The Potential Impact of Pre-Exposure Prophylaxis for HIV Prevention among Men Who Have Sex with Men and Transwomen in Lima, Peru: A Mathematical Modelling Study
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{"title"=>"The Potential Impact of Pre-Exposure Prophylaxis for HIV Prevention among Men Who Have Sex with Men and Transwomen in Lima, Peru: A Mathematical Modelling Study", "type"=>"journal", "authors"=>[{"first_name"=>"Gabriela B.", "last_name"=>"Gomez", "scopus_author_id"=>"13410760100"}, {"first_name"=>"Annick", "last_name"=>"Borquez", "scopus_author_id"=>"25649124000"}, {"first_name"=>"Carlos F.", "last_name"=>"Caceres", "scopus_author_id"=>"7101909859"}, {"first_name"=>"Eddy R.", "last_name"=>"Segura", "scopus_author_id"=>"8878114100"}, {"first_name"=>"Robert M.", "last_name"=>"Grant", "scopus_author_id"=>"7402006018"}, {"first_name"=>"Geoff P.", "last_name"=>"Garnett", "scopus_author_id"=>"7005464007"}, {"first_name"=>"Timothy B.", "last_name"=>"Hallett", "scopus_author_id"=>"35614255000"}], "year"=>2012, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-84868089015", "pui"=>"365953478", "doi"=>"10.1371/journal.pmed.1001323", "isbn"=>"1549-1676 (Electronic)\\n1549-1277 (Linking)", "sgr"=>"84868089015", "pmid"=>"23055836"}, "id"=>"628fc635-0186-333b-9f66-146516828bf6", "abstract"=>"BACKGROUND: HIV pre-exposure prophylaxis (PrEP), the use of antiretroviral drugs by uninfected individuals to prevent HIV infection, has demonstrated effectiveness in preventing acquisition in a high-risk population of men who have sex with men (MSM). Consequently, there is a need to understand if and how PrEP can be used cost-effectively to prevent HIV infection in such populations. METHODS AND FINDINGS: We developed a mathematical model representing the HIV epidemic among MSM and transwomen (male-to-female transgender individuals) in Lima, Peru, as a test case. PrEP effectiveness in the model is assumed to result from the combination of a \"conditional efficacy\" parameter and an adherence parameter. Annual operating costs from a health provider perspective were based on the US Centers for Disease Control and Prevention interim guidelines for PrEP use. The model was used to investigate the population-level impact, cost, and cost-effectiveness of PrEP under a range of implementation scenarios. The epidemiological impact of PrEP is largely driven by programme characteristics. For a modest PrEP coverage of 5%, over 8% of infections could be averted in a programme prioritising those at higher risk and attaining the adherence levels of the Pre-Exposure Prophylaxis Initiative study. Across all scenarios, the highest estimated cost per disability-adjusted life year averted (uniform strategy for a coverage level of 20%, US$1,036-US$4,254) is below the World Health Organization recommended threshold for cost-effective interventions, while only certain optimistic scenarios (low coverage of 5% and some or high prioritisation) are likely to be cost-effective using the World Bank threshold. The impact of PrEP is reduced if those on PrEP decrease condom use, but only extreme behaviour changes among non-adherers (over 80% reduction in condom use) and a low PrEP conditional efficacy (40%) would adversely impact the epidemic. However, PrEP will not arrest HIV transmission in isolation because of its incomplete effectiveness and dependence on adherence, and because the high cost of programmes limits the coverage levels that could potentially be attained. CONCLUSIONS: A strategic PrEP intervention could be a cost-effective addition to existing HIV prevention strategies for MSM populations. However, despite being cost-effective, a substantial expenditure would be required to generate significant reductions in incidence. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/potential-impact-preexposure-prophylaxis-hiv-prevention-among-men-sex-men-transwomen-lima-peru-mathe-5", "reader_count"=>89, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>22, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>5, "Student > Master"=>21, "Other"=>4, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>22, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>5, "Student > Master"=>21, "Other"=>4, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>6, "Philosophy"=>1, "Business, Management and Accounting"=>3, "Computer Science"=>1, "Economics, Econometrics and Finance"=>2, "Nursing and Health Professions"=>5, "Mathematics"=>3, "Medicine and Dentistry"=>38, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>6, "Social Sciences"=>12, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>38}, "Social Sciences"=>{"Social Sciences"=>12}, "Psychology"=>{"Psychology"=>6}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>9}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>5}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"United States"=>4, "Brazil"=>1, "United Kingdom"=>1, "Switzerland"=>1}, "group_count"=>9}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/562138"], "description"=>"<p>The error bars reflect the iPrEx efficacy estimate of 92% (95% CI 40–99).