A comparative analysis of the prevalence of suicidal ideation among depressed and non-depressed pregnant women in rural Bangladesh
Mumu, R. T.; Shaikh, M. P.; Mitra, D. K.
Show abstract
BackgroundMore than 300 million people all over the world succumbed to depressive disorders in 2015. 680 per 100,000 expectant mothers worldwide bear suicidal ideation during the antenatal period. Despite suicidal ideation being a consequence of antenatal depression, there is a scarcity of information on the prevalence of suicidal ideation in depressed and non-depressed pregnant women in rural Bangladesh. ObjectiveThis study is directed to evaluate the point prevalence of suicidal ideation and compare the prevalence between depressed and non-depressed pregnant women in rural Bangladesh. MethodA cross-sectional study was performed in Lohagara, a rural subdistrict in Bangladesh between January 08 and 17, 2024. 351 pregnant women of various trimesters were recruited for the study. The Bengali-translated version of the Edinburgh Postnatal Depression Scale (EPDS) and another structured questionnaire were used for data collection. Data analyses were done by STATA version 17. ResultThe point prevalence of suicidal ideation is 11.4% (95% CI: 8.5% to 15.2%). It reveals a similarity between depressed and non-depressed pregnant women. The prevalence of suicidal thoughts in antenatally depressed women accounts for 10.2% (95% CI: 6.1% to 16.6%) and in non-depressed pregnant women it is 12.2% (95% CI: 8.4% to 17.3%) -obtained after analysis. ConclusionThe considerable prevalence of suicidal thoughts among depressed and non-depressed rural pregnant women in Bangladesh underscores the necessity of ensuring additional counseling, care, and support to expectant mothers during their antepartum.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Health-Related Quality of Life and Coping Strategies adopted by COVID-19 survivors: A nationwide cross-sectional study in Bangladesh 97%
- Preterm Birth and Neonatal Mortality in Selected Slums in and around Dhaka City of Bangladesh: A Cohort Study 97%
- Fetal macrosomia and its associated factors among pregnant women delivered at national referral hospital in Uganda, a case-control study 97%
Similar papers in this journal
- Prevalence and associated factors of gender-based violence for female: Evidence from school students in Nepal- a cross sectional study 97%
- Predictors of voluntary uptake of modern contraceptive methods in rural Sindh, Pakistan 95%
- Knowledge, perception, and attitude toward premarital screening among university students in Kurdistan region- Iraq 95%
Similar papers in this journal
- Psychiatric Manifestations and Associated Risk Factors among Hospitalized Patients with COVID-19 in Edo State, Nigeria 97%
- Risk Factors for Non-Communicable Diseases among Bangladeshi Adults: An Application of Generalized Linear Mixed Model on Multilevel Demographic and Health Survey Data 96%
- Women’s Awareness of Obstetric Fistula and its Associated Factors Among Reproductive-Age Women in Ethiopia: A Multilevel Analysis Based on National Survey Data 95%
Similar papers in this journal
- Seroprevalence of SARS-CoV-2 in Niger State: A Pilot Cross Sectional Study 96%
- Impact on Mental Health of students due to restriction caused by COVID-19 pandemic: Cross-sectional study 95%
- Patterns of physical activity among the students of an Indian university and their perceptions about the curricular content concerned with health 94%
Similar papers in this journal
- Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 14th and 28th day of the disease course: a retrospective cohort study in Bangladesh 93%
- Impact of COVID-19 on the indigenous population of Brazil: A geo-epidemiological study 92%
- Is Nigeria really on top of COVID-19? Message from effective reproduction number 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.