Abstract A017: Equity and the age cutoff problem: Rethinking early detection and screening in Nigeria
Notice bibliographique
Résumé
Abstract Background: Global screening guidelines for cancer form the backbone of international early detection strategies. These guidelines are designed around high-income country (HIC) epidemiology, where cancers typically present later in life, infrastructure is robust, and health systems support referral and follow-up. However, in low- and middle-income countries (LMICs) such as Nigeria, cancer epidemiology diverges significantly, with increasing incidence of early-onset cancers (<50 years), weak health systems, and inequitable access to care. Adopting unmodified global guidelines risks overlooking high-risk younger populations and exacerbating late-stage diagnoses. Methods: We conducted a comparative epidemiological and policy analysis. Data sources included GLOBOCAN 2022, Nigeria’s National Cancer Control Plan (2018–2022), and population-based cancer registry data from Ibadan (the oldest in Nigeria, with data dating back to the 1960s), Abuja, Calabar, and other contributing registries. These were supplemented by Global Health Observatory mortality indicators (maternal, infant, and under-five mortality) to provide health system context. Screening benchmarks from HICs were systematically reviewed (age thresholds, modalities, genetic risk assessment, culturally tailored prevention campaigns, and surveillance systems) and compared against Nigerian incidence, age-specific distribution, and rural–urban disparities. Gaps in adoption, contextualization, and implementation of screening strategies were then analyzed. Results: Cancer registry data from Ibadan, Abuja, and Calabar, triangulated with GLOBOCAN 2022, demonstrate a marked shift in Nigeria toward earlier onset compared with high-income countries (HICs). In Nigeria, 42% of breast cancers and 35% of colorectal cancers were diagnosed in individuals younger than 50, compared with 19% and 11% respectively in HICs. The median age at cervical cancer diagnosis was 44 years, nearly a decade earlier than the U.S. median (52). Application of HIC screening thresholds (45–50 years) therefore excludes approximately one in four Nigerians at risk from preventive detection. Screening participation was low, with <10% of women screened for breast cancer and <5% for colorectal cancer, versus >60% and >50% coverage in HICs. Preventive vaccination followed similar patterns, with HPV coverage <20% in Nigeria compared to >90% in Rwanda, where contextualized implementation has been achieved. Although Nigeria’s National Cancer Control Plan (2018–2022) identifies early detection as a priority, it does not provide age-specific national screening guidelines or referral protocols. Collectively, these findings quantify for the first time how uncontextualized duplication of international standards leads to systematic underdiagnosis of younger, high-risk populations and contributes directly to rising early-onset cancer prevalence. Conclusion: Nigeria needs contextualized early detection and screening strategies, with earlier age thresholds and resource-appropriate methods. Citation Format: Deloraine A. Dennis, Joy B. Gwong, Zainab M. Nasir, Faith A. Affi, Muhammad Aliyu, Babatunde Alausa, Adeoluwa O. Idowu, Chinyere Okafor, Musa Ali-Gombe, Usman W. Muhammad, Joy Iya-Benson, Usman M. Aliyu, Emmanuel Taylor. Equity and the age cutoff problem: Rethinking early detection and screening in Nigeria [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A017.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».