research about malnutrition in the philippines

Undernutrition in the Philippines: Scale, Scope, and Opportunities for Nutrition Policy and Programming

January 30, 2021--Filipino children stand next to each other at a slum area in Quezon City, Metro Manila, Philippines.

Ezra Acayan/World Bank

Key Findings

• For nearly thirty years, there have been almost no improvements in the prevalence of undernutrition in the Philippines. One in three children (29%) younger than five years old suffered from stunting (2019), being small in size for their age.
• The country is ranked fifth among countries in the East Asia and Pacific region with the highest prevalence of stunting and is among the 10 countries in the world with the highest number of stunted children.
• There are regions with levels of stunting that exceed 40% of the population. In Bangsamoro Autonomous Region in Muslim Mindanao 45% of children below five are stunted, in Southwestern Tagalog Region (MIMAROPA) 41%, Bicol Region it is 40%, Western Visayas 40%, and in the south-central Mindanao Region (SOCCKSARGEN) 40%
• Micronutrient undernutrition is also highly prevalent in the Philippines: 38% among infants six to 11 months old; 26% among children 12–23 months; and 20% of pregnant women are anemic. Nearly 17% of children aged 6–59 months suffered from vitamin A deficiency (2018), of which children aged 12–24 months had the highest prevalence (22%) followed by children aged six to 12 months (18%).
• The persistence of very high levels of childhood undernutrition, despite decades of economic growth and poverty reduction, could lead to a staggering loss of the country’s human and economic potential. A Filipino child with optimal nutrition will have greater cognitive development, stay in school longer, learn more in school, and have a brighter future as an adult, while undernutrition robs other children of their chance to succeed.
• The burden on the Philippine economy brought by childhood undernutrition was estimated at US$4.4 billion, or 1.5% of the country’s GDP, in 2015.
• The country’s Human Capital Index (HCI) of 0.52 indicates that the future productivity of a child born today will be half of what could have been achieved with complete education and full health.
• In 2013, it was estimated the benefit cost ratio for nutrition investments in the Philippines at 44 (Figure 3.2). In other words, every dollar invested in nutrition has the potential of yielding a $44 return. A lower estimate projecting the benefits accruing from a nutrition intervention scenario (NIS) at the national level through key nutrition-specific interventions rolled out over ten years at full coverage reaches US $12.8 billion over a 10-year period with a corresponding cost of $1,062 million, yielding a benefit cost ratio of 12:1.
• Hunger in the Philippines rose sharply following the start of the pandemic. Social Weather Stations (SWS) surveys show that in September 2020, after seven months of community quarantine, 31% of families reported experiencing hunger in the past 30 days, and 9% were suffering severe hunger—in both cases, the highest levels recorded in more than 20 years.
• The key determinants of undernutrition are multisectoral. At the immediate level, a child becomes undernourished because of inadequate or inappropriate food, health, and care.
• At the basic level, poverty is one of the most important causes of undernutrition: 42.4% of children from households in the poorest income quintile are stunted. Governance structures also pose significant challenges for the country’s efforts to combat undernutrition. 

Policy and programmatic actions to address the challenge of reducing childhood undernutrition:

• Strengthen the National Nutrition Council to provide the supervisory and oversight capacities needed for programs to run effectively and efficiently and be enabled to respond to gaps in program implementation
• High priority and strong support to nutrition should be the agenda of both the executive and legislative bodies in the municipalities
• “More Money for Nutrition and More Nutrition for the Money”: Secure domestic funding for nutrition-related programs
• Adopting an evidence-based package of nutrition-specific interventions that can be made available to each household in all priority LGUs
• Formulating a comprehensive, social behavior change communications strategy targeted at policy makers, health workers and households
• Maternal and child health programs provide the best opportunities for both nutrition-specific and nutrition-sensitive components.
• Implement nutrition sensitive programs aimed at improving dietary quality, access to clean water and sanitation, ensuring that PF serves the most vulnerable populations
• Establish geographic convergence by key sectors down to the household level and that focus on delivering a basic nutrition package of nutrition-specific and nutrition-sensitive programs to pregnant and lactating women and children younger than 2 years old.
• Ensure availability of subnational, ethnicity-disaggregated nutrition and nutrition-related data for targeted policy advice and interventions
• [FULL REPORT] Undernutrition in the Philippines: Scale, Scope, and Opportunities for Nutrition Policy and Programming
• Report Executive Summary
• [EVENT] Undernutrition and Stunting: From Crisis to Action
• [VIDEO] Addressing undernutrition in the Philippines, investing in the health of Filipino children
• Video in Filipino
• [PRESS RELEASE] Investing in Nutrition Can Eradicate the “Silent Pandemic” Affecting Millions of Poor Families in the Philippines – World Bank

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Open Access

Peer-reviewed

Research Article

Exploring the double burden of malnutrition at the household level in the Philippines: Analysis of National Nutrition Survey data

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

Affiliation School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada

Roles Conceptualization, Supervision, Writing – review & editing

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

Affiliation School of Public Health and Social Policy, University of Victoria, Victoria, British Colombia, Canada

• Josephine Gaupholm, 
• Warren Dodd, 
• Andrew Papadopoulos, 
• Matthew Little

• Published: July 17, 2023
• https://doi.org/10.1371/journal.pone.0288402
• Reader Comments

In the Philippines, the rising prevalence of obesity and related chronic diseases alongside persistent undernutrition presents a complex public health challenge. Understanding the patterns and dynamics of this ‘double burden of malnutrition’ (DBM) is crucial for developing effective intervention strategies. However, evidence of the occurrence of undernutrition and overnutrition within the same household is currently lacking.

Using cross-sectional data from the 2013 Philippines National Nutrition Survey this study examined the prevalence of different typologies of household-level DBM from an analytical sample of 5,837 households and 25,417 individuals. Multivariable logistic regression was performed to identify factors associated with overall occurrence of intrahousehold DBM.

The overall prevalence of double burden households was 56% based on a comprehensive definition. The most common typology of intrahousehold DBM characterized in this study (% of all households) comprised households with at least one adult with overnutrition and at least one separate adult with undernutrition. Household size, wealth quintile, food insecurity, and household dietary diversity were all associated with household-level DBM. Double burden households were also influenced by head of household characteristics, including sex, level of education, employment status, and age.

Conclusions

The findings from this study reveal that the coexistence of overnutrition and undernutrition at the household level is a major public health concern in the Philippines. Further comprehensive assessments of household-level manifestations of the DBM are needed to improve our understanding of the trends and drivers of this phenomenon in order to develop better targeted interventions.

Citation: Gaupholm J, Dodd W, Papadopoulos A, Little M (2023) Exploring the double burden of malnutrition at the household level in the Philippines: Analysis of National Nutrition Survey data. PLoS ONE 18(7): e0288402. https://doi.org/10.1371/journal.pone.0288402

Editor: Emmanuel Oladeji Alamu, International Institute of Tropical Agriculture (IITA), ZAMBIA

Received: November 14, 2022; Accepted: June 26, 2023; Published: July 17, 2023

Copyright: © 2023 Gaupholm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The datasets used in this study are publicly available online from the Philippines’ Food and Nutrition Research Institute of the Department of Science and Technology’s website ( http://enutrition.fnri.dost.gov.ph/site/home.php ).

Funding: This research was funded by the Social Sciences and Humanities Research Council of Canada ( https://www.sshrc-crsh.gc.ca/home-accueil-eng.aspx ) grant number 430-2019-00150 was awarded to ML and WD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Philippines, like many low- and middle-income countries, is currently facing multiple burdens of malnutrition [ 1 , 2 ]. Despite rapid economic growth, the Philippines continues to experience high levels of undernutrition (e.g., stunting, wasting, underweight) in addition to a rapid rise in the prevalence of overnutrition (e.g. overweight, obesity, and diet-related non-communicable diseases) [ 2 – 4 ]. Indeed, according to the 2022 Global Nutrition Report the prevalence of stunting among children under five is 28.8% in the Philippines, which is higher than the Asian regional average of 21.8% [ 2 ]. Meanwhile, approximately one third of adults are overweight or obese [ 2 ]. Micronutrient deficiencies also persist, with an estimated 70 to 80% of adults not meeting the recommended nutrient intakes for many vital micronutrients, including iron, calcium, and vitamin A [ 5 ]. This coexistence of both over- and undernutrition is known as the double burden of malnutrition (DBM) and can occur at the population, household, and/or individual level [ 6 ]. Understanding the patterns and dynamics of malnutrition is crucial for developing effective intervention strategies [ 7 ]. Studies of the DBM most frequently report on the coexistence of undernutrition and overnutrition at the population level [ 8 ]. DBM research in the Philippines is similarly population-level focused, with some recent work assessing individual-level DBM [ 9 , 10 ]. However, household-level manifestations of the DBM have not been widely studied in the Philippines.

DBM at the household level is broadly characterized as the coexistence of undernutrition and overnutrition within the same household, however, no definitive operational definition or indicators of double burden households exist [ 8 , 11 ]. While the majority of DBM research at the household level focuses solely on maternal-child relationships, where the presence of an overweight mother and a stunted child has largely become the archetypal characterization of a double burden household, numerous other typologies can, and do, exist [ 12 – 16 ]. In most studies, anthropometric measurements are used as indicators of undernutrition and overnutrition, however, exact indicators and cut-off points differ across studies [ 15 ]. As a result of such heterogeneity in DBM definitions and indicators, prevalence estimates of double burden households can vary widely between studies [ 8 , 15 , 16 ].

The cooccurrence of under- and overnutrition at the household level is often considered paradoxical, as historically these two forms of malnutrition were understood to arise from two different sets of determinants and behaviours [ 12 , 17 ]. However, intrahousehold DBM requires that both under- and overnutrition arise among people who share similar food environments and household characteristics. As such, there is an increasing recognition that under- and overnutrition should not be considered distinct conditions at opposite ends of the nutrition spectrum, as both are indicative of malnutrition, including diets high in calories but low in micronutrients [ 6 , 18 ]. Previous research has found associations between double burden households and socio-demographic factors, such as maternal age, educational level, and occupation, as well as household wealth and family size [ 11 , 12 , 14 ]. Suggested mechanisms driving intrahousehold DBM often relate to the nutrition transition and related changes in household dietary and lifestyle patterns [ 12 ]. Dietary behaviour and patterns are strongly influenced by social and familial dynamics. However, empirical evidence examining associations between intrahousehold DBM and dietary indicators, such as diet diversity, food security, and diet quality, is lacking [ 12 , 19 – 21 ].

Understanding the various types and correlates of malnutrition at the household level provides an opportunity to identify shared drivers of malnutrition, which is critical for effectively designing and targeting interventions. However, in the Philippines, the extent and determinants of intrahousehold DBM are relatively unknown because of scarce information. To our knowledge, the only published research investigating household-level DBM in the Philippines reported that among a study sample of 376 child-mother pairs living in Metro Manila, 59% were experiencing dual forms of malnutrition [ 22 ]. However, since this localized study conducted two decades ago, no further examinations of household-level DBM have been done. To begin addressing this knowledge gap, the current study aims to explore this phenomenon using nationally representative data and gain a high-level understanding of factors associated with intrahousehold DBM. Specifically, the objectives of this study are: 1) to determine the prevalence of different typologies of the DBM at the household level in the Philippines; and 2) to examine the broad factors associated with the DBM at the household level.

Data source and study population

This study used publicly available data from the 2013 National Nutrition Survey (NNS) in the Philippines. The NNS, conducted by the Philippines Food and Nutrition Research Institute, is a cross-sectional, nationally representative survey that reports on the health, nutrition, and dietary composition of the Filipino population [ 23 ]. The 2013 NNS employed a three-stage stratified sampling design. Primary Sampling Units (PSUs) were defined first as a barangay (i.e., the smallest administrative unit in the Philippines) or contiguous barangay with at least 500 households. PSUs were then used to identify enumeration areas (EAs) of 150–200 households. A random selection of households within each EA made up the final sampling unit. Overall, 35,825 households and 172,323 individuals were sampled across 80 provinces (the province of Batanes was not surveyed due to logistic reasons). The complete methodology for the 2013 NNS survey has been detailed elsewhere [ 23 , 24 ].

This study included multi-person households with complete data from seven survey components, including anthropometric, biochemical, clinical, food security, socioeconomic (household and individual), and dietary data. Data from pregnant women were excluded from the analysis because pregnancy affects the accuracy of anthropometric measurements [ 25 ]. A total of 5,837 households and 25,417 individuals were eligible for analysis ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0288402.g001

Outcome variables

Assessment of overnutrition and undernutrition at the individual level..

