Antenatal depression and its potential causal mechanisms among pregnant mothers in Gondar town: Application of structural equation model

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Study Justification:
– The study aimed to explore the causal mechanisms underlying antenatal depression in Gondar, Ethiopia.
– There is a lack of understanding of which forms of stress lead to antenatal depression and through what mechanisms.
– Modeling stress processes within a theoretical framework can enhance understanding of the relationships between stressors and stress outcomes.
Study Highlights:
– The study included 916 pregnant women in their second and third trimesters in Gondar, Ethiopia.
– Sixty-three participants (6.9%) reported symptoms of depression.
– Unplanned pregnancy, history of common mental health disorder, and fear of giving birth were associated with higher depression scores.
– Adequate food access was associated with decreased depression scores.
– Social support, marital agreement, and partner support partially mediated the link between stressors and antenatal depression.
Study Recommendations:
– Early screening for antenatal depression should be implemented.
– Enhancing psychosocial resources, such as marital agreement, social support, and partner support, can help improve maternal resilience.
Key Role Players:
– Researchers and data collectors
– Health professionals and counselors
– Community leaders and organizations
– Policy makers and government officials
Cost Items for Planning Recommendations:
– Training and capacity building for health professionals and counselors
– Development and implementation of screening tools
– Awareness campaigns and community outreach programs
– Provision of support services for pregnant women
– Monitoring and evaluation of the interventions

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study used a large sample size and employed a structured questionnaire to collect data. The use of a Structural Equation Model (SEM) allowed for the exploration of direct and indirect effects of stressors on antenatal depression. However, there are a few areas that could be improved. First, the abstract does not provide information on the representativeness of the sample, which could affect the generalizability of the findings. Second, the abstract does not mention any limitations of the study, such as potential biases or confounding factors. Finally, the abstract does not provide any recommendations for future research or implications for practice. To improve the evidence, it would be helpful to include information on the representativeness of the sample, acknowledge any limitations of the study, and provide recommendations for future research or implications for practice.

Background: Various forms of life stressors have been implicated as causes of antenatal depression. However, there is a lack of understanding of which forms of stress lead to antenatal depression and through what mechanisms. Modeling stress processes within a theoretical model framework can enhance an understanding of the mechanisms underlying relationships between stressors and stress outcomes. This study used the stress process model framework to explore the causal mechanisms underlying antenatal depression in Gondar, Ethiopia. Methods: Questionnaires, using an Online Data collection Kit (ODK) tool were administered face-to-face in 916 pregnant women in their second and third trimesters. Pregnant women were included from six randomly selected urban districts in Gondar, Ethiopia during June and August 2018. The Edinburgh Postnatal Depression Scale (EPDS) was used to screen for antenatal depression. A Structural Equation Model (SEM) was employed to explore the direct, indirect, and total effect of stressors and mediators of antenatal depression. Result: Sixty-three participants (6.9%) reported symptoms of depression. Of these, 16 (4.7%) and 47 (8.1%) were in their second and third trimesters, respectively. The SEM demonstrated several direct effects on antenatal depression scores including unplanned pregnancy (standardized β = 0.15), having a history of common mental health disorder (standardized β = 0.18) and fear of giving birth to the current pregnancy (standardized β = 0.29), all of which were associated with a higher depression score. Adequate food access for the last 3 months (standardized β =-0.11) was associated with decreased depression score. Social support (β =-0.21), marital agreement (β =-0.28), and partner support (β =-.18) appeared to partially mediate the link between the identified stressors and the risk of antenatal depression. Conclusion: Both direct and indirect effects contributed to higher antenatal depression score in Ethiopian women. The three psychosocial resources namely marital agreement, social and partner support, mediated reduced antenatal depression scores. Early screening of antenatal depression and enhancing the three psychosocial resources would help to improve maternal resilience.

