Assessment of the Impacts of Climate Change on Human ...

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Assessment of the Impacts of Climate Change on Human

Health and Nutrition

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Assessment of the Impacts of Climate Change on Human Health and Nutrition TECHNICAL REPORT Charmaine A. Duante Rovea Ernazelle G. Austria John Michael E. Borigas Cecilia S. Acuin

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The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), led by the International Center for Tropical Agriculture (CIAT), brings together some of the world’s best researchers in agricultural science, development research, climate science and Earth System science, to identify and address the most important interactions, synergies and tradeoffs between climate change, agriculture and food security. www.ccafs.cgiar.org. This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organisations.

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Abstract

Its geographic location and economic situation makes the Philippines highly-vulnerable to impacts of climate change and extreme weather events that cause considerable disruptions to food systems, affecting food security, nutrition and health especially of the most vulnerable groups. This study aims to assess the effects of exposure to extreme weather conditions, classified as natural disasters, on the proportion of households meeting the recommended energy intake (REI), and the prevalences of stunting and wasting among children under-five years old, chronic energy deficiency (CED) among lactating mothers and elderly adults and nutritionally at-risk pregnant women. This study utilized cross-sectional data from the 2013 and 2015 National Nutrition Surveys conducted nationwide by the Department of Science and Technology- Food and Nutrition Research Institute (DOST-FNRI). Exposure data came from the National Disaster Risk Reduction Management Council (NDRRMC) for typhoons and floods, the Philippine Rice Information System (PRISM) of the International Rice Research Institute (IRRI) for drought, and from the Bureau of Agricultural Statistics- Philippine Statistics Authority (BAS-PSA) for palay production. Logistic regression models were adjusted for sex, age, civil status, education, household size, work and place of work of the household head, ethnicity, illness for the past 2 weeks, avail of prenatal and mothers class for pregnant women, months of lactation for lactating mothers, hypertension for elderly, food security, membership to Philhealth, participation to Four Ps, place of residence, wealth index, palay production, and exposure to climate variables typhoons and floods one month up to six months prior to survey and drought for the first quarter of 2015 and 2016. Bivariate results showed that socioeconomic status, household size, food security status, sex, age, civil status, belonging to an indigenous group, exposure to typhoons, floods and drought had significant associations with nutrition outcomes. In full models, belonging to the poorest quintile, large and food insecure households increase the odds of stunting and wasting in children 0 to 59 months old, of chronic energy deficiency in elderly adults and lactating mothers and for pregnant women to become nutritionally at-risk . Households who are engaged in agriculture were more likely to meet the REI. The effect of exposure to typhoons and floods on meeting the REI at household level was positive at three (3) months but was negative at 6 months. Among households in the Mindanao areas, exposure to drought in either the first quarter of 2015 or 2016 only, increased the likelihood of children below five years old to become stunted and among elderly adults to become CED. However, elderly adults exposed to drought for both the first quarter of 2015 and the first quarter of 2016 made them less likely to become CED. The time of exposure to these natural

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disasters, whether typhoons, floods or drought, appears to affect the outcomes analyzed. Cohort data would help to better understand the continuing effects of such exposures. These results provide vital inputs for more strategic responses to climate change adaptation and mitigation programs of the government particularly for vulnerable population groups.

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Table of Contents

Introduction .................................................................................................................. 7 Statement of the Problem ............................................................................................... 9 Significance .................................................................................................................. 10 Objectives of the Study ................................................................................................ 10 Scope and Limitations of the Study ............................................................................. 10 Materials and methods .............................................................................................. 11 Study Design ................................................................................................................ 11 Data Sources ................................................................................................................ 11 Method of Data Collection and Analysis ..................................................................... 12 Scope and Limitations of the Study ............................................................................. 12 Study Design ................................................................................................................ 12 Data Sources ................................................................................................................ 12 Study Population and Subjects ..................................................................................... 17 Data Processing and Analysis ...................................................................................... 18 Statistical Analyses ...................................................................................................... 23 Ethical Considerations ................................................................................................. 23 Declaration of Potential Conflicts of Interest .............................................................. 23 Dissemination of Study Results ................................................................................... 23 Results and Discussion ............................................................................................... 24 Typhoons/Floods .......................................................................................................... 26 Drought ........................................................................................................................ 78 Summary and conclusion .......................................................................................... 95 Results and Discussion ............................................................................................... 95 Appendices ................................................................................................................... 96

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Introduction

Background of the Study

Climate change is defined by the United Nations Framework Convention on Climate Change (UNFCC, 2011) as “a change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and that is in addition to natural climate variability observed over comparable time periods”. As a consequence of climate change, natural disasters occur that influence the environment and in turn, adversely affecting human health.

The past forty years has shown a continuous increase in the frequency of natural disasters as recorded in the Emergency Events Database (EM-DAT) as shown by a three-time increase than what it was in 1975 to 1984, from over 1,300 events to 3,900 seen from 2005 to 2014 (Asian Development Bank). Human activities affected global climate through the release of carbon dioxide and other greenhouse gases that increased the carbon dioxide atmospheric concentration by more than 30% since pre-industrial times.

Climate change has inevitable impact on human health, livelihood assets and food production and distribution channels as well as changing purchasing power and market flows. The pathway may not be direct but the consequences of the changes in global climate bring about health risks, from deaths in extreme temperatures to changing patterns of infectious diseases. Temperature fluctuations which can be intense and short-term may cause heat stress or extreme cold, leading to increased mortality rates from heart and respiratory diseases. Sea levels may rise, increasing the risk of coastal flooding, causing displacement of population and injury, deaths, increased risks of infection from water and vector-borne diseases (World Health Organization, 2018).

The Food and Agriculture Organization of the United Nations (FAO-UN) lists three (3) main causal pathways that will aggravate the problem of undernutrition as a consequence of climate change. These include the following: impact on accessibility to sufficient, safe and adequate food; impact on care and feeding practices and impact on environmental health and access to health services. Food security is compromised because of crop yield reduction due to rising temperatures and variable rainfall patterns. It is estimated that climate change can cause 250,000 additional deaths per year between 2030 and 2050, from malnutrition, malaria, diarrhea and heat stress (World Health Organization, 2018).

The Philippines is one of the countries in the world that is highly vulnerable to the impacts of climate change and extreme weather events because of its geographic location and economic situation. (2013 Climate Change Vulnerability Index, Maplecroft). Among the extreme weather events, tropical cyclones ranked first at

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82%1 and flood ranked third at 5% in terms of the number of affected population in 2013a. Tropical cyclones also had the largest share of casualties at 92%b.

(a) (b)

Figure 1. (a) Top 5 disasters in the Philippines in terms of affected population, 20131 and (b) top 5 disasters in terms of casualties, 20131 (a)

In 2014, tropical cyclones accounted for the top disaster in terms of the number of affected people at 13,081,129. Flashflood ranked second at 141,052 and ITCZs/ monsoon, continuous rains at 47,759 ranked fourth.

Figure 2. Top 5 disasters in terms of the number of affected people, 20142

The impacts of climate change particularly from extreme weather conditions may result in considerable damage to food systems and affect the food security and nutritional and health status particularly of the most vulnerable groups. In terms of food production, the El Nino effects may also be considered a natural calamity, and cause considerable impact on food security and nutrition. The economic cost of climate change- related calamities is also

1 2013 Philippine Disaster Report, Citizens’ Disaster Response Center (CDRC) Research and Public Information Department

2 Philippine Disaster Report 2014: Understanding the Root Causes of the Country’s Vulnerability to Disasters, Citizens’ Disaster

Response Center (CDRC) Research

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alarming with billions of pesos worth of damages in the infrastructure, agriculture and commercial sectors.

Figure 3. Cost of 2014 disasters on economic sectors

10 Most Disaster-Affected Countries in 2014 World Disaster Risk Index 2014 1. China (58.45 M)

1. Vanuatu

2. Philippines (10.14 M) 2. Philippines 3. India (5.66 M) 3. Tonga 4. Burkina Faso (4 M) 4. Guatemala 5. Sri Lanka (3 M) 5. Bangladesh 6. Bangladesh (2.80 M) 6. Solomon Islands 7. Malaysia (2.40 M) 7. Costa Rica 8. Pakistan (2.28 M) 8. El Salvador 9. Serbia (1.61 M) 9. Cambodia 10. Kenya (1.60 M) 10. Papua New Guinea

* Philippine Disaster Report 2014: Understanding the Root Causes of the Country’s Vulnerability to Disasters, Citizens’ Disaster Response Center (CDRC) Research

On a global scale, the Philippines ranked second in the list of most disasters-affected countries in 2014 and ranks second in the World Disaster Risk Index 2014.

Statement of the Problem

Climate change results to both positive and negative outcomes. Occurrence of natural disasters due to the increasing temperature of the earth brings forth an impact on the ecological makeup of the earth, which, in virtue of chain reaction, will consequently impact the health and nutrition status of individuals. In the Philippines, the impact of climate change to nutrition, health and food security status of individuals and households has not yet been explored. The availability of national-scale data on nutrition and health statistics through the conduct of nutrition surveys is an opportune activity to assess the impact of climate change on the nutrition and health status of vulnerable population groups by exploring the association between exposure to typhoons, floods and drought and nutrition and health indicators.

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Significance

The results of the study provided information on the impacts of extreme weather conditions on food security status particularly on food access and availability and on human health and nutrition using available data on food consumption, climate data, weather and rice production data, food production data and exposure from disasters. They are vital inputs to climate change adaptation programs of the government for vulnerable population groups. It may also be utilized to understand how the natural disasters impact human life and be a call for action and prevent being overcome by the aftermath of calamities.

Objectives of the Study

General

1. To assess the effects of exposure to extreme weather conditions classified as natural disasters on the Filipino households in terms of food security and selected health and nutritional status of household members;

2. To determine if the effects on food intake and nutrition outcomes of extreme weather conditions classified as natural disasters have changed from 2013 to 2016

Specific

1. To identify the vulnerable households or those that do not meet the recommended energy requirement by region and province;

2. To characterize the vulnerable households according to socio-economic status of the household, household head profile, food consumption patterns and exposure to climate shocks;

3. To determine the factors (including the effect of climate variables) that may affect the proportions of households not meeting the recommended energy requirement;

4. To identify the possible climate change-related determinants of the nutritional status (stunting, wasting, chronic energy deficiency) of children, adolescents, adults, the elderly, pregnant and lactating women and vulnerable groups such as the poor, agricultural households and indigenous peoples.

Scope and Limitations of the Study

The study covered typhoons, floods and drought that occurred in the Philippines within six (6) months prior to data collection in the survey years 2013, 2015 and 2016.

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Materials and methods

a. Study Design

This study is an analysis of cross-sectional data from the 2013 National Nutrition Survey and the 2015 Updating Survey of Nutritional Status of Filipino Children and Other Population Groups conducted nationwide by the Department of Science and Technology- Food and Nutrition Research Institute (DOST-FNRI). Data from the National Disaster Risk Reduction Management Council (NDRRMC), the Philippine Rice Information System (PRiSM) of the International Rice Research Institute (IRRI), and the Philippine Statistics Authority (PSA) were merged with the nutrition data.

b. Data Sources

National Nutrition Survey of the DOST-FNRI

Sampling Design of the 2013 8th NNS and 2015 Updating Survey Both the 8th NNS and 2015 Updating Survey employed a stratified three-stage sampling design adopting the 2003 Master Sample of the Philippine Statistics Authority (PSA). The first stage is the selection of Primary Sampling Units (PSUs) composed of one barangay or a combination of contiguous barangays with at least 500 households per sampling units. The second stage is the identification of enumeration areas (EAs) within each PSU, comprising of 150 to 200 households. The third stage is the random selection of housing units within the enumeration areas with the household as the sampling unit.

The 2013 NNS was conducted from June to December 2013 and February to April 2015. The 2015 Updating Survey was conducted from July to November 2015. This survey used four replicates of the 2003 Master Sample to obtain the national, regional, and provincial estimates for the following components: anthropometric measurements, blood pressure status and interview schedule-based information for food security, government program participation and socioeconomic components. For the dietary component, one replicate was used to obtain national as well as regional estimates.

The detailed methodology of the 8th NNS and 2015 Updating Survey is published elsewhere

(Department of Science and Technology- Food and Nutrition Research Institute, 2015) & (Department of Science and Technology- Food and Nutrition Research Institute, 2016).

Method of Data Collection and Analysis a. Household Food Consumption

A digital dietetic scale was used to weigh foods at the household. All weighing scales were calibrated using a one (1) kilogram standard weight. During food weighing day, all food items prepared and served for the entire day from breakfast, lunch and supper, including snacks were weighed before cooking or serving. These include raw as purchased foods to be cooked for each meal and snacks, food served and eaten raw, and cooked and processed foodstuff

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served directly on the dining table. Leftover foods not consumed during the food weighing day were weighed and together with the weights of plate wastes and given-out foods were deducted from the weighed food to come up with the actual food consumed by the household.

Data for household food consumption was evaluated using the Household Dietary Evaluation System (HDES) which is a computer software system developed by DOST-FNRI. A food item code for individual food items was assigned prior to data entry. The HDES converts all weight of food items into a uniform unit which is gross weight or as purchased (AP) weight.

a.

b.

Where 1 CU is equivalent to 1 member or visitor who consumes all the whole day major meals (breakfast, lunch, supper) at home. If a member consumes only one (1) meal in a day, this should be computed as one (1) meal divided by the meal pattern of the household.

The HDES converts the actual intake into raw edible portion (raw EP weight). Net edible weight (EP) is then computed using the following formula:

c.

The energy content of foods is determined using the following formula:

d.

b. Anthropometric Survey

The weight and height/ recumbent length of children were measured using standard anthropometric techniques. A digital double window weighing scale with 200-kilogram capacity (SECA® 874) was used to measure weight. A stadiometer (SECA® 874) and medical plastic infantometer (SECA® 417) was used for the standing height of children 2 years and above and recumbent length of children below 2 years, respectively.

Stunting and wasting among children under-five years old was determined using the WHO Child Growth Standards, based on weight and height measurements. Data was analyzed using the WHO Anthro 3.3.2 for children 0 to 60 months old software package. A child is stunted or wasted if his/her length/height-for-age and weight-for-length/height, respectively, fall below -2SD.

Chronic energy deficiency (CED) among elderly adults 60.0 years and over was determined using Body Mass Index (BMI) with cut-off points defined by the National Center for Health Statistics (NCHS)/ World Health Organization (WHO). BMI is computed by dividing the height (square meters) by weight in kilograms and multiplying by 100. A BMI less than 18.5 is classified as chronic energy deficiency (CED).

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The nutritional status of pregnant women was assessed using the Philippine reference for weight-for-height index developed by Magbitang et.al. (1988). A pregnant woman whose weight falls below the 95th percentile was classified as nutritionally at-risk.

c. Clinical and Health Survey

The variables used from this component included blood pressure status and illness for the past two (2) weeks prior to data collection.

Blood pressure was measured using a digital non-mercurial sphygmomanometer (A&D UM-101™) and KaweTM dual stethoscope following standard procedures. The BP level of adults were classified according to the 7th Joint National Committee (JNC) on detection and treatment of high blood pressure (Table 1)

Table 1. Classification of blood pressure according to JNC VII, 2004

BP Classification Systolic BP (mmHg) Diastolic BP (mmHg) Normal <120 and <80 Prehypertension 120-139 or 80-89 Hypertension, Stage 1 140-159 or 90-99 Hypertension, Stage 2 > 160 or > 100

For the purposes of the study, blood pressure level was collapsed into two (2) categories - normal (normal and prehypertension) and hypertensive (hypertension, stage 1 and hypertension, stage 2).

Presence of illness for the previous two (2) weeks prior to data collection was also collected. These include measles, chicken pox, upper respiratory tract infection, respiratory tract infection, diarrhea/ acute gastroenteritis, fever/headache, dengue, influenza/flu, asthma, allergy, and hypoglycemia/hyperglycemia.

d. Food Security Survey

Data for food security status was collected through face-to-face interview. The 2013 NNS, 2015 Updating Survey and 2016 LFHNS utilized two (2) sets of interview guides to assess household food insecurity using the Household Food Insecurity Access Scale (HFIAS) and the Household Dietary Diversity (HDD). For the purposes of this study, only data from the HFIAS results will be utilized.

The HFIAS included nine (9) occurrence questions based on a 30-day recall period, followed up by probing on the frequency of conditions experienced by the household. The HFIAS provides information on the prevalence and magnitude of food insecurity at the household level. Table 2 shows the standard procedure for scoring. The range of the total HFIAS score for each household could be from 0 (food security) to 27 (maximum food insecurity). The higher the score, the more food insecurity the household experienced3.

3Coates, Swindale, & Bilinsky, 2007

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Table 2. HFIAS household food access scores

Frequency of Occurrence Scoring (points) Never occurred (0 times) 0 Rarely (1-2 times) 1 Sometimes (3-10 times) 2 Often (>10 times) 3

The Household Food Insecurity Access Scale is divided into four (4) levels: food secure, mildly food insecure, moderately food insecure, and severely food insecure (Table 3). The more severe conditions the household experience and the more frequently they are experienced increases the level of food insecurity of the household.

Table 3. Categories of food insecurity (access)4

Situation(s) experienced in the past month

Frequency Rarely 1-2x

Sometimes 3-10x

Often >10x

1. Worry about food 2. Unable to eat preferred food 3. Eat just a few kinds of food 4. Eat food they really do not

want to eat

5. Eat a smaller meal 6. Eat fewer meals a day 7. No food of any kind in the

household

8. Go to sleep hungry 9. Go a whole day and night

without eating

Legend Food Secure Mildly Moderately Severely

e. Government Program Participation Survey

Two (2) variables from the government program participation (GP) component of the 8th NNS were included in the study- Philhealth membership and participation to 4Ps. Data for this component is gathered through face-to-face interview.

The response for both variables was self-reported as DOST-FNRI did not have the actual list of participants for each program. To compute for the participation rate, an eligibility criteria was set, based on the program objectives.

4 Household Food Insecurity Access Scale Indicator Guide, v.3

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a. Pantawid Pamilyang Pilipino Program (4Ps) membership

Pantawid Pamilyang Pilipino Program (4Ps) (English: Bridging Program for the Filipino Family) is a human development program of the national government providing cash grants to beneficiaries upon compliance with co-responsibilities.

The eligibility criteria used for the participation to the Pantawid Pamilyang Pilipino Program (4Ps) were the following:

i. Residents of the poorest municipalities, based on 2003 Small Area Estimates (SAE) of the National Statistical Coordination Board (NSCB)

ii. Households whose economic condition is equal to or below the provincial poverty threshold

iii. Households that have children 0-18 years old and/or have a pregnant woman at the time of assessment

iv. Households that agree to meet conditions specified in the program

For this variable, the percentage participation of households was computed using the following equation:

b. PhilHealth Membership

PhilHealth is a government corporation attached to the Department of Health which functions to administer the National Health Insurance Program (NHIP) of the Philippine government aiming to provide health insurance coverage for Filipinos.

All Filipinos are eligible for PhilHealth membership, as a principal member or dependent.

The percentage participation of households to PhilHealth was computed using the following equation:

f. Maternal and Infant and Young Child Feeding (IYCF) Survey

The exposure variables derived from the Maternal and IYCF component were used for generating the final model for pregnant women. These included maternal status, availment of prenatal checkup and stage in pregnancy. Data for this component was collected through face-to-face interview. Maternal status was categorized into three (3) according to the current pregnancy of the woman- first pregnancy, pregnant with child <30 months and pregnant with child >30 months. Availment of prenatal checkup is an indicator of good health-seeking

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behavior. Responses were coded 0 for non-availment of prenatal checkup and 1 for availment of at least one (1) prenatal checkup. Stage in pregnancy refers to how far along the pregnant subject was at the time of the survey- first trimester, second trimester or third trimester.

g. Socioeconomic and Sociodemographic Survey

Household wealth index was computed using principal component analysis (PCA) based on household assets, household characteristics, access to utilities and infrastructure variables. Asset-based wealth index was utilized for this survey data because of the absence of income and expenditure data. Sociodemographic characteristics for the household-level data consisted of the household head’s sex, age, civil status, highest educational attainment, work and place of work and the size of the household and place of residence. Data for this component was collected through face-to-face interview.

National Disaster Risk Reduction and Management Council (Typhoons and Floods)

Data on typhoons and floods that occurred in the Philippines was available in the provincial level from reports published online by the National Disaster Risk Reduction and Management Council.

A province was included in the study if any of the following criteria was met based on the NDRRMC report:

a. Provinces that declared state of calamity

b. Provinces that were severely destroyed after the calamity

c. Provinces with high casualties reported caused by the calamity

For the 2013 NNS data, typhoon/flood data used was from December 2012 to April 2014 and for 2015 Updating Survey data, typhoon/flood data used was from January 2015 to December 2016.

The complete list of typhoon/flood occurrence in the Philippines is presented in Appendix.

Philippine Rice Information System (PRiSM)- International Rice Research Institute IRRI) (Drought)

The International Rice Research Institute (IRRI) provided the data for drought occurrence through the Philippine Rice Information System (PRiSM). PRiSM is a satellite-based rice crop monitoring system which provides accurate and timely data by mapping calamity flooding and drought. Data was available on the municipal level in Mindanao only, for the first quarter of 2015 and first quarter of 2016.

Philippine Statistics Authority (Palay production)

Palay production data is available as public use file from the Philippine Statistics Authority - CountrySTAT Philippines website. Annual data of palay production per province was obtained for the period of 2012 to 2016. The unit of measurement is palay production in

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metric tons. This data is used as an independent variable for nutrition outcomes of drought exposure. The summary of sources of data and variables used in the analysis are presented in the Table below. Table 4. Source of data and variables used in the analysis Data source Component and Variables Used National Nutritional Survey and Updating Survey - Food and Nutrition Research Institute- Department of Science and Technology (FNRI-DOST) website: http://www.fnri.dost.gov.ph/

Anthropometric Component - Ethnicity, Stunting, Wasting, Chronic Energy Deficiency (CED), Nutritionally at-risk pregnant. Clinical Component – Blood pressure status and presence of illness Dietary Component – Households meeting 100% of recommended energy intake Food Security Component – Household food security status Government Programs Component – Membership of household head to PhilHealth and participation of household to 4Ps Maternal and IYCF Component - Maternal status, availment of prenatal checkup and stage in pregnancy Socio-economic and Socio-demographic Component - Age, sex, civil status, highest educational attainment, occupation, place of work, household size, wealth index and place of residence

National Disaster Risk Reduction and Monitoring Council (NDRRMC) website: www.ndrrmc.gov.ph/

Basis of inclusion of province in the study:

1. Provinces that declared state of calamity.

2. Provinces that was severely destroyed after the calamity. 3. Provinces with high casualties reported caused by the calamity.

Philippine Statistics Authority - CountrySTAT Philippines website: http://countrystat.psa.gov.ph/

Annual data and provincial disaggregation of palay production for year 2012 to 2016

International Rice Research Institute website: http://irri.org/

Municipal areas affected by drought in first quarter of 2015 and first quarter of 2016, using satellite imagery (PRiSM)

c. Study Population and Subjects

The study population was categorized into two (2) levels - a) household and b) individual and grouped according to survey year. The households were taken as is, and the individuals were grouped according to population/ physiologic group. A household was defined as “a group of persons who may be related or not, who sleep in the same dwelling unit and have common arrangements for the preparation and consumption of food.” (Department of Science and Technology- Food and Nutrition Research Institute, 2016). Table 5 summarizes the number of households and individuals by component --anthropometric, food security, dietary and socioeconomic components. The determinant of the final number of households and individuals was the dietary and anthropometric component, respectively.