Table 1 provides an overview of each of the indicators used to classify under- and overnutrition in adults and children and the clinical cut-offs. For adults, we used a combination of anthropometric, biochemical, and clinical indicators to classify malnutrition. Body mass index (BMI) was classified based on the World Health Organization (WHO) standard cut-offs [ 26 ]. Under- and overnutrition among children/adolescents were identified using height-for-age Z-scores (HAZ), weight-for-height Z-scores (WHZ), weight-for-age Z-scores (WAZ), and body-mass-index- for-age Z-scores (BAZ) [ 27 ]. Z-score calculations were performed using the Anthro and AnthroPlus macro packages for Stata developed by the WHO and United Nations Children’s Fund (UNICEF) which apply the WHO Child Growth Standards as reference data [ 28 , 29 ]. Adults were classified as having overnutrition if they had any of the following conditions: (1) overweight/obesity or abdominal obesity; (2) hypertension; (3) hyperglycemia, or; (4) dyslipidemia. Adults were classified as having undernutrition if they had any of the following conditions: (1) underweight; (2) anemia; (3) vitamin A deficiency; or, (4) iodine deficiency. A child was considered under-nourished if they were stunted, wasted, and/or underweight. A child was considered over-nourished if they were overweight. Micronutrient deficiencies were not assessed for children and adolescents due to a limited number of complete micronutrient data from children and adolescents in our sample.

https://doi.org/10.1371/journal.pone.0288402.t001

Anthropometric, biochemical, and clinical data collection.

Height and weight measurements were obtained for all eligible household members using an electronic calibrated portable stadiometer (SECA 217, Hamburg, Germany) and a digital double window weighing scale (SECA 874, Hamburg, Germany). The recumbent length of children under 2 years-old was measured using an infantometer or a wooden length board. Waist circumference was measured among participants aged 10 and older using a calibrated tape measure. Blood pressure measurements were taken by trained nurses using a non-mercurial sphygmomanometer (A&D Um-101TM) and stethoscope. Registered medical technologists collected blood samples which were used to measure biomarkers of cardio-metabolic risk factors (CMRF), namely fasting blood glucose and blood lipids. Following collection, they were analyzed using the enzymatic colorimetric method with Roche COBAS Integra and Hitachi 912. Registered medical technologists also collected blood and urine samples to assess micronutrient deficiencies. The cyanmethemoglobin method was used to evaluate hemoglobin levels. Serum retinol was examined using High-Performance Liquid Chromatography. Urinary iodine excretion levels were determined using the acid digestion/colorimetric method.

Assessment of the double burden of malnutrition at the household level.

To comprehensively evaluate the prevalence of double burden households in the Philippines, we considered all possible typologies, or combinations, of under- and overnutrition among household members. A household was considered a double burden household if they had any combination of undernutrition and overnutrition in two or more household members ( Table 2 ). We also explored the prevalence of several specific household DBM subtypes relevant to the literature, including the co-occurrence of child undernutrition and maternal overnutrition (represented by women of reproductive age 18–49 years).

https://doi.org/10.1371/journal.pone.0288402.t002

Independent variables

Covariates were identified from previous literature and included both household factors and individual-level characteristics of the head of household [ 11 – 14 ]. These include the age, sex, educational attainment, and occupation of the head of household. Household factors included the number of people living in the household (household size), wealth quintile, food security, and diet diversity.

The head of household was reported by participants during the survey. The sex of the head of household was classified as either male or female, while the age variable was categorized as 18–29, 30–39, 40–49, 50–64, and ≥65. Educational attainment was classified as either having a post-secondary education or not. Household size was categorized into four groups: 2–3, 4–6, 7–10, and ≥11. Wealth status was derived from principal component analysis scores based on household assets such as possession of vehicles, appliances, housing construction materials, and sanitation facilities [ 23 ]. These scores were used to define wealth quintiles.

The Household Food Insecurity Access Scale (HFIAS) was used to assess household food security. The nine-question HFIAS measures the occurrence and frequency of various conditions related to food insecurity over a one-month (30 day) period. Participants are first asked an occurrence question—i.e., whether a condition occurred at all in the past 30 days (yes or no). If they respond yes, they are then asked the frequency-of-occurrence, that is, if the condition happened rarely (once or twice), sometimes (three to ten times) or often (more than ten times) in the past four weeks. Using the scoring system developed by USAID’s Food and Nutrition Technical Assistance Project [ 37 ], households were categorized as either “food secure”, “mildly food insecure”, “moderately food insecure”, or “severely food insecure”.

Household diet diversity scores (HDDS) were calculated based on data collected through one-day food records, which including weighing foods consumed inside the home. Trained registered nutritionist-dietitians used a digital dietetic scale to weigh (before cooking or serving) all food items prepared and served in the home for an entire day. Unconsumed or leftover food was deducted from the total in order to obtain the final amount of food and beverages consumed by the household. Foods consumed outside the house were recalled by household members and added to the household food record. Dietary data were aggregated into 12 food groups based on Food and Agriculture Organization guidelines [ 38 ]. HDDS were computed by summing up the total number of food groups consumed by members of the household. Scores were then categorized into three groups representing low diet diversity (score of 1 to 4), moderate diet diversity (5 to 7), and high diet diversity (≥8).

Statistical analysis

We generated descriptive statistics for the socio-demographic and individual over- and undernutrition characteristics of the study sample using weighted means and percentages. Survey sample weights were computed and adjusted for non-response, then post-stratified based on the projected population obtained from the Philippines Statistics Authority. We calculated the prevalence of all possible combinations of overnutrition and undernutrition within households, with overall presence being categorized as a binary outcome (0 = not a double burden household, 1 = a double burden household). The rationale for this approach was based on the desire to gain a high-level understanding of factors associated with the phenomenon of intrahousehold DBM in general across the Philippines, as this has not yet been explored in the literature. Additionally, following preliminary explorations of the data, we found that assessing all possible typologies of household DBM separately increased the risk of misclassification bias and diminished the statistical power of the analyses. It was also for this reason that micronutrient deficiencies were not assessed separately from underweight.

Logistic regression analyses were used to examine factors associated with double burden households of any type. First, bivariate regression models were constructed to examine the unconditional association between each of the independent variables and our dependent variable (i.e., household-level DBM). As we had identified our variables of interest a priori based on previous research, all covariates, regardless of significance in the unconditional analysis, were included in the multivariable logistic regression model. Prior to fitting our main model, we examined all pairwise correlations among predictor variables to test for possible collinearity using Spearman’s rho. However, we found no evidence of multicollinearity between independent variables as all correlation coefficients were less than |0.80|. As such, a model containing all variables of interest was constructed. This model also included dummy variables for the 17 administrative regions in the Philippines to control for geographic differences. Subsequently, we tested for all plausible two-way interactions among the covariates included in our main model, however, none were found to be significant. Goodness of fit was assessed using the Hosmer-Lemeshow goodness of fit test for binary data. All statistical analyses were performed using the Stata statistical software package version 16.1 (2022; StataCorp). The level of significance was set at p<0.05.

Individual nutrition status among study participants

We analysed the individual nutrition status of 13,843 adults (≥18 y) and 11,574 children/adolescents (≤17 y). The mean BMI of adults in the sample was 23.06 kg/m 2 (SD = 4.18), with 31.2% of women and 26.2% of men being either overweight or obese ( Table 3 ). Dyslipidemia was the most prevalent CMRF, affecting nearly one in three adults within the study sample. The prevalence of abdominal obesity was notably 6.8 times higher in women than in men (20.5% compared to 3%). Overall, 55% of adults had one or more indicator of overnutrition. Conversely, undernutrition affected 41.7% of adults. Approximately 11% of adults were underweight (BMI <18.5 kg/m 2 ), while over one in three adults had some form of micronutrient deficiency.

https://doi.org/10.1371/journal.pone.0288402.t003

Table 4 presents the prevalence of malnutrition among children and adolescents. Overall, over one in three children and adolescents were classified as having some form of undernutrition, while only 3.3% were considered overweight. Stunting was the most prevalent form of undernutrition, affecting nearly 30% of all children/adolescents.

https://doi.org/10.1371/journal.pone.0288402.t004

Household-level double burden

The overall prevalence of double burden households was 56% when we included all possible combinations of over- and undernutrition among household members ( Table 5 ). The majority of these double burden households (83.3%) had at least one adult with overnutrition and at least one adult with undernutrition. The proportion of households with DBM type 1, i.e., at least one adult with overnutrition and at least one child/adolescent with undernutrition, was 26.8%. When we restricted DBM type 1 to only include the co-existence of overnutrition among women of reproductive age (18–49 y) and undernutrition among children/adolescents, the prevalence of DBM fell to 12%. Further, if we define child undernutrition using only stunting and wasting metrics among children under five, household-level DBM prevalence was 4.3%.

https://doi.org/10.1371/journal.pone.0288402.t005

Socio-demographic characteristics

Table 6 describes the sociodemographic characteristics of the study population overall and by household DBM type. Overall, households had on average five members, with the majority having a male head of household. Households were relatively evenly distributed across each of the wealth quintiles, however, close to 60% of households were classified as either moderately or severely food insecure. Household heads were mainly employed as farmers, laborers, or unskilled workers, with only 15.7% having attended postsecondary school.

https://doi.org/10.1371/journal.pone.0288402.t006

Factors associated with household-level double burden

Table 7 presents the results from both our bivariate and multivariable logistic regression models, reporting the unadjusted odds ratios (uOR) and adjusted odds ratios (aOR), respectively. The remainder of the results section focuses on the results of the multivariable analysis. Female-headed households had significantly lower odds of household-level DBM compared to male-headed households (95% confidence interval (CI): 0.63–0.86). Similarly, the odds of being a double burden household were lower when the head of household attended post-secondary school (95% CI: 0.63–0.89). The age of the head of the household was positively associated with the household experiencing a DBM. For example, intrahousehold DBM was 2.22 times more likely in households with a head of household aged 65 or older, compared to households with a head of household between the ages of 18 to 29 (95% CI: 1.51–3.14). We found no significant differences in household-level DBM between head of households employed as farmers, laborers, or in unskilled work and other occupation categories. However, households with an unemployed head of household were found to have 1.24 times higher odds of household-level DBM compared to household heads employed in farming, labour, or unskilled work (95% CI: 1.03–1.49).

https://doi.org/10.1371/journal.pone.0288402.t007

Larger household size was associated with higher odds of household-level DBM. Additionally, households experiencing food insecurity had higher odds of intrahousehold DBM compared to food-secure households. Household wealth quintile was also associated with household DBM, with households in the 5 th (or wealthiest) quintile having 1.26 times the odds of household-level DBM when compared to the 1 st (or lowest) quintile (95% CI: 1.00–1.59). Lastly, higher household diet diversity scores were associated with increased odds of household-level DBM. For instance, households with high household diet diversity (i.e., a HDDS of 8 or above) had 1.46 times the odds of household DBM when compared to households with low diet diversity (i.e., a HDDS of 4 or below) (95% CI: 1.14–1.87).

The current study sought to evaluate the prevalence, typologies, and correlates of the household-level DBM in the Philippines. Using data from a large nationally representative survey, we adopted a comprehensive definition of household-level DBM to examine this complex phenomenon. Results indicate that various types of household-level DBM exist in the Philippines, with over half of all households experiencing some form of intrahousehold DBM. This reflects both the high prevalence of individual-level indicators of over- and undernutrition among the Filipino population, but also the number and type of individual-level indicators we measured which included anthropometric measurements, micronutrient deficiencies, and CMRF. The growing concern of overweight and associated CMRF in the Philippines was emphasized in this study, as over one in two adults were affected by some form of overnutrition. On the other hand, undernutrition remains a serious public health concern in the Philippines, with 41.7% of adults and 33.5% of children and adolescents exhibiting at least one indicator of undernutrition. At the household-level, these forms of malnutrition manifested in a myriad of ways. The prevalence of households containing a woman of reproductive age (18–49 years) with overnutrition and a child with undernutrition, which is the most frequently assessed household-level DBM type in the literature, was 12%. While this figure is comparable to the global average prevalence, there is large variation between studies, regions, and countries [ 6 , 11 ]. A recent systematic review by Kosaka and Umezaki found that household prevalence of mother overweight–child underweight pairs ranged from <3% (e.g., in Bangladesh, Cambodia, and Nepal) to >10% (e.g., in Bolivia, Egypt, and Guatemala) [ 11 ]. Of the household-level DBM typologies characterized in this study, the most common (46.4% of all households) comprised households with at least one adult with overnutrition and at least one different adult with undernutrition. Of these pairings, 54.2% (or 25% of all households) consisted of a man (≥18 years) with overnutrition and a woman (≥18 years) with undernutrition. The high prevalence of this pairing is notable, as there is a substantial lack of research exploring such adult-adult DBM parings in the literature [ 8 ]. Previous studies on DBM may have overlooked this important manifestation of household-level DBM. This typology of DBM may be indicative of inequitable allocation of resources (e.g., food, financial resources, and decision-making power) among adults at the household level, which bears further investigation.

