Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy.

Background: The HPV-automated visual evaluation (PAVE) Study is an extensive, multinational initiative designed to advance cervical cancer prevention in resource-constrained regions. Cervical cancer disproportionally affects regions with limited access to preventive measures. PAVE aims to assess a novel screening-triage-treatment strategy integrating self-sampled HPV testing, deep-learning-based automated visual evaluation (AVE), and targeted therapies. Methods: Phase 1 efficacy involves screening up to 100,000 women aged 25-49 across nine countries, using self-collected vaginal samples for hierarchical HPV evaluation: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68 else negative. HPV-positive individuals undergo further evaluation, including pelvic exams, cervical imaging, and biopsies. AVE algorithms analyze images, assigning risk scores for precancer, validated against histologic high-grade precancer. Phase 1, however, does not integrate AVE results into patient management, contrasting them with local standard care.Phase 2 effectiveness focuses on deploying AVE software and HPV genotype data in real-time clinical decision-making, evaluating feasibility, acceptability, cost-effectiveness, and health communication of the PAVE strategy in practice. Results: Currently, sites have commenced fieldwork, and conclusive results are pending. Conclusions: The study aspires to validate a screen-triage-treat protocol utilizing innovative biomarkers to deliver an accurate, feasible, and cost-effective strategy for cervical cancer prevention in resource-limited areas. Should the study validate PAVE, its broader implementation could be recommended, potentially expanding cervical cancer prevention worldwide. Funding: The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/ NIH under Grant T32CA09168.

Reproducible and clinically translatable deep neural networks for cervical screening.

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. In this work, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-geography, multi-institution, and multi-device dataset of 9462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our model also produced reliable and consistent predictions, achieving a strong quadratic weighted kappa (QWK) of 0.86 and a minimal %2-class disagreement (% 2-Cl. D.) of 0.69%, between image pairs across women. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.

Adapting a model of cervical carcinogenesis to self-identified Black women to evaluate racial disparities in the United States.

BACKGROUND: Self-identified Black women in the United States have higher cervical cancer incidence and mortality than the general population, but these differences have not been clearly attributed across described cancer care inequities. METHODS: A previously established microsimulation model of cervical cancer was adapted to reflect demographic, screening, and survival data for Black US women and compared with a model reflecting data for all US women. Each model input with stratified data (all-cause mortality, hysterectomy rates, screening frequency, screening modality, follow-up, and cancer survival) was sequentially replaced with Black-race specific data to arrive at a fully specified model reflecting Black women. At each step, we estimated the relative contribution of inputs to observed disparities. RESULTS: Estimated (hysterectomy-adjusted) cervical cancer incidence was 8.6 per 100 000 in the all-race model vs 10.8 per 100 000 in the Black-race model (relative risk [RR] = 1.24, range = 1.23-1.27). Estimated all-race cervical cancer mortality was 2.9 per 100 000 vs 5.5 per 100 000 in the Black-race model (RR = 1.92, range = 1.85-2.00). We found the largest contributors of incidence disparities were follow-up from positive screening results (47.3% of the total disparity) and screening frequency (32.7%). For mortality disparities, the largest contributor was cancer survival differences (70.1%) followed by screening follow-up (12.7%). CONCLUSION: To reduce disparities in cervical cancer incidence and mortality, it is important to understand and address differences in care access and quality across the continuum of care. Focusing on the practices and policies that drive differences in treatment and follow-up from cervical abnormalities may have the highest impact.

Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy.

