Background: Young children with diets lacking diversity with low consumption of animal source foods are at risk of iron deficiency anemia (IDA). Objectives: Our objectives were to determine the impact of supplementing diets with 1 egg/d on 1) plasma ferritin, soluble transferrin receptor (sTfR), body iron index (BII), and hemoglobin concentrations and 2) the prevalence of iron deficiency (ID), anemia, and IDA. Methods: Malawian 6-9-mo-old infants in the Mazira trial (clinicaltrials.gov; NCT03385252) were individually randomly assigned to receive 1 egg/d for 6 mo (n = 331) or continue their usual diet (n = 329). In this secondary analysis, hemoglobin, plasma ferritin, sTfR, C-reactive protein (CRP), and α-1-acid glycoprotein (AGP) were measured at enrollment and 6-mo follow-up. Iron biomarkers were corrected for inflammation. Ferritin, sTfR, BII, and hemoglobin were compared between groups using linear regression. Prevalence ratios (PRs) for anemia (hemoglobin <11 g/dL) and ID (ferritin <12 μg/L, sTfR >8.3 mg/L, or BII <0 mg/kg) between groups were compared using log binomial or modified Poisson regression. Results: A total of 585 children were included in this analysis (Egg: n = 286; Control: n = 299). At enrollment, the total prevalence of anemia was 61% and did not differ between groups. At 6-mo follow-up, groups did not differ in geometric mean concentration of hemoglobin [mean (95% CI); Egg: 10.9 (10.7, 11.1) g/dL; Control: 11.1 (10.9, 11.2) g/dL] and inflammation-adjusted ferritin [Egg: 6.52 (5.98, 7.10) μg/L; Control: 6.82 (6.27, 7.42) μg/L], sTfR [Egg: 11.34 (10.92, 11.78) mg/L; Control: 11.46 (11.04, 11.89) mg/L] or BII [Egg: 0.07 (0.06, 0.09) mg/kg; Control: 0.07 (0.05, 0.08) mg/kg]. There were also no group differences in anemia [Egg: 46%; Control 40%; PR: 1.15 (95% CI: 0.96, 1.38)], ID [PR: 0.99 (0.94, 1.05)], or IDA [PR: 1.12 (0.92, 1.36)]. Conclusions: Providing eggs daily for 6 mo did not affect iron status or anemia prevalence in this context. Other interventions are needed to address the high prevalence of ID and anemia among young Malawian children. This trial is registered at http://www.clinicaltrials.gov as NCT03385252.
The Mazira Project was conducted between February 2018 and January 2019 in the Mangochi District of Malawi (clinicaltrials.gov registry {"type":"clinical-trial","attrs":{"text":"NCT03385252","term_id":"NCT03385252"}}NCT03385252). Children were randomly assigned to an intervention group, receiving 1 egg/d for 6 mo, or a control group that did not receive additional eggs. Details of the study design have been reported previously (20, 21). The study was promoted through community outreach events and study participants were recruited by home visits from household listings. Children were eligible if they were between the ages of 6.0 and 9.9 mo, were of singleton birth, and planned to reside in the catchment areas of the Lungwena or Malindi health centers for the study duration. Children were excluded based on wasting (midupper arm circumference ≤12.5 cm), severe anemia (hemoglobin ≤5 g/dL), bipedal edema, acute illness warranting hospital referral, history of egg allergy, congenital defects, or other morbidities that may impede growth or development. Children were referred to a health center if they presented with signs of severe dehydration or screened positive for wasting, bipedal edema, malaria, or severe anemia during any study visits. Caregivers were oriented to the clinic facilities, activities, purpose, and procedures of the research study and had opportunities to ask questions and discuss concerns in a group setting and privately with staff members. They provided written informed consent at enrollment by signature or thumbprint to confirm their study participation, consent to future use of collected blood samples, and right to withdraw at any time. This study followed principles of ethical conduct approved by the Institutional Review Board at the University of California, Davis and the Research Ethics Committee at the University of Malawi College of Medicine. The target sample size for the main trial was 662 children, based on the desire to detect a 0.25 SD difference between groups in the primary outcome measure of length-for-age z-score with α = 0.05 and 80% power. Children were block-randomized in groups of 10 and allocated to the egg intervention or non-intervention group in a 1:1 ratio after enrolling and completing baseline assessments. From the current block, caregivers randomly selected 1 opaque, unmarked envelope containing a card with a unique randomization code to reveal their group assignment. Study staff conducting assessments were masked to group assignments. A full description of the intervention groups has been published elsewhere (20). Briefly, each week caregivers in the egg intervention group received 7 eggs to feed the enrolled child 1 egg/d, plus 7 additional eggs to share with other household members. Study staff delivered eggs to intervention households twice per week and conducted recalls on the most recent egg feeding. The control group continued their usual diet, and their households were visited twice per week to report on the child's most recent meal. They received wash tubs, buckets, and plastic bins as participation incentives during the study and a mixed basket of foods, including eggs, at the completion of the study. This package of goods was selected to be of equal value to that of the eggs provided to the intervention group. All study participants received fabric cloth, sugar, and soap tablets after completing