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Updated: May 15, 2025

Collection, Isolation, and Flow Cytometric Analysis of Human Endocervical Samples
Published on: July 6, 2014
Advancing Cervical Cancer Prevention Equity: Innovations in Self-Sampling and Digital Health Technologies Across
Michelle Gomes1,2, Elena Provaggi2, Andrea Barnabas Pembe3
1Department of Global Health and Development, The London School of Hygiene and Tropical Medicine (LSHTM), London WC1E 7HT, UK.
Abstract:
Cervical cancer causes 350,000 deaths annually, with 90% occurring in low- and middle-income countries (LMICs), despite being largely preventable through vaccination and screening. This review examines innovative approaches to address screening coverage gaps worldwide, analysing both established programmes in high-income countries and implementation strategies for LMICs. Self-sampling technologies demonstrate significant potential to improve the uptake of cervical screening, thereby improving cervical cancer prevention compared to traditional methods, particularly benefiting underserved populations across all healthcare settings. Among self-collection devices, vaginal brushes achieve sensitivity of 94.6% (95% CI: 92.4-96.8) for HPV detection, while novel approaches like the tampon show promising results (sensitivity 82.9-100%, specificity 91.6-96.8%) with high user acceptability. Implementation strategies vary by healthcare context, with high-income countries achieving success through integrated screening programmes and digital solutions, while LMICs demonstrate effective adaptation through community-based distribution (20-35% uptake) and innovative delivery methods. In resource-limited settings, self-sampling increases participation through enhanced patient comfort and cultural acceptability, while reducing costs by 32-48%. Progress toward WHO's cervical cancer elimination goals require careful consideration of local healthcare infrastructure, cultural contexts and sustainable financing mechanisms. Future research priorities include optimising self-sampling technologies for sustainability and scalability, developing context-specific implementation strategies and validating artificial intelligence applications to enhance screening efficiency across diverse healthcare settings.
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