INK4a, a surrogate marker for HPV-driven OPSCC, is sufficient to correctly identify those tumours and avoid substantial HPV misattribution and thus undertreatment of patients by de-escalation. Robust data estimating the proportion of potentially undertreated patients are missing.A remarkably better prognosis is associated with oropharyngeal squamous cell carcinomas (OPSCC) driven by human papillomaviruses (HPV) compared with HPV-negative OPSCC. Consequently, de-escalation of standard treatment has been suggested. Due to modest specificity rates, debates are ongoing, whether p16INK4a expression and multiple tumour- and patient-related risk factors, and investigated their impact on patients’ survival in comprehensive uni- and multivariate analyses.We assessed a large-scale cohort of consecutively included OPSCC diagnosed between 2000 and 2017 for HPV–DNA, HPV genotypes, p16INK4a- and high-risk HPV–DNA-positivity) was detected in 27.1% (n = 192) of OPSCC, with HPV16 being the most abundant HPV type (94.6%). In 5.5% patients (n = 39), p16INK4a overexpression but no HPV–DNA was detected. Principal component and survival analyses revealed that 60.6% of these p16INK4a-positive OPSCC lacking HPV–DNA did not resemble HPV16-driven but HPV-negative OPSCC regarding risk-factor profile and overall survival. Notably, this group represented 10.6% of all p16INK4a-overexpressing OPSCC.Aetiological relevance of HPV (p16INK4a as a single marker appears insufficient to indicate OPSCC patients suitable for treatment de-escalation.p16 Among the various consequences of HPV E6/E7 oncogene signalling, the cellular cyclin-dependent kinase inhibitor 2A (p16INK4a) becomes overexpressed as a sequel to E7-induced epigenetic remodelling.16 Thus, p16INK4a has been suggested as a biomarker of HPV-transformed cells, and evaluation of p16INK4a expression can be easily performed by immunohistochemistry using formalin-fixed tissue. Detection of p16INK4a has been introduced as a surrogate marker in the AJCC-8/UICC-8 staging system for the classification of OPSCC.The detection of viral oncogene E6/E7 mRNA or protein can be considered as gold standard to define an aetiological relevance of HPV for carcinogenesis, since E6 and E7 expression is essential for initiation and maintenance of the transformed phenotype in HPV-induced tumours.INK4a overexpression in about 5–20% of OPSCC, indicating that a considerable subset of p16INK4a-positive OPSCC is not causally driven by HPV.19 This fact has raised concerns whether a group of OPSCC patients could be undertreated if the decision for therapy de-escalation was solely based on p16INK4a positivity.14 However, there is currently a paucity of robust data estimating the proportion of potentially undertreated patients in this context.Several studies, however, have demonstrated a lack of association between HPV–DNA or RNA detection and p16INK4a represented the single-decision criterion for treatment de-escalation in OPSCC patients. We therefore assessed p16INK4a, HPV–DNA and HPV genotypes in relation to tumour- and patient-related risk factors and survival in a cohort of more than 800 consecutively included OPSCC patients at a large German University Hospital diagnosed between 2000 and 2017. Specifically, we aimed to determine risk groups among OPSCC with discordant test results for p16INK4a expression and HPV–DNA to assess whether the respective patient groups could be considered suitable for treatment de-escalation.The major goal of our study was to determine the proportion of potentially undertreated patients if p16Between 2000 and 2017, 802 patients were diagnosed with OPSCC (according to ICD10) and treated at our hospital  by upfront surgery with or without neck dissection, or by definitive radio- or chemo–radiotherapy according to the local guidelines and upon patient’s decision (Table n = 721) and expression of p16INK4a (n = 717) by immunostaining  diagnostic biopsies  and resected OPSCC was available to perform both tests.We have analysed all primary tumours with sufficient formalin-fixed, paraffin-embedded tissue for the presence of HPV–DNA, HPV genotypes n = 72 and expr21 as an eigenvector-based multivariate method to reveal the internal structure of our data, especially regarding the four combinations of HPV–DNA testing and p16INK4a expression of OPSCC to define HPV status (HPV-negative and HPV-driven for concordant tests vs. discordant test results). Overall survival  was used to generate survival curves by the Kaplan–Meier method. SPSS Statistical Software (IBM SPSS 26.0) was used for statistical analysis, and significance was considered P ≤ .05 for all tests, unless otherwise indicated.We used principal component analysis (PCA)16 was the most frequent among all HPV types, identified as the exclusive type in 91.2%  of HPV–DNA-positive cases. HR–HPV types other than 16 were detected alone or in combination with one or two other types in 16 cases (Table We detected HR–HPV–DNA in 234/721 (32.5%) OPSCC samples. DNA from non-HR–HPV types was detected in 5 (0.7%) OPSCC only, two of them corresponding to the low-risk (LR) types 6 and 11  and specifically for HPV16–DNA in 78.7% (185/235) of cases. Importantly, in 39 (16.6%) OPSCC overexpressing p16INK4a, no HPV–DNA was detected. p16INK4a expression pattern in these HPV– DNA-negative tumours did not differ from HPV-driven OPSCC  and HPV16-driven (HPV16–DNA-positive and p16INK4a-positive) OPSCC. Cases with discordant results were not considered in the statistical comparison of this sub-analysis  than all other patient groups with HPV-negative OPSCC or discordant results for HPV–DNA and p16INK4a expression , dichotomised variables for age, localisation, comorbidity, tobacco and alcohol consumption and T- and N-stage showed significant impact on patients’ overall survival  and the highest in comparison with all other positive predictors . Again, a 3.2-fold reduction of hazard in the case of HPV16-driven OPSCC had the strongest impact on survival  and lymph node (N-) stage together with patient’s performance (ECOG). Age and tonsillar localisation of the primary tumour did not cluster together with other factors.Patients with discordant HPV-test results differed from those with HPV-negative and HPV OS Fig. , tumour  OS Fig. . In thisThe distribution of all OPSCCs plotted according to the two main components separated two main groups of patients Fig. . Risk pr16-driven OPSCC were clearly separated upon plotting of the individual cases, and a separation border regarding component 1 could be defined according to the distribution of cases. Eighteen of 385 (4.7%) HPV-negative and only one of 155 (0.6%) HPV16-driven OPSCC were located outside their respective clusters  OPSCC with p16SCC Fig. . This reSCC Fig. , and clu16-driven OPSCC , therefore representing “HPV-negative-like” OPSCC. In contrast, no difference in OS existed between group 2 patients in comparison with HPV16-driven (P = 0.845) but compared with HPV-negative OPSCC  and head and neck (85%) cancers worldwide.1 In our study, HPV16 was almost the only relevant HPV type for OPSCC. Therefore, we excluded non-16 HPV types from our further investigation.About 30% of OPSCC worldwide are caused by oncogenic HPV types.16-driven OPSCC were not younger, but significantly older than patients with HPV-negative OPSCC in our cohort. This is in contrast to the literature, although most published data rely on selected populations from clinical trials,6 comparatively small random cohorts or cancer registry-based data without inclusion of any experimental data regarding HPV.24 Interestingly, the unimodal age incidence distribution was peaking in the United States at ages 60–64 for all patients with