Psychosocial and environmental determinants of child cognitive development in rural south africa and tanzania: Findings from the mal-ed cohort

BMC Public Health, Volume 20, No. 1, Year 2020

Interpretation

Background: Approximately 66% of children under the age of 5 in Sub-Saharan African countries do not reach their full cognitive potential, the highest percentage in the world. Because the majority of studies investigating child cognitive development have been conducted in high-income countries (HICs), there is limited knowledge regarding the determinants of child development in low- and middle-income countries (LMICs). Methods: This analysis includes 401 mother-child dyads from the South Africa and Tanzania sites of the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) longitudinal birth cohort study. We investigated the effect of psychosocial and environmental determinants on child cognitive development measured by the Wechsler Preschool Primary Scales of Intelligence (WPPSI) at 5 years of age using multivariable linear regression. Results: Socioeconomic status was most strongly associated with child cognitive development (WPSSI Score Difference (SD):14.27, 95% CI:1.96, 26.59). Modest associations between the organization of the home environment and its opportunities for cognitive stimulation and child cognitive development were also found (SD: 3.08, 95% CI: 0.65, 5.52 and SD: 3.18, 95% CI: 0.59, 5.76, respectively). Conclusion: This study shows a stronger association with child cognitive development at 5 years of age for socioeconomic status compared to more proximal measures of psychosocial and environmental determinants. A better understanding of the role of these factors is needed to inform interventions aiming to alleviate the burden of compromised cognitive development for children in LMICs. This analysis includes data from the Venda, South Africa and Haydom, Tanzania sites of the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study [27–29]. This study was a multi-disciplinary prospective community-based birth cohort study in eight global sites (Bangladesh, Brazil, India, Nepal, Peru, Pakistan, South Africa, and Tanzania). From November 2009 to February 2017, mother and child dyads were enrolled shortly after birth and followed until 5 years of child age. The MAL-ED study design and description of the study sites has been extensively described elsewhere [27–30]. A total of 576 pregnant women over a period of two years were enrolled in the South African (SA) and Tanzanian (TZ) sites. Each site was responsible for enrolling and following the cohort of children. Exclusion criteria were (1) family’s intention to move outside the area in the next 6 months, (2) mother’s age (< 16 years), (3) twin pregnancy, (4) underweight infant (< 1.5 kg), (5) presence of diagnosable congenital disease or severe neonatal disease, and (6) sibling’s enrollment in the study. For the present analysis, only children with cognitive development scores at 5 years of age were included in the analysis (N = 230 for SA; N = 171 for TZ). The main outcome of interest was child cognitive development at 5 years (±30 days) of age. Cognitive development was assessed using the Wechsler Preschool Primary Scales of Intelligence (WPPSI). This clinical tool assesses cognitive function by testing children on six subscales (Block Design, Information, Matrix Reasoning, Picture Concepts, Word Reasoning, and Vocabulary). The WPPSI measures progress and functioning in areas such as problem-solving, thinking processes, and decision-making skills. Some items in the WPPSI were adapted to account for cultural differences and to reduce the potential for the test to be culturally bias (e.g., in the information subscale, shower was changed to bath or bucket) [31]. Because the WPPSI provides both subtest and composite scores, the outcomes of interest were treated as three continuous scores representing the children’s: (1) general cognitive development and functioning (Full Scale IQ), (2) verbal reasoning and comprehension and attention to verbal stimuli (Verbal IQ), and (3) fluid reasoning, spatial processing, and visual-motor integration (Performance IQ). In comparing these three outcomes, we assessed the role that psychosocial and environmental factors play not only on the overall child development but also in specific functioning domains (i.e., verbal and performance). Maternal depression was assessed using the self-reporting questionnaire (SRQ-20) at 1, 6, 12, 24, 36, and 60 (±30 days) months of child age. The SRQ-20 consists of 20 dichotomously coded items. We used a reduced version of SRQ-20 (SRQ-16) for this analysis because it excludes items reflecting somatic symptoms and has been used previously in the MAL-ED cohort [32]. To distinguish between the effects of exposure to postpartum depression and prolonged exposure to depressive symptoms, we assessed (1) a measure of post-partum depressive symptoms defined by the average SRQ-16 scores at 1, 6, and 12 months of child age, (2) one measure of maternal depressive symptoms defined by the average SRQ-16 scores at 24 and 36 months of child age, and (3) one measure of maternal depressive symptoms defined by the SRQ-16 score at 60 months, or 5 years, of child age. Socioeconomic status was assessed through the WAMI index (Water, Assets, Maternal Education and Income) [33]. This measure of household socioeconomic status includes: (1) access to improved water and sanitation, (2) wealth measured by ownership of a set of eight assets, (3) maternal education, and (4) monthly household income. This index has been standardized and validated across the eight MAL-ED study sites [33]. This study assessed environmental factors that may impact child development (i.e., organization of the environment, provision of play material, opportunities for stimulation, and cleanliness of the child) through the Home Observation for the Measurement of the Environment (HOME) tool [34]. This tool was also used to measure some psychosocial factors (i.e., responsivity of the caregiver, avoidance of restrictions and punishment, and promotion of child development). This assessment tool has been used in studies worldwide [35, 36]. Furthermore, it was adapted and validated across the eight international sites of the MAL-ED study [21]. The HOME variable was measured at 6, 24, and 36 (±15 days) months of child age. HOME assessments at each of the three points in time were averaged and coded dichotomously at the overall median (i.e., for both sites together). The organization of the environment (SA median [IQR]: 11.0 [10.3,11.5]; TZ median [IQR]: 4.3 [3.3, 5.5] and maternal education (SA median: [IQR]: 10.5 [9.0, 12.0]; TZ median [IQR]: 7.0, [3.0, 7.0] were coded dichotomously at the site-specific medians due to non-overlapping distributions of these variables across the two sites. Following MAL-ED procedures, children were weighed and measured at enrollment. Weight at enrollment was converted to weight-for-age Z-scores (WAZ) following the WHO 2006 growth standards [37]. We used enrollment WAZ as a proxy for birthweight in the analysis because weight at birth was missing for some children and because age at enrollment varied from 0-17 days. Additionally, we conducted homogeneity tests to identify significant differences in associations between the two sites. We selected covariates based on a directed acyclic graph [38]. We used multivariable linear regression for the continuous WPPSI outcomes using SAS version 9.4. The model included (1) environmental factors (organization of the environment, provision of play material, opportunities for stimulation, cleanliness of the child, and WAMI index for socioeconomic status), (2) psychosocial factors (responsivity of the caregiver, avoidance of punishment, maternal depressive symptoms, and maternal education), (3) child birthweight, and (4) indicators for the fieldworker who collected the data on the home environment (HOME field assessors). We included the HOME field assessor as a covariate because the assessor was significantly associated with both the HOME inventory scale measurements and the WPSSI outcomes. We obtained ethical approval from the Institutional Review Boards for the original and follow-up studies at the University of Venda (Limpopo, South Africa), at the Haydom Lutheran Hospital (Haydom, Tanzania), and the University of Virginia School of Medicine (Charlottesville, United States).

