Socioeconomic predictors of cognition in Ugandan children: Implications for community interventions

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Study Justification:
– The study aimed to identify key variables predicting cognition in Ugandan children in order to guide interventions to improve cognition in at-risk children.
– This is important because several interventions have been suggested, but understanding the predictors of cognition is necessary to effectively target these interventions.
Highlights:
– The study followed a cohort of 89 healthy children aged 5 to 12 years old over 24 months.
– Cognitive tests measuring visual spatial processing, memory, attention, and spatial learning were administered at baseline, 6 months, and 24 months.
– Nutritional status, child’s educational level, maternal education, socioeconomic status, and quality of the home environment were also measured at baseline.
– The study found that a higher child’s education level was associated with better memory, attention, and spatial learning scores over the 24 months.
– Higher nutrition scores predicted better visual spatial processing and spatial learning scores.
– A higher home environment score predicted a better memory score.
– The conclusion is that cognition in Ugandan children is predicted by child’s education, nutritional status, and the home environment.
– Community interventions to improve cognition may be effective if they target multiple socioeconomic variables.
Recommendations:
– Based on the findings, community interventions to improve cognition in Ugandan children should target multiple socioeconomic variables, including child’s education, nutritional status, and the home environment.
Key Role Players:
– Researchers and scientists in the field of child development and education
– Policy makers and government officials responsible for education and healthcare in Uganda
– Teachers and educators who can implement interventions in schools
– Parents and caregivers who play a crucial role in supporting children’s education and providing a nurturing home environment
Cost Items for Planning Recommendations:
– Educational resources and materials for schools
– Teacher training and professional development programs
– Nutritional programs and interventions
– Home visitation programs to support parents and caregivers
– Research and evaluation to monitor the effectiveness of interventions
– Funding for implementation and sustainability of interventions

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 a cohort study, which provides valuable longitudinal data. The sample size of 89 children is relatively small, which may limit the generalizability of the findings. The cognitive tests used have been validated in previous studies, which strengthens the validity of the results. However, the abstract does not provide information on the statistical analysis methods used, which makes it difficult to assess the robustness of the findings. To improve the strength of the evidence, it would be helpful to include more details on the statistical analysis, such as the specific regression model used and any adjustments made for potential confounding variables. Additionally, increasing the sample size and including a more diverse population would enhance the generalizability of the findings.

Background: Several interventions to improve cognition in at risk children have been suggested. Identification of key variables predicting cognition is necessary to guide these interventions. This study was conducted to identify these variables in Ugandan children and guide such interventions. Methods: A cohort of 89 healthy children (45 females) aged 5 to 12 years old were followed over 24 months and had cognitive tests measuring visual spatial processing, memory, attention and spatial learning administered at baseline, 6 months and 24 months. Nutritional status, child’s educational level, maternal education, socioeconomic status and quality of the home environment were also measured at baseline. A multivariate, longitudinal model was then used to identify predictors of cognition over the 24 months. Results: A higher child’s education level was associated with better memory (p = 0.03), attention (p = 0.005) and spatial learning scores over the 24 months (p = 0.05); higher nutrition scores predicted better visual spatial processing (p = 0.002) and spatial learning scores (p = 0.008); and a higher home environment score predicted a better memory score (p = 0.03). Conclusion: Cognition in Ugandan children is predicted by child’s education, nutritional status and the home environment. Community interventions to improve cognition may be effective if they target multiple socioeconomic variables. © 2009 Bangirana et al.

