ChAracterizing, Revlving, Supporting, Monitoring and MAnaging sustainable food systems to address malnutrition in Indigenous tribal communities (CARISMMA): exploring food systems and its drivers to develop multisectoral data-driven solutions. A study protocol.

backgroundIndigenous food systems of Indian tribal communities, hold potential to address the dual burden of malnutrition, and thus, comprehensive research on dynamics and complexity of food systems and their main drivers is critical to understand the root causes of poor nutrition in Indian tribes. Incorporating local food systems drivers into data-driven decision-making algorithms, can help improve food production, distribution, and consumption among Indian tribes through customized interventions to address malnutrition. The present study aims to assess indigenous food systems elements, their drivers and nutritional outcomes among diverse tribal communities of India and use novel data analytics to develop decision support algorithms for sustainable food systems solutions.

methodsA prospective observational cohort study using mixed methods will be implemented across three tribal communities from three states of India: i) Gond tribal community of Mandla, Madhya Pradesh, ii) Garo tribal community of West Garo Hills, Meghalaya and iii) Pahari Korwa tribal community of Surguja, Chhattisgarh. Longitudinal data will be collected in all three tribal cohorts for a period of three years on food system elements (food supply chain and food environment), and different drivers like policy and governance, demographic, sociocultural, infrastructure and technology, biophysical and environment and economic drivers. Additionally, nutritional outcomes will be assessed as proximal outcomes (including household dietary patterns, nutritional intake, indigenous food consumption and dietary diversity in women and children under-five and the gut microbiota profile of women), and distal outcomes (including anthropometric indicators of women and children and nutritional and inflammatory biomarkers in women). The accumulated data from tribal cohorts will be used to formulate data-driven decision support tools using classical statistical procedures and machine learning informed optimization pathways to assess food system vulnerabilities and develop customized food systems interventions. DISCUSSION: The diverse data from several cross-sectoral domains, generated in the present study, will facilitate an objective assessment of the nuances of indigenous food systems. This will further guide the development of equity-focused, resource-optimized food systems interventions, which will be piloted for acceptability through inclusion of bottom-up voices from indigenous tribal communities as well as the stakeholders including policymakers and non-government organizations.