The current study was conducted in Gondar town, which is one of the administrative zones of Amhara Regional State, Northwest Ethiopia. Gondar town is in the Northern part of the Amhara region at 747 km away from Addis Ababa and 170 km from Bahirdar (the regional capital city). Gondar town has 12 kebeles (the smallest administrative units in the country) and in 2017/2018, the number of pregnancies in the town was expected to be 6450 [48, 49]. The town has one government-operated referral hospital, more than eight health centers, and more than 15 private medical clinics [50]. The study population included pregnant women living in the randomly selected districts and in their second and third trimester of pregnancy. A house-to-house visit was conducted to identify pregnant women who were willing to participate in this mother- child cohort study. If nobody was found at home during the initial recruitment visit and after three attempts, they were non-respondents. Identified participants were recruited and were followed until 12 weeks post-delivery. Ethical approval was obtained from the Social and Behavioral Research Ethics Committee (SBREC) of the Flinders University [51] and the Institutional Review Board of University of Gondar. A support letter was obtained from Gondar town mayoral office and the respective kebeles administration offices. Participants of the study were informed about the purpose, objectives, their right to decline participation or withdraw their participation. A written consent was then obtained. Privacy and confidentiality were maintained throughout the study. Women who were found to be seriously ill and fulfilled the following criteria were referred to University of Gondar Specialized Hospital Psychiatry unit for further diagnosis and treatment: an overall Edinburgh Postnatal Depression Scale (EPDS) score of 13 (those with ≥17 were excluded from the study for further follow up) and those who had a score 1, 2, 3 on item ten (a question about thoughts of self-harm) [52]. Structured and pre-tested electronic questionnaires were administered face-to-face in pregnant women aided by an online, Open Data collection Kit (ODK) application tool [53]. Open data collection kit is an application developed by the ODK community for collecting, managing, and using data in resource limited countries [54]. The prepared questionnaire was designed on an excel spreadsheet, converted to XLS format online, and checked for its validity using Enketo (a preview provided by ODK). The validated form was uploaded on a Lenovo 7 tablet. During collection, data were stored on the Google cloud platform. Nine qualified and registered nurses were trained as data collectors and were each provided with a Lenovo 7 tablet to administer the questionnaire to the participants. After completion of each questionnaire, the data collectors uploaded the data to Google Cloud and the principal investigator then directly downloaded the data from the system. The electronic based data collection was helpful in maintaining the quality and completeness of the data. The questionnaire collected socio-demographic information such as: age; sex; educational status (no formal education, grade 1–8, grade 9–12, diploma and above); income (low, medium, high); and marital status (single, married, separated). Information on maternal characteristics was also collected, including pregnancy intention (planned, unplanned); gestational weeks; previous history of either low weight, preterm or still birth; and previous history of a caesarian section delivery. Finally, the questionnaire collected information on psychosocial and behavioral characteristics, such as: social support (good, poor); partner support (always, most of the time, some of the time, rarely); stress coping ability (very rarely, rarely, sometimes, most of the time); coffee drinking (daily, sometimes, never); and cigarette exposure (yes, no). Antenatal depression was measured using the Edinburgh Postnatal Depression Scale (EPDS) developed by Cox and colleagues [52] and adapted for use in an Ethiopian context [55]. The EPDS, which is the most commonly used screening tool for antenatal depression [56–60], is a brief screening tool for symptoms of emotional distress during pregnancy that contains 10 specific questions with four Likert scale response options (most of the time, sometimes, not often, never) and is intended to measure the distress that pregnant women have experienced over the previous week. It is a simple and free to use tool, can be scored by simple addition and has been validated in urban settings of Ethiopia [61] with a sensitivity and specificity of 84.7 and 77.0%, respectively. The validated cut off value for possible depression in urban population in Ethiopia was 12 [43, 62, 63]. In the current study the EPDS demonstrated high reliability for the single construct of distress with an internal consistency (α) of 0.74. The Oslo Social Support Scale (OSSS-3) [64] was used to measure maternal social support during pregnancy. Although the tool has not been validated in the Ethiopian context, it has shown a good utility in various studies [62, 65]. OSSS-3 has three items measured by a few Likert scales, which are summed to 14 points and categorized as ‘poor’ if the total