Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts
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{"title"=>"Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts", "type"=>"journal", "authors"=>[{"first_name"=>"Steve E.", "last_name"=>"Bellan", "scopus_author_id"=>"36459684100"}, {"first_name"=>"Jonathan", "last_name"=>"Dushoff", "scopus_author_id"=>"55748313900"}, {"first_name"=>"Alison P.", "last_name"=>"Galvani", "scopus_author_id"=>"7004067242"}, {"first_name"=>"Lauren Ancel", "last_name"=>"Meyers", "scopus_author_id"=>"7005055717"}], "year"=>2015, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491676", "pui"=>"603611520", "pmid"=>"25781323", "isbn"=>"1549-1676", "doi"=>"10.1371/journal.pmed.1001801", "sgr"=>"84926435472", "scopus"=>"2-s2.0-84926435472"}, "id"=>"470cf187-328c-3383-9f15-b46d7ce6c8d3", "abstract"=>"BACKGROUND The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk. METHODS AND FINDINGS We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own. From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79-57) and dacute = 1.7 mo (95% CrI: 0.55-6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3-9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design. Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure. CONCLUSIONS Previous EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses.", "link"=>"http://www.mendeley.com/research/reassessment-hiv1-acute-phase-infectivity-accounting-heterogeneity-study-design-simulated-cohorts", "reader_count"=>82, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>16, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>3, "Student > Master"=>10, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>16, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>3, "Student > Master"=>10, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Agricultural and Biological Sciences"=>14, "Business, Management and Accounting"=>1, "Computer Science"=>3, "Decision Sciences"=>1, "Environmental Science"=>1, "Nursing and Health Professions"=>4, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>5, "Medicine and Dentistry"=>30, "Design"=>1, "Psychology"=>3, "Social Sciences"=>5, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>30}, "Social Sciences"=>{"Social Sciences"=>5}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>11}, "Environmental Science"=>{"Environmental Science"=>1}, "Design"=>{"Design"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Argentina"=>1, "United States"=>5, "Israel"=>1, "Switzerland"=>1}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/1953141", "https://ndownloader.figshare.com/files/1953142", "https://ndownloader.figshare.com/files/1953143", "https://ndownloader.figshare.com/files/1953144", "https://ndownloader.figshare.com/files/1953145", "https://ndownloader.figshare.com/files/1953146", "https://ndownloader.figshare.com/files/1953147", "https://ndownloader.figshare.com/files/1953148", "https://ndownloader.figshare.com/files/1953149", "https://ndownloader.figshare.com/files/1953150", "https://ndownloader.figshare.com/files/1953151", "https://ndownloader.figshare.com/files/1953152", "https://ndownloader.figshare.com/files/1953153", "https://ndownloader.figshare.com/files/1953154", "https://ndownloader.figshare.com/files/1953155", "https://ndownloader.figshare.com/files/1953156", "https://ndownloader.figshare.com/files/1953157"], "description"=>"<div><p>Background</p><p>The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.</p><p>Methods and Findings</p><p>We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RH<sub>acute</sub>) and the acute phase duration (<i>d</i><sub>acute</sub>) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own.</p><p>From the Rakai data, we estimated RH<sub>acute</sub> = 5.3 (95% credibility interval [95% CrI]: 0.79–57) and <i>d</i><sub>acute</sub> = 1.7 mo (95% CrI: 0.55–6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHM<sub>acute</sub>) can be estimated more precisely: EHM<sub>acute</sub> = (RH<sub>acute</sub> - 1) × <i>d</i><sub>acute</sub>, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHM<sub>acute</sub> = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3–9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design.</p><p>Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.