Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Objectives To examine disparities between transgender and gender-diverse (TGD) and cisgender (CG) people through analysis of attendance rates for cancer screening and compare differences between types of cancer screened. Design Systematic review and meta-analysis. Data sources PubMed, EMBASE [via Ovid], CINAHL Complete [via EBSCO], and Cochrane Library from inception to 30 September 2023. Methods Studies for inclusion were case-control or cross-sectional studies with quantitative data investigating TGD adults attending any cancer screening services. Exclusion criteria were studies with participants ineligible for cancer screening or without samples from TGD individuals, qualitative data, and cancer diagnosis from symptomatic presentation or incidental findings. A modified Newcastle-Ottawa Scale was used to assess risk of bias and reports rated poor were excluded. Results were synthesised through random-effects meta-analysis and narrative synthesis. Results Searches identified 25 eligible records, whereby 18 met risk of bias requirements. These were cross-sectional studies, including retrospective chart reviews and survey analyses, and encompassed over 14.8 million participants. The main outcomes measured were up-to-date (UTD) and lifetime (LT) attendance. Meta-analysis found differences for UTD cervical (OR=0.37, 95% CI [0.23, 0.60], p<0.0001) and mammography screening (OR=0.41, 95% CI [0.20, 0.87], p=0.02). There were no meaningful differences seen in LT results. Pooling total odds ratios for each synthesis (cervical, breast, prostate, and colorectal cancer) showed reduced attendance in TGD participants (OR=0.50, 95% CI [0.37, 0.68], p<0.0001). Narrative synthesis of seven remaining articles supported meta-analysis results, finding generally reduced screening rates in TGD versus CG participants. Conclusions TGD individuals are overall less likely to utilise cancer screening compared to CG counterparts. The greatest disparity in attendance was seen specifically in UTD cervical screening. Limitations of this review included high risk of bias within studies, high heterogeneity, and a lack of resources for further statistical testing. Individual and structural factors such as psychological distress, socioeconomic status, and healthcare accessibility can prevent TGD people from accessing cancer screening. Bridging this gap will require consolidated efforts from healthcare systems including reviews of structural design, innovation of accessible and inclusive technology, education of HCPs, and reassessment of patient information resources. Joint production of future interventions with the TGD community is vital to improving both cancer screening experience and outcomes. Funding This work was supported by the INSPIRE grant generously awarded to the Hull York Medical School by the Academy of Medical Sciences through the Wellcome Trust [Ref: IR5\1018]. Systematic review registration PROSPERO CRD42022368911. KEY MESSAGES What is already known about this topic? Many transgender and gender-diverse (TGD) people experience difficulties accessing cancer screening and so face potentially increased risks in morbidity and mortality. What this study adds? This systematic review and meta-analysis investigated differences in attendance of cancer screening services between TGD and CG people and explored reasons underpinning present disparities. TGD individuals are less likely to attend cancer screening services overall, and are less likely to be up-to-date with breast and cervical cancer screening. How this study might affect research, practise of policy? To reduce inequities, individual and institutional barriers must be addressed through research, technological innovation, reviews of current structural design, and improved education. It is vital that future interventions for TGD people are jointly produced with the community to improve both cancer screening experience and outcomes.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| 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, 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".