Our analysis identified a number of socio-economic and demographic factors associated with intrahousehold DBM. Consistent with previous research, we found positive associations with both the age of the head of household and household size [ 11 ]. Household wealth was also associated with household-level DBM, with households in the 3 rd and 5 th wealth quintiles having significantly higher odds of household-level DBM compared to households in the 1 st (or lowest) wealth quintile. This indicates that middle- and high-income households may be at higher risk for intrahousehold DBM in the Philippines, which supports findings from similar research [ 14 , 39 ].

Our results showed female-headed households had lower odds of DBM compared to male-headed households. Correspondingly, a similar study using nationally-representative survey data from Indonesia found that female-headed households were less likely to experience the DBM, defined as households with at least one overweight and one underweight member [ 40 ]. This association underscores the importance of gender dynamics and decision-making power in household nutrition. Evidence suggests that women’s empowerment in the household, including decision-making power and autonomy, is associated with improved child nutrition in Asia [ 41 ], Africa [ 42 ], and other regions [ 43 ]. It has been hypothesized that when women have greater control and access to household resources, they tend to prioritize health and food spending, therefore leading to improved diets and nutrition [ 40 ]. However, such relationships are complicated by the reality that female-headed households are at increased risk of food insecurity and poverty [ 44 ] due to numerous economic and societal disadvantages, including unequal access to assets (e.g., land and livestock), markets, and services [ 45 , 46 ]. Indeed, contrary to our findings, Sansón-Rosas et al. found that female-headed households in rural Colombia had higher odds of intrahousehold DBM [ 47 ], while results from a pooled analysis of 23 African countries found no association with the sex of the head of household [ 14 ]. Conflicting results across studies and contexts could be influenced by the variation in sample populations, study design, and/or measures of household DBM.

It has been previously reported that the Philippines has one of the highest burdens of food insecurity in southeast Asia [ 48 ]. Food insecurity is strongly associated with a number of nutritional and cardio-metabolic health outcomes, including indicators of both under- and overnutrition [ 49 ]. In our analysis, food insecurity was associated with increased odds of household-level DBM, indicating that food insecurity may reduce household dietary quality and increases risk of multiple forms of malnutrition. Households experiencing food insecurity often modify their diets by decreasing the overall amount of food consumed and increasing consumption of cheaper, energy-dense, highly processed foods, thus creating a nutrition environment in which multiple forms of malnutrition can develop [ 48 ]. Studies from Brazil, Indonesia, and Colombia also found food insecurity was associated with the DBM at the household level [ 47 , 50 , 51 ]. Further research is needed to examine the precise mechanisms for this association, including how food insecurity interacts with environmental (e.g., food environment) and household (e.g., intrahousehold allocation of resources) factors to modify dietary consumption and influence risk of nutritional health outcomes [ 52 ].

We also found that household dietary diversity scores were associated with increased odds of intrahousehold DBM. This finding is contrary to our expectations, as the HDDS is often viewed as an indicator of a household’s economic ability to access food [ 38 ] and diversity is often seen as a key component of healthy diets [ 53 ]. While the HDDS is not intended as a proxy for dietary quality [ 38 ], studies elsewhere have established a positive association between household diet diversity and healthy diets [ 54 ], food security [ 55 ], and nutrition outcomes [ 56 ]. One might therefore expect that households with higher HDDS would have lower odds of DBM; however, we observed the inverse association in our analysis. This association was consistent across bivariate analyses and when controlling for household food security, wealth status, and occupation in the multivariable model. This finding may indicate that in the Philippines, households with greater dietary diversity consume larger quantities of non-nutrient-dense foods and/or live more sedentary lifestyles. Another possibility is the presence of intrahousehold disparities in resource allocation, leading to nutritional inequalities that are not identified by the household-level HDDS. However, such explanations remain purely conjecture given the limited information provided by HDDS. These findings may raise questions about the HDDS as an accurate indicator of food access and dietary quality. Researchers have previously criticized dietary diversity scores for their limited cross-cultural validity and misuse/misinterpretation in nutrition literature [ 57 – 59 ]. Other authors have critiqued the reliability and usefulness of the HDDS [ 57 , 60 ] as a research tool and promote tools (e.g., the World Food Programme’s Food Consumption Score) that assign higher weights to foods deemed most important for nutritional purposes [ 61 ]. Further research is therefore required to ensure validity and reliability of the HDDS across multiple global contexts [ 60 ], particularly since improved dietary diversity is often measured in DBM research [ 14 ] and is a stated goal of nutrition interventions. Future studies examining dietary determinants of intrahousehold DBM should also focus on measuring household dietary intake and quality, not just diversity.

This research provides important and novel insights into the household-level double burden of malnutrition in the Philippines. However, our study has several limitations. First, although we use data from a large, nationally representative sample, we were limited to the information and variables collected by the NNS. For instance, it was not possible to establish familial relations within each household (for example, in households with children and multiple women of reproductive age, we could not definitively identify mother-child pairs), which may limit the comparability of results with research on household-level DBM elsewhere. Second, while this the most recent NNS with publicly available data, the situation in the Philippines today has likely changed since 2013. Similar analysis of future NNS data should be done to get a more accurate picture of the intrahousehold DBM in the Philippines. Third, due to our use of a comprehensive definition of household-level DBM rather than focusing on the more common mother-child DBM pairs, the comparability of our study to other research on household-level DBM is limited. Further, although we did include some indicators of micronutrient deficiencies among adults, this paper did not assess the presence of these conditions as separate from underweight, or what is known as the triple burden of malnutrition. Due to limitations of the data set, which lacked an adequate number of complete micronutrient data from children and adolescents in our sample, assessing triple burden and double burden typologies separately increased the risk of misclassification bias and diminished the statistical power of the analyses.

In conclusion, we found a very high prevalence of intrahousehold DBM when considering all possible typologies of under- and overnutrition among household members in the Philippines, indicating that this form of the double burden of malnutrition is a major public health concern. This study highlights important gaps in the current understanding of household-level manifestations of the DBM, particularly those between adult-adult DBM parings. While further research is needed to investigate each of these specific typologies in greater detail, these findings support the need for more comprehensive surveillance of the DBM at the individual, household, and national levels in order to monitor progress and develop better targeted interventions. We identified a number of socio-economic and demographic factors associated with overall intrahousehold DBM, including household size, wealth quintile, food insecurity, and diet diversity. Double burden households were also influenced by head of household characteristics including sex, level of education, employment status, and age. These results suggest that key policy actions for reducing the DBM should center on supporting education and employment opportunities, gender equality, and social safety nets. Overall, this evidence provides important high-level insight into factors influencing household-level DBM in the Philippines and may help inform future research designs and directions.

Acknowledgments

We thank the Food and Nutrition Research Institute of the Department of Science and Technology, Philippines for providing access to the 2013 National Nutrition Survey data.

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• Open access
• Published: 16 April 2018

Nutritional status of children ages 0–5 and 5–10 years old in households headed by fisherfolks in the Philippines

• Mario V. Capanzana 1 ,
• Divorah V. Aguila 1 ,
• Glen Melvin P. Gironella 1 &
• Kristine V. Montecillo 1  

Archives of Public Health volume  76 , Article number:  24 ( 2018 ) Cite this article

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The study aimed to analyze the nutritional status of Filipino children ages 0–60 months (0–5.0 years old) and 61–120 months (5.08–10.0 years old) in households headed by fisherfolks.

The 8th National Nutrition Survey (NNS) data collected by the Food and Nutrition Research Institute, Department of Science and Technology (FNRI-DOST) was used in the study. There were 13,423 young children and 16,398 schoolchildren participants for anthropometry component. The World Health Organization Child Growth Standards (WHO-CGS) was used to assess the nutritional status of the young children while the WHO Growth Reference 2007 was used for schoolchildren. Occupational groups were categorized based on the 1992 Philippine Standard Occupational Classification (PSOC). Descriptive statistics were used for the profiling of the different variables while bivariate analysis, logistic regression and odds ratios were used to correlate the different variables to the nutrition status of the children. Data were analyzed using Stata 12.0.

Results showed that households headed by fisherfolks (HHF) were one of the occupational groups with highest malnutrition among young and school-aged children. The HHF had higher prevalence of malnutrition among young children compared to the overall prevalence of malnutrition among young children in the Philippines, except for overweight. This is also true for schoolchildren, except for wasting. Age of child, sex, household size, age, fishermen and farmer as household head and type of toilet (water-sealed) were correlated to stunting, underweight, overweight and obesity among children.

Conclusions

The high prevalence of stunting, underweight and wasting among young and schoolchildren in this occupational group poses immediate and serious nutrition intervention strategies such as health and nutrition information, health care, sanitation and hygiene, and physical activities. A national policy on the health, nutrition and welfare of households headed by fisherfolks and their children is highly recommended.

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Fisherfolks consistently top as the poorest sector in the Philippines from 2003 to 2012 based on the report of Philippine Statistics Authority (PSA) on Poverty Incidence of Basic Sectors [ 1 ]. Aside from the uncertainty of income among fishing communities, factors such as land ownership, debt, access to health, education and financial capital, as well as political and geographical marginalization also contribute to why poverty thrives in this sector [ 2 ]. Moreover, fisherfolks often live in places that have particularly high risk of extreme events; flooding, cyclones and tsunamis often visit coastal and floodplain fisheries, while inland fisheries can be significantly affected by droughts and floods. These disasters leave severe damages on infrastructures as well as productive assets such as boat, landing sites, post-harvesting facilities and road among fishing-dependent people. These consequently decrease their harvesting capacity and access to markets, affecting both local livelihood and the overall economy [ 3 ].

These kinds of disasters had also brought severe asset damages among fishing communities in the Philippines. In 2013, Typhoon Yolanda/Haiyan, one of the strongest typhoons that visited the country, affected Eastern Samar, Samar and Leyte. The National Disaster Risk Reduction and Management Council (NDRRMC) estimated that 16 million people were affected and 1.1 million houses were damaged. The livelihood of fisherfolks was also affected in the areas. There were nearly 30,000 small-scale fishing boats damaged while more than 100,000 were lost or destroyed [ 4 ]. Additionally, about 600,000 ha of agricultural land, 33 million coconut trees and 305 km farm-to-market road were damaged, whereas more than 400 health facilities and 1200 provincial, city and municipal and barangay halls and public markets were destroyed in the area [ 5 ].

Disasters, such as typhoon Yolanda/Haiyan, can have serious consequences for food security, nutrition and health. Damaged infrastructures due to extreme events or flooding can cut access to local markets, and consequently reduce the availability of food products and increase the food prices, resulting in higher incidences of malnutrition in communities [ 3 ].

These disturbances in nutrition as a result of inadequacy in food intake, health problems, or a combination of both, invariably affect the growth of children [ 6 ]. Hence, assessments on the nutritional status of children based on their anthropometric indicators of growth has been used not only in generating information on their nutritional and health status, but also in providing an indirect measurement of the quality of life of their community, and thereby as an indicator of the nutritional status and food intake adequacy of all members in that community [ 7 , 8 ].

Thus, this study aimed to focus and analyze the condition of households headed by fisherfolks (HHF) with respect to the nutritional situation by assessing the nutritional status of children ages 0–60 months (0–5.0 years old) and 61–120 months (5.08–10.0 years old), using the 8th National Nutrition Survey data. Factors affecting the nutritional status of the children in HHF were also analyzed to better understand the nutrition situation in the fishing communities in the Philippines.

The study was initiated to estimate the prevalence of malnutrition among young and school-aged children among fishing community as basis in planning and developing nutrition programs that will improve the nutritional situation of this occupational group. It is hoped that this study will provide significant information that can serve as basis for policy makers and program planners in the nutrition and fishery sectors among private or public organizations in drawing future strategies for improving the nutritional situation of the fisherfolks in the Philippines.

Conceptual framework

Malnutrition in children is the result of complex interaction of numerous and multifaceted factors. Thus, in the analysis of this study, the UNICEF’s conceptual framework for the causes of malnutrition was considered (Fig.  1 ). It was expected that factors such as age, gender, household size, occupation, and sanitation facilities were associated with the malnutrition among children in HHF.

UNICEF conceptual framework for the causes of malnutrition (Adapted from Mason, 2003)

The study used the data from 8th National Nutrition Survey (NNS) conducted by the Food and Nutrition Research Institute, Department of Science and Technology (FNRI-DOST) in 2013. The NNS was conducted in 79 provinces, 45,047 households and 172,323 individuals, adopting the 2003 master sample developed by Philippine Statistics Authority (PSA) [ 9 ]. A stratified multi-stage sampling design for household-based surveys covering all the 17 regions, including the National Capital Region was used.

