The Socioeconomic Landscape of Gestational Trophoblastic Disease: A Systematic Review of Risk, Presentation, and Outcomes
Notice bibliographique
Résumé
Introduction: Gestational Trophoblastic Disease (GTD) encompasses a spectrum of pregnancy-related disorders, from premalignant hydatidiform moles to malignant Gestational Trophoblastic Neoplasia (GTN). While highly curable with timely diagnosis and management, significant global disparities in incidence and mortality suggest a powerful role for socioeconomic factors. However, the precise nature of this relationship remains poorly defined in the literature. This systematic review aims to comprehensively examine and synthesize the evidence on the association between socioeconomic status (SES) and the etiological risk, clinical course, and ultimate outcomes of GTD. Methods: A systematic search was conducted in PubMed, Google Scholar, Semantic Scholar, Springer, Wiley Online Library for observational studies published in English up to January 2024. The search included studies that evaluated an association between at least one SES indicator (e.g., income, education, occupation, marital status, insurance) and a GTD-related risk or outcome. Data were extracted and synthesized narratively due to study heterogeneity. The methodological quality of included studies was rigorously assessed using the Newcastle-Ottawa Scale (NOS) for case-control and cohort studies and the Joanna Briggs Institute (JBI) checklist for cross-sectional studies. Results: Seventeen studies met the inclusion criteria, comprising case-control, cohort, and cross-sectional designs from diverse global settings. The evidence linking low SES to an increased primary risk of developing GTD was inconsistent and contradictory. While multiple descriptive studies in low- and middle-income countries (LMICs) reported a high proportion of cases among women with low income and education, a high-quality US-based case-control study found a significantly increased risk among women in professional occupations. In stark contrast, a strong and consistent association was found between lower SES and a wide array of adverse clinical outcomes. Indicators of socioeconomic disadvantage—including unemployment, unmarried/widowed status, low income, and residence in low-resource settings—were significantly associated with poorer prognosis, higher rates of loss to follow-up (up to 27%), delayed diagnosis, increased risk of chemoresistance, and decreased overall survival in patients with GTN. Discussion: The primary impact of SES in GTD appears to be as a powerful determinant of prognosis rather than a direct etiological risk factor. The link between SES and GTD risk is likely confounded by mediating factors such as nutrition and reproductive age patterns, which vary across socioeconomic strata. However, socioeconomic barriers directly impede a patient's ability to navigate the complex, costly, and prolonged clinical management required for a cure. Key mechanisms include financial toxicity from treatment and surveillance, lack of social and logistical support, structural barriers to accessing specialized healthcare, and lower health literacy, which collectively contribute to treatment non-adherence, disease progression, and worse survival outcomes. Conclusion: Socioeconomic deprivation is a critical and independent determinant of adverse outcomes in Gestational Trophoblastic Disease. While the disease is highly curable under optimal conditions, poverty and lack of social capital can transform it into a fatal condition by obstructing access to and completion of essential care. Clinical protocols and public health strategies must be designed to proactively identify and address these socioeconomic disparities to ensure equitable outcomes for all women affected by GTD.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».