Benchmarking policy goals and actions for healthy food environments in Ethiopia to prevent malnutrition in all its forms using document analysis

OBJECTIVE

Unhealthy diets resulting in overweight and obesity and diet-related non-communicable diseases are of increasing concern in Ethiopia, alongside persistent undernutrition, and have been linked to unhealthy food environments. Little is known about the policy response to unhealthy food environments in Ethiopia. The objective of this study was to assess how different food environment domains have been addressed in Ethiopian policy goals and action over time and how this compares with global good practice benchmarks.

SETTING

Ethiopia.

PRIMARY AND SECONDARY OUTCOME MEASURES

We analysed intentions and plans of the government to act, using policy documents (outputs of decision-making in the form of published strategies, plans or policies) related to improving diets and nutritional status through healthy food environments in Ethiopia between 2008 and 2020. Our coding framework was guided by the policy component (n=7 domains) of the Healthy Food-Environment Policy Index, which was modified to include food quality and safety as an eighth domain.

RESULTS

From the 127 policy outputs identified, 38 were retained, published by 9 different government ministries and institutions. Our results show that eight food environment domains have been addressed to some extent, but gaps remain compared with global best practice, especially in food promotion, processing, retail, price and trade. From 2018, policy began to embrace the wider food system, with more explicit food environment interventions becoming apparent.

CONCLUSIONS

Policy efforts achieved in food safety, food processing, marketing and labelling are important stepping stones to building future policy actions addressing the food environment domains of food retail, food provision and food trade. Benchmarking of food environment policy actions should also consider actions on food fortification, agro-processing and informal markets in the context of multiple forms of malnutrition.

Modeling impacts of faster productivity growth to inform the CGIAR initiative on Crops to End Hunger

In 2017-2018, a group of international development funding agencies launched the Crops to End Hunger initiative to modernize public plant breeding in lower-income countries. To inform that initiative, USAID asked the International Food Policy Research Institute and the United States Department of Agriculture's Economic Research Service to estimate the impacts of faster productivity growth for 20 food crops on income and other indicators in 106 countries in developing regions in 2030. We first estimated the value of production in 2015 for each crop using data from FAO. We then used the IMPACT and GLOBE economic models to estimate changes in the value of production and changes in economy-wide income under scenarios of faster crop productivity growth, assuming that increased investment will raise annual rates of yield growth by 25% above baseline growth rates over the period 2015-2030. We found that faster productivity growth in rice, wheat and maize increased economy-wide income in the selected countries in 2030 by 59 billion USD, 27 billion USD and 21 billion USD respectively, followed by banana and yams with increases of 9 billion USD each. While these amounts represent small shares of total GDP, they are 2-15 times current public R&D spending on food crops in developing countries. Income increased most in South Asia and Sub-Saharan Africa. Faster productivity growth in rice and wheat reduced the population at risk of hunger by 11 million people and 6 million people respectively, followed by plantain and cassava with reductions of about 2 million people each. Changes in adequacy ratios were relatively large for carbohydrates (already in surplus) and relatively small for micronutrients. In general, we found that impacts of faster productivity growth vary widely across crops, regions and outcome indicators, highlighting the importance of identifying the potentially diverse objectives of different decision makers and recognizing possible tradeoffs between objectives.

Effect of Differences in Month and Location of Measurement in Estimating Prevalence and Trend of Wasting and Stunting in India in 2005-2006 and 2015-2016

BACKGROUND

Child undernutrition in India remains widespread. Data from the National Family Health Survey 3 and 4 (NFHS-3 and NFHS-4) suggest that wasting prevalence has increased while stunting prevalence has declined.

OBJECTIVE

The objectives of this study were to do the following: 1) describe wasting and stunting by month of measurement in India in children <5 y of age in NFHS-3 and NFHS-4 surveys, and 2) test whether differences in the timing of anthropometric data collection and in states between survey years introduced bias in the comparison of estimates of wasting and stunting between NFHS-3 and NFHS-4.

METHODS

Data on wasting and stunting for 42,608 and 232,744 children aged >5 y in the NFHS-3 and NFHS-4 survey rounds were analyzed. Differences in the prevalence of wasting and stunting by month of year and by state of residence were examined descriptively. Regression analyses were conducted to test the sensitivity of the estimate of differences in wasting and stunting prevalence across survey years to both state differences and seasonality.

RESULTS

Examination of the patterns of wasting and stunting by month of measurement and by state across survey years reveal marked variability. When both state and month were adjusted, regardless of the method used to account for sample size, there was a small negative difference from 2005-2006 to 2015-2016 in the prevalence of wasting (-0.8 ± 0.6 percentage points; P = 0.2) and a negative difference in stunting prevalence (-8.3 ± 0.7 percentage points; P < 0.001), indicating a small bias for wasting but not for stunting in unadjusted analyses.

CONCLUSIONS

State and seasonal differences may have introduced bias to the estimated difference in prevalence of wasting between the survey years but did not do so for stunting. Future data collection should be designed to maximize consistency in coverage of both time and place.