Association of maternal depression and anxiety with toddler social-emotional and cognitive development in South Africa: A prospective cohort study

BMJ Open, Volume 12, No. 4, Year 2022

Interpretation

Objective A robust literature has identified associations between prenatal maternal depression and adverse child social-emotional and cognitive outcomes. The majority of prior research is from high-income countries despite increased reporting of perinatal depression in low/middle-income countries (LMICs). Additionally, despite the comorbidity between depression and anxiety, few prior studies have examined their joint impact on child neurodevelopment. The objective of the current analysis was to examine associations between prenatal maternal depression and anxiety with child social-emotional and cognitive development in a cohort from the Western Cape Province of South Africa. Design Prenatal maternal depression and anxiety were measured using the Edinburgh Postnatal Depression Scale and the State-Trait Anxiety Inventory Scale at 20-24 weeks’ gestation. Child neurobehaviour was assessed at age 3 using the Brief Infant-Toddler Social Emotional Assessment and the Bayley Scales of Infant Development III Screening Test (BSID-III ST). We used linear regression models to examine the independent and joint association between prenatal maternal depression, anxiety and child developmental outcomes. Results Participants consisted of 600 maternal-infant dyads (274 females; gestational age at birth: 38.89 weeks±2.03). Children born to mothers with both prenatal depression and trait anxiety had higher social-emotional problems (mean difference: 4.66; 95% CI 3.43 to 5.90) compared with children born to mothers with no prenatal depression or trait anxiety, each condition alone, or compared with mothers with depression and state anxiety. Additionally, children born to mothers with prenatal maternal depression and trait anxiety had the greatest reduction in mean cognitive scores on the BSID-III ST (mean difference: -1.04; 95% CI -1.99 to -0.08). Conclusions The observed association between comorbid prenatal maternal depression and chronic anxiety with subsequent child social-emotional and cognitive development underscores the need for targeting mental health support among perinatal women in LMICs to improve long-term child neurobehavioural outcomes. Participants were a subset of infants with available outcome data at age 3 enrolled in the Safe Passage study conducted by the Prenatal Alcohol and SIDS and Stillbirth Network, a multi-centre study investigating the role of prenatal exposure in risk for sudden infant death syndrome, stillbirth and fetal alcohol spectrum disorders. Eligibility criteria for the Safe Passage study included the ability to provide informed consent in English or Afrikaans, 16 years of age or older at the time of consent, and a gestational age between 6 weeks and 40 weeks at the time of consent based on estimated delivery date.31 Exclusion criteria for prenatal maternal enrolment into the Safe Passage study included planned therapeutic abortion, moving out of the catchment area prior to estimated date of delivery and clinical judgement. Maternal-infant medical charts were abstracted to obtain maternal age at delivery, gestational age at birth, mode of delivery and the infant’s biological sex. Measures to collect prenatal alcohol, tobacco and recreational drug exposure have been previously described.31 32 Prenatal maternal alcohol and tobacco use behaviours were previously characterised using cluster analysis.33 34 Through study specific case report forms, participants indicated demographic and socioeconomic information including race, maternal educational attainment, household crowding (persons per room in household), access to running water inside the house, prenatal care during pregnancy and marital status. Information regarding maternal mental health during pregnancy was obtained at 20–24 weeks’ gestation. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS), a depression screening tool developed to specifically assess depressive symptoms in perinatal women where higher scores indicate more severe depressive symptoms.35 36 The EPDS is widely used and has been validated in English and Afrikaans in South Africa.35 37 Prior studies have used a cut-off score of ≥12 or ≥13 to be indicative of major depression within perinatal women living in South Africa.35 37 Maternal anxiety symptoms were measured using the State-Trait Anxiety Inventory (STAI),38 an anxiety screening tool to distinguish anxiety symptoms from depressive symptoms which has also been validated in both languages.39 The STAI has two subscales, state anxiety which reflects the participant’s current state of anxiety when completing the questionnaires and trait anxiety, which is thought to be consistent across time and reflect personality traits. In HICs, the STAI has a cut-off score of ≥40 on both the state anxiety and trait anxiety subscales to indicate a threshold for clinical levels of anxiety. Based on these prior studies, we used a cut-off of ≥13 to indicate maternal depression, a cut-off of >40 on the STAI-state subscale to indicate state anxiety and a cut-off of >40 on the STAI-trait subscale to indicate trait anxiety. The Bayley Scales of Infant Development III Screening Test (BSID-III ST) were designed as a rapid assessment of cognitive, language and motor functioning in infants and young children in order to determine if a child’s development is within normal limits and identify risk for developmental delay. The BSID-III ST has high test–retest reliability: cognitive (0.85), receptive language (0.88), expressive language (0.88), fine motor (0.82) and gross motor (0.86). Although the BSID-III ST does not identify degree of impairment, the cut-off points indicate whether a child shows competence in age-appropriate tasks, evidence of emerging age-appropriate skills and evidence of being at risk for developmental delay. The BSID has been validated and widely used throughout South Africa.40 41 The Brief Infant-Toddler Social and Emotional Assessment (BITSEA) is a 42-item parental report measure of social-emotional development, behavioural problems and delays in competence.42 Domains assessed within the BITSEA include: externalising (activity/impulsivity, aggression/defiance, peer aggression), internalising (depression/withdrawal, anxiety, separation distress, inhibition to novelty), dysregulation (sleep, negative emotionality, eating, sensory sensitivity) and competence (compliance, attention, imitation/play, mastery motivation, empathy and pro-social peer relations).42 Findings from the BITSEA validation study provide preliminary support for the BITSEA as a reliable and valid brief screener for infant-toddler social-emotional and behavioural problems in addition to delays in competence.43 When used in socioeconomically and ethnically diverse community-based populations, the BITSEA demonstrated excellent test–retest reliability and good inter-rater agreement between parents.42 Using multiple linear regression models, we estimated independent and joint effects of maternal depression and state and trait anxiety on social-emotional problem, social emotional competence and cognitive development scores. Two, separate four-level categorical prenatal maternal mental health variables were created to assess the impact of prenatal maternal depression, trait anxiety and state anxiety. We created a prenatal maternal depression and trait anxiety variable with four categories: (1) no prenatal depression or trait anxiety (n=199; 33.17%, reference category), (2) prenatal depression only (106; 17.67%), (3) prenatal trait anxiety only (n=68; 11.33%) and (4) prenatal maternal depression and trait anxiety (n=227; 37.83%) (table 1). In separate models we additionally examined the independent and joint effects of prenatal maternal depression and state anxiety. We created a prenatal maternal depression and state anxiety variable with four categories: (1) no prenatal depression or state anxiety (n=248; 41.33%; reference category), (2) prenatal depression only (n=237; 39.50%), (3) prenatal state anxiety only (n=19; 3.17%) and (4) prenatal maternal depression and state anxiety (n=96; 16%) (table 1). For each regression model, either no prenatal maternal depression or trait anxiety or no prenatal maternal depression and state anxiety was set as the reference category. Minimally adjusted models included sex, gestational age at birth and age at follow-up as covariates. Fully adjusted models additionally controlled for prenatal maternal alcohol use, prenatal maternal tobacco use, maternal employment status at delivery, maternal educational attainment at delivery, parity and the household crowding index. We used missing indicator methods and median imputation to account for missing categorical and continuous covariate data, respectively (described in table 1). All analyses were performed in SAS software V.9.4 (SAS Institute). Sociodemographic characteristics