The 2013 NNS used the four subsample or replicates of the master sample for its anthropometry component. There were 13,423 young children ages 0–60 months (0–5.0 years old) and 16,398 schoolchildren ages 61–120 months (5.08–10.0 years old) participated in the survey. The World Health Organization-Child Growth Standards (WHO-CGS) [ 10 ] was used to assess the nutritional status of young children, while WHO Growth Reference 2007 [ 11 ] was used for schoolchildren.

A written informed consent was obtained from all the participants of this study through the mother or guardian. Ethical clearance was provided by the FNRI Institutional Ethics Review Committee (FIERC).

To analyze the nutrition situation of the fisherfolks in the Philippines, the households were categorized into occupational groups based on the 1992 Philippine Standard Occupational Classification (PSOC) of PSA [ 12 ]. Descriptive statistics was employed to summarize data on the prevalence of malnutrition among young children and schoolchildren in different occupational groups including the fisherfolks. Logistic regression and multinomial logistic regression analyses were used to determine the association of variables to underweight, stunting, and overweight or obesity among children 0–5 years and 6–10 years children. Statistical analysis was conducted using Stata version 12.0.

The prevalence of malnutrition among Filipino children ages 0–60 months (0–5.0 years old) by occupational group in the Philippines is summarized in Table  1 . Results showed that stunting (30.3%) was the most prevalent malnutrition among children of this age group in the country, followed by underweight (19.9%). The prevalence of stunting and underweight in this age group are considered high based on the 1995 WHO cut-off for public health significance [ 13 ]. On the other hand, the prevalence of wasting among this age group was 7.9%. The prevalence of overweight was 5.0%.

The young children in household headed by fisherfolks (HHF) had higher prevalence of malnutrition compared to the overall prevalence of malnutrition among young children in the Philippines, except for overweight. The magnitude of underweight (26.4%), stunting (37.7%) and wasting (11.2%) were all alarmingly high and pose as serious public health concerns. The HHF had lower prevalence of overweight (3.2%) compared to the overall prevalence of overweight among young children in the Philippines.

Among the occupational groups, young children in HHF had the highest prevalence of underweight, stunting and wasting next to children belonging to household headed by forestry and related worker. They also had the lowest prevalence of overweight among all occupational groups.

The prevalence of malnutrition among Filipino children ages 61–120 months (5.08–10.0 years old) by occupational group in the Philippines is summarized in Table  2 . Findings showed that stunting (29.9%) is the foremost form of malnutrition that is prevalent among schoolchildren in the Philippines followed by underweight (29.1%). However, unlike the observation on the young children, the prevalence of overweight (9.1%) among schoolchildren was higher compared to the prevalence of wasting (8.6%).

Among HHF, underweight (39.9%) and stunting (39.9%) were the primary forms of malnutrition among schoolchildren. Compared to other occupational groups, schoolchildren in the fishing communities had the highest prevalence of underweight. Furthermore, the prevalence of underweight among fisherfolks was even higher compared to the prevalence of underweight among all schoolchildren in the Philippines (29.1%).

The prevalence of stunting among HHF was also higher compared to the prevalence of stunting among all schoolchildren in the Philippines (29.9%). However, compared to other occupational groups, schoolchildren in HHF had the highest prevalence of stunting next to forestry and related workers (43.3%) and farmers and other plant growers (40.1%).

Moreover, there was the same magnitude of wasting among all schoolchildren in the Philippines (8.6%) and those in fishing communities (8.6%). The magnitude of wasting among schoolchildren in fishing communities was also relatively high compared to the prevalence of wasting in other occupational groups.

Fisherfolks had lower prevalence of overweight among schoolchildren (4.0%) compared to the prevalence of overweight among all schoolchildren in the Philippines (9.1%). They even had the lowest prevalence of overweight among other occupational groups, next to forestry and related workers (2.6%).

With the other variables were held constant, logistic regression indicated that both stunting and underweight were influenced by the same variables. Older children (OR = 1.03; OR = 1.11), male gender (OR = 1.15; OR = 1.08), larger household (HH) size (OR = 1.08; OR = 1.07), and the occupation of the HH head, specifically fishermen (OR = 1.36; OR = 1.44) and farmers (OR = 1.51; OR = 1.32), increases the risk for stunting and underweight respectively. While older age of the HH head (OR = 0.99; OR = 0.99) and the use of water sealed toilet (OR = 0.57; OR = 0.59) manifested a protective effect on the nutrition status. Wasting or thinness was also correlated to gender of child but was not correlated to households headed by fishermen and farmer. For overweight and obesity, older children (OR = 0.93), household size (OR = 0.96), and the occupation of the household head, fishermen (OR = 0.71) and farmer (OR = 0.77) were found to decrease its risk while the use-of water-sealed toilet (OR = 1.29), gender (OR = 1.08) and age of household head increases it. Details were shown in Table 3 .

The present study showed that malnutrition is highly prevalent among children in HHF. Colds and cough, diarrhea, skin infections and asthma, sore eyes and various intestinal parasites are the common illness among children livisdng in coastal rural areas [ 14 , 15 ]. These kinds of illnesses affect the growth and nutritional status of children [ 16 ]. Perhaps, the nutritional status of children in HHF was compromised due to their poor health caused by their living environment.

Another factor that affects the nutritional status of children living in the coastal areas is the high risk of extreme events, such as typhoons and tsunamis [ 3 ]. In the Philippines, many families lost their livelihood, especially the famers and fishers, when typhoon Yolanda/Haiyan visited the country. The typhoon also damaged the means of transportation (i.e. boats) of people living in far flung areas. Because of this, they experienced difficulties going to city proper or mainland where most of the evacuation areas are located and relief goods are distributed making them to go on up to four days without food. As a result, malnutrition among children increased because of difficulties in accessing nutritious foods. It has been reported that 1.5 million children were identified as at risk of acute malnutrition [ 17 , 18 ].

Household socioeconomic status remains to be crucial determinant of nutritional status of children. Children in the poorest quintile had worse nutritional status than the ones from the richest group [ 19 ]. Thus, with HHF being the poorest sector in the country, the prevalence of undernutrition among fisherfolks may be due to their low economic capacity that limits their access to food and nutrition.

However, while poverty is a strong determinant of undernutrition among young children, it may not be true for schoolchildren. A study suggests that poverty is predictive factor to the poor nutrition among young children but not to the nutritional outcomes among schoolchildren [ 20 ]. On the other hand, factors such as household food insecurity, low maternal education and poor health are stronger predictor for the undernutrition of schoolchildren [ 21 ].

Furthermore, women in fishing communities often engage in economic activities to complement men’s decreasing income. In some areas, this results to substantial reduction in breast-feeding when mothers resumed their economic activities soon after delivery. Consequently, the quality of nutrition provided to the young children is compromised [ 22 ]. Moreover, various factors were also observed that are contributing to the nutritional status of schoolchildren. Quality of food intake, food availability, household size, literacy of person in charge of food preparation and household head are some of the factors associated to the nutritional status of schoolchildren. Food availability and nutrition education on balanced diet, food production and consumption are necessary, although not sufficient, to improve the nutritional status of schoolchildren [ 23 ].

Perhaps, in the case of fisherfolk communities in the Philippines, the living condition of this occupational group coupled with poor health, high risk to extreme events, poverty and poor quality of diet may have contributed to the occurrence of various forms of undernutrition among young and school-aged children.

On the other hand, along with high prevalence of undernutrition, overnutrition was also observed among young children in fishing community in the Philippines. Generally, overweight among young children is usually attributed to excessive calorie consumption and low calorie expenditure. The increased consumption of more energy-dense, nutrient-poor foods with high levels of sugar and saturated fats, together with reduced physical activity, have led to obesity rates that have increased three-fold or more since 1980 [ 24 ]. However, these reasons may be unlikely in poor households. A study suggests that overweight among young children in food insecure households may be due to other potential factors linked to obesity, like low activity levels and excessive television watching [ 25 ].

Moreover, overweight among children observed in coastal areas may be due to geographical disparities in terms of income. One study observed that those in coastal area with high economic status exhibited increase in the prevalence of overweight and obesity than those in other areas with less economic development [ 26 ]. Perhaps, in the present study, the overweight children in HHF may be located in coastal areas with high economic status.

Looking into the prevalence of malnutrition among children in HHF, it can be observed that there is a low prevalence of overweight and high prevalence of underweight, stunting and wasting children. This scenario can be attributed to limited access of children to high-calorie snacks and fast food which are hardly affordable. Thus, there are only few who are identified to overweight. Another plausible explanation given is that the children may have been engaged in a more physically demanding activities compared to their contemporaries [ 27 ].

Based on the results of the present study, there is a need for immediate action to intervene in the magnitude of undernutrition among this occupational group with priority on the needs of children. Young children are more prone to poor nutrition and health conditions than adults. Poor nutrition among children of this age could not only contribute to increased likelihood of contracting serious illnesses but may also have a permanent effect on their health and development [ 28 ].

In addition, aside from the direct welfare and financial costs of illness due to poor nutrition, schoolchildren with poor health can also grow up as an adult with poor lower health status with less education which in turn could result to intergenerational transmission of poverty [ 29 ]. Childhood undernutrition exposure has permanent socioeconomic and health consequences. These consequences may include metabolic and cardiovascular diseases, reduced learning ability, lowered years of schooling and intellectual performance in adulthood, reduced working ability, productivity and income, poor quality of life and overall poverty that could be transferred to the future generations [ 22 ].

Thus, investing in the nutritional status of young children and schoolchildren in HHF could break the cycle of poverty in this occupational group. Alleviating the poverty has been a challenge for the longest time now and starting from the poorest of the poor sectors in the country should be a good start.

Findings showed that young children in HHF had higher prevalence of malnutrition compared to the overall prevalence of malnutrition among young children in the Philippines, except for overweight. This is also true for schoolchildren in HHF, except for wasting. It also identified that fisherfolks were one of the occupational groups with highest malnutrition among young and school-aged children. The high magnitude of public health concern in terms of underweight, stunting and wasting among these age groups pose an immediate and serious need for action, prioritizing the nutritional needs of this occupational group.

Various factors could affect the nutritional status of children in fishing communities. Food security, socio-economic capacity, availability of health services, environmental sanitation and geographical disparity in terms of economic development are some of the factors that should be considered in formulating and prioritizing in nutrition programs for children in this occupational group. Possible interventions should include health and nutrition education program that advocates the promotion of children’s nutrition at home, physical activity, capacity building, sanitation and hygiene in the community. A national focus on the health, nutrition and welfare of fisherfolks is highly recommended.

Furthermore, forestry and related workers is another occupational group that exhibited alarmingly high prevalence of malnutrition among young and school-aged children. Perhaps, for future studies, it would be an interesting topic to explore and understand the determining factors of malnutrition in this occupation group.

Abbreviations

Annual Poverty Indicators Survey

FNRI Institutional Ethics Review Committee

Food and Nutrition Research Institute, Department of Science and Technology

households headed by fisherfolks

National Disaster Risk Reduction and Management Council

National Nutrition Survey

Philippine Statistics Authority

Philippine Standard Occupational Classification

World Health Organization-Child Growth Standards

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Acknowledgements

The authors would like to express their sincere gratitude to the children and their parents/guardians for being part of the study. The authors would also like to thank the WorldFish Philippines for funding this study.

This study was funded by the WorldFish Philippines. The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Capanzana, M.V., Aguila, D.V., Gironella, G.M.P. et al. Nutritional status of children ages 0–5 and 5–10 years old in households headed by fisherfolks in the Philippines. Arch Public Health 76 , 24 (2018). https://doi.org/10.1186/s13690-018-0267-3

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Prevalence and Determinants of Multiple Forms of Malnutrition among Adults with Different Body Mass Index: A Population-Based Survey in the Philippines

Wan-chen hsu.

1 Department of Public Health, College of Medicine, National Cheng Kung University, Tainan City 701, Taiwan

Aileen R. de Juras

2 Institute of Human Nutrition and Food, College of Human Ecology, University of the Philippines Los Baños, Laguna 4031, Philippines

Susan C. Hu

Associated data.

The data is publicly available and can be found at http://enutrition.fnri.dost.gov.ph/site/home.php .

The multiple forms of malnutrition, including overnutrition, undernutrition, and diet-related noncommunicable diseases, are emerging crises in Asian countries. Past studies have focused more on malnutrition among overweight/obese individuals; however, limited research has examined chronic energy-deficient adults. Therefore, this study is aimed at investigating the prevalence and determinants of different forms of malnutrition among adults with different body mass index, using the Philippines as an example. Findings from this study will guide the development and implementation of public health nutrition programs and policies.

