National trends in acute lymphocytic leukemia (ALL) incidence (1999–2021): A longitudinal outcome study using joinpoint analysis of age, sex, and regional disparities
Bibliographic record
Abstract
Abstract INTRODUCTION Acute Lymphocytic Leukemia (ALL) is a highly aggressive hematological malignancy characterized by rapid proliferation of lymphoid progenitor cells, requiring prompt diagnosis and intensive treatment to achieve optimal outcomes. This study systematically investigates the incidence trends from 1999 to 2021, with a particular focus on demographic and regional disparities. Method: The data for this study were sourced from the Centers for Disease Control and Prevention (CDC) Wonder Database. This dataset includes the incidence rates of ALL across various age groups: under 1 year, 5-9 years, 10-14 years, 15-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-69 years, 70-74 years, 75-79 years, 80-84 years, and 85 years and older. Each year's incidence rates are expressed per 100,000 population. To determine the percentage increase, the incidence rates from 1999 were compared with those from 2021. Joinpoint regression was used to estimate average annual percent change (AAPC) with 95% confidence intervals. RESULTS From 1999 to 2021, there were 111133 reported cases of ALL. Throughout this period, the overall incidence rate showed an increase by 34.75% over two decades. This corresponds to an AAPC of 0.9073 with a 95%CI of 0.7191 (1.0958 - 10.0618) and P value < 0.000001. When examining the data by gender, men experienced lower incidence rates than women, with an incidence rate of 33.196% for men and 36.82% for women. The AAPC for women was 1.0002 with 95% CI (0.7672 - 1.2338 ), while for men, it was 0.9681 with 95% CI (0.7562 -1.1805). When looking at racial groups, Blacks or African Americans showed the greatest increase in incidence rates with AAPC of 0.8035 with 95% CI of (0.3479 - 1.2612) followed by Asian or Pacific Islanders with an AAPC of 0.7639 with 95% CI (0.2581 -1.2723), compared to other races, When it comes to regions, Southern USA had the highest incidence rate with an AAPC of 1.1902 95% CI (0.9556 - 1.4255). Age-specific trends revealed that the 1-4 year age group saw the largest relative increase in incidence, surging by 26972 with a crude incidence rate of 7.47, with the sharpest decline in later age groups until 55-59. Surprisingly, after 55-59, it started rising again from age groups 60-64 until 80-84. After age 85, however, the incidence rates started decreasing once again. CONCLUSION Incidence of ALL in children is obvious, but it has also significantly increased across the U.S. adult population, particularly after 59, with disparities most pronounced by sex, race/ethnicity & region. Targeted interventions are needed to address disparities and mitigate rising incidence rates.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".