The development of an ultra-short, maternal mental health screening tool in South Africa

Abstract

Purpose.The burden of common perinatal mental disorders (CPMD) in low-and-middle-income countries is substantially higher than high-income countries, with low levels of detection, service provision and treatment in resource-constrained settings. We describe the development of an ultra-short screening tool to detect antenatal depression, anxiety disorders and maternal suicidal ideation.Methods.A sample of 376 women was recruited at a primary-level obstetric clinic. Five depression and anxiety symptom-screening questionnaires, demographics and psychosocial risk questionnaires were administered. All participants were assessed with the Mini-International Neuropsychiatric Interview (MINI), a structured, diagnostic interview. Screening tool items were analysed against diagnostic data using multiple logistic regression and receiver operating curve (ROC) analysis.Results.The prevalence of MINI-defined major depressive episode (MDE) and/or anxiety disorders was 33%. Overall, 18% of participants expressed suicidal ideation and behaviour, 54% of these had no depression or anxiety diagnosis. Multiple logistic regression identified four screening items that were independently predictive of MDE and anxiety disorders, investigating depressed mood, anhedonia, anxiety symptoms and suicidal ideation. ROC analysis of these combined items yielded an area under the curve of 0.83 (95% CI 0.78–0.88). A cut-off score of 2 or more offered a sensitivity of 78% and specificity of 82%.Conclusion.This novel screening tool is the first measure of CPMD developed in South Africa to include depressed mood, anxiety symptoms and suicidal ideation. While the tool requires further investigation, it may be useful for the early identification of mental health symptoms and morbidity in the perinatal period.

Cultural Adaptation of Community Informant Tool for Detection of Maternal Depression in Rural Pakistan

“…During feedback from the end-users, it was observed that the tool was easy to use due to its simple questions, binary (Yes/No) responses, and the use of glass analogy, which were less time consuming. It was similar to the previous findings for an ideal detection tool ( 26 ). During the finalization of the draft tool with mental health experts the domains of adaptation such as comprehensibility, relevance, and motivation were all taken into consideration as the modified draft was based on these three domains ( 27 – 29 ).…”

Reliability and validity of a perinatal depression screening instrument in rural Mali

“…depression and anxiety, are estimated to be experienced by 15.6% of pregnant women and 19.8% of women who have recently given birth in low- and middle-income countries (LMIC) ( Fisher et al, 2012 ). Risk factors for CPMDs in LMIC include low educational attainment, poverty, mood changes during pregnancy, intimate partner violence, unemployment, and poor newborn health ( Abiodun, 2006 ; Adewuya, 2006 ; Brown et al, 2020 ; Ebeigbe & Akhigbe, 2008 ; Fekadu Dadi et al, 2020 ; Garman et al, 2019 ; Gausman et al, 2020 ; Patel, 2007 ; Rahman et al, 2013 ; van Heyningen et al, 2019 ; Weobong et al, 2015 ). Beyond causing suffering and impairing women’s functioning, negative health and developmental outcomes have been observed among children of women with CPMDs ( Bass et al, 2008 ; Fekadu Dadi et al, 2020 ; Gureje et al, 2015 ; Santoro & Peabody, 2010 ; Shidhaye & Giri, 2014 ) including malnutrition, stunting, elevated rates of illness, and cognitive and emotional delays ( Mughal et al, 2019 ; Surkan et al, 2011 ; Walker et al, 2007 ).…”

Perinatal depression screening using smartphone technology: Exploring uptake, engagement and future directions for the MGH Perinatal Depression Scale (MGHPDS)

“…To date, use of a mobile phone application to screen for perinatal depression has been demonstrated to be feasible in a low resource environment in South Africa where community health workers administered the EPDS using mobile phones [ 19 ]. A 4-item screening tool for symptoms of common mental disorders and suicidality has been developed, validated and found to be effective at identifying pregnant women with symptoms of common mental disorders across commonly spoken languages and cultures in Cape Town, South Africa [ 37 , 38 ]. A recent review of the literature on digital technology for treating and preventing mental disorders in LMIC concluded that, while continued research is needed, findings across 49 studies were promising and demonstrated potential effectiveness of online, text-messaging, and telephone support interventions [ 39 ].…”