A representative dataset from the 2013 Philippine National Nutrition Survey was used in the study. Adults aged ≥20 years ( n = 16,826) were included in the analysis after excluding those with missing values. Six phenotypes of malnutrition were assessed, including three in overweight/obese adults (overweight/obese with metabolic syndrome; those with micronutrient deficiency–anemia, vitamin A deficiency, and iodine insufficiency; and those with both metabolic syndrome and micronutrient deficiency) and three in chronic energy-deficient (CED) adults (CED with either metabolic syndrome or micronutrient deficiency and with both metabolic syndrome and micronutrient deficiency). Sociodemographic and lifestyle factors were examined as the determinants of different forms of malnutrition, and multinomial logistic regression analyses were performed.

The prevalence of the six phenotypes of malnutrition ranged from 0.4% to 10.2%, where overweight/obese with metabolic syndrome was the most predominant type. The multinomial logistic regression models indicated that older age was the major risk factor across all phenotypes. Sex was associated with the outcomes in the overweight/obesity group, whereas employment status was correlated with CED adults. Furthermore, higher educational levels, being married, living in affluent households, and not smoking were protective factors for conditions related to CED but not overweight/obese individuals.

Malnutrition in all its forms is a significant public health concern that must be understood and addressed. Policymakers should implement appropriate intervention programs to control these nutritional problems considering the specific risk factors for the adult population.

1. Introduction

The different forms of malnutrition are a worldwide crisis affecting numerous countries, especially those in Asia [ 1 ]. The 2020 Global Nutrition Report estimates that 87% of the 143 countries are confronted with high levels of at least two forms of malnutrition (3 countries, overweight and stunting; 28 countries, anemia and stunting; and 56 countries, anemia and overweight) [ 2 ]. Worse still, 26% of those countries have experienced the cooccurrence of childhood stunting, anemia among women of reproductive age, and overweight/obesity (Ow/Ob) among adult women. Regarding the metabolic risk factors, high blood pressure and diabetes affect 22.1% and 8.5% of the adult population, respectively. These nutritional burdens have marked differences by the physiological group and sociodemographic characteristics such as age, sex, education, and wealth [ 2 ].

The Philippines is not exempted from these adversities and is persistently challenged by the various forms of malnutrition [ 3 , 4 ]. The recent data in the Philippines indicate a twofold increase in overweight and obesity among adults, from 16.6% to 37.2% during 1993-2018. Parallel to this is the rising prevalence of metabolic syndrome (MetS) components, including abdominal obesity, hypertension, high fasting blood glucose, and dyslipidemia. Moreover, poor body weight, anemia, and vitamin A deficiency continue to have public health significance [ 3 , 4 ].

However, past studies have focused more on overweight/obese people in the Philippines, and only limited studies have been conducted on underweight adults (also called chronic energy deficiency (CED)) [ 5 – 7 ]. In addition, most researchers have only examined a single measure of adult nutritional status (i.e., obesity, diet-related noncommunicable diseases, or anemia) [ 5 – 7 ]. This approach may fail to capture the severity of the nutritional problems, given that some adults concurrently suffer from more than one disorder. The consequences of the conditions mentioned above during adulthood are vast, ranging from reduced labor productivity to increased risk of morbidity and mortality. Thus, using a nationally representative sample, this study is aimed at investigating the prevalence and determinants of the different forms of malnutrition in both Ow/Ob and CED adults in the Philippines.

2. Materials and Methods

2.1. participants and sampling procedure.

The 2013 National Nutrition Survey (NNS) data from the Philippines was used [ 8 ]. The NNS data are cross-sectional and nationally representative and are collected every five years. Briefly, the survey is aimed at evaluating Filipinos' food intake, nutrition, and health status. It utilized the 2003 Master Sample of the National Statistics Office and employed a multi-stage-stratified sampling design. Barangays, enumeration areas, and households were the sampling units in the first, second, and third stages [ 3 , 9 ]. The NNS provides bases for the country's programs and nutrition and health improvement plans. Further details of the methodology have been published elsewhere [ 10 ].

The current analysis was limited to adults (≥20 years) with complete subject identification in five survey components: anthropometry, biochemical, clinical, and socioeconomic (individual and household). Pregnant and lactating women and those with missing values for the body mass index (BMI), MetS components, hemoglobin, serum retinol, and urinary iodine excretion (UIE) were excluded, accounting for 8.7% of the overall sample size. The final study sample included 16,826 adults ( Figure 1 ).

Flow diagram of participant selection. Missing data on metabolic syndrome was defined as no available data in at least 3 of the following: (1) waist circumference, (2) triglyceride, (3) HDL cholesterol, (4) blood pressure, and (5) fasting blood glucose.

2.2. Outcome Variables

The primary outcome variables were six phenotypes. The phenotypes included three categories for adults with overnutrition: (1) overweight/obese with metabolic syndrome (Ow/Ob+MetS), (2) overweight/obese with micronutrient deficiency (Ow/Ob+MnD), and (3) overweight/obese with both metabolic syndrome and micronutrient deficiency (Ow/Ob+MetS+MnD), and three categories for adults with undernutrition: (4) chronic energy deficiency with metabolic syndrome (CED+MetS), (5) chronic energy deficiency with micronutrient deficiency (CED+MnD), and (6) chronic energy deficiency with both metabolic syndrome and micronutrient deficiency (CED+MetS+MnD).

The anthropometry, biochemical, and clinical NNS datasets were used to classify individuals under the different phenotypes. Ow/Ob and CED were defined based on BMI derived from the weight and height measurements. Weight was measured by the Detecto™ platform beam balance weighing scale, while height was obtained using Seca™ microtoise [ 3 ]. The BMI of each participant was categorized according to the World Health Organization guidelines as CED (<18.5 kg/m 2 ), Ow (25.0–29.9 kg/m 2 ), and Ob (≥30.0 kg/m 2 ) [ 11 ].

In this study, the National Cholesterol Education Program Adult Treatment Panel III clinical criteria were employed to assess MetS [ 12 ]. Based on the criteria, MetS was diagnosed if a person has any three of the following five criteria: (1) abdominal obesity (waist circumference > 102 cm in men or >88 cm in women); (2) dyslipidemia (triglyceride ≥ 150 mg/dL); (3) dyslipidemia, second criteria (HDL cholesterol < 40 mg/dL in men or <50 mg/dL in women); (4) hypertension (blood pressure ≥ 130/85 mmHg); and (5) hyperglycemia (fasting blood glucose ≥ 100 mg/dL). In addition, waist circumference was measured with a calibrated tape measure at the midpoint between the lowest rib and tip of the hip bone while the participants were standing and breathing normally. Blood pressure readings were performed with a calibrated nonmercurial sphygmomanometer (A&D Um-101™) and stethoscope on the right arm of seated participants after resting for a minimum of five minutes. The systolic and diastolic blood pressures were taken twice with two-minute intervals between the first and second measurements. Fasting venous blood samples were drawn from the participants for glucose and lipid assessments and analyzed through the enzymatic colorimetric method using the Roche COBAS Integra and Hitachi 912 clinical laboratory analyzer [ 3 ].

Furthermore, micronutrient deficiency (MnD) was characterized by anemia, vitamin A deficiency, and iodine insufficiency. Venous blood samples and urine samples were utilized to evaluate these conditions. Anemia was determined by measuring hemoglobin in the blood using a portable spectrophotometer [ 3 , 13 ], where a hemoglobin value of <13 g/dL (males) or <12 g/dL (females) indicated anemia [ 14 ]. Vitamin A deficiency was determined from serum retinol levels using high-performance liquid chromatography [ 3 , 15 ] and was defined as serum retinol < 10  μ g/dL [ 16 ]. Iodine insufficiency was assessed by measuring iodine excretion in the urine using the acid digestion/colorimetric method [ 3 , 17 ]. The cut-off used for iodine insufficiency was UIE < 50  μ g/dL [ 18 ]. Healthy adults with normal weight (i.e., without MetS and MnD and BMI equivalent to 18.5–24.9 kg/m 2 ) served as the reference group.

2.3. Explanatory Variables

The explanatory variables in this study were identified based on the literature review and information available in the NNS datasets. The individual-level sociodemographic factors included sex (male or female), age (20–39, 40–59, and ≥60 years), educational levels (highest level completed), marital status (single, married/with a partner, and others or those who were widowed/separated/divorced), and employment status (whether employed or not). The household-level sociodemographic factors covered the household size and wealth quintile. Household size was created from the socioeconomic dataset and categorized as 1–3, 4–6, and ≥7. Wealth status was divided into five groups (poorest, poor, middle, rich, or richest). Lifestyle factors, including smoking (current smoker or not), alcohol consumption (current drinker or not), and physical activity (low or high), were also controlled in the analysis. All variables were collected using standardized interviewer-administered questionnaires [ 3 ].

The study was conducted according to the guidelines in the Declaration of Helsinki and certified for exemption by the Human Research Ethics Committee of National Cheng Kung University, Tainan City, Taiwan (HREC No. 110–280).

2.4. Statistical Analysis

Descriptive statistics were used to summarize the weighted percentage of the participants' characteristics and outcome variables. A chi-square test was utilized to identify differences in the percentages obtained for the sociodemographic and lifestyle variables according to BMI. A bivariate analysis (chi-square tests) was also carried out to analyze factors associated with malnutrition phenotypes. Multinomial logistic regression models were performed on adults in the overnutrition and undernutrition groups. Healthy adults with normal weight were used as the reference group. The results were reported as odds ratios (OR) and 95% confidence intervals (95% CI). If the 95% CI does not include 1.0, it means statistically significant. Variance inflation factors (all < 2) were evaluated to verify the multicollinearity in the explanatory variables before running the models. All analyses considered the sampling design and survey weights and were performed using R software version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).

3.1. Participants' Characteristics

Table 1 describes the sociodemographic characteristics and lifestyle factors of the 16,826 valid adults (8428 men and 8398 nonpregnant and nonlactating women) included in this study. Compared to the average of the total sample size, the prevalence of Ow/Ob was higher among females (55.8%), middle-aged adults (47.8%), married individuals (75.8%), and noncurrent smokers (80.5%). In contrast, older adults (25.5%) with an elementary school education or less (42.0%), unemployed (49.4%), and noncurrent drinkers (54.7%) had a higher CED prevalence. Regarding wealth status, the prevalence of Ow/Ob increased with the quintile, ranging from 9.5% in the poorest to 31.1% in the richest households. Contrariwise, the prevalence of CED decreased from the poorest to the richest households (27.0% to 12.1%). The median household size was four, with no differences noted across BMI categories.

Characteristics of participants according to the body mass index by chi-square tests ∗ .

∗ Values are weighted percentages (%). † Variables with missing observations: educational levels ( n = 71), smoking and drinking status ( n = 971), and physical activity classification ( n = 1160). ‡ Chronic energy deficiency (CED) also refers to being underweight and is defined as a body mass index < 18.5 kg/m 2 .

Table 2 shows that 29.6% of the adult population were Ow/Ob, 59.3% had normal weight, and 11.1% had CED. MetS was present in approximately one-quarter of the adults (24.6%) and was highest among those who were Ow/Ob (47.2%). In terms of MnD, 28.5% were either anemic, vitamin A-deficient, or iodine-insufficient and were more prevalent among adults who were CED (36.4%). The prevalence of both MetS and MnD was 7.5% and mainly affected adults who were Ow/Ob (12.7%).

Prevalence of metabolic syndrome, micronutrient deficiency, and their combination according to the body mass index by chi-square tests ∗ .

MetS: metabolic syndrome; HDL: high-density lipoprotein; MnD: micronutrient deficiency. ∗ Values are weighted percentages (%). † Metabolic syndrome component variables with missing observation as follows: abdominal obesity ( n = 254), hyperglycemia ( n = 457), hypertension ( n = 65), and high triglycerides and low HDL cholesterol ( n = 16). ‡ Chronic energy deficiency (CED) also refers to being underweight and is defined as a body mass index < 18.5 kg/m 2 .

3.2. Phenotypes of Malnutrition

The prevalence of the different phenotypes of malnutrition is shown in Table 3 . Over one-third of the participants were healthy and with normal weight (35.5%), and nearly 40% only had a single form of malnutrition or MetS. Among the Ow/Ob adults, 1 in every 10 had MetS (10.2%), while a few had MnD (3.3%) and MetS+MnD (3.8%). CED was a concern in only a small percentage of these adults (0.4% for both MetS and MetS+MnD and 3.7% for MnD), and 3.3% were normal weight along with MetS+MnD.

Prevalence of different phenotypes of malnutrition based on related determinants by chi-square tests ∗ ( n = 16,826).

Nw: normal weight; Ow/Ob: overweight or obese; CED: chronic energy deficiency; MetS: metabolic syndrome; MnD: micronutrient deficiency. ∗ Values are weighted percentages (%).