AI Digest

Study Justification:

This study aimed to investigate the association between maternal depression and anxiety during pregnancy and the social-emotional and cognitive development of toddlers in South Africa. The existing research on this topic has primarily focused on high-income countries, despite the increasing prevalence of perinatal depression in low/middle-income countries. Additionally, few studies have examined the combined impact of depression and anxiety on child neurodevelopment. This study fills these gaps in knowledge and highlights the importance of addressing mental health support for perinatal women in low/middle-income countries to improve long-term child outcomes.

Study Highlights:

– The study included 600 maternal-infant dyads from the Western Cape Province of South Africa.
– Maternal depression and anxiety were measured during pregnancy using validated screening tools.
– Child social-emotional and cognitive development was assessed at age 3 using standardized tests.
– Children born to mothers with both prenatal depression and trait anxiety had higher social-emotional problems compared to other groups.
– Children born to mothers with prenatal depression and trait anxiety also had the greatest reduction in cognitive scores.
– These findings emphasize the association between comorbid maternal depression and chronic anxiety with adverse child outcomes.

Recommendations for Lay Readers:

– Pregnant women should receive mental health support to address depression and anxiety.
– Addressing maternal mental health can have long-term benefits for child social-emotional and cognitive development.
– Healthcare providers should screen for and provide appropriate interventions for perinatal depression and anxiety.
– Policy makers should prioritize the integration of mental health services into maternal and child healthcare programs.

Recommendations for Policy Makers:

– Develop and implement policies that promote mental health support for perinatal women.
– Allocate resources to train healthcare providers in screening and managing perinatal depression and anxiety.
– Integrate mental health services into existing maternal and child healthcare programs.
– Collaborate with community organizations to raise awareness about the importance of maternal mental health.

Key Role Players:

– Healthcare providers: Responsible for screening, diagnosing, and providing interventions for perinatal depression and anxiety.
– Mental health professionals: Provide specialized support and treatment for women with perinatal mental health disorders.
– Community organizations: Raise awareness, provide support groups, and promote mental health education.
– Policy makers: Develop and implement policies to support maternal mental health and allocate resources for mental health services.

Cost Items for Planning Recommendations:

– Training programs for healthcare providers: Budget for training sessions, materials, and ongoing professional development.
– Mental health services: Allocate funds for mental health professionals, counseling services, and support groups.
– Awareness campaigns: Budget for marketing materials, community events, and educational resources.
– Integration of mental health services: Consider costs associated with program development, coordination, and monitoring.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available.

