Abstract A04: Childhood cancer incidence among specific Asian and Pacific Islander populations in the U.S.
Bibliographic record
Abstract
Abstract Background: We sought to explore variation in childhood cancer incidence rates among specific Asian and Pacific Islander (API) groups. Despite high genetic diversity in this region of the world, API groups in the United States are often combined into a single race/ethnic group. Methods: The Surveillance, Epidemiology, and End Results (SEER) Detailed Asian/Pacific Islander (2000-Centered) data was used for this analysis. Cancer diagnoses occurring between January 1998 to December 2002 from 14 SEER registries were included. Detailed racial/ethnic population data from the 2000 census was used to calculate incidence rates for individual races with at least 500,000 individuals. We also combined this data into groups based on geographic regions and genetic similarity, which included East Asia (China, Japan, Korea; n=377 cases), Southeast Asia (Vietnam, Laos, Cambodia; n=136 cases), Asian Indian/Pakistan (n=163 cases), Oceania (Guam, Samoa, Tonga; n=29 cases), and the Philippines (n=199 cases). Incidence rate ratios (IRR) and 95% confidence intervals (CI) were calculated comparing each API regional group to non-Hispanic Whites (NHW), and each API regional group to East Asians. Results: Incidence rates among detailed Asian and Pacific Islander groups varied. Acute lymphoblastic leukemia (ALL) was significantly lower in children of SE Asian (IRR 0.59, 95% CI 0.42, 0.82) and Filipino (IRR 0.73, 95% CI 0.57, 1.00) descent compared to non-Hispanic Whites. Acute myeloid leukemia (AML) was more common among children from Oceania compared to NHW (IRR 3.44, 95% CI 1.63, 7.28). Central nervous system (CNS) cancers were less common among East Asian (IRR 0.74, 95% CI 0.58, 0.96), SE Asian (IRR 0.45, 95% CI 0.28, 0.73), and Filipino (IRR 0.48, 95% CI 0.32, 0.72) children compared to NHW. When comparing the incidence of cancers among API regions, few clear patterns emerged. The incidence of AML in children from Oceania was nearly four times that in East Asians (IRR 3.88, 95% CI 1.64, 9,.11), though roughly the same among all other regions. The incidence rates of ALL (IRR 0.63, 95% CI 0.41, 0.97) and malignant gonadal germ cell tumors (IRR 0.29 95% CI 0.08, 0.97) were lower in SE Asians compared to East Asians. Lymphoma was twice as common in Asian Indians/Pakistani children compared to East Asians (IRR 2.13, 95% CI 1.33, 2.29). Conclusions: The variation observed in cancer incidence patterns among these groups is important and may indicate differences in underlying etiology and/or exposure patterns. These findings highlight possible disparities in cancer incidence between specific API groups. Citation Format: Kristin J. Moore, Aubrey K. Hubbard, Lindsay A. Williams, Logan G. Spector. Childhood cancer incidence among specific Asian and Pacific Islander populations in the U.S. [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A04.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".