What Drives the US and Peruvian HIV Epidemics in Men Who Have Sex with Men (MSM)?
Publication Date
November 29, 2012
Journal
PLOS ONE
Authors
Steven M. Goodreau, Nicole B. Carnegie, Eric Vittinghoff, Javier R. Lama, et al
Volume
7
Issue
11
Pages
e50522
DOI
https://dx.plos.org/10.1371/journal.pone.0050522
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0050522
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23209768
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510067
Europe PMC
http://europepmc.org/abstract/MED/23209768
Web of Science
000312104900062
Scopus
84870513120
Mendeley
http://www.mendeley.com/research/drives-peruvian-hiv-epidemics-men-sex-men-msm
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Mendeley | Further Information

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CrossRef

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/287837"], "description"=>"<div><p>In this work, we estimate the proportions of transmissions occurring in main vs. casual partnerships, and by the sexual role, infection stage, and testing and treatment history of the infected partner, for men who have sex with men (MSM) in the US and Peru. We use dynamic, stochastic models based in exponential random graph models (ERGMs), obtaining inputs from multiple large-scale MSM surveys. Parallel main partnership and casual sexual networks are simulated. Each man is characterized by age, race, circumcision status, sexual role behavior, and propensity for unprotected anal intercourse (UAI); his history is modeled from entry into the adult population, with potential transitions including HIV infection, detection, treatment, AIDS diagnosis, and death. We implemented two model variants differing in assumptions about acute infectiousness, and assessed sensitivity to other key inputs. Our two models suggested that only 4–5% (Model 1) or 22–29% (Model 2) of HIV transmission results from contacts with acute-stage partners; the plurality (80–81% and 49%, respectively) stem from chronic-stage partners and the remainder (14–16% and 27–35%, respectively) from AIDS-stage partners. Similar proportions of infections stem from partners whose infection is undiagnosed (24–31%), diagnosed but untreated (36–46%), and currently being treated (30–36%). Roughly one-third of infections (32–39%) occur within main partnerships. Results by country were qualitatively similar, despite key behavioral differences; one exception was that transmission from the receptive to insertive partner appears more important in Peru (34%) than the US (21%). The broad balance in transmission contexts suggests that education about risk, careful assessment, pre-exposure prophylaxis, more frequent testing, earlier treatment, and risk-reduction, disclosure, and adherence counseling may all contribute substantially to reducing the HIV incidence among MSM in the US and Peru.</p> </div>", "links"=>[], "tags"=>["drives", "peruvian", "hiv", "epidemics", "men"], "article_id"=>116698, "categories"=>["Information And Computing Sciences", "Cancer", "Neuroscience", "Biotechnology"], "users"=>["Steven M. Goodreau", "Nicole B. Carnegie", "Eric Vittinghoff", "Javier R. Lama", "Jorge Sanchez", "Beatriz Grinsztejn", "Beryl A. Koblin", "Kenneth H. Mayer", "Susan P. Buchbinder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050522", "stats"=>{"downloads"=>5, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/What_Drives_the_US_and_Peruvian_HIV_Epidemics_in_Men_Who_Have_Sex_with_Men_MSM__/116698", "title"=>"What Drives the US and Peruvian HIV Epidemics in Men Who Have Sex with Men (MSM)?", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:51:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/535167"], "description"=>"<p>a) Changes in main partnership duration relative to base model (100%). b) Changes in main and casual UAI relative to base model (100%). c) Changes in testing frequency relative to base model (100%).</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "urology", "computer science", "neuroscience"], "article_id"=>205653, "categories"=>["Cancer", "Neuroscience", "Information And Computing Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Steven M. Goodreau", "Nicole B. Carnegie", "Eric Vittinghoff", "Javier R. Lama", "Jorge Sanchez", "Beatriz Grinsztejn", "Beryl A. Koblin", "Kenneth H. Mayer", "Susan P. Buchbinder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050522.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_analyses_/205653", "title"=>"Sensitivity analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:34:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/535001"], "description"=>"<p>Bars represent the means across ten stochastic simulations over 25 years. The variance of these estimates across runs was less than 2 percentage points for all measures, so only means are shown. For year-on-year variation, see the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050522#pone-0050522-g003\" target=\"_blank\">Figure 3</a> and the online technical supplement.</p>", "links"=>[], "tags"=>["events"], "article_id"=>205492, "categories"=>["Cancer", "Neuroscience", "Information And Computing Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Steven M. Goodreau", "Nicole B. Carnegie", "Eric Vittinghoff", "Javier R. Lama", "Jorge Sanchez", "Beatriz Grinsztejn", "Beryl A. Koblin", "Kenneth H. Mayer", "Susan P. Buchbinder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050522.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_transmission_events_by_key_variables_/205492", "title"=>"Distribution of transmission events by key variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:31:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/535280"], "description"=>"<p>Model features.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "urology", "computer science", "neuroscience"], "article_id"=>205762, "categories"=>["Cancer", "Neuroscience", "Information And Computing Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Steven M. Goodreau", "Nicole B. Carnegie", "Eric Vittinghoff", "Javier R. Lama", "Jorge Sanchez", "Beatriz Grinsztejn", "Beryl A. Koblin", "Kenneth H. Mayer", "Susan P. Buchbinder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050522.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_features_/205762", "title"=>"Model features.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:36:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/535091"], "description"=>"<p>Each boxplot covers the values for a given outcome metric measured for each of the 25 years in a single run. We show US Model 1 here as demonstration; comparable plots for the other three country/model combinations are in the online technical supplement.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "urology", "computer science", "neuroscience"], "article_id"=>205578, "categories"=>["Cancer", "Neuroscience", "Information And Computing Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Steven M. Goodreau", "Nicole B. Carnegie", "Eric Vittinghoff", "Javier R. Lama", "Jorge Sanchez", "Beatriz Grinsztejn", "Beryl A. Koblin", "Kenneth H. Mayer", "Susan P. Buchbinder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050522.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Range_of_variation_from_year_to_year_for_outcome_metrics_/205578", "title"=>"Range of variation from year to year for outcome metrics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:32:58"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Medicine and health sciences", "average_usage"=>[312, 547, 668, 769, 861, 953, 1046, 1135, 1224, 1307, 1388, 1464, 1536, 1606, 1676, 1744, 1812, 1882, 1954, 2020, 2089, 2155, 2218, 2282, 2344]}, {"subject_area"=>"/Medicine and health sciences/Public and occupational health", "average_usage"=>[327, 554, 673, 780, 874, 968, 1060, 1133, 1222, 1304, 1376, 1463, 1536, 1602, 1663, 1742, 1796, 1853, 1931, 1985, 2044, 2108, 2170, 2248, 2305]}, {"subject_area"=>"/People and places/Geographical locations", "average_usage"=>[318, 550, 661, 755, 847, 941, 1034, 1126, 1222, 1320, 1395, 1476, 1573, 1629, 1693, 1751, 1832, 1910, 1975, 2037, 2106, 2172, 2235, 2309, 2388]}, {"subject_area"=>"/People and places/Population groupings", "average_usage"=>[325, 565, 687, 787, 880, 971, 1065, 1156, 1244, 1339, 1424, 1502, 1582, 1654, 1724, 1794, 1855, 1925, 1997, 2058, 2132, 2207, 2285, 2347, 2412]}]}
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