A Community-Led Central Kitchen Model for School Feeding Programs in the Philippines: Learnings for Multisectoral Action for Health

In devolved governments like the Philippines, local government units (LGUs) must be engaged to develop and coordinate responses to tackle the multisectoral problem of childhood undernutrition. However, current Philippine nutrition interventions, such as decentralized school feeding programs (SFPs), generally rely on the national government, public school teachers, or the private sector for implementation, with mixed results. The central kitchen model for SFPs was developed by 2 Philippine nongovernmental organizations and facilitated large-scale in-school feeding through community multisectoral action. This case study documented coordination processes in February 2018 for 1 urban city and 1 rural province-the model\u27s earliest large-scale implementation sites-that contributed to its institutionalization and sustainability. Data from 24-hour dietary recalls with 308 rural and 310 urban public school students and household surveys with their caregivers showed undernutrition was an urgent problem. Enabling factors and innovative local solutions were explored in focus group discussions with 160 multisector participants and implementers in health care, education, and government, as well as volunteers, parents, and central kitchen staff. The locally led and operated central kitchens promoted community ownership by embedding volunteer pools in social networks and spurring demand for related social services from their LGU. With the LGU as the face of implementation, operations were sustained despite political leadership changes, fostering local government stewardship over nutrition. Leveraging national legislation and funding for SFPs and guided by the Department of Education\u27s standards for SFP eligibility, LGUs had room to adapt the model to local needs. Central kitchens afforded opportunities for scale-up and flexibility that were utilized during natural disasters and the coronavirus disease (COVID-19) pandemic. The case demonstrated empowering civil society can hold volunteers, local implementers, and local governments accountable for multisectoral action in decentralized settings. The model may serve as a template for how other social services can be scaled and implemented in devolved settings

Predicting undernutrition among elementary schoolchildren in the Philippines using machine learning algorithms

Objectives This study aimed to compare the accuracy of four machine-learning (ML) algorithms, using two classification schemes, to predict undernutrition based on individual and household risk factors. Methods Data on public-school children were collected from a rural province (310 children) and a highly urbanized city (308 children) in the Philippines using 24-h dietary recalls and a household socioeconomic and demographic survey. Children\u27s nutritional risk was classified based on acceptable macronutrient distribution ranges (AMDRs) developed by the National Academy of Medicine (NAM) and Philippine Dietary Reference Intakes (PDRIs). Four algorithms (random forest, support-vector machine, linear discriminant analysis, and logistic regression) predicted undernutrition in the sample, and their accuracy, sensitivity, and specificity were compared. Predictions were also compared with the national school feeding program\u27s anthropometric classifications. Results The prevalence of undernutrition was greater under NAM AMDRs (82.67%) compared with PDRI AMDRs (78.71%). Random forest was the most accurate ML algorithm (78.55%), able to predict undernutrition based on household expenditures, child and household age, food insecurity, and dietary diversity. Compared with anthropometric classification (213 children), AMDRs classified more children as at risk for inadequate dietary intake (477 children). Conclusions The random forest algorithm performed best in predicting undernutrition among Filipino elementary schoolchildren, although results could be improved with bootstrap aggregation. The AMDR classification shows potential for targeting feeding beneficiaries. However, local dietary culture should be considered in the development of nutrition interventions. Government use of big-data techniques such as ML must also address underrepresentation in health data collected from and accessible to poor populations or risk further marginalizing them

Predicting Undernutrition Among Elementary Schoolchildren in the Philippines Using Machine Learning Algorithms

Objectives This study aimed to compare the accuracy of four machine-learning (ML) algorithms, using two classification schemes, to predict undernutrition based on individual and household risk factors. Methods Data on public-school children were collected from a rural province (310 children) and a highly urbanized city (308 children) in the Philippines using 24-h dietary recalls and a household socioeconomic and demographic survey. Children\u27s nutritional risk was classified based on acceptable macronutrient distribution ranges (AMDRs) developed by the National Academy of Medicine (NAM) and Philippine Dietary Reference Intakes (PDRIs). Four algorithms (random forest, support-vector machine, linear discriminant analysis, and logistic regression) predicted undernutrition in the sample, and their accuracy, sensitivity, and specificity were compared. Predictions were also compared with the national school feeding program\u27s anthropometric classifications. Results The prevalence of undernutrition was greater under NAM AMDRs (82.67%) compared with PDRI AMDRs (78.71%). Random forest was the most accurate ML algorithm (78.55%), able to predict undernutrition based on household expenditures, child and household age, food insecurity, and dietary diversity. Compared with anthropometric classification (213 children), AMDRs classified more children as at risk for inadequate dietary intake (477 children). Conclusions The random forest algorithm performed best in predicting undernutrition among Filipino elementary schoolchildren, although results could be improved with bootstrap aggregation. The AMDR classification shows potential for targeting feeding beneficiaries. However, local dietary culture should be considered in the development of nutrition interventions. Government use of big-data techniques such as ML must also address underrepresentation in health data collected from and accessible to poor populations or risk further marginalizing them