Cancer screening in sexual and gender minority populations: A systematic review and meta-analysis.
Bibliographic record
Abstract
10540 Background: The sexual and gender minority (SGM) populations experience a greater cancer burden than their heterosexual or cisgender counterparts. Screening rates for cancer within this cohort are frequently suboptimal, highlighting notable deficiencies in screening recommendations. Inadequate culturally competent care and screening guidelines may contribute to delays in cancer diagnosis and treatment, ultimately impacting survival and quality of life. We sought to investigate the existing disparities in cancer screening among SGM populations. Methods: We conducted a systematic review and meta-analysis to assess the cancer screening rates in SGM. The SGM population included in this study were gays, lesbians, bisexuals, transgender men, and transgender women. Two reviewers conducted a systematic search of numerous databases, including PubMed, PsycINFO, and CINAHL, and then extracted relevant information from eligible studies. For this study, we conducted a meta-analysis using a random-effects model. Pooled estimates of the odds ratio were calculated for any combination of outcomes and population when at least two studies had relevant data. Study heterogeneity was assessed using I² statistics. Meta-regression, accounting for the study weight, year, and latitude, was performed on variables potentially associated with heterogeneity. Newcastle-Ottawa Scale was used to assess the quality of selected studies. Results: We analyzed data from a total of 60 eligible studies with 65,315 patients. Pooled analysis showed that sexual minority groups were at lower risk for cancer screening such as breast cancer screening (OR: 0.79, 95% CI, 0.75-0.82; Chi2=835.23; p<0.001; I2=97%), cervical cancer screening (OR: 0.62, 95% CI, 0.56-0.72; Chi2=926.23; p<0.001; I2=96%), colorectal cancer screening (OR: 0.51, 95% CI, 0.45-0.61; Chi2=3272.21; p<0.001; I2=98%), and prostate cancer screening (OR: 0.71, 95% CI, 0.63-0.82; Chi2=1282.92; p<0.001; I2=99%). The prevalence of lung cancer (p<0.001) and anal cancer screening (p<0.001) were also lower in sexual minority groups. The analysis also showed no small study effects (Egger test: 1.33; 95% CI: −5.42, −0.63; p=0.182). All the subgroups (geographical location, screening procedure, screening period) had high between-study heterogeneity. Conclusions: Our findings indicate that cancer screening rates for breast, cervical, colorectal, prostate, lung, and anal cell cancer are significantly lower in SGM populations and emphasize the urgent need for targeted interventions, culturally competent care, and inclusive screening guidelines to address these disparities. Understanding and addressing these issues are crucial for reducing delays in cancer diagnosis and improving survival and quality of life for SGM population. Further research and comprehensive strategies are warranted to bridge these gaps in cancer screening accessibility and uptake.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".