The present study was conducted at Mulago Hospital, Kampala, Uganda. Participants were children aged 5 to 12 years recruited as healthy community controls for children with cerebral malaria and uncomplicated malaria taking part in prospective studies looking at the cognitive sequelae of cerebral malaria [21], [22]. They were recruited from the homes or neighbourhoods of children with cerebral malaria and uncomplicated malaria. All children had a medical history and physical examination done to ensure they were healthy at the time of recruitment. Children with a positive smear for malaria were treated with chloroquine and sulfadoxine/pyrimethamine (the first line antimalarial treatment at that time) while those with intestinal parasites were given appropriate antihelminthic medication as per the national health guidelines. Inclusion criteria were age 5–12 years with no acute illness and signed informed consent from the parent/guardian. Exclusion criteria were (1) a history of meningitis, encephalitis, or any brain disorder, including cerebral malaria; (2) a history of developmental delay; (3) prior admission to the hospital for malnutrition; (4) a history of chronic illness; (5) treatment for an acute illness during the preceding month and (6) admission for malaria during the preceding 6 months. Ethical approval for the study was granted by the Institutional Review Boards for Human Studies at Makerere University Faculty of Medicine, University Hospitals of Cleveland, Case Western Reserve University, Indiana Wesleyan University, University of Minnesota and the Uganda National Council for Science and Technology. Cognitive testing was done at baseline after physical examination with follow up testing at 6 months and 24 months by testers trained in the administration of the tests. Tests instructions from the test manuals were administered in the local language for children who did not understand English. Instructions were repeated when necessary in cases where the children failed to understand them. In some instances where the child still had difficultly comprehending, the mother or caretaker was asked to explain to the child. Visual spatial processing and memory were measured by the Kaufmann Assessment Battery for Children (KABC) [23] while spatial learning and attention were measured by the Tactual Performance Test (TPT) [24] and the Test of Variables of Attention (TOVA) [25] respectively. These tests have been validated in previous studies with children in Africa and South East Asia [12], [14], [16]. The two scales of the KABC that were administered were the Sequential Processing Scale where problems are solved by arranging the input in sequential order and the Simultaneous Processing Scale where problems are spatial, analogic or organisational and are solved by integrating the input simultaneously [23]. The TPT was administered to the blindfolded child who was required to place six wooden blocks into corresponding holes in a board. The child was first given the blocks to feel their shapes, then feel the holes in the board and their location. The child was given three trials lasting ten minutes each to place the blocks into the holes, the first trial was with the preferred hand, then the non preferred hand and finally with both hands. The TOVA was administered on a laptop where the child was asked to press a switch whenever the target stimulus (a small black box in the top position) appeared and not to press when the non target stimulus (a small black box in the bottom position) appeared. Outcome scores are inattention (failure to respond), commission (responding to non target), response time (time to respond to target), response time variability (variance in response times) and d’ prime (measure of response sensitivity). Visual spatial processing scores were derived from the Simultaneous Processing Scale of the KABC which comprises of Face Recognition, Gestalt Closure, Triangles, Matrix Analogies, Spatial Memory and Photo Series subscales while memory scores were derived from the Sequential Processing Scale which comprises of Hand Movements, Number Recall and Word Order subscales. Spatial learning was measured by the average time per block for the three trials on the TPT while attention was measured by the d prime score of the TOVA which is one’s ability to discriminate between the target and non target stimuli. While the child was doing the baseline cognitive tests, the parent/caregiver was asked about the quality of child’s home environment. The quality of the home environment was measured by the Middle Childhood Home Observation for the Measurement of the Environment (MC-HOME) [26]. The MC-HOME is used to assess the stimulation and learning opportunities offered by the child’s home environment. Studies using similar home evaluations have shown that the child’s home environment affects its cognitive development [13], [27]. The MC-HOME consists of eight subscales; Responsivity, Encouragement of maturity, Emotional climate, Learning materials and opportunities, Enrichment, Family companionship, Family integration and Physical environment. It has 59 items however item 40 ‘Family member has taken child to (or arranged for child to visit) a scientific, historical or art museum within past year’ was omitted because it was deemed not applicable to most of the children in the sample thus leaving 58 items for use in the study. This modified MC-HOME had an inter-item reliability of 0.85. Nutrition was assessed as in our previous studies [21], [22] by comparing weight for age to published norms [28] and obtaining a standardized z-score (Statistical Analysis System (SAS) release 9.1, SAS Institute, Inc., Cary, North Carolina). Socioeconomic status was assessed using a scoring instrument incorporating a checklist of material possessions, house structure, living density, food resources and access to electricity and clean water. Level of education of the child and mother were scored as follows: None = 0, Nursery = 1, Primary school grades 1−7 = 2−8, Secondary education = 9, Post-secondary school = 10. Children spend one to three years in nursery school (pre-primary) and seven years in primary school for classes Primary one to Primary seven (P1 to P7). The age of entry into nursery and primary varies because parents may delay to take children to school for various reasons. The Uganda government has a Universal Primary Education policy where all children are entitled to free primary education where schools are urged to promote children to the next class regardless of the performance. Six socioeconomic variables were obtained from the above assessments; quality of the home environment (MC-HOME score), nutritional status, maternal education level, child’s education level and socioeconomic status (SES) score. Data were entered into databases using FileMaker Pro 7 and analysed using Statistical Package for the Social Sciences (SPSS) version 11.0 and SAS 9.1. Raw cognitive test scores were log transformed to generate normal distributions, with a higher score for visual spatial processing, memory and attention reflecting a better score and a lower score for spatial learning reflecting a better score. Pearson’s correlations were run between test scores at baseline and 6 months and between 6 months scores and 24 months scores to determine the test-retest reliabilities of the tests. Similar correlations were also run between the socioeconomic factors to determine the relationships between them. A longitudinal mixed effects model [29] was used to study the effects of socioeconomic factors and other covariates (baseline age, gender, weight-for-age z-score, child’s education level, home score, social economic status (SES), and maternal education) on cognitive assessments, since the same cognitive assessments were performed at three time points. In the regression analyses, the predictor variable coefficients were calculated for each of the four outcome variables (log-transformed scores in the areas of visual spatial processing, learning, attention and working memory). Exponentiated coefficients represent the percent change in geometric mean per unit on the non-transformed scale of the predictor variable [30].

