Factors associated with dietary diversity and length-for-age z-score in rural Ethiopian children aged 6–23 months: A novel approach to the analysis of baseline data from the Sustainable Undernutrition Reduction in Ethiopia evaluation

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Study Justification:
This study aimed to evaluate factors associated with dietary diversity and length-for-age z-score (LAZ) in rural Ethiopian children aged 6-23 months. The study is important because infants and young children require nutrient-dense and diverse diets for optimal growth and development. Understanding the factors that influence dietary diversity and LAZ can inform interventions and policies to improve child nutrition in Ethiopia.
Study Highlights:
– The study used a novel approach called directed acyclic graphs (DAGs) to analyze the data and identify causal relationships between factors of interest and outcomes.
– Child dietary diversity was positively associated with LAZ, with children consuming 4 or more food groups having higher LAZ scores compared to those consuming no complementary foods.
– Household production of fruits and vegetables was associated with increased child dietary diversity and LAZ.
– Other factors positively associated with child dietary diversity included age, socio-economic status, maternal education, women’s empowerment, paternal childcare support, household food security, fruit and vegetable cultivation, and land ownership.
– LAZ was positively associated with age, socio-economic status, maternal education, fruit and vegetable production, and land ownership.
Recommendations for Lay Reader:
– Encourage caregivers to provide a diverse diet to children aged 6-23 months, including at least 4 food groups.
– Promote household production of fruits and vegetables to improve child dietary diversity and LAZ.
– Support initiatives that improve socio-economic status, maternal education, women’s empowerment, and household food security to enhance child nutrition.
– Advocate for policies that promote fruit and vegetable cultivation and land ownership to improve child dietary diversity and LAZ.
Recommendations for Policy Maker:
– Implement interventions that promote dietary diversity in children aged 6-23 months, such as nutrition education programs and access to diverse food sources.
– Support agricultural initiatives that focus on fruit and vegetable production to improve child nutrition.
– Invest in programs that improve socio-economic status, maternal education, women’s empowerment, and household food security to address the underlying determinants of child dietary diversity and LAZ.
– Develop policies that promote fruit and vegetable cultivation and land ownership to enhance child nutrition outcomes.
Key Role Players:
– Ministry of Health: Responsible for implementing and coordinating nutrition interventions and policies.
– Ministry of Agriculture: Involved in promoting agricultural practices, including fruit and vegetable production.
– Non-Governmental Organizations (NGOs): Engaged in implementing nutrition programs and providing support to vulnerable populations.
– Community Health Workers: Play a crucial role in delivering nutrition education and counseling to caregivers.
– Local Government Authorities: Responsible for implementing and monitoring nutrition programs at the community level.
Cost Items for Planning Recommendations:
– Nutrition Education Programs: Budget for developing and implementing nutrition education materials, training community health workers, and conducting awareness campaigns.
– Agricultural Initiatives: Allocate funds for promoting fruit and vegetable production, providing training and resources to farmers, and establishing market linkages.
– Socio-Economic Support: Consider budgeting for income-generating activities, vocational training, and social protection programs to improve household socio-economic status.
– Women’s Empowerment Programs: Allocate resources for initiatives that promote gender equality, women’s education, and economic empowerment.
– Monitoring and Evaluation: Set aside funds for data collection, analysis, and monitoring the impact of interventions on child dietary diversity and LAZ.
Note: The provided cost items are general categories and do not represent actual cost estimates.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a quasi-experimental study design and includes a large sample size. However, to improve the evidence, the abstract could provide more details on the methods used, such as the specific statistical analyses performed and any potential limitations of the study.

Infants and young children need diets high in nutrient density and diversity to meet the requirements of rapid growth and development. Our aim was to evaluate sociodemographic, agricultural diversity, and women’s empowerment factors associated with child dietary diversity and length-for-age z-score (LAZ) in children 6–23 months using data collected as part of the Sustainable Undernutrition Reduction in Ethiopia (SURE) evaluation study baseline survey in May–June 2016. We here present a novel analysis using directed acyclic graphs (DAGs) to represent our assumptions about the causal influences between the factors of interest and the outcomes. The causal diagrams enabled the identification of variables to be included in multivariable analysis to estimate the total effects of factors of interest using ordinal logistic/linear regression models. We found that child dietary diversity was positively associated with LAZ with children consuming 4 or more food groups having on average an LAZ score 0.42 (95% CI [0.08, 0.77]) higher than those consuming no complementary foods. Household production of fruits and vegetables was associated with both increased child dietary diversity (adjusted OR 1.16; 95% CI [1.09, 1.24]) and LAZ (adjusted mean difference 0.05; 95% CI [0.005, 0.10]). Other factors positively associated with child dietary diversity included age in months, socio-economic status, maternal education, women’s empowerment and dietary diversity, paternal childcare support, household food security, fruit and vegetable cultivation, and land ownership. LAZ was positively associated with age, socio-economic status, maternal education, fruit and vegetable production, and land ownership.

