Household food access and child malnutrition: Results from the eight-country MAL-ED study

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Study Justification:
The study aims to investigate the relationship between household food access and indicators of nutritional status in early childhood across eight different countries. This is important because stunting, which is caused by factors such as decreased food intake and poor diet quality, contributes to significant morbidity and mortality worldwide. Understanding the relationship between food access and child malnutrition can help identify children at risk and inform interventions to improve their health outcomes.
Study Highlights:
– The study used a recently validated nine-item food access insecurity questionnaire to assess household food access across eight country sites.
– The average food access insecurity score was 5.8, with variations across countries.
– The prevalence of stunting (42%) was much higher than the prevalence of wasting (6%).
– A 10-point increase in food access insecurity score was associated with a 0.20 SD decrease in height-for-age Z score.
– The relationship between food access insecurity and anthropometry was consistent across countries.
Study Recommendations:
– The study recommends using a simple household food access insecurity score to identify children at risk of illness and death related to malnutrition.
– Interventions should be directed towards improving food access for households with high food insecurity scores.
Key Role Players:
– Researchers from thirteen academic and research institutions involved in the MAL-ED Network cohort study.
– Field supervisors and workers responsible for data collection.
– Institutional Review Boards at participating research sites.
– Site investigators who translated the survey questionnaire into local languages.
Cost Items for Planning Recommendations:
– Budget items may include funding for data collection, training of field workers, translation of survey questionnaire, ethical approval processes, and data analysis using statistical software.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it presents findings from a study conducted across eight countries, using a standardized protocol and a large sample size. The study used multivariable regression models to assess the relationship between household food access insecurity and anthropometry in children. The results showed a consistent relationship across countries, indicating the validity of using a simple household food access insecurity score to investigate childhood growth faltering. To improve the evidence, the abstract could provide more details about the study design, sampling methods, and statistical analysis techniques used.

Background: Stunting results from decreased food intake, poor diet quality, and a high burden of early childhood infections, and contributes to significant morbidity and mortality worldwide. Although food insecurity is an important determinant of child nutrition, including stunting, development of universal measures has been challenging due to cumbersome nutritional questionnaires and concerns about lack of comparability across populations. We investigate the relationship between household food access, one component of food security, and indicators of nutritional status in early childhood across eight country sites.Methods: We administered a socioeconomic survey to 800 households in research sites in eight countries, including a recently validated nine-item food access insecurity questionnaire, and obtained anthropometric measurements from children aged 24 to 60 months. We used multivariable regression models to assess the relationship between household food access insecurity and anthropometry in children, and we assessed the invariance of that relationship across country sites.Results: Average age of study children was 41 months. Mean food access insecurity score (range: 0-27) was 5.8, and varied from 2.4 in Nepal to 8.3 in Pakistan. Across sites, the prevalence of stunting (42%) was much higher than the prevalence of wasting (6%). In pooled regression analyses, a 10-point increase in food access insecurity score was associated with a 0.20 SD decrease in height-for-age Z score (95% CI 0.05 to 0.34 SD; p = 0.008). A likelihood ratio test for heterogeneity revealed that this relationship was consistent across countries (p = 0.17).Conclusions: Our study provides evidence of the validity of using a simple household food access insecurity score to investigate the etiology of childhood growth faltering across diverse geographic settings. Such a measure could be used to direct interventions by identifying children at risk of illness and death related to malnutrition. © 2012 Psaki et al; licensee BioMed Central Ltd.

