Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,021 | 0,041 |
| Bibliométrie | 0,008 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».