score is less than nine and ‘moderate’ to ‘strong’ support if the score is 9–14. In this study OSSS-3 demonstrated a high reliability for social support with an internal consistency of α = 0.76. Partner support was assessed by a question “My husband helps me a lot” with five response scales, “Always”, “Most of the time”, “Some of the time”, “Rarely”, and “Never”. Marital agreement was assessed by a question “How often do you discuss and agree with your husband in day to day life?” with a response category, “Most of the time”, “Some of the time”, “Rarely”, and “Never”. The women’s Middle-Upper Arm Circumference (MUAC) tape was used to measure nutritional status. MUAC is a validated and recommended tool for measuring nutritional status during pregnancy, with cutoff scores of 18–22 as ‘normal’ and 22.5 to 31 as ‘underweight’ [66]. Women were asked if they participated in moderate-intensity physical activity such as brisk walking, dancing, gardening, and usual housework for 2 to 3 h per week [67]. Exposure to cigarette smoking during pregnancy was assessed by the question, “Have you been smoking since your pregnancy or has there been anybody who smokes near you in your home or in your workplace?” [15]. To assess coffee exposure, we asked “How often do you drink coffee after your pregnancy?” if her answer was “daily” or “sometimes in a week”, she was labeled as exposed to coffee drinking and if not, she was labeled as non-exposed [68, 69]. Women’s health condition was assessed using the question “How do you rate your daily general health condition?” with response options of “Very good”, “Good” or “Poor”. A women’s stress coping level was assessed using the four customized internally-consistent coping subscales of the Perinatal Coping Inventory (PCI-4), which was specifically developed for pregnancy [70]. Coping styles within this tool included: (1) Preparation for motherhood, “planned how you will handle the birth” (2) Avoidance “avoided being with people in general” (3) Positive appraisal “felt that being pregnant has enriched your life” and (4) Prayer “prayed that the birth will go well”. Women were asked to report how often they used each of the above coping styles and their response was recorded using a 4-point Likert scale; 0 (Never), (1) rarely, (2) sometimes, (3) most of the time [71]. In this study, PCI-4 demonstrated a moderate reliability with an internal consistency of α = 0.50. The sample size calculation was based on the estimated effect of perinatal depression on adverse infant health outcomes. To calculate this, we used a double population proportion formula in Epi Info version 7 [72] with the following assumptions: 95% confidence level, 90% power, an exposed to non-exposed ratio of 1:2, a prevalence of underweight among those free from common mental disorder of 25%, and a difference of 1.5. A total sample size of n = 809 was estimated which was then increased by 20% to account for expected losses to follow up. The final sample size was therefore estimated as n = 970. Completed data were downloaded from the Google Cloud platform in Excel spreadsheet form, checked for completeness and imported to Stata version 14 (StataCorp, USA) for further cleaning and analysis. Descriptive statistics included mean, median, proportion/percentage, interquartile range, and standard deviations as appropriate. A chi-squared test was used to test for crude associations between the categorical stressors and evidence of depression based on a cut of score of 12. A Structural Equation Model (SEM) was constructed that reflected the stress-process model framework and which explored the direct and indirect relationships between the independent (stressors) and the dependent (antenatal depression) variables. This allowed us to assess the strength of the hypothesized direct and indirect causal paths [73, 74]. In order to better fit the measurement model for depression, the measurement items for the depression scale were parceled into three categories using a random based parceling algorithm. Parceling allows for recategorizing multiple items of a scale in order to get better model fit and convergence [75, 76]. The first parcel contained the EPDS items 1, 4, and 9. The second parcel contained the EPDS items 6, 7, and 8. The third parcel contained the EPDS items 2, 3, 5, and 10. Since the subsequent parcels displayed evidence of non-normality we used the Satorra-Bentler scaled chi-squared test when estimating model fit since this is robust to non-normality [77]. The potential stressors and hypothesized causal paths were selected based on prior subject knowledge (which informed the questionnaire). In addition, a multivariate mixed effects regression analysis was performed to help determine variables suitable for inclusion in the SEM conditioning for socio-demographic, maternal obstetrics and psychosocial factors that were significantly associated (P  0.05); Tucker Lewis Index (TLI) and Comparative Fit Index (CFI) value ≥0.90; and Root Mean Square Error of Approximation (RMSEA) ≤ 0.08 [79]. The direct, indirect, and total effects of the stressors on antenatal depression were reported in the form of standardized beta coefficients. Estimated effects for which p < 0.05 were considered as being statistically significant.