</p><p>Conclusions</p><p>Previous EHM<sub>acute</sub> estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses</p></div>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339085, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001801.s001", "https://dx.doi.org/10.1371/journal.pmed.1001801.s002", "https://dx.doi.org/10.1371/journal.pmed.1001801.s003", "https://dx.doi.org/10.1371/journal.pmed.1001801.s004", "https://dx.doi.org/10.1371/journal.pmed.1001801.s005", "https://dx.doi.org/10.1371/journal.pmed.1001801.s006", "https://dx.doi.org/10.1371/journal.pmed.1001801.s007", "https://dx.doi.org/10.1371/journal.pmed.1001801.s008", "https://dx.doi.org/10.1371/journal.pmed.1001801.s009", "https://dx.doi.org/10.1371/journal.pmed.1001801.s010", "https://dx.doi.org/10.1371/journal.pmed.1001801.s011", "https://dx.doi.org/10.1371/journal.pmed.1001801.s012", "https://dx.doi.org/10.1371/journal.pmed.1001801.s013", "https://dx.doi.org/10.1371/journal.pmed.1001801.s014", "https://dx.doi.org/10.1371/journal.pmed.1001801.s015", "https://dx.doi.org/10.1371/journal.pmed.1001801.s016", "https://dx.doi.org/10.1371/journal.pmed.1001801.s017"], "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reassessment_of_HIV_1_Acute_Phase_Infectivity_Accounting_for_Heterogeneity_and_Study_Design_with_Simulated_Cohorts_/1339085", "title"=>"Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953114"], "description"=>"<p>Acute phase infectivity estimates from Rakai cohort data.</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339067, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Acute_phase_infectivity_estimates_from_Rakai_cohort_data_/1339067", "title"=>"Acute phase infectivity estimates from Rakai cohort data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953113"], "description"=>"<p>Published estimates of the proportion of incidence attributable to early transmission (AF<sub>early</sub>) versus the assumed excess hazard-months attributable to physiologically elevated acute phase infectivity (EHM<sub>acute</sub>). Shapes indicate whether EHM<sub>acute</sub> was estimated from epidemic growth rates, viral load trajectories and viral load–infectivity relationships, the Rakai retrospective cohort, phylogenetics, or a combination thereof. Points reflecting studies that published more than one result are identified with letters; explanations of differences between estimates are available in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.s016\" target=\"_blank\">S6 Table</a>. Points and error bars below the <i>x</i>-axis indicate our estimated EHM<sub>acute</sub> from the Rakai retrospective cohort data and based on viral load trajectories; we do not specify a sexual network model and therefore do not estimate AF<sub>early</sub> in this study.</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339066, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_transmission_due_to_acute_infectivity_/1339066", "title"=>"Proportion of transmission due to acute infectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953108"], "description"=>"<p>(A) The hazard of transmission by viral load category (horizontal bars with 95% confidence intervals [95% CIs]) from [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref007\" target=\"_blank\">7</a>] with a fitted log-linear model (blue line). We compare these data to other studies in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.s008\" target=\"_blank\">S7 Fig</a>. (B) The average viral load trajectory of 19 recently infected individuals in East Africa from the ECHO cohort [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref032\" target=\"_blank\">32</a>]. (C) Combining the fitted log-linear model (and 95% CIs on model coefficients) from (A) and the average viral load trajectory from (B), we estimated the relative hazard by disease phase (black line). The expected EHM<sub>acute</sub> is the excess hazard-months occurring in the acute phase (area of the purple region), which can be compared with the baseline chronic hazard of equal duration (hatched orange area). While we drew the acute phase cutoff at 100 d based on the stabilization of the viral load near this time, it can be seen that EHM<sub>acute</sub>, because it is defined as excess hazard-months, is relatively insensitive to the cutoff time once the relative hazard approaches that of the chronic phase level (orange).</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339061, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Viral_load_based_estimates_of_excess_hazard_months_due_to_the_acute_phase_/1339061", "title"=>"Viral-load-based estimates of excess hazard-months due to the acute phase.