In the bivariate analysis, the prevalence of the three conditions related to overnutrition (coexistence of Ow/Ob with MetS, MnD, and MetS+MnD) was higher among females, those 40–59 years old, those who were married, the richest quintile, noncurrent smokers, and noncurrent drinkers. Educational levels and household size were associated with all phenotypes in varying degrees of prevalence. For example, higher-educated adults tend to be more Ow/Ob+MetS, especially those with college degrees (12.6%). Adults with fewer family members had a higher proportion of Ow/Ob and MetS+MnD (3.4% to 4.2%). In contrast, the prevalence for adults with undernutrition (coexistence of CED with MetS, MnD, and MetS+MnD) was higher among those aged ≥60 years old, without a spouse, with elementary education or lower, unemployed, living in small-sized households (1–3 members), from the poorest or poor wealth quintiles, current smokers, and noncurrent drinkers. No differences were found in the level of physical activity in any phenotype.

3.3. Factors Associated with Overnutrition

Table 4 shows the factors associated with the different phenotypes of malnutrition relative to normal-weight and healthy adults, using multinomial logistic regressions. In the case of adults with overnutrition, sex, age, marital status, and the wealth quintile were risk factors for all three phenotypes. Women and married or widowed/separated/divorced adults were more likely to experience these conditions. Regarding the age group, the odds of any phenotype were higher among those 40–59 and ≥60 years old. Adults from the richest households had the highest risk of suffering from all phenotypes compared with those in the poorest quintile, with a significant dose-response relationship. In addition, having a high school and college education was related to MetS and MnD. Medium-sized households and not currently smoking were correlated solely with MnD. Notably, those from households with 4–6 members had a lower likelihood of MnD, and this was the only protective factor for Ow/Ob adults.

Factors associated with the different phenotypes of malnutrition among overweight/obese and chronic energy-deficient adults by multinomial logistic regression ∗ ,† .

Ow/Ob: overweight or obese; CED: chronic energy deficiency; MetS: metabolic syndrome; MnD: micronutrient deficiency. ∗ Values are odds ratios (OR) and 95% confidence intervals (95% CI). If the 95% CI does not include 1.0, it means statistically significant. † All models were controlled for variables shown in the first column. Reference category: healthy and normal weight ( n = 5685).

3.4. Factors Associated with Undernutrition

Table 4 also indicates that age was the common determinant of the three phenotypes for adults with undernutrition, while other variables had mixed effects on at least one phenotype. The odds of having any form of undernutrition and MetS were significantly higher among the older age group (≥60 years) as compared to the younger age group (20–39 years). Also, women, unemployed adults, and noncurrent drinkers were more likely to experience MnD. The remaining variables were protective factors for different phenotypes, including a college education or higher, married, big households (≥7 members), the middle wealth quintile, and noncurrent smokers. Interestingly, the odds of having MnD declined with improvements in wealth status.

4. Discussion

The results of this national study demonstrate the coexistence of Ow/Ob or CED alone or in combination with nutritional deficiency among adults ≥ 20 years old in the Philippines. The most predominant phenotype was Ow/Ob+MetS (10.2%). The other phenotypes were similar at approximately 3% (Ow/Ob+MnD, Ow/Ob+MetS+MnD, and CED+MnD) and 0.4% (CED+MetS and CED+MetS+MnD). Given the limited studies presenting the cooccurrence conditions among adults, the prevalence of Ow/Ob+MnD was lower in this study compared with the figures reported in Burkina Faso (3.3% vs. 8.5%) [ 19 , 20 ].

This study found that age was the significant risk factor across all malnutrition phenotypes. The odds of older adults (≥60 years) were 1.6 to 14.0 times higher than that of younger adults. This finding could be linked to the high prevalence of single forms of malnutrition (i.e., Ow/Ob, CED, and MnD) and MetS components among elderly Filipinos, which is consistent with national estimates and previous studies [ 3 , 5 , 6 , 21 ]. The strong correlation between age and the different phenotypes demonstrated in the outcomes may have been driven by the interactions among biological and behavioral factors [ 22 ].

Women were more likely to suffer from all categories of overnutrition. The higher prevalence of the coexistence of Ow/Ob, MetS, and MnD among Filipino women was expected because they are at greater risk for these conditions [ 3 ]. Some predisposing factors for women to develop these nutritional disorders are their reproductive biology and body fat distribution. Besides the reasons described above, socioeconomic and environmental factors also play crucial roles [ 23 , 24 ].

The analysis also showed that educational levels, marital status, employment status, household size, wealth quintile, smoking, and alcohol consumption had mixed effects on the outcomes. Adults with higher educational levels, married, living in households with better wealth status, and not currently smoking had a greater risk of having conditions related to overnutrition. Remarkably, all these factors had an inverse relationship with the categories for adults with undernutrition.

Achieving a college degree or higher was a risk factor for the two phenotypes of adults with overnutrition. The opposite was true among adults with undernutrition, i.e., a higher level of education was a protective factor. This may partly be explained by the knowledge and skills gained from studying that enable individuals to make positive or negative choices about their diet, physical activity, and lifestyle [ 25 ]. Being married or living with a partner was also associated with the different phenotypes. The occurrence of stressors, perceptions of attractiveness, availability of resources, and the presence of a support system are posited to affect how marital status affects health outcomes [ 26 , 27 ].

Employment was only associated with undernutrition, as CED+MnD was higher among unemployed adults. It is common in the Philippines to reside in urban areas with more job opportunities [ 28 ]. Consequently, these areas' physical and food environments may contribute to poor nutritional status. This finding also aligns with a past study wherein CED and anemia were more prevalent in certain occupational groups [ 5 ]. Larger household size was a protective factor for two phenotypes of malnutrition. This could have been due to changes in food intake quantity and quality with increasing family size, as supported by the national dietary survey results [ 3 ].

The influence of household wealth status on the study outcomes varied. Among the Ow/Ob adults, the rich and richest quintiles were related to all three phenotypes that could be attributed to the obesogenic effect of household wealth as it improves [ 29 ]. This result corresponds with previous research in India wherein the wealth index was a determinant of being overweight as well as MetS and anemia [ 30 ]. On the other hand, household wealth was protective for two phenotypes for adults experiencing undernutrition. The advancement in wealth status possibly had a lesser effect since most adults experiencing CED belonged to the poorest quintile. Additionally, this observation concurred with the study of Angeles-Agdeppa et al., wherein diet adequacy and diversity in Filipino households were similar across quintiles [ 31 ].

Furthermore, this study found that adults not currently smoking had greater susceptibility to Ow/Ob + MnD. It should be noted that the nonsmokers in this study included adults who had never smoked or were former smokers. Hence, it is probable that the relationship observed was for adults who had quit smoking. Evidence suggests that smoking cessation may lead to overnutrition and MnD through increased energy intake, inflammatory reactions, and oxidative stress [ 32 – 34 ]. Conversely, nonsmokers had a lower risk for CED+MnD and CED+MetS+MnD. This was ascribed to the clustering of healthy behaviors. As seen in this study, the prevalence of CED was higher among those who were not current smokers, those who were not current alcohol drinkers, and those who engaged in high levels of physical activity. However, the results on alcohol consumption warrant careful interpretation since the consumption of alcoholic beverages could alter nutrient metabolism and absorption, which could lead to malnutrition and MetS [ 35 – 37 ].

This study has two merits: the large sample size and the use of biochemical markers for the MetS and MnD assessment. However, it also has some limitations. First, the dataset did not include nonnutritional factors, such as disease history, medication use, and presence of infection, which might have provided more information on the determinants of malnutrition. Second, the lifestyle information was based on self-reporting, and certain behaviors may have been under- or overreported. Third, the exclusion of missing data may have introduced some bias. Lastly, the cross-sectional study design does not infer causality between risk factors and the development of overnutrition and undernutrition.

5. Conclusions

Our findings indicated that the cooccurrence of multiple forms of malnutrition among adults in the Philippines is a significant public health concern. Older age was the strongest risk factor in all phenotypes. In addition, being a woman was correlated with the categories for overnutrition, while being unemployed was associated with undernutrition. On the other hand, higher education, marriage, better-off households, and nonsmokers were protective factors related to undernutrition but not overnutrition. These results significantly contribute to understanding the different phenotypes of malnutrition and their potential determinants.

Therefore, public health policies and interventions are essential to address these threats from both ends of the spectrum. It highlights the importance of having adequate nutrition and health programs that consider socioeconomic status, for example, focusing on women who are overweight/obese in wealthy households and older adults who are underweight and unemployed. In addition, a healthy, sustainable food system and an increased investment in healthcare services are equally essential to improve malnutrition in all its forms.

Acknowledgments

The authors wish to thank the Food and Nutrition Research Institute of the Department of Science and Technology, Philippines, for providing the 2013 National Nutrition Survey data.

Data Availability

Ethical approval.

The study was conducted according to the guidelines in the Declaration of Helsinki and certified for exemption by the Human Research Ethics Committee of National Cheng Kung University, Tainan City, Taiwan (HREC No. 110–280). Before the survey was conducted, the 2013 NNS obtained ethical clearance from the Institutional Ethics Review Committee of the Department of Science and Technology-Food and Nutrition Research Institute, Manila, Philippines.

Written informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors' Contributions

W-C.H., A.R.D.J., and S.C.H. designed the study; W-C.H. and A.R.D.J. conducted the statistical analysis and drafted the manuscript; S.C.H. supervised the research and revised the manuscript. All the authors contributed to preparing the final manuscript and approved it for publication. Wan-Chen Hsu and Aileen R. de Juras are joint first authors.

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Article Contents

Data resource basics, data collected, data resource use, strengths and weaknesses, data resource access, acknowledgements.

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Data Resource Profile: The Philippine National Nutrition Survey (NNS)

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Chona F Patalen, Nayu Ikeda, Imelda Angeles-Agdeppa, Marina B Vargas, Nobuo Nishi, Charmaine A Duante, Mario V Capanzana, Data Resource Profile: The Philippine National Nutrition Survey (NNS), International Journal of Epidemiology , Volume 49, Issue 3, June 2020, Pages 742–743f, https://doi.org/10.1093/ije/dyaa045

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The Philippine National Nutrition Survey (NNS) is the official nationwide survey on nutritional status, diet and other lifestyle-related risk factors for noncommunicable diseases. The NNS is designated to generate critical data for decision making by the government and the private sector in the Philippines. Data from the NNS have been used as a reference for formulating the Philippine Plan of Action for Nutrition 1 and other policies. The NNS also provides selected indicator metadata for the World Health Organization (WHO) Global Action Plan for the Prevention and Control of Noncommunicable Diseases (NCDs) 2 and the United Nations Sustainable Development Goals. 3

The first NNS was conducted in 1978, and this survey was repeated in 1982, 1987 and then every 5 years from 1993 ( Table 1 ). In 2018, the NNS was redesigned as a rolling sample survey for 3 consecutive years, named the Expanded National Nutrition Survey (Expanded NNS). The shift to the Expanded NNS aimed to improve reliability of estimates at the local level, thereby enhancing programme planning, monitoring and evaluation, and assisting with developing timely policies. Moreover, a survey known as the Updating of the Nutritional Status of Filipino Children and Other Population Groups (Updating Survey) was implemented in 1989, 1996, 2001, 2005, 2011 and 2015 to provide updates on the nutritional status of the population, particularly children and pregnant or lactating women. The Updating Survey was discontinued with the launch of the Expanded NNS in 2018–20.

Data collection period, survey regions and sample sizes of households and individuals in the Philippine National Nutrition Survey, 1978–2019

NA, not available.

Western and Central Mindanao regions were not surveyed in 1978 and 1982, and Southern Mindanao region was not surveyed in 1987.

Percentage of eligible households out of sampled households in the 2003 Master Sample.

Percentage of eligible households that participated in the NNS.

Number of respondents of the anthropometric survey component in 2003, 2008, and 2013.

Percentage of individuals with anthropometric measurements out of eligible household members.

Postponements in Regions 6 (because of Typhoon Haiyan), and in Autonomous Region in Muslim Mindanao (due to unrest related to the siege in Zamboanga City).

The Philippines is an archipelagic country comprising the three island groups of Luzon, Visayas and Mindanao. The country consists of 17 regions (16 administrative and one autonomous), 4 which are subdivided into 81 provinces and 38 independent cities. Independent cities are outside the jurisdiction of provinces and consist of 33 highly urbanized cities and five independent component cities. Provinces further comprise 107 component cities and 1489 municipalities. Independent cities, component cities and municipalities are subdivided into 42 045 barangays, the smallest local government units in the country.

