Potential Impact of Antiretroviral Chemoprophylaxis on HIV-1 Transmission in Resource-Limited Settings
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{"title"=>"Potential impact of antiretroviral chemoprophylaxis on HIV-1 transmission in resource-limited settings", "type"=>"journal", "authors"=>[{"first_name"=>"Ume L.", "last_name"=>"Abbas", "scopus_author_id"=>"6506214347"}, {"first_name"=>"Roy M.", "last_name"=>"Anderson", "scopus_author_id"=>"7408244444"}, {"first_name"=>"John W.", "last_name"=>"Mellors", "scopus_author_id"=>"7007008426"}], "year"=>2007, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"351425883", "sgr"=>"41149091104", "issn"=>"19326203", "pmid"=>"17878928", "scopus"=>"2-s2.0-41149091104", "doi"=>"10.1371/journal.pone.0000875", "isbn"=>"1932-6203 (Electronic)"}, "id"=>"32cafd4b-d1f1-3878-8880-775d6d0282f3", "abstract"=>"BACKGROUND The potential impact of pre-exposure chemoprophylaxis (PrEP) on heterosexual transmission of HIV-1 infection in resource-limited settings is uncertain. METHODOLOGY/PRINCIPLE FINDINGS A deterministic mathematical model was used to simulate the effects of antiretroviral PrEP on an HIV-1 epidemic in sub-Saharan Africa under different scenarios (optimistic, neutral and pessimistic) both with and without sexual disinhibition. Sensitivity analyses were used to evaluate the effect of uncertainty in input parameters on model output and included calculation of partial rank correlations and standardized rank regressions. In the scenario without sexual disinhibition after PrEP initiation, key parameters influencing infections prevented were effectiveness of PrEP (partial rank correlation coefficient (PRCC) = 0.94), PrEP discontinuation rate (PRCC = -0.94), level of coverage (PRCC = 0.92), and time to achieve target coverage (PRCC = -0.82). In the scenario with sexual disinhibition, PrEP effectiveness and the extent of sexual disinhibition had the greatest impact on prevention. An optimistic scenario of PrEP with 90% effectiveness and 75% coverage of the general population predicted a 74% decline in cumulative HIV-1 infections after 10 years, and a 28.8% decline with PrEP targeted to the highest risk groups (16% of the population). Even with a 100% increase in at-risk behavior from sexual disinhibition, a beneficial effect (23.4%-62.7% decrease in infections) was seen with 90% effective PrEP across a broad range of coverage (25%-75%). Similar disinhibition led to a rise in infections with lower effectiveness of PrEP (< or = 50%). CONCLUSIONS/SIGNIFICANCE Mathematical modeling supports the potential public health benefit of PrEP. Approximately 2.7 to 3.2 million new HIV-1 infections could be averted in southern sub-Saharan Africa over 10 years by targeting PrEP (having 90% effectiveness) to those at highest behavioral risk and by preventing sexual disinhibition. This benefit could be lost, however, by sexual disinhibition and by high PrEP discontinuation, especially with lower PrEP effectiveness (< or = 50%).", "link"=>"http://www.mendeley.com/research/potential-impact-antiretroviral-chemoprophylaxis-hiv1-transmission-resourcelimited-settings-3", "reader_count"=>56, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>7, "Student > Doctoral Student"=>1, "Researcher"=>10, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>3, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>7, "Student > Doctoral Student"=>1, "Researcher"=>10, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>3, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Agricultural and Biological Sciences"=>10, "Arts and Humanities"=>1, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Nursing and Health Professions"=>2, "Mathematics"=>4, "Medicine and Dentistry"=>19, "Design"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Social Sciences"=>9, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Social Sciences"=>{"Social Sciences"=>9}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Design"=>{"Design"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>6, "Ireland"=>1, "Kenya"=>1}, "group_count"=>8}

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  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"8", "xml_views"=>"2", "html_views"=>"17"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"2", "xml_views"=>"0", "html_views"=>"4"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"15"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"4", "xml_views"=>"1", "html_views"=>"9"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"14"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"3", "xml_views"=>"0", "html_views"=>"3"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"4"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"13"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"6", "xml_views"=>"1", "html_views"=>"2"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"3", "xml_views"=>"0", "html_views"=>"0"}
  • {"month"=>"6", "year"=>"2019", "pdf_views"=>"1", "xml_views"=>"0", "html_views"=>"6"}
  • {"month"=>"7", "year"=>"2019", "pdf_views"=>"2", "xml_views"=>"0", "html_views"=>"4"}
  • {"month"=>"8", "year"=>"2019", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"5"}
  • {"month"=>"9", "year"=>"2019", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"13"}