Strength of Evidence

N/A

Abstract

Participants were a subset of infants with available outcome data at age 3 enrolled in the Safe Passage study conducted by the Prenatal Alcohol and SIDS and Stillbirth Network, a multi-centre study investigating the role of prenatal exposure in risk for sudden infant death syndrome, stillbirth and fetal alcohol spectrum disorders. Eligibility criteria for the Safe Passage study included the ability to provide informed consent in English or Afrikaans, 16 years of age or older at the time of consent, and a gestational age between 6 weeks and 40 weeks at the time of consent based on estimated delivery date.31 Exclusion criteria for prenatal maternal enrolment into the Safe Passage study included planned therapeutic abortion, moving out of the catchment area prior to estimated date of delivery and clinical judgement. Maternal-infant medical charts were abstracted to obtain maternal age at delivery, gestational age at birth, mode of delivery and the infant’s biological sex. Measures to collect prenatal alcohol, tobacco and recreational drug exposure have been previously described.31 32 Prenatal maternal alcohol and tobacco use behaviours were previously characterised using cluster analysis.33 34 Through study specific case report forms, participants indicated demographic and socioeconomic information including race, maternal educational attainment, household crowding (persons per room in household), access to running water inside the house, prenatal care during pregnancy and marital status. Information regarding maternal mental health during pregnancy was obtained at 20–24 weeks’ gestation. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS), a depression screening tool developed to specifically assess depressive symptoms in perinatal women where higher scores indicate more severe depressive symptoms.35 36 The EPDS is widely used and has been validated in English and Afrikaans in South Africa.35 37 Prior studies have used a cut-off score of ≥12 or ≥13 to be indicative of major depression within perinatal women living in South Africa.35 37 Maternal anxiety symptoms were measured using the State-Trait Anxiety Inventory (STAI),38 an anxiety screening tool to distinguish anxiety symptoms from depressive symptoms which has also been validated in both languages.39 The STAI has two subscales, state anxiety which reflects the participant’s current state of anxiety when completing the questionnaires and trait anxiety, which is thought to be consistent across time and reflect personality traits. In HICs, the STAI has a cut-off score of ≥40 on both the state anxiety and trait anxiety subscales to indicate a threshold for clinical levels of anxiety. Based on these prior studies, we used a cut-off of ≥13 to indicate maternal depression, a cut-off of >40 on the STAI-state subscale to indicate state anxiety and a cut-off of >40 on the STAI-trait subscale to indicate trait anxiety. The Bayley Scales of Infant Development III Screening Test (BSID-III ST) were designed as a rapid assessment of cognitive, language and motor functioning in infants and young children in order to determine if a child’s development is within normal limits and identify risk for developmental delay. The BSID-III ST has high test–retest reliability: cognitive (0.85), receptive language (0.88), expressive language (0.88), fine motor (0.82) and gross motor (0.86). Although the BSID-III ST does not identify degree of impairment, the cut-off points indicate whether a child shows competence in age-appropriate tasks, evidence of emerging age-appropriate skills and evidence of being at risk for developmental delay. The BSID has been validated and widely used throughout South Africa.40 41 The Brief Infant-Toddler Social and Emotional Assessment (BITSEA) is a 42-item parental report measure of social-emotional development, behavioural problems and delays in competence.42 Domains assessed within the BITSEA include: externalising (activity/impulsivity, aggression/defiance, peer aggression), internalising (depression/withdrawal, anxiety, separation distress, inhibition to novelty), dysregulation (sleep, negative emotionality, eating, sensory sensitivity) and competence (compliance, attention, imitation/play, mastery motivation, empathy and pro-social peer relations).42 Findings from the BITSEA validation study provide preliminary support for the BITSEA as a reliable and valid brief screener for infant-toddler social-emotional and behavioural problems in addition to delays in competence.43 When used in socioeconomically and ethnically diverse community-based populations, the BITSEA demonstrated excellent test–retest reliability and good inter-rater agreement between parents.42 Using multiple linear regression models, we estimated independent and joint effects of maternal depression and state and trait anxiety on social-emotional problem, social emotional competence and cognitive development scores. Two, separate four-level categorical prenatal maternal mental health variables were created to assess the impact of prenatal maternal depression, trait anxiety and state anxiety. We created a prenatal maternal depression and trait anxiety variable with four categories: (1) no prenatal depression or trait anxiety (n=199; 33.17%, reference category), (2) prenatal depression only (106; 17.67%), (3) prenatal trait anxiety only (n=68; 11.33%) and (4) prenatal maternal depression and trait anxiety (n=227; 37.83%) (table 1). In separate models we additionally examined the independent and joint effects of prenatal maternal depression and state anxiety. We created a prenatal maternal depression and state anxiety variable with four categories: (1) no prenatal depression or state anxiety (n=248; 41.33%; reference category), (2) prenatal depression only (n=237; 39.50%), (3) prenatal state anxiety only (n=19; 3.17%) and (4) prenatal maternal depression and state anxiety (n=96; 16%) (table 1). For each regression model, either no prenatal maternal depression or trait anxiety or no prenatal maternal depression and state anxiety was set as the reference category. Minimally adjusted models included sex, gestational age at birth and age at follow-up as covariates. Fully adjusted models additionally controlled for prenatal maternal alcohol use, prenatal maternal tobacco use, maternal employment status at delivery, maternal educational attainment at delivery, parity and the household crowding index. We used missing indicator methods and median imputation to account for missing categorical and continuous covariate data, respectively (described in table 1). All analyses were performed in SAS software V.9.4 (SAS Institute). Sociodemographic characteristics

Materials

Innovations

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 in remote or underserved areas to access prenatal care and mental health support through virtual consultations with healthcare providers.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, self-assessment tools, and reminders for prenatal care appointments can help women stay informed and engaged in their own maternal health.

3. Community health workers: Training and deploying community health workers who can provide prenatal education, mental health support, and referrals to healthcare facilities can improve access to maternal health services, especially in rural or low-resource settings.