HPV-positive OPSCC in a recent investigation,25 which is comparable to our data, and more importantly, not younger than HPV-negative OPSCC in our cohort.To our surprise, patients with HPV16-driven cancers. Age, HPV status, T stage and comorbidity remained independent risk factors in the Cox regression model. In contrast to other studies,6 tobacco and alcohol consumption did not have significant impact on this model. However, both are highly related with HPV status, and in a previous study we showed that tobacco and alcohol consumption were less important for survival prediction in our patients.26 In contrast to other studies,6 the frequency of smokers in our cohort was higher in general, and especially in HPV-negative OPSCC, which might cover its influence in our multivariate Cox model. This is supported by studies showing that HPV positivity in OPSCC is a significant prognostic factor, irrespective of smoking.27However, other lifestyle-related risk factors were obviously reduced in these patients. Furthermore, the type of therapy differed in our cohort according to HPV status, which might be a direct consequence of patient (comorbidity) and tumour characteristics (primary size and N-/M-status) and, therefore, contributes to superior outcome of patients with HPV16–DNA and p16INK4a had almost similar values of components 1 and 2, and were accompanied by smoking and alcohol consumption on the component-1 axis  explains as much of the data as possible. The second dimension (component 2) explains as much as possible of the remaining, unexplained data (and so on), which reduces complexity by summarising the most contributing features of the dataset. In conclusion, HPV status, smoking and alcohol consumption explained most of the variance in our data by contributing (positively and negatively) to component 1, followed by patient’s performance (ECOG), T- and N-category as they have similar values contributing to component 2 , which is supported by similar OS of these patients. In contrast, only 39% (13/33) of OPSCC with p16INK4a overexpression in the absence of any HPV–DNA resembled HPV-driven OPSCC. The remaining patients were similar to patients with HPV-negative OPSCC  and constituted 10.6% (20/188) of all p16INK4a-overexpressing OPSCC included in this analysis. Consequently, if the selection of patients for therapy de-escalation solely relies on p16INK4a expression as an indicator of viral aetiology and improved survival, those patients would be incorrectly selected and thus be put at jeopardy for treatment failure. Our findings clearly show that patient selection for de-escalated treatments based on a single marker bears a substantial risk for OPSCC patients, particularly in populations with high tobacco consumption.Component-1 values efficiently separated cases with HPVPCA Fig. . The disINK4a positivity. HPV16 is by far the most important HPV type in OPSCC in central Germany. Patients with HPV16-driven OPSCC consumed fewer amounts of cigarettes and alcohol, but were significantly older at diagnosis. OPSCC positive for p16INK4a but negative for any HPV– DNA, accounting for 10.6% of all p16INK4a-overexpressing OPSCC, resembled HPV-negative OPSCC in risk-factor profile and overall survival.We have analysed a consecutive cohort of patients and a relevant number of samples lacked either HPV–DNA or p16INK4a expression as a single marker is insufficient to define HPV aetiology in OPSCC, and that stratification of patients for treatment de-escalation thus requires the integration of further risk factors and/or diagnostic tests, primarily confirmatory HPV testing.In conclusion, our data imply that p16Supplemental Tables S1–S6


In 2017, 46,157 and 3,127 new oropharyngeal cancer (OPC) cases were reported in the U.S. and Texas, respectively. About 70% of OPC were attributed to human papillomavirus (HPV). However, only 51% of U.S. and 43.5% of Texas adolescents have completed the HPV vaccine series. Therefore, modeling the demographic dynamics and transmission of HPV and OPC progression is needed for accurate estimation of the economic and epidemiological impacts of HPV vaccine in a geographic area. An age-structured population dynamic model was developed for the U.S. state of Texas. With Texas-specific model parameters calibrated, this model described the dynamics of HPV-associated OPC in Texas. Parameters for the Year 2010 were used as the initial values, and the prediction for Year 2012 was compared with the real age-specific incidence rates in 23 age groups for model validation. The validated model was applied to predict 100-year age-adjusted incidence rates. The public health benefits of HPV vaccine uptake were evaluated by computer simulation. Compared with current vaccination program, increasing vaccine uptake rates by 50% would decrease the cumulative cases by 4403, within 100 years. The incremental cost-effectiveness ratio of this strategy was $94,518 per quality-adjusted life year (QALY) gained. Increasing the vaccine uptake rate by 50% can: (i) reduce the incidence rates of OPC among both males and females; (ii) improve the quality-adjusted life years for both males and females; (iii) be cost-effective and has the potential to provide tremendous public health benefits in Texas. In the United States, more than 70% of OPC cases are attributable to HPV2. Over the past decades, the incidence of HPV-related OPC has been increasing and is now higher than that of HPV-related cervical cancer3. HPV-related OPC incidence differs by sex in the United States, with overall incidence rates for men being two to four times higher than those for women4. Since there is currently no validated screening tool for OPC, primary prevention is the most effective available public health strategy for reducing the burden of HPV-related OPC in the United States.Human papillomavirus (HPV) is etiologically linked with several cancers, including oropharyngeal cancer (OPC). In 2017, 46,157 and 3,127 new OPC cases were reported in the United States and Texas5, and thus has the potential to protect against most of the HPV-related OPCs. The HPV vaccine has been shown to be long-lasting; multiple studies have shown sustained effectiveness and high immunogenicity for at least 10 years after vaccination8. Although HPV-vaccine trials were not designed to evaluate oropharyngeal end-points, data from the Costa Rica vaccine trial showed 93% (95% CI 63 to 100%) protection against prevalent oral HPV infection, similar to the efficacy for cervical lesions11. The Advisory Committee on Immunization Practices of the U.S. Centers for Disease Control and Prevention (CDC) currently recommends routine vaccination of boys and girls aged 11 to 12 years against HPV; vaccination may be started as young as age 9 years12. Catch-up vaccination for all individuals up to age 26 years is also recommended. Vaccination of adults aged 26 years and older is considered of limited public health benefit; however, vaccination of adults aged 27–45 years who are not adequately vaccinated may benefit and is recommended through shared clinical decision-making12. HPV vaccination rates remain low in the United States, with approximately half (54%) of adolescents having completed the vaccine series in 2018 from the most recent estimate available13. Meanwhile, only 48% of Texas teenagers received a complete series of HPV vaccine, which is much lower than the vaccine uptake rate in the National Immunization Survey-Teen (NIS-Teen) 2019, Texas14. To determine the societal benefit of dedicating resources to public health policies, including those related to immunization, policy makers and stakeholders rely on cost-effectiveness and disease burden studies. Such studies rely on accurate and up-to-date mathematical models.Prophylactic vaccination against HPV is associated with a significant decrease in the prevalence of oral infection by HPV types that can cause OPC among young adults16.In general, modeling HPV-related cancers is a challenging task for a number of reasons, including, for example, the need for a sophisticated model structure, with numerous model variables and unknown parameters, and poor data availability. Particularly for HPV-related OPC, there is little to no previous modeling work on OPC progression. To our knowledge, this is the first attempt to develop and apply a comprehensive mathematical model to quantitatively understand HPV-related OPC incidence and its economic consequence in Texas. For this purpose, an age-structured population model of the natural history of HPV-related OPC was developed for this study, based on the literature and our current understanding of demographic dynamics, disease progression, and incidence rate of OPC. Whenever possible, Texas data were utilized to model the disease, to model the behavior of at-risk individuals, and to validate the model. The mathematical model incorporated HPV infection, progression, treatment, and other characteristics; strategies for vaccination; and the epidemiologic and economic effects of HPV vaccination. Also, given the heterogeneities among different age groups, age structures were explicitly incorporated into the ordinary differential equation models17, large diverse states such as Texas require information relevant to their populations in order to inform state-level decision-makers about the health and economic consequences of public health disease-prevention investments. This paper provides valuable information for Texas and an example for other states to consider.The primary aim of this paper was to develop and apply a model for comparing the OPC-related health and economic consequences of proposed investments for increasing the HPV vaccination rates in Texas over 100 years. While there are well-developed HPV vaccination economic models for the nation that have been utilized to inform the recommendations of the CDC19, which provided a solid foundation for depicting the population dynamics, HPV transmission, and vaccination simultaneously. In this study, the epidemiologic model of HPV-associated OPC was incorporated and the predicted HPV-associated OPC incidence rates were used for model validation.The model consisted of a large system of ordinary differential equations (ODEs) similar to the previous work of Elbasha and PengThe parameters and initial variables collected from Year 2010 in Texas used to validate the model. The predicted age-specific incidence rates  compared with the actual data for Years 2011 and 2012 in Texas, and both the real incidence rates and the predicted incidence rates were calculated Fig. . For mal20 set the starting age group at 12 years, Graham et al.21 listed the vaccine target age for starting HPV vaccination for females in the U.S. at 11 years. Therefore, age-adjusted incidence rate was predicted by covering age groups started from 11 years  increased until Year 2030 and then declined for the remainder of the time span. The predicted male incidence rates were much higher than for females, which clearly reflects the known differences in OPC between genders. The predicted incidence rates covered Years 2019 to Year 2115 .Cumulative cases prevented in Texas are presented in Table Policy focuses on the health gains from investments to increase the vaccination uptake rate among the target groups in Texas. We assessed the impact of different vaccination strategies or scenarios on health and economic consequences, such as HPV-related OPC incidence rates, years of life, QALYs, and costs. Incremental cost-effectiveness ratios (ICERs) estimated for different vaccination coverage rates, vaccination covered age groups, and whether male vaccination rates increased in addition to those of females. The effects of rate of sexual partner change and detection rate of the OPC parameters on health and economic outcomes are unknown or highly uncertain and therefore assessed with sensitivity analysis.22 and the values were presented in Supplements Text Vaccination adherence is one of the most important factors in determining the level of disease prevention. In our model, age and gender specific vaccination uptake rates, including uptake rates of first dose and second dose, were collected from CDCAs shown in Fig. By changing the proportion of those with uptake receiving two doses, the incidence rate changed less than the incidence rate changes when we varied the first dose uptake rate Fig. c,d. CompWe also performed sensitivity analysis by changing the vaccine uptake rate for the first and second doses at the same time. As shown in Fig. The starting age of vaccine uptake has a notable effect on incidence rate. We predicted the incidence rates for three different scenarios: (1) the current vaccination coverage strategy, starting from age 13–14 years for both males and females, (2) assuming that people would receive vaccination two age groups earlier, that is, age 9–10 years for males and females, and (3) assuming that people would receive vaccination two age groups later, that is, age 18 years for males and females  in the top row to the most resource intensive  in the bottom row. We varied combinations of vaccine acquisition cost and vaccine promotion uptake costs in a sensitivity analysis of the ICERs. Base-case vaccination cost values were derived from CDC data 24. The best-case cost assumptions  and the worst-case cost assumptions  were calculated25. The base-case results showed that changing from a strategy of a 50% increase over current vaccination levels among females only to a strategy of a 50% increase among females and males . While estimated tends in OPC incidence rates and the cost-effectiveness of increasing vaccination rates for boys and girls were similar to previous estimates of U.S. trends, there were some differences in the Texas results. A Canadian study27. Addition of these diagnoses to the current work would make the increased investment in vaccination even more cost-effective and possibly cost saving, as shown in the work by investigators at the CDC17. Second, we did not attempt model fitting because of poor data availability and the lack of efficient regression techniques for large-scale ordinary differential equation models. Therefore, the parameter values obtained from the literature or were calibrated based on the actual data and assumptions. A better match would be possible if model fitting becomes feasible in the future. Third, the vaccination uptake rate held constant during the 100-year prediction period, which may not reflect the real evolution of vaccine policy and vaccination behavior. Fourth, because of the lack of population data, the validation was limited to only data for 2011 and 2012 in Texas. With more data in the future, we will improve model validation. For example, the predicted age-specific incidence rate is a little higher than the actual incidence rate in people aged 60 to 79 years, and the difference becomes smaller in people aged 80 years and older. Fifth, there is no dependable literature on the rates of detection of OPC. In one study28, the detection rate for oral cancer was 86%. We estimated that the detection rates for local and regional stages of OPC were lower than 86% and assumed the detection rate for distant stage OPC as 1. Therefore, detection rates were assumed 0.68, 0.85, and 1 for local, regional, and distant OPC. Finally, the predicted incidence rate declined briefly in the beginning of the prediction period; this