Effect of perinatal depression on risk of adverse infant health outcomes in mother-infant dyads in Gondar town: a causal analysis

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Study Justification:
– Perinatal depression is a significant health burden for pregnant and postnatal women in Ethiopia, affecting approximately one-third of women.
– Associations between postnatal depression and adverse infant health outcomes have been observed, but there have been no studies assessing the causal effects of perinatal depression on infant health in Ethiopia.
– This study aims to fill this gap by using longitudinal data and causal inference methods to estimate the associations between perinatal depression and infant diarrhea, Acute Respiratory Infection (ARI), and malnutrition in Gondar Town, Ethiopia.
Highlights:
– The study was conducted in Gondar Town, Ethiopia, with a cohort of 866 mother-infant dyads followed for 6 months.
– The cumulative incidence of diarrhea, ARI, and malnutrition during the follow-up period was 17.0%, 21.6%, and 14.4%, respectively.
– There was no evidence of an association between perinatal depression and the risk of infant diarrhea, ARI, and malnutrition in Gondar Town.
– Previous reports suggesting increased risks resulting from maternal depression may be due to unobserved confounding.
Recommendations:
– Based on the findings, it is recommended that further research be conducted to explore other potential factors contributing to adverse infant health outcomes in Gondar Town.
– Interventions should focus on addressing the underlying causes of infant diarrhea, ARI, and malnutrition, such as improving access to clean water, sanitation, and nutrition.
Key Role Players:
– Researchers and data collectors
– Health professionals and clinicians
– Community health workers
– Local government officials
– Non-governmental organizations (NGOs) working in maternal and child health
Cost Items for Planning Recommendations:
– Research funding for further studies
– Training and capacity building for health professionals and community health workers
– Implementation of interventions, including infrastructure improvements and health education programs
– Monitoring and evaluation of intervention effectiveness
– Collaboration and coordination between stakeholders

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, methods, and results. However, it does not provide information on the statistical significance of the associations between perinatal depression and infant health outcomes. To improve the evidence, the abstract could include p-values or confidence intervals for the risk differences estimated using TMLE. Additionally, it would be helpful to provide information on the sample size and any limitations of the study.

Background: Approximately one-third of pregnant and postnatal women in Ethiopia experience depression posing a substantial health burden for these women and their families. Although associations between postnatal depression and worse infant health have been observed, there have been no studies to date assessing the causal effects of perinatal depression on infant health in Ethiopia. We applied longitudinal data and recently developed causal inference methods that reduce the risk of bias to estimate associations between perinatal depression and infant diarrhea, Acute Respiratory Infection (ARI), and malnutrition in Gondar Town, Ethiopia. Methods: A cohort of 866 mother-infant dyads were followed from infant birth for 6 months and the cumulative incidence of ARI, diarrhea, and malnutrition were assessed. The Edinburgh Postnatal Depression Scale (EPDS) was used to assess the presence of maternal depression, the Integrated Management of Newborn and Childhood Illnesses (IMNCI) guidelines were used to identify infant ARI and diarrhea, and the mid upper arm circumference (MUAC) was used to identify infant malnutrition. The risk difference (RD) due to maternal depression for each outcome was estimated using targeted maximum likelihood estimation (TMLE), a doubly robust causal inference method used to reduce bias in observational studies. Results: The cumulative incidence of diarrhea, ARI and malnutrition during 6-month follow-up was 17.0% (95%CI: 14.5, 19.6), 21.6% (95%CI: 18.89, 24.49), and 14.4% (95%CI: 12.2, 16.9), respectively. There was no association between antenatal depression and ARI (RD = − 1.3%; 95%CI: − 21.0, 18.5), diarrhea (RD = 0.8%; 95%CI: − 9.2, 10.9), or malnutrition (RD = -7.3%; 95%CI: − 22.0, 21.8). Similarly, postnatal depression was not associated with diarrhea (RD = -2.4%; 95%CI: − 9.6, 4.9), ARI (RD = − 3.2%; 95%CI: − 12.4, 5.9), or malnutrition (RD = 0.9%; 95%CI: − 7.6, 9.5). Conclusion: There was no evidence for an association between perinatal depression and the risk of infant diarrhea, ARI, and malnutrition amongst women in Gondar Town. Previous reports suggesting increased risks resulting from maternal depression may be due to unobserved confounding.

