metadata: authors: 0: author: Capanzana,Mario V.
1: author: Demombynes,Gabriel
2: author: Gubbins,Paul Michael
count: Philippines
docna: 0: docna: Why Are So Many Children Stunted in the
            Philippines ?
docty: Policy Research Working Paper
subtopic: Reproductive Health,Early Child and Children's Health,Nutrition,Health Care Services Industry,Educational Sciences,Hydrology
teratopic: Health, Nutrition and Population,Industry,Education,Water Resources
abstracts: cdata!: Nearly one in three children under age
            five in the Philippines is stunted, a key marker of
            undernutrition. This rate is high for the country's
            level of income. This paper provides the first detailed
            multivariate analysis of potential drivers of stunting in
            the Philippines, using data from the 2015 National Nutrition
            Survey. Potential drivers are analyzed individually and
            grouped in major categories. The analysis finds that
            stunting between 24-60 months is principally associated with
            suboptimal prenatal conditions and inadequate food security
            and diversity. If the results are given a causal
            interpretation, they imply that if all Filipino newborns had
            adequate prenatal conditions, the fraction stunted at age
            24-60 months would fall by 20 percent. Similarly, providing
            adequate food security and diversity to all Filipino
            children would reduce stunting by 22 percent. Other factors
            -- including access to water, sanitation, and environmental
            conditions -- have less strong associations with stunting.
            The results point to a series of policy priorities to reduce
            stunting: supporting the nutrition and health of expectant
            mothers, ensuring access to contraception to reduce
            adolescent pregnancy, and ensuring that children consume a
            variety of healthy foods, including protein-dense foods such
            as milk, meat, and eggs.
geo_reg_key: 517198,517191
repnme: repnme: Why Are So Many Children Stunted in the
            Philippines ?
display_title: Why Are So Many Children Stunted in
            the Philippines
listing_relative_url: /research/2020/06/32163167/so-many-children-stunted-philippines
topicv3: Water m1326522 137,Nutrition m1326779 644,Education m1327048 470,Stunting m1327504 1445,Health m1504661 3480
publishtoextweb_dt: 2020-06-23T00:00:00Z
historic_topic: Health, Nutrition and Population,Industry,Education,Water Resources
origu: HD - E.Asia Director (HEADR)
no_of_pages: 67
author_keys: 0: author_key: 145125
1: author_key: 1104757
model_extractions: Policy Research Working Paper 9294

# Why Are So Many Children Stunted in the Philippines?

_Mario V. Capanzana Gabriel Demombynes Paul Gubbins_ Health, Nutrition and Population Global Practice June 2020
Policy Research Working Paper 9294

# **Abstract**

Nearly one in three children under age five in the Philippines is stunted, a key marker of undernutrition. This rate is high for the country’s level of income. This paper provides the first detailed multivariate analysis of potential drivers of stunting in the Philippines, using data from the 2015 National Nutrition Survey. Potential drivers are analyzed individually and grouped in major categories. The analysis finds that stunting between 24–60 months is principally associated with suboptimal prenatal conditions and inadequate food security and diversity. If the results are given a causal interpretation, they imply that if all Filipino newborns had adequate prenatal conditions, the fraction stunted at age 24–60 months would fall by 20 percent. Similarly, providing adequate food security and diversity to all Filipino children would reduce stunting by 22 percent. Other factors—including access to water, sanitation, and environmental conditions—have less strong associations with stunting. The results point to a series of policy priorities to reduce stunting: supporting the nutrition and health of expectant mothers, ensuring access to contraception to reduce adolescent pregnancy, and ensuring that children consume a variety of healthy foods, including protein-dense foods such as milk, meat, and eggs.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gdemombynes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Why Are So Many Children Stunted in the Philippines?**

Mario V. Capanzana, Gabriel Demombynes, and Paul Gubbins<sup>*</sup>

**Keywords:** nutrition, stunting. **JEL Codes:** I1, I12, I14

> * Mario V. Capanzana is the Director of the Food and Nutrition Research Institute of the Philippines Department of Science and Technology (FNRI-DOST). Gabriel Demombynes is the World Bank Program Leader for Human Development for Brunei, Malaysia, Philippines and Thailand. Paul Gubbins is a consultant with the World Bank. The authors thank Sharon Piza of the World Bank and the staff of the FNRI-DOST for assistance with the data. They thank Nkosinathi Mbuya and participants at a seminar at FNRI-DOST for their useful comments. The Government of Japan provided financial support for this work through the Japan Trust Fund for Scaling Up Nutrition.
# **1 Introduction**

Despite recent economic growth, increased health spending and improved coverage of key health interventions, the Philippines continues to struggle with persistently high child undernutrition. In nationally representative 2015 survey data, nearly 1 in 3 children under age 5 were stunted, a principal marker of undernutrition, and that figure was essentially unchanged from a decade previous. The World Bank’s Systematic Country Diagnostic highlights addressing the high level of undernutrition as a principal policy for the country. The government’s core planning document, the Philippine Development Plan, also recognizes the undernutrition challenge, and the government’s nutrition strategy, the Philippine Plan of Action for Nutrition, sets out an ambitious target of reducing stunting to 20 percent by 2022.<sup>1</sup> Policy attention to the issue is motivated by a large body of international research which shows that undernutrition in utero and early childhood has severe consequences. Childhood undernutrition is associated with increased risks of morbidity, mortality and cognitive deficits.<sup>2</sup> Longitudinal studies show links between childhood undernutrition and reduced schooling, learning outcomes, and economic productivity as an adult. One of these studies is the Cebu Longitudinal Health and Nutrition Study, which has tracked children born in the Philippines in the early 1980s. Children in the Cebu study who were stunted between birth and age 2 were found to have on average lower cognitive test scores at the ages of 8 and 11 years, delayed enrollment in school, higher absenteeism and repetition of grades, and a lower likelihood of holding a formal job in early adulthood.<sup>3</sup> Recent studies suggest that the total costs of childhood undernutrition in the Philippines is in the range of 1.5 to 3 percent of GDP per year.<sup>4,5</sup> There is growing evidence that cognitive, motor and socio-emotional deficits in early childhood, as well as schooling and learning outcomes later in life, are mediated through the brain’s architecture as it develops in an environment cut off from adequate nutrition, stimulation and care. The vast majority of neural development occurs between conception and infancy and the neural circuits that underpin key functions such as seeing and hearing, speech production and higher cognitive ability are established before the age of two.<sup>6</sup> Key processes such as the creation of neurons, the growth of axons and dendrites, synapse formation and pruning as well as myelination are dependent on key micronutrients.<sup>7</sup> New studies that use neuroimaging technologies to measure brain size and structure show alarming differences in brain architecture associated with chronic malnutrition and sensory deprivation.<sup>8</sup> While the Philippines has an excellent survey program that routinely monitors the nutritional status of children, there is limited published empirical evidence on the size, direction, interdependence and relative importance of stunting’s risk factors and interventions. The objective of this paper is to help fill that knowledge gap by conducting a systematic analysis of potential drivers of stunting. The paper begins with a discussion of the data used for the analysis, and then reviews the broad patterns of stunting in the Philippines and across countries. It next describes a conceptual framework for understanding undernutrition and presents the analysis and discussion.

# **2 Data**

The analysis in this report relies on the nationally representative 2015 National Nutrition Survey (NNS) conducted by the Department of Science and Technology’s Food and Nutrition Research Institute (DOST-FNRI). The 2015 NNS is an ‘updating’ survey, meaning that it was implemented in the years intervening the more comprehensive NNS surveys which are conducted every 5 years. The 2015 NNS 2
collected data across 9 topic areas: anthropometry, clinical and health, dietary, socioeconomic, food security, government nutrition and health program participation, maternal health and nutrition, infant and young child feeding and MDG health related outcomes. For the analyses in this report, 10 NNS modules made available from DOST-FNRI were used (Table 1), representing 42,310 households and 202,570 household members. Data on household income and consumption from the 2015 Family Income & Expenditure Survey (FIES) are also used, given that households in the 2015 FIES can be matched to the 2015 NNS, since both samples use the same sampling frame.

**Table 1**

|**Survey program**|**Module**|**Population**|**Observations**|
|---|---|---|---|
|2015 Family Income &<br>Expenditure Survey (FIES)<br>(1st semester)|--|Households|43,882|
|||Household members (all ages)|202,570|
||Form 1.1|Household members (fathers)|28,140|
|||Household members (mothers)|31,654|
||Form 1.2|Households|42,310|
||Form 1.3|Households|42,310|
||Form 1.5|Households|42,310|
|2015 National Nutrition|Form 1.6|Households|42,310|
|Survey||Anthropometry (children < 61 months)|18,629|
||Form 2.1|Anthropometry (fathers)|28,140|
|||Anthropometry (mothers)|31,654|
||Form 3.1|Mothers & pregnant women with children|8,568|
||Form 4.1|Children (0-71 months)|20,303|
||Form 4.2|Children (0-71 months)|22,332|
||Form 4.3|Children (0-23 months)|6,653|

The NNS anthropometry module contains data on 18,629 children spanning the ages of 0 and 1,856 completed days of age (60.9 months) which can be matched to the WHO’s international growth standards reference table. However, for 8.7 percent of these cases, height measurements are missing, leaving 17,014 children for whom height-for-age z-scores (HAZ) can be computed.<sup>9</sup> Of these children, 141 had biologically implausible z-scores (> 6 sds or < -6 sds), leaving 16,873 children under the age of 61 months with valid HAZ measurements (Table 2).