each visit. During the initial clinic visit and 6-mo follow-up, children were assessed for growth, development, and dietary intake. Child recumbent length and weight were converted to z-scores using the sex- and age-specific WHO Growth Standards (22). Enrollment surveys and initial household visits assessed demographic characteristics of the study child and household members, including household assets and food insecurity using the Household Food Insecurity Access Scale (HFIAS) (23) and home environment using the Home Observation for Measurement of the Environment (HOME) (24) indicator. At clinic visits, trained nurses collected venous blood samples to measure hemoglobin concentration using a portable spectrophotometer (Hemocue Hb 201; HemoCue, Inc.) and presence of malaria antigens using a rapid diagnostic test kit (SD Bioline Malaria Ag P.f/Pan; Abbott Diagnostics) with >85% sensitivity and ≥90% specificity for Plasmodium falciparum (25, 26). Blood samples were collected in lithium heparin tubes, immediately placed in a cooler with ice, centrifuged at ambient temperature for 15 min at 1040 × g, and placed in aliquots on site. Plasma samples were temporarily stored at –20°C and transported in coolers at the end of each day to a storage freezer maintained at –80°C. Aliquots were shipped on dry ice to laboratories completing plasma analyses. Plasma ferritin, sTfR, C-reactive protein (CRP), α-1-acid glycoprotein (AGP), and retinol binding protein (RBP) were measured by combined sandwich techniques with ELISA methods by the VitMin Lab (27). All analytes were measured from a single well containing 50–75 µL plasma for all children who provided a minimum of 450 µL plasma sample from blood draws. A 10% subset of samples was reanalyzed for quality assurance. Replicates of pooled plasma samples were run with each tray, and the CV for each indicator was calculated as the following: ferritin (2.3%), sTfR (3.6%), CRP (5.8%), and AGP (8.1%). A detailed statistical analysis plan was developed and posted (https://osf.io/vfrg7) prior to analysis and analyst unblinding. All data cleaning, management, and analyses were performed using de-identified data in Stata (version 15; StataCorp LLC) (28). Indices above the upper limit of detection were replaced by the maximum observed values, and indices below the lower limit of detection were replaced by zeroes and converted to half the limit of detection as needed for analytical models performed on the log scale. Ferritin and sTfR were corrected for subclinical inflammation on the log-transformed scale using the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) regression approach. This method adjusts for elevated CRP and AGP above the lowest decile set by an external reference group of preschool-aged children (29, 30). Body iron index (BII) was calculated according to Cook’s formula (31) by applying constants to the ratio of sTfR:ferritin using inflammation-adjusted values, such that the quantitative estimates of iron stores are indicated by positive values and the magnitude of iron deficit is depicted with negative values (32). Dichotomous variables were created for anemia (hemoglobin <11 g/dL), ID (ferritin <12 µg/L, sTfR >8.3 mg/L, or BII <0 mg/kg), and IDA (both anemia and ID) (33–35). Descriptive statistics were calculated for demographic characteristics, iron indices, and inflammation (CRP >5 mg/L or AGP >1 g/L) (36) at enrollment by group assignment. Linear regression models assessed groupwise differences in mean concentrations of hemoglobin and inflammation-corrected ferritin, sTfR, and BII. The prevalences of anemia, ID, and IDA were compared by group assignment using prevalence ratios estimated using logistic regression with a logarithmic link function and prevalence differences estimated using linear probability models with heteroscedasticity-consistent SEs (37). Modified Poisson models were used when log binomial models failed to converge (38). Our primary inferences were drawn from minimally adjusted models that controlled for baseline values of the outcome variable. For fully adjusted models, covariates were selected based on a bivariate association with the outcome variable (P < 0.1) from the following set of a priori identified variables: child age, child sex, maternal education, household asset index, number of children under 5 in the household, month of assessment, blood processing time, and inflammation-adjusted retinol binding protein (RBP). Malaria was examined for inclusion as a covariate based on a bivariate association (P < 0.1) with hemoglobin and anemia but not for ferritin, sTfR, BII, ID, or IDA, since these indicators included malaria in the correction for inflammation. Linear regression models were used to impute missing baseline values, which affected 11% of hemoglobin and 20% of ferritin, sTfR, CRP, and AGP covariates included in analytical models. Demographic variables were evaluated for bivariate associations with each biomarker and were used for imputation of missing baseline measures when they retained significance (P < 0.1) in multivariable linear regression models. Additional sensitivity analyses were conducted excluding children missing baseline data and imputing with the mean value. Participant characteristics of children lost to follow-up or missing outcome measures were compared to children with complete measures. We used an inverse probability of censoring-weighted approach to reweight the analytic sample to match the enrolled sample and then compared these results with those from the principal models.