AI Digest

Study Justification:

This study aimed to investigate the psychosocial and environmental determinants of child cognitive development in rural South Africa and Tanzania. The justification for this study is based on the high percentage of children in Sub-Saharan African countries who do not reach their full cognitive potential, and the limited knowledge regarding the determinants of child development in low- and middle-income countries. By understanding these determinants, interventions can be developed to alleviate the burden of compromised cognitive development for children in these countries.

Highlights:

– The study included 401 mother-child dyads from the South Africa and Tanzania sites of the MAL-ED longitudinal birth cohort study.
– The main outcome of interest was child cognitive development at 5 years of age, measured using the Wechsler Preschool Primary Scales of Intelligence (WPPSI).
– The study found that socioeconomic status was most strongly associated with child cognitive development, followed by the organization of the home environment and its opportunities for cognitive stimulation.
– The findings suggest that interventions targeting socioeconomic factors may have the greatest impact on improving child cognitive development in these settings.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Interventions should focus on improving socioeconomic status in rural areas to support child cognitive development.
2. Efforts should be made to enhance the organization of the home environment and provide opportunities for cognitive stimulation.
3. Further research is needed to better understand the role of psychosocial and environmental factors in child cognitive development in low- and middle-income countries.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Government agencies responsible for implementing policies related to early childhood development and education.
2. Non-governmental organizations (NGOs) working in the field of child development and poverty alleviation.
3. Community leaders and local organizations involved in community development and support for families.
4. Researchers and academics specializing in child development and interventions in low- and middle-income countries.

