Analysis of the Hematology Health Equity Literature: Trends By Topic and Disparity, across Journals, and over Time
Bibliographic record
Abstract
Introduction: Health equity is increasingly being recognized as a critical priority across medicine. Through an initiative of the ASH Health Equity Task Force, we recently published the ASH Health Equity Compendium (Fingrut et al, Blood Adv. 2024) to describe/catalogue the health equity manuscripts published across the Blood journals. Extending from this work, herein we report the results of a scoping review of health equity in hematology. We aimed to gather published articles together to highlight hematology health equity works, note areas that would benefit from more attention and inspire efforts to spearhead projects characterizing/addressing disparities. Methods: We applied PRISMA-P extension guidelines to develop/publish a scoping review protocol, https://osf.io/exuzc. Articles were eligible if focused on health equity in hematology and published in one of 22 hematology journals 1/1/18-3/13/24 or 46 oncology/medicine/pediatrics journals 1/1/18-4/4/24 which had an impact factor >9.2 or were listed in Clarivate's Journal Citation Reports first quartile or were a major hematology/oncology society's official journal. A research librarian crafted a search to retrieve all articles with ≥1 health equity subject heading or ≥1 title/abstract health equity keyword from a predefined list. Articles underwent title/abstract then full text review and data extraction by 2 independent team members. All extracted data were published to docs.google.com/spreadsheets/d/1rFeGrNVdIDnYEzL9KPB1rqyZUafWGcoN11FsKGUzyCY. Results: The search returned 5,494 articles; 1,009 underwent full-text screening with 602 included in the review. The greatest number of articles were published in Pediatric Blood & Cancer (n=77) and Blood Advances (n=65), with the highest proportions in Lancet Haematology (3.3%) and Transplantation and Cellular Therapy (3.1%). Most articles were about patients, with 64 (11%) about blood/stem cell donors and 26 (4%) the workforce. 132 (22%) had a pediatric/adolescent/young adult focus. 262 (44%) were retrospective studies, with 116 (19%) prospective/trials and 23 studying basic science correlates of disparities. Only 64 (11%) described interventions to advance equity, 20 (3%) social determinants of health data collection process improvement and 1 health equity educational initiatives. Of 427 research articles (ie excluding 175 editorials/reviews), nearly half (204/427, 48%) reported disparities in disease phenotype/outcomes, with 69/427 (16%) on access to care. Of 562 disease-related articles, most (372/562, 66%) examined hematologic malignancies or BMT/cell therapy with only one third (190/562, 34%) about classical hematologic conditions. Most articles (342/602, 57%) focused on racial/ethnic disparities, with 239 (40%) on socioeconomics; 87 (14%) sex/parity; 65 (11%) geographic; 54 (9%) age; and 46 (8%) sexual/gender minorities (22 on transgender peoples). Less than a third (179/602, 30%) considered intersectionality across disparities. Only 12 focused on Indigenous peoples, 5 disability, 8 religion, 9 immigration and 2 incarceration. Only 8 noted partnerships with the populations impacted. Analyzing by year from 2018-2023, health equity articles increased over time, with nearly two thirds (356/545, 65%) of included articles published 2021-2023 versus one third (189/545, 35%) 2018-2020, p<.001. Focusing on the 22 included hematology journals, 491 health equity articles were published 2018-2023, reflecting 1.1% of all articles published in those journals over these years. Hematology journals also had a 75% increase in the proportion of health equity articles in the recent vs earlier periods (0.8% vs 1.4%, p<.001). Conclusion: Our analysis, the first of its kind to our knowledge, highlights recent progress characterizing inequities and clarifies areas warranting further study (eg disparities impacting understudied populations with consideration of intersectionality, across classical and malignant hematologic disorders, and testing interventions to address disparities, partnering with advocates from populations impacted). We also establish benchmarks for the proportion of health equity articles published, within/across journals. This review will serve as an important educational resource to advance equity, helping researchers and the field in general to assimilate the rapidly expanding hematology health equity literature
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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 | 0.081 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.154 | 0.155 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".