2 research outputs found

    Prevalence and predictors of postpartum depression among postnatal women in Lagos, Nigeria

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    Background: Globally, postpartum depression is one of the most common but often unrecognized complications of childbirth, yearly affecting about 10\u201315% of postnatal women. This study aimed to determine the prevalence of postpartum depression and its predictors among postnatal women in Lagos. Methods: A descriptive cross-sectional study was conducted among 250 mothers in Eti-Osa Local Government Area of Lagos State, Nigeria, attending six Primary Health Care centers for infant immunization at six weeks post-delivery. Data was collected using a pretested semi-structured interviewer administered questionnaire which included the Edinburgh Postnatal Depression Scale. Analysis was carried out using SPSS version 23TM. Chi-square and logistic regression analyses were used to determine associations and predictive relationships between various factors and the presence of postpartum depression. The level of significance was set at <0.05. Results: The prevalence of postpartum depression was 35.6%. Multiparity, delivery by cesarean section, mother being unwell after delivery, and not exclusively breastfeeding the baby were the factors linked with postpartum depression. Following multiple logistic regression, having postpartum blues (p=0.000; OR=32.77; 95%CI=7.23-148.58)., not getting help with caring for the baby (p=0.008; OR=2.64; 95%CI=1.29-5.42), experiencing intimate partner violence (p=0.000; OR=5.2; 95%CI=2.23-11.91) and having an unsupportive partner (p=0.018; OR=2.6; 95%CI=1.17-5.78) were identified as predictors of postpartum depression. Conclusion: This study revealed a high prevalence of postpartum depression, identifying both the obstetric and psychosocial predictors. Social support for women both in the pre- and postnatal periods and routine screening of women for postpartum depression should be encouraged for early detection and immediate intervention

    Diagnosing malaria from some symptoms: a machine learning approach and public health implications

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