Knowledge of Ovarian Cancer Risk Factors and Symptoms Among Iraqi Women in Baghdad: A Cross-Sectional Study

Background: Ovarian cancer is a significant global health concern, often diagnosed at advanced stages due to the absence of specific symptoms and the limited awareness of risk factors and symptoms among the general population. This study aimed to assess the knowledge of ovarian cancer risk factors and symptoms among Iraqi women in Baghdad and identify socio-demographic predictors of knowledge. Methods: A cross-sectional study was conducted among 492 Iraqi women in Baghdad using a self-administered questionnaire. The questionnaire contained sections on socio-demographic characteristics, knowledge of ovarian cancer risk factors, and symptoms. Data analysis involved the use of descriptive statistics and chi-square tests to ascertain associations between socio-demographic variables and knowledge levels. Results: The results indicated that the majority of participants exhibited limited knowledge of ovarian cancer risk factors and symptoms. Key risk factors, such as aging (81 slightly familiar) and family history (73 slightly familiar), were poorly recognized. Additionally, misconceptions were observed, with some participants identifying non-risk factors, such as benign ovarian cysts, as risk factors. Symptoms like pelvic pain (89% familiar) and bloating (97% familiar) were moderately recognized, while others, such as fatigue and loss of appetite, were less familiar. Significant predictors of knowledge included higher education (p = 0.003 for risk factors; p = 0.045 for symptoms) and urban residency (p = 0.003 for both risk factors and symptoms). Conclusion: This study underscores substantial gaps in knowledge of ovarian cancer risk factors and symptoms among Iraqi women in Baghdad, with education and urban residency emerging as key predictors. The findings underscore the imperative for targeted educational initiatives, particularly among rural and less-educated populations, to enhance awareness, facilitate early detection, and optimize outcomes for ovarian cancer.