Prevalence and correlates of female sexual dysfunction and sexual distress in reproductive-aged women: a systematic review and meta-analysis
Notice bibliographique
Résumé
Abstract Background Female sexual dysfunction (FSD) is defined as any dissatisfaction with sexual function domains such as sexual desire/arousal disorder, orgasmic disorders, and genital-pelvic pain/penetration disorder that in some cases leads to sexual distress (SD). Objective This systematic review and meta-analysis aimed to examine the prevalence and associated factors of FSD and SD among healthy reproductive-aged women. Methods In this systematic review and meta-analysis, Google Scholar and electronic databases such as Scopus, ScienceDirect, PubMed, Web of Science, and the Iranian database including the Scientific Information Database (SID), were searched and the publication year of the included articles were limited to January 1, 2015, through January 1, 2024 to identify studies that assessed FSD and SD among reproductive-aged women. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale for cross-sectional and cohort studies. Results Twenty studies were included in this review. According to the results of 18 studies, the prevalence of FSD ranged between 20.6% and 95.0%. In addition, the prevalence of sexual desire disorder (SDD) was estimated between 8.0% and 91.0% based on the results of 16 studies. The prevalence of arousal disorder (9.0–91.0%), orgasm disorder (7.9–93.0%), lubrication problems (9.3–99.0%), pain disorder (8.2–99.0%), and satisfaction (21.4–86.0%). SD prevalence was 31.8–83.4%. Meta-analysis revealed a pooled FSD prevalence of 47.81% (95% CI: 39.19–56.43%). The factors related to FSD were classified into five main categories: sociodemographic, reproduction, interpersonal, psychological, and medical factors. Also, the associated factors of SD were classified into four categories: sociodemographic, interpersonal, psychological, and medical factors. The risk of bias for all of the included studies was good. Conclusion The results of this study showed that reproductive-aged women are at high risk of FSD and SD due to various factors. As the literature regarding SD among the reproductive-aged population was limited, conducting high-quality cross-sectional studies with representative samples and using validated questionnaires are required to provide more precise data regarding its prevalence and associated factors across diverse cultural contexts. Trial registration PROSPERO; Registration no. CRD42024491942.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 tête enseignante, 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 ».