We conducted our study at the eight field sites in the Malnutrition and Enteric Infections: Consequences for Child Health and Development (MAL-ED) Network cohort study. The MAL-ED Network, comprising researchers from thirteen academic and research institutions, aims to explore the relationship between malnutrition and intestinal infections and their consequences for various aspects of child growth and development. Sites are utilizing a standardized protocol for the collection of twice-weekly diarrhea surveillance information, monthly anthropometry, urine for gut function and iodine status, stool for enteric pathogens, blood for micronutrients and vaccine response, and cognitive development assessments. Study sites are located in rural, urban, and peri-urban areas of Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa and Tanzania (See Additional file 1). The MAL-ED study began enrolling pregnant women in 2009, and plans to follow a cohort of approximately 200 newborns per site for up to 36 months. We report on pilot study activities that preceded enrollment for the cohort study, aimed at characterizing the relationship between food access and child nutritional status. In preparation for the MAL-ED cohort study, we sought to develop and test cross-country indicators of food access insecurity and socioeconomic status (SES). We administered a standardized survey including demographic, SES, and food access questions to 100 households in each of the eight field sites between September 2009 and August 2010. Households were randomly selected from census results collected within the previous year at each study site. Households were eligible to participate if they were located within the MAL-ED study area and had an index child aged 24 to 60 months. Data collection lasted approximately two to four weeks in each site. We obtained ethical approval from the Institutional Review Boards at each of the participating research sites, at the Johns Hopkins Bloomberg School of Public Health (Baltimore, USA) and at the University of Virginia School of Medicine (Charlottesville, USA). Demographic and SES questions were adapted from the most recent Demographic and Health Surveys [16] in collaboration with site investigators. Questions focused on age and education of the head of household and child’s mother, as well as the mother’s fertility history. The SES section included a series of questions on household assets, housing materials, and water and sanitation facilities. The questionnaire was developed in English, and then translated into local languages by site investigators using appropriate local terms (See Additional file 2). The questionnaire was accompanied by standard operating procedures based on existing guidelines for administration of the HFIAS [17]. Field supervisors trained field workers prior to survey administration, and used locally appropriate management techniques to support complete, accurate and timely data collection, including weekly review of all data to ensure quality. To assess food access insecurity, our survey included the nine-question HFIAS (See Online Supplement), adapted in 2006 by the Food And Nutrition Technical Assistance (FANTA) project for use in low resource settings [18]. Although this scale has been validated and adapted in individual country settings through previous studies [18-20], to our knowledge it has not been used in its original form in a multi-country study. The nine-item scale uses a four-week recall period and captures three dimensions of the access component of household food insecurity: anxiety and uncertainty about household food access (item 1); insufficient quality (items 2–4); and insufficient food intake and its physical consequences (items 5–9) [18]. Responses on the nine items were summed to create the food access insecurity score, with a minimum score of 0 indicating the most food access secure households, and a maximum score of 27 indicating the most food access insecure households. We also categorized households into four groups [17]: food access secure, and mildly, moderately and severely food access insecure. We measured height and weight in one child aged 24 to 60 months in each participating household. When multiple children in this age range lived in one household, we randomly chose one child to avoid intra-household correlation in our data. Trained field staff measured standing height to the nearest 0.1 cm using a locally produced platform with sliding headboard. Digital scales were used to measure weight to the nearest 100 grams. Height-for-age (HAZ) and weight-for-height (WHZ) Z-scores were calculated based on World Health Organization child growth standards [21]. We defined stunting and wasting as a HAZ and WHZ that were two standard deviations below the WHO standard, respectively. Exploratory analyses involved examination of the distribution of each variable and inter-relationships between variables within and across sites. We then conducted a series of pooled analyses, including data from all eight country sites. We used a generalized additive model with a smoothing spline to characterize the relationship between food access insecurity and nutritional indicators. Our findings indicated that the pooled relationship between food access insecurity and both nutritional indicators was approximately linear, indicating the appropriateness of linear regression models. We then examined bivariate relationships between food access insecurity, HAZ, WHZ and SES indicators. Last, we used linear regression to model the relationship between food access insecurity and each nutritional outcome in the pooled sample of households, adjusted for child age, sex, maternal education, household bank account, people per room in the household, and access to an improved water source and sanitation facilities. We selected these SES indicators based on their relevance to the outcomes and sufficient variation within each country site. We compared the results to a model including a household SES score generated through principal components analysis based on 17 indicators of household wealth. The results were similar, and we felt that the selection of individual indicators provided more interpretable information on the relationships between food access insecurity and SES. To control for differences in baseline levels of HAZ and WHZ, we included indicator variables for all but one country. We conducted a likelihood ratio test comparing a full model with interactions between food access insecurity score and the eight country dummy variables with a reduced model lacking those interactions. The results of this test provided evidence of the extent of heterogeneity in the relationship between food access insecurity and HAZ across countries. We used R (http://www.r-project.org) and STATA 12 (STATA Corp., College Station, USA) for statistical analysis.

Based on the information provided, it appears that the study focused on investigating the relationship between household food access and indicators of nutritional status in early childhood across eight country sites. The study utilized a standardized survey, including a nine-item food access insecurity questionnaire, to assess food access insecurity. The study found that a higher food access insecurity score was associated with a decrease in height-for-age Z score, indicating a relationship between food access insecurity and childhood growth faltering.

Based on this study, potential innovations to improve access to maternal health could include:

1. Mobile-based surveys: Developing mobile applications or platforms that allow for the administration of standardized surveys, including the food access insecurity questionnaire, to households in remote or underserved areas. This would streamline data collection and improve efficiency.