Objective: To describe the HPV-Automated Visual Evaluation (PAVE) Study, an international, multi-centric study designed to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (Large Loop Excision of the Transformation Zone). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine acceptability and feasibility of the PAVE strategy into clinical practice, cost-effectiveness, and health communication within the PAVE sites. Study design: Phase 1 Efficacy: Around 100,000 nonpregnant women, aged 25-49 years, without prior hysterectomy, and irrespective of HIV status, are being screened at nine study sites in resource-limited settings. Eligible and consenting participants perform self-collection of vaginal specimens for HPV testing using a FLOQSwab (Copan). Swabs are transported dry and undergo testing for HPV using a newly-redesigned isothermal DNA amplification HPV test (ScreenFire HPV RS), which has been designed to provide HPV genotyping by hierarchical risk groups: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68. HPV-negative individuals are considered negative for precancer/cancer and do not undergo further testing. HPV-positive individuals undergo pelvic examination with collection of cervical images and targeted biopsies of all acetowhite areas or endocervical sampling in the absence of visible lesions. Accuracy of histology diagnosis is evaluated across all sites. Cervical images are used to refine a deep learning AVE algorithm that classifies images as normal, indeterminate, or precancer+. AVE classifications are validated against the histologic endpoint of high-grade precancer determined by biopsy. The combination of HPV genotype and AVE classification is used to generate a risk score that corresponds to the risk of precancer (lower, medium, high, highest). During the efficacy phase, clinicians and patients within the PAVE sites will receive HPV testing results but not AVE results or risk scores. Treatment during the efficacy phase will be performed per local standard of care: positive Visual Inspection with Acetic Acid impression, high-grade colposcopic impression or CIN2+ on colposcopic biopsy, HPV positivity, or HPV 16,18/45 positivity. Follow up of triage negative patients and post treatment will follow standard of care protocols. The sensitivity of the PAVE strategy for detection of precancer will be compared to current SOC at a given level of specificity.Phase 2 Effectiveness: The AVE software will be downloaded to the new dedicated image analysis and thermal ablation devices (Liger Iris) into which the HPV genotype information can be entered to provide risk HPV-AVE risk scores for precancer to clinicians in real time. The effectiveness phase will examine clinician use of the PAVE strategy in practice, including feasibility and acceptability for clinicians and patients, cost-effectiveness, and health communication within the PAVE sites. Conclusion: The goal of the PAVE study is to validate a screen-triage-treat protocol using novel biomarkers to provide an accurate, feasible, cost-effective strategy for cervical cancer prevention in resource-limited settings. If validated, implementation of PAVE at larger scale can be encouraged. Funding: The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/NIH under Grant T32CA09168. Date of protocol latest review: September 24 th 2023.

Estimating human papillomavirus vaccine efficacy from a single-arm trial: proof-of-principle in the Costa Rica Vaccine Trial.

BACKGROUND: The World Health Organization recommends a 1- or 2-dose human papillomavirus (HPV) vaccination schedule for females aged 9 to 20 years. Studies confirming the efficacy of a single dose and vaccine modifications are needed, but randomized controlled trials are costly and face logistical and ethical challenges. We propose a resource-efficient single-arm trial design that uses untargeted and unaffected HPV types as controls. METHODS: We estimated HPV vaccine efficacy (VE) from a single arm by comparing 2 ratios: the ratio of the rate of persistent incident infection with vaccine-targeted HPV 16 and 18 (HPV 16/18) and cross-protected types HPV 31, 33, and 45 (HPV 31/33/45) to vaccine-unaffected types HPV 35, 39, 51, 52, 56, 58, 59, and 66 (HPV 35/39/51/52/56/58/59/66) vs the ratio of prevalence of these types at the time of trial enrollment. We compare VE estimates using only data from the bivalent HPV 16/18 vaccine arm of the Costa Rica Vaccine Trial with published VE estimates that used both the vaccine and control arms. RESULTS: Our single-arm approach among 3727 women yielded VE estimates against persistent HPV 16/18 infections similar to published 2-arm estimates from the trial (according-to-protocol cohort: 91.0% , 95% CI = 82.9% to 95.3% [single-arm] vs 90.9% , 95% CI = 82.0% to 95.9% [2-arm]; intention-to-treat cohort: 41.7%, 95% CI = 32.4% to 49.8% [single-arm] vs 49.0% , 95% CI = 38.1% to 58.1% [2-arm]). VE estimates were also similar in analytic subgroups (number of doses received; baseline HPV serology status). CONCLUSIONS: We demonstrate that a single-arm design yields valid VE estimates with similar precision to a randomized controlled trial. Single-arm studies can reduce the sample size and costs of future HPV vaccine trials while avoiding concerns related to unvaccinated control groups. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00128661.

