Female Labour Force Participation and the Prices of Household Durable Goods: A Philippine Study

This paper investigated whether a decrease in the prices of household durable goods increases the Female Labour Force Participation (FLFP) in the Philippines. The paper used the theoretical model of Pirani, Leon, and Lugauers (2010), who theorized that a decrease in the prices of household appliances would increase FLFP due to their time being freed up for non-household tasks. To study this, the regression model of Cavalcanti and Tavares (2008) was used to test the hypothesis. The results of this paper were consistent with the theoretical and empirical results from the two models

Trends in National-Level Governance and Implementation of the Philippines\u27 Responsible Parenthood and Reproductive Health Law from 2014 to 2020

In 2012; the Philippines passed the Responsible Parenthood and Family Planning Law; a landmark legislation billed as a multisectoral and rights-based approach to further sustainable human development. This article is part of the first comprehensive evaluation of the implementation of the law by national-level actors. This evaluation is intended to assess the progress of implementers in the conduct of mandates; roles; and responsibilities described in the law and its implementing guidelines. Interviews with key national government officials and data from official documents and literature revealed 3 major trends in governance and implementation from 2014 to 2020. First; despite being a multisectoral policy; performance was siloed within individual units of implementing agencies; with limited interagency coordination. Second; although the law explicitly called for interventions to invest in human capital and address socioeconomic disparities for sustainable human development; performance focused on biomedical and health interventions; particularly in the area of family planning. Third; national-level governance for reproductive health interventions concentrated on programmatic and operational concerns. Overall; this case in the Philippines illustrates that fragmented implementation has contributed to the slow improvement of reproductive health outcomes. This study highlights the challenges of governance and multisectoral coordination to implement multidimensional interventions in a low- and middle-income country; and it provides potential areas for political and administrative reform in reproductive health governance in the Philippines. By creating a common narrative and onboarding multiple sectors; officials can better identify and address structural determinants with holistic policy solutions to improve reproductive health outcomes

The Impact of COVID-19 on Hospital Admissions for Twelve High-Burden Diseases and Five Common Procedures in the Philippines: A National Health Insurance Database Study 2019-2020

Background The Philippines has the highest cumulative COVID-19 cases and deaths in the Western-Pacific. To explore the broader health impacts of the pandemic, we assessed the magnitude and duration of changes in hospital admissions for 12 high-burden diseases and the utilization of five common procedures by lockdown stringency, hospital level, and equity in patient access. Methods Our analysis used Philippine social health insurance data filed by 1,295 hospitals in 2019 and 2020. We calculated three descriptive statistics of percent change comparing 2020 to the same periods in 2019: (1) year-onyear, (2) same-month-prior-year, and (3) lockdown periods. Findings Disease admissions declined (-54%) while procedures increased (13%) in 2020 versus 2019. The increase in procedures was caused by hemodialysis surpassing its 2019 utilization levels in 2020 by 25%, overshadowing declines for C-section (-5%) and vaginal delivery (-18%). Comparing months in 2020 to the same months in 2019, the declines in admissions and procedures occurred at pandemic onset (March-April 2020), with some recovery starting May, but were generally not reversed by the end of 2020. Non-urgent procedures and respiratory diseases faced the largest declines in April 2020 versus April 2019 (range: -60% to -70%), followed by diseases requiring regular follow-up (-50% to -56%), then urgent conditions (-4% to -40%). During the strictest (April-May 2020) and relaxed (May-December 2020) lockdown periods compared to the same periods in 2019, the declines among the poorest (-21%, -39%) were three-times greater than in direct contributors (-7%, -12%) and two-times more in the south (-16%, -32%) than the richer north (-8%, -10%). Year-on-year admission declines across the 12 diseases and procedures (except for hemodialysis) was highest for level three hospitals. Compared to public hospitals, private hospitals had smaller year-on-year declines for procedures, because of increases in utilization in lower level private hospitals. Interpretation COVID-19’s prolonged impact on the utilization of hospital services in the Philippines suggests a looming public health crisis in countries with frail health systems. Through the periodic waves of COVID-19 and lockdowns, policymakers must employ a whole-of-health strategy considering all conditions, service delivery networks, and access for the most vulnerable. Funding Open Philanthropy

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

National Multisectoral Governance Challenges of Implementing the Philippines\u27 Reproductive Health Law

In recognition of the role of reproductive health in individual and national development; the Responsible Parenthood and Reproductive Health (RPRH) Law of 2012 was passed in the Philippines after 30 years of opposition and debate. Seven years later; this article examined the cohesiveness of national multi-sectoral governance among state and non-state actors and identified challenges in coordination as part of the first comprehensive evaluation of the landmark policy. Using a qualitative intrinsic case study design and guided by the World Health Organization\u27s systems checklist for governing health equity as our theoretical perspective; we conducted 20 semi-structured interviews with national implementers from health agencies (n=11); non-health agencies (n=6) and non-state actors (n=3) that included civil society organizations (CSOs). Key themes identified through thematic analysis were supported with document reviews of policy issuances; accomplishment reports and meeting transcripts of the RPRH National Implementation Team (NIT). The study found that despite aspirations for vibrant multi-sectoral coordination; the implementation of the RPRH Law in the Philippines was incohesive. National leaders; particularly the health sector; were unable to rally non-health sector actors around RPRH nor strategically harness the power of CSOs. Local resource limitations associated with decentralization were exacerbated by paternalistic financing; coordination; and monitoring. The absence of multi-agency plans fostered a culture of siloed opportunism; without consideration to integrated implementation. This case study shows that even for neutral policies; the interest and buy-in of non-health state actors cannot be assumed. Moreover; possible conflicts in interests and perspectives between state and civil society actors must be managed. Overall; there is need for participatory policymaking and health-sector advocacy to set health equity as an intersectoral goal; involving subnational leaders in developing concrete action plans; and strengthening NIT\u27s accountability systems

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