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Table 5. Actual number of sample households and individuals by component: Philippines, 2013a and 2015b Population group/ physiologic group 2013 NNS 2015 Updating Survey

A FS SE D A FS SE D Households - 35,573 35,584 8,592 - 41,282 41,972 9,925 Individuals

Children 0-59m 9,990 - - - 12,738 - - - Elderly >60y 10,808 - - - 16,613 - - - Pregnant 1,196 - - - 1,491 - - - Lactating 2,605 - - - 4,005 - - -

Total (Individuals) a (Department of Science and Technology- Food and Nutrition Research Institute, 2015) b (Department of Science and Technology- Food and Nutrition Research Institute, 2016)

The final number of households and individuals based on the merged datasets of the a) NNS (nutrition outcomes) and typhoons and floods (nationwide) and b) NNS (nutrition outcome) and drought in Mindanao with complete and plausible anthropometric and dietary data included in the study is summarized in the table below:

Table 6. Final study population for the climate change study

Population group/ physiologic group 2013 NNSa 2015 Updatingb

2015 Updating (Mindanao)c

Householdsd 8,592 9,925 2,803 Individuals 24,487 28,483 6,694 Children 0-59 monthse 9,890 12,578 3,697 Elderly 60.0 years and overf 10,808 11,809 2,997 Pregnant Women 1,184 1,491 - Lactating Mothers 2,605 4,005 - a 8th National Nutritional Survey, 2013 b Updating of the Nutritional Status of Filipino Children and Other Population Groups, 2015 c Updating Survey (Mindanao Areas Only) Drought analysis d Household with dietary component only (1 replicate out of 4 replicates)

e Reference child: youngest child 0-59 months

f Reference elderly adult: oldest elder 60 years and over

Inclusion and Exclusion Criteria

The households and individuals included in the study are those with complete and plausible dietary and individual data, respectively. For households with at least two (2) children 0-60 months, the youngest child was taken as the reference child. For households with at least two (2) elderly 60 years and over, the older one by age was taken as the reference elder.

d. Data Processing and Analysis

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Outcome Variables

The outcome variables or dependent variables for the study were derived from the anthropometry and dietary components of the 2013 8th NNS and 2015 Updating Survey: a) households meeting 100% of recommended energy intake (REI); b) stunting and wasting among children under-five (0-59 months old); c) chronic energy deficiency among elderly adults 60.0 years and over and lactating mothers; and d) nutritionally at-risk among pregnant women

A summary of the operational definition of the outcome variables and reference standards used is presented in Table 7.

Table 7. Summary of outcome variables, definition and reference standards Outcome Variable Definition Reference standards Household

Households meeting energy intake

Households who met 100% of Recommended Energy Intake (REI)

Philippine Dietary Reference Intake (PDRI)

Individual

a. Stunted children 0 to 59 months

Children 0-59 months (under-five) whose length/height-for-age fall below -2SD

WHO-Child Growth Standards

b. Wasted children 0 to 59 months

Children 0-59 months (under-five) whose weight-for-length/ height fall below -2SD

WHO-Child Growth Standards

c. Chronic energy deficient elderly 60.0 years and over

Elderly adults 60.0 years and over with Body Mass Index (BMI) <18.5

National Center for Health Statistics (NCHS)/ World Health Organization (WHO), 1978

d. Nutritionally at-risk pregnant women

Pregnant women whose weight fall below the 95th percentile

Philippine reference for weight-for-height index developed by Magbitang et.al. (1988).

e. Chronic energy deficient lactating mothers

Below 20y Lactating mothers <20y with BMI-for-age below -2SD

2007 WHO Growth Reference

20y and above

Lactating mothers 20y and above with BMI <18.5

National Center for Health Statistics (NCHS)/ World Health Organization (WHO), 1978

Exposure Variables

The exposure variables used in the study were generated using data from NNS, NDRRMC, IRRI-PRiSM and DA-BAS.

a. Sociodemographic and socioeconomic characteristics. Household food security status,

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government program participation and maternal and IYCF variables

b. Exposure to typhoons and floods

Data was consolidated using past records and reports of calamities listed by the National Disaster Risk Reduction and Management Council (NDRRMC). Provinces affected by typhoons/floods were listed with the dates of calamity occurrence and then matched with the date of actual data collection of the 2013 NNS and 2015 Updating. Those areas exposed to typhoons/ floods within six months before actual data collection were tagged. Dummy variables for exposure starting with one (1) month up to six (6) months were generated, coding 1 for not exposed and 2 for exposed.

Figure 4. Provinces affected by typhoons and floods within six (6) months prior to

data collection of the 2013 8th NNS and 2015 Updating Survey

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Figure 4 shows the areas affected by typhoons and floods in 2013 and 2015 within 6 months prior to data collection survey. The complete list of natural calamity and schedule of data collection matrix is presented in Appendix.

c. Exposure to drought

Data for drought occurrence was provided by the International Rice Research Institute- PRiSM. Municipalities in Mindanao affected by drought were listed and matched with date of actual data collection of the 2015 Updating Survey. Those exposed to drought for the first quarter of 2015 and 2016 were tagged. Dummy variables for drought was generated- non-exposure or exposure to drought in the first quarter of 2015, non-exposure or exposure to drought in the first quarter of 2016 and non-exposure or exposure to drought in both 2015 and 2016. Population groups with generated data for drought exposure include the household, children 0-59 months and elderly only.

d. Palay Production

Data gathered for palay production was measured in metric tons by province. The variable “palay production” was computed using a slope formula with the recent, present and succeeding data of production, where year of production was “x” and the total production “y”. Computing for the slope can show if there is a difference between palay production before NNS and after NNS. Palay production was used as an exposure variable for the outcome variables merged with drought data.

The summary of exposure variables, categories used and operational definition are presented in Table 8.

Table 8. Summary of exposure variables and their definitions

Exposure variables and categories Definition Socioeconomic status Household (HH) wealth index

1- Poorest 2- Poor 3- Middle 4- Rich 5- Richest

Proxy measure of relative wealth of household; based on the following characteristics: type of floor, roof, wall and fuel used, household toilet facility, source of drinking water and ownership of household assets such as appliances, vehicles and presence of electricity connection

Sociodemographic Characteristics Sex of HH Head

Male Female

Refers to the biological sex of the respondent

Sex of child/ elderly adult Male Female

Age of HH head Less than 30y 30-39.9y 40-49.9y 50-59.9y 60.0y and over

Refers to age of respondent at the time of survey

Age of child 0-11m 12-23m 24-35m 36-47m 48-59m

Age of elderly adult 60-64y

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65-69y 70-74y 75-79y >80y

Age of lactating mother/ pregnant woman <20y >20y

Civil Status of HH head/ respondent With Partner (Married, Live-in) Without Partner (Single, Widowed/ Widower, Divorced/ Separated)

Refers to the marital status of the household head/ subject at the time of the survey; 5 categories of civil status was collapsed into two (2)- with partner and without partner

Highest Educational Attainment of HH head/ subject No grade completed Elementary level High school level Vocational level College undergraduate At least college graduate

Refers to the highest level of education the household head/ subject has attained

Household Size 1-3 members 4-6 members 7 or more members

Refers to the actual number of household members in the dwelling unit

Work of HH Head Agricultural Non-agricultural

Refers to the type of work the HH head/ subject is engaged in

Work of HH Head/ subject No occupation Agricultural Non-agricultural

Refers to the type of work the subject is engaged in

Place of work of HH head At home Local away from home Abroad

Refers to the actual place of work of the household head/ subject

Place of Residence Rural Urban

Refers to which type of human settlement the household resides; based on the Philippine Standard Geographic Code by the Philippine Statistics Authority (PSA)

Ethnicity of child Non-IP IP

Refers to the self-reported ethnicity of the household members; children were categorized as IP if at least one of the following conditions were met: a) both biological parents are IPs, b) one of the biological parents is an IP, c) one biological grandparent is an IP. A household was considered an IP household if at least one (1) member is an IP

Food Security Status Food Secure Mildly Food Insecure Moderately Food Insecure Severely Food Insecure

Refers to the household food security status based on the Household Food Insecurity Access Scale scores

Government Program Participation Participation to 4Ps of household

No Yes

Refers to the self-reported membership of the household to the Pantawid Pamilyang Pilipino Program (4Ps) (English: Bridging Program for the Filipino Family) which is a human development program of the national government providing cash grants to beneficiaries upon compliance with co-responsibilities.

Membership of household head to PhilHealth No Yes

Refers to the self-reported membership of the household head to PhilHealth, a government corporation attached to the Department of Health which functions to administer the National Health Insurance Program (NHIP) of the Philippine government which aims to provide health insurance coverage for Filipinos.

Presence of illness for the past two (2) weeks No Yes

Refers to the presence of illness of the respondent the past two weeks prior to data collection; includes measles, chicken pox, upper respiratory tract infection, respiratory tract infection, diarrhea/ acute gastroenteritis, fever/headache, dengue, influenza/flu, asthma, allergy, and hypoglycemia/hyperglycemia.

Blood pressure status Normal Hypertensive

Refers to the absence or presence of hypertension of elderly adult; determined through single-visit blood pressure measurement using JNC VII (2003) cut-off points

Maternal characteristics/ health-seeking behavior Maternal status

First time pregnant Pregnant mother with child 0-36 months Pregnant mother with child >36m

Refers to the maternal status of pregnant mother

Availment of prenatal check-up No

Refers to the presence or absence of availment of prenatal check-up of pregnant mother; reflects health-seeking behavior

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Yes Pregnancy trimester

First Second Third

Refers to the trimester of pregnancy of respondent

Exposure to Calamities Exposure to Typhoons and Floods

Ever exposure (within six months prior to data collection) Exposed 1 month prior to data collection Exposed 2 months prior to data collection Exposed 3 months prior to data collection Exposed 4 months prior to data collection Exposed 5 months prior to data collection Exposed 6 months prior to data collection

Refers to the window of exposure, defined in months, of households and individuals to typhoons and floods prior to data collection; data available was in provincial disaggregation

Exposure to Drought Exposure in 2015

Not Exposed Exposed

Exposure in 2016 Not Exposed Exposed

Exposure in 2015 and 2016 Not Exposed Exposed

Refers to the window of exposure, defined in months, of households and individuals to data collection; data available was in municipal disaggregation

Palay Production (for drought exposure) Refers to the slope of palay production measured in metric tons; data available was in provincial disaggregation

e. Statistical Analyses

For the NNS data, sampling weights were computed and adjusted for non-response and were post-stratified based on the projected population obtained from the PSA. Weighted statistical analyses were done using Stata Version 15.1 designed for complex analysis, taking into consideration the stages of household selection for participation in the survey.

Stata Version 15.1 was used for the merging of NNS data, typhoon/flood exposure and drought exposure. Descriptive statistics such as means, standard deviations, frequencies and percentages, and confidence intervals were generated to describe the distribution of the outcome and exposure variables.

Chi-square test was employed to determine if there was a significant association between the outcome and exposure variable. Bivariate analysis using simple logistic regression was used to determine the association between the outcome and exposure variables.

Multivariate logistic regression with a significance level of 5% (p<0.05) was adopted to determine the final model of the nutrition outcome variables. Backward elimination method was chosen in fitting the final models, the method started with the full model and insignificant variables were listed. Then, one at a time, one insignificant variable was removed and regression model was predicted, goodness of fit was inspected for each generated models. And the model with the highest fit among all the predicted models in the method was chosen. The elimination procedure was repeated until no insignificant variables were present in the model, thus the final model was predicted.

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Post estimation tests were also applied in the final model, first was the goodness of fit test is used to test the overall fit of the observed data to the predicted model, and model with a level of significance above 5% (p>0.05) was considered a good fit. Second was the Wald’s test which was performed to determine which among variables in the final model had no effect on the model, using a 5% (p<0.05) level of significance. The slope of palay production was computed using Microsoft Excel. And reliability of estimates for the descriptive analysis and odds ratio (OR) was set at 95%.

f. Ethical Considerations

The 8th NNS study was approved by the Food and Nutrition Research Institute Institutional Ethics Review Committee (FIERC) on February 19, 2013 with Protocol Code FIERC-2012-001. The 2015 Updating Survey was approved by the FIERC on July 20, 2015 with protocol code FIERC-2015-006. Consent was obtained in writing from study respondents prior to actual data collection. The Informed Consent Form contained all the components of the 8th NNS and 2015 Updating Survey, detailed data collection procedures and non-disclosure of information for anonymity and confidentiality purposes. The contents of the consent form used is published elsewhere

g. Declaration of Potential Conflicts of Interest

The proponents of the study declare that there is no conflict of interest.

h. Dissemination of Study Results

The results of the collaborative research will be jointly published in the public interest as mutually agreed upon, and subject to IRRI’s Intellectual Property guidelines as well as Philippine government and DOST Intellectual Property Guidelines.

Results and Discussion

In 2013, typhoons/floods mostly affected Luzon and Visayas areas which coincided with actual data collection phase of the 8th NNS. These typhoons were Maring, Nando, Labuyo, Odette and Yolanda which cost the Philippines substantial human life and economic losses. Among households, about four (4) in 10 households (37.8%) were exposed to typhoons/floods in 2013 and one-fourth (24.3%) in 2015. Majority of households (93.5%) in Mindanao were exposed to drought in 2015.

Under-five children, elderly adults, pregnant women and lactating mothers are among the most vulnerable population groups because of increased nutrition

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requirements/’’’hy and other special needs, even without exposure to natural calamities.

The proportion of children under-five years old exposed to typhoons/floods was 36.3% in 2013 and 23.3% in 2015 and 90.3% of children living in Mindanao were exposed to drought. Among elderly adults, about two-fifths (39.3%) and one-fourth (25.7%) were exposed to typhoons/floods in 2013 and 2015, respectively. Similarly, nine (9) in 10 (90.9%) were affected by drought.

More than one-third (34.2%) of pregnant women in 2013 and 22.5% in 2015 were exposed to typhoons/floods. Similarly, four (4) in 10 (38.5%) of lactating mothers were exposed to typhoons/floods in 2013 and 23.0% in 2015.

Table 9. Proportion of households and selected population groups by exposure

status to typhoons/flood and droughts: Philippines, 2013 and 2015

Exposure 2013 2015

n % (95% CI) n % (95% CI) HOUSEHOLD Typhoons/ Floods

No 5,709 62.2 (59.2-65.1) 7,265 75.7 (73.5-77.8) Yes 2,883 37.8 (34.9-40.8) 2,660 24.3 (22.2-26.5)

Drought* No - - 193 6.5 (4.3-9.6) Yes - - 2,610 93.5 (90.4-95.7)

CHILDREN 0-59 MONTHS OLD Typhoons/ Floods

No 6,654 63.7 (62.5-64.9) 9,344 76.7 (75.7-77.7) Yes 3,336 36.3 (35.1-37.5) 3,394 23.3 (22.3-24.3)

Drought* No - - 293 9.7 (7.9-11.8) Yes - - 3,441 90.3 (88.2-92.1)

ELDERLY ADULTS > 60 YEARS Typhoons/ Floods

No 6,850 60.7 (59.6-61.8) 8,379 74.3 (73.4-75.2) Yes 3,958 39.3 (38.2-40.4) 3,430 25.7 (24.8-26.6)

Drought* No - - 281 9.1 (7.0-11.7) Yes - - 3,421 90.9 (88.3-93.0)

Exposure 2013 2015 n % (95% CI) n % (95% CI)

PREGNANT WOMEN Typhoons/ Floods

No 827 65.8 (63.6-67.9) 1,123 77.5 (76.0-78.9) Yes 369 34.2 (32.1-36.4) 368 22.5 (21.1-24.0)

Drought* No - - - - Yes - - - -

LACTATING MOTHERS Typhoons/ Floods

No 1,683 61.5 (59.5-63.4) 2,936 77.0 (75.7-78.2) Yes 922 38.5 (36.6-40.5) 1,069 23.0 (21.8-24.3)

Drought* No - - - - Yes - - - -

*Mindanao only

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A. Typhoons/ Floods

Profile of Households Meeting the Recommended Energy Intake (REI): Philippines, 2013 and 2015

More than one-third (35%) of households meeting recommended energy intake (REI) in both reference years were female- headed, significantly higher than the male-headed households. The proportion of households meeting REI was significantly higher among those headed without partners (single/ widowed/ separated) than those with partners for both reference years. An increasing trend can also be seen in terms of increasing proportion of households meeting REI as the educational attainment of household head improves.

In terms of employment, there were more households engaged in agricultural work who met the REI than those who were not engaged in agricultural work, although differences were not statistically significant. The proportion of households who met REI was highest among those whose heads primarily work abroad.

Table 10. Profile of households meeting the REI: Philippines, 2013 and 2015

Characteristics 2013 2015

n % (95% CI) n % (95% CI)

Philippines 8,592 31.7 (30.4-33.0) 9,925 31.0 (29.8-32.2)

Sex of household (hh) head

Male 6,882 30.6 (29.2-32.0) 7,938 29.8 (28.5-31.1) Female 1,710 35.8 (33.4-38.2) 1,987 35.6 (33.4-37.9)

Age of hh head

Less than 30y 257 32.1 (26.2-38.7) 473 37.7 (32.7-43.0) 30-39.9y 1,269 30.6 (28.0-33.4) 1,674 29.3 (27.0-31.6) 40-49.9y 2,333 28.6 (26.6-30.7) 2,553 28.0 (26.0-30.1) 50-59.9y 2,223 29.7 (27.7-31.8) 2,461 28.1 (26.2-30.1) More than 60y 2,510 36.7 (34.4-39.1) 2,764 36.3 (34.4-38.2)

Civil status of hh head

With partner 6,464 30.7 (29.4-32.1) 7,371 29.4 (28.1-30.7) Without partner 2,124 34.5 (32.3-36.8) 2,549 35.7 (33.6-37.9)

Highest educational attainment of hh head

No grade completed 253 26.0 (20.8-32.0) 329 32.0 (27.1-37.3) Elementary level 3,703 29.4 (27.8-31.1) 4,101 30.9 (29.2-32.7) High School level 2,776 30.6 (28.6-32.6) 3,367 30.3 (28.6-32.0) Vocational level 421 35.9 (30.9-41.3) 563 29.5 (25.3-34.0) College undergraduate 585 37.1 (33.3-41.0) 636 32.7 (29.2-36.5) At least college graduate 812 39.6 (35.8-43.5) 927 33.3 (30.0-36.9)

Work of hh head

Agricultural work 2,532 31.2 (29.3-33.1) 2,882 33.9 (31.7-36.3) Non-agricultural work 4,021 30.7 (29.1-32.4) 5,115 29.0 (27.6-30.5)

Place of work of hh head

At home 754 34.9 (31.6-38.4) 805 33.8 (30.3-37.5) Local away from home 5,548 29.8 (28.4-31.2) 6,998 30.0 (28.7-31.4) Abroad 160 46.4 (38.4-54.6) 194 34.3 (27.9-41.2)

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The proportion of households meeting REI was significantly higher among non-4Ps than 4P households for both reference years, similar with households headed by Philhealth members although not statistically significant.

As the level of food security decreases, the proportion of households meeting REI significantly decreases as well, as evidenced by a clear and distinct pattern from food secure to being severely food insecure. The proportion of households meeting REI among those identified as food secure was almost twice the proportion of those severely food insecure in 2013. Households with relatively smaller sizes, i.e. 1-3 members, had significantly higher percentage of meeting REI than those with larger sizes, for both reference years.

The proportion of households meeting REI increased as wealth quintile improved.



n % (95% CI) n % (95% CI)

Philhealth membership of hh head

No 3,240 31.0 (29.1-33.0) 2,600 30.4 (28.4-32.4) Yes 5,036 32.0 (30.4-33.7) 6,501 31.3 (29.9-32.7)

4Ps membership of hh

No 5,037 33.9 (32.2-35.6) 7,312 33.3 (31.9-34.6) Yes 1,743 21.4 (19.4-23.6) 2,359 23.7 (21.6-26.0)

Food security

Food secure 2,383 40.9 (38.3-43.5) 3,142 36.7 (34.7-38.7) Mildly food insecure 1,200 32.9 (30.3-35.6) 1,254 31.4 (28.7-34.2) Moderately food insecure 3,220 28.4 (26.6-30.3) 3,146 29.6 (27.7-31.5) Severely food insecure 1,766 22.5 (20.4-24.7) 2,131 24.6 (22.7-26.7)

Household size

1-3 members 2,332 43.1 (40.9-45.3) 2,813 42.1 (40.2-44.1) 4-6 members 2,898 33.0 (31.0-35.1) 3,501 32.3 (30.6-34.1) 7 or more members 3,362 22.7 (21.2-24.4) 3,611 21.1 (19.5-22.8)

Place of residence

Rural 4,881 31.9 (30.3-33.5) 6,053 32.8 (31.2-34.4) Urban 3,711 31.5 (29.5-33.5) 3,872 29.0 (27.3-30.7)

Wealth index

Poorest 1,972 23.4 (21.3-25.7) 2,197 29.3 (26.9-31.9) Poor 1,862 29.1 (26.8-31.4) 2,110 29.6 (27.4-31.9) Middle 1,666 30.9 (28.6-33.4) 2,022 29.2 (27.0-31.5) Rich 1,511 34.1 (31.2-37.2) 1,881 32.0 (29.8-34.3) Richest 1,359 40.0 (37.2-42.9) 1,614 34.7 (32.2-37.3)

In 2013, about one-third of households (32.0%) not exposed to typhoons/floods within 6 months prior to survey met the REI. This percentage was slightly higher than those exposed to typhoons/floods but was not statistically significant. In terms of exposure, the proportion of households meeting REI was higher among those exposed for one

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(1) month to at least four (4) months than those who were not in the same reference period. Meanwhile, the proportion of households meeting REI that were not exposed longer to typhoons and floods, i.e. five- and six- month exposure, was higher than among those exposed.



n % (95% CI) n % (95% CI)

Ever exposed [within 6m prior to survey (PTS)]

No 5,709 32.0 (30.5-33.5) 7,265 30.7 (29.3-32.0) Yes 2,883 31.2 (28.9-33.5) 2,660 32.1 (29.7-34.6)

Exposed 1 month PTS

No 7,413 31.5 (30.1-32.9) 8,008 30.9 (29.6-32.2) Yes 1,179 33.1 (30.6-35.8) 1,917 31.6 (28.9-34.3)

Exposed 2 months PTS

No 7,515 31.5 (30.2-32.9) 9,094 30.9 (29.7-32.1) Yes 1,077 32.6 (29.6-35.7) 831 32.7 (28.8-36.8)


No 7,708 31.3 (30.0-32.7) 9,306 30.9 (29.7-32.1) Yes 884 34.8 (31.3-38.4) 619 32.8 (27.6-38.3)


No 8,121 31.5 (30.2-32.8) 8,913 30.7 (29.5-31.9) Yes 471 35.4 (30.2-40.9) 1,012 34.8 (30.7-39.0)


No 7,334 32.3 (31.1-33.6) 8,629 30.8 (29.5-32.0) Yes 1,258 29.2 (25.8-32.9) 1,296 33.3 (29.4-37.4)


No 7,230 32.4 (31.1-33.7) 9,721 31.1 (29.9-32.3) Yes 1,362 29.2 (25.9-32.7) 204 25.4 (16.7-36.7)

Full Model of Factors Associated with Households Meeting the Recommended Energy Intake (REI): Philippines, 2013 and 2015

The full model of the factors associated with meeting the REI of households for both 2013 and 2015 is presented in the succeeding Table. In 2013, age of household head, household size, work of household head, food security, place of residence and wealth index were significantly associated (p<0.05) with meeting REI. In 2015, the significantly associated factors of the full model included household size, work of household head, food security, place of residence and wealth index.