drop may have resulted from the initial conditions.This study has some limitations. First, the study focused on only one of several diseases related to HPV infection. We focused on OPC because its incidence is increasing and it has overtaken cervical cancer as the leading health problem likely to decrease due to HPV immunization. Researchers have demonstrated the value of preventing other cancers, genital warts, and recurrent respiratory papillomatosis with HPV vaccinationThe paper adds to the literature on health/economic policy analysis of HPV immunization by adapting a comprehensive population disease model for a state-level assessment of the long-term health and economic consequences of increasing adherence to HPV immunization guidelines. We incorporated up-to-date treatment and vaccine cost estimates for OPC based on Texas health insurance claims, published reports, up-to-date disease and sexual behavior data and cost estimates for promoting vaccine uptake among males and females to achieve a 50% increase in the HPV vaccination rates. Thus, this work provides information that may be actionable at the state level of government to decide resource allocation regarding the prevention of HPV-related conditions compared to other state investment opportunities in health. Finally, this work may provide a template for similar studies in other states that contemplate HPV-related public health investments.29 provided a solid foundation for a comprehensive mathematical model to depict population growth, HPV transmission, and vaccination. This modeling framework included a demographic model, epidemiological model, and the natural history of OPC for different sex and age groups. Model parameter notations, definitions, and the source of parameters presented in Table 29, the natural history of OPC was modeled and incorporated, and the entire framework was updated and adapted to the Texas population for making specific predictions for Texas. In the demographic model, people transfer from their current age group to the next age group, except for the oldest age group (≥ 85 years), at age- and gender-specific rates. For instance, the calibrated transfer rate for Texas suggested that about 12.4% of females from the second age group (age 1–8 years) move to the third age group (age 9–10 years) each year, compared to 13.4% of males. The population size for every age group depended on the probability of transferring to the next age group and the probability of transferring from the younger group, and on the OPC-related and non-OPC-related death rates. The population growth for the first age group (0–1 years) calculated based on the birth rate because there is no younger age group, and there was no transfer out for the oldest age group.Previous modeling work by Elbasha et al.19, which was determined by the number of sexual partners, relative infectivity of vaccine breakthrough cases, and probability of people being in different groups.In the epidemiological model, HPV transmission specified by gender, age, and sexual activity. The important subpopulations related to HPV transmission included the susceptible population, infected population, persistently infected population, vaccinated population, infectious vaccinated population, persistently infected vaccinated population, and recovered vaccinated population. Additionally, since the three-dose HPV vaccination strategy has been changed into a two-dose strategy recently, the force of HPV infection (λ) was redefined bytR represented the progression rate from local to regional status.In the OPC natural history model, the OPC stage contained detected local, regional, and distant cases and survived cases. We updated parameters of published models to reflect current information on HPV transmission and OPC progression. For OPC progression, equations now reflect the epidemiologic variations among different stages of detected OPC. The population of individuals with detected local OPC determined by progression rate, detection rate, and various subpopulations, as given below:Equations given below model how the disease develops from local to regional:For the detected regional disease, when the disease progressed from regional to distant, we used the following equations:tL, θtR, and θtD represent the progression rates for three stages for an individual with OPC. Specifically, θtL denotes the progression rate from HPV infection to local OPC, θtR denotes the progression rate from local to regional OPC, and θtD denotes the progression rate from regional to distant OPC. The median time of disease progression in all age groups was around 8 months30. The progression rates were age-specific, as the disease usually progresses more quickly in the older groups, and the θtR and θtD were larger than the θtL since the disease progression accelerates when the disease progresses to regional or distant status compared with local status.In the equations, θ33, the incremental cost-effectiveness ratios (ICERs) were calculated for 50% increases in the female vaccination rate only and in the female and male rates  by life years and quality-adjusted life years (QALY) gained. Sensitivity analyses altering the vaccination strategies and HPV transmission rates examined the impact of behavior and vaccination on the incidence of OPC. To estimate the ICERs for the vaccination strategies, we calculated the vaccination cost, treatment cost, and QALYs based on the following formulas. All costs valued in 2018 U.S. dollars.Applying the 2010 and 2015 Texas base vaccination ratesl,c denote the proportion of newborns vaccinated; φl,i,c represent the vaccine uptake rate for the first dose; Φ1 and Φ2 represent the proportion of receiving only one dose or two dose vaccine respectively; and Yl,i,c, Xl,i,c, Ul,i,c, denote the population vaccinated18. The cost of the HPV vaccine was $178.14 per dose25. 37. Total vaccination costs included the unit cost per vaccination and the number of people vaccinated. Let 38. Cost estimated from experience of a retrospective cohort of 467 Texas patients with OPC. The Truven MarketScan database used to identify commercially insured patients with OPC newly diagnosed during the period 2011 to 2014. All dollars adjusted to year 2015 values using the Consumer Price Index from the U.S. Bureau of Labor Statistics. A generalized linear model estimated the total healthcare cost for OPC patients.The per-case cost of OPC treatment was $151,62229. T denotes the time span, a 100-year analytic horizon.The lost quality-adjusted life years due to OPC calculated using the following equation:To compare vaccination strategies Here We computed the cost-effectiveness acceptability curve (CEAC) by performing probabilistic sensitivity analysis. Parameters for vaccine uptake rate and quality of life for patients with OPC were included in probabilistic sensitivity analyses. For quality of life, beta distribution was applied to generate 1000 random samples as inputs . For vaccine uptake rate, the Latin Hypercube sampling technique based on multivariate normal distribution conducted to generate random samples as input values. Because of the computational complexity, only 100 random samples for each parameter were generated as inputs in the simulation.43. The HPV vaccines function primarily by inducing durable systemic virus-neutralizing antibody responses, thereby preventing infection and possibly guarding against reinfection and disease21. Therefore, we assumed the corresponding parameters  to be 0.All of the values of variables, parameters were listed in Supplementary Text –7 in the ordinary differential equation solver; we also set maximum step size as 1.0 × 10–2 and restricted the total number of persons (variable N) as non-negative.The model structure was implemented in MATLAB  and the ode15s solver was applied to obtain numeric solutions. Both the relative and the absolute error tolerances were set as 1.0 × 10Simulations were performed for the Texas population. The entire population was stratified into 23 age groups, two gender groups, and three sexual activity groups. The variable values of initial conditions were from Year 2010 in Texas and we calculated the age-specific incidence rates  and age-adjusted incidence rates  for validation, prediction and sensitivity analyses.To check the validity of the model, we compared the predicted age-specific incidence rates  with the real OPC incidence rates  from the Surveillance, Epidemiology, and End Results Program (SEER) database for Texas and the Texas Cancer Registry database. Because of limitations on data availability, comparisons conducted for Texas in 2011 and 2012 only. One-way sensitivity analyses implemented by changing the value of some parameters according to pre-specified ranges.Supplementary Legends.Supplementary Text S1.Supplementary Text S2.Supplementary Text S3.