  • {"month"=>"10", "year"=>"2019", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"6"}
  • {"month"=>"11", "year"=>"2019", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"3"}
  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"2", "xml_views"=>"0", "html_views"=>"0"}
  • {"month"=>"1", "year"=>"2020", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"2"}
  • {"month"=>"2", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"0"}
  • {"month"=>"3", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"4"}
  • {"month"=>"4", "year"=>"2020", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"3"}
  • {"month"=>"5", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"1"}
  • {"month"=>"6", "year"=>"2020", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"0"}
  • {"month"=>"7", "year"=>"2020", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"0"}
  • {"month"=>"8", "year"=>"2020", "pdf_views"=>"3", "xml_views"=>"2", "html_views"=>"1"}
  • {"month"=>"9", "year"=>"2020", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"7"}
  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"8"}
  • {"month"=>"11", "year"=>"2020", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"1"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/945212"], "description"=>"*<p>The p-value is 0.0000 except for ж where the p-value < 0.05.</p>‡<p><i>Y<sup>R′′</sup></i> represents individuals with secondary resistance.</p>", "links"=>[], "tags"=>["affecting", "cumulative", "hiv-1", "infections"], "article_id"=>615584, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.t003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_Sensitivity_Analyses_for_Model_Parameters_Affecting_the_Decrease_in_Cumulative_New_HIV_1_Infections_/615584", "title"=>"Results of Sensitivity Analyses for Model Parameters Affecting the Decrease in Cumulative New HIV-1 Infections (%)", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 01:33:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/945270"], "description"=>"‡<p>These are conservative projections based on estimates of the size of adult population <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-UNAIDS1\" target=\"_blank\">[47]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Department1\" target=\"_blank\">[71]</a> and assuming constant incidence <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Shisana1\" target=\"_blank\">[73]</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-UNAIDSWHO3\" target=\"_blank\">[75]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Surveillance1\" target=\"_blank\">[89]</a>, prevalence <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-UNAIDS1\" target=\"_blank\">[47]</a> and growth rate <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-UNAIDS1\" target=\"_blank\">[47]</a>.</p>¶<p>For southern sub-Saharan Africa overall, projections are based on the UNAIDS/WHO statement that the total number of infections in this region were 1.1 million for three consecutive years including 2005 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-UNAIDSWHO3\" target=\"_blank\">[75]</a>. With this estimate as a constant, low projection assumes 86% of these infections occur in adults, while the high projection assumes the full estimate.</p>§<p>Excludes Angola, Madagascar, Mauritius and Seychelles.</p>†<p>Refers to median country-prevalence.</p>", "links"=>[], "tags"=>["prep", "introduced", "2007", "hiv-1", "infections", "sub-saharan"], "article_id"=>615643, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.t005", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Potential_Impact_of_PrEP_Introduced_in_2007_on_HIV_1_Infections_in_Southern_Sub_Saharan_Africa_8225_182_/615643", "title"=>"Potential Impact of PrEP Introduced in 2007 on HIV-1 Infections in Southern Sub-Saharan Africa<sup>‡</sup><sup>¶</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 01:34:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/945174"], "description"=>"‡<p>Except for the most relevant, the subscripts/superscripts have been omitted from the symbols for clarity.</p>¶<p>For the analyses reported in this paper, we assumed that infections would be detected in the on-PrEP individuals after an average duration of 6 months, when they would stop PrEP and resume baseline sexual activity. Multivariate sensitivity analyses, where we varied the total persistence of drug-resistant virus in infected individuals (i.e. sum of the periods of persistence on- and off-PrEP) between 1 month to 2 years, showed that the model output was not sensitive to changes in this parameter (data not shown).</p>§<p>HIV-1 disease progression was assumed the same for drug resistant and drug sensitive virus because: i) a temporary predominance of drug-resistant mutants was assumed in the model; and ii) though lower viremia has been observed in the experimental setting <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-GarcaLerma1\" target=\"_blank\">[24]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Lifson1\" target=\"_blank\">[85]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Rosenwirth1\" target=\"_blank\">[86]</a>, it is unknown whether PrEP would attenuate the course of HIV-1 infection.</p>†<p>Inverse of the average duration of PrEP use. For the optimistic scenario we assumed the average duration of PrEP use to be equal to the average duration of sexual activity.