Evaluation of the SURE intervention uses a quasi‐experimental study design to determine the impact on child minimum acceptable diet (MAD) and stunting. This study analyses data from the SURE baseline survey completed in May–July 2016 and comprising 1,848 children 6–23 months of age in 36 intervention districts (Oromia: n = 18; Amhara: n = 8; Tigray: n = 4; and SNNP: n = 6) and 36 comparison districts selected by region in equal proportion. Complete details are available in the SURE evaluation study protocol (Moss et al., 2018). At baseline 4,980 children 0–47 months (761 children 0–5 months; 1,848 children 6–23 months; 2,371 children 24–47 months) were selected from 4,299 households. Sample size calculations were based on detecting a change at endline in LAZ/height‐for‐age z‐score (HAZ) score and MAD attributable to the intervention. Detectable differences of 0.15 HAZ and of 4% MAD were calculated for intracluster correlation coefficients of 0.03 with 80% power with a significance level of 5%; and differences of 0.21 HAZ and 6% MAD were calculated for intracluster correlation coefficients of 0.08 with 80% power with a significance level of 5%. Kebeles were selected at study outset using probability proportional to size sampling from lists and population data provided by district officials. Gotes (sub‐kebeles) were selected by simple random sampling (paper in hat) during data collection. A complete listing of all households with children under 47 months in the gote was conducted, and 15 were selected using systematic random sampling. Resident children 0–47 months within a selected household were listed in the following age groups: 0–5 months, 6–23 months, and 24–47 months. Where only one child for any or all age categories was present, all eligible children were selected (up to three children). Where multiple children from a single age category were present, one child was randomly selected per age group by the computer‐assisted personal interview programme. This study uses data from children sampled who were between 6 and 23 months of age only as this is the age group for which WHO complementary feeding indicators apply and for whom dietary data were collected. The household questionnaire (see Data S1) comprised modules on child feeding and care practices, child anthropometry and haemoglobin, household characteristics including food security, mother’s dietary diversity, agricultural practices including diversity of food production, and women’s empowerment. Child anthropometric measurements comprising length were taken using a portable measuring board (UNICEF Supply Division, Copenhagen, 2016). Data collectors were trained in anthropometric measurement for 5 days and completed a standardisation exercise prior to survey deployment. Survey training on the questionnaire, operating procedures, and piloting was completed in 12 days for a total of 17 days of training. Child LAZs were generated using the WHO growth standards (WHO Multicentre Growth Reference Study Group & de Onis, 2006). Scores of 6 LAZ were excluded for biological implausibility as per WHO guidelines (World Health Organisation, 2006). Dietary diversity was first generated for children as a score ranging from 0 to 7 food groups as defined by WHO indicators for IYCF (WHO et al., 2010). We then combined 4–7 food groups consumed into a single category to create a five‐category child dietary diversity outcome variable: 0, 1, 2, 3, and 4–7 food groups consumed. Women’s minimum dietary diversity was generated as a scale variable ranging from 0 to 10 food groups as defined by FAO/FANTA (Food and Agriculture Organisation & USAID’s Food and Nutrition Technical Assistance III Project (FANTA), 2016). A scale variable from 0 to 27 based on the Household Food Insecurity Access Score was also used (Coates, Swindale, & Bilinsky, 2007). All dietary data were cleaned by comparing 24‐hr recall foods first entered in computer‐assisted personal interview questionnaire as free text with final food group assignments made by data collectors. A household wealth index was created using principle components analysis applied to proxy indicators of household socio‐economic status, namely, ownership of consumer goods, electricity, livestock (non‐food producing), source of water, type of toilet, and type of materials used for floor, roof, and walls. We created tertiles and checked internal validity by assessing ownership of consumer goods and housing characteristics by socio‐economic