The study aimed to investigate the effects of supplementing the diets of young Malawian children with 1 egg per day on iron and anemia status. This was important because children with diets lacking diversity and low consumption of animal source foods are at risk of iron deficiency anemia (IDA). Understanding the impact of egg supplementation could inform interventions to address the high prevalence of ID and anemia among young Malawian children.
Highlights:
– The study included 585 children aged 6-9 months from the Mazira trial in Malawi.
– Children were randomly assigned to receive 1 egg per day for 6 months (intervention group) or continue their usual diet (control group).
– Hemoglobin, plasma ferritin, soluble transferrin receptor (sTfR), C-reactive protein (CRP), and α-1-acid glycoprotein (AGP) were measured at enrollment and 6-month follow-up.
– The study found that providing eggs daily for 6 months did not affect iron status or the prevalence of anemia in this context.
– Other interventions may be needed to address the high prevalence of ID and anemia among young Malawian children.
Recommendations:
Based on the study findings, the following recommendations can be made:
1. Further research: Conduct additional studies to explore alternative interventions that can effectively address iron deficiency and anemia among young Malawian children.
2. Dietary diversification: Promote the consumption of a diverse range of nutrient-rich foods, including animal source foods, to improve iron status and reduce the risk of anemia.
3. Nutrition education: Implement nutrition education programs targeting caregivers to increase their knowledge and awareness of the importance of a balanced diet for children’s health and development.
4. Public health interventions: Develop and implement public health interventions that address the underlying causes of iron deficiency and anemia, such as poverty, food insecurity, and limited access to healthcare.
Key Role Players:
1. Researchers: Conduct further studies to investigate alternative interventions and evaluate their effectiveness in addressing iron deficiency and anemia.
2. Healthcare professionals: Provide guidance and support to caregivers on nutrition and the prevention of iron deficiency and anemia.
3. Policy makers: Develop and implement policies that promote dietary diversification, nutrition education, and public health interventions to address iron deficiency and anemia among young children.
4. Community leaders: Engage community leaders to raise awareness and mobilize communities to support interventions aimed at improving iron and anemia status in children.
Cost Items for Planning Recommendations:
1. Research funding: Allocate resources for conducting further studies on alternative interventions and evaluating their effectiveness.
2. Training and capacity building: Invest in training healthcare professionals and community leaders to effectively deliver nutrition education and implement public health interventions.
3. Program implementation: Allocate funds for the implementation of nutrition education programs, public health interventions, and community mobilization activities.
4. Monitoring and evaluation: Set aside resources for monitoring and evaluating the impact of interventions on iron and anemia status, as well as the cost-effectiveness of the programs implemented.
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs will depend on the scale and scope of the interventions implemented.
The strength of evidence for this abstract is 7 out of 10. The evidence in the abstract is based on a randomized controlled trial, which is a strong study design. The study included a large sample size and measured multiple outcomes related to iron and anemia status. However, the evidence could be improved by providing more details on the methods used, such as the randomization process and blinding of assessors. Additionally, the abstract does not mention any limitations of the study, which would be helpful for interpreting the results.