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Table 13. Unadjusted odds ratio of factors associated with meeting REI:

Philippines, 2013


Wald’s Test OR (95% CI) p-value Wald’s Test OR (95% CI) p-value

Sex of household (hh) head 0.2384 0.1610

Male reference reference

Female 1.18 (0.90-1.55) 0.2380 1.15 (0.95-1.40) 0.1610

Age of hh head 0.0327 0.6010

Less than 30y 0.58 (0.37-0.91) 0.0170 1.11 (0.84-1.48) 0.4690

30-39.9y 0.82 (0.63-1.06) 0.1340 0.97 (0.80-1.17) 0.7360

40-49.9y 0.81 (0.66-1.00) 0.0530 0.96 (0.81-1.14) 0.6320

50-59.9y 0.77 (0.63-0.94) 0.0110 0.91 (0.77-1.08) 0.2740

More than 60y reference reference

Civil status of hh head 0.2204 0.9271

With partner reference reference

Without partner 0.86 (0.67-1.10) 0.2200 0.99 (0.84-1.18) 0.9270 Highest educational attainment of hh head 0.2850 0.8923

No grade completed 0.95 (0.57-1.56) 0.8330 0.95 (0.65-1.38) 0.7750

Elementary level 0.87 (0.67-1.14) 0.3140 1.00 (0.80-1.24) 0.9680

High School level 1.00 (0.76-1.32) 0.9880 1.05 (0.84-1.30) 0.6670

Vocational level 1.00 (0.68-1.47) 0.9850 0.92 (0.67-1.26) 0.5960

College undergraduate 1.23 (0.88-1.72) 0.2260 1.07 (0.83-1.37) 0.6120 At least college

graduate reference reference

Household size 0.0000 0.0000

1-3 members reference reference

4-6 members 0.62 (0.52-0.75) 0.0000 0.68 (0.59-0.79) 0.0000

7 or more members 0.37 (0.31-0.45) 0.0000 0.41 (0.34-0.48) 0.0000

Work of hh head 0.0071 0.0000

Agricultural work 1.25 (1.06-1.47) 0.0070 1.36 (1.19-1.56) 0.0000

Non-agricultural work reference reference

Place of work of hh head 0.0068 0.9060

At home 1.09 (0.88-1.36) 0.4160 1.02 (0.85-1.22) 0.8680

Local away from home reference reference

Abroad 2.06 (1.30-3.25) 0.0020 1.07 (0.77-1.49) 0.6760

Food security 0.0001 0.0011

Food secure reference reference

Mildly food insecure 0.79 (0.62-1.02) 0.0680 0.86 (0.73-1.02) 0.0920

Moderately food insecure 0.69 (0.56-0.85) 0.0000 0.86 (0.75-0.99) 0.0330

Severely food insecure 0.57 (0.45-0.73) 0.0000 0.69 (0.58-0.83) 0.0000 Philhealth membership of hh head 0.7696 0.8612

No 0.98 (0.83-1.15) 0.7700 0.99 (0.87-1.13) 0.8610

Yes reference reference

4Ps membership of hh 0.1296 0.1615

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No 1.15 (0.96-1.38) 0.1300 1.12 (0.95-1.32) 0.1620


Place of residence 0.0138 0.0086

Rural 1.25 (1.05-1.48) 0.0140 1.22 (1.05-1.42) 0.0090

Urban reference reference

Wealth index 0.0004 0.0106

Poorest 0.53 (0.38-0.72) 0.0000 0.76 (0.58-0.99) 0.0400

Poor 0.77 (0.57-1.03) 0.0790 0.76 (0.59-0.97) 0.0280

Middle 0.75 (0.57-0.99) 0.0440 0.74 (0.59-0.93) 0.0110

Rich 0.83 (0.63-1.10) 0.1880 0.97 (0.80-1.19) 0.7960

Richest reference reference Ever exposed [within 6m prior to survey (PTS)] 0.2825 0.8346

No reference reference

Yes 1.21 (0.85-1.73) 0.2830 1.03 (0.76-1.41) 0.8350

Exposed 1 month PTS 0.2985 0.5261


Yes 0.80 (0.53-1.22) 0.2980 0.90 (0.66-1.24) 0.5260

Exposed 2 montha PTS 0.8084 0.4473


Yes 0.97 (0.73-1.27) 0.8080 0.88 (0.64-1.22) 0.4470

Exposed 3 months PTS 0.1580 0.8736


Yes 1.40 (0.88-2.24) 0.1580 1.03 (0.72-1.48) 0.8740



Yes 0.97 (0.63-1.49) 0.8740 1.17 (0.77-1.79) 0.4670



Yes 1.57 (0.77-3.18) 0.2150 1.01 (0.68-1.50) 0.9670


No reference

Yes 0.53 (0.27-1.05) 0.0680 0.59 (0.27-1.31) 0.1980

Constant 1.19 (0.80-1.77) 0.3760 0.72 (0.52-0.99) 0.0440

Final Model of Factors Associated with Households Meeting the Recommended Energy Intake (REI): Philippines, 2013 and 2015

Controlling for the effects of other variables, the final model of factors associated with households meeting the REI in 2013 is presented in Table. Households with 7 or more members were 65% less likely to meet the REI than households with 1-3 members. In addition, households with 4 to 6 members were 40% less likely to meet REI than households with 1-3 members.

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Those households engaged in agricultural work, i.e. farming, fishing etc. were 1.19 times more likely to meet the REI than those engaged in non-agricultural work. Additionally, those who work at home were 1.10 times more likely and those who work abroad were 1.79 times more likely to meet REI than those who work away from home.

In terms of food security, a decreasing trend in the odds ratio showed that compared to food secure households, the likelihood of meeting REI decreases as food insecurity progresses in intensity. Mildly food insecure, moderately food insecure and severely food insecure households were 20%, 29% and 41% less likely to meet the REI than food secure households.

Rural households were 20% more likely to meet REI than urban households. In contrast with the trend in food security, the likelihood of meeting the REI increases as wealth index improved. That is, the poorest were 50% less likely to meet the REI compared to the richest and the rich were only 20% less likely to meet REI compared to the richest quintile counterparts.

Households exposed to typhoons/floods three (3) months prior to survey were 1.54 times more likely to meet the REI compared to those who were not exposed. Interestingly, households exposed for a longer reference time, i.e. 6 months prior to survey were only 0.82 times more likely to meet REI compared to those unexposed.

Table 14. Final model of factors associated with meeting REI: Philippines, 2013 GOODNESS-OF-FIT: 0.502

Final model Wald’s Test OR (95% CI) p-value

Household size 0.0000 1-3 members reference

4-6 members 0.60 (0.52-0.69) 0.0000

7 or more members 0.35 (0.30-0.41) 0.0000

Work of hh head 0.0142 Agricultural work 1.19 (1.04-1.36) 0.0140

Non-agricultural work reference

Place of work of hh head 0.0094 At home 1.10 (0.92-1.32) 0.2790

Local away from home reference

Abroad 1.79 (1.23-2.62) 0.0030

Food security 0.0000 Food secure reference

Mildly food insecure 0.80 (0.65-0.98) 0.0290

Moderately food insecure 0.71 (0.60-0.85) 0.0000

Severely food insecure 0.59 (0.48-0.72) 0.0000

Place of residence 0.0174

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Rural 1.20 (1.03-1.39) 0.0170

Urban reference

Wealth index 0.0000 Poorest 0.50 (0.40-0.63) 0.0000

Poor 0.67 (0.55-0.82) 0.0000

Middle 0.73 (0.60-0.89) 0.0020

Rich 0.80 (0.65-0.99) 0.0360

Richest reference

Exposed 3 months PTS 0.0001 No reference

Yes 1.54 (1.25-1.91) 0.0000


Yes 0.82 (0.68-0.98) 0.0330

Constant 1.19 (0.97-1.45) 0.0920

Figure 5. Proportion and 95% CI of final model of factors associated with

households meeting REI: Philippines, 2013

Figure above shows the bivariate characteristics of the final model of the factors associated with meeting REI of households. It can be seen that the proportion of households meeting REI were significantly higher among those with 1-3 members

HH Size

Work

Place of work

Food

Place of residence

Wealth index

Exposed 3m PTS

Exposed 6m PTS

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compared to those with 4-6 members or 7 or more members which may explain the decreasing odds of meeting REI as household membership increases. In terms of food security, a clear decreasing trend can be noted wherein the proportion of households meeting REI declines as level of food insecurity worsens. In terms of socioeconomic status, a stark and consistent contrast can be seen when comparing the proportion of households meeting REI in the poorest and richest wealth quintiles.

For the reference year of 2015, the final model of factors associated with meeting REI of households are presented in Table. After controlling for other variables, the factors significantly associated with meeting REI in 2015 included household size, work of household head, food security, place of residence and wealth index.

Households with 4 to 6 members were 36% less likely and those with 7 or more members were 63% less likely to meet the REI than those with 1-3 members only. The odds of meeting the REI was 37% higher among those households engaged in agricultural work compared to those engaged in non-agricultural work. Consistently, the odds of meeting REI decreases as level of food insecurity increased. That is, mildly food insecure, moderately food insecure and severely food insecure households were 16%, 17% and 37% less likely to meet REI compared to food secure households.

There was also a higher likelihood of meeting the REI for rural households than urban households. In terms of socioeconomic status, the odds of meeting REI increased as wealth improved, consistent and similar with the results of the final model for 2013.

Table 15. Final model of factors associated with meeting REI: Philippines, 2015 GOODNESS-OF-FIT: 0.5737



4-6 members 0.64 (0.56-0.73) 0.0000

7 or more members 0.37 (0.32-0.42) 0.0000







Place of residence 0.0045 Rural 1.23 (1.07-1.41) 0.0050

Urban reference

Wealth index 0.0017

34

Poorest 0.74 (0.60-0.92) 0.0060

Poor 0.74 (0.60-0.92) 0.0060

Middle 0.75 (0.61-0.91) 0.0050

Rich 0.97 (0.82-1.16) 0.7670

Richest reference

Constant 0.85 (0.71-1.00) 0.0560

Figure 6. Proportion and 95% CI of final model of factors associated with

households meeting REI: Philippines, 2015

The figure above supplements the findings for the final model of factors significantly associated with meeting REI of households for the reference year 2015. It can be seen that the proportion of households meeting REI was highest among those with 1-3 members (42.1%) only, compared to those with 4-6 members (32.5%) and 7 or more members (21.1%). The proportion of households meeting REI was also significantly higher among those engaged in agricultural work (33.9%), food secure (36.7%) and rural households (32.8%).

HH Size

Work

Food security

Place of residence

Wealth index

35

Profile of Stunted Children 0-59 Months Old: Philippines, 2013 and 2015

The profile of stunted children under five years old is presented in Table. In the Philippine, about three (3) in 10 children under-five were stunted in 2013 (28.0%) and 2015 (31.0%), with the 2015 figure significantly higher than the 2013 figure. In 2013, the prevalence of stunting was higher among males (29.6%), those in the 24 to 35-month age group (35.4%), among indigenous peoples (IPs) (40.3%) and among those whose household heads were engaged in agricultural occupation (34.3%).

In 2015, there were significantly more stunted males (32.3%) than females, older children, IPs and among agricultural households.

Table 16. Profile of stunted children 0-59 months old: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI)

Philippines 9,890 28.0 (27.0-29.0) 12,578 31.0 (30.2-31.9)

Sex of child

Male 5,097 29.6 (28.3-31.0) 6,478 32.3 (31.1-33.6)

Female 4,793 26.3 (24.9-27.7) 6,100 29.7 (28.4-30.9)

Age of child

0-11m 2,337 14.6 (13.2-16.2) 2,947 15.0 (13.7-16.5)

12-23m 2,176 31.3 (29.4-33.4) 2,742 36.2 (34.3-38.1)

24-35m 1,753 35.4 (33.1-37.7) 2,349 37.1 (35.0-39.2)

36-47m 1,918 33.3 (31.1-35.6) 2,338 36.4 (34.4-38.5)

48-59m 1,706 28.4 (26.2-30.8) 2,202 33.9 (31.9-36.0)

Ethnicity of child

Non-IP 9,201 27.4 (26.4-28.4) 11,672 30.4 (29.5-31.3)

IP 689 40.3 (36.4-44.3) 906 43.5 (39.8-47.3)

Presence of illness in past 2 weeks

No 7,419 27.7 (26.6-28.9) 9,914 30.6 (29.6-31.5)

Yes 2,454 28.8 (26.9-30.8) 2,293 32.1 (30.1-34.2)

Work status of hh head

No occupation 1,999 25.5 (23.6-27.6) 1,867 25.8 (23.7-28.1)

Agricultural 2,979 34.3 (32.5-36.2) 3,986 38.4 (36.8-40.0)

Non-agricultural 4,909 25.9 (24.6-27.2) 6,678 29.0 (27.8-30.1)

There were significantly more stunted children in larger households (7 or more members) than in smaller member households for both survey years. The prevalence of stunting increases as the level of food insecurity increases in both reference years. The proportion of stunted children in severely food insecure households was almost double than that of the proportion of stunted children in food secure households in 2013.

In terms of government program participation for social security, the prevalence of stunting was higher among households with no Philhealth membership of household

36

head than those with Philhealth membership. Similarly, stunting was significantly higher among those who reported that their household is a 4P member.

There were significantly more stunted children in rural areas than in urban areas, for both reference years. The trends in the stunting prevalence of children in terms of socioeconomic showed that the proportion decreased as wealth index improved. The proportion of stunting in the poorest wealth quintile was more than thrice the proportion of stunting in the richest wealth quintile.

Cont. Table 16. Profile of stunted children 0-59 months old: Philippines, 2013 and

2015


n % (95% CI) n % (95% CI)

Household size

1-3 members 1,813 24.7 (22.6-26.8) 822 26.8 (23.7-30.1)

4-6 members 3,657 27.8 (26.2-29.4) 4,292 30.9 (29.4-32.4)

7 or more members 4,420 29.6 (28.2-31.0) 7,464 31.6 (30.5-32.8)

Food security

Food secure 2,286 18.7 (17.0-20.5) 2,957 22.5 (20.9-24.2)

Mildly food insecure 1,433 27.1 (24.8-29.6) 1,593 27.6 (25.4-29.9)

Moderately food insecure 4,038 30.5 (29.1-32.0) 4,587 33.5 (32.1-35.0)

Severely food insecure 2,109 34.9 (32.7-37.2) 3,178 37.1 (35.3-39.0)


No 3,841 28.7 (27.2-30.3) 3,280 34.2 (32.4-36.0)

Yes 5,808 27.6 (26.4-28.9) 8,637 29.9 (28.9-31.0)


No 5,330 24.7 (23.5-25.9) 8,560 27.3 (26.3-28.4)

Yes 2,850 39.0 (37.1-40.9) 3,750 40.2 (38.5-42.0)

Place of residence

Rural 5,638 32.2 (30.9-33.5) 7,539 35.2 (34.0-36.4)

Urban 4,252 23.8 (22.4-25.3) 5,039 26.4 (25.1-27.8)

Wealth index

Poorest 2,650 41.2 (39.2-43.2) 3,367 45.5 (43.7-47.3)

Poor 2,167 32.7 (30.7-34.8) 2,939 36.3 (34.5-38.1)

Middle 1,996 26.7 (24.7-28.8) 2,493 30.4 (28.5-32.4)

Rich 1,642 20.6 (18.7-22.7) 2,017 21.0 (19.1-22.9)

Richest 1,347 12.8 (11.0-14.8) 1,661 14.3 (12.4-16.3)

In 2013, 28.6% of stunted children under-five were exposed to typhoons/floods, slightly higher than the proportion of stunted children not exposed. In 2015, however, there were significantly more stunted children not exposed to typhoons/floods 6 months prior to data collection than those who were exposed. It can be seen in Table that the prevalence of stunting was significantly higher among households exposed 1 month, 2 months, 3 months and 4 months prior to survey than those unexposed for the same reference periods. However, this trend flips such that the prevalence of stunting is lower among those exposed for longer time periods, i.e. 5 and 6-month exposure.

37

Taking into account the 2015 results, the prevalence of stunting was lower among those exposed to typhoons/floods for 1-4 months than those were not exposed. Similarly, the prevalence of stunting was higher among those exposed to typhoons/floods for longer period of time.

Table 17. Profile of stunted children 0-59 months old: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI)

Ever exposed [within 6m prior to survey (PTS)]

No 6,579 27.7 (26.5-28.9) 9,226 31.8 (30.8-32.8)

Yes 3,311 28.6 (26.9-30.3) 3,352 28.6 (26.9-30.3)

Exposed 1 month PTS

No 8,551 27.3 (26.2-28.3) 10,123 32.0 (31.0-33.0)

Yes 1,339 33.1 (30.5-35.8) 2,455 26.3 (24.5-28.2)


No 8,599 26.9 (25.9-28.0) 11,514 31.1 (30.2-32.0)

Yes 1,291 35.5 (32.7-38.4) 1,064 30.2 (27.2-33.3)


No 8,822 27.2 (26.2-28.2) 11,831 31.2 (30.3-32.1)

Yes 1,068 35.4 (32.3-38.6) 747 28.1 (24.8-31.6)


No 9,376 27.7 (26.7-28.7) 11,287 31.1 (30.2-32.1)

Yes 514 33.3 (28.9-38.0) 1,291 29.7 (26.9-32.8)


No 8,483 28.8 (27.8-29.9) 10,939 31.0 (30.1-32.0)

Yes 1,407 24.5 (22.1-27.1) 1,639 31.1 (28.5-33.8)


No 8,354 28.7 (27.7-29.8) 12,356 30.9 (30.1-31.8)

Yes 1,536 24.9 (22.6-27.5) 222 36.7 (29.4-44.7)

Full Model of Factors Associated with Stunting among Children 0-59 Months: Philippines, 2013 and 2015

The full model of factors associated with stunting among children 0 to 59 months is presented in Table. For both survey years, stunting was determined by sex of child, age of child, ethnicity of child, household size, food security, Philhealth membership of household head, 4Ps membership of household and wealth index.

38

Table 18. Unadjusted odds ratio of factors associated with stunting among children 0-59 months: Philippines, 2013



Sex of child 0.0053 0.0022


Female 0.85 (0.77-0.95) 0.0050 0.87 (0.79-0.95) 0.0020

Age of child 0.0000 0.0000

0-11m reference reference

12-23m 2.92 (2.45-3.49) 0.0000 3.38 (2.91-3.92) 0.0000

24-35m 3.80 (3.17-4.56) 0.0000 3.55 (3.02-4.17) 0.0000

36-47m 3.63 (3.00-4.39) 0.0000 3.52 (3.03-4.10) 0.0000

48-59m 2.85 (2.35-3.45) 0.0000 3.17 (2.72-3.70) 0.0000

Ethnicity of child 0.0083 0.0466

Non-IP reference reference

IP 1.30 (1.07-1.57) 0.0080 1.19 (1.00-1.42) 0.0470 Presence of illness in past 2 weeks 0.3024 0.6754


Yes 0.94 (0.83-1.06) 0.3020 0.98 (0.87-1.09) 0.6750

Work status of hh head 0.2819 0.5184

No Occupation 1.08 (0.93-1.24) 0.3080 0.93 (0.80-1.07) 0.2910

Agricultural 0.94 (0.83-1.07) 0.3790 1.01 (0.91-1.12) 0.8510

Non-agricultural reference reference



4-6 members 1.29 (1.09-1.53) 0.0030 1.26 (1.04-1.54) 0.0210

7 or more members 1.46 (1.23-1.73) 0.0000 1.46 (1.21-1.77) 0.0000






No 1.17 (1.04-1.32) 0.0100 1.20 (1.08-1.33) 0.0010



No 0.76 (0.67-0.87) 0.0000 0.84 (0.76-0.94) 0.0020



Rural 0.98 (0.86-1.12) 0.7830 0.98 (0.88-1.08) 0.6420



Poorest 3.65 (2.80-4.75) 0.0000 4.38 (3.53-5.44) 0.0000

Poor 2.77 (2.15-3.57) 0.0000 3.06 (2.49-3.77) 0.0000

Middle 2.09 (1.62-2.69) 0.0000 2.47 (2.03-2.99) 0.0000

Rich 1.65 (1.30-2.11) 0.0000 1.50 (1.21-1.88) 0.0000

Richest reference reference

39

Ever exposed (within 6m prior to survey (PTS) 0.8058 0.0711


Yes 0.96 (0.72-1.29) 0.8060 1.29 (0.98-1.71) 0.0710



Yes 1.21 (0.90-1.62) 0.2140 0.71 (0.53-0.94) 0.0190



Yes 1.09 (0.84-1.41) 0.5340 1.26 (0.95-1.67) 0.1030



Yes 1.22 (0.90-1.65) 0.2020 0.91 (0.66-1.25) 0.5490



Yes 0.90 (0.65-1.24) 0.5040 1.08 (0.82-1.43) 0.5750



Yes 0.87 (0.52-1.46) 0.6060 0.79 (0.59-1.05) 0.1000



Yes 1.20 (0.72-2.02) 0.4850 1.26 (0.80-2.00) 0.3220

Constant 0.05 (0.04-0.07) 0.0000 0.06 (0.04-0.08) 0.0000

Final Model of Factors Associated with Stunting among Children 0 to 59 Months: Philippines, 2013 and 2015

After controlling for the effects of other variables, the final model of factors

associated with stunting among children 0 to 59 months for reference year 2013

included sex of child, ethnicity of child, household size, Philhealth membership of

household head, 4Ps membership of household, wealth index and exposure to

typhoons/floods 1 months and 3 months prior to survey.

Females were 15% less likely to be stunted than males. IP children were 27% more

likely to be stunted than non-IPs. The likelihood of being stunted was 1.22 times and

1.23 times more likely among children in households with 4-6 members and 7 or more

members, respectively, compared to those with smaller size households. The odds of

stunting was 1.15 times higher among those belonging in households whose heads

40

were not Philhealth members. The likelihood of being stunted was 29% less among

non-4Ps households than 4Ps households.

The likelihood of becoming stunted decreased as wealth status improved. Those

belonging in the poorest quintile were 3.59 times more likely to become stunted than

those in the richest quintile. Those who belonged in households who were exposed to

typhoons/floods 1 month and 3 months prior to survey were 21% and 19% more

likely to become stunted than those who were not exposed.

Table 19. Final model of factors associated with stunting among children 0 to 59 months: Philippines, 2013

GOODNESS-OF-FIT: 0.9055


Sex of child 0.0030 Male reference

Female 0.85 (0.76-0.95) 0.0030

Ethnicity of child 0.0122 Non-IP reference

IP 1.27 (1.05-1.53) 0.0120


4-6 members 1.22 (1.03-1.44) 0.0210

7 or more members 1.23 (1.04-1.44) 0.0130

Philhealth membership of hh head 0.0189 No 1.15 (1.02-1.29) 0.0190

Yes reference

4Ps membership of hh 0.0000 No 0.71 (0.63-0.81) 0.0000

Yes reference


Poor 2.78 (2.21-3.50) 0.0000

Middle 2.19 (1.73-2.76) 0.0000

Rich 1.69 (1.34-2.12) 0.0000

Richest reference

Exposed 1 month PTS 0.0100 No reference

Yes 1.21 (1.05-1.40) 0.0100

41


Yes 1.19 (1.02-1.40) 0.0310

Constant 0.18 (0.13-0.24) 0.0000

Figure 7. Proportion and 95% CI of final model of factors associated with stunting among children 0 to 59 months: Philippines, 2013

The figure above shows the bivariate analysis of the final model of stunting among

children under-five in 2013. Results showed that stunting was significantly higher

among those belonging in IP groups, among households with 7 or more members,

among those in 4P households, those in the poorest quintile and among those exposed

to typhoons/floods 1 month and 3 months prior to survey.


associated with stunting among children 0 to 59 months in 2015 included sex of child,

household size, Philhealth membership of household head, 4Ps membership of

HH Size

Wealth index

Exposed 1m PTS

Exposed 3m PTS

Sex of child

Ethnicity of child

PhilHealth membership of hh head


42

household and wealth index, which is consistent with the final model for stunting in

2013.

Females were 12% less likely to be stunted than males. Those living in households

with 4 to 6 members were 23% more likely and those living in 7 or more member-

households were 27% more likely to be stunted compared to their smaller-member

household counterparts. Those whose household heads were not Philhealth members

were 1.18 times more likely to become stunted than those who were members.

Conversely, children in non-4Ps households were 20% less likely to be stunted than

those who were 4Ps households. Consistent with the previous findings, the odds of

becoming stunted decreased as socioeconomic status improved. The odds was 4.40

times higher among children belonging to the poorest quintile compared to their

richest counterparts.

Table 20. Final model of factors associated with stunting among children 0 to 59 months: Philippines, 2015



Sex of child 0.0039 Male reference

Female 0.88 (0.81-0.96) 0.0040


4-6 members 1.23 (1.01-1.49) 0.0370

7 or more members 1.27 (1.06-1.53) 0.0100


Yes reference


Yes reference


Poor 3.07 (2.54-3.71) 0.0000

Middle 2.44 (2.04-2.93) 0.0000

Rich 1.49 (1.21-1.83) 0.0000

43

Richest reference

Constant 0.17 (0.13-0.23) 0.0000

Figure 8. Proportion and 95% CI of final model of factors associated with stunting among children 0 to 59 months: Philippines, 2015

In this figure, it can be seen that stunting prevalence was significantly higher among

households with Philhealth membership of household head (34.2%), 4Ps membership

of household (40.2%) and among those in the poorest quintile (45.5%). Wealth index

showed a clear trend in terms of the prevalence of stunting as socioeconomic status

improved.