3
**Table 2**

|**Data**|**Children**<br>**under 61**<br>**months of**<br>**age**|**Children**<br>**under 61**<br>**months of age**<br>**with height**<br>**measurement**|**Children under**<br>**61 months of age**<br>**with height**<br>**measurement &**<br>**biologically**<br>**plausible height**<br>**for age z-scores**|**Children under 61**<br>**months of age**<br>**with height**<br>**measurement &**<br>**biologically**<br>**plausible height**<br>**for age z-scores &**<br>**in the 2015 FIES**|
|---|---|---|---|---|
|Observations (unique individuals)|18,629|17,014|16,873|13,172|
|Females (%)|48.3|48.4|48.4|48.7|
|Males (%)|51.7|51.6|51.6|51.3|
|Urban (%)|33.6|33.4|33.4|31.5|
|Cases with missing age (%)|0|0|0|0|
|Age range (completed months)|0-60|0-60|0-60|0-60|
|Mean age (completed months)|32|32|32|32|
|Median age (completed months|33|33|33|33|
|Cases with missing height (%)|8.7|0|0|0|
|Height range (cm)|24.5-144.55|24.5-144.55|41.9-131.45|41.9-131.45|
|Mean height (cm)|85.2|85.2|85.3|85.3|
|Median height (cm)|86.9|86.9|87|87.1|

Given that different modules of the NNS focus on specific segments of the population, there is a tradeoff between the scope of nutrition drivers included and the size and age-composition of the sample considered in the analysis (Figure 1). For example, a total of 16,873 children have valid HAZ scores in the NNS and while all of these children are represented in each of the household-level modules of the NNS (Form 1.2, Form 1.5 and Form 1.6), only 10,188 (around 60 percent) are represented in Form 3.1, which captures variables on maternal health care, and only 5,945 (around 35 percent) are captured in Form 4.3, which captured data on infant and young child feeding practices for children under the age of 2. If data from the FIES are matched to the NNS sample, a total of 4,049 children with valid z- scores (all under the age of 2 years due to the inclusion criteria of Form 3.1) have information from all of the available NNS and FIES modules. There is no evidence that factors that influence stunting other than age are being affected as a result of inclusion/exclusion criteria of the different NNS modules, as the unweighted stunting rates for children under the age of 24 completed months do not vary with the sample size.

4
<!-- Start of picture text --> 0-23 months 0-60 months<br>NNS Form 2.1: Anthropometry (with non-missing _ 5,974 16,873<br>height measure & biologically valid HAZ) 0-23m) 0-60m)<br>: . . 5,974 16,873<br>. 5,974 16,873<br>. : 5,974 16,873<br>. :<br>NNS Form 3.1: Maternal care, knowledge & behaviors ee. 5,344 so ga10,430<br>po : . 5,777 16,239<br>- Chi . 5,974 16,873<br>NNS Form 4.3: Infant & young childpractices feeding _ 90(0-23m)5,945 2590 |(0-23m)5,945<br>: . 4,655 13,172<br>Represented in. all modules/datasets 500g4,049 255 ghee4,049<br>Represented with non-missing values in a simple _ 4,732 4,732<br>model (without a variable from FIES) 0-23m) 0-23m)<br>Represented with non-missing values in a simple _ 3,681 3,681<br>model (with a variable from FIES) (0-23m) Q23m)<br>0.00 0.25 0.50 0.75 1.00 1.28.00 025 050 075 1.00 12<br>Percent of sample<br><!-- End of picture text -->

The unweighted stunted rates for each respective sample are shown as red circles with the values labeled
<!-- Start of picture text --> HAZ<br>-0.5 ian: B BoysGirls<br>ae<br>-15 \ e O eo i 4 O O T OD<br>- Ow e } I yh .2F 6 Te ' O OF Y |<br>-2 sds.<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br>Source: Author's own calculation based on 2015 National Nutrition Survey (NNS). Notes: HAZ = Height-for-age z-<br>score. Point estimates for the mean and 95% confidence intervals are based on groups of children defined by<br>complete age in months. This analysis uses 2 month intervals where, for example, 1 represents children with between<br>0 and 2 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with<br>a generalized additive model (gam) using on all observations. The 95% confidence interval around the predictions<br>from the gam gam smoothed line is shown as a grey area.<br><!-- End of picture text -->

Source: Author's own calculation based on 2015 National Nutrition Survey (NNS). Notes: HAZ = Height-for-age z- score. Point estimates for the mean and 95% confidence intervals are based on groups of children defined by complete age in months. This analysis uses 2 month intervals where, for example, 1 represents children with between 0 and 2 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using on all observations. The 95% confidence interval around the predictions from the gam gam smoothed line is shown as a grey area.
<!-- Start of picture text --> 2 6 22 32 35<br>Income aggregates - High ome oer middle income wa Lower middle Qome Qw income<br>;<br>East Asia & Pacific~ (Ope,2 cring,TR10 11 ° ° “Philippines3336Qionesia ° Timeteste50<br>Mongolia<br>17 45<br>South Asia- Sri Qoka ° sees PakRian<br>Sub-Saharan Africa-. senSai17 *Cotstivore”22 er eS er er ee oe Bundi56<br>5 7 12 46<br>Middle East & North Africa- kuwait-O- © * O—-aeria - ° ° y oO<br>West Bank and Gaza emen, Rep.<br>568 11 20 27<br>Europe & Central Asia- Macedonia HYP SeaKhstan “Georgia C28ekistan® TajfPetan<br>Moldova<br>2 6 13 46<br>Latin; America; & Caribbean-,Q-; PaiQusy cee eee eee Gua@nala<br>0 10 20 30 40 50<br>Stunting prevalence (% under-5)<br>© High performer 9° Other<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Notes: Countries within each region with the highest & lowest stunting rates as well as those who have achieved more than 6 percent per year reductions in stunting (high performers) are labeled. Data correspond to the year with the most recently available stunting estimate for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2002 (Romania) and the most recent estimates are from 2016.
<!-- Start of picture text --> % of children under 5 years of age that are stunted, 2000 - 2016<br>Global leaders (Top 8 fastest declines .<br>in stunting) Regional comparators<br>53<br>50<br>42 43 43<br>40 40 42<br>36 36 .<br>31 32 33 v4 Indonesia33 aes<br>30 Lesotho fe N77 Philippines<br>30 27 27<br>Tajikistan Burkina Faso 25<br>Vietnam<br>29<br>Cote d'Ivoire 21 .<br>20 19 48 17 Malaysia<br>Ghana,<br>Bolivia<br>13 16<br>14 Peru 40<br>10 Mongolia 8 Thailand<br>China<br>2000 2005 2010 2016 2000 2005 2010 2016<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Only countries with the following criteria are considered for inclusion in the 'Global leaders; group: (1) had stunting prevalences higher than 25% at the start of the period, (2) have at least three stunting estimates that cover more than a 5 year interval between 2000 and 2016, (3) Have populations exceeding 1,000,000.
<!-- Start of picture text --> Stunting prevalence (% under-5)<br>60 © High performer<br>© Low performer<br>Other<br>; © Philippines<br>50 Papua New Guinea Tigor-Leste © Regional comparator<br>Pakistangemen2. PDR éigeniaGyatemala<br>40<br>igdonesia<br>Fallippines<br>30<br>Viet °<br>e<br>20 Nam laysia<br>10 Armenia Thailand:<br>o Tonga en and Hefcegovina<br>Republic 0° Jordan Fill Oc” China<br> of Moldova State of Palgstine Samoa Quamaica<br>0 Ukraine Belarus<br>USD 250 500 1,000 2,000 4,000 8,000 16,000 32,000<br>GDP per capita (Constant 2010 USD) [log scale]<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Notes: 'High performers' denotes countries with GDP per capita in the USD 1,000 to 5,000 range whose national stunting rates are below 10 percent. 'Low performers’ denotes countries in the same GDP per capita range whose national stunting rates are above 40 percent. Data correspond to the year with the most recently available stunting estimate for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2001 (Czech Republic) and the most recent estimates are from 2016. The line shown is based on a locally weighted scatterlpot smoothing algorithm (loess) to more clearly show the average relationship between stunting and average income per capita levels across countries.
<!-- Start of picture text --> .<br>Burundi Richest 20% National Poorest 20%<br>Guatemala 31 - 69<br>Yemen 17 nt 66<br>Pakistan 26 + 59<br>Lao PDR QQ|24 “te FE614<br>Nigeria 18 + 63<br>CentralentraAfricaniMRErf GSu fic *: 30 + *: 45 “8 "<br>Rgp aS 28 + 47<br>Chad 31 +0 41<br>Ethiopia 26 + 45<br>. SIndian 2122 ++ 44 51<br>Sierra [sone 29 + 42<br>igo a 20 + 47<br>BanTanzania9Ma Nepal awies 181920 25 + ++ + 40 46 4849<br>BhutaBeni n 18 21 ++ 41 46<br>CamtLiberiaodia 14 {9 CA+ |} 442<br>SolomonCameroonIslands 20 25 -+ 3637<br>Comoros 24 =e 39<br>Mali 15 + 41<br>Myanmar [ CT Zo<br>. Ugan a 17 + 32<br>Guinea- issauogo 11 15 ++ 31 33<br>Zimbabweajikistan 17 22 ate+ 3233<br>Kenya 14 + 36<br>Namibiaganania2 9 "6 ++ * .3132<br>Vie nam EE} 44<br>Honduras 8 + 42<br>Haitgy.aqt 7 21 24+OF © 2425 31<br>Uzbekistan 16 +9 21<br>Sao Tome and eine,saan e 7 ° 15 + + 22 *25<br>abon 6 + 31<br>E] SalvadorSeng 5 6 + + 24 26<br>Kyrgyzstan 1 + 18<br>SlombiaeXxICO 5 6 ++ 19 23<br>TurkmenistanAlgeria 111+O+0 13 16<br>Guyana 7 + 20<br>Mongoliahaiflan 6 1%+ @@ 13 19<br>Tunisia 8 Ot 16<br>Armenia 6 + 12<br>. Montenegro SQ-o+<br>Bosnia and Hegegov' na 16 10<br>Kazakhstanu hota 66 O +9+ 10 13<br>Jordan 2 + 14<br>State of BarbadosPalestine 3 6 +7+ 8<br>Dominican Republic 4 nts 12 Other —<br>Republic of Meldovaerbia 3 4 0++ 11 14 ®© PhilippinesRegional comparator<br>Pagagquay 1 + 12<br>_Belarus4 le} 23 ++o 6 10<br>Saint Lucia 0 + 5<br>0 20 40 60<br>Stunting prevalence (% under-5)<br><!-- End of picture text -->

Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Data by income quintile for the Philippines are from the 2015 National Nutrition Survey. Data for Vietnam and Thailand are from World Bank country programs. Countries are sorted by the overall national stunting prevalance. Data correspond to the year with the most recently available stunting estimates by wealth quintile for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2005 (Belarus) and the most recent estimates are from 2016.
<!-- Start of picture text --> Average annual rate of stunting change (%)<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>e Malaysia<br>1 ; 2006-2016<br>Pakistan<br>0 ° 3001-2012 an<br>Burundi Egypt, Arab Philippines Timor-Leste<br>©2000-2016 Rep. 2000-2014 @ 2003-2015 2002-2013<br>-1 © Jamaica Yemen, Rep. 2000-2013Indonesia e<br>2000-2014 2003-2013<br>-24 Haiti2000-2012 Guinea-Bissau Azerbaijan<br>ee emp0.2014 © Myanmar 2000-2013<br>Mexico. 2000-2016<br>3 2006-2015<br>© Thailand ® Vietnam<br>-4 United States 2006-2016 2000-2015<br>2001-2012 ® Equatorial<br>5 Guinea<br>® 2003-2014 El Salvador 2000-2011<br>-6 Cote d'Ivoire @Peru 2000-2016<br>e©* 2006-2016 West Bank and<br>-7 2000-2012Algeria Gaza 2002-2014<br>© sao Tome @China 2000-2013<br>“8 and2006-2014Principe ® Mongolia<br>2000-2013<br>“9 kazakhstan2006-2015<br>-10 ©@ Paraguay<br>2005-2016<br>-1 0 1 2 3 4 5 6 7 8 9 10 11<br>Average annual rate of GDP per capita change (%)<br><!-- End of picture text --> Other ® Outliers © Philippines ® Regional comparator Source: World Bank World Development Indicators. Notes: Only countries with at least 3 stunting estimates between 2000 and 2016 are considered. Labeled countries are either Philippines' regional comparators or 'outliers' - countries with the 5 highest and 5 lowest growth elasticities of stunting as well as those with average annual reductions in stunting of below -6% per year and average annual increases in per capita growth of 8% per year. The line indicates the average relationship between changes in growth and changes in stunting based on ordinary least squares (OLS) fit of the data points, the shaded area represents the 95% confidence interval of the OLS line.
Length(cm) <!-- Start of picture text --> Boys<br>120<br>85th pctile (WHO)<br>110 50th pctile (WHO)<br>15th pctile (WHO)<br>100 cat Stunting threshold (WHO)<br>Extreme stunting threshold (WHO)<br>90<br>80<br>70<br>60<br>50<br>40<br>Girls<br>120<br>85th pctile (WHO)<br>110 Oth pctile (WHO)<br>15th pctile (WHO)<br>100 Toph Stunting threshold (WHO)<br>cj Extreme stunting threshold (WHO)<br>90<br>80<br>70<br>60<br>50<br>40<br>0 6 12 18 24 30 36 42 48 54 60<br>Age (months)<br>Source: WHO growth standards & 2015 National Nutrition Survey (NNS). Notes:<br><!-- End of picture text -->
% of children under-5 who are stunted <!-- Start of picture text --> 50%<br>40 am - a e e T v e dsv 1 ai 6———~" e e e<br>1 | 9<br>30 f .<br>20 J<br>Girls<br>0<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br><!-- End of picture text -->

Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Notes: Point estimates and 95% confidence intervals are based on groups of children defined by complete age in months. This chart uses 2 month intervals where, for example, 1 represents children with between 0 and 2 completed months of age. The line represents a non-linear prediction of the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from the gam smoothed line is shown as a grey area.
<!-- Start of picture text --> Stunting prevalance (%)<br>0-5 completed months 6-18 completed months 19-60 completed months<br>All individuals- g 2 2<br>Income quintile- | 75, gk - g &<br>Mother's education- oR g & 3 g<br>Urban/rural HH residence- 11 %3 oe %, gS 2<br>Sex" | jig % 38738<br>0% 20 40 55% 0% 20 40 55% 0% 20 40 55%<br><!-- End of picture text -->

© Poorest 20% © Some or complete college © Urban © Girls © Richest 20% © Some or complete elementary; or no schooling © Rural © Boys Source: Author's calculations using 2015 NNS survey data. Notes: 95% confidence intervals of the stunting prevalance by age group and population subgroup are displayed.
<!-- Start of picture text --> Stunting prevalence (%) a ;<br>6 Poorest 20% © 3rd quintile © Richest 20%<br>60%<br>50 pS e pf e e<br>20 ° 4 o o ° i<br>10 + T<br>0<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br>Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Income groups<br>based on the 2015 FIES matched to households in the NNS. Notes: Stunting prevalance point estimates and 95% confidence<br>intervals correspond to groups of children defined by complete age in months. This chart uses 4 month intervals where,<br>for example, 2 represents children with between 0 and 4 completed months of age. age. The line represents a non-linear<br>prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95%<br>confidence interval around the predictions from from the gam gam smoothed line is shown asaa grey area.<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Income groups based on the 2015 FIES matched to households in the NNS. Notes: Stunting prevalance point estimates and 95% confidence intervals correspond to groups of children defined by complete age in months. This chart uses 4 month intervals where, for example, 2 represents children with between 0 and 4 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from from the gam gam smoothed line is shown asaa grey area.
<!-- Start of picture text --> Stunting prevalence (%)<br>60%<br>50 0-5 completed months<br>6-18 completed months<br>19-60 completed months<br>40<br>30<br>20<br>10<br>0<br>Poorest 10% 2 3 4 5 6 7 8 9 Richest 10%<br>Income decile<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Notes: Income deciles are based on monthly household income from the 2015 FIES matched to households in the NNS. The line represents a non-linear prediction of the stunting rate by income decile fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from the gam smoothed line is shown as a grey area.
<!-- Start of picture text --> Age group:<br>© 0-5 completed months<br>© 6-18 completed months<br>© 19-60 completed months<br>11 19 25<br>Region Ill: Central Luzon —o—o—o-<br>National Capital Region bs S. o<br>(NCR)<br>10 22 31<br>Region IV-A: CALABARZON —o— —o— -o-<br>5 26 32<br>Region II: Cagayan Valley —o— —o—o—<br>13 26 35<br>Region |: Ilocos —_Oo——__O0—_0—<br>11 19 37<br>Region XI: Davao —o—_-o— —o—<br>16 27 41<br>Region X: Mindanao —oO—o—— —o—<br>Region VII: Central 5 3 o<br>Visayas<br>10 21 43<br>Region XIII: Caraga —_o——_O0—_- —o—<br>Cordillera Administrative ye 25 43<br>: —_o———_o—-_ —O—--<br>Region (CAR)<br>Region IX: Zamboanga 6 ra ©<br>Peninsula<br>14 27 45<br>Region V: Bicol —o—_ —o— —-o-<br>Region VI: Western S A PS<br>Visayas<br>10 31 46<br>MIMAROPA Region}. —-O0-—- —_0—- —Oo—<br>12 26 47<br>Region XII: SOCCSKSARGEN —o—- —-o— —o—<br>Region VIII: Eastern 6 S o<br>Visayas<br>Autonomous Region in } 3 }<br>Muslim Mindanao (ARMM)<br>0 10 20 30 40 50%<br>% of infants/children<br><!-- End of picture text -->
<!-- Start of picture text --> Stunting prevalence by region (% of infants/children)<br>0-5 completed months 6-18 completed months 19-60 completed months<br>50%<br>Autonomous Region<br>40 inin Muslim(ARMMMindjanao<br>30 utonomous Region<br>in Muslim Mindanao<br>(ARMM)<br>20 National Capital<br>Region (NCR)<br>Region Ill: Central<br>Luzon<br>10 ° NationalRegion (NCR)Capital<br>Region III: Central<br>National Capital Region|X: Mindanao Luzon<br>Region (NCR) Autonomous Region<br>0 Region II: Cagayan in Muslim Mindanao<br>Valley (ARMM) $$ SS —<br>0 10 20 30 40 50% 0 10 20 30 40 50% 0 10 20 30 40 50%<br>Poverty rate (% of population)<br><!-- End of picture text -->

