Background: Cervical cancer is largely preventable, yet major gaps persist in detection and timely management of cervical precancer, particularly in low- and middle-income countries. Using data from the ESTAMPA multicentric study, we derived stratified absolute risk estimates for cervical precancer and cancer based on HPV testing, cervical cytology, and colposcopic impression. Methods: ESTAMPA recruited women aged 30–64 years across 12 centres in nine Latin American countries. Women were screened with HPV testing and cervical cytology. Those with positive screening results were referred to colposcopy using a standardised protocol including colposcopic impression, directed biopsies, and treatment as indicated. Women with normal colposcopy or histology less than CIN2 were recalled after 18 months for repeat HPV testing and colposcopy if HPV-positive. Absolute risks of histologically confirmed CIN3+ detected at baseline or follow-up were estimated integrating HPV testing, cytology, and colposcopic impression. Findings: 44,135 women with a valid HPV test were included, of whom 6,236 (14·1%) were HPV-positive. Overall, 764 CIN3+ lesions were detected (1·73%; 95% CI 1·61–1·86). CIN3+ risk was 12·0% (747/6,236; 95% CI 11·2–12·8) among HPV-positive women compared with 0·04% (17/37,899; 95% CI 0·03–0·07) among HPV-negative women. Among HPV-positive women, risk increased with cervical cytological severity, reaching 62·9% (95% CI 57·1–68·5) for HSIL+, 22·4% (95% CI 18·4–26·8) for LSIL, and 19·1% (95% CI 15·1–23·6) for ASC-US; a positive colposcopic impression further increased CIN3+ risk to 70·2% (95% CI 64·1–75·9), 28·5% (95% CI 23·5–34·0), and 24·5% (95% CI 19·4–30·2), respectively. Among HPV16/18-positive women, CIN3+ risk was 32·8% (95% CI 28·9–37·0) increasing to 46·2% (95% CI 40·8–51·6) when abnormal colposcopy was present. Interpretation: We confirmed a high underlying risk of CIN3+ in the Latin American population, with risk strongly stratified by HPV positivity, increasing cervical cytological severity, and positive colposcopic impression. These estimates may support WHO-aligned cervical screening and risk-based management strategies in LMICs and contribute to accelerating cervical cancer elimination.

Cervical cancer is caused by persistent high-risk human papillomavirus (HR-HPV) infection and remains a major global health burden despite available preventive strategies. Most HR-HPV infections and associated low-grade squamous intraepithelial lesions (LSIL) regress spontaneously, yet increasing commercial promotion of topical therapies targeting HPV clearance has emerged in clinical practice. This FIGO/International Gynecologic Cancer Society (IGCS) Position Statement critically appraises the available evidence on topical treatments for HR-HPV infection and LSIL, and provides evidence-based recommendations for clinical practice and future research. No randomized controlled trial has been adequately designed or powered to evaluate the effect of topical therapies on the prevention of histologically confirmed high-grade squamous intraepithelial lesions (HSIL/CIN3+). Available studies are characterized by small sample sizes, short follow-up periods, heterogeneous populations, inadequate comparators, and reliance on surrogate endpoints of uncertain clinical relevance, including cytological regression, HPV nondetection, and immunohistochemical markers such as p16/Ki-67. Published meta-analyses reporting statistically significant improvements in virological and cytological surrogates are limited by substantial heterogeneity, publication bias, and follow-up periods under 1 year. Product-specific appraisal of Coriolus versicolor-based and silicon dioxide/selenite-based vaginal gels reveal additional methodological concerns, including retrospective trial registration, industry funding, and misinterpretation of findings consistent with the spin phenomenon. In the absence of high-quality evidence demonstrating clinically meaningful benefit, FIGO and IGCS do not endorse the routine clinical use of any currently available topical agent for HR-HPV infection or LSIL. Evidence-based surveillance remains the standard of care. Future research should prioritize rigorous trial design with histologically confirmed HSIL/CIN3+ as the primary endpoint, adequate follow-up of at least 3 years, and appropriate comparators. Clinical management should center on informed shared decision-making and evidence-based counseling, rather than on interventions lacking proven clinical benefit.

Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of image quality control. Diagnostic models are often trained on images with no denotation of image quality in the training data; this, in turn, can lead to misclassifications by these models when implemented in the clinical setting. In the case of cervical images, quality classification is a crucial task to ensure accurate detection of precancerous lesions or cancer; this is true for both gynecologic-oncologists’ (manual) and diagnostic AI models’ (automated) predictions. Factors that impact the quality of a cervical image include but are not limited to blur, poor focus, poor light, noise, obscured view of the cervix due to mucus and/or blood, improper position, and over- and/or under-exposure. Utilizing a multi-level image quality ground truth denoted by providers, we generated an image quality classifier following a multi-stage model selection process that investigated several key design choices on a multi-heterogenous “SEED” dataset of 40,534 images. We subsequently validated the best model on an external dataset (“EXT”), comprising 1,340 images captured using a different device and acquired in different geographies from “SEED”. We assessed the relative impact of various axes of data heterogeneity, including device, geography, and ground-truth rater on model performance. Our best performing model achieved an area under the receiver operating characteristics curve (AUROC) of 0.92 (low quality, LQ vs. rest) and 0.93 (high quality, HQ vs. rest), and a minimal total %extreme misclassification (%EM) of 2.8% on the internal validation set. Our model also generalized well externally, achieving corresponding AUROCs of 0.83 and 0.82, and %EM of 3.9% when tested out-of-the-box on the external validation (“EXT”) set. Additionally, our model was geography agnostic with no meaningful difference in performance across geographies, did not exhibit catastrophic forgetting upon retraining with new data, and mimicked the overall/average ground truth rater behavior well. Our work represents one of the first efforts at generating and externally validating an image quality classifier across multiple axes of data heterogeneity to aid in visual diagnosis of cervical precancer and cancer. We hope that this will motivate the accompaniment of adequate guardrails for AI-based pipelines to account for image quality and generalizability concerns.

Background:Cancer morbidity disproportionately affects patients in low- and middle-income countries (LMICs), where timely and accurate tumor profiling is often nonexistent. Immunohistochemistry-based assessment of estrogen receptor (ER) status, a critical step to guide use of endocrine therapy (ET) in breast cancer, is often delayed or unavailable. As a result, ET is often prescribed empirically, leading to ineffective and toxic treatment for ER-negative patients. To address this unmet need, we developed ESPWA (Estrogen Receptor Status Prediction for Haitian patients using deep learning-enabled histopathology Whole Slide Imaging Analysis), a deep-learning (DL) model that predicts ER status directly from hematoxylin-and-eosin (H&E)-stained whole slide images (WSIs). Methods:We curated two cohorts of H&E WSIs with tissue-matched ER status: The Cancer Genome Atlas (TCGA, n = 1085) and Zanmi Lasante (ZL, n = 3448) from Haiti. We trained two models using weakly supervised attention-based multiple instance learning: a "TCGA" model, trained on TCGA data, and ESPWA, trained on the ZL dataset. Model performance was evaluated using 10-fold cross validation. Results:Performance of the "TCGA" model was sensitive to the domain shift between the TCGA and ZL datasets, with a performance of an area under receiver operating characteristic (AUROC) of 0.846 on the TCGA test sets and 0.671 on the ZL test sets. Compared to the "TCGA" model, ESPWA demonstrated improved performance on the ZL cohort (AUROC=0.790; p=0.005). Subgroup analyses revealed clinically relevant populations in which ESPWA demonstrated improved performance relative to the overall cohort. Finally, ESPWA outperformed an academic breast pathologist (accuracy: 0.726 vs 0.639 respectively; p <0.001) in determining ER status from H&E WSIs. Conclusion:ESPWA ("Hope" in Haitian Creole) offers an accessible framework to identify individualized therapeutic insights from H&E WSIs in LMICs. We have initiated clinical trials, using ESPWA, in ZL and sub-Saharan African countries to inform precision-based use of ET for prospective patients.

Visual assessment is currently used for primary screening or triage of screen-positive individuals in cervical cancer screening programs. Most guidelines recommend screening and triage up to at least age 65 years old. We examined cervical images from participants in three National Cancer Institute funded cervical cancer screening studies: ALTS (2864 participants recruited between 1996 to 1998) in the United States (US), NHS (7548 in 1993) in Costa Rica, and the Biopsy study (684 between 2009 to 2012) in the US. Specifically, we assessed the visibility of the squamocolumnar junction (SCJ), which is the susceptible zone for precancer/cancer by age, as reported by colposcopist reviewers either at examination or review of cervical images. The visibility of the SCJ declined substantially with age: by the late 40s the majority of people screened had at most partially visible SCJ. On longitudinal analysis, the change in SCJ visibility from visible to not visible was largest for participants from ages 40-44 in ALTS and 50-54 in NHS. Of note, in the Biopsy study, the live colposcopic exam resulted in significantly higher SCJ visibility as compared to review of static images (Weighted kappa 0.27 (95% Confidence Interval: 0.21, 0.33), Asymmetry chi-square P-value<0.001). Lack of SCJ visibility leads to increased difficulty in diagnosis and management of cervical precancers. Therefore, cervical cancer screening programs reliant on visual assessment might consider lowering the upper age limit for screening if there are not adequately trained personnel and equipment to evaluate and manage participants with inadequately visible SCJ.