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with access to information, resources, and support related to maternal health. These apps can include features such as personalized pregnancy tracking, educational content, appointment reminders, and access to healthcare professionals through telemedicine.

2. Community Health Workers: Train and deploy community health workers to provide maternal health education, counseling, and support to pregnant women in rural areas. These workers can conduct home visits, organize community health events, and serve as a bridge between pregnant women and healthcare facilities.

3. Telemedicine: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone consultations. This can help address the shortage of healthcare providers in rural areas and improve access to prenatal care.

4. Transportation Solutions: Develop innovative transportation solutions to overcome geographical barriers and improve access to healthcare facilities for pregnant women. This can include initiatives such as mobile clinics, community-based transportation services, or partnerships with ride-sharing companies to provide affordable transportation options.

5. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to cover the costs of prenatal care, delivery, and postnatal care. These vouchers can be distributed to women in low-income communities, enabling them to access quality maternal healthcare services.

6. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and support to pregnant women. These hotlines can be accessible 24/7 and serve as a resource for women seeking guidance on maternal health issues.

7. Public-Private Partnerships: Foster collaborations between government agencies, private healthcare providers, and non-profit organizations to improve access to maternal health services. These partnerships can leverage resources, expertise, and infrastructure to expand healthcare facilities, train healthcare workers, and implement innovative programs.

8. Maternal Health Education Programs: Develop and implement comprehensive maternal health education programs that target pregnant women, their families, and communities. These programs can focus on promoting healthy behaviors, raising awareness about the importance of prenatal care, and addressing cultural beliefs and practices that may impact maternal health.

9. Maternal Health Monitoring Systems: Implement digital health solutions that enable real-time monitoring of maternal health indicators, such as blood pressure, weight, and fetal movements. These systems can help identify high-risk pregnancies early on and facilitate timely interventions to prevent complications.

10. Financial Incentives: Introduce financial incentives, such as cash transfers or conditional cash transfers, to encourage pregnant women to seek and adhere to prenatal care. These incentives can help overcome financial barriers and improve access to essential maternal health services.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of the community.
AI Innovations Description
The study conducted in Gondar town, Ethiopia, aimed to explore the causal mechanisms underlying antenatal depression and identify potential stressors and mediators associated with it. The study used a Structural Equation Model (SEM) to analyze the data collected from 916 pregnant women in their second and third trimesters.

The findings of the study revealed several direct effects on antenatal depression scores. Unplanned pregnancy, having a history of common mental health disorder, and fear of giving birth to the current pregnancy were associated with higher depression scores. On the other hand, adequate food access for the last 3 months was associated with decreased depression scores. The study also found that social support, marital agreement, and partner support partially mediated the link between the identified stressors and the risk of antenatal depression.

Based on these findings, the study recommends early screening of antenatal depression and enhancing the three psychosocial resources, namely marital agreement, social support, and partner support, to improve maternal resilience. These recommendations can be used to develop innovative interventions that focus on improving access to maternal health and addressing the specific stressors and mediators identified in the study.
AI Innovations Methodology
Based on the provided description, the study focuses on understanding the causal mechanisms underlying antenatal depression in Gondar, Ethiopia. To improve access to maternal health, the following recommendations can be considered:

1. Early screening for antenatal depression: Implementing routine screening for antenatal depression can help identify women at risk and provide timely interventions and support.

2. Enhancing social support: Promoting social support networks for pregnant women can help reduce the risk of antenatal depression. This can be done through community-based programs, support groups, and involving family members in the care and support of pregnant women.

3. Improving access to mental health services: Ensuring that pregnant women have access to mental health services, including counseling and therapy, can help address antenatal depression and provide necessary support.

4. Addressing stressors: Identifying and addressing specific stressors that contribute to antenatal depression, such as unplanned pregnancy and fear of giving birth, can help reduce the risk and improve maternal mental health.

To simulate the impact of these recommendations on improving access to maternal health, a methodology can be developed as follows:

1. Define the target population: Determine the specific population of pregnant women in Gondar town who would benefit from improved access to maternal health services.

2. Collect baseline data: Gather data on the current state of access to maternal health services, including availability, affordability, and utilization rates. This can be done through surveys, interviews, and data collection from healthcare facilities.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on improving access to maternal health. This model should consider factors such as the number of women screened for antenatal depression, the increase in social support networks, the availability of mental health services, and the reduction in specific stressors.

4. Input data and run simulations: Input the collected baseline data into the simulation model and run multiple simulations to assess the impact of the recommendations on improving access to maternal health. This can involve varying parameters and assumptions to explore different scenarios and outcomes.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on access to maternal health. This can include assessing changes in utilization rates, reduction in antenatal depression cases, and improvements in overall maternal well-being.

6. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data and feedback from healthcare providers and stakeholders. Refine the model based on the validation process to ensure accuracy and reliability.

7. Communicate findings and implement recommendations: Present the findings of the simulation study to relevant stakeholders, including policymakers, healthcare providers, and community organizations. Advocate for the implementation of the identified recommendations to improve access to maternal health services.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of specific recommendations on improving access to maternal health and make informed decisions to enhance maternal well-being in Gondar town.

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