4. Integrated care models: Implementing integrated care models that combine maternal health services with mental health support can ensure that women receive comprehensive care that addresses both their physical and mental well-being.

5. Task-shifting: Training and empowering non-specialist healthcare providers, such as nurses or midwives, to provide basic mental health screening and support can help bridge the gap in mental health services for pregnant women.

6. Public awareness campaigns: Launching public awareness campaigns to reduce the stigma surrounding mental health issues during pregnancy and encourage women to seek help can promote early detection and intervention.

7. Collaborative partnerships: Establishing partnerships between healthcare providers, community organizations, and government agencies can facilitate the coordination of resources and services, leading to improved access to maternal health care.

It’s important to note that these recommendations are general and may need to be adapted to the specific context and resources available in South Africa.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health and address the association between maternal depression/anxiety and child development in low/middle-income countries (LMICs) is as follows:

1. Increase mental health support for perinatal women: LMICs should prioritize the provision of mental health support services for pregnant women and new mothers. This can include screening for depression and anxiety during prenatal care visits and providing counseling or therapy services for those in need. Access to mental health professionals should be improved, and community-based interventions can be implemented to reach women in remote or underserved areas.

2. Enhance awareness and education: Efforts should be made to raise awareness about perinatal mental health issues among healthcare providers, community leaders, and the general public. Education campaigns can help reduce stigma and promote early identification and intervention for maternal depression and anxiety. Information on available resources and support services should be widely disseminated.

3. Integrate mental health into maternal health programs: Maternal health programs should incorporate mental health components into their services. This can involve training healthcare providers to recognize and address mental health issues, integrating mental health screening and support into routine prenatal and postnatal care, and establishing referral pathways for women in need of specialized mental health treatment.

4. Strengthen healthcare systems: LMICs should invest in strengthening their healthcare systems to ensure adequate resources and infrastructure for maternal mental health services. This includes increasing the number of mental health professionals, improving access to medications for those who require them, and integrating mental health into existing primary healthcare services.

5. Foster collaboration and research: Collaboration between researchers, policymakers, and healthcare providers is essential to drive innovation and improve access to maternal mental health services. Continued research is needed to better understand the impact of maternal depression and anxiety on child development in LMICs and to identify effective interventions. Findings from studies like the one described can inform evidence-based practices and policies.

By implementing these recommendations, LMICs can work towards improving access to maternal health services and addressing the association between maternal depression/anxiety and child development, ultimately leading to better long-term outcomes for both mothers and children.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Telemedicine: Implementing telemedicine programs can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and consultations without the need for travel.

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 information.

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

4. Transportation services: Establishing transportation services, such as ambulances or shuttle services, can ensure that pregnant women have access to timely and safe transportation to healthcare facilities for prenatal care, delivery, and postnatal care.

5. Maternal health clinics: Setting up dedicated maternal health clinics in underserved areas can provide comprehensive prenatal care, delivery services, and postnatal care in one location, making it easier for pregnant women to access the care they need.

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 would benefit from the recommendations, such as pregnant women in a particular region or socioeconomic group.

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

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 could include variables such as the number of telemedicine consultations, the number of community health workers deployed, or the availability of transportation services.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations on improving access to maternal health. Vary the input parameters to explore different scenarios and assess the sensitivity of the results.

5. 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 pregnant women receiving prenatal care, the reduction in travel time to healthcare facilities, or the improvement in overall healthcare utilization.

6. Validate the model: Validate the simulation model by comparing the projected results with real-world data, if available. This step helps ensure the accuracy and reliability of the simulation findings.

7. Communicate findings and make recommendations: Present the simulation findings to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. Use the results to inform decision-making and advocate for the implementation of the recommended interventions to improve access to maternal health.

Authors & Co-Authors

Statistics:

Authors: 10

Identifiers:

DOI: 10.1136/bmjopen-2021-058135

Research Areas:

Community Interventions, Disparities, Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Technology and Innovations

Study Countries:

South Africa

Study Design:

Cohort Study

Study Approach:

Quantitative

Participants Gender:

Female