Cost Items:

While the actual cost of implementing the recommendations cannot be estimated without a detailed budget analysis, the following cost items should be considered in planning the recommendations:

1. Funding for socioeconomic development programs, including income generation initiatives and education support.
2. Resources for improving the organization of the home environment, such as providing play materials and promoting cleanliness.
3. Training and capacity building for caregivers and parents to enhance their knowledge and skills in supporting child cognitive development.
4. Monitoring and evaluation systems to assess the impact of interventions and ensure accountability.
Please note that the above cost items are general categories and the specific costs will vary depending on the context and scale of the interventions.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is robust, using a longitudinal birth cohort study with a large sample size. The analysis includes multivariable linear regression to investigate the effect of psychosocial and environmental determinants on child cognitive development. The findings show a strong association between socioeconomic status and child cognitive development, as well as modest associations with the organization of the home environment and cognitive stimulation. However, the abstract could be improved by providing more specific information about the methodology, such as the inclusion and exclusion criteria, and the statistical analysis methods used. Additionally, it would be helpful to include information about the limitations of the study and suggestions for future research.

Abstract

This analysis includes data from the Venda, South Africa and Haydom, Tanzania sites of the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study [27–29]. This study was a multi-disciplinary prospective community-based birth cohort study in eight global sites (Bangladesh, Brazil, India, Nepal, Peru, Pakistan, South Africa, and Tanzania). From November 2009 to February 2017, mother and child dyads were enrolled shortly after birth and followed until 5 years of child age. The MAL-ED study design and description of the study sites has been extensively described elsewhere [27–30]. A total of 576 pregnant women over a period of two years were enrolled in the South African (SA) and Tanzanian (TZ) sites. Each site was responsible for enrolling and following the cohort of children. Exclusion criteria were (1) family’s intention to move outside the area in the next 6 months, (2) mother’s age (< 16 years), (3) twin pregnancy, (4) underweight infant (< 1.5 kg), (5) presence of diagnosable congenital disease or severe neonatal disease, and (6) sibling’s enrollment in the study. For the present analysis, only children with cognitive development scores at 5 years of age were included in the analysis (N = 230 for SA; N = 171 for TZ). The main outcome of interest was child cognitive development at 5 years (±30 days) of age. Cognitive development was assessed using the Wechsler Preschool Primary Scales of Intelligence (WPPSI). This clinical tool assesses cognitive function by testing children on six subscales (Block Design, Information, Matrix Reasoning, Picture Concepts, Word Reasoning, and Vocabulary). The WPPSI measures progress and functioning in areas such as problem-solving, thinking processes, and decision-making skills. Some items in the WPPSI were adapted to account for cultural differences and to reduce the potential for the test to be culturally bias (e.g., in the information subscale, shower was changed to bath or bucket) [31]. Because the WPPSI provides both subtest and composite scores, the outcomes of interest were treated as three continuous scores representing the children’s: (1) general cognitive development and functioning (Full Scale IQ), (2) verbal reasoning and comprehension and attention to verbal stimuli (Verbal IQ), and (3) fluid reasoning, spatial processing, and visual-motor integration (Performance IQ). In comparing these three outcomes, we assessed the role that psychosocial and environmental factors play not only on the overall child development but also in specific functioning domains (i.e., verbal and performance). Maternal depression was assessed using the self-reporting questionnaire (SRQ-20) at 1, 6, 12, 24, 36, and 60 (±30 days) months of child age. The SRQ-20 consists of 20 dichotomously coded items. We used a reduced version of SRQ-20 (SRQ-16) for this analysis because it excludes items reflecting somatic symptoms and has been used previously in the MAL-ED cohort [32]. To distinguish between the effects of exposure to postpartum depression and prolonged exposure to depressive symptoms, we assessed (1) a measure of post-partum depressive symptoms defined by the average SRQ-16 scores at 1, 6, and 12 months of child age, (2) one measure of maternal depressive symptoms defined