Xinxin Huang

County-level colposcopy attendance in China’s national cervical cancer screening programme: a cross-sectional ecological study of Fujian province

Objectives This study aims to analyse the county-level spatiotemporal disparities in colposcopy non-attendance rates and to explore their associated factors in Fujian, China. Design A county-level observational study. Setting This study was conducted using data from the Fujian Provincial Maternal and Child Health Information System and Statistical Yearbooks, from 2021 to 2023. Participants Data from 60 counties in Fujian province across 3 years (2021–2023) were included, comprising 1080 county-age group-year observations. Counties from Xiamen Municipality were excluded due to data unavailability. Primary and secondary outcome measures The primary outcome was the county-level colposcopy non-attendance rate. Secondary outcomes included the Theil indices (measuring inequality) and the geographically varied associations of factors (eg, population density and transportation network density) with non-attendance. Results The average county-level colposcopy non-attendance rate in Fujian decreased from 24.41% in 2021 to 16.52% in 2023. The Theil-T and Theil-L indices of county-level non-attendance rates across the 3 years were 0.31 and 0.24, respectively. Lower population density was associated with higher rates of colposcopy non-attendance in the west of Fujian, while higher transportation network density and a larger per capita number of healthcare professionals were associated with higher rates of non-attendance, particularly in the west. Over time, the effects of these factors weakened. Three clusters of the coefficients were identified. Conclusions While colposcopy non-attendance rates have declined in recent years in Fujian, the persistent geographical disparity shown in this study indicates the importance of addressing service and information gaps. It is likely helpful to integrate the information system and service delivery across counties in cervical screening programmes to address the effects of potentially higher mobility and low population density in western Fujian. Considering the geographically varied effects of such factors, we also suggested a classified approach be adopted to improve colposcopy attendance.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.