Source: Author's calculations using 2015 NNS and official poverty statistics. Notes: Poverty rates are based on the Philippines national poverty line. The 95% confidence intervals for regional estimates of stunting prevalence are displayed. In addition, the line indicates the average relationship between regional poverty and stunting prevalance based on ordinary least squares (OLS) fit of the data points, the shaded area represents the 95% confidence interval of the OLS line.
<!-- Start of picture text --> eee TIT Beene<br>Adolescent girls Oe<br>Pregnant mother & ;<br>embryo Newborn , Stunted chid at age 2 not<br>Infants Early childhood likely to reach<br>detemninantsimmediat Accumulating adversities developmentP Ppotential<br>PRE NATAL RISK FACTORS POST NATAL RISK FACTORS<br>Health & nutritional status of newborn Malnutrition during infancy & early childhood Disease incidence during childhood<br>Birth outcomes: Small-for-gestational age at birth/ Low Inadequate nutritient intake. Number, severity, duration of disease episodes during early<br>birth weight/ Preterm delivery childhood. Most prominently diarrhea.<br>Underlying Z :<br>determinants . oo ~<br>Pre-natal environment Caregiving environment Water & Sanitation << Child Health care .<br>environment a envrionment .<br>Maternal nutrition, health Intrauterine/fetal growth nedeciste Inadequate complementary Unclean environment, excessive Inadequate utilization of health<br>+ Anemia — ; restriction breastfeeding feeding routine & diet exposure to pathogens at home & services, technologies &<br>+ Micronutrient deficiency _ Fetus is smaller than ee . . A : .<br>ppt) 7 ' + No early initiation + Late introduction community interventions<br>(Vitamin A, Zinc, lodine, expected for the number of . . . anki j<br>Calcii Vitamin D) ks i + Not exclusive during + Insufficient frequency + Inadquate drinking water + Vaccines<br>«smokinMaternal8 a.drinkin 7 Maternal age/MASEL [ple HELIS birth parity/ + Notthe first continued6 months +(leading Insufficient to lackdiversity of protein, ++ Exposure Inadequate to disposal indoor smokeof human waste +monitoring Postnatal screening & growth<br>+ anthropometry birth spacing through 2 years of age other micronutrients) + Exposure to livestock + Micronutrient supplementation<br> short stature/ stunting; + Adolescent pregnancy + Inadequate food handling<br>+ low BMI<br>Food environment<br>Inadequate household food security& accessto diverse foods<br>Resources, systems &<br>services Enabling environment for caregiver. family and communitv<br>Caregiver knowledge, beliefs, autonomy, time & control of Household financial resources Food system & markets Health system<br>resources + Income level & volatility + Food supply + Availability of facilities, professionals, medicines<br>+ Years of education, sources of information, independent income<br>Context Social, economic, political, climatic, and cultural contexts<br>Social norms & strength of their Informal & formal social support Politics & public policies Investment & innovation Employment<br>enforcement + Social network (remittances, caregiving support & advice)<br>+ Institutional programs, cash transfers, advice<br><!-- End of picture text -->
# **_4.1 Prenatal risk factors_**

In the prenatal period, fetal growth is governed by the mother’s nutrition, endocrine and metabolic signals, and placental development. Newborn birth weight and gestational age reflect the intrauterine environment. While low birth weight (LBW, defined as < 2500g) is used as a proxy for undernutrition in utero, it really encompasses two related phenomena that have distinct underlying causes: small for gestational age (SGA)<sup>14</sup> and preterm delivery. SGA describes a fetus that is smaller than expected for the number of weeks in pregnancy and is closely associated with fetal growth restriction (FGR). Preterm denotes live births that occur before 37 weeks of completed gestation. Filipino newborns are among the world’s most likely to be born too small or too soon. An estimated 15 percent of live births in LMICs are LBW, with LBW prevalence highest in South Asia (25.6 percent), Sub-Saharan Africa (14.2 percent) and Southeast Asia (12.2 percent).<sup>15</sup> In the Philippines, the 2017 Demographic and Health Survey (DHS)<sup>16</sup> estimated 14.5 percent of live births were LBW, suggesting a significant drop since 2011 when LBW prevalence was estimated at 21.4 percent. Using data from the INTERGROWTH-21st project,<sup>17</sup> a recent study examined the global prevalence and burden of smallfor-gestational-age (SGA) and estimated that in 2012, 25.6 percent of Filipino newborns were SGA and that 22.7 percent of neonatal deaths were attributable to SGA (either term or preterm), placing the Philippines among the 10 countries globally with the highest SGA burden.<sup>18</sup> Recent global and national estimates<sup>19</sup> suggest preterm birth prevalence in the Philippines was in the range of 13.3 percent in 2014 and more than doubled since 2000.<sup>20</sup> Among SGA live births, 7 percent (or 1.8 percent among all live births) were also delivered preterm. The Philippines ranks 9th globally for the share of live births that are preterm and 3rd for the share of live births that are both SGA and preterm.

Using data from 19 birth cohorts and longitudinal studies representing countries in 3 LMIC regions (including the Cebu, Philippines cohort), Christian et al. explore the relationship between SGA and preterm birth with stunting between the ages of 12 and 60 months.<sup>21</sup> The study finds that the risk of stunting associated with LBW, SGA and preterm – even when adjusting for characteristics of the mother - is significantly elevated compared to appropriate for gestational age (AGA) and term births, with the risks being compounded for births that are both SGA and preterm. For example, for birth cohorts in the Southern/Eastern Asia region, the odds ratios (ORs) for stunting associated with LBW, SGA and preterm were 2.64, 2.13 and 1.34. Babies born both SGA and preterm had even higher odds of stunting (OR 3.63) compared to babies born AGA and term. Given that the study did not find significant variation in these associations by region, the authors consider highly plausible that the associations are biological. The combined population attributable risk for stunting from SGA was 0.16, suggesting that close to one-fifth of childhood stunting could have its origins in the fetal period.

Available evidence suggests that SGA (both term and pre-term) is strongly associated with maternal height and low BMI which are in turn associated with adolescent health and pregnancy. A pooled analysis of DHS data from 109 countries adjusting for socioeconomic characteristics, such as the wealth of the household and education of the mother, found a strong relationship between maternal height and stunting<sup>22</sup> : children of mothers measuring 145 cm or less had an 80.5 percent probability of being stunted, compared to 18.9 percent for children of mothers measuring 160 cm or more, controlling for other factors. Other studies have established links between maternal birth outcomes – such as birthweight and SGA – with offspring outcomes including stunting. A study using the Cebu longitudinal health and nutrition survey conducted between 1983 to 1995 in the Philippines reinforces this evidence, finding that the likelihood of stunting is inversely related to mother’s height and low birth weight.<sup>23</sup> 20
Risk factors for LBW include maternal anemia. A meta-analysis that included 11 experimental trials identified a significant 20% reduction in the risk of LBW associated with antenatal iron supplementation.<sup>24</sup> Risk factors for preterm births include zinc deficiency, parity and maternal age.

# **_4.2 Postnatal risk factors_**

During infancy and childhood, epidemiological studies point to suboptimal complementary feeding, recurrent infections and micronutrient deficiencies as important proximal determinants of stunting. Findings from the Cebu longitudinal study, for example, show that the likelihood of stunting is increased by diarrhea, febrile respiratory infections and early supplemental feeding.<sup>25</sup> Notably, the study finds a clear risk of early weaning off of breastmilk, identifying protective effects of breastfeeding and negative effects of supplementary feeding that were strongest in the first year of life, suggesting that among breast-fed infants _“high quality nutrients from breast milk are replaced with lower quality nutrients from weaning diets based on corn or rice”_ .

The existing experimental evidence suggests that nutrient supplementation, counseling for maternal, infant and young child feeding and the promotion of complementary feeding have been marginally successful at boosting linear growth (none have been able to normalize early childhood growth) and reducing the incidence of stunting. Experimental studies on breastfeeding promotion that included nutritional status outcomes did not show any effect on the length of infants (though the beneficial impact of breastfeeding in reducing mortality risk underpins why it is strongly promoted).<sup>26</sup> In response to the underwhelming effect of nutrient supplementation and counseling interventions on linear growth, three recent randomized controlled trials in Kenya, Bangladesh and Zimbabwe tested whether reduced exposure to pathogens in the environment are a key constraint to early growth among children. More specifically, these studies tested the hypothesis that environmental enteric dysfunction – a condition of the gut caused by fecal contamination which impairs nutrient absorption- is a key bottleneck for childhood growth, thus constraining the impact of nutrition interventions in settings with poor environmental conditions. In each of the studies, respondents were randomly assigned to receive water, sanitation, hygiene and nutrition interventions in isolation and in combination, testing whether synergies across different interventions exist.<sup>27</sup> In the Kenya study, none of the interventions reduced the incidence of diarrhea and only the nutrition intervention improved the growth of children.<sup>28</sup> In the Bangladesh study, all interventions reduced the incidence of diarrhea and again, only the nutrition intervention improved the growth of children, correcting one-sixth of the growth deficit compared to international norms of healthy growth. These studies were all consistent in their failure to find any effect of WSH interventions in isolation or in combination on the linear growth of children.