PDF file - 107K, Supplemental Tables 1A and 1B: Agreement between worst study histopathologic diagnosis. Supplemental Table 2: The relationship of E6 and DNA detection of human papillomavirus (HPV) genotypes 16, 18, and/or 45 (HPV16/18/45) on cervical exfoliated cells with HPV genotypes detected in the tissue of cervical intraepithelial neoplasia grade 2 (CIN2), grade 3 (CIN3), and cervical cancer, overall (all) and by clinical site.

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.

Abstract Background In low-resource countries, interpretation of the transformation zone (TZ) using the classification of the International Federation for Cervical Pathology and Colposcopy (IFCPC), adopted by the World Health Organization, is critical for determining if visual inspection with acetic acid (VIA) screening and thermal ablation treatment are possible. We aim to assess inter- and intra-observer agreement in TZ interpretation. Methods We performed a prospective multi-observer reliability study. One hundred cervical digital images of Human papillomavirus positive women (30–49 years) were consecutively selected from a Cameroonian cervical cancer screening trial. Images of the native cervix and after VIA were obtained. The images were evaluated for the TZ type at two time points (rounds one and two) by five VIA experts from four countries (Côte d’Ivoire, Cameroon, Peru, and Zambia) according to the IFCPC classification (TZ1 = ectocervical fully visible; TZ2 = endocervical fully visible; TZ3 = not fully visible). Intra- and inter-observer agreement were measured by Fleiss’ kappa. Results Overall, 37.0% of images were interpreted as TZ1, 36.4% as TZ2, and 26.6% as TZ3. Global inter-observer reliability indicated fair agreement in both rounds (kappa 0.313 and 0.288). The inter-observer agreement was moderate for TZ1 interpretation (0.460), slight for TZ2 (0.153), and fair for TZ3 (0.329). Intra-observer analysis showed fair agreement for two observers (0.356 and 0.345), moderate agreement for two other (0.562 and 0.549), and one with substantial agreement (0.728). Conclusion Interpretation of the TZ using the IFCPC classification, adopted by the World Health Organization, is critical for determining if VIA screening and thermal ablation treatment are possible. However, the low inter- and intra-observer agreement suggest that the reliability of the referred classification is limited in the context of VIA. It’s integration in treatment recommendations should be used with caution since TZ3 interpretation could lead to an important referral rate for further evaluation. Trial registration Cantonal Ethics Board of Geneva, Switzerland: N°2017–0110. Cameroonian National Ethics Committee for Human Health Research N°2018/07/1083/CE/CNERSH/SP.