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953107"], "description"=>"<p>(A) Schematic diagram of relative infectiousness during HIV progression. In scenarios 1–3, the duration (<i>d</i><sub>acute</sub>) and relative hazard of the acute phase (RH<sub>acute</sub>) differ; however, they all generate 75 excess hazard-months (EHM<sub>acute</sub> = [RH<sub>acute</sub> − 1] × <i>d</i><sub>acute</sub>). The area of each acute phase rectangle (red; drawn to scale) represents the magnitude of EHM<sub>acute</sub>. Scenario 2 is the widely assumed acute phase infectivity that was estimated from the Rakai retrospective cohort using a variable hazard survival model [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref018\" target=\"_blank\">18</a>]. Scenario 4 is our revised estimate obtained by fitting a couples transmission model to the same Rakai data (EHM<sub>acute</sub> = 8.4). Unlike previous estimates, it accounts for unmodeled heterogeneity and the Rakai study’s exclusion criteria. (B) RH<sub>acute</sub> versus <i>d</i><sub>acute</sub> for scenarios 1–3, along with 95% credibility intervals (95% CrIs) and a 95% credibility contour around estimates from the variable hazard survival model (scenario 2). Colors indicate EHM<sub>acute</sub>. Because couples in the Rakai cohort were observed at 10-mo intervals, the duration of the acute phase is not easily identified—shorter, highly infectious and longer, mildly infectious acute phases are both consistent with the data. EHM<sub>acute</sub>, however, can be estimated with greater relative precision. (C) Our best estimate of acute phase characteristics (scenario 4) and associated 95% CrI and credibility contour.</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339060, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Excess_hazard_months_due_to_the_acute_phase_/1339060", "title"=>"Excess hazard-months due to the acute phase.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953112"], "description"=>"<p>(A) The log-normal distributions used to model variability in individual hazard of infection (color-coding of σ<sub>hazard</sub> used throughout the figure). (B and C) Estimated excess hazard-months attributable to the acute phase (EHM<sub>acute</sub>) versus the true (simulated) EHM<sub>acute</sub> when analyzing simulated cohort data with the (B) Wawer et al. [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref017\" target=\"_blank\">17</a>] Poisson regression and (C) Hollingsworth et al. [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref018\" target=\"_blank\">18</a>] variable hazard survival model. Thick gray diagonal lines represent unbiased estimates. Arrows 1–4 indicate how each bias affects estimates of EHM<sub>acute</sub>. Arrow locations along the <i>x</i>-axis are chosen for ease of display only; for any true EHM<sub>acute</sub>, each bias is quantified by the vertical separations between lines. Dotted lines show the “best case” scenario for these models: if the underlying population is truly homogenous, the analysis includes all seroincident couples, and late and chronic phase infectivity are equal. The small downward bias (1) in the Poisson regression arises from assumptions regarding person-time at risk. The dashed lines reveal additional downward bias (2) in both models stemming from misclassification of late couples as prevalent couples (assuming the excess hazard-months attributable to the late phase was 40). Solid lines show estimates from simulated cohorts when seroincident couples lost to follow-up are excluded (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.g004\" target=\"_blank\">Fig. 4</a>), causing bias (3). Finally, both analyses are further biased upward (4) when used to analyze heterogeneous populations. (D) The same trends for σ<sub>hazard</sub> = 0 and 3 from (B), but also showing how bias (4) can be partly mitigated (4*) when variance between individuals is controlled for by adjusting for measured covariates corresponding to some (but not all) of the heterogeneity. (B–D) were created by fitting smoothers through individual simulations (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.s004\" target=\"_blank\">S3 Fig</a>.).</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339065, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g006", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multiple_sources_of_bias_for_acute_phase_estimates_/1339065", "title"=>"Multiple sources of bias for acute phase estimates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953110"], "description"=>"<p>In both the original Rakai study and our simulated cohorts (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.g003\" target=\"_blank\">Fig. 3</a>), retrospectively identified serodiscordant couples (SDCs) were divided into those in which (1) the index partner’s infection occurred between study visits (incident SDCs), (2) the index partner’s infection occurred prior to study enrollment and neither partner died during follow-up (prevalent SDCs), and (3) the index partner’s infection occurred prior to study enrollment and the index partner died of AIDS during follow-up (late SDCs). Incident, prevalent, and late SDCs were assumed to reflect acute, chronic, and late phase infectivity exposure for the secondary partner (i.e., non-index partner). Couples recorded as serodiscordant only once and never seen again were excluded from the analysis under the assumption that these couples did not contribute any person-time at risk for transmission, while couples transitioning directly from concordant negative to concordant positive were included, whether or not they were subsequently observed. However, just as an immediate transition from concordant negative to concordant positive provides evidence for higher acute phase infectivity, a transition from concordant negative to serodiscordant provides evidence for lower acute phase infectivity. Thus, a sampling bias arises from this asymmetric exclusion of couples.