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Cervical cancer screening strategies using visual inspection or cytology may have suboptimal diagnostic accuracy for detection of precancer in women living with HIV (WLHIV). The optimal screen and screen–triage strategy, age to initiate, and frequency of screening for WLHIV remain unclear. This study evaluated the sensitivity, specificity, and positive predictive value of different cervical cancer strategies in WLHIV in Africa.p < 0.001), 28.6% and 49.6% ever had prior cervical cancer screening (p < 0.001), and 69.9% and 64.2% were taking ART at enrolment (p = 0.045) in BF and SA, respectively. CIN2+ prevalence was 5.8% and 22.4% in BF and SA (p < 0.001), respectively. VIA had low sensitivity for CIN2+  and CIN3+  in both countries, with specificity for ≤CIN1 of 78.7% (95% CI 76.0%–81.3%). HC2 had sensitivity of 88.8% (95% CI 82.9%–93.2%) for CIN2+ and 86.4% (95% CI 75.7%–93.6%) for CIN3+. Specificity for ≤CIN1 was 55.4% (95% CI 52.2%–58.6%), and screen positivity was 51.3%. Specificity was higher with a restricted genotype (HPV16/18/31/33/35/45/52/58) approach , with lower screen positivity (33.7%), although there was lower sensitivity for CIN3+ . In BF, HC2 was more sensitive for CIN2+/CIN3+ compared to VIA/VILI . Triage of HC2-positive women with VIA/VILI reduced the number of colposcopy referrals, but with loss in sensitivity for CIN2+ (58.1%) but not for CIN3+ (84.6%). In SA, cytology high-grade squamous intraepithelial lesion or greater (HSIL+) had best combination of sensitivity  and specificity . HC2 had similar sensitivity for CIN3+  but lower specificity compared to HSIL+ , resulting in almost twice as many referrals. Compared to HC2, triage of HC2-positive women with HSIL+ resulted in a 40% reduction in colposcopy referrals but was associated with some loss in sensitivity. CIN2+ incidence over a median 16 months was highest among VIA baseline screen-negative women  and women who were baseline double-negative with HC2 and VIA  and lowest among HC2 baseline screen-negative women . Limitations of our study are that WLHIV included in the study may not reflect a contemporary cohort of WLHIV initiating ART in the universal ART era and that we did not evaluate HPV tests available in study settings today.WLHIV aged 25–50 years attending HIV treatment centres in Burkina Faso (BF) and South Africa (SA) from 5 December 2011 to 30 October 2012 were enrolled in a prospective evaluation study of visual inspection using acetic acid (VIA) or visual inspection using Lugol’s iodine (VILI), high-risk human papillomavirus DNA test (Hybrid Capture 2 [HC2] or careHPV), and cytology for histology-verified high-grade cervical intraepithelial neoplasia (CIN2+/CIN3+) at baseline and endline, a median 16 months later. Among 1,238 women , median age was 36 and 34 years  test targeting 14 high-risk (HR) types had higher sensitivity to detect CIN2+ compared to visual inspection but had low specificity, although a restricted genotype approach targeting 8 HR types decreased the number of unnecessary colposcopy referrals. Cytology HSIL+ had optimal performance for CIN2+/CIN3+ detection in SA. Triage of HPV-positive women with HSIL+ maintained high specificity but with some loss in sensitivity compared to HC2 alone. In this cohort study, Helen Kelly and colleagues explore cervical cancer screening strategies for women living with HIV. Invasive cervical cancer is the second most common cancer among women in low- and middle-income countries and a leading cause of cancer-related death in women in sub-Saharan Africa.Women living with human immunodeficiency virus (WLHIV) have an increased risk of cervical cancer and precancer. The majority of WLHIV live in low- and middle-income countries, where access to cervical cancer screening and treatment of precancerous cervical lesions is limited.Screening approaches most commonly used in sub-Saharan Africa, including visual inspection of the cervix and cervical cytology, which checks for cervical cell abnormalities, have shown variable diagnostic accuracy to detect precancerous lesions. Molecular-based screening approaches, such as human papillomavirus (HPV) DNA testing, which screens for oncogenic HPV infection, have shown high sensitivity for cervical precancer but can result in over-referral to colposcopy, a procedure to determine eligibility for treatment.The optimal screening test, age to begin screening, and frequency of screening for WLHIV remain uncertain.We evaluated several cervical cancer screening strategies in over 1,200 WLHIV in sub-Saharan Africa.We found that an HPV DNA test identified a greater number of women with precancer compared to visual inspection and cytology methods. However, there was a greater proportion of women without precancer who had a positive HPV DNA test, meaning a triage test using cytology or visual inspection was required to determine treatment eligibility.Simple user-applied modifications to the HPV-DNA-based test resulted in fewer women without precancer testing positive and fewer women needing triage.In settings with adequate infrastructure, cervical cytology was a useful triage test for HPV-positive women.Our data contribute to the evidence on choice of screening strategies for detection of cervical precancer among WLHIV in low- and middle-income settings.HPV DNA tests can play an important role in cervical cancer screening, especially in the era of universal antiretroviral therapy and where availability of self-sampling will facilitate screening participation.Prevention of cervical cancer should rank high as a public health priority in sub-Saharan Africa since WLHIV represent a group at very high risk of cervical precancer. In May 2018, the Director-General of the World Health Organization (WHO) announced a global call for action towards the elimination of invasive cervical cancer (ICC) as a public health problem, calling for more innovative technologies for detection of high-grade cervical intraepithelial neoplasia (CIN) grade 2 and higher (CIN2+) and better strategies to increase screening coverage and uptake  = 1/PPV) , and theWe evaluated the diagnostic accuracy of a restricted genotyping approach using results of the INNO-LiPA genotyping assay in the following combinations (positive for any genotype): HPV16; HPV16/18/45 (3 high-risk [HR] types), and HPV16/18/45/31/33/35/52/58 (8 HR types). Because of the low limit of detection of INNO-LiPA and to improve clinical relevancy, test positivity was defined as positivity for any of those genotypes among