HH Size

Wealth index

Sex of child



44

Profile of Wasted Children 0-59 Months Old: Philippines, 2013 and 2015

This section presents the profile of wasted children 0 to 59 months old for both

reference years. Results show that about 9 in 100 (8.7%) children in 2013 and about 8

in 100 (7.6%) children in 2015 were wasted or thin. Wasting was generally more

prevalent among males, among those in the 0-11 month age group, among IPs and

among those in agricultural households.

Table 21. Profile of wasted children 0-59 months old: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI)

Philippines 9,885 8.7 (8.1-9.3) 12,585 7.6 (7.1-8.1)

Sex of child

Male 5,094 9.0 (8.2-9.9) 6,485 8.2 (7.5-8.9)

Female 4,791 8.4 (7.6-9.3) 6,100 6.9 (6.3-7.7)

Age of child

0-11m 2,336 12.4 (11.0-13.9) 2,952 11.4 (10.1-12.8)

12-23m 2,175 10.8 (9.5-12.3) 2,742 9.4 (8.3-10.6)

24-35m 1,753 6.7 (5.6-7.9) 2,352 5.8 (4.9-6.8)

36-47m 1,916 6.3 (5.2-7.6) 2,338 5.2 (4.4-6.3)

48-59m 1,705 5.8 (4.8-7.0) 2,201 4.5 (3.6-5.5)

Ethnicity of child

Non-IP 9,197 8.7 (8.1-9.4) 11,679 7.5 (7.0-8.0)

IP 688 8.4 (6.3-11.0) 906 8.3 (6.3-10.9)


No 7,414 8.7 (8.0-9.4) 9,921 7.4 (6.9-8.0)

Yes 2,454 8.8 (7.7-10.1) 2,293 8.4 (7.3-9.7)


No occupation 1,998 7.8 (6.6-9.3) 1,868 7.6 (6.4-9.0)

Agricultural 2,979 10.2 (9.1-11.4) 3,987 8.0 (7.2-9.0)


Furthermore, consistent with stunting, wasting prevalence was higher among

households with 7 or more members, among those moderately and severely food

insecure, among non-Philhealth member heads and among 4P households. There were

also more wasted children in rural areas and among those in poorest households.

45



n % (95% CI) n % (95% CI)

Household size

1-3 members 1,811 7.2 (6.0-8.6) 823 5.6 (4.2-7.4)

4-6 members 3,657 8.7 (7.8-9.7) 4,294 7.2 (6.5-8.1)

7 or more members 4,417 9.3 (8.4-10.3) 7,468 8.0 (7.3-8.7)

Food security

Food secure 2,286 7.3 (6.2-8.7) 2,961 6.0 (5.1-7.0)





No 3,837 9.8 (8.8-10.8) 3,281 9.2 (8.2-10.3)

Yes 5,807 7.9 (7.2-8.7) 8,642 7.0 (6.4-7.6)


No 5,329 8.4 (7.6-9.3) 8,566 7.2 (6.6-7.8)

Yes 2,847 8.9 (7.8-10.0) 3,750 8.5 (7.6-9.6)

Place of residence

Rural 5,635 9.0 (8.2-9.8) 7,544 7.8 (7.1-8.4)

Urban 4,250 8.4 (7.6-9.4) 5,041 7.3 (6.6-8.1)

Wealth index

Poorest 2,647 11.1 (9.9-12.5) 3,367 9.1 (8.1-10.2)

Poor 2,166 7.8 (6.7-9.1) 2,941 8.2 (7.2-9.3)

Middle 1,995 9.3 (8.1-10.8) 2,493 7.4 (6.4-8.6)

Rich 1,642 8.2 (7.0-9.7) 2,020 6.4 (5.4-7.7)

Richest 1,347 6.0 (4.6-7.7) 1,663 6.1 (4.9-7.5)

In terms of exposure to typhoons/ floods, no significant differences were found in the

proportion of wasted children in exposed and unexposed areas. However, a general

pattern can be observed wherein the prevalence of wasting was higher among those

exposed 1 to 4 months prior to survey, for both reference years. Meanwhile, among

those exposed 5 to 6 months prior to survey, the prevalence of wasting was higher

among the unexposed population.



n % (95% CI) n % (95% CI)

Ever exposed (within 6m prior to survey (PTS)

46

No 6,575 8.7 (7.9-9.4) 9,231 7.5 (7.0-8.1)

Yes 3,310 8.8 (7.8-9.9) 3,354 7.7 (6.8-8.7)

Exposed 1 month PTS

No 8,546 8.6 (8.0-9.3) 10,128 7.6 (7.1-8.1)

Yes 1,339 9.4 (7.8-11.2) 2,457 7.5 (6.4-8.7)


No 8,594 8.6 (7.9-9.2) 11,520 7.5 (7.0-8.0)

Yes 1,291 9.8 (8.3-11.5) 1,065 8.1 (6.4-10.2)


No 8,818 8.6 (8.0-9.3) 11,838 7.5 (7.0-8.0)

Yes 1,067 9.5 (7.8-11.4) 747 8.5 (6.5-11.2)


No 9,372 8.6 (8.0-9.3) 11,294 7.6 (7.1-8.2)

Yes 513 10.1 (7.7-13.1) 1,291 6.8 (5.5-8.3)


No 8,478 8.8 (8.1-9.4) 10,946 7.6 (7.1-8.1)

Yes 1,407 8.5 (7.1-10.1) 1,639 7.7 (6.4-9.2)


No 8,349 8.8 (8.1-9.4) 12,363 7.5 (7.1-8.0)

Yes 1,536 8.5 (7.2-10.1) 222 8.9 (6.0-12.9)

Full Model of Factors Associated with Wasting among Children 0-59 Months: Philippines, 2013 and 2015

The full model of the factors associated with wasting among children 0 to 59 months

is presented in the Table below. Preliminary results showed that in 2013, factors

significantly associated with wasting (Wald’s Test < 0.05), before controlling for the

effect of other variables, included age of child, Philhealth membership of household

head and wealth index. In 2015, the factors significantly associated with wasting

before controlling for the effect of other variables were sex of child, age of child, and

Philhealth membership of household head.

Table 24. Unadjusted odds ratio of factors associated with wasting among children 0-59 months: Philippines, 2013


Wald’s Test OR (95% CI) p-

value Wald’s

Test OR (95% CI) p-value

Sex of child 0.6993 0.0174


Female 0.97 (0.82-1.15) 0.6990 0.83 (0.72-

0.97) 0.017

0

47

Age of child 0.0000 0.0000

0-11m reference reference

12-23m 0.90 (0.71-1.13) 0.3570 0.82 (0.66-

1.01) 0.058

0

24-35m 0.50 (0.38-0.66) 0.0000 0.46 (0.36-

0.58) 0.000

0

36-47m 0.52 (0.39-0.68) 0.0000 0.45 (0.35-

0.56) 0.000

0

48-59m 0.47 (0.35-0.62) 0.0000 0.38 (0.29-

0.50) 0.000

0 Ethnicity of child 0.3230 0.9444


IP 0.84 (0.59-1.19) 0.3230 1.01 (0.74-

1.38) 0.944

0 Presence of illness in past 2 weeks 0.5097 0.1468


Yes 1.07 (0.88-1.30) 0.5100 1.14 (0.95-

1.37) 0.147

0 Work status of hh head 0.3959 0.7161

No Occupation 0.97 (0.75-1.25) 0.7880 1.04 (0.83-

1.30) 0.746

0

Agricultural 1.13 (0.92-1.38) 0.2340 0.94 (0.79-

1.12) 0.513

0 Non-agricultural reference reference



4-6 members 1.19 (0.92-1.53) 0.1880 1.23 (0.88-

1.71) 0.231

0

7 or more members 1.12 (0.85-1.48) 0.4060 1.26 (0.90-

1.76) 0.175

0 Food security 0.4652 0.1287


Mildly food insecure 0.94 (0.69-1.28) 0.6970 0.97 (0.73-

1.30) 0.856

0

Moderately food insecure 0.95 (0.73-1.23) 0.7070 1.19 (0.95-

1.50) 0.127

0

Severely food insecure 0.81 (0.59-1.10) 0.1790 1.26 (0.99-

1.60) 0.063

0 Philhealth membership of hh head 0.0345 0.0004

No 1.22 (1.01-1.46) 0.0350 1.36 (1.15-

1.61) 0.000

0 Yes reference reference


No 0.97 (0.79-1.20) 0.7990 0.84 (0.69-

1.02) 0.072

0 Yes reference reference


Rural 0.95 (0.77-1.17) 0.6130 0.91 (0.77-

1.08) 0.292

0 Urban reference reference


Poorest 1.77 (1.17-2.68) 0.0070 1.31 (0.93-

1.83) 0.120

0

Poor 1.14 (0.75-1.71) 0.5410 1.21 (0.87-

1.68) 0.256

0

Middle 1.38 (0.93-2.05) 0.1130 1.06 (0.77-

1.46) 0.722

0

Rich 1.39 (0.93-2.06) 0.1060 0.97 (0.70-

1.34) 0.850

0 Richest reference reference



48

Yes 0.91 (0.58-1.41) 0.6680 1.37 (0.82-

2.26) 0.225

0 Exposed 1 month PTS 0.7161 0.4039


Yes 1.09 (0.68-1.75) 0.7160 0.79 (0.46-

1.37) 0.404

0 Exposed 2 months PTS 0.5137 0.5635


Yes 1.14 (0.77-1.69) 0.5140 1.12 (0.76-

1.66) 0.564



Yes 1.01 (0.64-1.59) 0.9730 1.43 (0.87-

2.35) 0.161



Yes 1.10 (0.67-1.81) 0.7120 0.73 (0.43-

1.22) 0.231



Yes 0.79 (0.36-1.73) 0.5480 0.92 (0.57-

1.49) 0.733



Yes 1.29 (0.58-2.83) 0.5310 0.94 (0.50-

1.78) 0.847

0

Constant 0.09 (0.05-0.15) 0.0000 0.09 (0.06-

0.15) 0.000

0

Final Model of Factors Associated with Wasting among Children 0-5 Months: Philippines, 2013 and 2015

The final model for factors associated with wasting among children 0 to 59 months

are presented in Table. Findings show that age of child, Philhealth membership of

household head and wealth index were significantly associated with wasting among

under-five. The odds of becoming wasted decreased as the age of child progressed.

Those belonging in the 12 to 23 month age group were 14% less likely to become

wasted and those in the 48 to 59 age group were 56% less likely to become wasted

compared to the youngest age group of 0 to 11 months.

Those who had household heads with no Philhealth membership were 1.21 times

more likely to become wasted than those with Philhealth membership. Furthermore,

the odds of becoming wasted decreased as wealth improved. Those in the poorest

quintile were 83% more likely to become wasted than those in the richest quintile and

49

those in the middle quintile were 55% more likely to become wasted than their richest

counterparts.

Table 25. Final model of factors associated with wasting among children 0 to 59 months: Philippines, 2013



Age of child 0.0000 0-11m reference 12-23m 0.86 (0.71-1.06) 0.1580

24-35m 0.51 (0.40-0.65) 0.0000

36-47m 0.49 (0.38-0.63) 0.0000

48-59m 0.44 (0.34-0.57) 0.0000


Yes reference Wealth index 0.0001

Poorest 1.83 (1.35-2.50) 0.0000

Poor 1.22 (0.89-1.67) 0.2270

Middle 1.55 (1.14-2.12) 0.0060

Rich 1.32 (0.95-1.84) 0.0950

Richest reference Constant 0.09 (0.07-0.12) 0.0000

50

Figure 9. Proportion and 95% CI of final model of factors associated with wasting among children 0 to 59 months: Philippines, 2013

Results showed that there were three (3) factors significantly associated with wasting

among children 0 to 59 months. It can be seen in the preliminary analysis as presented

above that wasting prevalence was highest among the youngest age group of 0 to 11

months compared to the older age groups and among those in the poorest wealth

quintile.

The factors significantly associated with wasting among children 0 to 59 months for

the reference year 2015 were sex of child, age of child, food security, Philhealth

membership of household head and 4Ps membership of household.

Similar with the previous findings, females were 16% less likely to become wasted

compared to males. The odds of becoming wasted decreased as age progressed. That

is, those in the 12 to 23 age group, 24 to 35 age group, 36 to 47 age group and 48 to

Wealth index

Age of child


51

59 age group were 18%, 54%, 56% and 63%, respectively, less likely to become

wasted compared to their younger counterparts of 0 to 11 months.

Compared to food secure households, those mildly food insecure, moderately food

insecure and severely food insecure were 1.01 times, 1.27 times and 1.38 times more

likely to become wasted, again showing an increasing trend of odds as level of food

insecurity increases.

Those children with non-Philhealth member heads were 37% more likely to become

wasted than those who were Philhealth members. Consistently, those children who

belonged to non-4Ps recipient households were 21% likely to become wasted than

those who were.

Table 26. Final model of factors associated with wasting among children 0 to 59 months: Philippines, 2015



Sex of child Male 0.0201 reference Female 0.84 (0.72-0.97) 0.0200

Age of child 0-11m 0.0000 reference 12-23m 0.82 (0.67-1.00) 0.0560

24-35m 0.46 (0.37-0.58) 0.0000

36-47m 0.44 (0.35-0.56) 0.0000

48-59m 0.37 (0.29-0.49) 0.0000

Food security Food secure 0.0088 reference Mildly food insecure 1.01 (0.77-1.34) 0.9210



Philhealth membership of hh head No 0.0002 1.37 (1.16-1.62) 0.0000

Yes reference 4Ps membership of hh

No 0.0078 0.79 (0.67-0.94) 0.0080

Yes reference Constant 0.12 (0.10-0.16) 0.0000

52

Figure 9. Proportion and 95% CI of final model of factors associated with wasting among children 0 to 59 months: Philippines, 2015

The results showed that wasting was significantly higher among those in the 0 to 11

age group, severely food insecure households and in non-Philhealth households.

Profile of Chronic Energy Deficient Elderly 60.0 Years and Over: Philippines, 2013 and 2015

In the Philippines, about two (2) in 10 elderly adults 60.0 years and over were chronic

energy deficient (CED) in both 2013 (18.1%) and 2015 (18.5%). CED was generally

more prevalent among those in the older age group of 80 and above, among IPs,

among elderly adults who had an illness the previous 2 weeks before data collection.

The proportion of CED adults were significantly higher among those without partners

in 2013 and among those with partners in 2015.



Sex of child

Age of child

Food security

53

As educational attainment improved, the proportion of CED adults decreased. The

prevalence of CED among those with no grade completed were almost 4.3 times

higher than among those who were at least college graduates. The proportion of

normotensive elderly adults who were CED were significantly higher than the

hypertensive adults. In terms of household size.

Consistent with other population groups, the prevalence of CED was higher among

those in agricultural households than those in non-agricultural and no occupation

households. This might also explain the increasing trend of CED prevalence as

household food security worsens. There were twice as many CED elderly adults who

were severely food insecure as there were food secure.

CED prevalence was significantly higher among Philhealth-member households and

among those who were members of 4P-recipient households. There were also

significantly more CED adults in rural areas than in urban areas. As with other

population groups, the prevalence of CED decreased as socioeconomic status

improved.

In terms of exposure to typhoons/floods, the prevalence of CED was slightly higher

among those not exposed in 2013 and among those exposed in 2015 than their

counterparts but the difference was not statistically significant. It can also be noted

that the prevalence of CED was generally higher among those exposed to

typhoons/floods within 1 to 4 months prior to data collection for both reference years.

Meanwhile, using a longer reference frame of exposure for 5 months or 6 months, the

prevalence of CED was lower among those unexposed than those exposed.

54

Table 27. Profile of chronic energy deficient elderly adults >60.0 years Philippines, 2013 and 2015


n % (95% CI) n % (95% CI)

Philippines 10,808 18.1 (17.4-18.9) 11,809 18.5 (17.8-19.3)

Sex of elderly adult

Male 5,131 18.3 (17.2-19.4) 5,711 18.0 (17.0-19.1)

Female 5,677 18.0 (16.9-19.1) 6,098 18.9 (17.9-20.0)

Age of elderly adult

60-64 3,645 13.2 (12.1-14.3) 3,820 13.4 (12.3-14.6)

65-69 2,598 15.5 (14.2-17.0) 3,176 17.3 (16.0-18.7)

70-74 2,052 20.2 (18.3-22.2) 2,107 20.7 (19.0-22.5)

75-79 1,450 26.5 (24.3-29.0) 1,539 23.9 (21.8-26.2)

80 and up 1,063 28.5 (25.8-31.4) 1,167 28.6 (25.9-31.5)

Ethnicity

Non-IP 10,191 17.9 (17.1-18.7) 11,152 18.0 (17.3-18.8)

IP 617 24.8 (21.1-28.8) 657 30.9 (27.3-34.7)


No 8,648 17.2 (16.4-18.0) 9,884 17.6 (16.8-18.4)

Yes 2,149 22.0 (20.1-23.9) 1,607 23.3 (21.2-25.5)

Civil status of elderly adult

With partner 5,722 15.9 (15.0-16.9) 692 24.7 (21.3-28.5)

Without partner 5,085 20.6 (19.5-21.9) 5,580 16.0 (15.0-17.0)

Highest educational attainment of elderly adult

No grade completed 640 30.3 (26.7-34.1) 631 31.7 (27.9-35.7)

Elementary level 6,157 21.8 (20.7-22.9) 6,577 22.0 (21.0-23.1)

High School level 2,322 14.6 (13.2-16.2) 2,678 15.0 (13.7-16.5)

Vocational level 198 7.7 (4.6-12.6) 305 13.7 (10.0-18.3)

College undergraduate 495 7.6 (5.5-10.4) 547 8.6 (6.4-11.5)

At least college graduate 948 6.9 (5.4-8.8) 1,069 7.7 (6.2-9.5)

Blood pressure status of elderly adult

Normal 6,200 21.4 (20.4-22.5) 6,918 22.4 (21.4-23.5)

Hypertensive 4,499 13.5 (12.5-14.6) 4,867 13.0 (12.1-14.0)

Household size

1-3 members 5,072 18.7 (17.6-19.9) 5,421 19.1 (18.0-20.3)

4-6 members 2,781 17.7 (16.3-19.3) 3,086 18.3 (16.9-19.7)

7 or more members 2,955 17.5 (16.1-19.0) 3,302 17.7 (16.4-19.2)


No Occupation 5,216 17.8 (16.8-18.9) 5,094 18.8 (17.7-19.9)

Agricultural 2,665 22.0 (20.4-23.6) 2,863 22.9 (21.3-24.6)


Food security

Food secure 3,857 12.1 (11.1-13.1) 4,349 12.8 (11.8-13.9)





55

No 4,620 21.3 (20.1-22.6) 2,561 23.8 (22.1-25.5)

Yes 5,973 15.8 (14.8-16.8) 8,720 17.1 (16.2-17.9)


No 6,348 17.4 (16.4-18.4) 10,239 17.9 (17.1-18.7)

Yes 1,020 23.3 (20.7-26.2) 1,340 24.2 (21.9-26.7)

Place of residence

Rural 6,086 22.7 (21.5-23.8) 6,794 22.5 (21.5-23.6)

Urban 4,722 13.6 (12.6-14.6) 5,015 14.3 (13.3-15.4)

Wealth index

Poorest 2,200 31.1 (29.0-33.1) 2,371 31.6 (29.7-33.6)

Poor 2,305 23.4 (21.6-25.3) 2,405 25.0 (23.2-26.9)

Middle 2,245 17.3 (15.7-19.0) 2,355 19.7 (18.0-21.4)

Rich 2,017 12.5 (11.0-14.2) 2,239 13.0 (11.6-14.6)

Richest 1,939 8.1 (7.0-9.4) 2,360 7.3 (6.3-8.5)

Ever exposed (within 6m prior to survey (PTS)

No 6,850 18.6 (17.7-19.6) 8,379 17.6 (16.8-18.5)

Yes 3,958 17.3 (16.0-18.7) 3,430 21.0 (19.6-22.5)

Exposed 1 month PTS

No 9,198 17.5 (16.7-18.4) 9,166 18.0 (17.2-18.8)

Yes 1,610 21.8 (19.7-24.1) 2,643 20.6 (19.0-22.3)


No 9,218 17.3 (16.4-18.1) 10,759 18.3 (17.5-19.1)

Yes 1,590 23.4 (21.3-25.6) 1,050 22.0 (19.3-24.9)


No 9,470 17.5 (16.7-18.4) 10,993 18.2 (17.4-18.9)

Yes 1,338 22.8 (20.4-25.2) 816 25.0 (21.9-28.3)


No 10,104 17.8 (17.0-18.6) 10,537 18.2 (17.4-19.0)

Yes 704 23.0 (19.9-26.4) 1,272 21.9 (19.4-24.5)


No 9,152 19.2 (18.3-20.1) 10,255 18.1 (17.3-18.9)

Yes 1,656 13.9 (12.2-15.8) 1,554 22.2 (20.0-24.5)


No 8,997 19.2 (18.4-20.1) 11,615 18.4 (17.7-19.2)

Yes 1,811 13.9 (12.3-15.8) 194 23.2 (18.5-28.6)

Full Model of Factors Associated with Chronic Energy Deficiency among Elderly Adults 60.0 Years and Over: Philippines, 2013 and 2015

Before controlling for the effects of other variables, the full model of factors

significantly associated with CED among the elderly in 2013 included sex, age,

presence of illness in past 2 weeks, civil status, highest educational attainment, blood

pressure status, household size, food security and wealth index. In 2015, the factors

56

significantly associated with CED among the elderly before controlling for the effect

of other variables included age, ethnicity, presence of illness in past 2 weeks, civil

status, blood pressure status, Philhealth membership, wealth index, exposure to

typhoons/floods 3 months prior to survey.

