here, we used a standardised approach to compare the UK HIV Drug Resistance Database and the Swiss HIV Cohort Study while maintaining data-protection requirements. Clusters were identified in subtype A1, B and C pol phylogenies. We generated degree distributions for each risk group and compared distributions between countries using Kolmogorov-Smirnov (KS) tests, Degree Distribution Quantification and Comparison (DDQC) and bootstrapping. We used logistic regression to predict cluster membership based on country, sampling date, risk group, ethnicity and sex. We analysed >8,000 Swiss and >30,000 UK subtype B sequences. At 4.5% genetic distance, the UK was more clustered and MSM and heterosexual degree distributions differed significantly by the KS test. The KS test is sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss dataset removed the difference. Only heterosexuals varied based on the DDQC, due to UK male heterosexuals who clustered exclusively with MSM. Their removal eliminated this difference. In conclusion, the UK and Swiss HIV epidemics have similar underlying dynamics and observed differences in clustering are mainly due to different population sizes.
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