Abstract A017: Equity and the age cutoff problem: Rethinking early detection and screening in Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".