</p>", "links"=>[], "tags"=>["infections", "averted", "10", "giving", "prep", "person-years", "subgroup"], "article_id"=>232621, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g003", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_total_new_infections_averted_over_10_y_by_giving_25_000_PrEP_person_years_to_each_subgroup_in_isolation_/232621", "title"=>"Proportion of total new infections averted over 10 y by giving 25,000 PrEP person-years to each subgroup in isolation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:43:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/562348"], "description"=>"<p>The top panel shows behaviour change and population impact of PrEP. The bottom panel shows behaviour change and PrEP cost per infection averted. This figure assumes there is no correlation between adherence and risk compensation. We explore this issue separately in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001323#pmed.1001323.s013\" target=\"_blank\">Figure S12</a>. The iPrEx adherence profile was used for these scenarios. In green: low coverage scenario: 5%; in blue: high coverage scenario: 20%. The red lines correspond to (1) the World Bank threshold for a cost-effective intervention, </p>", "links"=>[], "tags"=>["prep", "changes", "condom", "prioritisation"], "article_id"=>232842, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g006", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_impact_and_cost_of_PrEP_with_respect_to_changes_in_condom_use_for_a_high_prioritisation_strategy_/232842", "title"=>"Population impact and cost of PrEP with respect to changes in condom use for a high prioritisation strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:47:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/297835", "https://ndownloader.figshare.com/files/297992", "https://ndownloader.figshare.com/files/298036", "https://ndownloader.figshare.com/files/298096", "https://ndownloader.figshare.com/files/298171", "https://ndownloader.figshare.com/files/298230", "https://ndownloader.figshare.com/files/298281", "https://ndownloader.figshare.com/files/298335", "https://ndownloader.figshare.com/files/298416", "https://ndownloader.figshare.com/files/298476", "https://ndownloader.figshare.com/files/298547", "https://ndownloader.figshare.com/files/298637", "https://ndownloader.figshare.com/files/298695", "https://ndownloader.figshare.com/files/298755", "https://ndownloader.figshare.com/files/298827"], "description"=>"<div><h3>Background</h3><p>HIV pre-exposure prophylaxis (PrEP), the use of antiretroviral drugs by uninfected individuals to prevent HIV infection, has demonstrated effectiveness in preventing acquisition in a high-risk population of men who have sex with men (MSM). Consequently, there is a need to understand if and how PrEP can be used cost-effectively to prevent HIV infection in such populations.</p> <h3>Methods and Findings</h3><p>We developed a mathematical model representing the HIV epidemic among MSM and transwomen (male-to-female transgender individuals) in Lima, Peru, as a test case. PrEP effectiveness in the model is assumed to result from the combination of a “conditional efficacy” parameter and an adherence parameter. Annual operating costs from a health provider perspective were based on the US Centers for Disease Control and Prevention interim guidelines for PrEP use. The model was used to investigate the population-level impact, cost, and cost-effectiveness of PrEP under a range of implementation scenarios. The epidemiological impact of PrEP is largely driven by programme characteristics. For a modest PrEP coverage of 5%, over 8% of infections could be averted in a programme prioritising those at higher risk and attaining the adherence levels of the Pre-Exposure Prophylaxis Initiative study. Across all scenarios, the highest estimated cost per disability-adjusted life year averted (uniform strategy for a coverage level of 20%, US$1,036–US$4,254) is below the World Health Organization recommended threshold for cost-effective interventions, while only certain optimistic scenarios (low coverage of 5% and some or high prioritisation) are likely to be cost-effective using the World Bank threshold. The impact of PrEP is reduced if those on PrEP decrease condom use, but only extreme behaviour changes among non-adherers (over 80% reduction in condom use) and a low PrEP conditional efficacy (40%) would adversely impact the epidemic. However, PrEP will not arrest HIV transmission in isolation because of its incomplete effectiveness and dependence on adherence, and because the high cost of programmes limits the coverage levels that could potentially be attained.</p> <h3>Conclusions</h3><p>A strategic PrEP intervention could be a cost-effective addition to existing HIV prevention strategies for MSM populations. However, despite being cost-effective, a substantial expenditure would be required to generate significant reductions in incidence.</p> <p> <em>Please see later in the article for the Editors' Summary</em></p> </div>", "links"=>[], "tags"=>["pre-exposure", "prophylaxis", "hiv", "men", "transwomen", "mathematical"], "article_id"=>118709, "categories"=>["Cancer", "Medicine", "Biotechnology"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001323.s001", "https://dx.doi.org/10.1371/journal.pmed.1001323.s002", "https://dx.doi.org/10.1371/journal.pmed.1001323.s003", "https://dx.doi.org/10.1371/journal.pmed.1001323.s004", "https://dx.doi.org/10.1371/journal.pmed.1001323.s005", "https://dx.doi.org/10.1371/journal.pmed.1001323.s006", "https://dx.doi.org/10.1371/journal.pmed.1001323.s007", "https://dx.doi.org/10.1371/journal.pmed.1001323.s008", "https://dx.doi.org/10.1371/journal.pmed.1001323.s009", "https://dx.doi.org/10.1371/journal.pmed.1001323.s010", "https://dx.doi.org/10.1371/journal.pmed.1001323.s011", "https://dx.doi.org/10.1371/journal.pmed.1001323.s012", "https://dx.doi.org/10.1371/journal.pmed.1001323.s013", "https://dx.doi.org/10.1371/journal.pmed.1001323.s014", "https://dx.doi.org/10.1371/journal.pmed.1001323.s015"], "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Potential_Impact_of_Pre_Exposure_Prophylaxis_for_HIV_Prevention_among_Men_Who_Have_Sex_with_Men_and_Transwomen_in_Lima_Peru_A_Mathematical_Modelling_Study/118709", "title"=>"The