women who were also HC2 positive. We also evaluated the diagnostic accuracy of an HPV-based test targeting HR types previously reported to be most significantly associated with CIN3+ in the HARP cohort . RelativIn order to observe the performance of screening strategies in an already screened population, we evaluated the diagnostic accuracy of endline VIA/VILI, HPV DNA (careHPV), and cytology for cumulative CIN2+ detection at endline, excluding women who were treated for prevalent CIN2+ at baseline.10.17632/yd5ygw38vj.1.To evaluate the association of HIV-related factors with diagnostic accuracy of screening strategies, diagnostic accuracy for CIN2+/CIN3+ was evaluated separately among women on prolonged ART (>2 years), women on short-duration ART (≤2 years), and ART-naïve women at baseline . Diagnostic accuracy was also evaluated according to age at screening . The cumulative incidence of CIN2+ at endline was calculated among women who screened negative for each of the screening strategies at baseline . Analyses for diagnostic accuracy were conducted for discrete outcomes of CIN2+ and CIN3+. Data are presented separately for each country. Data were analysed using Stata (version 16) and according to the study statistical analysis plan . This arp < 0.001; p < 0.001).Of 1,395 women screened, 1,238 (89%) were enrolled in the HARP study , reflecting the 2:1 stratification ratio. Just over a third of women were taking ART for >2 years. The median CD4+ T cell count among women on ART for a prolonged duration (>2 years), women on ART for a short duration ART (≤2 years), and ART-naïve women was 478 (IQR 366–478), 394 (IQR 276–573), and 392 (IQR 310–591) cells/μl, respectively, in BF and 476 (IQR 372–626), 326 (IQR 207–453), and 440 (IQR 347–595) cells/μl, respectively, in SA. The corresponding values for HIV-1 plasma viral load were 40 (IQR 40–40), 40 (IQR 40–101), and 23,171  copies/ml, respectively, in BF and 109 (IQR 40–560), 141 (IQR 40–743), and 19,650  copies/ml, respectively, in SA.The median age of participants was 36 (IQR 32–42) years in BF and 34 (IQR 30–39) years in SA . About hp < 0.001). The prevalence of HR-HPV by HC2 was 41.8% (229/554) in BF and 59.7% (342/576) in SA  in BF and 79.3% (456/575) in SA (6) in SA .At baseline, positivity for each of the screening tests—VIA, VIA/VILI, ASCUS+, HSIL+, and HR-HPV DNA (HC2)—was 21.0%, 23.9%, 25.8%, 4.5%, and 41.8%, respectively, in BF, and 28.1%, 41.5%, 93.4%, 30.1%, and 59.7%, respectively, in SA .VIA had low sensitivity for CIN2+/CIN3+ , with specificity for ≤CIN1 of 78.7%  and specificity for ≤CIN1 . Increasing the threshold to ≥20 RLU resulted in fewer women screening positive  and increased the specificity to 71.4% (95% CI 68.4%–74.2%) but with loss in sensitivity . In BF, the number of colposcopy referrals was similar to current standard-of-care VIA/VILI , with a 2-fold increase in sensitivity for CIN2+  and CIN3+  but the lowest specificity  women using VIA or VIA/VILI had similarly low sensitivity for CIN2+ as using VIA or VIA/VILI as a screen test. In BF, although triage of HC2-positive (≥20 RLU) women with VIA/VILI had low sensitivity for CIN2+ , as was CIN3+ prevalence .In both countries, HR-HPV prevalence was higher among ART-naïve women (58.8%) and recent ART users (≤2 years’ duration: 60.6%) compared to prolonged ART users  compared to women on ART ≤2 years  or ART-naïve  for ≤CIN1 was higher in women on ART >2 years  compared to women on ART ≤2 years  or ART-naïve women , corresponding with lower HR-HPV prevalence in prolonged ART users . Consequp < 0.001; p < 0.001). There were no invasive cancer cases detected in either country at endline. Among 809 participants without CIN2+ at baseline, the incidence of CIN2+ over 16 months was 3.3% (95% CI 2.3%–4.8%) overall and was higher in SA .Of the 1,130 women evaluated at baseline, 1,042 (92.4%) were seen at the endline visit, at a median follow-up of 16.2 months (IQR 15.6–16.8), of whom 933 (89.5%) had histology data available at both time points  women in BF with CIN2+ detected at baseline who underwent management of their CIN2/3 lesions returned for the endline visit. In SA, 97 women with CIN2+ detected at baseline returned for the endline visit, and of these, 61 (63%) underwent management before the colposcopy/biopsy endline visit. Of the 36 participants who did not undergo treatment, 20 (55.6%) had CIN2/3 detected again at endline, and 16 (44.4%) had lower grade lesions (≤CIN1). Of the women who underwent management, the median time from colposcopy visit to management was similar in both countries .p-trend < 0.001). A test targeting HR-HPV type-specific persistence could detect 73.6%  cumulative CIN2+ cases at endline with a PPV of 16.9% (95% CI 12.3%–22.3%). The proportion of women without CIN2+ at endline and with HR-HPV type persistence was 22.6% .There were 903 women with matched histology and genotyping at both time points. Type-specific HR-HPV persistence was 20.7% (156/752) among women who were ≤CIN1 at both time points and 77.8% (21/27) among women with incident CIN2+. Among 87 women who received management for prevalent CIN2+, HR-HPV persistence was 37.0% (30/82) in those who remained ≤CIN1 and 66.7% (4/6) in women with CIN2+ redetected at endline . Among 3CIN2+ incidence at endline was 0.5% (95% CI 0.1–1.8) among women with a baseline negative HC2 (≥1 RLU) test or <LSIL on cytology, and 2.2% (95% CI 1.3%–3.7%) among women who were baseline VIA negative . Among HWe evaluated the diagnostic accuracy of screen and screen–triage approaches for CIN2+/CIN3+ in a large prospective cohort of WLHIV from 2 African countries with different HIV epidemics, different burdens of HPV infection and cervical cancer, and differing approaches to screening for cervical cancer. This allows the findings to be extended to a range of low- and middle-income settings. We found that an HPV-DNA-based test had high sensitivity but low specificity for CIN2+/CIN3+, but with simple modifications to increase the threshold for test positivity and with a restricted genotype approach resulted in higher specificity and correspondingly fewer referrals to colposcopy. Triage of HPV-positive women with VIA/VILI in BF and cytology (HSIL+) in SA resulted in a further reduction in referrals, with minimal impact on sensitivity for CIN3+, but not for CIN2+.HPV-based tests have high sensitivity for CIN2+/CIN3+ in both HIV-negative women and WLHIV, but specificity to distinguish CIN2+ is lower in WLHIV compared to HIV-negative women –34. HPV-We also found that the specificity of HC2 varied according to ART status, with the highest specificity observed in women taking ART for more than 2 years, corresponding with lower HR-HPV and CIN2+ prevalence. These findings are consistent with that reported in a cohort of WLHIV undergoing screening in Nairobi, Kenya , and JohAlternative approaches to the use of HPV tests could include repeat HPV DNA testing over time, which may distinguish HR-HPV persistent infection associated with CIN2+ from transient infections. We found in this study that 74% of WLHIV with CIN2+ detected at endline had type-specific persistence from baseline, compared with 23% of women without CIN2+ at endline. While such an approach may result in fewer women being unnecessarily treated or referred to colposcopy, the limitation is the potential for loss to follow-up of screen-positive women compared to a one-time HPV DNA test. On the other hand, repeat testing over a shorter interval  may be a feasible approach to integrate in routine HIV care, where WLHIV may be more frequently followed. Further data collection on the effectiveness and feasibility of such an approach is warranted.VIA is commonly used