While not well established, there is some empirical evidence that points to the role of animal-sourced foods (ASFs) in the promotion of early childhood growth. Randomized control studies in Jamaica, China and Ecuador, for example, supplemented children’s usual diets with high protein milk, minced pork and eggs and in all cases found that treated children had grown taller than the control. In the Ecuador study, stunting prevalence among randomly assigned children who were provided one egg per day over a 6-month period declined by 47 percent relative to the to the control group of children who followed their usual diet.<sup>29</sup> A cluster randomized evaluation of the Philippines’ conditional cash transfer program, Pantawid Pamilyang Pilipino Program (PPP),<sup>30</sup> found the program reduced severe stunting among children (ages of 6 and 36 months) in the treated group by 10 percentage points, relative to children in the control.<sup>31</sup> To try and understand the possible mechanisms contributing to the large reduction in severe stunting, the study also reports the effects of Pantawid on the use of health services for children and on parenting practices, finding large impacts on the utilization of growth monitoring check-ups, vaccination and treatment for illness as well as an 8.2 percentage point (pp) increase in egg consumption and a 6.9 pp increase in dairy consumption relative to children in the control group. While 21
utilization of child health care was a key condition to receive cash benefits, the greater exposure to nutrient rich ASFs among children participating in the program would have resulted from either the income effect of the cash transfer (relaxing budget constraints), Pantawid’s family development sessions which emphasized infant and young child feeding and nutrition, or both. In addition research using the 2013 NNS data - which collected detailed data on children’s nutrient intake and food sources through a 24 hour recall module - found that the diets of children aged 6-60 months were concentrated primarily to foods with low nutrient density such as refined rice with little contribution to total energy or nutrients from vegetables, meats, fruits and eggs.<sup>32</sup> A recent observational study looking at the relationship between ASF consumption and stunting globally using DHS data finds that children between the ages of 18 and 23 months who consume 2 or 3 types of ASFs the previous day are around 6 pp less likely to be stunted compared to children who do not consume any ASFs, controlling for other child and household factors.<sup>33</sup> In South, Central and Southeast (SE) Asia, 56.8 percent of children (6-23 months) consumed any ASF the prior day (compared to a high of 83 percent in Latin America & Caribbean and a low of 49.3 percent in Eastern & Southern Africa). Of the children who consumed any ASF, the vast majority (75 percent) only consumed 1 type of ASF - the highest rate across all regions - suggesting relatively low exposure to different kinds of ASFs among children in South, Central and SE Asia. High prices may be a key constraint to relatively low levels of ASF consumption. The prices per-calorie of ASFs relative to the prices per-calorie of the cheapest staple cereal in South, Central and SE Asia, range from a high of 7.8 for fresh cow’s milk to a low of 5.3 for fish; chicken eggs are 6.5 times more expensive, on average, per calorie than the cheapest staple cereal.<sup>34</sup>

# **_4.3 Stunting risk factor trends in the Philippines_**

In the Philippines, population-level trends for key prenatal and postnatal stunting risk factors are positive and creating a more favorable environment for children (Figure 16). The fact that under-5, infant and neonatal mortality have all declined since 2000 at least partly reflects this.

22
<!-- Start of picture text --> 2000 20102017 2000 20102017<br>Child nutrition outcomes Child mortality outcomes<br>Under-5 stunting prevalence (JME) 4 33 Under-5 mortality rate (UN IGME) 39<br>Under-5 underweight prevalence (JME) 21 22 Infantnfant mortalitymortality raterate ( (UN IGME ) 29 2822<br>17<br>Under-5 wasting prevalence (JME) 6 7 Neonatal mortality rate (UN IGME) 14<br>Adolescent pregnancy Maternal nutrition<br>a Pregnant women (% hb < 110 g/L) (NNS)42,44<br>Livebirths per 1,000 women, ages 15-19 (N) (UN Lactating women (% hb < 120 g/L) (NNS)<br>POP)<br>25<br>Nutritionally-at-risk pregnant women (%) (NNS) 5<br>Adolescent childbearing prevalence (% of women 8 3 y<br>15-19) (DHS)<br>Birth outcomes Anemia in children<br>Low birth weight prevalence (% of livebirths < 19| Children: 6mo - tyr (% hb < 110 g/L) (NNS)<br>2.5kg) (DHS) 14<br>13\/<br>Children 1-5yr (% hb < 110 g/L) (NNS)<br>{<br>Preterm birth rate (% of livebirths) (WHO) 4<br>Diarrhea Breastfeeding<br>Treatment of diarrhea, ORS/RHF (% of children<3yrs) (DHS) 57 54 Ever breastfed (% children < 2 years, last(DHS born ) g—&8<br>57<br>Early initiation of breastfeeding (% children < 2<br>Diarrhea. prevalence (% children. < 3 years) (DHS) bu,4 Exclusive breastfeeding (Sol4tenee-Onisa) 46<br>(NNS)<br>Complementary feeding Health system performance<br>Minimum dietary diversity (% children 6-23 mos) 22 Received all 8 basic vaccinations (% of children 80<br>(NNS) 9 12-23 mos) (DHS) 7<br>Minimum acceptable diet (% children 6-23 mos) 12<br>(NNS) 61<br>Skilled birth assistance (% of livebirths) (DHS)<br>Water & Sanitation Poverty<br>Ps 95 Poverty rate (% of pop, US $3.20 line) (FIES)<br>Improved drinking water (% of pop) (DHS) 7<br>78<br>14<br>Improved, non-shared sanitation (% of pop) (DHS) 67 Poverty rate (% of pop, US $1.90 line) (FIES) NS<br>2000 20102017 2000 20102017<br><!-- End of picture text -->

Source: The source of<sup>eachindicatorisindicatedinparentheses.DHS=Demographic&HealthSurvey,NNS=Philippines</sup> National Nutrition Survey, Food & Nutrition Institute, Department of Science & Technology (FNRI-DOST), WHO = World Health Organization, WB = World Bank. Notes: Risk factors labeled as showing a clear improvement/ clear worsening are those with average rates of change greater than 0.5% per year or less than -0.5% per year, depending on the indicator.
Average living standards in the Philippines have improved markedly since 2000. GDP per person (adjusted for inflation) rose at greater than 4 percent per year in nine of the 18 years between 2000 and 2017, contributing to an overall increase in real income per capita of close to USD 1,300 over this period. Overall, the distribution of income growth since 2006 has been inclusive and pro-poor, but especially so between 2012 and 2015 when income per capita among the poorest 20% of households increased by 16 percent.<sup>35</sup> As a result, between 2006 and 2015, poverty rates estimated on the basis of the national poverty line fell from 26.6 to 21.6 percent (and from 14.5 to 6.6 using the USD 1.90 international or “extreme” poverty line).

Other risk factors or interventions closely associated with stunting that clearly improved between 2000 and 2017 and that now affect less than 25% (or cover at least 75%) of the population, include anemia prevalence among women (pregnant or lactating), anemia prevalence among children between the ages of 1 and 5 years, diarrhea prevalence among children under the age of 3, breastfeeding prevalence, the share of births assisted by a skilled health practitioner and the share of the population with access to an improved, non-shared sanitation facility.

Risk factors or interventions that improved but still affect more than 25% (or cover less than 75%) of the population include anemia prevalence in children between 6 months and 1 year of age, exclusive breastfeeding prevalence among infants between 0 and 5 months of age, and children between 6 and 23 months of age receiving a diverse or acceptable diet.<sup>36</sup> Amid these positive trends are a few key areas of concern, some of which were pointed out previously. Adolescent fertility and childbearing prevalence are on the rise. Preterm births are estimated to have more than doubled since 2000<sup>37</sup> and DHS surveys conducted between 1993 and 2015 found that LBW prevalence (among all live births in the 5 years preceding the survey) increased from 14.7 to 21.4 percent. However, the most recent DHS estimate from 2017 found LBW prevalence to have fallen significantly to 14.5 percent. Estimates from the NNS of the share of children under the age of 4 whose mother reported (with or without a written record) a low birth weight was 15.9 percent in 2011, 11.1 percent in 2013, and 14.5 percent in 2015.<sup>38</sup> While not deteriorating, a measure of maternal nutrition used by the Department of Science & Technology’s Food and Nutrition Research Institute (DOSTFNRI) in its last three NNS reports - the percentage of pregnant women who are ‘nutritionally-at-risk’ - has not improved. In 2011, 2013 and 2015, around 1 in 4 pregnant women were estimated to be nutritionally-at-risk. Further, in 2015, pregnant women under the age of 20 were twice as likely to be nutritionally-at-risk than women above the age of 20 (39.6 vs. 21.9 percent). The relative risk of having deficient nutrition during pregnancy associated with adolescent age is greater than the risk associated with wealth.

Coverage of all 8 basic vaccinations according to the government’s vaccination schedule slipped from 79 to 71 percent between 2000 and 2003 and has remained around that level since.

The overall child nutrition picture in the Philippines presents a puzzle. Why did the country’s stunting rates stagnate between 2000 and 2015 amid improving living standards, improved child survival, expanding coverage of key health interventions, the implementation of a targeted cash transfer scheme and sustained progress against stunting elsewhere in the region?

Examination of the trends in key risk factors provides some clues. For example, combined with the fact that adolescent fertility is increasing, lower nutritional status among pregnant adolescents is a major headwind for child nutrition even as economic conditions improve. Closely associated is the rise of preterm birth and low birth weight prevalence, which signals a broader challenge with maternal health and nutrition. In addition, given that preterm births also face a significantly higher risk of neonatal mortality,<sup>39</sup> one implication of rising preterm birth rates in a context of improving care for 24
vulnerable neonates – which appears to be the case in the Philippines – is that as more preterm newborns survive past the first 30 days of life, the composition of children at older ages will include a larger share who are at a greater risk of stunting.

It is not clear how important these factors are in the Philippines’ overall stunting story. A major objective of the sections that follow is to more precisely identify which pathways seem to drive stunting, through an empirical assessment of the relationship between stunting and its known risk factors.

