The Impact of Undernutrition on Cognition in Children with Severe Malaria and Community Children: A Prospective 2-Year Cohort Study

listen audio

Study Justification:
– The frequency of recovery from undernutrition after severe malaria is not well understood.
– The relationship between undernutrition during severe malaria and clinical and cognitive outcomes is not well characterized.
– This study aimed to evaluate undernutrition and cognition in children with severe malaria and community children to better understand these relationships.
Study Highlights:
– The study was conducted in Kampala, Uganda, which has a high prevalence of undernutrition and malaria.
– Children with cerebral malaria (CM), severe malarial anemia (SMA), and community children (CC) were included in the study.
– Undernutrition, defined as being underweight or stunted, was compared with mortality, hospital readmission, and cognition over a 24-month follow-up period.
– Wasting and being underweight were more common in children with severe malaria compared to community children.
– Undernutrition at enrollment was not associated with mortality or hospital readmission, but it was associated with lower cognitive scores in both severe malaria and community children.
– Wasting and being underweight returned to population levels after treatment for severe malaria.
Recommendations for Lay Reader:
– The study found that undernutrition during severe malaria is associated with worse long-term cognition in children.
– Treating undernutrition during and after severe malaria is important for improving cognitive outcomes.
– Early identification and treatment of undernutrition in children with severe malaria can help prevent long-term cognitive impairments.
Recommendations for Policy Maker:
– Develop and implement strategies to improve nutrition in children with severe malaria, including during and after treatment.
– Strengthen healthcare systems to ensure early identification and treatment of undernutrition in children with severe malaria.
– Increase awareness among healthcare providers about the importance of addressing undernutrition in children with severe malaria.
– Invest in research and interventions to improve nutrition and cognitive outcomes in children with severe malaria.
Key Role Players:
– Healthcare providers: Responsible for identifying and treating undernutrition in children with severe malaria.
– Researchers: Conduct studies to further understand the relationship between undernutrition, severe malaria, and cognitive outcomes.
– Policy makers: Develop and implement policies to address undernutrition in children with severe malaria.
– Community leaders: Raise awareness and promote interventions to improve nutrition in children with severe malaria.
Cost Items for Planning Recommendations:
– Nutrition programs: Funding for programs that provide nutritional support to children with severe malaria.
– Healthcare infrastructure: Investment in healthcare facilities and equipment to support the identification and treatment of undernutrition.
– Research funding: Allocation of resources for research studies on undernutrition and cognitive outcomes in children with severe malaria.
– Training and education: Budget for training healthcare providers on the importance of addressing undernutrition in children with severe malaria.

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 prospective 2-year cohort study, which provides a good level of evidence. The study includes a comparison between children with severe malaria and community children, which strengthens the findings. The study also uses a validated measure of cognition (Mullen Scales of Early Learning) and includes a large sample size. However, the abstract does not provide information on the specific methods used for data collection and analysis, which could be improved. Additionally, the abstract does not mention any limitations of the study, which should be included to provide a balanced assessment of the evidence. To improve the evidence, the abstract could provide more details on the study methods, including information on the recruitment process, data collection procedures, and statistical analysis. It would also be helpful to include a section on the limitations of the study, such as potential sources of bias or confounding factors. Overall, the evidence in the abstract is moderately strong, but providing more methodological details and discussing limitations would further strengthen the study.

Background: The frequency of recovery from undernutrition after an episode of severe malaria, and the relationship between undernutrition during severe malaria and clinical and cognitive outcomes are not well characterized. Methods: We evaluated undernutrition and cognition in children in Kampala, Uganda 18 months to 5 years of age with cerebral malaria (CM), severe malarial anemia (SMA) or community children (CC). The Mullen Scales of Early Learning was used to measure cognition. Undernutrition, defined as 2 SDs below median for weight-for-age (underweight), height-for-age (stunting) or weight-for-height (wasting), was compared with mortality, hospital readmission and cognition over 24-month follow-up. Results: At enrollment, wasting was more common in CM (16.7%) or SMA (15.9%) than CC (4.7%) (both p < 0.0001), and being underweight was more common in SMA (27.0%) than CC (12.8%; p = 0.001), while prevalence of stunting was similar in all three groups. By 6-month follow-up, prevalence of wasting or being underweight did not differ significantly between children with severe malaria and CC. Undernutrition at enrollment was not associated with mortality or hospital readmission, but children who were underweight or stunted at baseline had lower cognitive z-scores than those who were not {underweight, mean difference [95% confidence interval (CI)]-0.98 (-1.66,-0.31),-0.72 (-1.16,-0.27) and-0.61 (-1.08,-0.13); and stunted,-0.70 (-1.25,-0.15),-0.73 (-1.16,-0.31) and-0.61 (-0.96,-0.27), for CM, SMA and CC, respectively}. Conclusion: In children with severe malaria, wasting and being underweight return to population levels after treatment. However, being stunted or underweight at enrollment was associated with worse long-term cognition in both CC and children with severe malaria.

