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Prevalence and predictors of depression, anxiety and stress symptoms among pregnant women during COVID-19-related lockdown in Abakaliki, Nigeria (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.08.30.20184697
ABSTRACT

Introduction:

Several studies on COVID-19 and pregnancy have been published recently, but few studies have evaluated the impact of this pandemic on maternal mental health particularly in low-resource setting.

Aim:

To determine the prevalence and predictors of COVID-19-related depression, anxiety and stress symptoms among pregnant women. Materials and

methods:

This was a questionnaire-based cross-sectional study conducted among 456 pregnant women attending prenatal care at Abakaliki, Nigeria during COVID-19-related lockdown. They were screened for psychological morbidities using DASS 21 (Depression, Anxiety and Stress Scale).

Results:

Severe and extremely severe depression were reported in 33 (7.2%) and 29 (6.4%) participants respectively. 15 (3.3%) and 35 (7.7%) women had severe and extremely severe anxiety respectively. 105 (23%) had severe anxiety whereas 76 (16.7%) reported extremely severe stress. Multiparity (2 - 4) and occupations such as trading and farming were predictors of depression whereas grandmultiparity, urban residence and trading were identified as predictors of anxiety and stress.

Conclusion:

Depression, anxiety and stress symptoms were relatively common among pregnant women during COVID-19-related lockdown in Abakaliki, Nigeria. There is a need to integrate screening for depression, anxiety and stress in existing antenatal care programs so as to identify and prevent long term adverse psychological outcome related to COVID-19 pandemic.
Subject(s)

Full text: Available Collection: Preprints Database: medRxiv Main subject: Anxiety Disorders / Depressive Disorder / COVID-19 Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: Anxiety Disorders / Depressive Disorder / COVID-19 Language: English Year: 2020 Document Type: Preprint