by the average SRQ-16 scores at 24 and 36 months of child age, and (3) one measure of maternal depressive symptoms defined by the SRQ-16 score at 60 months, or 5 years, of child age. Socioeconomic status was assessed through the WAMI index (Water, Assets, Maternal Education and Income) [33]. This measure of household socioeconomic status includes: (1) access to improved water and sanitation, (2) wealth measured by ownership of a set of eight assets, (3) maternal education, and (4) monthly household income. This index has been standardized and validated across the eight MAL-ED study sites [33]. This study assessed environmental factors that may impact child development (i.e., organization of the environment, provision of play material, opportunities for stimulation, and cleanliness of the child) through the Home Observation for the Measurement of the Environment (HOME) tool [34]. This tool was also used to measure some psychosocial factors (i.e., responsivity of the caregiver, avoidance of restrictions and punishment, and promotion of child development). This assessment tool has been used in studies worldwide [35, 36]. Furthermore, it was adapted and validated across the eight international sites of the MAL-ED study [21]. The HOME variable was measured at 6, 24, and 36 (±15 days) months of child age. HOME assessments at each of the three points in time were averaged and coded dichotomously at the overall median (i.e., for both sites together). The organization of the environment (SA median [IQR]: 11.0 [10.3,11.5]; TZ median [IQR]: 4.3 [3.3, 5.5] and maternal education (SA median: [IQR]: 10.5 [9.0, 12.0]; TZ median [IQR]: 7.0, [3.0, 7.0] were coded dichotomously at the site-specific medians due to non-overlapping distributions of these variables across the two sites. Following MAL-ED procedures, children were weighed and measured at enrollment. Weight at enrollment was converted to weight-for-age Z-scores (WAZ) following the WHO 2006 growth standards [37]. We used enrollment WAZ as a proxy for birthweight in the analysis because weight at birth was missing for some children and because age at enrollment varied from 0-17 days. Additionally, we conducted homogeneity tests to identify significant differences in associations between the two sites. We selected covariates based on a directed acyclic graph [38]. We used multivariable linear regression for the continuous WPPSI outcomes using SAS version 9.4. The model included (1) environmental factors (organization of the environment, provision of play material, opportunities for stimulation, cleanliness of the child, and WAMI index for socioeconomic status), (2) psychosocial factors (responsivity of the caregiver, avoidance of punishment, maternal depressive symptoms, and maternal education), (3) child birthweight, and (4) indicators for the fieldworker who collected the data on the home environment (HOME field assessors). We included the HOME field assessor as a covariate because the assessor was significantly associated with both the HOME inventory scale measurements and the WPSSI outcomes. We obtained ethical approval from the Institutional Review Boards for the original and follow-up studies at the University of Venda (Limpopo, South Africa), at the Haydom Lutheran Hospital (Haydom, Tanzania), and the University of Virginia School of Medicine (Charlottesville, United States).

Materials

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals for prenatal and postnatal care, allowing pregnant women in rural areas to receive medical advice and support without having to travel long distances.

2. Mobile clinics: Setting up mobile clinics that visit rural communities can bring healthcare services, including prenatal care and check-ups, closer to pregnant women who may have limited access to healthcare facilities.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support within rural communities can help bridge the gap in access to healthcare.

4. Health education programs: Implementing comprehensive health education programs that focus on maternal health and child development can empower women with knowledge and skills to take care of themselves and their children, even in resource-limited settings.

5. Maternal health incentives: Introducing incentives such as financial assistance or transportation support for pregnant women in rural areas can encourage them to seek regular prenatal care and access necessary healthcare services.

6. Partnerships with local organizations: Collaborating with local organizations, community leaders, and traditional birth attendants can help create a network of support and improve access to maternal health services in rural areas.

7. Mobile applications: Developing mobile applications that provide information, reminders, and resources related to maternal health can empower women to take control of their own health and access relevant information easily.

