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

Economic Losses From COVID-19 Cases in the Philippines: A Dynamic Model of Health and Economic Policy Trade-Offs

The COVID-19 pandemic forced governments globally to impose lockdown measures and mobility restrictions to curb the transmission of the virus. As economies slowly reopen, governments face a trade-off between implementing economic recovery and health policy measures to control the spread of the virus and to ensure it will not overwhelm the health system. We developed a mathematical model that measures the economic losses due to the spread of the disease and due to different lockdown policies. This is done by extending the subnational SEIR model to include two differential equations that capture economic losses due to COVID-19 infection and due to the lockdown measures imposed by the Philippine government. We then proceed to assess the trade-off policy space between health and economic measures faced by the Philippine government. The study simulates the cumulative economic losses for 3 months in 8 scenarios across 5 regions in the country, including the National Capital Region (NCR), to capture the trade-off mechanism. These scenarios present the various combinations of either retaining or easing lockdown policies in these regions. Per region, the trade-off policy space was assessed through minimising the 3-month cumulative economic losses subject to the constraint that the average health-care utilisation rate (HCUR) consistently falls below 70%, which is the threshold set by the government before declaring that the health system capacity is at high risk. The study finds that in NCR, a policy trade-off exists where the minimum cumulative economic losses comprise 10.66% of its Gross Regional Domestic Product. Meanwhile, for regions that are non-adjacent to NCR, a policy that hinges on trade-off analysis does not apply. Nevertheless, for all simulated regions, it is recommended to improve and expand the capacity of the health system to broaden the policy space for the government in easing lockdown measures

Reviving the Philippine Economy under a Responsible New Normal

After the reclassification of areas under enhanced community quarantine (ECQ) to general community quarantine (GCQ), the urgent task for the Philippine government is to provide an exit plan to revive the Philippine economy. Given the significant economic damage resulting from the shutdown of roughly 75 percent of the country’s total production in the National Capital Region (NCR) and in the CALABARZON and Central Luzon areas, a gradual reopening of the economy will be necessary to prevent further economic damage that could not only be difficult to repair, but also long to overcome. Indeed, based on recent directives from the government, a substantial number of industries and services have thus been allowed to operate in both the ECQ and GCQ areas. However, as the Philippine government begins to calibrate the opening of sectors, there remain concerns as to how this process will affect jobs and livelihoods now and beyond. In this context, an economic recovery plan that talks about short-term, a transition, and full recovery phases— encompassing a revision of the current Philippine Development Plan without losing sight of the long-term goals envisioned in Ambisyon Natin 2040— is still needed. Indeed, a key component of AmBisyon 2040 has been of building resiliency over the long-term, which includes resiliency in health and economic shocks apart from natural disasters. At the same time, this recovery plan should also be accompanied by structural reforms to enhance its implementation. The Department of Finance has crafted a four-pillar socio-economic strategy aimed at: (a) supporting the more vulnerable sectors of society; (b) increasing medical resources to contain the virus and offer safety to front-liners; (c) keeping the economy afloat through financial emergency initiatives; and (d) creating jobs and sustaining the economy. Yet while enumerating the costs of these plans, the said strategy lacked details on how the country could achieve some of the goals without the availability of widespread testing and adequate health facilities. Loan guarantees, cash transfers, and other forms of subsidies can revive disrupted supply chains but cannot restore productivity in the middle of a persisting health crisis, while the uncertainty of a possible outbreak can keep workers from supplying goods and services. It is crucial to have these programs and institutions in place since a number of cities, regions and provinces have started to reopen. A modified community quarantine without the necessary health system investments, protection measures, and economic recovery plan risks amounting to an unregulated herd immunity strategy. Opting for herd immunity allows governments to blame the failure of the health and economic system on the virus, rather than on bad governance. Under current GCQ protocols, the burden on containing the virus is mostly transferred to the public. Unless the government provides mass testing, the problem of information is aggravated, probably raising the transmission risks. Moreover, unregulated herd immunity will be differentially felt by the poor. As healthy workers may recover their earnings from the modified quarantine, the poor, who have limited access to the health services and are thus more susceptible to the virus, are unlikely to benefit from this system. In effect, this will only exacerbate the inequality that prevails in the country. Moving towards a responsible new normal requires a strategy that addresses both people’s wellbeing and the socio-economic weaknesses exposed by COVID-19. Thus, the strategy should have the following elements

Gender-Sensitive Remittances and Asset Building in the Philippines

This publication aims to add to the remittances for development discourse, as an input into policy, programme and services development. It offers information and sex-disaggregated data on remittance flows, patterns, recognizing the differences between women and men as senders and recipients of remittances. The study considers how these gender dimensions intersect with specific social and economic contexts so that programmes are responsive to the needs at different levels – local, national, international, as appropriate and in a collaborative manner among key stakeholders. The study recommends emphasis on the meaningful participation of women migrant workers in decision-making processes on remittance-oriented initiatives, not merely as remittances senders and investors but beneficiaries and protagonists of developmen

Gender, Migration and Development in the Philippines

This paper highlights the current situation of Filipino women migrant workers with gender analysis and examines their social and economic contributions to Philippines’ development

Multilevel Pathways of Rural and Urban Poverty as Determinants of Childhood Undernutrition in the Philippines

This paper identified and compared pathways of childhood undernutrition among 308 rural and 310 urban children from low-income households in the Philippines. Multidisciplinary analyses based on quantitative and qualitative data revealed unique urban and rural constraints accounting for differing nutritional outcomes. Urban poor families were more food secure, though vegetable avoidance and poor micronutrient adequacy were observed. Rather than mitigate threats to undernutrition, rural households’ reliance on home food agriculture heightened risk to food insecurity, as the Philippines is vulnerable to crop-destroying tropical storms. Our findings suggest the need to strengthen local governance institutions to implement context-specific multisectoral interventions

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