Table 28. Unadjusted odds ratio of factors associated with CED among elderly 60.0 years and over: Philippines, 2013



Philippines

Sex of elderly adult 0.0158 0.9749


Female 0.82 (0.70-0.96) 0.0160 1.0 (0.9-1.1) 1.0

Age of elderly adult 0.0000 0.0000

60-64 reference reference

65-69 1.21 (1.02-1.44) 0.0330 1.34 (1.1567-1.55) 0.0000

70-74 1.50 (1.24-1.82) 0.0000 1.68 (1.4290-1.98) 0.0000

75-79 2.31 (1.87-2.86) 0.0000 1.82 (1.5181-2.18) 0.0000

80 and up 2.71 (2.12-3.47) 0.0000 2.60 (2.1364-3.16) 0.0000

Ethnicity 0.5790 0.0030




Yes 1.30 (1.11-1.52) 0.0010 1.30 (1.13-1.50) 0.0000

Civil status of elderly adult 0.0018 0.0003

With partner reference 1.72 (1.35-2.20) 0.0000

Without partner 1.28 (1.10-1.50) 0.0020 reference Highest educational attainment of elderly adult 0.0029 0.1669

No grade completed 2.02 (1.32-3.11) 0.0010 1.33 (0.94-1.87) 0.1040

Elementary level 1.78 (1.25-2.55) 0.0020 1.23 (0.94-1.61) 0.1220

High School level 1.56 (1.07-2.26) 0.0200 1.19 (0.91-1.57) 0.2020

Vocational level 0.90 (0.37-2.16) 0.8070 1.53 (0.98-2.39) 0.0610

College undergraduate 1.05 (0.62-1.80) 0.8460 0.89 (0.58-1.37) 0.6070

At least college graduate reference reference Blood pressure status of elderly adult 0.0000 0.0000

Normal reference reference

Hypertensive 0.51 (0.44-0.58) 0.0000 0.50 (0.45-0.56) 0.0000



57

4-6 members 1.22 (1.04-1.43) 0.0170 1.16 (1.02-1.32) 0.0290

7 or more members 1.20 (1.02-1.42) 0.0320 1.14 (0.99-1.32) 0.0750

Work status of hh head 0.5867 0.2183

No Occupation 0.98 (0.83-1.15) 0.8070 1.12 (0.98-1.27) 0.0870

Agricultural 0.91 (0.76-1.10) 0.3310 1.04 (0.89-1.22) 0.5960







No 1.09 (0.95-1.25) 0.2220 1.19 (1.05-1.34) 0.0070



No 1.03 (0.84-1.27) 0.7610 1.00 (0.84-1.19) 0.9930



Rural 1.16 (0.99-1.36) 0.0650 1.05 (0.92-1.19) 0.4920



Poorest 2.77 (2.08-3.69) 0.0000 5.26 (4.05-6.82) 0.0000

Poor 2.12 (1.60-2.79) 0.0000 3.89 (3.03-4.99) 0.0000

Middle 1.62 (1.23-2.13) 0.0010 2.98 (2.35-3.79) 0.0000

Rich 1.38 (1.03-1.84) 0.0280 1.84 (1.44-2.36) 0.0000

Richest reference reference Ever exposed (within 6m prior to survey (PTS) 0.1237 0.1451


Yes 0.76 (0.54-1.08) 0.1240 1.34 (0.90-1.98) 0.1450



Yes 1.41 (0.96-2.07) 0.0800 1.03 (0.69-1.55) 0.8780



Yes 1.11 (0.82-1.48) 0.5040 0.68 (0.46-1.00) 0.0530



Yes 1.17 (0.84-1.65) 0.3560 1.40 (1.01-1.94) 0.0420



Yes 1.34 (0.95-1.90) 0.0980 1.04 (0.72-1.52) 0.8200



Yes 1.78 (0.94-3.36) 0.0780 0.99 (0.68-1.44) 0.9710



Yes 0.70 (0.36-1.35) 0.2890 1.03 (0.64-1.63) 0.9160

Constant 0.04 (0.02-0.06) 0.0000 0.04 (0.03-0.05) 0.0000

58

Final Model of Factors Associated with Chronic Energy Deficiency among Elderly Adults 60.0 Years and Over: Philippines, 2013 and 2015

After controlling for the effects of other variables, the factors significantly associated

with CED among elderly adults 60.0 years and over were sex, age, presence of illness

in past 2 weeks, civil status, highest educational attainment, blood pressure status,

food security, wealth index, ever exposure to typhoons/floods 6 months prior to

survey, exposure to typhoons/floods 1 and 3 months prior to survey.

Findings show that females were 14% less likely to be CED than males. The odds of

becoming CED increased as age group progressed. Adults 80 years old and over were

2.59 times more likely to become CED than those in the 60 to 64 age group. The odds

of becoming CED was 1.21 times more among those who had illness in the previous 2

weeks. Elderly adults with no partners (single/ widowed/ separated) were 24% more

likely to become CED than those with partners. Consistently, the odds of becoming

CED decreased with the improvement of educational attainment. Those with no grade

completed were 2.30 times more likely to become CED than those who at least

finished college. The likelihood of being CED was 45% less among the hypertensive

elderly adults.

The odds of becoming CED increased as level of food security increased. Elderly

adults were 1.22 times, 1.35 times and 1.36 times more likely to be CED if they were

from mildly, moderately and severely food insecure households. Inversely, the

likelihood of becoming CED decreases as wealth improves. Those from the poorest

and poor quintiles were 3.06 times and 2.22 times more likely to become CED

compared to their richest counterparts.

Elderly adults who were exposed to typhoons/floods within 6 months prior to survey

were 0.82 times more likely to become CED. Those exposed to typhoons/floods 1

59

month and 3 months prior to survey were 1.33 times and 1.36 times more likely to

become CED than those who were not exposed in the same reference period.

Table 29. Final model of factors associated with CED among elderly adults: Philippines, 2013

GOODNESS-OF-FIT: 0.3863 Final model Wald’s Test OR (95% CI) p-value Sex of elderly adult 0.0151

Male reference

Female 0.86 (0.75-0.97) 0.0150

Age of elderly adult 0.0000 60-64 reference

65-69 1.21 (1.04-1.40) 0.0150

70-74 1.62 (1.38-1.90) 0.0000

75-79 2.28 (1.93-2.70) 0.0000

80 and up 2.59 (2.15-3.12) 0.0000

Presence of illness in past 2 weeks 0.0031 No reference

Yes 1.21 (1.07-1.38) 0.0030

Civil status of elderly adult 0.0008 With partner reference

Without partner 1.24 (1.09-1.40) 0.0010

Highest educational attainment of elderly adult 0.0000 No grade completed 2.30 (1.63-3.25) 0.0000

Elementary level 2.02 (1.50-2.71) 0.0000

High School level 1.83 (1.35-2.48) 0.0000

Vocational level 1.12 (0.60-2.10) 0.7170

College undergraduate 1.03 (0.65-1.63) 0.8910

At least college graduate reference Blood pressure status of elderly adult 0.0000

Normal reference

Hypertensive 0.55 (0.49-0.61) 0.0000






Poor 2.22 (1.78-2.76) 0.0000

Middle 1.67 (1.35-2.08) 0.0000

Rich 1.32 (1.05-1.66) 0.0160

Richest reference

Ever exposed (within 6m prior to survey (PTS) 0.0437 No reference

Yes 0.82 (0.67-0.99) 0.0440

Exposed 1 month PTS 0.0050 No reference

Yes 1.33 (1.09-1.62) 0.0050

60


Yes

1.36 (1.11-1.67) 0.0030

Constant 0.04 (0.03-0.06) 0.0000

Figure 10. Proportion and 95% CI of final model of factors associated with CED among elderly 60 years and over: Philippines, 2013

The proportion of CED adults was found to be significantly higher among those in the

80 and above age group compared to the younger elder groups, among those who

were ill the previous 2 weeks prior to data collection, among those without partners

Sex of elderly adult




Highest educational attainment of

Blood pressure status of elderly adult

Food security

Wealth index

Exposed 1m PTS

Exposed 3m PTS

Ever exposed (within 6m PTS)

61

and those with no grade completed. It was also noteworthy that there were

significantly more CED adults who were normotensive than hypertensive. The

prevalence of CED increased as food insecurity level increased and inversely, the

prevalence of CED decreased as socioeconomic status improved. The proportion of

CED adults was significantly higher among those who were exposed to typhoons/

floods for 1 month and 3 months prior to survey.


associated with CED among elderly adults in 2015 included age, ethnicity, presence

of illness in past 2 weeks, civil status, household size, Philhealth membership of

household head, wealth index, exposure to typhoons/floods within 6 months prior to

survey and exposure to typhoons/floods 2 or 3 months prior to survey.

The odds of becoming CED increased as age group progressed. Those in the 65-69,

70 to 74, 75 to 79 and 80 and above age group were 1.33 times, 1.65 times, 1.83 times

and 2.54 times more likely, respectively, to be CED compared to the 60 to 64 age

group. IP elderly adults were 34% more likely to be CED than non-IP elders.

Compared to households with 1 to 3 members, those with 4 to 6 members and those

with 7 or more members were 21% and 20% more likely, respectively, to become

CED.

Elderly adults living in households whose household heads were not Philhealth

members were 1.21 times more likely to become CED than those who were members.

As can be seen in the results, the odds of becoming CED decreased as socioeconomic

status improved. The likelihood of being CED was more than 6-fold among those in

the poorest quintile compared to the richest quintile.

Those in the poor wealth quintile were 4.50 times more likely and those in the middle

wealth quintile were 3.30 times more likely to be CED than those in the richest

quintile. Those who were exposed to typhoons/ floods within 6 months prior to survey

62

were 1.41 times more likely to become CED than those who were not. The odds of

becoming CED were 38% less likely among those exposed to typhoons/floods 2

months prior to survey but the odds were increased by 58% among those exposed 3

months prior to survey.

Table 30. Final model of factors associated with CED among elderly adults: Philippines, 2015

GOODNESS-OF-FIT: 0.2368 Final model Wald’s Test OR (95% CI) p-value

Age of elderly adult 0.0000 60-64 reference

65-69 1.33 (1.15-1.53) 0.0000

70-74 1.65 (1.41-1.93) 0.0000

75-79 1.83 (1.54-2.17) 0.0000

80 and up 2.54 (2.11-3.06) 0.0000

Ethnicity 0.0032 Non-IP reference

IP 1.34 (1.10-1.63) 0.0030

Presence of illness in past 2 weeks 0.0001 No reference

Yes 1.33 (1.16-1.53) 0.0000

Civil status of elderly adult 0.0062 With partner 1.16 (1.04-1.30) 0.0060

Without partner reference


4-6 members 1.21 (1.07-1.38) 0.0030

7 or more members 1.20 (1.05-1.37) 0.0060


Yes reference


Poor 4.50 (3.66-5.52) 0.0000

Middle 3.30 (2.69-4.07) 0.0000

Rich 1.99 (1.58-2.50) 0.0000

Richest reference


Yes 1.41 (1.24-1.61) 0.0000


Yes 0.62 (0.47-0.82) 0.0010


Yes 1.58 (1.20-2.07) 0.0010

63

Constant

0.04 (0.03-0.04) 0.0000

Figure 11. Proportion and 95% CI of final model of factors associated with CED among elderly 60 years and over: Philippines, 2015

The prevalence of CED was significantly higher among those in the 80 and above age

group, among IP elderly, those with illness the previous 2 weeks prior to survey,

amng those with partners, among those with no Philhealth membership of household

head and among those in the poorest and poor quintiles. In terms of exposure to

typhoon and floods, there was a significantly higher proportion of CED among those

who were exposed within 6 months prior to data collection and among those exposed

3 months prior to survey.


Ethnicity of elderly adult



Wealth index

Exposed 2m PTS

Exposed 3m PTS



HH size

64

Profile of Nutritionally At-Risk Pregnant Women: Philippines, 2013 and 2015

Findings revealed that one in every four pregnant women was nutritionally at risk.

The proportion of nutritionally at-risk among pregnant women was higher among

those in the younger age group of less than 20 years, those without partners, those

with low educational attainment. First-time pregnant, those who did not avail of

prenatal checkups and those in the first or second trimester of pregnancy.

Consistent with the findings of other population groups, the proportion of nutritionally

at-risk pregnant women increase as food insecurity level also increased and

conversely, the proportion decreased as socioeconomic status improved.

In general, the prevalence of being nutritionally at-risk was higher among those who

were exposed to typhoons/floods for 1 to 4 months prior to survey and this flips for

exposure of longer reference time, i,e, for 5 to 6 months wherein the prevalence was

higher among those who were not exposed.

Table 31. Profile of nutritionally at-risk pregnant women: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI) Philippines 1,184 24.8 (22.3-27.4) 1,491 24.7 (22.3-27.2) Age of pregnant woman

less than 20y 188 37.2 (30.1-44.9) 234 39.7 (33.3-46.6) 20y and above 996 23.0 (20.3-25.9) 1,257 22.4 (19.9-25.0)

Ethnicity of pregnant woman Non-IP 1,096 24.8 (22.2-27.5) 1,386 24.3 (21.9-26.8) IP 88 24.5 (15.9-35.8) 105 32.3 (22.0-44.7)

Presence of illness in past 2 weeks No 1,016 24.4 (21.7-27.4) 1,248 23.3 (20.9-26.0) Yes 167 26.5 (20.2-34.0) 136 32.2 (24.3-41.3)

Civil status of pregnant woman With partner 1,043 23.9 (21.3-26.7) 1,283 18.8 (15.9-22.2) Without partner 139 31.5 (23.7-40.4) 208 30.3 (26.8-34.0)

Highest educational attainment of pregnant woman No grade completed 13 14.5 (3.4-44.7) 17 39.3 (18.3-65.2) Elementary level 228 23.2 (17.9-29.5) 291 24.4 (19.5-30.0) High School level 631 27.2 (23.7-31.1) 796 26.9 (23.8-30.1) Vocational level 43 26.6 (14.9-42.7) 82 21.2 (12.6-33.6) College undergraduate 132 22.3 (16.0-30.1) 143 21.9 (15.4-30.3)

65

At least college graduate 131 18.3 (12.7-25.5) 160 17.2 (11.7-24.6) Maternal status

First time pregnant 424 29.1 (24.8-33.9) 466 31.9 (27.2-36.9) Pregnant mother with children 0-36mos 348 27.6 (22.6-33.2) 479 24.7 (20.6-29.3) Pregnant mother with children >36 months 392 17.0 (13.4-21.4) 510 17.1 (14.0-20.8)

Availment of prenatal checkup No 189 32.5 (25.9-39.9) 172 23.1 (20.6-25.8) Yes 975 22.9 (20.3-25.7) 1,283 33.8 (27.2-41.2)

Stage in pregnancy First trimester 211 26.6 (20.9-33.3) 267 23.7 (18.7-29.6) Second trimester 499 25.5 (21.5-30.0) 614 25.3 (21.9-29.1) Third trimester 474 23.1 (19.4-27.3) 610 24.4 (20.8-28.4)

Work of pregnant woman No Occupation 230 26.2 (20.5-32.7) 201 25.4 (19.8-32.0) Agricultural 388 24.9 (20.7-29.7) 494 22.7 (18.4-27.6) Non-agricultural 565 24.2 (20.7-28.2) 785 25.5 (22.4-28.9)

Household size 1-3 members 119 16.5 (10.5-24.8) 203 24.5 (18.8-31.2) 4-6 members 371 23.4 (19.2-28.2) 491 20.6 (17.1-24.6) 7 or more members 694 27.1 (23.7-30.7) 797 27.2 (24.0-30.6)

Food security Food secure 265 19.1 (14.9-24.2) 324 19.7 (15.2-25.2) Mildly food insecure 166 20.3 (14.5-27.6) 182 26.0 (20.0-33.1) Moderately food insecure 485 27.5 (23.4-31.9) 586 24.0 (20.3-28.1) Severely food insecure 266 30.0 (24.3-36.3) 366 30.3 (25.7-35.5)

Philhealth membership of hh head No 481 25.8 (21.9-30.1) 433 24.6 (20.2-29.5) Yes 670 23.7 (20.6-27.1) 989 24.5 (21.8-27.5)

4Ps membership of hh No 617 23.6 (20.3-27.3) 1,028 23.9 (21.1-26.9) Yes 345 26.8 (22.1-32.0) 432 26.4 (22.4-30.8)

Place of residence Rural 665 25.2 (21.9-28.9) 907 24.2 (21.2-27.5) Urban 519 24.3 (20.8-28.3) 584 25.2 (21.6-29.1)

Wealth index Poorest 334 30.0 (25.0-35.7) 386 30.0 (24.8-35.7) Poor 270 26.5 (21.2-32.5) 373 24.4 (20.0-29.3) Middle 239 24.7 (19.4-31.1) 311 24.7 (20.1-29.9) Rich 170 25.9 (19.8-33.2) 241 22.7 (17.6-28.8) Richest 161 14.4 (9.5-21.2) 174 19.5 (13.9-26.8)

Ever exposed (within 6m prior to survey (PTS) No 816 24.1 (21.2-27.1) 1,123 24.9 (22.2-27.8) Yes 368 26.1 (21.4-31.5) 368 24.0 (19.4-29.3)

Exposed 1 month PTS No 1,039 24.1 (21.5-27.0) 1,225 25.0 (22.4-27.7) Yes 145 29.7 (22.5-38.1) 266 23.2 (17.8-29.7)

Exposed 2 months PTS No 1,046 24.3 (21.7-27.1) 1,375 24.8 (22.3-27.4) Yes 138 28.6 (21.1-37.5) 116 22.7 (15.7-31.6)


Exposed 4 months PTS No 1,130 24.7 (22.2-27.5) 1,338 24.6 (22.1-27.2)

66

Yes 54 25.5 (17.1-36.1) 153 26.1 (19.0-34.7) Exposed 5 months PTS

No 1,026 25.1 (22.4-27.9) 1,309 24.8 (22.2-27.5) Yes 158 23.5 (17.2-31.3) 182 23.9 (17.7-31.5)


Full Model of Factors Associated with being Nutritionally At-Risk among Pregnant Women: Philippines, 2013 and 2015

Before controlling for the effects of other variables, the factors significantly

associated with being nutritionally at-risk among pregnant women included availment

of prenatal checkup in 2013 and in 2015 included age, maternal status and exposure of

3 months and exposure of 4 months prior to survey.

Table 32. Unadjusted odds ratio of factors associated with being nutritionally at-risk among pregnant women: Philippines, 2013


Wald’s Test OR (95% CI) p-value Wald’s


Age of pregnant woman 0.1957 0.0033

less than 20y 1.40 (0.84-2.32) 0.1960 1.98 (1.26-3.11) 0.0030 20y and above reference reference

Ethnicity of pregnant woman 0.5774 0.5577




Yes 1.18 (0.75-1.85) 0.4690 1.36 (0.85-2. 19) 0.2020 Civil status of pregnant woman 0.6844 0.1338

With partner reference reference

Without partner 1.12 (0.65-1.94) 0.6840 1.37 (0.91-2.06) 0.1340 Highest educational attainment of pregnant woman

0.6608 0.2994

No grade completed 0.18 (0.01-2.31) 0.1860 3.88 (0.96-15.71) 0.0580 Elementary level 0.76 (0.37-1.54) 0.4450 1.43 (0.74-2.75) 0.2820

High School level 0.87 (0.45-1.67) 0.6710 1.57 (0.87-2.80) 0.1310 Vocational level 1.41 (0.59-3.36) 0.4330 1.09 (0.45-2.65) 0.8560

College undergraduate 0.95 (0.47-1.93) 0.8980 1.19 (0.60-2.37) 0.6110 At least college graduate reference reference

Maternal status 0.0584 0.0010

First time pregnant 1.83 (1.10-3.05) 0.0200 2.16 (1.44-3.24) 0.0000

67

Pregnant mother with children 0-36mos 1.48 (0.93-2.33) 0.0950 1.51 (1.05-2.16) 0.0260

Pregnant mother with children >36 months reference reference

Availment of prenatal checkup 0.0133 0.9452

No 1.79 (1.13-2.84) 0.0130 1.02 (0.63-1.64) 0.9450


Stage in pregnancy 0.6097 0.8130

First trimester 1.18 (0.72-1.95) 0.5090 0.98 (0.64-1.51) 0.9290 Second trimester 1.20 (0.82-1.76) 0.3390 1.10 (0.79-1.52) 0.5880 Third trimester reference reference

Work of pregnant woman 0.9396 0.1272

No Occupation 0.97 (0.62-1.53) 0.9010 1.06 (0.71-1.58) 0.7630 Agricultural 0.93 (0.62-1.40) 0.7240 0.71 (0.50-1.01) 0.0590



1-3 members reference reference 4-6 members 1.27 (0.66-2.46) 0.4720 0.97 (0.61-1.54) 0.9070

7 or more members 1.56 (0.81-3.00) 0.1790 1.26 (0.81-1.97) 0.3000

Food security 0.5201 0.3427 Food secure reference reference

Mildly food insecure 0.93 (0.49-1.77) 0.8230 1.43 (0.87-2.34) 0.1600 Moderately food

insecure 1.33 (0.81-2.18) 0.2630 1.12 (0.71-1.75) 0.6310


No 1.29 (0.88-1.89) 0.1930 0.93 (0.65-1.33) 0.6930 Yes reference reference


No 0.76 (0.50-1.17) 0.2100 1.14 (0.78-1.65) 0.4990 Yes reference reference


Rural 0.82 (0.56-1.20) 0.3020 0.74 (0.54-1.01) 0.0560 Urban reference reference

Wealth index 0.3289 0.0646 Poorest 1.82 (0.86-3.86) 0.1160 2.70 (1.32-5.53) 0.0070 Poor 1.26 (0.59-2.69) 0.5580 1.91 (1.00-3.67) 0.0510

Middle 1.27 (0.61-2.65) 0.5220 1.57 (0.83-2.97) 0.1640 Rich 1.64 (0.81-3.30) 0.1680 1.30 (0.72-2.37) 0.3840

Richest reference reference Ever exposed (within 6m prior to survey (PTS) 0.1390 0.4098

No reference reference Yes 2.10 (0.79-5.60) 0.1390 0.76 (0.39-1.47) 0.4100



Exposed 2 months PTS 0.2711 0.2646 No reference reference

Yes 1.55 (0.71-3.36) 0.2710 1.46 (0.75-2.86) 0.2650


68

Yes 0.45 (0.19-1.09) 0.0770 0.26 (0.11-0.61) 0.0020



Exposed 5 months PTS 0.7447 0.5565 No reference reference Yes 0.71 (0.09-5.72) 0.7450 0.78 (0.34-1.80) 0.5560


Yes 0.78 (0.09-6.98) 0.8220 1.11 (0.29-4.18) 0.8810

Constant 0.09 (0.03-0.26) 0.0000 0.06 (0.03-0.16) 0.0000

Final Model of Factors Associated with being Nutritionally At-Risk among Pregnant Women: Philippines, 2013 and 2015

After controlling for the effects of other variables, the factors associated with being

nutritionally at-risk among pregnant women were age, maternal status, availment of

prenatal checkup and wealth index.

Pregnant women aged less than 20 years has increased odds of 56% of becoming

nutritionally at-risk compared to the older age group of 20 years and above. Being a

first time pregnant also increases the likelihood of being nutritionally at-risk at 83%

and having a child aged 0 to 36 months alongside being pregnant also is 1.80 times

more likely to be nutritionally at-risk. Those who did not avail of any prenatal

checkup were 1.49 times more likely to be nutritionally at-risk compared to those who

at least had one (1) visit. In terms of socioeconomic status the odds of becoming

nutritionally at-risk generally decreased as wealth improved. Those in the poorest

quintile were 2.32 times more likely to be nutritionally at-risk than those in the richest

quintile and those in the poor quintile were 1.92 times more likely to be nutritionally

at-risk than those in the richest quintile.

69

Table 33. Final model of factors associated with being nutritionally at-risk among pregnant women: Philippines, 2013



Age of pregnant woman 0.0306 less than 20y 1.56 (1.04-2.34) 0.0310

20y and above reference

Maternal status 0.0047 First time pregnant 1.83 (1.21-2.76) 0.0050

Pregnant mother with children 0-36mos 1.80 (1.22-2.65) 0.0030

Pregnant mother with children >36 months reference

Availment of prenatal checkup 0.0347 No 1.49 (1.03-2.14) 0.0350

Yes reference


Poor 1.92 (1.08-3.43) 0.0270

Middle 1.75 (0.98-3.10) 0.0570

Rich 2.15 (1.19-3.88) 0.0110

Richest reference

Constant 0.10 (0.06-0.17) 0.0000

Figure 12. Proportion and 95% CI of final model of factors associated with being nutritionally at-risk among pregnant women: Philippines, 2013

Age of pregnant woman

Maternal status

Availment of prenatal checkup

Wealth index

70

Preliminary analysis showed that the proportion of nutritionally at-risk pregnant

women were significantly higher among those less than 20 years of age, among first

time pregnant women, among those who did not avail of any prenatal checkup and

among those in the poorest quintile.

After controlling for the effects of other variables, findings reveal that the factors

significantly associated with being nutritionally at-risk among pregnant women for

reference year 2015 were age of pregnant woman, maternal status and wealth index,

similar with the final model for reference year 2013.

The odds of becoming nutritionally at-risk were 72% higher among those less than 20

years of age compared to the older age group of 20 years and above. First time

pregnant mothers had a two-fold odds of being nutritionally at-risk compared to

pregnant mothers with children more than 36 months and those pregnant with children

0 to 36 months were 1.51 times more likely to be nutritionally at-risk than those

pregnant with children 36 months and above.

Consistently, the likelihood of becoming nutritionally at-risk decreased as

socioeconomic status improved. Pregnant mothers belonging in the poorest quintile

were 2.08 times more likely to be nutritionally at-risk than those in the richest

quintile.