status tertile. Maternal education, land ownership, and ownership of livestock producing animal sources foods such as cows or sheep were excluded from the index due to known effects on nutrition outcomes that we wished to explore independently. Variables for household food production were constructed from crops grown, animals reared, and resulting food types produced by the household within the past major and minor growing seasons (1‐year reference period). We constructed a variable for fruit and vegetable production as the sum of all distinct types of fruit and vegetable crops grown in the past year. The score ranged from 0 (i.e., no fruit and vegetable crops grown) to a maximum of 21 as reported by the household. A variable for animal source food production was constructed as the sum of all such food types produced on a scale of 0–10 including eggs, meats, milks and other dairy, and fish. Descriptive statistics were generated for child, mother, and household risk factors and outcomes. Continuous variables were summarised using means and standard deviations or medians and interquartile ranges (IQRs) for nonnormally distributed variables. Categorical variables were summarised using numbers and percentages. We hypothesised pathways of impact between risk factors and dietary diversity and linear growth in children. To represent these pathways, a DAG was developed using DAGitty software (Textor, van der Zander, Gilthorpe, Liśkiewicz, & Ellison, 2016; see Figure 1). DAGs represent a set of assumptions about the causal relationships between variables that are made by researchers based on evidence and logical reasoning and provide a basis on which to reduce bias in statistical modelling (Sauer & Vanderweele, 2013; Shrier & Platt, 2008). Recent evidence on agricultural production and women’s empowerment factors supported construction of our diagram (Cunningham, Ruel, Ferguson, & Uauy, 2015; Hirvonen & Hoddinott, 2016; Ruel & Alderman, 2013). Occurrence of fever and diarrhoea in the past 2 weeks were hypothesised to influence child feeding practices but not growth due to the limited reference period. Directed acyclic graph (DAG) mapping causal relationships. DAGitty web‐based software was used to develop the diagram and to determine the minimal adjustment variable set to estimate total effect when regressing each explanatory risk factor on the outcome of interest—in this study, dietary diversity or length‐for‐age z‐score (Textor et al., 2016) Based on ancestor variables for each individual association, we used the DAGitty software to identify the unique set of covariates to be included in a multivariate model of that association. Unlike stepwise regression, covariates were not forced into a single multivariate model thus avoiding the table 2 fallacy (i.e., presentation of multiple adjusted effect estimates from a single model in a single table, often misunderstood to represent effects of the same direct causal type and therefore misinterpreted; Westreich & Greenland, 2013). We also decided a priori to add age and sex into all multivariate models, and age squared into multivariate model for LAZ. Associations between factors and child dietary diversity were investigated using univariate and multivariate ordinal logistic regression modelling. Associations between factors and LAZ were investigated using univariate and multivariate linear regression modelling. Robust standard errors were used to account for clustering at the kebele level. All analyses were conducted using Stata/IC (version 15). The baseline protocol was approved by the Scientific and Ethical Review Committee at EPHI (Ref number: SERO‐54‐3‐2016) and by the LSHTM ethics committee (Ref number: 10937) prior to data collection. By approval of both institutional review boards, informed written consent was given by caregivers of young children and witnessed by the local health extension worker.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and educational resources.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to have virtual consultations with healthcare providers, reducing the need for travel and improving access to prenatal care.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal and postnatal care, as well as education and support to pregnant women and new mothers in their communities.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access quality maternal healthcare services, including prenatal care, delivery, and postnatal care.