Background: Young children with diets lacking diversity with low consumption of animal source foods are at risk of iron deficiency anemia (IDA). Objectives: Our objectives were to determine the impact of supplementing diets with 1 egg/d on 1) plasma ferritin, soluble transferrin receptor (sTfR), body iron index (BII), and hemoglobin concentrations and 2) the prevalence of iron deficiency (ID), anemia, and IDA. Methods: Malawian 6-9-mo-old infants in the Mazira trial (clinicaltrials.gov; NCT03385252) were individually randomly assigned to receive 1 egg/d for 6 mo (n = 331) or continue their usual diet (n = 329). In this secondary analysis, hemoglobin, plasma ferritin, sTfR, C-reactive protein (CRP), and α-1-acid glycoprotein (AGP) were measured at enrollment and 6-mo follow-up. Iron biomarkers were corrected for inflammation. Ferritin, sTfR, BII, and hemoglobin were compared between groups using linear regression. Prevalence ratios (PRs) for anemia (hemoglobin <11 g/dL) and ID (ferritin 8.3 mg/L, or BII 85% sensitivity and ≥90% specificity for Plasmodium falciparum (25, 26). Blood samples were collected in lithium heparin tubes, immediately placed in a cooler with ice, centrifuged at ambient temperature for 15 min at 1040 × g, and placed in aliquots on site. Plasma samples were temporarily stored at –20°C and transported in coolers at the end of each day to a storage freezer maintained at –80°C. Aliquots were shipped on dry ice to laboratories completing plasma analyses. Plasma ferritin, sTfR, C-reactive protein (CRP), α-1-acid glycoprotein (AGP), and retinol binding protein (RBP) were measured by combined sandwich techniques with ELISA methods by the VitMin Lab (27). All analytes were measured from a single well containing 50–75 µL plasma for all children who provided a minimum of 450 µL plasma sample from blood draws. A 10% subset of samples was reanalyzed for quality assurance. Replicates of pooled plasma samples were run with each tray, and the CV for each indicator was calculated as the following: ferritin (2.3%), sTfR (3.6%), CRP (5.8%), and AGP (8.1%). A detailed statistical analysis plan was developed and posted (https://osf.io/vfrg7) prior to analysis and analyst unblinding. All data cleaning, management, and analyses were performed using de-identified data in Stata (version 15; StataCorp LLC) (28). Indices above the upper limit of detection were replaced by the maximum observed values, and indices below the lower limit of detection were replaced by zeroes and converted to half the limit of detection as needed for analytical models performed on the log scale. Ferritin and sTfR were corrected for subclinical inflammation on the log-transformed scale using the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) regression approach. This method adjusts for elevated CRP and AGP above the lowest decile set by an external reference group of preschool-aged children (29, 30). Body iron index (BII) was calculated according to Cook’s formula (31) by applying constants to the ratio of sTfR:ferritin using inflammation-adjusted values, such that the quantitative estimates of iron stores are indicated by positive values and the magnitude of iron deficit is depicted with negative values (32). Dichotomous variables were created for anemia (hemoglobin <11 g/dL), ID (ferritin 8.3 mg/L, or BII 5 mg/L or AGP >1 g/L) (36) at enrollment by group assignment. Linear regression models assessed groupwise differences in mean concentrations of hemoglobin and inflammation-corrected ferritin, sTfR, and BII. The prevalences of anemia, ID, and IDA were compared by group assignment using prevalence ratios estimated using logistic regression with a logarithmic link function and prevalence differences estimated using linear probability models with heteroscedasticity-consistent SEs (37). Modified Poisson models were used when log binomial models failed to converge (38). Our primary inferences were drawn from minimally adjusted models that controlled for baseline values of the outcome variable. For fully adjusted models, covariates were selected based on a bivariate association with the outcome variable (P < 0.1) from the following set of a priori identified variables: child age, child sex, maternal education, household asset index, number of children under 5 in the household, month of assessment, blood processing time, and inflammation-adjusted retinol binding protein (RBP). Malaria was examined for inclusion as a covariate based on a bivariate association (P < 0.1) with hemoglobin and anemia but not for ferritin, sTfR, BII, ID, or IDA, since these indicators included malaria in the correction for inflammation. Linear regression models were used to impute missing baseline values, which affected 11% of hemoglobin and 20% of ferritin, sTfR, CRP, and AGP covariates included in analytical models. Demographic variables were evaluated for bivariate associations with each biomarker and were used for imputation of missing baseline measures when they retained significance (P < 0.1) in multivariable linear regression models. Additional sensitivity analyses were conducted excluding children missing baseline data and imputing with the mean value. Participant characteristics of children lost to follow-up or missing outcome measures were compared to children with complete measures. We used an inverse probability of censoring-weighted approach to reweight the analytic sample to match the enrolled sample and then compared these results with those from the principal models.
