HIV Treatment as Prevention: Models, Data, and Questions—Towards Evidence-Based Decision-Making
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"id"=>"b94b298c-88e6-36db-b25e-6264fc9e0ee8", "abstract"=>"Antiretroviral therapy (ART) for those infected with HIV can prevent onward transmission of infection, but biological efficacy alone is not enough to guide policy decisions about the role of ART in reducing HIV incidence. Epidemiology, economics, demography, statistics, biology, and mathematical modelling will be central in framing key decisions in the optimal use of ART. PLoS Medicine, with the HIV Modelling Consortium, has commissioned a set of articles that examine different aspects of HIV treatment as prevention with a forward-looking research agenda. Interlocking themes across these articles are discussed in this introduction. We hope that this article, and others in the collection, will provide a foundation upon which greater collaborations between disciplines will be formed, and will afford deeper insights into the key factors involved, to help strengthen the support for evidence-based decision-making in HIV prevention.", "link"=>"http://www.mendeley.com/research/hiv-treatment-prevention-models-data-questionstowards-evidencebased-decisionmaking", "reader_count"=>107, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>7, "Librarian"=>3, "Researcher"=>28, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Student > Master"=>14, "Other"=>7, "Student > Bachelor"=>3, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>14}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>7, "Librarian"=>3, "Researcher"=>28, "Student > Doctoral Student"=>3, "Student > Ph. D. 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Scopus | Further Information

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  • {"month"=>"7", "year"=>"2020", "pdf_views"=>"10", "xml_views"=>"2", "html_views"=>"17"}
  • {"month"=>"8", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"12"}
  • {"month"=>"9", "year"=>"2020", "pdf_views"=>"23", "xml_views"=>"0", "html_views"=>"27"}
  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"23", "xml_views"=>"0", "html_views"=>"18"}
  • {"month"=>"11", "year"=>"2020", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"40"}
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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/611055"], "description"=>"<p>The published results of models <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Montaner1\" target=\"_blank\">[38]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-AbuRaddad1\" target=\"_blank\">[53]</a>–<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Goodreau1\" target=\"_blank\">[55]</a> that have estimated the contribution of different stages of HIV infection to onward transmission are compiled in a median cumulative distribution of infections generated by one infected person over the course of his/her infection in the absence of treatment (red line). The horizontal axis shows time from the time of infection to 12 years, which is the mean survival time for those with untreated HIV infection <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Todd1\" target=\"_blank\">[56]</a>. The vertical axis shows the cumulative transmission, from 0% (no new infections generated yet) to 100% (all onward transmission completed). (Note that the uncertainty in this distribution is not indicated.) The shading indicates the approximate CD4 cell count category at each time point <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Eligibility1\" target=\"_blank\">[25]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Lodi1\" target=\"_blank\">[26]</a>. Currently, treatment tends to be initiated well below a CD4 cell count of 200 cells/μl <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Cornell1\" target=\"_blank\">[32]</a>, meaning that the contribution of treatment to prevention is minimal because most of the transmission from that person has already occurred before treatment starts. If increased testing and improved linkages to care enabled individuals to start treatment at a CD4 cell count very close to 200 cells/μl, this could result in a substantial reduction in HIV incidence, because ∼25%–30% of transmission normally arises from individuals after that point. The prevention impact would be expected to be even greater with initiation close to a CD4 cell count of 350 cells/μl. If the average number of new infections arising from an infected person in a susceptible population exceeds one before treatment could be feasibility initiated, then treatment could not eliminate the HIV epidemic. In this framework, the influence of other forms of prevention will be to change the shape of the graph. For instance, if many men are circumcised or individuals have fewer new sexual partners per time unit, then new infections arising from an infected person will grow more slowly over time, so that on average one new infection might be generated only after the point at which a feasible programme could have initiated treatment.</p>", "links"=>[], "tags"=>["epidemiological", "hiv"], "article_id"=>281552, "categories"=>["Biological Sciences"], "users"=>["The HIV Modelling Consortium Treatment as Prevention Editorial Writing Group"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001259.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_framework_for_understanding_the_epidemiological_impact_of_HIV_treatment_/281552", "title"=>"A framework for understanding the epidemiological impact of HIV treatment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:39:55"}

PMC Usage Stats | Further Information

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Relative Metric

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