Table 34. Final model of factors associated with being nutritionally at-

risk among pregnant women: Philippines, 2015



Age of pregnant woman 0.0032 less than 20y 1.72 (1.20-2.46) 0.0030 20y and above reference

Maternal status 0.0011 First time pregnant 2.00 (1.38-2.88) 0.0000 Pregnant mother with children 0-36mos 1.51 (1.07-2.13) 0.0190

Pregnant mother with children >36 months reference

71


Poor 1.54 (0.97-2.47) 0.0700 Middle 1.42 (0.88-2.31) 0.1540

Rich 1.32 (0.80-2.17) 0.2700 Richest reference

Constant 0.13 (0.09-0.21) 0.0000

Figure 12. Proportion and 95% CI of final model of factors associated with being nutritionally at-risk among pregnant women: Philippines, 2015

Consistent with the 2013 results, the prevalence of being nutritionally at-risk among

pregnant was significantly higher among those less than 20 years of age, first time

pregnant and among those in the poorest quintile.

Profile of Chronic Energy Deficient Lactating Mothers: Philippines, 2013 and 2015

The profile of chronic energy lactating mothers are presented in the Table below.

Findings show that one (1) in every 10 lactating mothers in 2013 (12.5%) and in 2015

(13.6%) were chronic energy deficient.

Age of pregnant woman

Maternal status

Wealth index

72

CED among lactating mothers was significantly more prevalent among those in

severely food insecure households and among those in the poorest households.

In terms of exposure to typhoons and floods, generally, the prevalence of CED was

higher among those exposed for 1 to 4 months prior to survey for both reference

years. However, for longer exposure of 5 to 6 months prior to survey, the prevalence

of CED was higher among those unexposed than those who were exposed.

Table 35. Profile of CED lactating mothers: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI)

Philippines 2,605 12.5 (11.2-13.9) 4,005 13.6 (12.4-14.8) Age of lactating mother

less than 20y 222 12.4 (8.4-17.9) 309 11.4 (8.1-15.7) 20y and above 2,383 12.5 (11.1-14.0) 3,696 13.7 (12.5-15.0)

Ethnicity of lactating mother Non-IP 2,387 12.5 (11.1-13.9) 3,678 13.7 (12.5-15.0) IP 218 12.7 (8.6-18.4) 327 11.5 (7.6-17.1)

Presence of illness in past 2 weeks No 2,092 12.2 (10.8-13.8) 3,385 13.8 (12.5-15.1) Yes 511 13.5 (10.7-17.0) 475 13.6 (10.7-17.2)

Civil status of lactating mother With partner 2,372 12.0 (10.7-13.5) 3,577 13.4 (12.2-14.7) Without partner 232 17.2 (12.6-23.0) 427 15.1 (11.8-19.0)

Highest educational attainment of lactating mother No Grade Completed 48 15.9 (8.0-29.3) 75 18.7 (9.6-33.4) Elementary Level 577 14.6 (11.8-17.9) 898 13.8 (11.6-16.3) High School Level 1,374 13.0 (11.2-15.0) 2,101 13.8 (12.2-15.5) Vocational Level 102 8.8 (4.6-16.2) 210 15.2 (10.7-21.2) College Undergraduate 279 10.5 (7.3-15.0) 403 14.5 (11.1-18.7) Atleast College Graduate (ref) 217 7.0 (4.1-11.7) 318 8.1 (5.5-11.7)

Work of lactating mother No occupation 460 11.1 (8.3-14.6) 495 17.7 (14.2-21.7) Agricultural 896 13.7 (11.5-16.2) 1,424 12.9 (11.1-15.1) Non-agricultural 1,249 12.3 (10.4-14.4) 2,070 12.9 (11.4-14.5)

Household size 1-3 members 72 16.6 (9.2-28.2) 175 12.8 (8.4-19.0) 4-6 members 650 11.2 (8.8-14.2) 1,201 11.8 (9.9-14.0) 7 or more members 1,883 12.8 (11.3-14.4) 2,629 14.4 (13.0-16.0)

Food security Food secure 442 8.4 (6.0-11.6) 757 9.9 (7.8-12.5) Mildly food insecure 363 11.6 (8.7-15.3) 444 14.2 (11.0-18.2) Moderately food insecure 1,159 12.9 (11.0-15.1) 1,586 13.2 (11.5-15.2) Severely food insecure 637 15.4 (12.4-18.8) 1,139 16.3 (14.0-18.8)

Philhealth membership of hh head No 1,083 12.4 (10.4-14.7) 1,095 16.6 (14.2-19.3) Yes 1,465 12.0 (10.4-13.8) 2,709 12.3 (11.0-13.7)

73

4Ps membership of hh No 1,401 12.2 (10.5-14.2) 2,605 13.6 (12.2-15.2) Yes 855 14.5 (12.3-17.1) 1,310 13.8 (11.8-16.0)

Place of residence Rural 1,562 13.5 (11.8-15.4) 2,581 13.1 (11.6-14.6) Urban 1,043 11.3 (9.3-13.5) 1,424 14.2 (12.4-16.3)

Wealth index Poorest 845 16.4 (13.9-19.3) 1,282 14.7 (12.6-17.1) Poor 627 14.4 (11.6-17.8) 1,040 12.5 (10.5-14.8) Middle 534 11.5 (9.0-14.7) 751 16.6 (13.6-20.0) Rich 364 10.3 (7.6-13.9) 547 11.6 (9.0-14.8) Richest 215 2.1 (0.8-5.2) 362 10.5 (7.7-14.2)

Ever exposed (within 6m prior to survey (PTS) No 1,683 12.4 (10.9-14.1) 2,936 12.8 (11.5-14.3) Yes 922 12.6 (10.3-15.3) 1,069 16.0 (13.6-18.6)

Exposed 1 month PTS No 2,202 12.2 (10.8-13.8) 3,245 12.9 (11.6-14.2) Yes 403 13.9 (10.5-18.3) 760 17.3 (14.5-20.4)






Full Model of Factors Associated with Chronic Energy Deficiency among Lactating Mothers: Philippines, 2013 and 2015

Before controlling for the effects of other variables, the factors significantly

associated with CED among lactating mothers included civil status and wealth index

for reference year 2013 and Philhealth membership for reference year 2015.

74

Table 36. Unadjusted odds ratio of factors associated with meeting REI: Philippines, 2013


Wald’s Test OR (95% CI) p-value Wald’s


Age of lactating mother 0.2023 0.6726 less than 20y 0.70 (0.40-1.22) 0.2020 0.91 (0.58-1.42) 0.6730 20y and above reference

Ethnicity of lactating mother 0.3548 0.1771

Non-IP reference IP 0.77 (0.44-1.34) 0.3550 0.71 (0.43-1.17) 0.1770

Presence of illness in past 2 weeks 0.2220 0.8451

No reference Yes 1.22 (0.89-1.67) 0.2220 1.03 (0.76-1.39) 0.8450

Civil status of lactating mother 0.0212 0.5731

With partner reference Without partner 1.70 (1.08-2.66) 0.0210 1.10 (0.79-1.54) 0.5730

Highest educational attainment of lactating mother

0.9548 0.3825

No Grade Completed 0.74 (0.21-2.54) 0.6290 1.80 (0.70-4.63) 0.2190 Elementary Level 1.03 (0.50-2.11) 0.9300 1.39 (0.81-2.37) 0.2300 High School Level 0.97 (0.50-1.87) 0.9260 1.52 (0.93-2.48) 0.0940 Vocational Level 0.67 (0.23-1.95) 0.4650 1.83 (1.02-3.28) 0.0430 College Undergraduate 0.93 (0.44-1.96) 0.8440 1.55 (0.90-2.67) 0.1130 Atleast College Graduate

(ref) reference

Work of lactating mother 0.1571 0.0567 No occupation 0.69 (0.46-1.05) 0.0850 1.44 (1.05-1.97) 0.0230 Agricultural 0.79 (0.57-1.09) 0.1460 0.99 (0.78-1.26) 0.9330 Non-agricultural reference

Household size 0.2066 0.1535 1-3 members reference 4-6 members 0.57 (0.26-1.27) 0.1690 0.79 (0.46-1.35) 0.3850 7 or more members 0.73 (0.33-1.58) 0.4220 0.99 (0.59-1.67) 0.9800

Food security 0.9725 0.3168 Food secure reference Mildly food insecure 1.14 (0.65-1.99) 0.6470 1.39 (0.91-2.13) 0.1300 Moderately food

insecure 1.11 (0.67-1.83) 0.6830 1.26 (0.89-1.79) 0.1950


No 1.00 (0.71-1.42) 0.9790 1.00 (0.77-1.30) 0.9910 Yes reference

4Ps membership of hh 0.9792 0.9908 No 0.96 (0.70-1.32) 0.8210 1.34 (1.05-1.71) 0.0170 Yes reference

Place of residence 0.9295 0.0974 Rural 1.02 (0.73-1.42) 0.9290 0.81 (0.64-1.04) 0.0970 Urban reference


Poorest 12.76 (3.26-

50.04) 0.0000 1.60 (0.92-2.78) 0.0970

75

Poor 8.90 (2.30-34.40) 0.0020 1.13 (0.68-1.88) 0.6330 Middle 8.48 (2.25-31.93) 0.0020 1.49 (0.90-2.46) 0.1170 Rich 7.45 (1.99-27.96) 0.0030 1.01 (0.62-1.66) 0.9710 Richest reference


No reference Yes 0.76 (0.37-1.56) 0.4490 1.38 (0.81-2.35) 0.2410

Exposed 1 month PTS 0.7559 0.6173 No reference Yes 1.12 (0.54-2.34) 0.7560 1.15 (0.67-1.96) 0.6170

Exposed 2 months PTS 0.2780 0.6398 No reference Yes 1.41 (0.76-2.62) 0.2780 0.86 (0.45-1.63) 0.6400





Constant 0.02 (0.00-0.11) 0.0000 0.06 (0.03-0.14) 0.0000

Final Model of Factors Associated with Chronic Energy Deficiency among Lactating Mothers: Philippines, 2013 and 2015

After controlling for the effect of other variables, the factors significantly associated

with chronic energy deficiency among lactating mothers include civil status and

wealth index. Lactating mothers without partners had a 60% increased odds of

becoming CED than lactating mothers with partners. Similar with other nutrition

outcomes, the odds of becoming CED decreases as wealth improves. Lactating

mothers in the poorest quintile had almost a 10-fold odds of being CED compared to

those in the richest quintile. Those in the poor had an 8-fold odds of becoming CED,

those in the middle with a 6-fold odds and those in the rich with a 5-fold odds of

becoming CED compared to their richest counterparts.

76

Table 37. Final model of factors associated with chronic energy deficiency among lactating mothers: Philippines, 2013

GOODNESS-OF-FIT: 1.0000 Final model Wald’s Test OR (95% CI) p-value Civil status of lactating mother 0.0193

With partner reference Without partner 1.60 (1.08-2.38) 0.0190

Wealth index 0.0000 Poorest 9.52 (3.62-25.04) 0.0000 Poor 8.07 (2.99-21.79) 0.0000 Middle 6.23 (2.31-16.76) 0.0000 Rich 5.43 (2.00-14.73) 0.0010 Richest reference

Constant 0.02 (0.01-0.05) 0.0000

Figure 13. Proportion and 95% CI of final model of factors associated with chronic energy deficiency among lactating mothers: Philippines, 2013

The figure shows that the prevalence of CED among lactating mothers was higher

among those without partners although differences were not statistically significant.

However, it can be seen clearly that the proportion of CED lactating mothers were

highest among those in the poorest and poor quintiles.

After controlling for the effects of other variables, the factors associated with chronic

energy deficiency among lactating mothers were work, food security status, Philhealth

Wealth index

Civil status of lactating mother

77

membership of household help and ever exposure to typhoons/floods within 6 months

prior to survey.

Lactating mothers with no occupation were 1.45 times more likely to be CED than

those engaged with non-agricultural work. Having agricultural work, on the other

hand, has a a protective effect in terms of being CED compared to those engaged in

non-agricultural work.

In terms of food security, the odds of becoming CED was highest among those in

severely food insecure households with an OR of 1.73. Those in the mildly food

insecure and moderately food insecure households were 56% and 43% more likely to

become CED compared to those in food secure households. Non-membership of the

household head in Philhealth increased the odds of becoming CED by 37%. Lactating

mothers who were exposed to typhoons/floods within 6 months prior to data

collection were 1.29 times more likely to be CED than those who were not.

Table 38. Final model of factors associated with chronic energy deficiency among lactating mothers: Philippines, 2015


Work of lactating mother 0.0341 No occupation 1.45 (1.07-1.96) 0.0170

Agricultural 0.99 (0.78-1.24) 0.9080

Non-agricultural reference






Yes reference


Yes 1.29 (1.02-1.62) 0.0330

Constant 0.09 (0.06-0.12) 0.0000

78

Figure 14. Proportion and 95% CI of final model of factors associated with chronic energy deficiency among lactating mothers: Philippines, 2015

Preliminary analysis showed that the prevalence of CED among lactating mothers

were significantly higher among those who were severely food insecure. IT was also

higher among those with no occupation, those with no Philhealth membership of

household head and among those exposed to typhoons/calamities within 6 months

prior to data collection, although differences were not statistically significant.

B. DROUGHT

Profile of Households Meeting the Recommended Energy Intake (REI): Mindanao, 2015

In Mindanao, three (3) in 10 (31.1%) households met the REI. Households that met

the REI were predominantly headed by females, by those in the 60 and above age

group, by those without partners, those engaged in agricultural work and those who

Food security

Work of lactating mother

Philhealth membership of HH head


79

work at home. There were also relatively a higher proportion of households who were

non- 4Ps recipients who met the REI.

The proportion of households meeting REI decreased as level of food insecurity

increased and as household size increased. There were also more households meeting

REI in rural areas. No definite pattern can be seen in terms of socioeconomic status,

there was no significant pattern on the proportion of households meeting REI.

The proportion of households meeting REI was higher among those not exposed to

drought in 2015, stretching to 2016.

Table 39. Profile of households meeting the REI: Mindanao, 2015

Characteristics 2015

n % (95% CI)

Mindanao 2,803 31.1 (28.7-33.6)

Sex of household (hh) head

Male 2,286 30.7 (28.1-33.4)

Female 517 32.8 (28.6-37.4)

Age of hh head

Less than 30y 176 36.3 (28.5-45.0)

30-39.9y 511 30.6 (26.4-35.1)

40-49.9y 742 24.7 (20.9-28.9)

50-59.9y 679 30.1 (26.4-34.0)

More than 60y 695 38.0 (34.3-41.9)

Civil status of hh head

With partner 2,147 30.3 (27.6-33.1)

Without partner 654 33.8 (29.7-38.1)

Highest educational attainment of hh head

No grade completed 179 30.6 (24.0-38.1)

Elementary level 1,269 33.7 (30.4-37.2)

High School level 838 27.7 (24.5-31.2)

Vocational level 104 31.0 (22.5-41.1)

College undergraduate 169 30.7 (23.2-39.5)

At least college graduate 243 30.3 (23.4-38.2)

Work of hh head

Agricultural work 1,081 35.1 (31.1-39.3)

Non-agricultural work 1,245 26.7 (24.1-29.5)

Place of work of hh head

At home 265 35.1 (28.0-42.9)

Local away from home 2,029 29.8 (27.2-32.6)

Abroad 32 33.4 (21.4-48.0)

Philhealth membership of hh head No 691 30.4 (26.3-34.8)

80

Yes 1,963 30.2 (27.6-33.0)


No 1,870 33.0 (30.4-35.6)

Yes 911 26.3 (22.2-30.7)

Food security

Food secure 827 36.8 (32.6-41.1)

Mildly food insecure 309 33.1 (27.6-39.1)

Moderately food insecure 971 27.5 (24.3-31.0)

Severely food insecure 669 26.8 (22.7-31.3)

Household size

1-3 members 760 43.6 (40.3-47.0)

4-6 members 1,346 28.9 (25.7-32.3)

7 or more members 697 21.4 (17.8-25.4)

Place of residence

Rural 2,035 32.2 (29.3-35.3)

Urban 768 28.4 (24.5-32.8)

Wealth index

Poorest 1,064 31.4 (27.2-35.9)

Poor 697 28.3 (24.3-32.7)

Middle 449 30.5 (26.5-34.8)

Rich 306 32.2 (27.1-37.9)

Richest 270 33.9 (26.9-41.7)

Exposed in 2015

No 174 38.9 (22.3-58.6)

Yes 2,629 30.6 (28.3-32.9)

Exposed in 2016

No 94 45.5 (23.4-69.5)

Yes 2,709 30.6 (28.2-33.0)

Exposed in 2015 & 2016

No 193 39.5 (23.8-57.8)

Yes 2,610 30.5 (28.2-32.9)

Palay Production

Meeting 882 -10632.2 (-12731.8--8532.6)

Full Model of Factors Associated with Households Meeting the Recommended Energy Intake (REI): Mindanao, 2015

Before adjusting for the effects of other variables, the factors significantly associated

with meeting the REI of households include household size, food security, and work

of household head (Wald’s test: <0.05).

Table 40. Unadjusted odds ratio of factors associated with meeting REI: Mindanao, 2015 Characteristics 2015

81

Wald’s Test OR (95% CI) p-value

Sex of household (hh) head 0.7185 Male reference

Female 0.93 (0.63-1.38) 0.7180

Age of hh head 0.4996 Less than 30y 0.99 (0.58-1.67) 0.9560

30-39.9y 0.96 (0.68-1.35) 0.8040

40-49.9y 0.79 (0.54-1.14) 0.2080

50-59.9y 1.00 (0.71-1.42) 0.9980

More than 60y reference

Civil status of hh head 0.5348 With partner reference

Without partner 0.89 (0.61-1.30) 0.5350

Highest educational attainment of hh head 0.1216 No grade completed 1.11 (0.56-2.18) 0.7660





At least college graduate reference


4-6 members 0.52 (0.39-0.69) 0.0000

7 or more members 0.35 (0.25-0.48) 0.0000



Place of work of hh head 0.5359 At home 1.17 (0.84-1.64) 0.3390

Local away from home reference

Abroad 1.16 (0.46-2.92) 0.7520





Philhealth membership of hh head 0.8227 No 0.97 (0.76-1.25) 0.8230 Yes reference


Yes reference


82

Rural 1.15 (0.86-1.55) 0.3360

Urban reference


Poor 0.74 (0.45-1.23) 0.2430

Middle 0.79 (0.48-1.30) 0.3470

Rich 0.74 (0.45-1.20) 0.2120

Richest reference

Exposed in 2015 0.2967 No reference

Yes 2.35 (0.47-11.78) 0.2970

Exposed in 2016 0.3823 No reference

Yes 0.41 (0.05-3.10) 0.3820

Exposed in 2015 & 2016 0.4925 No reference

Yes 0.2773 0.49 (0.06-3.77) 0.4930

Palay Production 1.00 (1.00-1.00) 0.2770

Constant 1.51 (0.26-8.65) 0.6380

Final Model of Factors Associated with Households Meeting the Recommended Energy Intake (REI): Mindanao, 2015

After controlling for the effects of other variables, the factors significantly associated with meeting REI included household size, work of household head and food security. Households with 4 to 6 members were 49% less likely to meet the REI compared to smaller size households with 1 to 3 members. Furthermore, households with 7 or more members were 65% less likely to meet the REI compared to smaller size households with 1 to 3 members.

Those engaged in agricultural work were 1.69 times more likely to meet the REI compared to those engaged in non-agricultural work.

In terms of food security, the odds of meeting REI decreases as food insecurity level increases. The likelihood of households meeting REI was 15%, 29% and 33% less for mildly, moderately and severely food insecure households compared to food secure households.

Table 41. Final model of factors associated with meeting REI: Mindanao, 2015



Household size 0.0000 1-3 members 4-6 members 0.51 (0.41-0.63) 0.0000

83

7 or more members 0.35 (0.28-0.46) 0.0000


Non-agricultural work Food security 0.0270

Food secure Mildly food insecure 0.85 (0.61-1.18) 0.3390



Constant 0.76 (0.61-0.95) 0.0160

Profile of Stunted Children 0-59 Months Old: Mindanao, 2015

In Mindanao, more than one-third (36.0%) of children under five years of age were stunted. Prevalence of stunting was high among children belonging to the 36 to 47 and 48 to 59-month age group, among IPs and among those households engaged in agricultural work.

The proportion of stunted children was also relatively higher among households with 4 to 6 members and among moderately, severely food insecure households, rural dwellers and among those in the poorest and poor wealth quintile. Those non-members of Philhealth and 4Ps recipients had higher proportions of stunting.

Table 42. Profile of stunted children 0-59 months old: Mindanao, 2015


n % (95% CI)

Mindanao 3,696 36.0 (34.3-37.8) Sex of child

Male 1,904 36.1 (33.7-38.6) Female 1,792 36.0 (33.7-38.3)

Age of child 0-11m 843 14.2 (12.0-16.9)

12-23m 809 40.8 (37.3-44.4) 24-35m 675 42.4 (38.3-46.6) 36-47m 702 43.5 (39.7-47.3) 48-59m 667 43.0 (39.2-46.8)

Ethnicity of child Non-IP 3,237 34.8 (32.9-36.6)

IP 459 45.0 (40.4-49.7) Presence of illness in past 2 weeks

No 2,992 36.3 (34.4-38.2) Yes 659 34.8 (31.0-38.7)

Work status of hh head No occupation 449 30.9 (26.2-36.1)

Agricultural 1,574 40.6 (38.2-43.1) Non-agricultural 1,661 33.3 (30.6-36.1)

84

Household size 1-3 members 254 31.1 (25.5-37.3) 4-6 members 1,320 38.4 (35.3-41.6) 7 or more members 2,122 35.1 (33.1-37.2)

Food security Food secure 735 28.5 (25.0-32.3)

Mildly food insecure 339 34.1 (29.2-39.4) Moderately food insecure 1,535 35.7 (33.2-38.3) Severely food insecure 1,074 42.2 (39.4-45.1)


Yes 2,592 34.7 (32.6-36.8) 4Ps membership of hh

No 2,250 32.7 (30.5-35.0) Yes 1,430 41.5 (39.0-44.1)

Place of residence Rural 2,608 38.3 (36.3-40.2)

Urban 1,088 31.1 (27.7-34.7) Wealth index

Poorest 1,665 44.6 (42.1-47.1) Poor 1,007 36.0 (33.0-39.2) Middle 480 28.5 (24.5-32.9) Rich 306 20.0 (16.2-24.5) Richest 227 13.2 (9.6-17.7)

Exposed in 2015 No 226 36.4 (29.8-43.4) Yes 3,470 36.0 (34.2-37.8)

Exposed in 2016 No 170 35.5 (27.0-44.9) Yes 3,526 36.1 (34.3-37.9)

Exposed in 2015 & 2016 No 288 34.9 (29.0-41.4) Yes 3,408 36.1 (34.4-38.0)

Palay Production

Meeting 3,696 -10,116.4 (-11,070.0--9,162.8)

Full Model of Factors Associated with Stunting among Children 0-59 Months: Mindanao, 2015

Before controlling for the effect of other variables, the factors significantly associated

with stunting among children in Mindanao included age of child and wealth index.

85

Table 43. Unadjusted odds ratio of factors associated with stunting among children

0-59 months: Mindanao, 2015



Sex of child 0.4006

Male

Female 0.94 (0.80-1.09) 0.4010

Age of child 0.0000 0-11m 12-23m 4.36 (3.29-5.79) 0.0000

24-35m 4.71 (3.38-6.56) 0.0000

36-47m 5.12 (3.89-6.74) 0.0000

48-59m 4.78 (3.59-6.36) 0.0000

Ethnicity of child 0.0501 Non-IP IP 1.25 (1.00-1.56) 0.0500

Presence of illness in past 2 weeks 0.2255 No

Yes 0.88 (0.72-1.08) 0.2260

Work status of hh head 0.9947 No Occupation 1.01 (0.75-1.37) 0.9280

Agricultural 1.00 (0.83-1.20) 0.9790

Non-agricultural Household size 0.1106

1-3 members 4-6 members 1.41 (1.02-1.95) 0.0360

7 or more members 1.32 (0.97-1.79) 0.0740

Food security 0.2660 Food secure Mildly food insecure 0.96 (0.70-1.32) 0.8250




Yes 4Ps membership of hh 0.1278

No 0.86 (0.71-1.04) 0.1280

Yes Place of residence 0.6143

Rural 1.05 (0.86-1.29) 0.6140

Urban Wealth index 0.0000

Poorest 5.69 (3.64-8.90) 0.0000

Poor 3.85 (2.49-5.97) 0.0000

Middle 2.86 (1.84-4.43) 0.0000

Rich 1.70 (1.04-2.76) 0.0340

Richest Exposed in 2015 0.0911

86

No Yes 0.62 (0.36-1.08) 0.0910

Exposed in 2016 0.8088 No Yes 0.93 (0.51-1.69) 0.8090

Exposed in 2015 & 2016 0.0501 No

Yes 2.08 (1.00-4.34) 0.0500

Palay Production 0.6256 1.00 (1.00-1.00) 0.6260

Constant 0.03 (0.01-0.06) 0.0000

Final Model of Factors Associated with Stunting among Children 0 to 59 Months: Mindanao, 2015

After controlling for the effect of other variables, the factors associated with stunting among children in Mindanao included age of child, household size, wealth index and exposure to drought in 2015 stretching to 2016.