# **5 Methods**

# **_5.1 Conceptual framework_**

The underlying environmental conditions and risk factors that influence stunting in the ante-natal and postnatal period can be grouped into five categories : (1) maternal anthropometry, health and nutrition (such as whether a pregnant women is stunted or has anemia) and birth outcomes, (2) the care and feeding practices that a child receives at home (such as breastfeeding and timely transition to complementary foods), (3) the degree of household food security and access to diverse foods, (4) the ability of the household to prevent transmission of infectious disease with adequate water, sanitation and hygiene (WSH) infrastructure and (5) the utilization of child health care services, technologies and interventions to prevent disease and boost nutrition. While building off the original UNICEF conceptual framework, this categorization, and the analyses that follow also draw on two recent reports by the World Bank examining the drivers of stunting in Indonesia<sup>40</sup> and across Sub-Saharan Africa.<sup>41,</sup> 42 The multiplicity and interlinked nature of stunting’s potential causal pathways carries at least three implications. The first is that the specific factors that most heavily influence growth failure in one context may not prevail in another. For example, stunting among children in poor, low-lying coastal neighborhoods affected by floodwater may be in large part caused by increased exposure to infectious disease in a setting without adequate WSH infrastructure. Whereas in neighborhoods without flood risk and good WSH infrastructure stunting may prevail due to inadequate dietary diversity caused by the high prices or limited supply of nutritious foods. The second implication is that presence of one driver (such as exclusive breastfeeding) may not necessarily prevent stunting without the presence of another driver (for example, if the mix of complementary foods introduced at 6 months inadequately meets the caloric needs of the child). In other words, there may be a subset of drivers that are strictly necessary for healthy growth that cannot be substituted for by the presence of other drivers. The third implication is that stunting is cyclical (Figure 17). Several studies have established a link between maternal anthropometry – such as the birthweight, SGA status and height of the mother – with offspring outcomes including stunting. Some frameworks make the cyclical nature of stunting explicit and suggest that making sustained progress against stunting requires interrupting its intergenerational causal pathways.<sup>43</sup> 25
<!-- Start of picture text --> Early childhood risk factors<br>Antenatal risk factors<br>+ Introduction of non-breast milk foods <6 months<br>+ Inadquate diet + Poor complementary feeding practices<br>+ Intrauterine infection + Poor WSH resulting in diarrhea & EED<br>+ Systemic infections/inflammation .. + Recurrent infections<br>+ Ambient air pollution Newborn ) + Exposure to mycotoxins, arsenic, biomass fuels<br>+ Environmental Enteric Dysfunction (EED) + Low birthweight + Poor infant stimulation & nurturing<br>+ Small for gestational age + Maternal depression<br>+ Preterm<br>Potential for / Y .<br>intervention/treatment Conception | 2-years<br>zs ~ + Stunted, HAZ <-2 : :<br>.<br>mw HighMedium (First 1,000 days) + MorbidityDelayed motor & mortaski l lsity& from executiveinfectiofu n ctions Late childhood risk factors<br>~ Low la ~\ \ ) nutrients+ Inadequate and dien e taryrgy<br>Reproductive age adult + Poor WSH resulting in<br>+ Short stature diarrhea & EED<br>+ Low physical stamina (— \ i i<br>\ + Lower lifetime earnings & assets + Fewer years of schooling fr y lef “<br>NN yy, Puberty : +__Poor school performance (from staple foods)<br><!-- End of picture text -->
<!-- Start of picture text --> % of children under 2 years of age<br>All individuals Income quintile Mothers UrbanicuralHH<br>Adequate pre-natal conditions (maternal nutrition,Low birth weightbirth outcomes) (<2,500g)-- v v 1405 7 a 18 3 “ °o 13,15 of B<br>Mother has short stature (height < 145cm)- iy é § 8 g 19,12<br>Mother has low body mass index (BMI < 18.5)- 8 1hd6 19.44 13,13<br>Attended at least 4 antenatal visits- ¥ @ B 86 99 78 §5<br>Received assistance during delivery by skilled practitioner (Doctor, nurse- 8 gy $ & 8 a y<br>HH member smokes or drinks alcohol- gy 63,87 83 g8 6389<br>Adequate home care & feeding practices- 3 e 4B 2 8 29,31<br>Mother reads & plays with child- g 8 2 2 4% 3a<br>Mother always handwashes bf foodprep & feeding, bf/af eating & af toilet u- 8 % §s 78 §8 TM$1<br>Age appropriate feeding (custom)- y 8 3! 84 3! 87,89<br>Child is currently breastfed- y bal A 8 A 2 %<br>Child is currently fed solid or semi-solid foods- 8 75/6 498 Te 78<br>Age appropriate infant & young child feeding- g z y $ & 9 8<br>Adequate HH food security & diversity- 8 8 8 $ 8 i nv<br>Household is food secure- 8 g 2 8 g 24,46<br>HH consumed protein rich foods in past 7 days (meat & eggs & milk product- $ ¥ B 8 86 8 99<br>Dietary diversity score 10 or above (75th percentile or higher) - g 3 8 8 8 % 8<br>HH consumed protein intense diet in past 7 days (meat & eggs & milk produ- e ¥ B 2 8 2 30<br>Adequate post-natal maternal & child health care- 8 38 8. 6 3 40,3Z<br>Received postnatal check-up right after birth by skilled practitioner (Doc- § 8 98 98 98 78 §2<br>Underwent neonatal screening - e Bd §§ 8 8 & A<br>Age appropriate vaccine status- gy 66,7 66.8 6%g°<br>Had acute diarrhea in past 2 weeks- i 14) 6 1% d 7 1 5,1 :<br>Adequate HH water, sanitation infrastructure & environment- Cy a % g §5 e fo<br>Improved drinking water (main source is piped, borehole, protected dug wel- S &§ 9 §§ 8 % 8<br>Improved sanitation (toilet is water sealed)- 8 88 y 8 ¥ % 8<br>At least 75% of housholds in municipality have improved sanitation- & §§ 8 8 88 8 3<br>Household has electricity- § g ¥ Ed Ey 83 3<br>Functioning refrigerator in household- S 8 8 8 a 8 8<br>HH member smokes- g 4G,47 Ba oy 4851<br>Uses improved cooking fuel (Elec/LPG/Nat gas) & does not burn garbage- g 8 § g 8 2 9<br>0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100<br>© Poorest 20% 9 Some or complete college 9° Urban<br>© Richest 20% © Some or complete elementary; or no schooling © Rural<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS and FIES data. Notes: Indicators in italics are not used as components of the aggregate indicators of overall adequacy in each of the five nutrition driver categories
# **_5.2 Prenatal environment: Measures of maternal health, nutrition and care_**

Given extensive evidence about the importance of the prenatal environment for subsequent child nutrition and health outcomes, a set of 10 indicators that are proxies for the likely nutritional status of the mother during pregnancy as well as the health care services that were available to - and used by - mothers during their pregnancy were identified in the NNS. Of these, five are considered necessary evidence for an adequate prenatal environment for the child: normal birthweight (greater than or equal to 2.5kg),<sup>44</sup> maternal height above 145 cm, mother’s BMI > 18.5 and at least four prenatal visits and birth assistance during delivery. Three additional ideal measures in this domain that were not available in the data include whether the child was born preterm, whether the child was born small for gestational age and whether the mother had anemia while she was pregnant.

**Table 3**

|Category|Dataset|Driver|Definition|Available<br>in NNS<br>dataset?|Required<br>for<br>adequacy<br>in<br>domain?|
|---|---|---|---|---|---|
||NNS Form 1.6|**Low birth weight**|**Child weighted less than**<br>**2.5kg at birth**|**Yes**|**Yes**|
|Birth<br>outcomes|Not in questionnaire|Preterm delivery|Child is born before 37 weeks<br>of completed gestation|No|No|
||Not in questionnaire|Small for gestational age|Child is as smaller than the<br>10th centile for fetal growth.|No|No|
|||**Short maternal stature**|**Mother’s height is less than**<br>**145 cm**|**Yes**|**Yes**|
|Maternal<br>nutrition|NNS Form 2.1|**Mother has low BMI**<br>**(underweight)**|**Mother’s BMI is less than**<br>**18.5**|**Yes**|**Yes**|
||Not in questionnaire|Maternal anemia|NA|No|No|
|||**Use of prenatal services**|**At least four prenatal visits**|**Yes**|**Yes**|
|||Neonatal tetanus|Received tetanus toxoid<br>during prenatal visit|Yes|No|
|Utilization of<br>antenatal<br>l|NNS Form 3.1|Prenatal nutrition<br>counseling|Received nutrition counseling<br>during prenatal visit|Yes|No|
|materna<br>health care||Micronutrient supplements<br>taken during pregnancy|Took iron or other vitamin<br>supplements during pregnancy|Yes|No|
||NNS Form 3.1/NNS<br>Form 4.1|Birth assisted by a skilled<br>health care professional|Birth assisted by skilled<br>practitioner (Doctor, nurse or<br>midwife)|Yes|Yes|

28
Separate indications of low maternal nutritional and health status during pregnancy - low birth weight of child during birth, maternal short stature and low BMI - are evident in 11 to 14 percent of children under the age of 2 and are strongly associated with mother’s education and family income. For example, the prevalence of LBW is 19 percent among children whose mothers completed elementary school at most, compared to 12 percent of children whose mothers went to college. The association of educational attainment and maternal short stature is even stronger. Of these three indicators, the prevalence of low BMI is most weakly associated with income and mother’s education.