Table 1 summarizes data collection periods, survey regions and sample sizes by survey year. The NNS has covered all regions since 1993. The NNS survey duration varied from 4 to 10 months, before becoming year-round in the Expanded NNS. There were about 35 000 participating households in the 2013 NNS, increasing to about 46 000 in the Expanded NNS in 2018, although response rates declined slightly between the two surveys.

The Department of Science and Technology-Food and Nutrition Research Institute (DOST-FNRI) in the Philippines has conducted the NNS as part of its mandate to undertake research on the population’s nutritional status, particularly regarding malnutrition. The DOST-FNRI plans the survey implementation, and the Philippine government, together with collaborative partners, financially supports the conduct of the survey. Ethics approval for the survey protocols and pre-tested questionnaires are obtained from the DOST-FNRI Institutional Ethics Review Committee. Questionnaires are also approved by the Philippine Statistics Authority through the Statistical Survey Review and Clearance System.

Sampling design

The NNS is a cross-sectional household interview and health examination survey conducted on a nationally representative sample of households in the Philippines. In the 1978, 1982 and 1987 NNS, selection of the sample provinces for each region was based on probability, proportional to the number of households. Urban and rural barangays were randomly selected from each sample province. In the final stage, households were systematically selected from each sample barangay, with replacements. All provinces were covered in the 1993 and 1998 NNS; however, in the 1998 NNS, the ultimate sampling unit was the individual rather than the household because there was no household food consumption survey.

Starting with the 2003 NNS, the Master Sample design developed by the then National Statistics Office for use in national surveys (such as the family income and expenditure survey, demographic and health survey and labour force survey) was employed in the NNS. The 2003 Master Sample had 17 regions as sampling domains and four independent replicates ( Figure 1 ). Each replicate possessed properties of the full Master Sample to generate reliable national-level estimates. 5 The 2003, 2008 and 2013 NNS each employed a multistage stratified sampling design. 6 Sampling units in the first, second and third stages were barangays, enumeration areas and households, respectively.

The Expanded NNS for 2018–20 employs the 2013 Master Sample design ( Figure 2 ). It has 117 sampling domains (81 provinces, 33 highly urbanized cities and three other areas) and 16 independent sample replicates drawn from each domain to generate sufficiently precise estimates at the province or city level. The number of sampled households has increased 4-fold through the expansion in numbers of sampling domains and replicates in the 2013 Master Sample.

The Expanded NNS, using the 2013 Master Sample design, requires considerable resources for its highly specialized data collection. As such, it is not possible to complete the survey and yield reliable national- and local-level estimates within a year. Therefore, in replicated sampling for the Expanded NNS, data collection is spread over 3 years. Domains are divided into replicates based on the similarity of certain characteristics, then randomly assigned to the years 2018, 2019 and 2020.

Survey schedule

Figure 3 illustrates the entire survey schedule, including pre-survey activities and field data collection. A series of training and coordination activities is completed before field data collection. All field survey researchers receive intensive training, field practice and reliability tests for conducting face-to-face interviews, health examinations, 1-day food weighing and food recall in accordance with standardized techniques and protocols. Field survey researchers are qualified health professionals, such as nutritionist-dieticians for anthropometric and dietary assessments, nurses and allied health professionals for clinical measurements and health examinations, and licensed medical technologists for collecting biochemical samples.

Field data collection usually takes 3–7 days, depending on the number of households in a barangay. A survey team starts with a courtesy visit and orientation for local officials and staff. The team, with volunteer survey aides, then locates sampled households to explain the purpose of the survey and the method of data collection. If the household head agrees to participate, household members aged 15 years or older are asked to sign the informed consent form translated into a local language. Parental permission and a signed assent form are required for children aged 7–14 years. These forms emphasize information confidentiality, data anonymization and voluntary participation. Withdrawal from the survey is permitted at any time without penalty. After consent is obtained, household membership data are collected. Survey questions are formatted for data entry in both English and Filipino. The volunteer survey aide in a barangay also facilitates translation during interviews.

Survey components and items

The NNS has nine survey components: anthropometric, biochemical, clinical and health, dietary, socioeconomic status, food insecurity, government programme participation, maternal health and nutrition and infant and young child feeding practices. Table 2 lists survey items of the anthropometric, biochemical and clinical and health survey components by survey year.

Survey items in the Philippine National Nutrition Surveys, 1978–2018

Haematological indices which included haemoglobin and other red blood cell parameters are collected in Metro Manila only: red blood cell count and red cell distribution width, serum ferritin, mean cell volume, mean cell haemoglobin, mean corpuscular haemoglobin concentration, haemoglobin A2, and haemoglobin A.

Lipids included total cholesterol, triglycerides, high-density lipoprotein-cholesterol and low-density lipoprotein-cholesterol.

Thyroid disorder was recorded by palpation in 1982, 1987, 1993 and 2008, and history in 2018.

The anthropometric survey examines nutritional status by measuring weight, height and body circumferences. Weight and height (for participants aged 2 years or older) or length (for infants and toddlers) are recorded twice. For individuals aged 10 years or older, three readings of waist and hip circumferences are obtained. Mid-upper arm circumference for those aged 4 months to 19 years, and pregnant or lactating women, is measured using an ergonomic circumference measuring tape. 7 Participants are excluded from the anthropometric survey if they have physical disability that makes it difficult to obtain measurements.

The biochemical survey determines levels of biomarkers such as haemoglobin, serum retinol and urinary iodine excretion in blood and urine samples ( Table 2 ). A portable spectrophotometer is used for absorbance measurements, and the results of haemoglobin levels are reported to the participants for those aged 6 months and older. For adults aged 18 years or older, 10-h fasting is required for the lipid profile and blood glucose determination. All biochemical samples are kept frozen in household freezers or ice chests until they are shipped to the DOST-FNRI. Biochemical samples are analysed in DOST-FNRI laboratories with ISO/IEC 17025 accreditation, following international guidelines and quality assurance measures. 8

The clinical and health survey focuses on risk factors for NCDs, including levels of lipids and fasting blood glucose measured in the biochemical survey. Blood pressure is measured using a non-mercurial, hybrid sphygmomanometer. Health interviews regarding behavioural risk factors, such as smoking, excessive alcohol consumption, physical inactivity and unhealthy diet, are also conducted ( Table 2 ).

The dietary survey provides direct measures of food consumption at both household and individual levels. Using a dietetic scale, a dietary researcher weighs (before cooking or serving) all food items prepared and served in the home for the entire day. The weight of unconsumed leftover food, including plate waste and food given away, is deducted to obtain the amount of food and beverages consumed by the household. A household food inventory is also performed by weighing non-perishable food items at the beginning and end of the food-weighing day. Intakes of household members who eat outside the home are also recorded, with sample weighing of food items eaten at those times. For individual food consumption, a 2-day, non-consecutive, 24-h food recall interview is conducted to estimate intake. Weights of actual food consumed are entered into a computer library of the 1997 Food Composition Tables, with 16 nutrients and 17 food groups, to estimate intake. Dietary adequacy is assessed by comparing energy and nutrient intake against the nutritional requirements indicated in the Philippine Dietary Reference Intakes. 9 In 2016, the DOST-FNRI released the online version of the 1997 Food Composition Tables. This updated version will be used in the Expanded NNS.

The remaining survey components are completed via a face-to-face interview, using an electronic data collection system. The socioeconomic status survey enquires about household membership and demographic information, including household members' educational backgrounds and occupations. It also asks about household living conditions, such as type and tenure of housing unit, ownership of household assets, toilet facilities and garbage disposal systems. These variables are used to compute the wealth index of households. 10 The food insecurity survey, which has been conducted since the 2001 Updating Survey, involves an interview with the household head or meal planner to assess the prevalence and magnitude of food insecurity. The Radimer/Cornell measure had been used to assess food insecurity until the Household Food Insecurity Access Scale 11 was adopted in the 2013 NNS to determine the prevalence and magnitude of food insecurity at the household level. The Food Insecurity Experience Scale was introduced in the Expanded NNS to measure the severity of food insecurity. 12 The government programme participation survey has been used since the 2008 NNS to examine the household’s participation in selected nutrition and related government programmes. 13 The maternal health and nutrition survey asks pregnant or lactating women and biological mothers of children aged 0–36 months about their health-seeking behaviours and health-related practices, including pre- and post-natal care. In the infant and young child feeding survey, the feeding practice for children aged 0–23 months is reported using 24-h food recall. Breastfeeding and complementary feeding indicators are also included. 14

Data organization and analysis

A team leader consolidates data collected in the field and transmits them online through the electronic data collection system before transferring to the next area. The survey team ships biochemical samples and completed questionnaires to DOST-FNRI on a weekly basis. Rounds of validation start immediately on completion of transmitted data.

The validated data are analysed using statistical software (e.g. Stata and the WHO Anthro Survey Analyser) to generate descriptive statistics, including means, medians, percentages, ranges, confidence intervals, design effects and coefficients of variation. National-level estimates are released first, before domain-level estimates. Indicators are disaggregated by sex, age group, place of residence (urban or rural) and wealth status. Time trends for key indicators are also examined to track the country’s progress towards the attainment of global targets (Figure 3). For example, height is measured in children under 5 years of age, and height-for-age z-scores are computed. Using the WHO Child Growth Standards, prevalence of stunting among children aged under 5 years is reported at the national level, and time trends are presented to monitor progress in achieving the global target of ending all forms of malnutrition.

Potential sources of bias or errors

Refusal to participate or unavailability of a household member constitutes non-response. Follow-up visits and application of post-survey adjustment techniques are used to minimize non-response. All instruments are calibrated and regularly tested to minimize measurement bias.

Data from the NNS have been mainly used for national- and local-level programme and policy development. Micronutrient supplementation, food fortification and local ordinances on salt iodization are some of the programmes implemented in the Philippines which used the survey results. The data are also used in primary scientific research on the population’s nutritional status and NCD risk factors. For example, in the 2003 NNS, the prevalence of metabolic syndrome varied from 12% to 19% by different criteria; high blood pressure, high blood glucose levels and low levels of high-density lipoprotein cholesterol were seen in 33%, 7% and 70% of participants, respectively. 15 The prevalence of smoking decreased between the 2003 and 2008 NNS, although prevalences of high blood pressure and dyslipidaemia increased. 16 The 9-year incidence of type 2 diabetes among Filipino adults was estimated at 16.3% in a cohort established from the 1998 NNS. 17 The data have also been used in reviewing excise taxation of alcohol and tobacco products, 18 legislating the national feeding programme for undernourished children 19 and developing Philippine clinical practice guidelines for dyslipidaemia. 20

Survey results have been used for global or regional research collaborations on NCD risk factors, such as overweight and obesity, 21 diabetes 22 , 23 and physical inactivity. 24 The Global Burden of Disease Study showed that dominant risk factors in the Philippines were high systolic blood pressure and smoking in 2013 and 2015, 25 , 26 and high fasting plasma glucose levels, high systolic blood pressure, and smoking in 2017. 27

The NNS has been conducted regularly since 1978. It provides a valuable source of information on the health and nutritional status of the Philippine population over the past four decades. The Master Sample designs adopted since the 2003 NNS have saved resources for designing surveys and developing sampling frames. With the Expanded NNS, confidence in providing statistically reliable estimates at the local level has increased. Timeliness has improved since the establishment of the electronic data collection system in 2013. Results are disseminated and accessible less than 6 months after the data collection period, which is much earlier than previously. Additionally, survey results are comparable to data from other countries through introduction of UNICEF core indicators on child nutrition 28 and the WHO STEPwise approach to NCD risk factor surveillance. 29

The NNS does have limitations. First, although the country has dry and wet seasons that affect food availability and consumption, the survey design does not consider seasonal variations in nutritional status, food insecurity and food consumption. Second, because of changes in reference standards and values over time, results are not always directly comparable across survey years. For example, the international reference standards from the US National Center for Health Statistics had been used for generating statistics on children’s nutritional status until the WHO Child Growth Standards were adopted in the 2008 NNS. Difficulties in retrieving individual-level data made it impossible to reanalyse anthropometric results from the 1978, 1982 and 1987 NNS in accordance with the new standards. Moreover, reference values for energy and nutrient intakes were obtained from the Recommended Dietary Allowances for Filipinos for the 1978 to 1998 NNS, 30 , 31 from the Recommended Energy and Nutrient Intakes for Filipinos for the 2003 to 2013 NNS 32 and from the Philippine Dietary Reference Intakes in the 2018 Expanded NNS. 9 These changes in reference values are vital for adapting to the new guidelines of the Joint Food and Agriculture Organization/WHO/United Nations University Expert Consultation, US Food and Nutrition Board, Institute of Medicine and WHO Child Growth Standards. 9 Finally, coefficients of variation for estimates at the provincial level are high for selected indicators based on the full Master Sample. Standard errors and coefficients of variation are presented in monographs as references for users.