</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339063, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rakai_retrospective_cohort_study_design_/1339063", "title"=>"Rakai retrospective cohort study design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953111"], "description"=>"<p>Our estimates (black) of the excess hazard-months attributable to the acute phase (EHM<sub>acute</sub>) based on (A) viral load trajectories (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.g002\" target=\"_blank\">Fig. 2</a>) and (B) our fit of a couples transmission model to the Rakai retrospective cohort. We compare these estimates with previous Rakai-based estimates that did not adjust for these biases (gray). These include (C) Wawer et al.’s adjusted and (D) unadjusted Poisson regressions [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref017\" target=\"_blank\">17</a>], (E) Hollingsworth et al.’s variable hazard survival analysis [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref018\" target=\"_blank\">18</a>], and (F) Powers et al.’s estimates that used a Bayesian framework to combine estimates from (E) and a mathematical modeling fit to an epidemic curve [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref019\" target=\"_blank\">19</a>].</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339064, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Revised_acute_phase_estimates_/1339064", "title"=>"Revised acute phase estimates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1953109"], "description"=>"<p>(A) The relationship history of an example couple. Male (M, upper) and female (F, lower) branches begin at each partner’s sexual debut and then join together into a single thick gray line when they form a couple. Male and female partners are at risk of transmission prior to couple formation at a rate equal to the product of a transmission coefficient (β<sub>M,pre</sub> and β<sub>F,pre</sub>) and the time-varying population prevalence in the opposite gender (P<sub>F</sub> and P<sub>M</sub>). Transmission after the couple has formed from extra-couple partners is similarly dependent on the population prevalence. Infected individuals infect their stable partner at a rate equal to the product of a chronic phase transmission rate (β<sub>M,within</sub> or β<sub>F,within</sub>) and the relative hazard of their current disease phase versus the chronic phase (not shown). Once infected, individuals are given Weibull distributed survival times [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref033\" target=\"_blank\">33</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.ref034\" target=\"_blank\">34</a>] (not shown). (B) A simulated time series of infection, AIDS mortality, and censorship histories for ten couples. Small arrows indicate longitudinal observations of each couple, up to five times at 10-mo intervals if they have already formed at the start of observation, if they are not censored due to loss to follow-up or couple dissolution, and if both partners remain alive. These observations are then used to create a retrospective cohort (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001801#pmed.1001801.g004\" target=\"_blank\">Fig. 4</a>).</p>", "links"=>[], "tags"=>["incident serodiscordant couples", "load trajectories", "Simulated Cohorts BackgroundThe infectivity", "couples transmission model", "serodiscordant heterosexual couples", "Rakai data", "network structure.ConclusionsPrevious EHMacute estimates", "couple dissolution", "estimate infectivity trajectories", "Acute Phase Infectivity", "Rakai retrospective cohort", "retrospective cohort exclusion criteria", "Rakai retrospective cohort data range", "hiv", "study termination", "retrospective cohort study design.Our results", "phase infectivity"], "article_id"=>1339062, "categories"=>["Biological Sciences"], "users"=>["Steve E. Bellan", "Jonathan Dushoff", "Alison P. Galvani", "Lauren Ancel Meyers"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001801.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Diagram_/1339062", "title"=>"Model Diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-17 04:36:04"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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