This study was conducted in Kampala, Uganda. Kampala is the capital and largest city in Uganda with an estimated population of 1 507 080 according to the 2014 census [33]. Children with CM and SMA were enrolled from Mulago Hospital which is the national referral hospital in Uganda. In 2019, Uganda accounted for about 5% of all malaria cases globally [34]. Uganda also has a high prevalence of undernutrition among children under 5 years (29% stunting, 11% underweight and 4% wasting in 2016) [35]. Between 2008 and 2013, Ugandan children aged 18 months to 5 years with CM, SMA and asymptomatic CC with no acute illness were enrolled in Kampala, Uganda. Children with CM and SMA meeting the study criteria were recruited and enrolled from the Acute Care Unit, which is the emergency pediatric unit of Mulago Hospital. On admission, national treatment guidelines were used to provide emergency care to children with CM or SMA. After stabilization, the caregivers of children meeting the eligibility criteria were approached for participation in the study. Children were enrolled after written informed consent was provided. The CC were recruited from the nuclear family, extended family or household compound area of children with CM or SMA. Parents of children with CM or SMA were given information about the study, asked whether any eligible children were present in their extended family, and requested to bring the eligible children to the center for evaluation. Parents of children in the household compound of a child with CM or SMA were notified about the study during a home visit. CM was defined as: (i) Plasmodium falciparum on blood smear; (ii) coma [Blantyre Coma Scale (BCS) score ≤2, Glasgow Coma Scale (GCS) ≤ 8] after ruling out other causes of coma (hypoglycemia, postictal state and meningitis). SMA was defined as the presence of P.falciparum on blood smear and a hemoglobin level ≤5 g/dl. Ugandan Ministry of Health national guidelines define SMA as a hemoglobin ≤5 g/dl with any P.falciparum parasitemia. No parasitemia threshold is required to meet this definition. We made our study definition of SMA to be consistent with the Ugandan Ministry of Health guidelines. Children with CM and severe anemia were classified as CM. Exclusion criteria for all children included: (i) previous history of head trauma, coma or prior hospitalization for undernutrition; (ii) cerebral palsy. Exclusion criteria for children with SMA included: (i) impaired consciousness to any degree on physical exam (e.g. GCS < 15 or BCS < 5); (ii) seizure activity prior to admission; (iii) any other clinical evidence of central nervous system (CNS) disease. Among children with severe malaria, those with concomitant infections were included but we excluded those with a known chronic illness. Exclusion criteria for CC included (i) any active illness or illness within the past 4 weeks requiring medical care; (ii) chronic illness requiring medical care; (iii) major medical abnormalities on screening history or physical exam; (iv) known developmental delay; and (v) prior history of coma. Children who had known HIV infection at the time of recruitment were not included in the study, but those who were diagnosed on enrollment (all children were tested) and were not symptomatic were not excluded from the study. Symptomatic HIV is known to affect cognition in children, so we excluded children with a known diagnosis of HIV. The number of children with asymptomatic, newly diagnosed HIV infection was small, and the effect of this infection on cognition was not known, so we did not exclude them from the study. Socioeconomic status (SES) was assessed using a scoring instrument that included material possessions, house structure, living density, food resources and access to electricity and clean water [36]. Emotional stimulation in the home was measured by age-appropriate versions of the Home Observation for the Measurement of the Environment (HOME). The Infant–Toddler version of the HOME Inventory was used for the children in the younger age group (<3 years), and the Early Childhood version used for the older age group (3–6 years) [37]. These age-appropriate versions of the HOME assessment were administered by trained staff during a home visit. Data on SES, home environment, dietary habits, parent and child education were collected at the home visit after discharge therefore it was missing for children who died or were lost to follow-up. Children with CM or SMA were managed according to the Ugandan Ministry of Health treatment guidelines. These included intravenous quinine or artesunate until a patient is alert and then oral quinine and artemether–lumefantrine therapy for outpatient [5]. In addition to the antimalarials, all children with SMA were transfused with blood at a dose of 20 mg/kg of whole blood or 10 mg/kg of packed cells. After correction of the severe anemia, the children were put on