Use of risk-based cervical screening programs in resource-limited settings.

Cervical cancer screening and management in the U.S. has adopted a risk-based approach. However, the majority of cervical cancer cases and deaths occur in resource-limited settings, where screening and management are not widely available. We describe a conceptual model that optimizes cervical cancer screening and management in resource-limited settings by utilizing a risk-based approach. The principles of risk-based screening and management in resource limited settings include (1) ensure that the screening method effectively separates low-risk from high-risk patients; (2) directing resources to populations at the highest cancer risk; (3) screen using HPV testing via self-sampling; (4) utilize HPV genotyping to improve risk stratification and better determine who will benefit from treatment, and (5) automated visual evaluation with artificial intelligence may further improve risk stratification. Risk-based screening and management in resource limited settings can optimize prevention by focusing triage and treatment resources on the highest risk patients while minimizing interventions in lower risk patients.

Opportunities for Achieving the Cancer Moonshot Goal of a 50% Reduction in Cancer Mortality by 2047.

On February 2, 2022, President Biden and First Lady Dr. Biden reignited the Cancer Moonshot, setting a new goal to reduce age-standardized cancer mortality rates by at least 50% over the next 25 years in the United States. We estimated trends in U.S. cancer mortality during 2000 to 2019 for all cancers and the six leading types (lung, colorectum, pancreas, breast, prostate, liver). Cancer death rates overall declined by 1.4% per year from 2000 to 2015, accelerating to 2.3% per year during 2016 to 2019, driven by strong declines in lung cancer mortality (-4.7%/year, 2014 to 2019). Recent declines in colorectal (-2.0%/year, 2010-2019) and breast cancer death rates (-1.2%/year, 2013-2019) also contributed. However, trends for other cancer types were less promising. To achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. We reviewed opportunities to prevent, detect, and treat these common cancers that could further reduce population-level cancer death rates and also reduce disparities. SIGNIFICANCE: We reviewed opportunities to prevent, detect, and treat common cancers, and show that to achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. See related commentary by Bertagnolli et al., p. 1049. This article is highlighted in the In This Issue feature, p. 1027.

REPRODUCIBLE AND CLINICALLY TRANSLATABLE DEEP NEURAL NETWORKS FOR CANCER SCREENING.

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.

Upper age limits for US male human papillomavirus vaccination for oropharyngeal cancer prevention: a microsimulation-based modeling study.

BACKGROUND: Human papillomavirus (HVP)-positive oropharyngeal cancer is the most common HPV-associated cancer in the United States. The age at acquisition of oral HPV infections that cause oropharyngeal cancer (causal infections) is unknown; consequently, the benefit of vaccination of US men aged 27-45 years remains uncertain. METHODS: We developed a microsimulation-based, individual-level, state-transition model of oral HPV16 and HPV16-positive oropharyngeal cancer among heterosexual US men aged 15-84 years, calibrated to population-level data. We estimated the benefit of vaccination of men aged 27-45 years for prevention of oropharyngeal cancer, accounting for direct- and indirect effects (ie, herd effects) of male and female vaccination. RESULTS: In the absence of vaccination, most (70%) causal oral HPV16 infections are acquired by age 26 years, and 29% are acquired between ages 27 and 45 years. Among men aged 15-45 years in 2021 (1976-2006 birth cohorts), status quo vaccination of men through age 26 years is estimated to prevent 95% of 153 450 vaccine-preventable cancers. Assuming 100% vaccination in 2021, extending the upper age limit to 30, 35, 40, or 45 years for men aged 27-45 years (1976-1994 cohorts) is estimated to yield small benefits (3.0%, 4.2%, 5.1%, and 5.6% additional cancers prevented, respectively). Importantly, status quo vaccination of men through age 26 years is predicted to result in notable declines in HPV16-positive oropharyngeal cancer incidence in young men by 2035 (51% and 24% declines at ages 40-44 years and 45-49 years, respectively) and noticeable declines (12%) overall by 2045. CONCLUSION: Most causal oral HPV16 infections in US men are acquired by age 26 years, underscoring limited benefit from vaccination of men aged 27-45 years for prevention of HPV16-positive oropharyngeal cancers.

Secondary Prevention of Cervical Cancer: ASCO Resource-Stratified Guideline Update.

PURPOSE: To update resource-stratified, evidence-based recommendations on secondary prevention of cervical cancer globally. METHODS: American Society of Clinical Oncology convened a multidisciplinary, multinational Expert Panel to produce recommendations reflecting four resource-tiered settings. A review of existing guidelines, formal consensus-based process, and modified ADAPTE process to adapt existing guidelines was conducted. Other experts participated in formal consensus. RESULTS: This guideline update reflects changes in evidence since the previous update. Five existing guidelines were identified and reviewed, and adapted recommendations form the evidence base. Cost-effectiveness analyses provided indirect evidence to inform consensus, which resulted in ≥ 75% agreement. RECOMMENDATIONS: Human papillomavirus (HPV) DNA testing is recommended in all resource settings; visual inspection with acetic acid may be used in basic settings. Recommended age ranges and frequencies vary by the following setting: maximal: age 25-65 years, every 5 years; enhanced: age 30-65 years, if two consecutive negative tests at 5-year intervals, then every 10 years; limited: age 30-49 years, every 10 years; basic: age 30-49 years, one to three times per lifetime. For basic settings, visual assessment is used to determine treatment eligibility; in other settings, genotyping with cytology or cytology alone is used to determine treatment. For basic settings, treatment is recommended if abnormal triage results are obtained; in other settings, abnormal triage results followed by colposcopy is recommended. For basic settings, treatment options are thermal ablation or loop electrosurgical excision procedure; for other settings, loop electrosurgical excision procedure or ablation is recommended; with a 12-month follow-up in all settings. Women who are HIV-positive should be screened with HPV testing after diagnosis, twice as many times per lifetime as the general population. Screening is recommended at 6 weeks postpartum in basic settings; in other settings, screening is recommended at 6 months. In basic settings without mass screening, infrastructure for HPV testing, diagnosis, and treatment should be developed.Additional information is available at www.asco.org/resource-stratified-guidelines.

Different human papillomavirus types share early natural history transitions in immunocompetent women.

Necessary stages of cervical carcinogenesis include acquisition of a carcinogenic human papillomavirus (HPV) type, persistence associated with the development of precancerous lesions, and invasion. Using prospective data from immunocompetent women in the Guanacaste HPV Natural History Study (NHS), the ASCUS-LSIL Triage Study (ALTS) and the Costa Rica HPV Vaccine Trial (CVT), we compared the early natural history of HPV types to inform transition probabilities for health decision models. We excluded women with evidence of high-grade cervical abnormalities at any point during follow-up and restricted the analysis to incident infections in all women and prevalent infections in young women (aged <30 years). We used survival approaches accounting for interval-censoring to estimate the time to clearance distribution for 20 529 HPV infections (64% were incident and 51% were carcinogenic). Time to clearance was similar across HPV types and risk classes (HPV16, HPV18/45, HPV31/33/35/52/58, HPV 39/51/56/59 and noncarcinogenic HPV types); and by age group (18-29, 30-44 and 45-54 years), among carcinogenic and noncarcinogenic infections. Similar time to clearance across HPV types suggests that relative prevalence can predict relative incidence. We confirmed that there was a uniform linear association between incident and prevalent infections for all HPV types within each study cohort. In the absence of progression to precancer, we observed similar time to clearance for incident infections across HPV types and risk classes. A singular clearance function for incident HPV infections has important implications for the refinement of microsimulation models used to evaluate the cost-effectiveness of novel prevention technologies.

A case study and proposal for publishing directed acyclic graphs: The effectiveness of the quadrivalent human papillomavirus vaccine in perinatally HIV Infected girls.

BACKGROUND: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. OBJECTIVES: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. METHODS: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study. CONCLUSION: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.

Potential effectiveness of a therapeutic HPV intervention campaign in Uganda.

Cervical cancer is a major source of morbidity and mortality in Uganda. In addition to prophylactic HPV vaccination, secondary prevention strategies are needed to reduce cancer burden. We evaluated the potential cancer reductions associated with a hypothetical single-contact therapeutic HPV intervention-with 70% coverage and variable efficacy [30%-100%]-using a three-stage HPV modeling framework reflecting HPV and cervical cancer burden in Uganda. In the reference case, we assumed prophylactic preadolescent HPV vaccination starting in 2020 with 70% coverage. A one-time therapeutic intervention targeting 35-year-old women in 2025 (not age-eligible for prophylactic vaccination) averted 1801 cervical cancers per 100 000 women over their lifetime (100% efficacy) or 533 cancers per 100 000 (30% efficacy). Benefits were considerably smaller in birth cohorts eligible for prophylactic HPV vaccination (768 cases averted per 100 000 at 100% efficacy). Evaluating the population-level impact over 40 years, we found introduction of a therapeutic intervention in 2025 with 100% efficacy targeted annually to 30-year-old women averted 139 000 incident cervical cancers in Uganda. This benefit was greatly reduced if efficacy was lower (30% efficacy; 41 000 cases averted), introduction was delayed (2040 introduction; 72 000 cases averted) or both (22 000 cases averted). We demonstrate the potential benefits of a single-contact HPV therapeutic intervention in a low-income setting, but show the importance of high therapeutic efficacy and early introduction timing relative to existing prophylactic programs. Reduced benefits from a less efficacious intervention may be somewhat offset if available within a shorter time frame.

The development of "automated visual evaluation" for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing: Interdisciplinary principles of automated visual evaluation in cervical screening.

There is limited access to effective cervical cancer screening programs in many resource-limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long-term reassurance when negative and adaptability to self-sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource-limited settings, either for primary screening or for triage of HPV-positive individuals. A deep learning (DL)-based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL-based AVE tool for broad use as a clinical test.

Rationale and design of a double-blind randomized non-inferiority clinical trial to evaluate one or two doses of vaccine against human papillomavirus including an epidemiologic survey to estimate vaccine efficacy: The Costa Rica ESCUDDO trial.

HPV vaccination of adolescent girls is the most effective measure to prevent cervical cancer. The World Health Organization recommends that adolescent girls receive two doses of vaccine but only a small proportion of girls from regions with the highest disease burden are vaccinated because of cost and logistical considerations. Our Costa Rica HPV Vaccine trial suggested that one dose of the bivalent HPV vaccine provides robust and lasting protection against persistent HPV infections for over a decade. Data from a post-licensure trial of the quadrivalent vaccine in India also suggested that a single dose may be effective in reducing cervical cancer risk. To formally compare one versus two doses of the bivalent and nonavalent HPV vaccines, we implemented a large, randomized, double-blind trial to investigate the non-inferiority of one compared to two vaccine doses in the prevention of new HPV16/18 infections that persist 6 or more months. Bivalent and nonavalent vaccines will be evaluated separately. The trial enrolled and randomized (1:1:1:1 to 1- and 2-dose arms of the bivalent and nonavalent vaccines) 20,330 girls 12 to 16 years old residing in Costa Rica. Trial participants are followed every 6 months for up to 5 years. We also aim to estimate vaccine efficacy by comparing the rates of 6 month persistent infection in unvaccinated women with the rates in the follow-up visits of trial participants. We included one survey of unvaccinated women at the start of the study (N = 4452) and will include another survey concomitant with follow up visits of trial participants at year 4.5 (planned N = 3000). Survey participants attend two visits 6 months appart. Herein, we present the rationale, design, and enrolled study population of the ESCUDDO trial. ClinicalTrials.gov Identifier: NCT03180034.

Cost-Effectiveness of Offering Cervical Cancer Screening with HPV Self-Sampling among African-American Women in the Mississippi Delta.

