Literature DB >> 33765091

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees.

David Niyukuri1,2, Peter Nyasulu1,3, Wim Delva1,2,4,5,6.   

Abstract

Understanding age-mixing patterns in Human Immunodeficiency Virus (HIV) transmission networks can enhance the design and implementation of HIV prevention strategies in sub-Saharan Africa. Due to ethical consideration, it is less likely possible to conduct a benchmark study to assess which sampling strategy, and sub-optimal sampling coverage which can yield best estimates for these patterns. We conducted a simulation study, using phylogenetic trees to infer estimates of age-mixing patterns in HIV transmission, through the computation of proportions of pairings between men and women, who were phylogenetically linked across different age groups (15-24 years, 25-39 years, and 40-49 years); and the means, and standard deviations of their age difference. We investigated also the uncertainty around these estimates as a function of the sampling coverage in four sampling strategies: when missing sequence data were missing completely at random (MCAR), and missing at random (MAR) with at most 30%-50%-70% of women in different age groups being in the sample. The results suggested that age-mixing patterns in HIV transmission can be unveiled from proportions of phylogenetic pairings between men and women across age groups; and the mean, and standard deviation of their age difference. A 55% sampling coverage was sufficient to provide the best values of estimates of age-mixing patterns in HIV transmission with MCAR scenario. But we should be cautious in interpreting proportions of men phylogenetically linked to women because they may be overestimated or underestimated, even at higher sampling coverage. The findings showed that, MCAR was the best sampling strategy. This means, it is advisable not to use sequence data collected in settings where we can find a systematic imbalance of age and gender to investigate age-mixing in HIV transmission. If not possible, ensure to take into consideration the imbalance in interpreting the results.

Entities:  

Year:  2021        PMID: 33765091      PMCID: PMC7993798          DOI: 10.1371/journal.pone.0249013

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  27 in total

1.  Identifying Transmission Clusters with Cluster Picker and HIV-TRACE.

Authors:  Rebecca Rose; Susanna L Lamers; James J Dollar; Mary K Grabowski; Emma B Hodcroft; Manon Ragonnet-Cronin; Joel O Wertheim; Andrew D Redd; Danielle German; Oliver Laeyendecker
Journal:  AIDS Res Hum Retroviruses       Date:  2016-12-13       Impact factor: 2.205

2.  Can antiretroviral therapy be used to prevent sexual transmission of human immunodeficiency virus type 1?

Authors:  Mina Hosseinipour; Myron S Cohen; Pietro L Vernazza; Angela D M Kashuba
Journal:  Clin Infect Dis       Date:  2002-04-22       Impact factor: 9.079

3.  The generational effect on age disparate partnerships and the risk for human immunodeficiency virus and sexually transmitted infections acquisition.

Authors:  Renée A Street; Tarylee Reddy; Gita Ramjee
Journal:  Int J STD AIDS       Date:  2015-07-02       Impact factor: 1.359

4.  Do age-disparate relationships drive HIV incidence in young women? Evidence from a population cohort in rural KwaZulu-Natal, South Africa.

Authors:  Guy Harling; Marie-Louise Newell; Frank Tanser; Ichiro Kawachi; S V Subramanian; Till Bärnighausen
Journal:  J Acquir Immune Defic Syndr       Date:  2014-08-01       Impact factor: 3.731

5.  New insights into HIV epidemic in South Africa: key findings from the National HIV Prevalence, Incidence and Behaviour Survey, 2012.

Authors:  Khangelani Zuma; Olive Shisana; Thomas M Rehle; Leickness C Simbayi; Sean Jooste; Nompumelelo Zungu; Demetre Labadarios; Dorina Onoya; Meredith Evans; Sizulu Moyo; Fareed Abdullah
Journal:  Afr J AIDS Res       Date:  2016       Impact factor: 1.300

6.  Age-disparate sex and HIV risk for young women from 2002 to 2012 in South Africa.

Authors:  Meredith Evan; Kathryn Risher; Nompumelelo Zungu; Olive Shisana; Sizulu Moyo; David D Celentano; Brendan Maughan-Brown; Thomas M Rehle
Journal:  J Int AIDS Soc       Date:  2016-12-26       Impact factor: 5.396

7.  Recent patterns in population-based HIV prevalence in Swaziland.

Authors:  George T Bicego; Rejoice Nkambule; Ingrid Peterson; Jason Reed; Deborah Donnell; Henry Ginindza; Yen T Duong; Hetal Patel; Naomi Bock; Neena Philip; Cherry Mao; Jessica Justman
Journal:  PLoS One       Date:  2013-10-15       Impact factor: 3.240

8.  What is the difference between missing completely at random and missing at random?

Authors:  Krishnan Bhaskaran; Liam Smeeth
Journal:  Int J Epidemiol       Date:  2014-04-04       Impact factor: 7.196

9.  HIV Infection and AIDS in Sub-Saharan Africa: Current Status, Challenges and Opportunities.

Authors:  Ayesha B M Kharsany; Quarraisha A Karim
Journal:  Open AIDS J       Date:  2016-04-08

Review 10.  Inferring the age difference in HIV transmission pairs by applying phylogenetic methods on the HIV transmission network of the Swiss HIV Cohort Study.

Authors:  Katharina Kusejko; Claus Kadelka; Alex Marzel; Manuel Battegay; Enos Bernasconi; Alexandra Calmy; Matthias Cavassini; Matthias Hoffmann; Jürg Böni; Sabine Yerly; Thomas Klimkait; Matthieu Perreau; Andri Rauch; Huldrych F Günthard; Roger D Kouyos
Journal:  Virus Evol       Date:  2018-09-18
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