Prevalence and knowledge of polycystic ovary syndrome (PCOS) and health-related practices among women of Syria: a cross-sectional study.
Haidara BohsasHidar AlibrahimSarya SwedYasmeen AbouainainAhmed AljabaliLazaward KazanYazan Khair Eldien JabbanQasim MehmoodBisher SawafNourhan EissaMeriam AlkasemYasmine EdreesIvan Cherrez-OjedaSherihan FatheyGowhar RashidWael HafezElrashed AbdElrahimHamid OsmanTalha Bin EmranRefat Khan PathanMayeen Uddin KhandakerPublished in: Journal of psychosomatic obstetrics and gynaecology (2024)
Polycystic Ovarian Syndrome (PCOS) is a prevalent metabolic and hormonal disorder affecting women of reproductive age. Limited data exists on Syrian women's PCOS awareness and health behaviors. This study aimed to gauge PCOS prevalence, knowledge, awareness, and health-related practices among Syrian women. A cross-sectional online survey was conducted from 11 February to 27 October 2022, targeting Syrian women aged 18-45. Collaborators from specific medical universities distributed a questionnaire adapted from a Malaysian paper through social media platforms. Out of 1840 surveyed Syrian women, 64.2% were aged 21-29, and 69.6% held bachelor's degrees. Those with a bachelor's degree exhibited the highest mean knowledge score (12.86), and women previously diagnosed with PCOS had a higher mean knowledge score (13.74) than those without. Approximately 27.4% were confirmed PCOS cases, and 38.9% had possible cases. Women with PCOS were 3.41 times more likely to possess knowledge about the condition. The findings suggest a moderate level of PCOS knowledge and health-related practices among Syrian women, emphasizing the need for increased awareness. Consistent local PCOS screening programs, in collaboration with obstetrics and gynecology professionals, are crucial for improving understanding and clinical symptom recognition of this condition among Syrian women.
Keyphrases
- polycystic ovary syndrome
- insulin resistance
- healthcare
- social media
- primary care
- adipose tissue
- risk factors
- type diabetes
- health information
- metabolic syndrome
- machine learning
- pregnant women
- risk assessment
- cross sectional
- case report
- pregnancy outcomes
- deep learning
- artificial intelligence
- data analysis
- cervical cancer screening
- patient reported