in LMICs, but we have shown it has low sensitivity for CIN2 lesions in WLHIV, consistent with other studies in Africa ,5,36, buCytology was the strategy with the best combination of sensitivity and specificity in SA, but only when the threshold for test positivity was increased to HSIL+. Similar high accuracy of cytology for CIN2+/CIN3+ has been reported in other studies in SA, which has an established cytology-based screening programme with quality control measures routinely implemented . StudiesThis study has several limitations. The study maximised the chances of obtaining histological results by basing the biopsy decision on positivity of any of 3 screening tests  or colposcopy , to which all participants were subjected . This approach and the threshold to trigger biopsy for histology are in excess of usual recommendations to minimise ascertainment bias. The number of post-biopsy adverse events was low; 6 (1.0%) women in BF and 4 (0.6%) in SA reported post-biopsy bleeding and/or abdominal pain. Women negative by all tests were considered to be at extremely low risk of CIN2+ since in particular HPV DNA and cytology have very high NPV for CIN2 diagnosis , and it HPV-based tests may be sufficient as a screening strategy in WLHIV if a restricted genotype approach is utilised and a higher threshold for test positivity is established. Molecular-based tests such as HPV tests have the added advantage of being automatable and less prone to training and interpretational errors than morphological tests such as VIA/VILI and cytology and can be performed using the same clinician-collected or self-collected sample, thereby simplifying sample collection, which may facilitate cervical cancer screening without the need for women to attend clinical services. Cytology remains optimal in settings with an existing cytology-based programme, such as SA. ART users with low or unknown nadir CD4+ cell count and ART-naïve women should be screened frequently, although the optimal screening intervals remain unclear. Although cervical cancer screening is not widely implemented in LMICs, integration of cervical cancer screening within HIV treatment services would ensure that women at high risk of developing cervical cancer precursor lesions are screened, and would lead to continuity in primary prevention, favouring early detection and management of HPV-related cervical lesions with minimal loss to follow-up . More loS1 Checklist(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S1 Text(PDF)Click here for additional data file. 1 Sep 2020Dear Dr Kelly, Thank you for submitting your manuscript entitled "Defining optimal cervical cancer screen and triage strategies for women living with HIV-1 in Sub-Saharan Africa: a diagnostic accuracy study" for consideration by PLOS Medicine.Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.Please re-submit your manuscript within two working days, i.e. by .https://www.editorialmanager.com/pmedicineLogin to Editorial Manager here: Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review. plosmedicine@plos.org if you have any queries relating to your submission.Feel free to email us at Kind regards,Helen Howard, for Clare Stone PhD Acting Editor-in-ChiefPLOS Medicine plosmedicine.org 30 Sep 2020Dear Dr. Kelly,Thank you very much for submitting your manuscript "Defining optimal cervical cancer screen and triage strategies for women living with HIV-1 in Sub-Saharan Africa: a diagnostic accuracy study" (PMEDICINE-D-20-04079R1) for consideration at PLOS Medicine. Your paper was evaluated by a senior editor and discussed among all the editors here. It was also evaluated by three independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:[LINK]In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers. http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. 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Finally when completing the checklist for resubmission, please use section and paragraph numbers, rather than page numbers (because the latter are amended when the paper is reformatted for publication). -----------------------------------------------------------Comments from the reviewers:Reviewer #1: This is an interesting diagnostic accuracy study on cervical cancer screening and triage strategies for women living with HIV-1 in Sub-Saharan Africa. However, there are quite a few issues needing attention.1) The title of "Defining optimal cervical cancer screen and triage strategies..." is a bit misleading. This is basically an observational studies on the diagnostic accuracies of a range of cervical cancer tests on the two cohorts for BF and SA. To develop and validate a diagnostics test, needs to develop/establish a test in a training set and then validate the test in the independent/external validation set, the performance of the diagnostic test needs to be evaluated for both discrimination and calibration. So far, we didn't see any of the above in the paper, therefore it's mainly an observational study reporting on the test results of a range of diagnostic tests.2) As this is a diagnostic study, it's not clear what is the gold standard of all the tests. Also, it could be better structured and written in a way - primary outcome, secondary outcome, incremental test accuracies of test 1, 2 and 3. Now it's all over the place. What exactly is the research question? What's the rationale and link of involving two cohorts from BF and SA? It's like two separate studies. Also, what's the point of baseline and endline measurements? For the diagnostic studies, it is 'on the spot' test which is very different from prognostic test with follow-ups. Basically the follow-ups in the study didn't contribute to the evaluation of the tests. It would be good for authors to refer to the TRIPOD statement to the principles of diagnostic and prognostic studies.3) ROC curve is very important for the reporting of diagnostic tests. However, it's not mentioned at all in the statistics analysis section in methods. In Figure 2 and 3, there needs to be curves there not just the dots, and ROC values need to have 95% CIs. Also, the ROC values with 95% CI need to be presented in all the tables for test performance.4) In statistics analysis of the methods section, it says "We modelled the diagnostic accuracy of restricted genotyping approach using results of INNO-LiPA genotyping assay in the following combinations...", however, it's not clear what exactly the statistical model being used for this?5) Table 1. P-value is missing for the variable "HIV-1 PVL in ART users".-----------------------------------------------------------Reviewer #2: Thank you for this manuscript. This is a very important topic and a well written manuscript. In this time when the need for a specific cervical pre-cancer/cancer screening and prevention strategy for women living with HIV, such a research and manuscript adds to the accumulating evidence to inform policy making. I have very few comments and suggestions to help improve the manuscript:1. line 9: I suggest you make "cervical screening" to read cervical cancer screening. This will be clearer to the readers and also consistent with the rest of the manuscript2. Line 64-54: rephrase possible to read 'All participants were referred for colposcopy performed AT A median OF 12 weeks (interquartile range [IQR]: 8-15)...'3. tables 2-5: I understand you have tried to put all the required information on the tables but as they look now, they are difficult to read. Kindly see what can be done to improve the legibility. I think they are very important tables and so any efforts to make them more legible is important.4. line 425: it should read '.....test positivity IS established.....'