oral hematinic—ferrous sulfate at a dose of 2 mg/kg for 3 months [38]. A child’s weight was measured with a digital weight scale. Children who could stand were weighed on the scale. Those unable to stand were weighed in their caregiver’s arms, and the caregiver’s weight was subtracted from the child’s weight. Recumbent length was taken for all children with CM or SMA at enrollment. For CC at enrollment and follow-up and for children with CM or SMA at follow-up, recumbent length of children <2 years was determined using a stadiometer, and a wall mounted-tape measure was used to assess the standing height of children ≥2. Heights and weights were converted into z-scores (height-for-age, weight-for-age and weight-for-height) based on 2006 World Health Organization (WHO) growth standards for infants and children <5 years and 2007 WHO growth references for children ≥5 years by follow-up visits [39, 40]. We defined nutritional status using standard anthropometric z-score cutoffs: more than 2 SD units below the reference median for weight-for-age (underweight), height-for-age (stunted) or weight-for-height (wasted). Children underwent cognitive evaluation either a week after discharge (CM or SMA) or at enrollment (CC) and then at 6, 12 and 24 month follow-up. Neuropsychology testers were blinded to study group. The Mullen Scales of Early Learning [41], which have been used and validated in Ugandan children in previous studies [4, 5], were used to measure cognitive ability. The early learning composite score was calculated from the sum of fine motor, visual reception, receptive language and expressive language scores [5]. To account for differences in child age, we converted each raw score into a z-score using scores of the CC children. The z-scores were computed as (actual score—mean score for a child’s age)/SD, where the mean score for a child’s age and SD were computed by fitting a quadratic mixed effects model, including a random intercept for the child and where correlations within a child were based on time between visits, to data for all visits for all CC children. Children who crossed the 5-year age limit for the Mullen Scales of Early Learning were tested by the Kauffman Assessment Battery for Children-II (KABC-II) when ≥ 5 years. Outcomes from the Mullen Scales and the KABC-II are not directly comparable, as they measure different components of cognition, so only results from the Mullen Scales are reported. At later follow-up time points, more children crossed the 5 year age threshold and therefore did not have Mullen Scales data available (Supplementary Fig. S1). Written informed consent was obtained from parents/guardians of study participants. Ethical approval was granted by the Institutional Review Boards at Makerere University School of Medicine and University of Minnesota. Additional regulatory approval was obtained from The Uganda National Council of Science and Technology. Comparisons between categorical variables were assessed using Pearson χ2 and one-way analysis of variance for continuous variables. Pairwise comparisons of continuous variables were assessed using Student’s t-test. Logistic regression was used to assess the odds ratio (OR) and 95% CI of mortality by nutritional status (stunting, underweight and wasting) at enrollment. Negative binomial regression was used to assess incidence rate ratio (IRR; 95% CI) of hospital readmission by nutritional status at enrollment. Multiple linear mixed effects (LMEs) models were used to evaluate the correlation between demographic or social factors and weight-for-age, height-for-age and weight-for-height z-scores over the full 24-month study period. Within subject observations were correlated using a subject specific intercept and time-points were treated as categorical variables. A banded diagonal covariance matrix was used to model the within-subject variance–covariance errors, and the mixed model was fit by restricted maximum likelihood. Kenward–Roger approximations were used to estimate the denominator degrees of freedom. Age at enrollment, sex, SES, home environment, weekly meat consumption, preschool education, maternal and paternal education were evaluated as independent predictor variables. To assess whether a child’s nutritional status at enrollment was associated with differences in cognition in children with CM, SMA or in CC, we used multiple LME models with child cognitive z-score as the dependent variable. Cognitive testing at all time points was incorporated. Cognitive models included a subject specific intercept and time-points were treated as a categorical variable. We adjusted for age at enrollment, sex, SES, home environment, preschool education, maternal and paternal education. To adjust for multiple comparisons, the Benjamini–Hochberg false discovery rate was applied at a threshold of 0.05. All analyses used Stata version 15 (StataCorp, College Station, TX).