5. Transportation Solutions: Develop innovative transportation solutions, such as mobile clinics or ambulances, to ensure that pregnant women in remote areas can safely and easily access healthcare facilities for prenatal care and delivery.

6. Health Education Programs: Implement comprehensive health education programs that focus on maternal health, including prenatal care, nutrition, breastfeeding, and postnatal care, to empower women with knowledge and improve their health outcomes.

7. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women and new mothers, addressing their concerns and answering their questions.

8. Maternity Waiting Homes: Set up maternity waiting homes near healthcare facilities, providing a safe and comfortable place for pregnant women to stay in the weeks leading up to their due dates, ensuring they are close to medical care when needed.

9. Partnerships with Traditional Birth Attendants: Collaborate with traditional birth attendants to improve their skills and knowledge, ensuring they can provide safe and appropriate care to pregnant women in their communities, while also facilitating referrals to healthcare facilities when necessary.

10. Maternal Health Financing: Develop innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to make maternal healthcare services more affordable and accessible for women in low-income communities.

These innovations aim to address barriers to accessing maternal health services, improve the quality of care, and empower women to make informed decisions about their health and the health of their children.
AI Innovations Description
The recommendation that can be used to develop an innovation to improve access to maternal health based on the provided description is to focus on improving dietary diversity and nutrition education for pregnant women and new mothers. This can be achieved through the following actions:

1. Implement nutrition education programs: Develop and implement programs that provide pregnant women and new mothers with information on the importance of a diverse and nutrient-rich diet during pregnancy and lactation. These programs should emphasize the benefits of consuming a variety of food groups and provide practical tips on how to achieve a balanced diet.

2. Promote household food production: Encourage households to engage in fruit and vegetable cultivation to increase the availability of nutritious foods. Provide training and resources to support families in growing their own fruits and vegetables, even in limited spaces. This can help improve dietary diversity and ensure access to fresh and nutritious foods.

3. Enhance women’s empowerment: Empower women by providing opportunities for education and skill development. This can include promoting women’s access to education, training in agricultural practices, and income-generating activities. Empowered women are more likely to make informed decisions about their own health and the health of their children.

4. Strengthen healthcare systems: Improve access to healthcare services by ensuring the availability of skilled healthcare providers, especially in rural areas. This can be achieved through training and deploying more midwives and other healthcare professionals to provide antenatal and postnatal care. Additionally, ensure that healthcare facilities are equipped with the necessary resources and infrastructure to support maternal health services.

5. Increase community awareness: Conduct community awareness campaigns to educate community members about the importance of maternal health and the role of nutrition in ensuring healthy pregnancies and optimal child development. These campaigns can include community meetings, radio programs, and the distribution of educational materials.

By implementing these recommendations, access to maternal health can be improved by addressing the factors associated with dietary diversity and length-for-age z-score in rural Ethiopian children. This approach focuses on empowering women, promoting nutrition education, and strengthening healthcare systems to ensure better maternal and child health outcomes.
AI Innovations Methodology
The provided text describes a study that aims to evaluate factors associated with child dietary diversity and length-for-age z-score (LAZ) in rural Ethiopian children aged 6-23 months. The study uses data collected as part of the Sustainable Undernutrition Reduction in Ethiopia (SURE) evaluation study baseline survey.

To improve access to maternal health, the following innovations could be considered:

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to information and resources related to maternal health. These apps can provide guidance on prenatal care, nutrition, breastfeeding, and postpartum care. They can also include features such as appointment reminders, medication tracking, and emergency contact information.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women and new mothers in remote areas to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to timely and quality healthcare services. Telemedicine consultations can cover prenatal check-ups, postpartum care, and breastfeeding support.

3. Community Health Workers: Train and deploy community health workers (CHWs) to provide maternal health services in underserved areas. CHWs can conduct prenatal visits, provide education on maternal health, assist with breastfeeding support, and refer women to higher-level healthcare facilities when needed. They can also play a crucial role in raising awareness about maternal health issues within the community.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with access to essential maternal health services. These vouchers can cover prenatal care, delivery services, postpartum care, and emergency obstetric care. By subsidizing the cost of these services, vouchers can help reduce financial barriers and improve access to quality care.

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 key indicators that measure access to maternal health, such as the number of prenatal visits, percentage of women receiving skilled birth attendance, or maternal mortality rates.

2. Collect baseline data: Gather data on the current status of maternal health access in the target population. This can be done through surveys, interviews, or analysis of existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the recommended innovations and their potential impact on the identified indicators. The model should consider factors such as population size, geographical distribution, healthcare infrastructure, and resource availability.

4. Input data and parameters: Input the collected baseline data into the simulation model, along with relevant parameters such as the coverage and effectiveness of the recommended innovations. This will allow the model to simulate the potential changes in access to maternal health.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommended innovations. This can involve varying parameters such as the scale of implementation, target population coverage, or resource allocation.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommended innovations on improving access to maternal health. This can include assessing changes in the identified indicators and comparing different scenarios to identify the most effective strategies.

7. Refine and iterate: Based on the simulation results, refine the recommendations and iterate the simulation model to further optimize the strategies for improving access to maternal health. This may involve adjusting parameters, exploring alternative innovations, or considering additional factors that influence access.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different innovations on improving access to maternal health and make informed decisions on implementation strategies.

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