We conducted a community-based cohort study in Gondar Town, Ethiopia. Pregnant women were recruited in their second to third trimester and followed for 6 months (from June 2018 to March 2019) after birth when their infants were assessed for the development of diarrhea, ARI, and malnutrition. Gondar Town is an administrative zone of Amhara Regional State, located 747 km north of Addis Ababa (the capital city of Ethiopia). The town has 12 ‘kebeles’ (the smallest administrative units in the country), and in 2017/2018 had 6450 pregnancies [29, 30]. Gondar town has one government-operated referral hospital, eight health centers and 15 private medical clinics [31]. This analysis forms part of a large mother-child health cohort study designed to examine the incidence and prevalence of perinatal depression and its effects on birth and infant health outcomes. The required sample size was determined using Epi Info version 7 [32], with the following assumptions: a type-1 error rate of alpha = 0.05, 90% power, an exposed to unexposed (perinatal depression) ratio of 1:2, and an odds ratio of low birth weight of 1.5 for infants born to women with depression compared to those without depression. A sample size of 809 was estimated and 20% was added for expected losses during follow up, giving a final recruitment target of 970. Ethics approval was obtained from the Institutional Review Board of the University of Gondar and the Social and Behavioral Research Ethics Committee (SBREC) of Flinders University in South Australia [33]. A support letter was provided by the mayor’s office for Gondar town. Participants were informed of the study’s aims and objectives and their right to withdraw from the research during follow-up. Each volunteer was asked to provide written consent and confidentiality was maintained throughout the study. Women with an overall Edinburgh Postnatal Depression Scale (EPDS) score of 13 to 16, and a score 1, 2, or 3 on item ten (thought of suicide) were referred to University of Gondar Specialized Hospital [34] for further diagnosis and treatment, whilst those with an overall EPDS ≥17 were excluded from the study. Trained nurse data collectors conducted face-to-face interviews with women in their home using a structured electronic-based questionnaire (supplementary material 1) to collect data on the exposure, outcomes, and potential confounders. The Open Data Collection Kit (ODK) was used to collect the data online using a Lenovo 7 tablet after being checked for validity using Enketo [35] and uploaded to the Google cloud platform. The Edinburgh Postnatal Depression Scale (EPDS) developed by Cox [34] and adapted for use in the Ethiopian context [36] was used to assess the mother’s depression. The tool measures the extent of stress that pregnant women experienced during the previous week [37–39] and has been validated in the urban population with a sensitivity, specificity, and misclassification rate of 78.9, 75.3, and 24.0% respectively. Women were considered to be depressed if they had an EPDS score of ≥12 during pregnancy (antenatal depression) and ≥ 6 during the postnatal period (postnatal depression) [40]. The Cronbach’s alpha for internal consistency was 0.74 in this study. The primary infant outcomes assessed were malnutrition, diarrhea, and ARI. Malnutrition was assessed using the measurement of Middle-Upper Arm-Circumference (MUAC) and defined as infant with MUAC of ≤110 mm [41, 42]. The Integrated Management of Newborn and Childhood Illnesses (IMNCI) guideline was used to identify infants for diarrhea and ARI [43]. Diarrhea was defined as three or more episodes of loose stools in 24 h [44] and ARI was defined as a cough/cold accompanying fever or rapid breathing [45]. Potential confounding variables were identified using the modified disjunctive cause criterion. According to this criteria, covariates were considered to be potential confounders if: (1) they had significant associations with the exposure, the outcome or both; (2) they were not an instrumental variable; and (3) they were not likely to be on the causal pathway between the primary exposure and the outcome [46, 47]. Accordingly, the following confounders for both exposure and outcome were identified: family food access, maternal age, infant age, maternal education and occupational status, pregnancy intention, maternal service uptake, parity, maternal nutritional status, fear of giving birth, a history of chronic mental disorder, level of partner support, quality of partner relationship, social support, and stress coping ability. The “targeting step” in TMLE involves the use of these confounders to estimate the predicted probability of exposure (maternal depression) for each participant given these confounders. This probability is then used to update the estimated risk of the outcome, which is modelled using the observed exposure (depression/no depression) and the same set of confounders. The updated estimates of the risk of the outcome are then used to generate updated pairs of potential outcomes. The “average treatment effect (ATE)”, which here is the risk difference, is finally calculated as the average difference between these pairs across individual [28]. Social support during pregnancy was measured using the Oslo Social Support Scale (OSSS-3) [48]. The three items from OSSS-3 Likert scales were summed to a possible 14 points and women were categorized as having either ‘poor’ (total score < 9) or moderate to strong (overall rating 9–14) support. The Cronbach’s alpha for internal consistency was 0.76 in this study. The support that participants received from their partner was assessed using a 5-point Likert scale via the question ‘my partner helps me a lot’, which had possible responses; ‘always,’ ‘most of the time,’ ‘some of the time,’ ‘rarely,’ and ‘never’. Quality of partner relationship was assessed using a 3-point Likert scale via the question “How do you rate your relationship with your partner in day to day life?” with response categories, ‘very good’, ‘good’, and ‘poor’. The maternal Middle-Upper Arm Circumference (MUAC) measured maternal nutritional status. The MUAC is validated for measuring nutritional status in the postnatal period and a cutoff score of 18-22 mm rated as ‘underweight’ and 22.5 to 31 mm as ‘normal” [49]. Participants were asked about their pregnancy intention via the question ‘at the time you became pregnant with this pregnancy, did you want to become pregnant, did you want to wait until later, or did you not want to have any more children?’. Their responses were categorized as ‘wanted now’, ‘wanted later’, and ‘not wanted at all’. The wanted now or later options were combined and labelled as ‘planned’ and ‘not wanted at all’ was labelled as ‘unplanned’. LBW was classified as a birth weight less than 2500 g [50]. The Perinatal Coping Inventory (PCI-4) was developed to assess maternal stress coping ability during pregnancy [51]. Coping styles within this tool included: (1) preparation for motherhood, ‘planned how you would 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 would go well’. Participants were asked to report how often they used each of the above coping styles and responses were recorded using a 4-point Likert scale as 0 (never), (1) rarely, (2) sometimes, (3) most of the time [52]. The Cronbach’s alpha for internal consistency was 0.50 in this study. Completed survey data were downloaded from the Google cloud platform in an Excel spreadsheet, checked for completeness, and imported to Stata version 14 [53] for analysis. Descriptive statistics including mean (SD), median (IQR), frequency (percentage) were used as appropriate. Targeted maximum likelihood estimation (TMLE) was used to investigate the causal effects of perinatal depression on the risk of infant diarrhea, ARI and malnutrition using the estimated average treatment effect (ATE) [54, 55] which was reported as a risk difference (RD). The ATE estimates the average difference in the outcome between participants had they all been exposed and had they all been unexposed, adjusting for potential confounders [56]. TMLE applies G-computation and propensity score methods that involve both exposure and outcome mechanisms [28, 57], and is a doubly robust estimator, providing unbiased estimates when either the exposure model or the outcome model are miss-specified [58]. TMLE is also unbiased in the presence of outliers, unmeasured confounding, sparsity, and other modeling challenges [59]. Assumptions of the model includes no loss to follow up, similar to that for a randomized trial design [60]. We also assessed the potential mediating effects of LBW, postnatal depression, and early initiation of breast feeding using Generalized Structural Equation Models (GSEM) [61]. We included these same potential mediator variables in the causal model as a sensitivity analysis. Interaction terms for antenatal and postnatal depression with social support, partner support, and stress coping ability were also assessed for inclusion in the causal model. A generalized estimating equation (GEE) model with a Poisson link function and exchangeable correlation structure was used as a comparison model to the TMLE model. Robust standard errors were used for the Poisson GEE given the clustering for the incidence of diarrhea, ARI and malnutrition within districts [62, 63]. Multicollinearity was assessed using correlation coefficients and the Variance Inflation Factor (VIF) with cut-off values of ≥0.8 and ≥ 10, respectively [64].

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive prenatal care and mental health support without having to travel long distances.

2. Mobile health applications: Developing mobile applications that provide educational resources, appointment reminders, and self-assessment tools can empower pregnant women to take control of their health and access information easily.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, mental health support, and education within local communities can improve access to maternal health services, especially in rural areas.

4. Integrated care models: Implementing integrated care models that combine maternal health services with other healthcare services, such as family planning and immunization, can ensure comprehensive and continuous care for pregnant women.

5. Task-shifting: Training and empowering non-specialist healthcare providers, such as nurses and midwives, to deliver certain aspects of prenatal care and mental health support can help alleviate the shortage of skilled healthcare professionals and improve access to maternal health services.

6. Mobile clinics: Establishing mobile clinics that travel to remote and underserved areas can bring essential maternal health services, including prenatal care and mental health support, directly to the communities that need them.

7. Public-private partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal health services by leveraging their resources, expertise, and infrastructure.

8. Health education campaigns: Conducting targeted health education campaigns that raise awareness about the importance of prenatal care, mental health, and nutrition can empower pregnant women to seek and access the necessary healthcare services.

9. Financial incentives: Implementing financial incentives, such as subsidies or cash transfers, for pregnant women who attend prenatal care visits and engage in mental health support programs can help overcome financial barriers and improve access to maternal health services.

10. Strengthening referral systems: Establishing efficient referral systems that ensure seamless coordination and communication between different levels of healthcare facilities can facilitate timely access to specialized maternal health services when needed.

It is important to note that the specific context and resources available in Gondar Town, Ethiopia, should be considered when implementing these innovations to ensure their effectiveness and sustainability.
AI Innovations Description
The study titled “Effect of perinatal depression on risk of adverse infant health outcomes in mother-infant dyads in Gondar town: a causal analysis” aimed to assess the causal effects of perinatal depression on infant health outcomes in Gondar Town, Ethiopia. The study followed a cohort of 866 mother-infant dyads for 6 months after birth and assessed the cumulative incidence of infant diarrhea, Acute Respiratory Infection (ARI), and malnutrition.

The study found no evidence of an association between perinatal depression and the risk of infant diarrhea, ARI, and malnutrition in Gondar Town. Previous reports suggesting increased risks resulting from maternal depression may be due to unobserved confounding.

The study used the Edinburgh Postnatal Depression Scale (EPDS) to assess maternal depression, the Integrated Management of Newborn and Childhood Illnesses (IMNCI) guidelines to identify infant ARI and diarrhea, and the mid upper arm circumference (MUAC) to identify infant malnutrition. The researchers applied targeted maximum likelihood estimation (TMLE), a doubly robust causal inference method, to estimate the risk difference (RD) due to maternal depression for each outcome.

The study was conducted as part of a larger mother-child health cohort study and obtained ethics approval from the Institutional Review Board of the University of Gondar and the Social and Behavioral Research Ethics Committee of Flinders University in South Australia. Participants provided written consent, and data were collected using face-to-face interviews with trained nurse data collectors.

The study identified potential confounding variables, such as family food access, maternal age, infant age, maternal education and occupational status, pregnancy intention, maternal service uptake, parity, maternal nutritional status, fear of giving birth, a history of chronic mental disorder, level of partner support, quality of partner relationship, social support, and stress coping ability. These variables were used in the TMLE analysis to estimate the causal effects of perinatal depression on infant health outcomes.

In conclusion, the study did not find evidence of an association between perinatal depression and the risk of infant diarrhea, ARI, and malnutrition in Gondar Town, Ethiopia. The findings suggest that previous reports of increased risks may be due to unobserved confounding. Further research is needed to better understand the relationship between perinatal depression and infant health outcomes.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement programs to raise awareness about perinatal depression and its impact on infant health outcomes. Provide education to pregnant women and their families about the signs, symptoms, and available support for perinatal depression.

2. Strengthen mental health services: Improve access to mental health services, including screening, diagnosis, and treatment for perinatal depression. Train healthcare providers to identify and manage perinatal depression effectively.

3. Enhance social support systems: Develop and strengthen social support systems for pregnant and postnatal women, including peer support groups, community-based programs, and counseling services. Encourage women to seek support from their partners, family members, and friends.

4. Integrate mental health into maternal health care: Integrate mental health screening and support into routine maternal health care services. Ensure that healthcare providers are trained to address mental health issues alongside physical health during pregnancy and the postnatal period.

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 population that will be affected by the recommendations, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather data on the current status of access to maternal health services, including rates of perinatal depression, utilization of mental health services, and availability of social support systems.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on access to maternal health. This model should consider factors such as population size, resource availability, and the potential reach of the interventions.

4. Input data and parameters: Input the baseline data and parameters into the simulation model. This may include information on the effectiveness of the interventions, the expected uptake of services, and the potential barriers or facilitators to implementation.

5. Run simulations: Use the simulation model to run multiple iterations or scenarios to estimate the potential impact of the recommendations on access to maternal health. This could involve varying the parameters or assumptions to explore different outcomes.

6. Analyze results: Analyze the results of the simulations to determine the potential changes in access to maternal health services. This may include quantifying the expected increase in utilization of mental health services, improvements in social support systems, and reductions in perinatal depression rates.

7. Interpret and communicate findings: Interpret the findings of the simulations and communicate the potential impact of the recommendations to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. This can help inform decision-making and resource allocation for improving access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. The steps outlined above provide a general framework for conducting such simulations.

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