Monographs of the Philippine Nutrition Facts and Figures have been published since 1978 and are available in print at the DOST-FNRI Library. Monographs of the 2013 NNS and 2015 Updating Survey are also available online at the eNutrition website of the DOST-FNRI [ http://enutrition.fnri.dost.gov.ph/site/home.php ]. 33 Public-use files of household or individual records in the 2013 NNS and the 2011 and 2015 Updating Surveys, with a data dictionary, are free to download in CSV format from that website. These records are anonymized, and the combination of region, province, household and household member codes is used to link variables of different survey components. Sample weights are assigned to correct for unequal probabilities of selection and non-response. Requests for remote data access to the 2003 and 2008 NNS and the 2005 Updating Survey can be made to the Director of the DOST-FNRI [ [email protected] ]. Required application documents include a request letter, approved study proposal, proof of clearance from an institutional ethics review board and dummy tables.

Philippine NNS in a nutshell

The Philippine National Nutrition Survey (NNS) is a cross-sectional household survey conducted by the Department of Science and Technology Food and Nutrition Research Institute every 5 years. It reports official statistics on nutritional status, diet and other lifestyle-related risk factors at the national and sub-national levels.

A multistage stratified and cluster sampling design has been adopted from the Philippine Statistics Authority. In 2018, the NNS was redesigned into a rolling sample survey for 3 consecutive years to provide reliable estimates for all 81 provinces, 33 highly urbanized cities and three other areas.

Survey components are: anthropometric, biochemical, clinical and health and dietary measures, socioeconomic status, food insecurity, government programme participation, maternal health and nutrition and infant and young child feeding practices. Data are collected through anthropometric measurement, food weighing, interviews and health examinations.

Results are disseminated and published within a year after the survey, in both printed and electronic versions [ http://enutrition.fnri.dost.gov.ph/site/home.php ]. Remote data access can also be requested through the Director [ [email protected] ].

Data from the NNS have been used for planning nutrition and development programmes and policies at the national and local levels and in primary scientific research, and have also contributed to global or regional research collaboration on risk factors for noncommunicable diseases.

Preparation of this paper was supported by the Fellowship Programme for Asian Researchers 2018–2019 from the National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan.

The authors would like to acknowledge the contributions of the following DOST-FNRI staff in the validation of survey information: Ma Lilibeth P Dasco for the anthropometric survey; Leah A Perlas, Michael E Serafico, Juanita M Marcos and Herbert P Patalen for the biochemical survey; and Glen Melvin D Gironella, Ma Lynell V Maniego, Eldridge B Ferrer and Apple Joy D Ducay for sampling and survey design. C.P. is grateful to Maria Stephanie N Parani for retrieval of reports used for this study, and acknowledges the support of the DOST-FNRI for granting travel authority in relation to this fellowship programme. The authors also thank Adam Goulston, MS, ELS, from Edanz Group [ www.edanzediting.com/ac ] for editing a draft of this manuscript.

Conflict of Interest

The authors declare that they have no competing interests.

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Child Rights Resource centre

Sizing Up: The stunting and child malnutrition problem in the Philippines

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Save the Children Philippines

Stunting in the Philippines, with a rate of 33%, is largely due to inequality of access to nutritious food, long period of hunger, and a lack of nutrition during the first 1,000 days of life. The first 1,000 days, from the time of conception up to the child’s first two years of life, is considered a “window of opportunity” which is a critical period of growth and development. Poor nutritional status of mother and child during this period is the primary cause of stunting.

This report aims to cull relevant findings of research studies on nutrition and stunting in order to provide background information about child malnutrition in the country, most especially in impoverished communities. The desk review findings from this report suggest that shortness is not a racial or genetic trait, but rather due to malnutrition.

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Cost of Hunger: Philippines

PSA Clears the Conduct of the 2023 National Nutrition Survey (NNS)

The Philippine Statistics Authority (PSA) granted clearance for the conduct of the 2023 National Nutrition Survey (NNS) by the Department of Science and Technology's Food and Nutrition Research Institute (DOST-FNRI).

The 2023 NNS will provide empirical data on the state of food security in Filipino households and the nutritional status and well-being of Filipino children, women, and other nutritionally vulnerable population groups for timely policy decisions at the national and regional levels.

The survey is composed of the following components:

Socio-economic Status – to determine the economic status of households as indicated by the wealth index;

Anthropometry – to evaluate the nutritional status of all population groups by determining the prevalence of underweight, under height, thinness, overweight and obesity, high waist circumference, high waist-hip ratio, and low birth weight;

Biochemical – to determine the prevalence of Vitamin A deficiency, iodine deficiency disorder, iron deficiency anemia and other micronutrient deficiencies;

Clinical Health and Nutrition of Adults and Other Members – to assess the prevalence of risk factors for non-communicable diseases such as elevated blood pressure and behavioral risk factors;

Dietary – to assess the sufficiency of all household food, energy, and nutritional intakes across all population groups;

Household Food Security – to assess level of experience-based food security status at the household as well as coping mechanisms and strategies adopted during times when food in the household is not available;

Government Nutrition/Health Program Participation – to assess households’ and members’ participation in selected nutrition and related government programs;

Maternal Health and Nutrition – to ascertain the health and nutrition of mothers with children aged 0 to 3 as well as their caregiving and health-seeking behaviors;

Infant and Young Child Feeding Practices – to provide updated data on the feeding practices of Filipino infants and toddlers aged 0 to 23 months and other relevant factors affecting feeding practices;

Mental Health – to evaluate mental health conditions of adolescents aged 10 to 17 years old and adults aged 18 and above; and

Food Environment – to determine the perceptions of households on food environment dimensions.

The 2023 NNS is a nationwide survey that adopts the 2013 Master Sample Design for household-based surveys and will cover 81 provinces, 33 highly urbanized cities (HUCs), and three other areas. The data will be collected on 42,768 sample households with an estimated number of 160,000 individuals from April 2023 to May 2024. A total budget amounting to PhP 346.1 million will be utilized, and the survey results are expected to be released between November and December 2024.

The survey was reviewed and cleared for conduct under the Statistical Survey Review and Clearance System (SSRCS), a mechanism being implemented by the PSA by virtue of Rule 28 of Implementing Rules and Regulations of Republic Act No. 10625 to:

ensure sound design for data collection;

minimize the burden placed upon respondents;

effect economy in statistical data collection;

eliminate unnecessary duplication of statistical data collection efforts; and

achieve better coordination of government statistical activities.

In line with this, the PSA enjoins all sampled households and individuals to participate in the survey by providing the necessary information.

For further information on SSRCS, please contact the Statistical Standards Division of the Standards Service with telephone numbers (02) 8376-1928 and (02) 8376-1931 , and email address [email protected] .

DENNIS S. MAPA, Ph.D. Undersecretary National Statistician and Civil Registrar General

See more at the Statistical Survey Review and Clearance System landing page .

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Psa approves the conduct of the senior bank loan officers’ survey, psa clears the conduct of the 2023 household energy consumption survey, psa clears the conduct of the 2024 rice and corn stocks survey: commercial and 2024 rice and corn stocks survey: household.

Malnutrition in the Philippines

• PMID: 12264685

PIP: In the Philippines poverty and pervasive malnutrition are not limited to families of deprived seasonal workers. Undernourishment is endemic and increasing throughout most of this archipelago of some 7100 islands, and is compounded by the prevalence of intestinal parasites and gastrointestinal diseases which health workers estimate deprive youngsters of at least 5-10% of the nutritional value in food they do consume. This problem is particularly prevalent in rural villages and city slums where many people eat with their fingers. According to the Philippine Ministry of Health, nearly 1/2 of all reported deaths are among infants and children through age 4, and about 1/2 of the accelerated death rate among those age 5 and younger is related to malnutrition, compounded by diarrhea, measles, and malaria which is returning to areas where it once was almost eradicated. 3 factors critically affect a newborn's survival prospects: the family size he or she is born into; the time or spacing between the mother's pregnancies; and the child's birth order. Evidence indicates that, during the 1970s, as US aid and other family planning assistance became available, they were used most among families in the 2 highest income classes, where reduction of family size is under way. Poverty is the most fundamental cause of malnutrition, although many other factors contribute. Land reform has brought security of tenure and increasingly is transferring ownership of fields to former tenants of rice and corn lands. For the former tenants enhanced security brings greater income and better eating for the farm families retain more of the crop. The undernourished and truly poor of the Philippines number about 1/2 of the population. Although dispersed throughout most of the archipelago, there are important regional differences. These related to marked geographic patterns that affect fertility of the soil, length of the dry season, fortunes of predominant crops, vulnerability to destructive typhoons, chronic warfare and other endemic lawlessness, major debilitating diseases, and especially population pressure. Malnutrition is not a hidden problem. The government, almost since the proclamation of 1972 martial law, has campaigned against malnutrition. During the 1970s, the government developed a major program of expanded production with the result that rice production expanded substantially. Even this achievement leaves the average Filipino short by 300 calories of food intake per day. It is not jiggering with food aid or government price incentives that will assure that future Filipinos will have enough to eat. Only a productive revolution of rural life that also educates mothers to know what makes for sound family nutrition will be adequate.

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Doh, un sign agreement to address malnutrition in the philippines, joint press release.

Manila, Nov. 13, 2023 -- The Department of Health (DOH), along with United Nations (UN) agency partners, UNICEF and the United Nations Office for Project Services (UNOPS), and World Bank inked a memorandum of understanding formalizing the partnership in addressing malnutrition in the Philippines.

As part of the broader Philippine Multisectoral Nutrition Project (PMNP), a four-year project spearheaded by the DOH and co-led by the Department of Social Welfare and Development (DSWD), this agreement adopts a bold multi-sectoral approach to achieve nutrition-specific and nutrition-sensitive interventions across 235 local government units (LGUs) in Luzon, Visayas, and Mindanao, as well as 40 municipalities in the Bangsamoro Autonomous Region of Muslim Mindanao (BARMM).

This project aims to increase the utilization of a package of nutrition-specific and nutrition-sensitive interventions and improve key behaviors and practices known to reduce stunting. It also enhances the capacity of the DOH and will support the delivery of nutrition and health care services at the primary care and community levels in municipalities known to have a high incidence of poverty and malnutrition.

"Improving the nutritional status of children is crucial for achieving the country's goals of enhancing human capital, strengthening economic recovery, and fostering long-term growth," said Ndiame Diop, World Bank Country Director for Brunei, Malaysia, Philippines, and Thailand.

The partnerships formalized today with UNICEF and UNOPS, as implementing partners, aim to strengthen primary health care and nutrition service delivery. This will be achieved through the provision of healthcare equipment and supplies, basic primary care and nutrition commodities, multisectoral information systems development for localized decision-making, capacity building for frontline healthcare workers, community health navigation, and health and nutrition leadership and governance for local chief executives. Additionally, the partnerships will focus on social behavior change and communication (SBCC), verification of LGU performance and finance systems for the performance-based grants, as well as project measurement and evaluation.

“Good nutrition is a fundamental child’s right. The need for healthy diets, multisectoral services, and practices that protect, promote, and support good nutrition has never been greater. By strengthening national and local systems and improving access to essential services in communities, we can help children not only survive but thrive. UNICEF is committed to supporting efforts to end child stunting, ensuring that every child has the opportunity to grow and develop to their full potential," said UNICEF Philippines Representative Oyunsaikhan Dendevnorov.

This is also in association with the community-driven development approach of DSWD Kapit-bisig Laban sa Kahirapan Comprehensive and Integrated Delivery of Social Services (KALAHI-CIDSS), where to date implementing communities have initiated more than 3,000 procurement packages using the community-based procurement system, with over 600 contracts successfully fulfilled. All these efforts are dedicated to improving access to and utilization of clean water, proper sanitation, enhanced hygiene practices (WASH), and access to Early Childhood Care and Development (ECCD) services.

"When children have better nutrition, they learn better. They can create opportunities to gradually break the cycles of poverty and hunger. By working together in the Philippine Multisectoral Nutrition Project, UNOPS and its partners will reach those furthest behind and help achieve the SDGs”, said UNOPS Philippines Country Manager, Oscar Marenco.

The partnership between the DOH and the UN will enhance the DOH's access to global expertise and resources, thereby expanding its knowledge base and network. Leveraging the UN's global portfolio of programs and initiatives, the DOH will gain a wealth of information and best practices, enabling it to enhance and optimize its public health programs and services.

“The PMNP is a recognition that nutrition is not just a matter of health. The Marcos Administration sets this intervention as part of its foundation for social and economic development by ensuring women and children’s access to the first 1,000 days of services as a critical investment in our country’s human capital. This collaboration between the DOH and the UN is a testament to our shared commitment to building a healthier and better future for the Filipino people, especially women and children," said DOH Secretary Teodoro Herbosa.

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