2. Targeted interventions: Using the food access insecurity score to identify households at risk of malnutrition and implementing targeted interventions, such as providing nutritional support, education on healthy eating habits, and access to affordable and nutritious food options.

3. Community-based programs: Implementing community-based programs that focus on improving household food access and nutrition, including initiatives such as community gardens, food cooperatives, and nutrition education workshops.

4. Policy advocacy: Using the findings from the study to advocate for policy changes that address food insecurity and improve access to nutritious food for pregnant women and mothers. This could include advocating for increased funding for social safety net programs, improving access to affordable and nutritious food in underserved areas, and implementing policies that support breastfeeding and maternal nutrition.

5. Integration of services: Integrating maternal health services with nutrition programs and initiatives to ensure that pregnant women and mothers have access to comprehensive care that addresses both their health and nutritional needs.

These innovations could help improve access to maternal health by addressing the underlying factors, such as food access insecurity, that contribute to poor maternal and child health outcomes.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health would be to implement interventions that address household food access insecurity. This can be achieved by:

1. Conducting a comprehensive assessment of household food access insecurity using a validated questionnaire, such as the nine-item food access insecurity questionnaire used in the study.

2. Identifying households that are at risk of food insecurity and malnutrition by analyzing the food access insecurity scores and categorizing households into different levels of food access insecurity (food access secure, mildly, moderately, and severely food access insecure).

3. Providing targeted interventions to households with higher levels of food access insecurity, such as:

a. Increasing access to nutritious food through food assistance programs or subsidies.

b. Promoting agricultural initiatives and income-generating activities to improve household food production and income.

c. Enhancing knowledge and skills on nutrition and food preparation through education and training programs.

d. Improving access to clean water and sanitation facilities to prevent waterborne diseases and improve overall health.

4. Collaborating with local communities, healthcare providers, and relevant stakeholders to implement and monitor the effectiveness of these interventions.

By addressing household food access insecurity, these interventions can contribute to improving maternal health outcomes by reducing the prevalence of malnutrition and its associated morbidity and mortality.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations to improve access to maternal health:

1. Increase availability and accessibility of healthcare facilities: Ensure that there are sufficient healthcare facilities, including hospitals, clinics, and maternity centers, in both urban and rural areas. This will make it easier for pregnant women to access prenatal care and delivery services.

2. Improve transportation infrastructure: Enhance transportation networks, such as roads and public transportation, to facilitate the movement of pregnant women to healthcare facilities. This is particularly important for women living in remote or underserved areas.

3. Strengthen community-based healthcare services: Implement community health programs that provide prenatal care, education, and support to pregnant women. This can be done through trained community health workers who can visit pregnant women in their homes and provide necessary care and information.

4. Increase awareness and education: Conduct awareness campaigns to educate women and their families about the importance of prenatal care, nutrition, and safe delivery practices. This can help dispel myths and misconceptions surrounding maternal health and encourage women to seek appropriate care.

5. Provide financial support: Implement policies and programs that provide financial assistance to pregnant women, especially those from low-income backgrounds, to cover the costs of prenatal care, delivery, and postnatal care. This can help remove financial barriers to accessing maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify the key indicators that will be used to measure the impact of the recommendations, such as the number of pregnant women accessing prenatal care, the number of deliveries attended by skilled healthcare professionals, and the reduction in maternal mortality rates.

2. Collect baseline data: Gather data on the current state of maternal health access, including the number of healthcare facilities, transportation infrastructure, and awareness levels among pregnant women. This will serve as a baseline for comparison.

3. Implement interventions: Implement the recommended interventions, such as building healthcare facilities, improving transportation infrastructure, and conducting awareness campaigns. Monitor the implementation process and collect data on the resources allocated and activities conducted.

4. Measure outcomes: Collect data on the indicators identified in step 1 after the interventions have been implemented. This could involve surveys, interviews, and data from healthcare facilities and government records.

5. Analyze and compare data: Analyze the data collected before and after the interventions to assess the impact on access to maternal health. Compare the indicators to determine if there have been improvements and quantify the extent of the changes.

6. Evaluate and adjust: Evaluate the effectiveness of the interventions and identify any areas that may require adjustments or additional interventions. This could involve conducting further research, seeking feedback from stakeholders, and consulting with experts in the field.

By following this methodology, policymakers and healthcare providers can gain insights into the effectiveness of different interventions and make informed decisions on how to further improve access to maternal health.

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