Background Colposcopy, currently included in WHO recommendations as an option to triage human papillomavirus (HPV)-positive women, remains as the reference standard to guide both biopsy for confirmation of cervical precancer and cancer and treatment approaches. We aim to evaluate the performance of colposcopy to detect cervical precancer and cancer for triage in HPV-positive women.Methods This cross-sectional, multicentric screening study was conducted at 12 centres (including primary and secondary care centres, hospitals, laboratories, and universities) in Latin America (Argentina, Bolivia, Colombia, Costa Rica, Honduras, Mexico, Paraguay, Peru, and Uruguay). Eligible women were aged 30-64 years, sexually active, did not have a history of cervical cancer or treatment for cervical precancer or a hysterectomy, and were not planning to move outside of the study area. Women were screened with HPV DNA testing and cytology. HPV-positive women were referred to colposcopy using a standardised protocol, including biopsy collection of observed lesions, endocervical sampling for transformation zone (TZ) type 3, and treatment as needed. Women with initial normal colposcopy or no high-grade cervical lesions on histology (less than cervical intraepithelial neoplasia [CIN] grade 2) were recalled after 18 months for another HPV test to complete disease ascertainment; HPV-positive women were referred for a second colposcopy with biopsy and treatment as needed. Diagnostic accuracy of colposcopy was assessed by considering a positive test result when the colposcopic impression at the initial colposcopy was positive minor, positive major, or suspected cancer, and was considered negative otherwise. The main study outcome was histologically confirmed CIN3+ (defined as grade 3 or worse) detected at the initial visit or 18-month visit.Findings Between Dec 12, 2012, and Dec 3, 2021, 42 502 women were recruited, and 5985 (14middot1%) tested positive for HPV. 4499 participants with complete disease ascertainment and follow-up were included in the analysis, with a median age of 40middot6 years (IQR 34middot7-49 & BULL;9). CIN3+ was detected in 669 (14middot9%) of 4499 women at the initial visit or 18-month visit (3530 [78middot5%] negative or CIN1, 300 [6middot7%] CIN2, 616 [13middot7%] CIN3, and 53 [1middot2%] cancers). Sensitivity was 91middot2% (95% CI 88middot9-93middot2) for CIN3+, whereas specificity was 50middot1% (48middot5-51middot8) for less than CIN2 and 47middot1% (45middot5-48middot7) for less than CIN3. Sensitivity for CIN3+ significantly decreased in older women (93middot5% [95% CI 91middot3-95middot3] in those aged 30-49 years vs 77middot6% [68middot6-85 & BULL;0] in those aged 50-65 years; p < 0middot0001), whereas specificity for less than CIN2 significantly increased (45middot7% [43middot8-47middot6] vs 61middot8% [58middot7-64middot8]; p < 0middot0001). Sensitivity for CIN3+ was also significantly lower in women with negative cytology than in those with abnormal cytology (p < 0middot0001).Interpretation Colposcopy is accurate for CIN3+ detection in HPV-positive women. These results reflect ESTAMPA efforts in an 18-month follow-up strategy to maximise disease detection with an internationally validated clinical management protocol and regular training, including quality improvement practices. We showed that colposcopy can be optimised with proper standardisation to be used as triage in HPV-positive women.

Sensitivity analyses for 2-visit HPV versus 2-visit HPV-VIA in Uganda

Cervical cancer screening and treatment of screen positives is an important and effective strategy to reduce cervical cancer morbidity and mortality. In order to have an accurate cervical cancer screening and evaluation of positives, the entire Squamocolumnar Junction (SCJ) must be visible. Throughout the life course, the position of the SCJ changes and affects its visibility. SCJ visibility was analyzed among participants screened at the League Against Cancer Clinic in Lima, Peru. Of the 4247 participants screened, the SCJ was fully visible in 49.7% of participants, partially visible in 23.1%, and not visible in 27.2%. Visibility decreased with age, and by age 45 years old, the SCJ was not fully visible in over 50% of participants. Our results show that a high percentage of participants at ages still recommended for screening do not have totally visible SCJ, and we may need to reconsider the upper age limit for screening and find new strategies for evaluation of those with a positive screening test and non-visible SCJ.

Cervical cancer disproportionately affects low and middle income countries. Automated visual evaluation – using deep learning to analyze a digital cervix photograph – has been proposed for patient management. Image quality remains a key challenge, as it can be degraded by many types of image defects. A series of such defects were artificially added to a test set consisting of N=344 digitized cervigram images from existing studies. Replicate test sets were created for different image defects: blur, recoloring, obstructions of different colors and directions, rotations, and white Gaussian noise. The augmented images were evaluated by a classifier. The two most significant image defects were blur and Gaussian noise.

Cervical tissue ablation is an effective treatment approach for excising high-grade precancerous lesions, which are a direct precursor to invasive cervical cancer. However, not all women are eligible for this ablative treatment due to their cervical characteristics. In our previous study, we presented a deep learning network that used pyramidal features to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. Our method demonstrated promising performance and valid visualization in the task of “treatability classification”. In this work, we propose using an image augmenter followed by a customized classification convolutional neural network (CNN) to overcome the challenges due to insufficient training data. We build the image augmenter using a CycleGAN model that is trained using three different datasets to ensure that the augmented images contain clinically significant morphological features. A gynecologic oncologist with more than 20 years of experience validated the augmented images. These are mixed into the set of original images to train our customized CNN. We note a performance improvement of 3.3

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). Image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97

BACKGROUND:There is growing evidence supporting the use of mobile health (mHealth) interventions in low- and middle-income countries to address resource limitations in the delivery of health information and services to vulnerable populations. In parallel, there is an increasing emphasis on the use of implementation science tools and frameworks for the early identification of implementation barriers and to improve the acceptability, appropriateness, and adoption of mHealth interventions in resource-limited settings. However, there are limited examples of the application of implementation science tools and frameworks to the formative phase of mHealth design for resource-limited settings despite the potential benefits of this work for enhancing subsequent implementation, scale-up, and sustainability. OBJECTIVE:We presented a case study on the use of an implementation science framework in mHealth design. In particular, we illustrated the usability of the Consolidated Framework for Implementation Research (CFIR) for organizing and interpreting formative research findings during the design of the mobile Inspección Visual con Ácido Acético (mIVAA) system in Lima, Peru. METHODS:We collected formative data from prospective users of the mIVAA intervention using multiple research methodologies, including structured observations, surveys, group and individual interviews, and discussions with local stakeholders at the partnering organization in Peru. These activities enabled the documentation of clinical workflows, perceived barriers to and facilitators of mIVAA, overarching barriers to cervical cancer screening in community-based settings, and related local policies and guidelines in health care. Using a convergent mixed methods analytic approach and the CFIR as an organizing framework, we mapped formative research findings to identify key implementation barriers and inform iterations of the mIVAA system design. RESULTS:In the setting of our case study, most implementation barriers were identified in the CFIR domains of intervention characteristics and inner setting. All but one barrier were addressed before mIVAA deployment by modifying the system design and adding supportive resources. Solutions involved improvements to infrastructure, including cellular data plans to avoid disruption from internet failure; improved process and flow, including an updated software interface; and better user role definition for image capture to be consistent with local health care laws. CONCLUSIONS:The CFIR can serve as a comprehensive framework for organizing formative research data and identifying key implementation barriers during mHealth intervention design. In our case study of the mIVAA system in Peru, formative research contributing to the CFIR domains of intervention characteristics and inner setting elicited the most key barriers to implementation. The early identification of barriers enabled design iterations before system deployment. Future efforts to develop mHealth interventions for low- and middle-income countries may benefit from using the approach presented in this case study as well as prioritizing the CFIR domains of intervention characteristics and inner setting.

BACKGROUND:Cervical cancer is a significant public health problem, with 570,000 new cases and 300,000 deaths of women per year globally, mostly in low- and middle-income countries. In 2018 the WHO Director General made a call to action for the elimination of cervical cancer as a public health problem. MAIN BODY:New thinking on programmatic approaches to introduce emerging technologies and screening and treatment interventions of cervical precancer at scale is needed to achieve elimination goals. Implementation research (IR) is an important yet underused tool for facilitating scale-up of evidence-based screening and treatment interventions, as most research has focused on developing and evaluating new interventions. It is time for countries to define their specific IR needs to understand acceptability, feasibility, and cost-effectiveness of interventions as to design and ensure effective implementation, scale-up, and sustainability of evidence-based screening and treatment interventions. WHO convened an expert advisory group to identify priority IR questions for HPV-based screening and treatment interventions in population-based programmes. Several international organizations are supporting large scale introduction of screen-and-treat approaches in many countries, providing ideal platforms to evaluate different approaches and strategies in diverse national contexts. CONCLUSION:For reducing cervical cancer incidence and mortality, the readiness of health systems, the reach and effectiveness of new technologies and algorithms for increasing screening and treatment coverage, and the factors that support sustainability of these programmes need to be better understood. Answering these key IR questions could provide actionable guidance for countries seeking to implement the WHO Global Strategy towards cervical cancer elimination.

ObjectiveColposcopy is an important part of cervical screening/management programs. Colposcopic appearance is often classified, for teaching and telemedicine, based on static images that do not reveal the dynamics of acetowhitening. We compared the accuracy and reproducibility of colposcopic impression based on a single image at one minute after application of acetic acid versus a time-series of 17 sequential images over two minutes.MethodsApproximately 5000 colposcopic examinations conducted with the DYSIS colposcopic system were divided into 10 random sets, each assigned to a separate expert colposcopist. Colposcopists first classified single two-dimensional images at one minute and then a time-series of 17 sequential images as ‘normal,’ ‘indeterminate,’ ‘high grade,’ or ‘cancer’. Ratings were compared to histologic diagnoses. Additionally, 5 colposcopists reviewed a subset of 200 single images and 200 time series to estimate intra- and inter-rater reliability.ResultsOf 4640 patients with adequate images, only 24.4% were correctly categorized by single image visual assessment (11% of 64 cancers; 31% of 605 CIN3; 22.4% of 558 CIN2; 23.9% of 3412 < CIN2). Individual colposcopist accuracy was low; Youden indices (sensitivity plus specificity minus one) ranged from 0.07 to 0.24. Use of the time-series increased the proportion of images classified as normal, regardless of histology. Intra-rater reliability was substantial (weighted kappa = 0.64); inter-rater reliability was fair ( weighted kappa = 0.26).ConclusionSubstantial variation exists in visual assessment of colposcopic images, even when a 17-image time series showing the two-minute process of acetowhitening is presented. We are currently evaluating whether deep-learning image evaluation can assist classification.

Background: The WHO’s cervical cancer elimination strategy calls for 70% screened and 90% with cervical disease treated. Human papillomavirus (HPV) testing increases access to cervix screening, improving screening and treatment uptake. Self-collection screening is acceptable and feasible. However, data are needed to inform policy on how health systems can best integrate self-collection HPV-based screening programs within existing infrastructure to optimize screening and treatment coverage.Methods: ASPIRE Mayuge was a pragmatic cluster-randomized trial in Uganda comparing implementation strategies for self-collection screening: door-to-door and community health days. Villages were randomized to strategy. Participants ages 25-29 with no prior cervix treatment were eligible. Participants completed a survey and self-collection screening. The primary outcome was follow-up attendance at local health centers after a positive screen. We ran mixed-effects logistic regression models to compare outcomes between arms.Findings: A total of 31 villages and 2,019 participants were randomized (Arm 1: 16 clusters, 1055 participants; Arm 2: 15 clusters, 964 participants). Among HPV positive participants, follow-up attendance rates were 75% (Arm 1) and 66% (Arm 2). The adjusted regression model showed Arm 2 had lower odds of follow-up attending (OR = 0·67, 95% CI: 0·38-1·19).Interpretation: Both models were feasibly integrated into existing infrastructure and led to high rates of follow-up attendance. The door-to-door model, with individualized education, may encourage better follow-up attendance; however, community health days required fewer personnel and allowed for bundling of services. Jurisdictions can use this trial as a roadmap for implementation and determine which approach is best suited for their setting.Trial Registration Details: ISRCTN, ISRCTN12767014. Registered 14 May 2019, https://doi.org/10.1186/ISRCTN12767014; clinicaltrials.gov, NCT04000503; Registered 27 June 2019, https://clinicaltrials.gov/ct2/show/NCT04000503.Funding Information: This work was supported by a Canadian Health Research Institutes Foundation grant awarded to Professor Gina Ogilvie (CIHR FDN-143339).Declaration of Interests: No conflicts of interest exist.Ethics Approval Statement: Ethics approval was obtained from the University of British Columbia / Children’s and Women’s Health Centre of British Columbia Research Ethics Board (UBC C&W REB # H17–0333) and the Uganda Cancer Institute (UCIREC REF-02-2018). All study participants provided informed consent.

The World Health Organization (WHO) is leading a call to action to eliminate cervical cancer by the end of the century through global implementation of two effective evidence-based preventive interventions: HPV vaccination and cervical screening and management (CSM). Models estimate that without intervention, over the next 50 years 12.2 million new cases of cervical cancer will occur, nearly 60% of which are preventable only through CSM. Given that more than 80% of the cervical cancer occurs in low- and middle-income countries (LMICs), scaling up sustainable CSM programs in these countries is a top priority for achieving the global elimination goals. Multiple technologies have been developed and validated to meet this need. Now it is critical to identify strategies to implement these technologies into complex, adaptive health care delivery systems. As part of the coordinated cervical cancer elimination effort, we applied a systems thinking lens to reflect on our experiences with implementation of HPV-based CSM programs using the WHO health systems framework. While many common health system barriers were identified, the effectiveness of implementation strategies to address them was context dependent; often reflecting differences in stakeholder's belief in the quality of the evidence supporting a CSM algorithm, the appropriateness of the evidence and algorithm to context, and the 'implementability' of the algorithm under realistic assessments of resource availability and constraints. A structured planning process, with early and broad stakeholder engagement, will ensure that shared-decisions in CSM implementation are appropriately aligned with the culture, values, and resource realities of the setting.