Potential Impact of Pre-Exposure Prophylaxis for HIV Prevention among Men Who Have Sex with Men and Transwomen in Lima, Peru: A Mathematical Modelling Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-10-09 02:25:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/562507"], "description"=>"<p>All values are proportions. Functional effectiveness is a function of the probability of transmission, the intrinsic efficacy, the adherence to PrEP, and its distribution, affecting only unprotected sex acts, which in turn are dependent on the number of partners, average condom use, and number of sex acts per partner (all values derived from iPrEx data <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001323#pmed.1001323-Grant1\" target=\"_blank\">[2]</a>).</p>a<p>Adherence 1, 2, and 3 refer to the proportion of doses taken consistently by an individual. The proportion in each adherence group is the population distribution (the number of people in each adherence group). This reflects the data reported in the iPrEx study: a distribution of participants by ranges of adherence. We included a point estimate within those ranges.</p>", "links"=>[], "tags"=>["prep", "adherence"], "article_id"=>232998, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scenario_definitions_impact_of_PrEP_by_adherence_and_functional_effectiveness_/232998", "title"=>"Scenario definitions: impact of PrEP by adherence and functional effectiveness.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-09 00:49:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/562198"], "description"=>"<p>The error bars reflect the uncertainty in the iPrEx efficacy estimate of 92% (95% CI 40–99). In this comparison, we show two scenarios (low coverage in green [A]; high coverage in blue [B]) including three prioritisation strategies—uniform, where the coverage is the same in each subgroup; some prioritisation, where higher coverage is achieved in the transwomen at higher risk and sex worker populations (but no more than 50% covered) than in MMSM and MMSW; and high prioritisation, where 90% of transwomen at higher risk and 11% of sex workers receive PrEP in the low coverage scenario, or 90% of transwomen at higher risk, 90% of sex workers, 3.9% of MMSM, and 21.5% of MMSW receive PrEP in the high coverage scenario.</p>", "links"=>[], "tags"=>["prep", "prioritisation"], "article_id"=>232687, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g004", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimated_impact_of_PrEP_with_respect_to_coverage_adherence_and_prioritisation_of_key_populations_/232687", "title"=>"Estimated impact of PrEP with respect to coverage, adherence, and prioritisation of key populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:44:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/561990"], "description"=>"<p>Grey shading represents selected runs; red lines represent the best fit. The runs were selected based on bounds (black bars) defined from prevalence data (blue x's) for four subgroups and the overall population.</p>", "links"=>[], "tags"=>["runs", "prevalence", "subgroups"], "article_id"=>232477, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_model_runs_and_best_fit_based_on_prevalence_data_for_four_subgroups_and_the_overall_population_/232477", "title"=>"Selected model runs and best fit based on prevalence data for four subgroups and the overall population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:41:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/562546"], "description"=>"a<p>Coverage in the uniform distribution strategy is equal in all subgroups (MMSW, MMSM, sex workers, and transwomen at higher risk). The coverage showed in both strategies involving prioritisation (some and high) are given as overall population coverage and, in parentheses, the coverage in each subpopulation (MMSW/MMSM/sex workers/transwomen at higher risk).</p>b<p>Total number of person-years on PrEP over 10 y.</p>c<p>The range represents the variation observed in the estimated costs per PrEP person-year.</p>d<p>Total cost of PrEP intervention over 10 y; the range represents the variation observed in the estimated costs per PrEP person-year.</p>", "links"=>[], "tags"=>["infections", "averted", "10"], "article_id"=>233035, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.t004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scenarios_to_achieve_one_third_of_new_infections_averted_over_10_y_/233035", "title"=>"Scenarios to achieve one third of new infections averted over 10 y.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-09 00:50:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/561925"], "description"=>"<p>Insertive (Ins) and receptive (Rec) men always take the insertive and receptive role during anal sex, respectively. Versatile (Ver) men take either the insertive or the receptive role during anal sex. Arrows indicate sexual partnerships being formed between individuals within groups—the width shows the number of partnerships, and the direction illustrates sexual positioning (from insertive towards receptive). For each category, insertive men will form partnerships only with receptive men or versatile men (the latter in a receptive role); versatile men will form partnerships with insertive men, versatile men, or receptive men, depending on their role per sex act; receptive men will form partnerships only with insertive men or versatile men (the latter in an insertive role).