BACKGROUND: African-American women in the United States have an elevated risk of cervical cancer incidence and mortality. In the Mississippi Delta, cervical cancer disparities are particularly stark. METHODS: We conducted a micro-costing study alongside a group randomized trial that evaluated the efficacy of a patient-centered approach ("Choice" between self-collection at home for HPV testing or current standard of care within the public health system in Mississippi) versus the current standard of care ["Standard-of-care screening," involving cytology (i.e., Pap) and HPV co-testing at the Health Department clinics]. The interventions in both study arms were delivered by community health workers (CHW). Using cost, screening uptake, and colposcopy adherence data from the trial, we informed a mathematical model of HPV infection and cervical carcinogenesis to conduct a cost-effectiveness analysis comparing the "Choice" and "Standard-of-care screening" interventions among un/underscreened African-American women in the Mississippi Delta. RESULTS: When each intervention was simulated every 5 years from ages 25 to 65 years, the "Standard-of-care screening" strategy reduced cancer risk by 6.4% and was not an efficient strategy; "Choice" was more effective and efficient, reducing lifetime risk of cervical cancer by 14.8% and costing $62,720 per year of life saved (YLS). Screening uptake and colposcopy adherence were key drivers of intervention cost-effectiveness. CONCLUSIONS: Offering "Choice" to un/underscreened African-American women in the Mississippi Delta led to greater uptake than CHW-facilitated screening at the Health Department, and may be cost-effective. IMPACT: We evaluated the cost-effectiveness of an HPV self-collection intervention to reduce disparities.

A proposed new generation of evidence-based microsimulation models to inform global control of cervical cancer.

Health decision models are the only available tools designed to consider the lifetime natural history of human papillomavirus (HPV) infection and pathogenesis of cervical cancer, and the estimated long-term impact of preventive interventions. Yet health decision modeling results are often considered a lesser form of scientific evidence due to the inherent needs to rely on imperfect data and make numerous assumptions and extrapolations regarding complex processes. We propose a new health decision modeling framework that de-emphasizes cytologic-colposcopic-histologic diagnoses due to their subjectivity and lack of reproducibility, relying instead on HPV type and duration of infection as the major determinants of subsequent transition probabilities. We posit that the new model health states (normal, carcinogenic HPV infection, precancer, cancer) and corollary transitions are universal, but that the probabilities of transitioning between states may vary by population. Evidence for this variability in host response to HPV infections can be inferred from HPV prevalence patterns in different regions across the lifespan, and might be linked to different average population levels of immunologic control of HPV infections. By prioritizing direct estimation of model transition probabilities from longitudinal data (and limiting reliance on model-fitting techniques that may propagate error when applied to multiple transitions), we aim to reduce the number of assumptions for greater transparency and reliability. We propose this new microsimulation model for critique and discussion, hoping to contribute to models that maximally inform efficient strategies towards global cervical cancer elimination.

Impact of COVID-19-related care disruptions on cervical cancer screening in the United States.

OBJECTIVES: To quantify the secondary impacts of the COVID-19 pandemic disruptions to cervical cancer screening in the United States, stratified by step in the screening process and primary test modality, on cervical cancer burden. METHODS: We conducted a comparative model-based analysis using three independent NCI Cancer Intervention and Surveillance Modeling Network cervical models to quantify the impact of eight alternative COVID-19-related screening disruption scenarios compared to a scenario of no disruptions. Scenarios varied by the duration of the disruption (6 or 24 months), steps in the screening process being disrupted (primary screening, surveillance, colposcopy, excisional treatment), and primary screening modality (cytology alone or cytology plus human papillomavirus "cotesting"). RESULTS: The models consistently showed that COVID-19-related disruptions yield small net increases in cervical cancer cases by 2027, which are greater for women previously screened with cytology compared with cotesting. When disruptions affected all four steps in the screening process under cytology-based screening, there were an additional 5-7 and 38-45 cases per one million screened for 6- and 24-month disruptions, respectively. In contrast, under cotesting, there were additional 4-5 and 35-45 cases per one million screened for 6- and 24-month disruptions, respectively. The majority (58-79%) of the projected increases in cases under cotesting were due to disruptions to surveillance, colposcopies, or excisional treatment, rather than to primary screening. CONCLUSIONS: Women in need of surveillance, colposcopies, or excisional treatment, or whose last primary screen did not involve human papillomavirus testing, may comprise priority groups for reintroductions.