-----------------------------------------------------------Reviewer #3: The authors tackle a key unanswered question for women living with HIV (WLHIV) globally. Cervical cancer has become the leading cause of death for WLHIV with access to ART in Africa, but achievable strategies to mitigate this burden have been illusive due to challenges of scale, prevalence of dysplasia, and pathology/cytology/PCR capacity. In this project, they prospectively enroll WLHIV in Burkina Faso and South Africa  to rigorously assess performance characteristics of visual, cytologic, and molecular approaches to screening. The core finding that molecular testing is superior  is a key piece of knowledge to move field/programs forward. However, as currently presented these findings are challenging for a general audience to gasps and understand importance. Revision for improved readability and focus could greatly improve impact.Suggestions:Major.1) Improve readability of abstract/manuscript for general medical audience- Abstract reads as though two different studies (BF and RSA), but could be improved by noting that evaluating in low and high/middle-income contexts with differing HIV and HPV prevalences for improved generalizability. Suggest considering merging findings as seems like molecular testing optimal strategy in both settings , for detecting cancers and reducing specialist-dependant services.2) More directly assess impact of weaknesses on findings/confidence. The key weaknesses include old study , unblinding of pathologists (how much bias would need to be present to modify findings?), use of careHPV , does different geography of HPV prevalence affect the conclusions, and can any intervention be effective if takes 8-14mo to treat dysplasia and 37% got no treatment?3) Age triage. Many programs considering threshold of 30yrs to improve specificity for HPV testing. Does this subgroup have different performance characteristics>Minor1) Unsure why some patients got VIA or VILI or both2) For an HIV audience, they would expect viral suppression as a categorical variable rather than continuous. And proportion with viral failure on ART (often >1000 copies)3) Introduction and elsewhere suggest relationship between HPV vaccination and HPVtesting, but not related4) Discussion long and lots of extrapolations re: CD4, ART timing beyond the data of this project. suggest focusing message.-----------------------------------------------------------Any attachments provided with reviews can be seen via the following link:[LINK] 21 Oct 2020AttachmentLetter of response_Reviewers_211020.docxSubmitted filename: Click here for additional data file. 9 Dec 2020Dear Dr. Kelly,Thank you very much for re-submitting your manuscript "Diagnostic accuracy of cervical cancer screening and screening-triage strategies among women living with HIV-1 in Africa" (PMEDICINE-D-20-04079R2) for review by PLOS Medicine.I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:[LINK]***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.Please also check the guidelines for revised papers at plosmedicine@plos.org) if you have any questions or concerns.We expect to receive your revised manuscript within 1 week. Please email us , which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.Please ensure that the paper adheres to the PLOS Data Availability Policy  and HC-II triage with VIA (2.1%) and lowest among HC-II screen-negative women (0.5%).”d. We had some difficulty locating the data in these statements for verification; please indicate which Table these can be found in:i. “Triage of HC-II positive women with VIA/VILI reduced the number of colposcopy referrals, but with loss in sensitivity for CIN2+ (58.1%) but not for CIN3+: (84.6%).”ii. “HC-II had higher sensitivity  but low specificity  resulting in almost twice as many referrals.”iii. “Compared to HC-II, triage of HC-II positive women with HSIL+ resulted in 40% reduction in colposcopy referrals but was associated with some loss in sensitivity.”e. Conclusions: Please start with “In this cohort study, …”f. Conclusions: Please mention WLHIV as well.6. Author summary: a. Please add “(WLHIV)” after first use.b. Please correct: “precancerous lesions cervical” to “precancerous cervical lesions” c. Please define/clarify “cytology” and “colposcopy” to a lay reader.d. Please avoid the phrasing “false-positive”, and instead merely describe this subset of patients, eg …infections that have not progressed to a cytology-detectable lesion…“”. Please also clarify the “second test” as being also for HPV.e. Please define “ART” at first use.f. Please delete “and in COVID-19 era”7. Please make your reference citations not superscript, and include a space between the text and the brackets, eg: “…increase screening coverage and uptake [1].” Please remove spaces from within citations themselves.8. Line 54: Please include days in the recruitment dates.9. Line 90: Please remove registered trademark symbol.10. PLOS does not permit "data not shown” . Please remove this claim, or do one of the following:a) If you are the owner of the data relevant to this claim, please provide the data in accordance with the PLOS data policy, and update your Data Availability Statement as needed.b) If the data not shown refer to a study from another group that has not been published, please cite personal communication in your manuscript text (it should not be included in the reference section). Please provide the name of the individual, the affiliation, and date of communication. The individual must provide PLOS Medicine written permission to be named for this purpose.c) For any other circumstance, please contact the journal office ASAP.11. Table 1 and throughout: Please show exact p values over 0.001, or p<0.001 otherwise.12. Line 346: Typo “..”13. Page 27: Please remove the declaration of interests and author contributions – these are taken from the online submission form.14. Please double check and correct the URLs for references 1, 2, 18. 15. Please provide a URL or DOI for references 11 and 33.16. Please remove duplicate URLs from references 14 and 15.17. Please include a title for reference 22.18. Please list the short journal name for “PLoS ONE” consistently in references.19. Please rename the STARD checklist file as “S1 Checklist”. Line 153: Please update the reference to this file.20. Please rename the analysis plan file as “S1 Text”. Please cite this file in the Methods, as with the STARD checklist.---Comments from Reviewers:Reviewer #1: Many thanks authors for their tremendous effort to improve the manuscript. All my comments were comprehensively addressed. I am satisfied with the response and revision. No further issues needing attention. Reviewer #2: Thank you for the extensive revision. I am satisfied with how the issues raised have been addressed. But I will suggest a final read through of the clean version for editing purpose. Reviewer #3: The presented work is important and is substantially improved following peer review. It will be of high utility to the field.Any attachments provided with reviews can be seen via the following link:[LINK] 18 Dec 2020AttachmentLetter to editor_161220.docxSubmitted filename: Click here for additional data file. 22 Dec 2020Dear Dr Kelly, On behalf of my colleagues and the guest Academic Editor, Scott L. Dryden-Peterson, I am pleased to inform you that we have agreed to publish your manuscript "Diagnostic accuracy of cervical cancer screening and screening-triage strategies among women living with HIV-1 in Burkina Faso and South Africa: A cohort study" (PMEDICINE-D-20-04079R3) in PLOS Medicine.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. 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