CHINTA Research Bangladesh (Centre for Health Innovation, Networking, Training, Action and Research - Bangladesh), Savar, Dhaka, Bangladesh; Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, Bangladesh.
Department of Child Health and the Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, United States.
Sleep Med. 2022 Mar;91:219-225. doi: 10.1016/j.sleep.2021.04.025. Epub 2021 Apr 26.
In a densely populated country like Bangladesh, mental health-related burden and associated adverse outcomes are quite prevalent. However, exploration of sleep-related issues in general, and more specifically of insomnia during the COVID-19 pandemic has been scarce and restricted to a single location. The present study investigated the prevalence of insomnia and its predictive factors in the general population, and included Geographic Information System (GIS) analysis to identify regional heterogeneities of insomnia in Bangladesh.
This cross-sectional study was conducted during the early period of the COVID-19 pandemic. Information related to socio-demographics, knowledge of COVID-19, behaviors related to COVID-19, fear of COVID-19, and insomnia were included in a questionnaire, and coupled with GIS-based spatial analysis to identify regional susceptibility to insomnia.
Approximately 30.4%, 13.1% and 2.8% of participants reported sub-threshold, moderate, and severe forms of insomnia, respectively. Independent predictive risk factors of insomnia symptoms included female gender, college education, urban residence, presence of comorbidities, using social media, taking naps during daytime, and fear of COVID-19. District-wide variations in the spatial distribution of fear of COVID-19 and insomnia were significantly associated.
Insomnia is frequently present during a pandemic, and exhibits regional variability along with multifactorial determinants. These analytic approaches should enable improved detection and targeting of at-risk sectors of the population, and enable implementation of appropriate measures to ensure improved sleep quality.
在孟加拉国这样人口密集的国家，与心理健康相关的负担和相关不良后果相当普遍。然而，对睡眠相关问题的探索，特别是在 COVID-19 大流行期间的失眠问题，一直很少且仅限于一个地点。本研究调查了普通人群中失眠的患病率及其预测因素，并包括地理信息系统 (GIS) 分析，以确定孟加拉国失眠的区域异质性。
这是一项在 COVID-19 大流行早期进行的横断面研究。问卷中包含了与社会人口统计学、COVID-19 知识、与 COVID-19 相关的行为、对 COVID-19 的恐惧以及失眠相关的信息，并结合 GIS 空间分析来识别失眠的区域易感性。
约 30.4%、13.1%和 2.8%的参与者分别报告存在亚阈值、中度和重度失眠症状。失眠症状的独立预测风险因素包括女性、大学教育、城市居住、合并症、使用社交媒体、白天小睡和对 COVID-19 的恐惧。对 COVID-19 和失眠恐惧的全区分布的空间差异具有显著相关性。
在大流行期间失眠很常见，并且存在与多因素决定因素相关的区域变异性。这些分析方法应能够提高对高危人群的检测和定位能力，并能够实施适当的措施来确保改善睡眠质量。