</p>", "links"=>[], "tags"=>["pre-exposure", "chemoprophylaxis"], "article_id"=>615556, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Parameters_for_Pre_exposure_Chemoprophylaxis_PrEP_Implementation_/615556", "title"=>"Model Parameters for Pre-exposure Chemoprophylaxis (PrEP) Implementation", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 01:32:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/465022"], "description"=>"<div><h3>Background</h3><p>The potential impact of pre-exposure chemoprophylaxis (PrEP) on heterosexual transmission of HIV-1 infection in resource-limited settings is uncertain.</p><h3>Methodology/Principle Findings</h3><p>A deterministic mathematical model was used to simulate the effects of antiretroviral PrEP on an HIV-1 epidemic in sub-Saharan Africa under different scenarios (optimistic, neutral and pessimistic) both with and without sexual disinhibition. Sensitivity analyses were used to evaluate the effect of uncertainty in input parameters on model output and included calculation of partial rank correlations and standardized rank regressions. In the scenario without sexual disinhibition after PrEP initiation, key parameters influencing infections prevented were effectiveness of PrEP (partial rank correlation coefficient (PRCC) = 0.94), PrEP discontinuation rate (PRCC = −0.94), level of coverage (PRCC = 0.92), and time to achieve target coverage (PRCC = −0.82). In the scenario with sexual disinhibition, PrEP effectiveness and the extent of sexual disinhibition had the greatest impact on prevention. An optimistic scenario of PrEP with 90% effectiveness and 75% coverage of the general population predicted a 74% decline in cumulative HIV-1 infections after 10 years, and a 28.8% decline with PrEP targeted to the highest risk groups (16% of the population). Even with a 100% increase in at-risk behavior from sexual disinhibition, a beneficial effect (23.4%–62.7% decrease in infections) was seen with 90% effective PrEP across a broad range of coverage (25%–75%). Similar disinhibition led to a rise in infections with lower effectiveness of PrEP (≤50%).</p><h3>Conclusions/Significance</h3><p>Mathematical modeling supports the potential public health benefit of PrEP. Approximately 2.7 to 3.2 million new HIV-1 infections could be averted in southern sub-Saharan Africa over 10 years by targeting PrEP (having 90% effectiveness) to those at highest behavioral risk and by preventing sexual disinhibition. This benefit could be lost, however, by sexual disinhibition and by high PrEP discontinuation, especially with lower PrEP effectiveness (≤50%).</p></div>", "links"=>[], "tags"=>["antiretroviral", "chemoprophylaxis", "hiv-1", "resource-limited", "settings"], "article_id"=>151668, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Potential_Impact_of_Antiretroviral_Chemoprophylaxis_on_HIV_1_Transmission_in_Resource_Limited_Settings/151668", "title"=>"Potential Impact of Antiretroviral Chemoprophylaxis on HIV-1 Transmission in Resource-Limited Settings", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 00:27:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/945064"], "description"=>"<p>Negative numbers reflect increase in infections.</p>", "links"=>[], "tags"=>["graph", "cumulative", "infections", "prep", "assuming"], "article_id"=>615438, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contour_Graph_for_Decline_in_Cumulative_Infections_as_a_Function_of_Effectiveness_of_PrEP_and_Increase_in_Risk_Behavior_Assuming_Optimistic_Scenario_/615438", "title"=>"Contour Graph for Decline in Cumulative Infections (%) as a Function of Effectiveness of PrEP and Increase in Risk Behavior Assuming Optimistic Scenario.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-19 01:30:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/944931"], "description"=>"<p>Simplified Flow Diagram of Model with PrEP Implementation.</p>", "links"=>[], "tags"=>["diagram", "prep"], "article_id"=>615314, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simplified_Flow_Diagram_of_Model_with_PrEP_Implementation_/615314", "title"=>"Simplified Flow Diagram of Model with PrEP Implementation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-19 01:28:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/945144"], "description"=>"*<p>The superscripts represent the disease stage.</p>Υ<p>Individuals with AIDS were assumed to be sexually inactive during the last 6 months of their life <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Wawer1\" target=\"_blank\">[12]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Deschamps1\" target=\"_blank\">[81]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Moatti1\" target=\"_blank\">[82]</a>.</p>", "links"=>[], "tags"=>["simulated", "hiv-1"], "article_id"=>615524, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.t001", "stats"=>{"downloads"=>6, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Parameters_for_the_Simulated_HIV_1_Epidemic_/615524", "title"=>"Model Parameters for the Simulated HIV-1 Epidemic", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 01:32:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/945006"], "description"=>"<p>Trends in HIV-1 Prevalence among Urban Antenatal Clinic Attendees in Zambia from 1994 to 2004 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Ministry1\" target=\"_blank\">[49]</a> and the Simulated Adult Female Population.