Based on the provided information, it appears that the study focused on the effects of supplementing diets with 1 egg per day on iron and anemia status among young Malawian children. The study did not find any significant impact of the intervention on iron status or anemia prevalence in this context. Therefore, the study suggests that other interventions are needed to address the high prevalence of iron deficiency and anemia among young Malawian children.
AI Innovations Description
The study described is titled “The Effects of 1 Egg per Day on Iron and Anemia Status among Young Malawian Children: A Secondary Analysis of a Randomized Controlled Trial.” The study aimed to determine the impact of supplementing diets with 1 egg per day on iron and anemia status among 6-9-month-old infants in Malawi.
The study was conducted in the Mangochi District of Malawi between February 2018 and January 2019. Children were randomly assigned to either the intervention group, which received 1 egg per day for 6 months, or the control group, which did not receive additional eggs. The study enrolled children between the ages of 6.0 and 9.9 months who planned to reside in the catchment areas of the Lungwena or Malindi health centers for the study duration. Children with certain health conditions or allergies were excluded from the study.
The primary outcome measures included plasma ferritin, soluble transferrin receptor (sTfR), body iron index (BII), and hemoglobin concentrations. The prevalence of iron deficiency (ID), anemia, and iron deficiency anemia (IDA) were also assessed. Blood samples were collected at enrollment and 6-month follow-up to measure these outcomes.
The results of the study showed that providing eggs daily for 6 months did not have a significant impact on iron status or the prevalence of anemia among the children in this context. There were no significant differences between the intervention and control groups in terms of hemoglobin concentrations, plasma ferritin, sTfR, BII, anemia, ID, or IDA.
In conclusion, the study suggests that supplementing diets with 1 egg per day may not be an effective intervention to improve iron and anemia status among young Malawian children. The findings highlight the need for other interventions to address the high prevalence of iron deficiency and anemia in this population.
Please note that this is a summary of the study’s findings and does not constitute a recommendation for developing an innovation to improve access to maternal health.
AI Innovations Methodology
Based on the provided information, the study aimed to determine the impact of supplementing diets with 1 egg per day on iron and anemia status among young Malawian children. The study was conducted in the Mangochi District of Malawi and involved randomly assigning children between the ages of 6.0 and 9.9 months to either an intervention group receiving 1 egg per day for 6 months or a control group that did not receive additional eggs.
To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:
1. Identify the recommendations: Start by identifying specific recommendations that can improve access to maternal health. These recommendations could include interventions such as increasing the number of healthcare facilities, improving transportation infrastructure, providing training for healthcare providers, implementing telemedicine programs, or increasing awareness and education about maternal health.
2. Define the indicators: Determine the indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include metrics such as the number of healthcare facilities per capita, average travel time to the nearest healthcare facility, the number of trained healthcare providers, the utilization rate of maternal health services, or the maternal mortality rate.
3. Collect baseline data: Gather baseline data on the selected indicators to establish a starting point for measuring the impact of the recommendations. This data could be obtained from existing sources such as government reports, surveys, or health records.
4. Develop a simulation model: Create a simulation model that incorporates the baseline data and simulates the impact of the recommendations on the selected indicators. The model should consider factors such as population growth, resource availability, and potential barriers to implementation.
5. Implement the recommendations: Apply the recommendations in the simulation model and observe the projected changes in the selected indicators. The model should provide estimates of how the indicators would change over time based on the implementation of the recommendations.
6. Validate the model: Validate the simulation model by comparing the projected changes in the indicators with real-world data from similar interventions or studies. This step helps ensure the accuracy and reliability of the simulation results.
7. Analyze the results: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. Identify any limitations or challenges that may arise from implementing the recommendations and propose potential solutions.
8. Refine and iterate: Based on the analysis of the simulation results, refine the recommendations and the simulation model as needed. Iterate the process to further optimize the recommendations and improve the accuracy of the simulation.
By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health. This information can guide decision-making and resource allocation to effectively address the challenges and improve maternal health outcomes.
Community Interventions, Disparities, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants