Determinants of Poor Sleep Quality During the COVID-19 Pandemic Among Women Attending Antenatal Care Services at the Health Facilities of Debre Berhan Town, Ethiopia: An Institutional-Based Cross-Sectional Study

  • Frontiers in Psychiatry, Volume 13, Year 2022

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Study Justification:
– The study aimed to assess the proportion of poor sleep quality during the COVID-19 pandemic among pregnant women attending antenatal care services.
– Understanding the determinants of poor sleep quality is important for identifying at-risk populations and developing interventions to improve sleep quality during stressful situations like the pandemic.
– This study provides valuable insights into the prevalence and factors associated with poor sleep quality among pregnant women, which can inform healthcare providers and policymakers in developing appropriate strategies to address this issue.
Study Highlights:
– The study found that the overall prevalence of poor sleep quality among pregnant women during the pandemic was 62.8%.
– Advanced maternal age, being in the third trimester, being multigravida, and having comorbidity were identified as determinants of poor sleep quality.
– These findings highlight the need for targeted interventions and support for pregnant women, especially those who are older, in the later stages of pregnancy, and with comorbidities, to improve their sleep quality during the COVID-19 pandemic.
Recommendations for Lay Readers:
– Pregnant women should prioritize their sleep and take steps to improve sleep quality, such as maintaining a regular sleep schedule, creating a comfortable sleep environment, and practicing relaxation techniques.
– Healthcare providers should screen pregnant women for sleep disturbances and provide appropriate support and interventions to improve sleep quality.
– Policy makers should consider incorporating sleep health promotion programs into antenatal care services and develop policies that address the unique sleep needs of pregnant women during the pandemic.
Recommendations for Policy Makers:
– Develop and implement sleep health promotion programs as part of antenatal care services to educate pregnant women about the importance of sleep and provide strategies to improve sleep quality.
– Allocate resources to train healthcare providers on identifying and addressing sleep disturbances among pregnant women.
– Incorporate sleep-related assessments and interventions into existing maternal health programs and policies.
– Collaborate with relevant stakeholders, such as public health institutions and community organizations, to raise awareness about the impact of poor sleep quality on maternal and fetal health and implement targeted interventions.
Key Role Players:
– Healthcare providers: Midwives, obstetricians, and other healthcare professionals involved in antenatal care services.
– Policy makers: Representatives from the Ministry of Health, Department of Maternal and Child Health, and other relevant government agencies.
– Researchers: Sleep experts, epidemiologists, and researchers specializing in maternal health.
– Community organizations: Non-governmental organizations, women’s groups, and community health workers involved in maternal health promotion.
Cost Items for Planning Recommendations:
– Training programs for healthcare providers on sleep health promotion: Includes costs for developing training materials, conducting training sessions, and monitoring the implementation of acquired knowledge.
– Development and dissemination of educational materials for pregnant women: Includes costs for designing and printing brochures, posters, and other educational materials.
– Implementation of sleep-related assessments and interventions in antenatal care services: Includes costs for integrating sleep assessments into existing healthcare systems, providing counseling services, and monitoring the effectiveness of interventions.
– Awareness campaigns: Includes costs for organizing workshops, seminars, and community outreach programs to raise awareness about the importance of sleep during pregnancy and the available support services.
– Research and evaluation: Includes costs for conducting further research to assess the effectiveness of interventions and evaluate the impact of sleep health promotion programs on maternal and fetal outcomes.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on an institutional-based cross-sectional study conducted in Debre Berhan Town, Ethiopia. The sample size was determined using appropriate statistical methods. The study used a validated tool, the Pittsburgh Sleep Quality Index, to assess sleep quality. The data collection process was described, including the training of data collectors and the checking of data consistency and completeness. The statistical analysis included both descriptive and inferential statistics, with a binary logistic regression model used to identify factors associated with poor sleep quality. The study was approved by the Institutional Review Board and obtained written informed consent from participants. However, the abstract does not provide information on the representativeness of the sample or the response rate. Additionally, the abstract does not mention any limitations of the study or potential sources of bias. To improve the evidence, it would be helpful to include information on the representativeness of the sample and the response rate, as well as a discussion of limitations and potential sources of bias.

Background: Women’s ability to get sleep can be affected by pregnancy-related hormonal changes or other external stressful situations like the coronavirus disease 2019 (COVID-19). Objective: The objective of this study was to assess the proportion of poor sleep quality during the COVID-19 pandemic and its determinants among pregnant women attending antenatal care (ANC) services. Methods: An institutional-based cross-sectional study was conducted among 423 women attending ANC services at the health facilities in Debre Berhan Town, Ethiopia, from May to June 2020. A systematic random sampling technique was used to select the required samples. The tool consisted of questions that assessed (1) socio-demographic characteristics, obstetric and health care service-related characteristics; and media exposure to get information regarding COVID-19 infection; (2) To assess sleep quality; the Pittsburgh Sleep Quality Index (PSQI) was applied. And a global score of >5 indicates poor sleep quality, and a global score of ≤5 indicates good sleep quality. Result: The overall prevalence of poor sleep quality was 62.8%, and was associated with pregnant women aged ≥46 years (AOR = 4.27), being in the third trimester (AOR = 2.51), being multigravida (AOR = 2.72), and having co-morbidity (AOR = 3.57). Conclusion: The prevalence of poor sleep quality among pregnant women during the pandemic was found to be high. Advanced maternal age, third trimester pregnancy, being multigravida, and having comorbidity were determinants of poor sleep quality among pregnant women during the COVID-19 pandemic.

An institutional-based cross-sectional study was conducted from May 1 to June 1, 2020, in Debre Berhan Town public health institutions. The source populations for the study were all pregnant women who are attending antenatal care services in Debre Berhan town. All pregnant women who are attending antenatal care services in the Town during the study period and fulfill the inclusion criteria were included as the study population. The sample size was determined by using the single population proportion formula with the assumption of 50% poor sleep quality, a 95% confidence interval, and a 5% marginal error. After adding a 10% non-response rate, the final sample size was 423. In this study, pregnant women who visited the public health institutions in Debre Berhan Town for ANC services were included in the study. And pregnant women who were unable to communicate effectively due to serious illness were excluded from the study. To select our study participants, all public health facilities in Debre Berhan town were considered, and then based on the number of pregnant women that visited the public health facilities during the preceding month before data collection, proportional allocation of the total sample size was carried out to get the required sample from each public health facility. Finally, the determined samples were selected with a mean age of 28 years (SD ± 4.86) by a systematic random sampling technique. Pretested and interviewer-administered questionnaires were used for the whole survey. The tool consisted of 33 items categorized in to two sections, (1) socio-demographic characteristics, obstetric and health care service-related characteristics; and media exposure to get information regarding COVID-19 infection with a total of 14 items; (2) items to assess sleep quality by the Pittsburgh Sleep Quality Index (PSQI). The Pittsburgh Sleep Quality Index contains 19 Likert-type and open-ended questions. Respondents were asked about their overall sleep quality and how frequently they had experienced certain sleep difficulties in the previous month. The 19 items were combined to form seven component scores, each of which had a range of 0–3, with a higher score indicating more acute sleep disturbances. Then, the seven component scores were added to yield a single global score ranging from 0 to 21, with the higher score indicating severe sleep difficulties in all areas. PSQI developers have suggested a cutoff score of 5 for the global scale as it was 88.5% valid to correctly identify the problem (27–29). The Cronbach alpha of PSQI in the current study was 0.72. Furthermore, the data was collected by trained BSc midwives, and the consistency and completeness of the data were checked daily by supervisors. Is defined based on the PSQI score; hence, a global score of >5 indicates poor sleep quality, and a global score of ≤5 indicates good sleep quality (27). Women who had access to either television, radio, or read newspapers at least once a week was considered exposed to the media. Is defined as the co-existence of diagnosed chronic medical conditions like asthma, diabetes mellitus, heart disease, hypertension, depression, cancer, and chronic kidney disease among pregnant women (30). The data was first entered into EPI INFO™ 7 and then exported to STATA version 14, statistical software for analysis. Frequencies and cross-tabulations were applied to summarize descriptive statistics of the data, and tables were used for data presentation. A binary logistic regression model was used to identify factors associated with poor sleep quality. Those variables with a p-value less than or equal to 0.2 from the bi-variable analysis were candidates for multivariable analysis. Variables with a p-value of less than 0.05 in multivariable analysis were declared as statistically significant factors for poor sleep quality. Moreover, the association was measured using odds ratios with a 95% confidence interval. Model fitness was also checked by the Hosmer-Lemeshow goodness of fit test (P-value = 0.491). This study was approved by the Institutional Review Board (IRB) of Debre Berhan University and an official permission letter was gained from the concerned body. Written informed consent was obtained from each participant before conducting the actual data collection process. Additionally, confidentiality was maintained by avoiding registration of personal identifiers and no raw data was given to anyone other than the investigator.

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Based on the provided information, here are some potential innovations that can be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can allow pregnant women to receive antenatal care remotely, reducing the need for in-person visits and improving access to healthcare services, especially for women in remote or underserved areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take an active role in their own healthcare and improve access to maternal health information.

3. Community health workers: Training and deploying community health workers who can provide basic antenatal care services, education, and support to pregnant women in their communities can help bridge the gap in access to healthcare services, particularly in rural or low-resource settings.

4. Transportation support: Establishing transportation support systems, such as providing subsidized or free transportation services, can help pregnant women overcome barriers related to distance and transportation costs, ensuring they can access timely and necessary maternal health services.

5. Maternal health clinics in remote areas: Setting up maternal health clinics in remote or underserved areas can bring essential antenatal care services closer to pregnant women, reducing the need for long-distance travel and improving access to care.

6. Maternal health hotlines: Establishing dedicated hotlines staffed by healthcare professionals can provide pregnant women with immediate access to medical advice, support, and information, ensuring they receive timely assistance and guidance.

7. Mobile clinics: Deploying mobile clinics equipped with necessary medical equipment and staffed by healthcare professionals can bring antenatal care services directly to communities, particularly in areas where healthcare facilities are scarce or inaccessible.

8. Health education campaigns: Conducting targeted health education campaigns to raise awareness about the importance of antenatal care, common pregnancy complications, and available healthcare services can empower pregnant women with knowledge and encourage them to seek timely care.

9. Partnerships with local organizations: Collaborating with local organizations, such as community-based organizations or non-governmental organizations, can help expand access to maternal health services by leveraging existing networks and resources.

10. Financial support programs: Implementing financial support programs, such as health insurance schemes or subsidies for maternal health services, can alleviate the financial burden associated with accessing antenatal care and improve affordability for pregnant women.

It’s important to note that the specific context and needs of the target population should be considered when implementing these innovations to ensure their effectiveness and sustainability.
AI Innovations Description
Based on the study titled “Determinants of Poor Sleep Quality During the COVID-19 Pandemic Among Women Attending Antenatal Care Services at the Health Facilities of Debre Berhan Town, Ethiopia: An Institutional-Based Cross-Sectional Study,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement sleep education programs: Develop and implement educational programs that focus on promoting good sleep hygiene and providing information on the importance of sleep during pregnancy. These programs can be integrated into antenatal care services and conducted by healthcare providers or trained sleep specialists.

2. Telemedicine for sleep consultations: Establish telemedicine services that allow pregnant women to consult with sleep specialists remotely. This can improve access to sleep-related healthcare services, especially for women in remote areas or those who face barriers to accessing in-person consultations.

3. Mobile applications for sleep tracking and support: Develop mobile applications that provide pregnant women with tools to track their sleep patterns, receive personalized sleep recommendations, and access educational resources on sleep during pregnancy. These applications can also include features for connecting with healthcare providers or sleep specialists for additional support.

4. Collaborate with community organizations: Partner with community organizations, such as women’s groups or local health centers, to raise awareness about the importance of sleep during pregnancy and provide resources for improving sleep quality. This can include organizing workshops, distributing educational materials, and facilitating support groups for pregnant women.

5. Integration of sleep assessment in routine antenatal care: Incorporate sleep assessment as a routine component of antenatal care visits. Healthcare providers can use validated sleep assessment tools, such as the Pittsburgh Sleep Quality Index (PSQI), to identify pregnant women who may be experiencing poor sleep quality and provide appropriate interventions or referrals.

6. Training healthcare providers on sleep management: Provide training to healthcare providers, including midwives and obstetricians, on sleep management during pregnancy. This can enhance their knowledge and skills in addressing sleep-related issues and providing evidence-based recommendations to pregnant women.

7. Research and innovation funding: Allocate funding for research and innovation in the field of maternal sleep health. This can support the development of new interventions, technologies, and approaches to improve access to maternal sleep health services and promote better sleep outcomes for pregnant women.

By implementing these recommendations, access to maternal health can be improved by addressing the issue of poor sleep quality during pregnancy, ultimately leading to better overall health outcomes for both mothers and babies.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Telemedicine and Telehealth Services: Implementing telemedicine and telehealth services can provide pregnant women with remote access to healthcare professionals, allowing them to receive prenatal care, consultations, and support without the need for in-person visits.

2. Mobile Health (mHealth) Applications: Developing and promoting mobile health applications specifically designed for maternal health can provide pregnant women with information, reminders, and tools to monitor their health during pregnancy. These apps can also facilitate communication with healthcare providers and offer access to educational resources.

3. Community Health Workers: Expanding the role of community health workers can help improve access to maternal health services, especially in remote or underserved areas. These workers can provide education, support, and referrals to pregnant women, ensuring they receive appropriate care throughout their pregnancy.

4. Transportation Support: Addressing transportation barriers by providing transportation support, such as subsidized or free transportation services, can help pregnant women reach healthcare facilities for prenatal visits, delivery, and postnatal care.

5. Maternal Health Education Programs: Implementing comprehensive maternal health education programs can empower pregnant women with knowledge about prenatal care, nutrition, hygiene, and warning signs during pregnancy. These programs can be conducted through community workshops, mobile apps, or online platforms.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific group of pregnant women who would benefit from the recommendations, considering factors such as location, socioeconomic status, and healthcare accessibility.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population, including factors such as the number of prenatal visits, distance to healthcare facilities, and utilization of available resources.

3. Develop a simulation model: Create a simulation model that incorporates the potential recommendations and their expected impact on access to maternal health. This model should consider factors such as the number of women reached, the frequency of utilization, and the expected outcomes (e.g., increased prenatal care visits, improved health outcomes).

4. Input data and parameters: Input the collected baseline data and relevant parameters into the simulation model. This may include data on population size, healthcare facility locations, transportation availability, and the effectiveness of the recommended interventions.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to estimate the potential impact of the recommendations on improving access to maternal health. This could involve varying factors such as the coverage of telemedicine services, the number of community health workers deployed, or the level of transportation support provided.

6. Analyze results: Analyze the simulation results to determine the projected changes in access to maternal health services. This could include metrics such as the increase in the number of prenatal visits, reduction in travel time to healthcare facilities, or improvements in health outcomes for pregnant women.

7. Validate and refine the model: Validate the simulation model by comparing the projected results with real-world data, if available. Refine the model based on feedback and further research to improve its accuracy and reliability.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health. This information can guide decision-making and resource allocation to prioritize interventions that are most likely to have a positive impact on maternal health outcomes.


Statistics:
Citations: 2
Identifiers:
DOI: 10.3389/fpsyt.2022.841097
Research Areas:
Cancer, COVID, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce
Study Countries:
Ethiopia
Study Design:
Cross Sectional Study, Grounded Theory
Study Approach:
Quantitative
Participants Gender:
Female
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