Nationally, the utilization of antenatal health care and care during delivery is common. The mothers of over 80 percent of children under-2 attended at least four antenatal visits and 85 percent received assistance during delivery by a skilled practitioner. Disparities in maternal prenatal care associated with income and education, however, are very high. For example, while 95 percent of mothers with college education had skilled assistance during delivery, only 62 percent of mothers with elementary education or less had skilled assistance.

Overall, 57 percent of children under-2 have strong evidence of an adequate antenatal environment leading up to their birth. Children in the richest income quintile are about 1.5 times as likely to have adequate prenatal conditions than children in the poorest income quintile. Children whose mothers have college education are 1.6 times as likely to have adequate prenatal conditions than children whose mothers had at most an elementary education.

# **_5.3 Measures of childcare and feeding practices at home_**

Early childhood nutritional intake is directly dependent on the feeding practices adopted by parents in the home, including whether mothers decide to breastfeed, for how long, and for older children, what kinds of solid foods are given, how frequently and how diverse they are. The WHO recommends exclusive breastfeeding for children between the ages of 0 and 5 completed months of age, the introduction of solid or semi-solid foods between the ages of 6 and 8 completed months and continued breastfeeding along with complementary foods through 2 years of age. The WHO also has guidelines on dietary frequency and diversity for children above the age of 6 months. Other important and related aspects of childcare in the home include the hygienic preparation of food and whether the child receives cognitive stimulation. A set of six indicators were identified in the NNS data that map to childcare and feeding practices at home, of these two are considered necessary evidence of an adequate childcare and feeding environment at home: the child is age-appropriately (breast) fed and the mother always handwashes before food preparation and feeding, before and after eating and after toilet use (Table 4). Three ideal indicators of child feeding practices that were not available for this study include minimum meal frequency, minimum dietary diversity and minimum acceptable diet for children 6 months and older.

**Table 4**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
||||**Mom always handwashes**|||
|Hygiene|NNS Form<br>3.1|**Handwashing habit**|**before food prep and feeding,**<br>**before and after eating and**|**Yes**|**Yes**|
||||**after toilet use**|||

29
|Stimulation|NNS Form<br>3.1|Cognitive stimulation|Caretaker reads & plays with<br>child|Yes|No|
|---|---|---|---|---|---|
|||Child is currently<br>breastfed||Yes|No|
|Feeding|NNS Form<br>3.1|Child is currently eating<br>complementary foods<br>**Age appropriate**<br>**breastfeeding (WHO)**|**Children up to 6 months are**<br>**exclusively breastfed, at or**<br>**above 6 months receive**<br>**complementary foods along**<br>**with breastmilk**|Yes<br>**Yes**|No<br>**Yes**|
||NNS Form<br>4.3|Early initiation of<br>breastfeeding||Yes|No|
|||Minimum dietary<br>diversity (6m & older)||No|No|
||NNS 24<br>hour-recall|Minimum meal<br>frequency (6m & older)||No|No|
|||Minimum acceptable<br>diet (6m & older)||No|No|

Good hygiene habits are quite common, 4 in 5 children under-2 have mothers who report always handwashing before and after food handling and toilet use, with only minor differences associated with family income or education. In contrast, only 39 percent of children are read to and played with by the caretaker, with moderate differences associated with family income and the mother’s education. Less than 1 in 2 children under-2 are appropriately fed for their age. Age appropriate feeding is the only nutrition intervention indicator that is negatively associated with family income and mother’s education. For example, children under-2 from the poorest quintile are twice as likely to be breastfed according to WHO standards for their age than children from the richest quintile. The reason is that women in higher earning households are less likely to breastfeed and more likely to use breast-milk substitutes. Overall, 16 percent of children under-2 are in a home environment which provides good hygiene, cognitive stimulation and WHO recommended breast- and complementary- feeding practices.

A closer examination of the age-profile of different infant and young child feeding practices reveals that women with elementary educational attainment or less (EE) are increasingly more likely to be breastfeeding their child compared to women with college education (CE) as the child gets older (Figure 19). For example, in the first two months of life, children of EE women are 16 percent more likely to be breastfed compared to children of CE women. At just over a year old, children of EE women are 66 percent more likely to be breastfed compared to children of CE women. EE women are both more likely to breastfeed and breastfeed for longer than CE women. The same is true for exclusive breastfeeding, though by 8 months of age, very few children are still exclusively breastfed. EE women are also more likely to introduce complementary foods earlier than CE women. For example, between the age of 4 and 6 completed months, 29 percent of children of EE women are being fed solid or semisolid foods, compared to 18 percent of CE women. As CE women transition away from breastfeeding, not only do they introduce complementary foods, they also introduce breastmilk substitutes, such that by the age of 2, almost all children of CE mothers are drinking milk of any kind, whereas the proportion of children of EE women near the age of 2 drinking milk of any kind is only 69 percent.

30
<!-- Start of picture text --> % of children<br><!-- End of picture text --> <!-- Start of picture text --> Child is currently exclusively breastfed Child is currently breastfed<br>100%<br>75<br>50 1 4<br>25 3 0<br>0%9, 2 0<br>Child is currently fed other milk Child is currently fed any milk (breastmilk or substitute)<br>100% 100 99) 4<br>75 ' 90 9<br>50<br>5 5<br>0%<br>Child is currently fed solid or semi-solid foods Age appropriate infant & young child feeding<br>100%9 5 38 100<br>75 4<br>50 1 4<br>25 0<br>0%11 a<br>0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24<br>Age (completed months)<br>© College (some or complete) © Elementary school or less<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS. Notes: Point estimates and 95% confidence intervals for the prevalence of feeding practices are shown for 2 month-interval age groups, where for example, children between the ages of 0 and 2 are positioned at 1 on the x-axis. The line shown is based on a locally weighted scatterlpot smoothing algorithm (loess) to more clearly show the average relationship between child feeding practices and child's age.
give up on preferred foods or cut back on quantity by reducing meal size or number of meals and does not run out of food, go to bed hungry or go a whole day or night without eating.

**Table 5**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|||Household<br>food insecurity<br>(access) scale<br>score (HFIAS)|Scale score (numeric, 0-27) based on HFIAS<br>questionnaire|Yes|No|
|||**Food**<br>**insecurity**<br>**status: Food**<br>**secure**|**A food secure household experiences none of the**<br>**food insecurity (access) conditions, or just**<br>**experiences worry, but rarely.**|**Yes**|**Yes**|
|Household<br>food<br>security|NNS Form<br>1.5|Food<br>insecurity<br>status: Mildly<br>food insecure<br>Food<br>insecurity<br>status:<br>Moderately<br>food insecure|A mildly food insecure (access) household worries<br>about not having enough food sometimes or often,<br>and/or is unable to eat preferred foods, and/or eats a<br>more monotonous diet than desired and/or some foods<br>considered undesirable, but only rarely. But it does not<br>cut back on quantity nor experience any of three most<br>severe conditions (running out of food, going to bed<br>hungry, or going a whole day and night without<br>eating).<br>A moderately food insecure household sacrifices<br>quality more frequently, by eating a monotonous diet<br>or undesirable foods sometimes or often, and/or has<br>started to cut back on quantity by reducing the size of<br>meals or number of meals, rarely or sometimes. But it<br>does not experience any of the three most severe<br>conditions.|Yes<br>Yes|No<br>No|
|||Food<br>insecurity<br>status:<br>Severely food<br>insecure|A severely food insecure household has graduated to<br>cutting back on meal size or number of meals often,<br>and/or experiences any of the three most severe<br>conditions (running out of food, going to bed hungry,<br>or going a whole day and night without eating), even<br>as infrequently as rarely. In other words, any<br>household that experiences one of these three<br>conditions even once in the last four weeks (30 days)<br>is considered severely food insecure.|Yes|No|

32
|||Household<br>Dietary<br>Diversity<br>Score (HDDS)|The HDDS is based on 12 aggregated food groups<br>proposed by FANTA and the score ranges from 0 to<br>12. The NNS has 16 food groups: Cereals & products,<br>Vitamin A rich vegetables & tubers, white tubers &<br>roots, dark leafy green vegetables, other vegetables<br>including wild vegetables; vitamin A rich fruits; other<br>fruits including wild fruits; meat (fresh and processed);<br>organ meats (iron-rich); eggs; fish and shellfish;<br>legumes, nuts and seeds; milk and milk products, oils<br>and fats; sweets; spices condiments and beverages.<br>Aggregate groups are created for vegetables, fruits and<br>|Yes|No|
|---|---|---|---|---|---|
|Household|||meats.|||
|access to|NNS Form|||||
|diverse<br>|1.6|Household||||
|foods/diet||with HDDS >=<br>10|Household has a total HDDS score of at least 10|Yes|No|
|||**Household**|**Household consumed protein rich foods any**|||
|||**with protein**<br>**rich diet**|**number of times in past 7 days (meat & eggs &**<br>**milk products)**|**Yes**|**Yes**|
|||Household<br>with protein<br>intense diet|Household consumed protein rich foods at least 3<br>times in past 7 days (meat & eggs & milk products)|Yes|No|
|||Household<br>with vitamin A<br>rich diet|Household consumed vitamin A rich food in past 7<br>days|Yes|No|
|Mother’s||Mothers||||
|access to<br>diverse<br>foods/diet|NNS 24<br>hour-recall|dietary<br>diversity score<br>(MDDS)|NA|No|No|
|Household<br>|i|The fraction of<br>household||||
|access to<br>diverse|Not n<br>questionnaire|calories<br>|NA|No|No|
|foods/diet||derived from<br>starchy stables||||