</p>", "links"=>[], "tags"=>["mixing", "positioning", "MSM"], "article_id"=>232412, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_representation_of_sexual_mixing_and_sexual_positioning_among_MSM_and_transwomen_/232412", "title"=>"Model representation of sexual mixing and sexual positioning among MSM and transwomen.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:40:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/562283"], "description"=>"<p>The top panel shows PrEP cost per DALY averted with regard to coverage levels and strategies. The bottom panel shows a comparison of PrEP cost per DALY averted with other HIV prevention strategies. The error bars reflect the uncertainty in the iPrEx efficacy estimate of 92% (95% CI 40–99). The height of the bars shows the variation due only to the cost assumptions of one person-year on PrEP. In the top panel, the iPrEx adherence profile is used for the scenarios. In green: low coverage scenario: 5%; in blue: high coverage scenario: 20%. In the bottom panel, the variation in cost for the interventions reflects different coverage strategies assumed and the uncertainty in the costing. Values from Aldridge et al. <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001323#pmed.1001323-Aldridge1\" target=\"_blank\">[38]</a> presented in this paper were calculated using assumptions from the GOALS model on coverage and intervention design (see also Stover et al. <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001323#pmed.1001323-Stover2\" target=\"_blank\">[54]</a>). PrEP estimated cost/DALY was calculated for both uniform and high prioritisation strategies (including 5% and 20% coverage scenarios). The red lines correspond to (1) the World Bank threshold for a cost-effective intervention, </p>", "links"=>[], "tags"=>["daly"], "article_id"=>232778, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_of_PrEP_estimated_as_cost_per_DALY_averted_/232778", "title"=>"Cost-effectiveness of PrEP, estimated as cost per DALY averted.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-09 00:46:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/562472"], "description"=>"<p>Downstream ARV costs averted not included.</p>a<p>Coverage in the uniform distribution strategy is equal in all subgroups (MMSW, MMSM, sex workers, and transwomen at higher risk). The coverage showed in both strategies involving prioritisation (some and high) are given as overall population coverage and, in parentheses, the coverage in each subpopulation (MMSW, MMSM, sex workers, transwomen at higher risk).</p>b<p>Total number of person-years on PrEP over 10 ys.</p>c<p>Variation in the cost/DALY due to cost assumptions only (corresponding to the height of the coloured bars in the top panel of <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001323#pmed-1001323-g005\" target=\"_blank\">Figure 5</a>).</p>d<p>Variation due to total uncertainty, including both cost and PrEP efficacy assumptions as well as epidemiological assumptions.</p>e<p>Total cost of PrEP intervention over 10 y; the range reflects the uncertainty of PrEP programme costs only.</p>", "links"=>[], "tags"=>["prep", "10"], "article_id"=>232965, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.t003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_and_total_cost_of_PrEP_over_10_y_/232965", "title"=>"Cost-effectiveness and total cost of PrEP over 10 y.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-09 00:49:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/562436"], "description"=>"<p>All values are proportions. MSW, men sex workers; trans, transwomen at higher risk.</p>", "links"=>[], "tags"=>["prep"], "article_id"=>232933, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Gabriela B. Gomez", "Annick Borquez", "Carlos F. Caceres", "Eddy R. Segura", "Robert M. Grant", "Geoff P. Garnett", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001323.t002", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scenario_definitions_impact_of_PrEP_by_coverage_and_prioritisation_/232933", "title"=>"Scenario definitions: impact of PrEP by coverage and prioritisation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-09 00:48:53"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"36", "full-text"=>"37", "pdf"=>"26", "abstract"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2012", "month"=>"10"}
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  • {"unique-ip"=>"12", "full-text"=>"9", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2013", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2013", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"14", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"9"}
  • {"unique-ip"=>"14", "full-text"=>"20", "pdf"=>"6", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"10"}
  • {"unique-ip"=>"11", "full-text"=>"16", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"11"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"12"}
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  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"3"}
  • {"unique-ip"=>"18", "full-text"=>"14", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2014", "month"=>"5"}
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Relative Metric

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