</p>", "links"=>[], "tags"=>["hiv-1", "prevalence", "antenatal", "attendees", "zambia", "1994", "2004", "simulated"], "article_id"=>615378, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trends_in_HIV_1_Prevalence_among_Urban_Antenatal_Clinic_Attendees_in_Zambia_from_1994_to_2004_49_and_the_Simulated_Adult_Female_Population_/615378", "title"=>"Trends in HIV-1 Prevalence among Urban Antenatal Clinic Attendees in Zambia from 1994 to 2004 [49] and the Simulated Adult Female Population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-19 01:29:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/945232"], "description"=>"‡<p>Assuming a 100% increase in at-risk behavior.</p>¶<p>Assuming $700 per person-year of PrEP; the market price of a generic version of tenofovir (Tenvir) manufactured by Cipla in India <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Gentleman1\" target=\"_blank\">[87]</a>.</p>§<p>Assuming $318 per person-year of PrEP ($0.87/day); the current cost of manufacturing tenofovir+emtricitabine (Truvada) by Gilead <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Gilead1\" target=\"_blank\">[88]</a>.</p>†<p>Assuming $208 per person-year of PrEP ($0.57/day); the current cost of manufacturing tenofovir (Viread) by Gilead <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Gilead1\" target=\"_blank\">[88]</a>.</p><p>Costs (defined as drug costs per person-year of PrEP) and health benefits (infections averted) are presented in their undiscounted form for clarity <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000875#pone.0000875-Drummond1\" target=\"_blank\">[80]</a>. Costs of PrEP exclude all other costs e.g. drug distribution, pharmacy and clinical services, communications and education, laboratory, treatment of complications including resistance, and counseling. Analyses also exclude the consequences of HIV-1 infection including costs of provision of antiretroviral therapy.</p>", "links"=>[], "tags"=>["pessimistic", "scenarios", "years", "prep"], "article_id"=>615609, "categories"=>["Computational Biology", "Mathematics", "Medicine"], "users"=>["Ume L. Abbas", "Roy M. Anderson", "John W. Mellors"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000875.t004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Outcomes_for_Optimistic_Neutral_and_Pessimistic_Scenarios_after_Ten_Years_of_PrEP_Implementation_/615609", "title"=>"Outcomes for Optimistic, Neutral and Pessimistic Scenarios after Ten Years of PrEP Implementation", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-19 01:33:29"}

PMC Usage Stats | Further Information

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  • {"month"=>"4", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"16", "year"=>"2010", "pdf"=>"10", "unique-ip"=>"12", "figure"=>"3", "scanned-summary"=>"0", "supp-data"=>"2"}
  • {"scanned-page-browse"=>"0", "month"=>"5", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"10", "unique-ip"=>"12", "pdf"=>"6", "year"=>"2010", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"month"=>"6", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"1", "full-text"=>"10", "year"=>"2010", "pdf"=>"1", "unique-ip"=>"8", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"scanned-page-browse"=>"0", "month"=>"7", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"9", "unique-ip"=>"10", "pdf"=>"3", "year"=>"2010", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"month"=>"8", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"11", "year"=>"2010", "pdf"=>"9", "unique-ip"=>"12", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"scanned-page-browse"=>"0", "month"=>"9", "cited-by"=>"0", "abstract"=>"1", "full-text"=>"6", "unique-ip"=>"9", "pdf"=>"8", "year"=>"2010", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"month"=>"10", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"1", "full-text"=>"7", "year"=>"2010", "pdf"=>"5", "unique-ip"=>"10", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"scanned-page-browse"=>"0", "month"=>"11", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"16", "unique-ip"=>"17", "pdf"=>"7", "year"=>"2010", "figure"=>"2", "scanned-summary"=>"0", "supp-data"=>"1"}
  • {"month"=>"12", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"1", "full-text"=>"10", "year"=>"2010", "pdf"=>"4", "unique-ip"=>"11", "figure"=>"2", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"scanned-page-browse"=>"0", "month"=>"1", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"5", "unique-ip"=>"5", "pdf"=>"2", "year"=>"2011", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
  • {"month"=>"2", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"15", "year"=>"2011", "pdf"=>"6", "unique-ip"=>"14", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
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  • {"scanned-page-browse"=>"0", "month"=>"5", "cited-by"=>"0", "abstract"=>"2", "full-text"=>"4", "unique-ip"=>"6", "pdf"=>"1", "year"=>"2011", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
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Relative Metric

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