The odds of becoming stunted were highest among those in the 36 to 47-month age group with 4.96. Households with 4 to 6 members had a 50% increased odds and those with 7 or more members had a 45% increased odds of becoming stunted compared to the smaller size households with 1 to 3 members. Moreover, compared to the richest quintiles, those in the poorest quintile were 5.81 times more likely and those in the poor quintile 3.92 times more likely to be stunted. Exposure to drought in 2015 to 2016 increased the odds of becoming stunted by 36%.

Table 44. Final model of factors associated with stunting among children 0 to 59

months: Mindanao, 2015 GOODNESS-OF-FIT: 0.9534


Age of child 0.0000

0-11m reference

12-23m 4.32 (3.32-5.61) 0.0000

24-35m 4.50 (3.30-6.12) 0.0000

36-47m 4.96 (3.80-6.48) 0.0000

48-59m 4.64 (3.55-6.08) 0.0000

Household size 0.0376

1-3 members reference

4-6 members 1.50 (1.09-2.07) 0.0140

7 or more members 1.45 (1.08-1.94) 0.0130

Wealth index 0.0000

Poorest 5.81 (3.99-8.46) 0.0000

87

Poor 3.92 (2.66-5.77) 0.0000

Middle 2.66 (1.77-3.99) 0.0000

Rich 1.68 (1.05-2.69) 0.0300

Richest reference

Exposed in 2015 & 2016 0.0249

No reference

Yes

1.36 (0.2-2.3) 0.0250

Constant 0.02 (0.0- -12.9) 0.0000

Profile of Wasted Children 0-59 Months Old: Mindanao, 2015

Findings reveal that in Mindanao, about 8 in 100 children 0 to 59 months old were wasted. Wasting was prevalent among males, among those in the 0 to 11 month age group, among IP children and among those who were ill for the previous 2 weeks before the survey. In terms of household-level characteristics, the proportion of wasting was high among those headed by households with no occupation, with 7 or more members, with no Philhealth membership and among 4P-recipient households. Wasting prevalence was also high in rural areas and among those in the poorest and poor quintiles.

Table 45. Profile of wasted children 0-59 months old: Mindanao, 2015


n % (95% CI)

Mindanao 3,697 7.5 (6.7-8.4) Sex of child

Male 1,906 8.1 (7.0-9.4) Female 1,791 6.8 (5.7-8.3)

Age of child

0-11m 844 12.1 (9.3-15.5) 12-23m 809 9.3 (7.2-12.0) 24-35m 675 6.5 (5.0-8.5) 36-47m 702 4.9 (3.5-6.7) 48-59m 667 3.4 (2.2-5.1)

Ethnicity of child

Non-IP 3,238 7.3 (6.4-8.3) IP 459 8.6 (6.2-12.0)

Presence of illness in past 2 weeks No 2,994 7.3 (6.3-8.3) Yes 659 8.5 (6.6-10.8)


No occupation 449 8.7 (6.1-12.3) Agricultural 1,575 7.7 (6.5-9.0) Non-agricultural 1,661 7.0 (5.9-8.3)

Household size

1-3 members 254 6.0 (3.6-9.7) 4-6 members 1,320 7.1 (5.8-8.7)

88

7 or more members 2,123 7.9 (6.7-9.4) Food security

Food secure 736 6.8 (5.0-9.0) Mildly food insecure 340 6.8 (4.4-10.2) Moderately food insecure 1,535 8.2 (6.7-9.9) Severely food insecure 1,073 7.3 (5.9-8.9)

Philhealth membership of hh head No 978 8.1 (6.5-10.1) Yes 2,593 7.1 (6.2-8.2)

4Ps membership of hh No 2,250 7.3 (6.3-8.5) Yes 1,431 7.6 (6.3-9.1)

Place of residence

Rural 2,609 7.8 (6.8-9.0) Urban 1,088 6.8 (5.4-8.6)

Wealth index

Poorest 1,666 8.4 (7.1-10.0) Poor 1,007 7.9 (6.4-9.7) Middle 480 5.5 (3.6-8.2) Rich 306 4.8 (2.8-8.2) Richest 227 6.9 (4.0-11.6)

Exposed in 2015 No 225 14.7 (10.1-21.0) Yes 3,472 6.8 (6.1-7.7)

Exposed in 2016 No 170 12.7 (8.4-18.7) Yes 3,527 7.2 (6.4-8.2)

Exposed in 2015 & 2016 No 287 14.6 (10.4-20.0) Yes 3,410 6.8 (6.0-7.6)

Palay Production

Meeting 3,697 -9,855.0 (-11,447.0--8,263.1)

Full Model of Factors Associated with Wasting among Children 0-59 Months: Mindanao, 2015

Before controlling for the effects of other variables, the factors significantly associated with wasting among children 0 to 59 months included age of child and exposure to drought in 2015 and 2016.

89

Table 46. Unadjusted odds ratio of factors associated with wasting among children

0-59 months: Philippines, 2013



Sex of child 0.1784

Male

Female 0.83 (0.64-1.09) 0.1780

Age of child 0.0000

0-11m

12-23m 0.81 (0.51-1.29) 0.3800

24-35m 0.45 (0.29-0.70) 0.0000

36-47m 0.41 (0.26-0.63) 0.0000

48-59m 0.26 (0.15-0.45) 0.0000

Ethnicity of child 0.7386

Non-IP

IP 1.07 (0.73-1.55) 0.7390

Presence of illness in past 2 weeks 0.3067

No

Yes 1.19 (0.85-1.66) 0.3070

Work status of hh head 0.4273

No Occupation 1.11 (0.74-1.68) 0.6040

Agricultural 0.86 (0.64-1.15) 0.3100

Non-agricultural

Household size 0.4909

1-3 members

4-6 members 1.35 (0.74-2.45) 0.3300

7 or more members 1.40 (0.81-2.42) 0.2340 Food security

0.6463

Food secure




Philhealth membership of hh head 0.9656

No 0.99 (0.71-1.39) 0.9660

Yes

4Ps membership of hh 0.5003

No 1.12 (0.80-1.57) 0.5000

Yes


Rural 0.99 (0.73-1.35) 0.9560

Urban

Wealth index 0.0287

Poorest 1.49 (0.71-3.11) 0.2870

Poor 1.33 (0.64-2.77) 0.4410

Middle 0.86 (0.38-1.93) 0.7110

90

Rich 0.58 (0.24-1.40) 0.2280

Richest

Exposed in 2015 0.8627 No Yes 1.07 (0.51-2.25) 0.8630

Exposed in 2016 0.2673 No Yes 1.53 (0.72-3.24) 0.2670

Exposed in 2015 & 2016 0.0192 No Yes 0.32 (0.13-0.83) 0.0190


Constant 0.15 (0.05-0.44) 0.0010

After controlling for the effect of other variables, no final model was produced for wasting among children 0 to 59 months old.

Profile of Chronic Energy Deficient Elderly 60.0 Years and Over: Mindanao, 2015

In Mindanao, two (2) out of 10 elderly adults (19.7%) were chronic energy deficient. CED among elderly adults was higher among females, those in the 80 and older age group, among IPs and among those who were ill the previous 2 weeks prior to survey.

The proportion of CED elderly was higher among those with partners, among those with no grades completed and among those with normal blood pressure status. CED was also more prevalent among elderly adults living in households with 1 to 3 members and in agricultural households.

CED was significantly more prevalent among severely food insecure households, rural households and among households in the poorest wealth quintile.

In terms of exposure to drought, the proportion of CED was higher among those who were not exposed in 2015 and not exposed from 2015 to 2016 although differences were not statistically significant.

Table 47. Profile of chronic energy deficient elderly adults >60.0 years: Mindanao,

2015


n % (95% CI)

Philippines 2,997 19.7 (18.2-21.2)

Sex of elderly adult Male 1,523 19.2 (17.2-21.3)

Female 1,474 20.1 (18.1-22.3)

91

Age of elderly adult 60-64 1,006 13.2 (11.3-15.5)

65-69 807 18.9 (16.2-21.8)

70-74 547 26.0 (22.5-29.9)

75-79 368 21.3 (17.4-25.7)

80 and up 269 32.8 (26.8-39.4)

Ethnicity Non-IP 2,730 18.2 (16.8-19.7)

IP 267 34.2 (29.3-39.4)

Presence of illness in past 2 weeks No 2,557 18.7 (17.2-20.4)

Yes 386 25.4 (21.1-30.2)

Civil status of elderly adult With partner 1,517 17.6 (15.7-19.6)

Without partner 1,478 21.9 (19.7-24.2)

Highest educational attainment of elderly adult No grade completed 303 33.0 (27.5-39.0)

Elementary level 1,656 22.6 (20.6-24.6)

High School level 611 14.0 (11.5-17.0)

Vocational level 50 11.3 (4.4-26.0)

College undergraduate 144 5.3 (2.6-10.6)

At least college graduate 233 7.1 (4.5-11.0)

Blood pressure status of elderly adult Normal 1,786 23.0 (21.1-25.1)

Hypertensive 1,207 14.6 (12.8-16.6)

Household size 1-3 members 1,464 21.3 (19.1-23.6)

4-6 members 779 18.5 (15.8-21.6)

7 or more members 754 17.8 (14.9-21.1)

Work status of hh head No Occupation 1,230 20.8 (18.6-23.2)

Agricultural 917 23.1 (20.4-26.0)

Non-agricultural 844 14.3 (12.0-16.9)

Food security Food secure 1,026 14.6 (12.5-17.0)

Mildly food insecure 289 20.5 (16.0-25.8)

Moderately food insecure 1,049 22.0 (19.4-24.9)

Severely food insecure 616 24.1 (20.5-28.0)


Yes 2,248 18.4 (16.8-20.1)

4Ps membership of hh No 2,498 18.8 (17.3-20.4)

Yes 482 24.7 (20.8-29.1)

Place of residence Rural 2,023 22.3 (20.5-24.3)

Urban 974 14.6 (12.2-17.4)

Wealth index Poorest 1,023 30.2 (27.4-33.1)

92

Poor 742 20.0 (17.1-23.2)

Middle 525 14.7 (11.8-18.2)

Rich 359 10.4 (7.5-14.4)

Richest 335 6.2 (4.1-9.4)

Exposed in 2015 No 169 22.1 (16.7-28.6)

Yes 2,828 19.5 (18.0-21.0)

Exposed in 2016 No 110 19.3 (13.6-26.6)

Yes 2,887 19.7 (18.2-21.2)

Exposed in 2015 & 2016 No 213 23.7 (18.7-29.6)

Yes 2,784 19.3 (17.8-20.8)

Palay Production Meeting 2,963 -11,036 (-12,077--9,995)

Full Model of Factors Associated with Chronic Energy Deficiency among Elderly Adults 60.0 Years and Over: Mindanao, 2015

Before controlling for the effects of other variables, the factors significantly associated with CED among elderly adults included age, ethnicity, wealth index, exposure in 2015, exposure in 2016 and exposure in 2015 and 2016.

Table 48. Unadjusted odds ratio of factors associated with CED among elderly 60.0

years and over: Mindanao, 2015



Sex of elderly adult 0.8559 Male Female 0.98 (0.77-1.24) 0.86

Age of elderly adult 0.0000 60-64 65-69 1.46 (1.10-1.93) 0.0090

70-74 2.11 (1.60-2.78) 0.0000

75-79 1.49 (1.03-2.16) 0.0330

80 and up 2.96 (1.97-4.45) 0.0000

Ethnicity 0.0001 Non-IP IP 1.80 (1.34-2.42) 0.0000

Presence of illness in past 2 weeks 0.2057 No Yes 1.21 (0.90-1.63) 0.2060

Civil status of elderly adult 0.5193

93

With partner Without partner 1.09 (0.85-1.39) 0.5190

Highest educational attainment of elderly adult 0.2122 No grade completed 1.56 (0.78-3.14) 0.2080





At least college graduate Blood pressure status of elderly adult 0.0000

Normal Hypertensive 0.53 (0.43-0.65) 0.0000

Household size 0.8625 1-3 members 4-6 members 1.01 (0.76-1.34) 0.9420

7 or more members 0.93 (0.68-1.26) 0.6370

Work status of hh head 0.0689 No Occupation 1.40 (1.05-1.85) 0.0210

Agricultural 1.19 (0.89-1.60) 0.2470

Non-agricultural Food security 0.9325

Food secure Mildly food insecure 1.11 (0.75-1.65) 0.5890




Yes 4Ps membership of hh 0.5100

No 0.89 (0.64-1.25) 0.5100

Yes Place of residence 0.8189

Rural 1.03 (0.79-1.35) 0.8190

Urban Wealth index 0.0000

Poorest 4.82 (2.52-9.22) 0.0000

Poor 3.22 (1.71-6.07) 0.0000

Middle 2.54 (1.33-4.84) 0.0050

Rich 1.76 (0.87-3.57) 0.1150

Richest Exposed in 2015 0.0011

No Yes 4.28 (1.80-10.18) 0.0010

Exposed in 2016 0.0015 No Yes 2.60 (1.44-4.68) 0.0010

Exposed in 2015 & 2016 0.0007 No Yes 0.24 (0.10-0.54) 0.0010

94


Constant 0.01 (0.01-0.03)

Final Model of Factors Associated with Chronic Energy Deficiency among Elderly Adults 60.0 Years and Over: Mindanao, 2015

After controlling for the effects of other variables, the factors found to be significantly associated with chronic energy deficiency among elderly adults included ethnicity, blood pressure status, wealth index, exposure to drought in 2015, exposure to drought in 2016 and exposure to drought in both 2015 and 2016.

IP elderly adults were 1.75 times more likely to be CED compared to non-IP adults. Hypertensive adults were found to be 44% less likely to be CED than among those who were normotensive.

The odds of becoming CED decreased as wealth index improved. Those in the poorest quintiles had 6-fold likelihood of becoming CED compared to the richest quintile. Elderly adults in the poor quintile were 3.61 times more likely and those in the middle quintile were 2.62 times more likely to be CED compared to those in the richest quintile.

Elderly adults exposed to drought in 2015 were 5.42 times more likely to become CED compared to those who were not exposed. Furthermore, exposure to drought in 2016 increases the likelihood of becoming CED by 2.64 times compared to those who were not exposed. However, the odds of being a CED elderly adult was 82% less likely among those who were exposed in both 2015 and 2016.

Table 49. Final model of factors associated with CED among elderly adults: Mindanao, 2015


Ethnicity 0.0000

Non-IP reference IP 1.75 (1.35-2.26) 0.0000

Blood pressure status of elderly adults 0.0000 Normotensive reference

Hypertensive 0.56 (0.46-0.69) 0.0000

Wealth Index 0.0000 Poorest 6.06 (3.76-9.77) 0.0000

Poor 3.61 (2.22-5.88) 0.0000

Middle 2.62 (1.55-4.43) 0.0000

Rich 1.78 (0.96-3.30) 0.0690

Richest reference Expose in 2015 0.0000

Not Exposed reference

95

Exposed 5.42 (2.45-12.02) 0.0000

Expose in 2016 0.0007 Not Exposed reference Exposed 2.64 (1.51-4.60) 0.0010

Expose in 2015 & 2016 0.0000 Not Exposed reference Exposed 0.18 (0.08-0.38) 0.0000

Constant

0.03 (0.01-0.07) 0.0000

Summary and Conclusion

• Bivariate results showed that socioeconomic status, household size, food security status, sex, age, civil status, belonging to an indigenous group, exposure to typhoons, floods and drought had significant associations with nutrition outcomes.

• In full models, belonging to the poorest quintile, large and food insecure households increase the odds of stunting and wasting in children 0 to 59 months old, of chronic energy deficiency in elderly adults and lactating mothers and for pregnant women to become nutritionally at-risk

• Households who are engaged in agriculture were more likely to meet the REI. The effect of exposure to typhoons and floods on meeting the REI at household level was positive at three (3) months but was negative at 6 months.

• Among households in the Mindanao areas, exposure to drought in either the first quarter of 2015 or 2016 only, increased the likelihood of children below five years old to become stunted and among elderly adults to become CED

• However, elderly adults exposed to drought for both the first quarter of 2015 and the first quarter of 2016 made them less likely to become CED.

Recommendations

• Points of action on policy formulation and legislation may be derived in terms of response and assistance during extreme natural disasters among the most vulnerable groups.

• Findings of this study may be utilized to understand how natural disasters impact life of Filipinos leading to a more proactive stance in prioritizing the nutrition and health of Filipinos in the aftermath of calamities.

96

Appendices

Appendix 1. Proportion of households not meeting REI by exposure status to

climate change: Philippines, 2013, 2015 and 2016

Exposure 2013 2015

n % (95% CI) n % (95% CI) HOUSEHOLD

Typhoons/ Floods No 5,709 62.2 (59.2-65.1) 7,265 75.7 (73.5-77.8) Yes 2,883 37.8 (34.9-40.8) 2,660 24.3 (22.2-26.5)

Drought No - - 193 6.5 (4.3-9.6) Yes - - 2,610 93.5 (90.4-95.7)

CHILDREN 0-59 MONTHS OLD Typhoons/ Floods

No 6,654 63.7 (62.5-64.9) 9,344 76.7 (75.7-77.7) Yes 3,336 36.3 (35.1-37.5) 3,394 23.3 (22.3-24.3)

Drought No - - 293 9.7 (7.9-11.8) Yes - - 3,441 90.3 (88.2-92.1)

ELDERLY ADULTS > 60 YEARS Typhoons/ Floods

No 6,850 60.7 (59.6-61.8) 8,379 74.3 (73.4-75.2) Yes 3,958 39.3 (38.2-40.4) 3,430 25.7 (24.8-26.6)

Drought No - - 281 9.1 (7.0-11.7) Yes - - 3,421 90.9 (88.3-93.0)

PREGNANT WOMEN Typhoons/ Floods

No 827 65.8 (63.6-67.9) 1,123 77.5 (76.0-78.9) Yes 369 34.2 (32.1-36.4) 368 22.5 (21.1-24.0)

Drought No - - - - Yes - - - -

LACTATING MOTHERS Typhoons/ Floods

No 1,683 61.5 (59.5-63.4) 2,936 77.0 (75.7-78.2) Yes 922 38.5 (36.6-40.5) 1,069 23.0 (21.8-24.3)

Drought No - - - - Yes - - - -

97

Appendix 2. Proportion of households meeting REI by region and province:

Philippines, 2013 and 2015

Regions/ Provinces 2013 2015

n % (95% CI) n % (95% CI) Philippines NCR 774 28.0 (24.0-32.4) 750 30.9 (27.3-34.8)

NCR 1st District 115 35.3 (26.2-45.7) 94 21.3 (16.9-26.4) NCR 2nd District 284 23.8 (17.2-31.9) 240 29.4 (24.0-35.4) NCR 3rd District 197 30.2 (23.7-37.6) 208 42.0 (31.7-53.0) NCR 4th District 178 28.9 (23.1-35.5) 208 27.0 (21.7-33.1)

CAR 373 40.8 (35.0-46.9) 403 38.7 (33.2-44.6) Abra 61 42.1 (36.0-48.5) 73 55.8 (42.1-68.8) Benguet 153 38.0 (29.6-47.2) 146 30.7 (24.2-38.0) Ifugao 36 52.8 (32.4-72.3) 53 45.2 (36.4-54.3) Kalinga 53 32.0 (16.2-53.3) 54 46.7 (41.6-51.8) Mountain Province 44 50.0 (49.9-50.0) 42 35.8 (31.5-40.3) Apayao 26 42.4 (20.8-67.3) 35 31.4 (8.6-69.0)

ILOCOS REGION 538 34.3 (30.6-38.3) 575 32.6 (26.8-39.0) Ilocos Norte 79 25.5 (19.0-33.3) 88 26.5 (15.4-41.7) Ilocos Sur 79 54.5 (44.2-64.4) 81 47.1 (34.7-59.9) La Union 86 31.3 (23.0-41.0) 95 27.5 (16.5-42.0) Pangasinan 294 32.4 (27.9-37.3) 311 31.9 (24.5-40.5)

CAGAYAN VALLEY 466 38.4 (33.9-43.2) 522 35.1 (29.0-41.7) Cagayan 172 38.4 (29.1-48.8) 174 31.4 (25.1-38.5) Isabela 208 38.4 (32.7-44.5) 252 35.7 (26.7-45.8) Nueva Vizcaya 62 40.4 (33.5-47.7) 60 45.1 (25.1-66.8) Quirino 24 33.3 (28.8-38.2) 36 30.6 (30.6-30.6)

CENTRAL LUZON 706 31.7 (27.6-36.1) 803 28.5 (25.6-31.6) Bataan 42 42.8 (28.3-58.7) 37 26.7 (23.2-30.5) Bulacan 215 31.1 (26.4-36.1) 223 27.9 (23.3-32.9) Nueva Ecija 167 30.1 (16.4-48.6) 216 34.5 (26.3-43.8) Pampanga 147 35.0 (28.2-42.4) 156 29.9 (24.9-35.5) Tarlac 73 28.7 (18.8-41.1) 111 20.3 (15.1-26.8) Zambales 44 18.2 (14.6-22.5) 44 23.0 (19.0-27.5) Aurora 18 44.4 (44.4-44.4) 16 25.0 (25.0-25.0)

CALABARZON 799 33.7 (30.3-37.3) 979 29.3 (26.3-32.5) Batangas 159 32.2 (26.0-39.0) 183 32.7 (27.2-38.6) Cavite 198 39.2 (32.9-45.8) 220 28.4 (22.1-35.6) Laguna 160 33.5 (27.1-40.6) 200 28.9 (23.8-34.7) Quezon 137 30.0 (25.8-34.7) 183 34.3 (26.5-43.2) Rizal 145 30.8 (19.2-45.4) 193 23.0 (18.5-28.2)

MIMAROPA 394 31.9 (27.5-36.6) 268 28.0 (21.4-35.6) Marinduque 38 42.1 (25.4-60.9) 36 38.9 (21.0-60.4) Occidental Mindoro 66 33.3 (21.0-48.5) 57 25.6 (14.1-42.0) Oriental Mindoro 104 27.7 (20.1-36.9) 54 22.4 (9.7-43.8) Palawan 132 34.6 (27.7-42.2) 96 29.5 (23.7-36.0) Romblon 54 24.1 (16.2-34.2) 25 25.2 (6.8-61.0)

BICOL 483 32.6 (28.3-37.3) 724 30.8 (26.5-35.5) Albay 122 39.9 (32.9-47.3) 169 35.2 (25.3-46.6) Camarines Norte 34 20.6 (14.6-28.2) 75 31.7 (22.2-42.9) Camarines Sur 144 35.5 (27.7-44.1) 210 29.1 (21.7-37.8) Catanduanes 27 37.0 (11.3-73.0) 40 39.8 (24.8-56.9) Masbate 72 22.1 (12.7-35.5) 129 17.0 (11.8-23.9) Sorsogon 84 28.6 (18.5-41.3) 101 41.6 (32.1-51.9)

98

Cont. Appendix 2. Proportion of households meeting REI by region and province:



n % (95% CI) n % (95% CI) WESTERN VISAYAS 572 32.3 (28.1-36.9) 859 33.2 (29.1-37.6)

Aklan 41 36.5 (21.5-54.7) 68 35.4 (33.3-37.5) Antique 50 28.0 (22.9-33.8) 60 26.7 (17.2-39.0) Capiz 60 28.1 (21.3-36.0) 104 39.6 (34.0-45.4) Iloilo 176 35.8 (26.9-45.8) 290 37.1 (27.2-48.4) Negros Occidental 226 31.0 (25.5-37.0) 315 29.0 (24.6-33.9) Guimaras 19 31.6 (31.6-31.6) 22 27.3