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health. These apps could provide guidance on prenatal care, nutrition, and exercise, as well as reminders for appointments and medication.

2. Telemedicine: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals remotely. This could involve video consultations, remote monitoring of vital signs, and the ability to send and receive medical records and test results electronically.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and pregnant women who may have limited access to transportation or face cultural barriers to seeking care.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers could cover the cost of prenatal care, delivery, and postnatal care, ensuring that women can afford the necessary healthcare services.

5. Maternal Health Clinics: Establish specialized maternal health clinics that provide comprehensive care for pregnant women. These clinics could offer a range of services, including prenatal check-ups, ultrasounds, childbirth classes, and postnatal care, all in one location.

6. Health Education Programs: Develop and implement health education programs that target pregnant women and their families. These programs could focus on topics such as nutrition, breastfeeding, safe childbirth practices, and newborn care, empowering women with the knowledge and skills to make informed decisions about their health and the health of their babies.

7. Transportation Support: Address transportation barriers by providing transportation support for pregnant women to access healthcare facilities. This could involve partnering with local transportation services or implementing dedicated transportation programs specifically for pregnant women.

8. Maternal Health Hotlines: Establish hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women. These hotlines could be available 24/7 and offer assistance in multiple languages to ensure accessibility for all women.

9. Maternal Health Monitoring Devices: Develop and distribute wearable devices or home monitoring kits that allow pregnant women to track their vital signs, fetal movements, and other relevant health indicators. These devices could provide real-time data to healthcare providers, enabling early detection of potential complications and timely interventions.

10. Public-Private Partnerships: Foster collaborations between government agencies, healthcare providers, and private organizations to improve access to maternal health services. These partnerships can leverage resources, expertise, and funding to implement innovative solutions and expand the reach of maternal healthcare initiatives.
AI Innovations Description
The study conducted in Uganda aimed to identify socioeconomic predictors of cognition in children and their implications for community interventions. The researchers found that a higher level of education for both the child and mother, better nutritional status, and a higher quality home environment were associated with better cognitive scores over a 24-month period. These findings suggest that community interventions to improve cognition in children should target multiple socioeconomic variables.
AI Innovations Methodology
Based on the information provided, the study conducted at Mulago Hospital in Uganda aimed to identify socioeconomic predictors of cognition in Ugandan children and their implications for community interventions. The study followed a cohort of 89 healthy children aged 5 to 12 years over 24 months and measured cognitive tests, nutritional status, child’s education level, maternal education, socioeconomic status, and quality of the home environment.

To improve access to maternal health, it is important to consider innovations that address the specific challenges faced in this area. Some potential recommendations for innovation to improve access to maternal health could include:

1. Telemedicine: Implementing telemedicine solutions that allow pregnant women in remote or underserved areas to access prenatal care and consultations with healthcare providers remotely. This can help overcome geographical barriers and improve access to essential maternal health services.

2. Mobile health (mHealth) applications: Developing mobile applications that provide pregnant women with information, reminders, and guidance on prenatal care, nutrition, and maternal health. These apps can also facilitate communication between pregnant women and healthcare providers, enabling them to seek advice and support when needed.

3. Community-based interventions: Implementing community-based programs that educate and empower women and their families about maternal health, including prenatal care, safe delivery practices, and postnatal care. These interventions can be tailored to the specific cultural and social context of the community to ensure maximum effectiveness.

4. Maternal health clinics: Establishing dedicated maternal health clinics in underserved areas, staffed with skilled healthcare providers who can provide comprehensive prenatal, delivery, and postnatal care. These clinics can also serve as centers for health education and community outreach.

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

1. Define the target population: Identify the specific population group that the recommendations aim to benefit, such as pregnant women in rural areas or low-income communities.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population, including factors such as healthcare infrastructure, availability of skilled healthcare providers, utilization rates of prenatal care, and maternal health outcomes.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on improving access to maternal health. The model should consider factors such as the number of women reached by each recommendation, the expected increase in utilization of maternal health services, and the potential improvement in maternal health outcomes.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Vary the parameters of the recommendations, such as the coverage of telemedicine services or the scale of community-based interventions, to explore different scenarios and their corresponding outcomes.

5. Analyze results: Analyze the simulation results to evaluate the potential impact of the recommendations on improving access to maternal health. Assess the changes in utilization rates, maternal health outcomes, and any other relevant indicators.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. Ensure that the model accurately represents the complexities of the maternal health system and the potential effects of the recommendations.

7. Communicate findings and make recommendations: Present the findings of the simulation study, including the potential impact of the recommendations on improving access to maternal health. Use the results to inform decision-making and make recommendations for implementing the most effective interventions.

By following this methodology, stakeholders can gain insights into the potential impact of different innovations and recommendations on improving access to maternal health. This information can guide the development and implementation of interventions that effectively address the challenges faced in this area.

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