Based on the provided information, it is difficult to determine specific innovations for improving access to maternal health. The study focuses on the impact of undernutrition on cognition in children with severe malaria and community children, rather than directly addressing maternal health. To provide recommendations for improving access to maternal health, it would be helpful to have more information specifically related to maternal health challenges and potential innovations.
AI Innovations Description
Based on the information provided, the study highlights the relationship between undernutrition during severe malaria and clinical and cognitive outcomes in children. To improve access to maternal health, the following recommendation can be developed into an innovation:

1. Strengthening Antenatal Care: Implement comprehensive antenatal care programs that focus on nutrition education and counseling for pregnant women. This can include providing information on the importance of a balanced diet, micronutrient supplementation, and breastfeeding practices.

2. Improving Nutritional Support: Develop innovative approaches to provide nutritional support to pregnant women, especially those at risk of undernutrition. This can include the distribution of nutrient-rich food supplements, fortified foods, or micronutrient powders to improve maternal nutrition.

3. Enhancing Healthcare Infrastructure: Invest in improving healthcare infrastructure, particularly in rural areas, to ensure access to quality maternal healthcare services. This can involve building and equipping healthcare facilities, training healthcare providers, and implementing telemedicine solutions to reach remote areas.

4. Community Engagement and Education: Conduct community-based awareness campaigns to educate communities about the importance of maternal health and nutrition. This can involve training community health workers to provide information and support to pregnant women, organizing community events, and using media platforms to disseminate educational materials.

5. Collaboration and Partnerships: Foster collaboration between government agencies, non-governmental organizations, and private sector entities to address the complex challenges related to maternal health. This can involve leveraging resources, expertise, and networks to implement innovative solutions and scale up successful interventions.

It is important to note that these recommendations should be tailored to the specific context and needs of the population. Continuous monitoring and evaluation should also be conducted to assess the effectiveness and impact of the implemented innovations.
AI Innovations Methodology
The study you provided focuses on the impact of undernutrition on cognition in children with severe malaria and community children in Kampala, Uganda. While the study does not directly address innovations to improve access to maternal health, I can provide some potential recommendations and a methodology to simulate their impact on improving access to maternal health.

Potential Recommendations:
1. Mobile Health (mHealth) Interventions: Develop and implement mobile health applications that provide pregnant women with access to information, resources, and reminders for prenatal care, nutrition, and maternal health services.
2. Community Health Workers: Train and deploy community health workers to provide education, support, and referrals for pregnant women in remote or underserved areas, bridging the gap between communities and formal healthcare systems.
3. Telemedicine: Establish telemedicine platforms to enable remote consultations and monitoring for pregnant women, reducing the need for travel and improving access to healthcare professionals.
4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access essential maternal health services, including antenatal care, skilled birth attendance, and postnatal care.
5. Transportation Support: Develop transportation initiatives, such as subsidized or free transportation services, to overcome geographical barriers and ensure pregnant women can reach healthcare facilities for prenatal and postnatal care.

Methodology to Simulate Impact:
1. Define the Parameters: Identify the key variables that influence access to maternal health, such as distance to healthcare facilities, availability of healthcare providers, financial constraints, and knowledge about maternal health services.
2. Data Collection: Gather data on the current status of these variables in the target population. This can be done through surveys, interviews, or existing data sources.
3. Model Development: Develop a simulation model that incorporates the identified variables and their relationships. This model should simulate the impact of the recommended interventions on improving access to maternal health.
4. Intervention Scenarios: Create different scenarios by adjusting the variables related to the recommended interventions. For example, simulate the impact of increasing the number of community health workers or implementing a maternal health voucher program.
5. Simulate Outcomes: Run the simulation model with each scenario to estimate the potential impact on access to maternal health. Measure outcomes such as the number of pregnant women accessing prenatal care, the reduction in travel distance, or the increase in knowledge about maternal health services.
6. Analyze Results: Compare the outcomes of different scenarios to identify the most effective interventions for improving access to maternal health. Consider factors such as cost-effectiveness, scalability, and sustainability.
7. Refine and Implement: Use the simulation results to inform decision-making and prioritize the implementation of interventions that have the greatest potential for impact. Continuously monitor and evaluate the implemented interventions to assess their effectiveness and make adjustments as needed.

Please note that the methodology provided is a general framework and may need to be adapted based on the specific context and data availability.

Share this:
Facebook
Twitter
LinkedIn
WhatsApp
Email