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Community Contribution

Adopting CLA for data driven programming: Lessons from Suaahara II

Subir Kole

In Nepal, USAID funded Suaahara II Good Nutrition program (2016- 2023) aimed to improve the nutritional outcomes of women and children in 42 districts reaching out to over 900,000 households with pregnant and lactating women and children under two years of age. Since the beginning, Suaahara II deployed a robust monitoring evaluation and research (MER) system to continuously collect program data, track and monitor progress of key indicators, learn from data driven insights, make necessary adaptive changes, and finally evaluate whether the program achieved its goals. Consequently, Suaahara II’s MER system was built on the principles of continuous improvement through collaboration, learning and adaptation (CLA). 

Suaahara II prioritized CLA due to at-scale programming, diverse implementation contexts and target groups, lack of intra-sectoral coordination within the government, and to leverage the experience of multiple USAID funded programs in Nepal. During the project’s lifespan, Suaahara II extensively collaborated with a wide range of internal and external stakeholders to continuously learn, share evidence-based insights, and disseminate program learnings to bring synergies among development partners thereby avoiding duplication of efforts, resources, and time. The CLA approach helped Suaahara II to quickly adapt to changing circumstances including COVID-19 pandemic and the food, fuel, and fertilizer crisis catalyzed by Russia-Ukraine war. An independent impact evaluation of Suaahara demonstrated that Suaahara significantly contributed to the decline of maternal underweight, stunting among children <6 months, and improved maternal and child dietary diversity in Nepal. These results were achieved by adopting a continuous quality improvement approach using CLA principles.

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