8. Maternal health awareness campaigns: Conducting targeted awareness campaigns to educate communities about the importance of maternal health and the available resources can help increase awareness and encourage women to seek timely care.

It’s important to note that the specific context and needs of each community should be considered when implementing these innovations to ensure their effectiveness and sustainability.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health and address the determinants of child cognitive development in low- and middle-income countries (LMICs) is as follows:

1. Strengthen Socioeconomic Support: Given that socioeconomic status was found to be strongly associated with child cognitive development, interventions should focus on improving the economic conditions of families in LMICs. This can include providing financial assistance, job training, and income-generating opportunities for mothers and families.

2. Enhance Home Environment: The study also found modest associations between the organization of the home environment and cognitive development. Interventions should aim to improve the home environment by providing resources and support for cognitive stimulation, such as access to educational materials, toys, and activities that promote cognitive development.

3. Address Maternal Mental Health: Maternal depression was assessed as a psychosocial factor that may impact child cognitive development. Interventions should prioritize maternal mental health support, including screening for and treating postpartum depression, providing counseling services, and promoting social support networks for mothers.

4. Improve Access to Education: Maternal education was identified as an important factor in child cognitive development. Efforts should be made to improve access to quality education for women in LMICs, including initiatives that provide scholarships, vocational training, and adult education programs.

5. Strengthen Healthcare Systems: To improve access to maternal health, it is crucial to strengthen healthcare systems in LMICs. This can be achieved by increasing the availability and affordability of maternal healthcare services, improving infrastructure and facilities, training healthcare providers, and implementing community-based healthcare programs.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to better cognitive development outcomes for children in LMICs.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in rural areas of Sub-Saharan African countries can improve access to maternal health services. This includes establishing well-equipped clinics and hospitals, ensuring availability of essential medicines and supplies, and training healthcare workers to provide quality maternal care.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to deliver maternal health information and services can help overcome geographical barriers. This can include sending SMS reminders for antenatal care appointments, providing access to teleconsultations with healthcare providers, and delivering educational content on maternal health through mobile applications.

3. Community-based interventions: Engaging and empowering local communities can improve access to maternal health services. This can involve training community health workers to provide basic maternal healthcare services, conducting awareness campaigns on maternal health, and establishing support groups for pregnant women and new mothers.

4. Financial incentives and subsidies: Implementing financial incentives, such as cash transfers or subsidies, can help overcome financial barriers to accessing maternal health services. This can include providing financial support for transportation to healthcare facilities, covering the cost of antenatal care visits and delivery expenses, and offering incentives for facility-based deliveries.

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 group or region where the recommendations will be implemented. This could be a specific rural area or a particular demographic group.

2. Collect baseline data: Gather data on the current status of maternal health access in the target population. This can include information on healthcare infrastructure, utilization of maternal health services, financial barriers, and other relevant factors.

3. Develop a simulation model: Create a simulation model that incorporates the recommended interventions and their potential impact on improving access to maternal health. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and financial resources.

4. Input intervention parameters: Define the parameters for each recommendation, such as the number of healthcare facilities to be established, the frequency and content of mobile health interventions, the number of community health workers to be trained, and the amount of financial incentives or subsidies to be provided.

5. Run simulations: Use the simulation model to run multiple scenarios with different combinations of intervention parameters. This will help assess the potential impact of each recommendation and identify the most effective combination of interventions.

6. Analyze results: Evaluate the simulation results to determine the projected improvements in access to maternal health services. This can include metrics such as increased utilization of antenatal care, facility-based deliveries, and postnatal care, as well as reductions in maternal mortality and morbidity rates.

7. Refine and validate the model: Continuously refine and validate the simulation model based on real-world data and feedback from experts in the field. This will ensure that the model accurately reflects the complexities of improving access to maternal health.

By following this methodology, policymakers and healthcare stakeholders can make informed decisions on implementing interventions that have the potential to significantly improve access to maternal health in low- and middle-income countries.

Authors & Co-Authors

Statistics:

Citations: 4

Authors: 9

Identifiers:

DOI: 10.1186/s12889-020-08598-5

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Multi-Countries, South Africa, Tanzania

Study Design:

Cohort Study

Study Approach:

Quantitative

Participants Gender:

Female