CENTRAL VISAYAS 591 33.5 (28.9-38.5) 614 30.3 (25.8-35.1) Bohol 110 22.9 (18.9-27.3) 120 34.7 (19.8-53.3) Cebu 352 36.3 (29.1-44.0) 331 27.4 (22.3-33.2) Negros Oriental 119 35.3 (31.2-39.7) 152 35.3 (28.6-42.6) Siquijor 10 20.0 (20.0-20.0) 11 18.2

EASTERN VISAYAS 492 31.4 (27.5-35.6) 625 31.9 (28.7-35.3) Eastern Samar 62 27.5 (17.4-40.5) 70 32.5 (20.6-47.2) Leyte 204 36.8 (29.5-44.9) 303 33.6 (29.8-37.6) Northern Samar 60 33.3 (31.5-35.2) 72 35.0 (31.0-39.2) Western Samar 86 22.0 (18.9-25.4) 95 23.9 (20.9-27.1) Southern Leyte 50 30.0 (22.4-38.9) 52 27.4 (14.8-44.9) Biliran 30 26.7 (7.2-63.1) 33 36.6 (33.0-40.4)

ZAMBOANGA PENINSULA 375 31.1 (24.5-38.5) 373 34.1 (27.5-41.4) Zamboanga Del Norte 124 32.7 (17.8-52.1) 116 32.4 (21.8-45.0) Zamboanga Del Sur 174 34.0 (26.8-42.1) 174 36.1 (27.5-45.7) Zamboanga Sibugay 65 27.7 (23.1-32.9) 73 30.9 (14.7-53.9)

NORTHERN MINDANAO 439 26.9 (21.8-32.6) 516 26.7 (23.0-30.8) Bukidnon 116 29.3 (20.1-40.6) 155 27.4 (21.1-34.8) Camiguin 16 18.8 6 0.0 (0.0-0.0) Lanao Del Norte 90 28.9 (23.4-35.0) 118 27.0 (18.7-37.3) Misamis Occidental 74 33.8 (22.9-46.8) 67 31.4 (30.3-32.4) Misamis Oriental 143 21.1 (12.4-33.6) 170 24.9 (17.8-33.7)

DAVAO 500 29.8 (26.0-34.0) 472 27.2 (23.2-31.5) Davao (Davao Del Norte) 89 34.7 (28.5-41.5) 104 24.7 (18.6-32.0) Davao Del Sur 296 29.1 (23.9-35.0) 246 26.0 (19.7-33.4) Davao Oriental 42 21.4 (12.0-35.2) 50 36.0 (32.2-40.0) Compostela Valley 73 32.9 (29.6-36.4) 72 30.4 (26.8-34.2)

SOCCSKSARGEN 479 25.8 (21.5-30.6) 527 33.1 (27.7-39.1) North Cotabato 143 19.0 (13.7-25.5) 175 42.7 (35.8-50.0) South Cotabato 190 29.6 (23.4-36.7) 204 25.4 (16.7-36.7) Sultan Kudarat 98 30.2 (17.8-46.5) 96 39.5 (30.5-49.3) Sarangani 48 21.3 (9.2-42.0) 52 22.9 (9.0-47.1)

ARMM 249 30.4 (25.5-35.8) 457 38.4 (29.5-48.0) Basilan 28 11.0 (2.1-41.6) 45 22.9 (12.2-38.8) Lanao Del Sur 70 34.7 (28.7-41.2) 137 36.8 (23.5-52.6) Maguindanao 102 34.0 (26.4-42.6) 158 40.9 (28.4-54.8) Sulu 28 28.4 (19.0-40.2) 66 40.0 (15.2-71.3) Tawi-Tawi 33 15.2 (9.8-22.6) 61 45.6 (17.9-76.2)

CARAGA 362 35.8 (30.2-41.7) 458 28.2 (24.6-32.1) Agusan Del Norte 99 29.0 (23.1-35.6) 126 34.4 (28.2-41.2) Agusan Del Sur 95 21.1 (9.9-39.4) 106 30.7 (23.2-39.4) Surigao Del Norte 80 50.0 (36.6-63.4) 110 25.4 (18.6-33.7) Surigao Del Sur 88 47.0 (41.3-52.7) 116 22.0 (16.9-28.3)

99

Appendix 3. Proportion of stunted children 0-59 months by region and province:



n % (95% CI) n % (95% CI) Philippines NCR 905 30.9 (27.3-34.8) 1,066 23.1 (20.5-25.9)


CAR 382 38.7 (33.2-44.6) 538 35.2 (31.1-39.6) Abra 74 55.8 (42.1-68.8) 95 39.1 (26.5-53.3) Benguet 142 30.7 (24.2-38.0) 191 33.2 (27.0-40.0) Ifugao 39 45.2 (36.4-54.3) 74 39.3 (30.2-49.2) Kalinga 68 46.7 (41.6-51.8) 67 32.2 (21.3-45.5) Mountain Province 30 35.8 (31.5-40.3) 78 33.8 (24.4-44.6) Apayao 29 31.4 (8.6-69.0) 33 39.2 (26.3-53.8)




CALABARZON 997 29.3 (26.3-32.5) 1,242 25.6 (23.2-28.2) Batangas 173 32.7 (27.2-38.6) 227 19.9 (15.3-25.3) Cavite 252 28.4 (22.1-35.6) 286 26.5 (22.3-31.2) Laguna 206 28.9 (23.8-34.7) 278 21.8 (16.9-27.5) Quezon 192 34.3 (26.5-43.2) 201 30.6 (24.1-37.8) Rizal 174 23.0 (18.5-28.2) 250 30.4 (24.5-36.9)



100

Cont. Appendix 3. Proportion of stunted children 0-59 months by region and

province: Philippines, 2013 and 2015



Aklan 37 35.4 (33.3-37.5) 50 42.8 (30.9-55.7) Antique 47 26.7 (17.2-39.0) 52 32.6 (23.0-44.0) Capiz 62 39.6 (34.0-45.4) 118 37.4 (24.4-52.4) Iloilo 193 37.1 (27.2-48.4) 253 33.4 (28.3-38.8) Negros Occidental 233 29.0 (24.6-33.9) 340 40.4 (35.2-45.8) Guimaras 21 27.3 23 36.3 (14.9-65.1)

CENTRAL VISAYAS 657 30.3 (25.8-35.1) 821 33.9 (30.7-37.3) Bohol 128 34.7 (19.8-53.3) 142 32.9 (25.7-40.9) Cebu 394 27.4 (22.3-33.2) 474 32.0 (27.8-36.4) Negros Oriental 125 35.3 (28.6-42.6) 195 39.9 (32.9-47.3) Siquijor 10 18.2 10 33.3 (15.7-57.3)



NORTHERN MINDANAO 509 26.7 (23.0-30.8) 571 34.9 (30.1-39.9) Bukidnon 141 27.4 (21.1-34.8) 181 34.8 (29.1-41.0) Camiguin 12 0.0 (0.0-0.0) 11 18.2 (3.6-56.8) Lanao Del Norte 124 27.0 (18.7-37.3) 130 41.4 (32.3-51.1) Misamis Occidental 71 31.4 (30.3-32.4) 64 24.8 (15.8-36.8) Misamis Oriental 161 24.9 (17.8-33.7) 185 35.1 (24.8-47.0)





101

Appendix 4. Proportion of wasted children 0-59 months by region and province:



n % (95% CI) n % (95% CI) Philippines NCR 905 7.4 (5.8-9.4) 1,066 7.3 (6.0-9.0)


CAR 382 5.6 (3.5-8.9) 538 4.8 (3.5-6.6) Abra 74 9.5 (4.8-18.0) 95 10.5 (7.1-15.3) Benguet 142 6.8 (3.2-13.8) 191 2.6 (1.1-5.8) Ifugao 39 - 74 5.4 (2.3-12.4) Kalinga 68 1.5 (0.2-9.4) 67 2.8 (0.7-10.0) Mountain Province 30 6.6 (1.4-25.7) 78 4.0 (1.5-10.3) Apayao 29 7.1 (1.8-24.6) 33 9.1 (3.0-24.3)





MIMAROPA 411 10.7 (7.9-14.3) 418 10.2 (8.0-12.8) Marinduque 32 3.1 (0.4-20.6) 50 16.0 (9.4-26.0) Occidental Mindoro 86 15.4 (8.0-27.7) 72 7.1 (3.9-12.6) Oriental Mindoro 130 10.2 (5.9-17.2) 110 9.4 (5.5-15.6) Palawan 122 12.7 (7.9-19.7) 145 11.6 (8.1-16.3) Romblon 41 41 4.9 (1.2-18.8)


102

Cont. Appendix 4. Proportion of wasted children 0-59 months by region and




Aklan 37 8.4 (3.4-19.2) 50 10.3 (4.3-22.9) Antique 47 12.6 (4.8-29.2) 52 13.6 (5.9-28.3) Capiz 62 11.5 (5.6-22.0) 118 6.0 (2.0-16.7) Iloilo 193 10.1 (6.9-14.6) 253 6.0 (3.7-9.7) Negros Occidental 233 10.4 (7.4-14.6) 340 5.9 (3.7-9.3) Guimaras 21 - 23 4.6 (0.6-29.0)

CENTRAL VISAYAS 657 8.8 (6.8-11.3) 821 7.7 (5.9-10.0) Bohol 128 3.8 (1.1-12.5) 142 9.7 (5.8-15.7) Cebu 394 11.3 (8.6-14.8) 474 7.7 (5.4-10.9) Negros Oriental 125 5.6 (2.8-10.6) 195 6.3 (3.6-10.8) Siquijor 10 10.0 (1.0-54.1) 10 11.0 (0.9-63.8)



NORTHERN MINDANAO 509 7.2 (5.3-9.8) 571 4.5 (3.0-6.7) Bukidnon 141 6.0 (3.4-10.3) 181 3.9 (1.8-8.2) Camiguin 12 8.3 (1.0-44.1) 11 - Lanao Del Norte 124 9.7 (5.9-15.5) 130 4.9 (2.2-10.6) Misamis Occidental 71 4.4 (1.6-11.3) 64 4.6 (1.8-11.4) Misamis Oriental 161 7.5 (4.0-13.7) 185 5.1 (2.6-10.1)





103

Appendix 5. Proportion of chronic energy deficient elderly adults 60.0 years and

over by region and province: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI) Philippines NCR 1,004 9.3 (7.5-11.5) 1,146 12.1 (10.3-14.2)


CAR 411 13.0 (10.6-15.9) 668 14.0 (11.2-17.4) Abra 79 30.4 (22.0-40.2) 130 26.6 (19.5-35.1) Benguet 184 5.6 (3.2-9.4) 255 3.0 (1.4-6.6) Ifugao 25 - 84 19.6 (10.5-33.6) Kalinga 51 10.3 (6.3-16.5) 84 18.1 (9.3-32.4) Mountain Province 39 13.2 (6.2-26.1) 74 12.7 (6.3-23.8) Apayao 33 35.9 (26.4-46.7) 41 35.1 (21.5-51.8)



CENTRAL LUZON 914 18.1 (15.7-20.7) 1,319 16.6 (14.4-18.9) Bataan 62 11.1 (5.7-20.5) 94 17.9 (9.8-30.5) Bulacan 239 18.5 (14.0-24.0) 355 12.7 (9.0-17.5) Nueva Ecija 187 19.1 (13.8-25.8) 289 21.2 (16.3-27.1) Pampanga 216 16.1 (12.3-20.9) 279 14.9 (10.8-20.2) Tarlac 131 20.9 (16.4-26.2) 183 19.3 (14.2-25.6) Zambales 65 22.8 (12.7-37.5) 92 19.9 (13.2-29.0) Aurora 14 14.3 (2.4-53.4) 27 9.7 (1.8-38.6)




104

Cont. Appendix 5. Proportion of chronic energy deficient elderly adults 60.0 years

and over by region and province: Philippines, 2013 and 2015


n % (95% CI) n % (95% CI) WESTERN VISAYAS 843 25.3 (22.5-28.4) 1,261 24.8 (22.0-27.9)

Aklan 58 23.1 (16.0-32.1) 86 21.4 (15.0-29.6) Antique 63 33.0 (23.1-44.6) 81 34.0 (20.3-51.0) Capiz 97 24.0 (15.4-35.5) 156 15.6 (9.8-23.9) Iloilo 291 24.3 (20.0-29.2) 469 24.9 (20.9-29.3) Negros Occidental 309 24.8 (20.2-30.0) 451 27.3 (22.2-33.0) Guimaras 25 35.8 (14.7-64.3) 18 21.4 (5.7-55.1)

CENTRAL VISAYAS 800 18.8 (16.1-21.8) 1,016 21.0 (18.1-24.1) Bohol 184 20.7 (15.5-27.1) 246 20.7 (15.0-27.9) Cebu 445 18.4 (14.7-22.7) 524 17.5 (14.1-21.5) Negros Oriental 148 19.9 (13.9-27.7) 221 29.7 (23.3-37.0) Siquijor 23 4.4 (0.9-19.9) 25 25.6 (14.7-40.7)



NORTHERN MINDANAO 498 15.7 (12.7-19.4) 666 14.2 (11.3-17.8) Bukidnon 108 16.0 (8.8-27.1) 154 18.1 (11.7-26.9) Camiguin 24 15.6 (4.0-45.3) 23 33.5 (12.0-65.1) Lanao Del Norte 88 16.7 (10.9-24.9) 136 16.5 (11.1-23.9) Misamis Occidental 87 15.5 (8.5-26.4) 107 9.4 (4.0-20.7) Misamis Oriental 191 15.3 (11.3-20.3) 246 11.3 (7.4-16.8)





105

Appendix 6. Proportion of nutritionally at-risk pregnant women by region and





CAR 36 18.6 (7.7-38.5) 47 22.9 (14.5-34.2) Abra 5 38.9 (12.8-73.4) 3 66.1 (7.9-97.8) Benguet 9 - 17 26.4 (16.3-39.8) Ifugao 1 - 6 - Kalinga 12 18.3 (4.2-53.4) 5 40.5 (8.0-84.2) Mountain Province 3 28.9 (5.3-74.5) 10 10.5 (2.6-34.3) Apayao 6 34.1 (6.6-79.1) 6 15.6 (1.7-66.4)

ILOCOS REGION 73 20.9 (13.3-31.2) 72 16.2 (10.9-23.4) Ilocos Norte 5 34.3 (5.6-82.2) 2 - Ilocos Sur 16 13.4 (3.3-41.3) 4 - La Union 12 25.3 (10.3-49.9) 16 - Pangasinan 40 21.1 (10.7-37.3) 50 23.5 (16.0-33.2)


CENTRAL LUZON 107 16.5 (10.7-24.4) 123 23.2 (15.6-33.0) Bataan 7 - 5 - Bulacan 31 23.3 (11.1-42.6) 43 26.2 (12.3-47.5) Nueva Ecija 16 13.2 (4.3-34.1) 35 31.3 (17.3-49.8) Pampanga 31 13.6 (7.0-25.0) 16 31.1 (15.3-53.1) Tarlac 13 22.7 (6.5-55.3) 15 - Zambales 7 16.5 (2.2-63.2) 7 14.2 (2.1-55.7) Aurora 2 - 2 -


MIMAROPA 46 29.5 (17.4-45.3) 47 35.1 (24.8-47.1) Marinduque 7 24.1 (9.5-49.0) 7 43.1 (13.5-78.5) Occidental Mindoro 8 36.8 (10.9-73.5) 12 24.9 (7.9-56.2) Oriental Mindoro 13 14.2 (2.9-47.4) 9 57.6 (37.3-75.6) Palawan 10 38.6 (9.4-79.2) 15 36.1 (20.1-55.9) Romblon 8 39.4 (18.4-65.2) 4 -


106

Cont. Appendix 6. Proportion of nutritionally at-risk pregnant women by region

and province: Philippines, 2013 and 2015



Aklan 6 83.4 (34.0-98.0) 10 18.7 (3.9-56.8) Antique 4 25.2 (9.2-52.8) 6 48.1 (27.6-69.3) Capiz 7 26.0 (4.0-74.9) 12 45.0 (12.8-82.0) Iloilo 20 18.5 (7.7-38.1) 31 22.8 (11.7-39.6) Negros Occidental 27 35.6 (21.1-53.2) 31 39.6 (22.7-59.3) Guimaras 2 - 2 -

CENTRAL VISAYAS 78 22.4 (15.1-32.0) 113 27.3 (19.6-36.6) Bohol 16 23.3 (7.4-53.8) 23 16.8 (5.4-41.7) Cebu 45 19.6 (11.0-32.4) 68 32.3 (22.3-44.2) Negros Oriental 16 32.2 (19.2-48.7) 18 19.8 (5.5-51.0) Siquijor 1 - 4 25.0 (0.7-94.4)

EASTERN VISAYAS 60 14.1 (6.6-27.4) 99 23.1 (15.2-33.6) Eastern Samar 6 - 9 21.1 (4.1-62.4) Leyte 24 12.8 (2.9-41.9) 47 27.2 (15.2-43.9) Northern Samar 9 9.5 (1.2-48.4) 12 34.8 (10.8-70.2) Western Samar 16 22.8 (6.7-54.7) 17 9.7 (2.6-30.5) Southern Leyte 3 - 10 15.7 (2.8-54.2) Biliran 2 44.9 (3.2-95.3) 4 25.2 (2.2-83.3)


NORTHERN MINDANAO 69 25.1 (15.9-37.3) 63 15.2 (8.3-26.0) Bukidnon 16 10.2 (2.9-30.0) 21 15.2 (5.6-35.3) Camiguin 3 33.3 (3.0-89.0) 0 0.0 (0.0-0.0) Lanao Del Norte 16 43.9 (20.6-70.3) 15 19.3 (6.9-43.4) Misamis Occidental 9 35.4 (10.5-72.1) 4 - Misamis Oriental 25 17.6 (7.3-36.5) 23 15.2 (5.4-36.2)

DAVAO 47 32.0 (18.7-49.0) 58 25.1 (16.1-36.9) Davao (Davao Del Norte) 7 29.2 (5.7-73.8) 9 11.5 (1.2-57.0) Davao Del Sur 25 41.2 (23.1-62.0) 28 26.6 (14.3-44.0) Davao Oriental 5 - 8 37.3 (17.8-61.9) Compostela Valley 10 29.0 (8.7-63.7) 13 23.5 (8.3-51.0)


ARMM 56 25.9 (12.6-45.7) 112 22.3 (12.5-36.8) Basilan 1 - 3 35.6 (13.8-65.7) Lanao Del Sur 28 13.2 (3.4-39.6) 59 13.3 (6.9-24.1) Maguindanao 14 23.1 (7.0-54.5) 38 12.5 (7.2-20.8) Sulu 5 74.1 (14.3-98.0) 5 - Tawi-Tawi 8 49.8 (36.6-63.0) 8 89.5 (33.6-99.3)

CARAGA 66 18.3 (11.4-28.0) 77 28.3 (18.7-40.5) Agusan Del Norte 21 37.1 (22.0-55.3) 14 14.1 (3.1-45.9) Agusan Del Sur 14 - 28 32.0 (18.6-49.2) Surigao Del Norte 7 - 14 23.2 (6.0-58.7) Surigao Del Sur 24 16.5 (6.5-36.0) 21 36.8 (19.2-58.9)

107

Appendix 7. Proportion of chronic energy deficient lactating mothers by region and





CAR 121 6.0 (3.1-11.2) 199 6.6 (4.1-10.5) Abra 21 20.1 (8.5-40.7) 31 9.9 (3.1-27.6) Benguet 46 2.2 (0.3-14.8) 67 3.0 (0.8-10.2) Ifugao 9 - 32 3.2 (0.4-21.2) Kalinga 28 7.6 (3.0-18.3) 33 5.9 (1.6-20.1) Mountain Province 5 - 26 4.3 (0.7-22.9) Apayao 12 - 10 49.2 (34.3-64.3)

ILOCOS REGION 156 15.9 (11.2-22.2) 220 19.3 (14.5-25.2) Ilocos Norte 21 14.4 (5.0-35.0) 25 12.7 (2.6-44.5) Ilocos Sur 14 - 23 8.5 (1.8-31.4) La Union 27 15.7 (6.1-34.9) 40 18.0 (9.9-30.5) Pangasinan 94 18.8 (12.4-27.4) 132 22.9 (16.6-30.7)


CENTRAL LUZON 175 14.0 (9.6-19.8) 254 17.8 (13.4-23.2) Bataan 13 8.6 (1.2-43.1) 8 26.4 (8.4-58.3) Bulacan 50 15.4 (7.1-30.4) 61 19.3 (11.4-31.0) Nueva Ecija 40 17.8 (9.0-32.2) 72 20.1 (11.4-33.0) Pampanga 38 10.4 (4.6-21.7) 51 17.4 (10.5-27.4) Tarlac 19 14.5 (4.9-36.0) 43 18.0 (8.4-34.3) Zambales 9 11.8 (1.8-49.9) 12 6.0 (0.8-33.2) Aurora 6 14.6 (2.5-52.9) 7 -


MIMAROPA 113 20.0 (12.3-30.9) 157 19.3 (13.7-26.6) Marinduque 5 38.1 (4.0-90.2) 22 13.5 (3.7-38.6) Occidental Mindoro 30 22.3 (7.1-51.8) 38 23.8 (12.8-40.0) Oriental Mindoro 29 16.5 (8.3-30.2) 37 17.6 (8.6-32.7) Palawan 36 24.9 (12.2-44.1) 50 21.5 (11.0-37.7) Romblon 13 - 10 8.3 (3.2-20.0)


108

Cont. Appendix 7. Proportion of chronic energy deficient lactating mothers by

region and province: Philippines, 2013 and 2015



Aklan 8 10.3 (1.3-51.0) 17 28.8 (16.7-44.9) Antique 17 22.6 (8.9-46.6) 23 26.8 (14.9-43.3) Capiz 20 21.3 (7.4-47.8) 53 19.4 (7.8-40.7) Iloilo 63 18.2 (9.8-31.3) 95 24.8 (16.0-36.3) Negros Occidental 86 13.5 (7.0-24.3) 120 9.3 (5.2-16.1) Guimaras 6 15.5 (2.0-62.8) 9 12.1 (1.4-57.2)

CENTRAL VISAYAS 166 9.6 (6.0-15.0) 275 10.7 (7.3-15.3) Bohol 31 7.6 (1.6-29.1) 52 13.9 (6.7-26.6) Cebu 96 10.6 (5.9-18.5) 155 10.4 (6.1-17.2) Negros Oriental 37 9.2 (4.5-18.0) 66 9.2 (4.0-19.7) Siquijor 2 - 2 -

EASTERN VISAYAS 186 11.5 (7.5-17.3) 259 10.5 (7.0-15.4) Eastern Samar 24 13.0 (5.2-28.9) 26 - Leyte 72 6.8 (2.8-15.7) 150 10.2 (6.4-15.9) Northern Samar 31 5.4 (1.1-21.8) 30 3.4 (0.5-18.2) Western Samar 35 19.1 (9.3-35.4) 34 20.6 (7.1-47.1) Southern Leyte 12 - 14 26.6 (9.5-55.4) Biliran 12 41.1 (15.5-72.7) 5 -

ZAMBOANGA PENINSULA 103 8.6 (4.8-14.8) 156 12.0 (7.6-18.5) Zamboanga Del Norte 34 14.0 (6.4-28.0) 57 17.5 (10.5-27.8) Zamboanga Del Sur 44 2.7 (0.4-17.2) 82 10.8 (5.0-21.9) Zamboanga Sibugay 24 12.4 (5.1-27.1) 15 -

NORTHERN MINDANAO 138 3.8 (1.6-8.6) 170 7.1 (3.5-13.6) Bukidnon 37 5.3 (1.4-18.5) 56 5.3 (1.7-15.7) Camiguin 3 - 4 - Lanao Del Norte 34 2.7 (0.5-14.0) 33 9.4 (3.1-25.0) Misamis Occidental 20 11.6 (2.9-36.7) 21 - Misamis Oriental 44 - 56 10.4 (3.4-28.1)





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