Compared to other groups of factors associated with childhood undernutrition, indicators of household food security and food diversity have the greatest differences associated with family income and mother’s education. Overall, 1 in 5 children under-2 live in food secure households, and children in the richest quintile are five times as likely to be food secure than children in the poorest quintile. Children in the richest quintile are also nearly twice as likely to live in households with access to a greater diversity of foods compared to children in the poorest quintile. Household access to animal source foods is also associated with income: 72 percent of children under-2 among the richest quintile are in households that consumed protein rich foods in the past week, compared to 37 percent of children in the poorest quintile. Even fewer children under-2, less than 5 percent in the poorest two deciles, live in households that regularly consume protein-rich food sources (from meat, eggs and milk, at least 3 times in the past week) compared to over 30 percent in the richest two deciles (Figure 20). The average number of times weekly that the poorest 20% of households consume milk or milk products is 2.7, compared to 4.8 times for the richest 20% of households. For eggs, the difference is 2 versus 3.7 times 33
<!-- Start of picture text --> % of children under-2<br>9 any 3 protein<br>100% 95 96 96 96 95 96 95 96 97 96 sources, at least 1<br>a a a in past week<br>all 3 protein<br>80 7 sources, at least 1<br>time in past week<br>60<br>all 3 protein<br>sources, at least 3<br>times in past week<br>40<br>1<br>20 1<br>1 13 13<br>7<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Income decile<br>Source: Author's calculations using 2015 NNS data (Household Dietary Diversity Module).<br><!-- End of picture text -->
Average number of days food is consumed in past week by household

||All<br>individuals|Poorest20%R|ichest20%|Some or<br>complete<br>elementary;<br>or no<br>schooling|Someor<br>complete<br>college|Rural|Urban|
|---|---|---|---|---|---|---|---|
|Cereals (eg rice bread noodles)-<br>Oils & Fats (eg. butter)-||||||||
|Milk & milk products (eg milk, cheese, yogurt)-||||||||
|Fish & shellfish (fesh, dried, processed)-||||||||
|Dark leafy green vegetables (eg kangkong, malunggay)-||||||||
|Eggs:<br>||||||||
|Otherfruits-<br>||P|||||(2.1)<br>||
|Other vegetables (eg eggplant, bamboo shoot)-<br>||||(2)<br>||(2.2]<br>|(2)|
|Meat, fresh & processed (eg beef pork, chicken)-<br>||(4||(1)<br>||(1.5)||
|Vitamin A rich vegetables & tubers (eg carrots, squash)-<br>||||(1.5)<br>||||
|White tubers & roots (eg potato, cassava)-|(1.4)|(1.4)|(1.3)|@||@|(1.2}|
|VitaminA<br>rich fruit (eg ripe mangos, papayas)-|@|(1)|(4.4)|{0.9}|(1.1}|@|[0.9]|
|Legumes, nuts & seeds (eg beens, peas, lentils)-|{0.9}|(0.8}|(1.4)|{0.8}|{0.9}|(0.8]|@|
|Organ meats (iron-rich) (eg liver, kidney)-|(02)|fe)|(0.3)|(02)|(03)|(02)|(03)|

Source: Author's calculations using 2015 NNS data.
## **Table 6**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|Utilization of<br>postnatal maternal<br>health care|NNS Form<br>3.1|**Postnatal care**<br>Postnatal breast<br>&<br>complementary<br>feeding<br>counseling|**Received postnatal check-up right after**<br>**birth by skilled practitioner (Doctor,**<br>**nurse or midwife)**<br>After birth, received counseling on breast<br>& complementary feeding|**Yes**<br>Yes|**Yes**<br>No|
|Health status|NNS Form<br>3.1|Acute diarrhea in<br>past 2 weeks|Child experienced acute diarrhea in prior<br>two weeks|Yes|No|
|||**Neonatal**<br>**screening**|**Child underwent neonatal screening**|**Yes**|**Yes**|
|Health screening|NNS Form<br>4.2|Postnatal growth<br>control visit|Child participated in operation Timbang<br>Plus or other growth monitoring<br>government program|Yes|No|
|Health<br>interventions|NNS Form<br>4.2|**Age**<br>**appropriate**<br>**vaccinations**|**Child vaccinated according to WHO**<br>**schedule. BCG, Oral Polio, DPT (or**<br>**pentavalent), Measles (allowance for**<br>**delayed vaccinations)**|**Yes**|**Yes**|
|||Dewormed|Child received deworming medication<br>from government program|Yes|No|
|Nutrition<br>supplementation|NNS Form<br>4.2|Vitamin A<br>supplementation|Child participated once or twice in<br>Vitamin A supplementation government<br>program|Yes|No|

Nationally, skilled postnatal care for the mother, neonatal screening and up to date vaccinations, cover 78, 64 and 67 percent of the target population. With the exception of vaccinations, differences in coverage associated with family income and maternal education are wide. For example, 86 percent of children in the richest quintile underwent neonatal screening shortly after birth, compared to 45 percent of children in the poorest quintile. About 40 percent of children under-2 nationally show evidence of having access to all three sub-indicators of postnatal care. While differences in coverage of adequate postnatal care associated with income and mother’s education are wide, it is not significant between urban and rural areas.

# **_5.6 Household environment: Water, sanitation and hygiene infrastructure_**

Whether a child comes into contact with infectious diseases or other environmental pathogens is partly determined by the conditions of the dwelling and of the surrounding community. A set of 6 indicators from the NNS were identified as measures of the sanitary and hygienic conditions of the household, of which three are considered necessary evidence of adequate WSH conditions for the child: the 36
household uses drinking water from an improved source, the household uses a toilet facility that is water sealed and at least 75% of households in the municipality have improved sanitation (Table 7).

37
# **Table 7**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|||**Access to**<br>**improved**<br>**drinking**<br>**water**|**Main source of drinking water is**<br>**piped, from a borehole, from a**<br>**protected dug well or spring,**<br>**rainwater, bottled water or truck**|**Yes**|**Yes**|
|Household facilities that|NNS Form<br>1.2|**Access to**<br>**improved**<br>**sanitation**|**Toilet facility is water sealed (either**<br>**flush or pour with septic tank)**|**Yes**|**Yes**|
|support hygiene,||Access to|Uses clean cooking fuel (electricity,|||
|sanitation and disease<br>prevention||clean cooking<br>fuels|LPG or natural gas) and does not burn<br>garbage|Yes|No|
|||Exposure to<br>farm animals|Household member owns or manages<br>livestock|Yes|No|
||Not in<br>questionnaire|Handwashing<br>facilities|Availability of handwashing facilities,<br>water, soap & detergent|No|No|
|Community-level<br>environment|NNS Form<br>1.2|**Community**<br>**level**<br>**sanitation**|**At least 75% of households in**<br>**municipality have improved**<br>**sanitation**|**Yes**|**Yes**|
|Behaviors that support<br>hygiene, sanitation and|NNS Form<br>1.2|Safely<br>manages<br>drinking water|Boils, chlorinates or filters water or<br>purchases bottled mineral water|Yes|No|
|<br>disease prevention|Not in<br>questionnaire|Feces disposal|Child’s stools are disposed into an<br>improved sanitation facility|No|No|

Access to improved WSH infrastructure is in general very high: 95 percent of children under-2 have access to improved drinking water, 85 percent have access to improved sanitation and 82 percent live in a municipality where at least three-quarters of the households have improved sanitation. Nationally, close to 3 in 4 children under-2 live in a household with adequate WSH infrastructure.

Several indicators associated with the household environment but not related to water and sanitation were identified in the NNS data. Whether the household has access to electricity or uses improved cooking fuel and does not burn garbage has a bearing on indoor air quality, and how efficiently food can be prepared. Whether there is a functioning refrigerator has a bearing on the household’s ability to preserve foods that are prone to spoilage, including meat, eggs, animal milk, and breastmilk. Nationally, only about 1 in 3 children under that age of 2 live in households that use improved cooking fuels and or have a functioning refrigerator. Use of improved cooking fuel and refrigerator ownership are even lower for the poorest quintile, 3 and 8 percent, respectively, compared to 56 and 79 percent for the richest quintile. And of all the indicators considered, the urban-rural difference is largest for the use of improved cooking fuel: Urban households are five times as likely to burn cleaner fuels for 38
cooking than rural households. Given the lack of evidence in the literature of the impact of these resources on child undernutrition, they are omitted from the overall measure of access to an adequate household environment but are explored independently in the analyses that follow.

# **_5.7 Resources for food security, home care and health care_**

A women’s ability to access sufficient nutrition and health care for herself and her child prior-to, during and after pregnancy; provide sufficient food and care for her child and a housing environment that facilitates hygiene, requires financial, social and knowledge resources. Several measures from the NNS that capture the resources available to families for childcare, nutrition and health were identified (Table 8).

39
**Table 8**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available in**<br>**NNS**<br>**dataset?**|
|---|---|---|---|---|
||NNS Form 3.1|Mother states child should be<br>exclusively breastfed for 6 months||Yes|
|Caregiver<br>knowledge &|NNS Form 3.2|Mother states child should be fed<br>other foods starting at 6 months||Yes|
|<br>beliefs|NNS Form 1.1|Mother’s educational attainment|Mother has high school or<br>tertiary education|Yes|
||NNS Form 3.1|Mother seeks advice for child’s<br>nutrition|Mother seeks advice from others<br>on child feeding timing,<br>consistency, type or amount|Yes|
|Income|FIES|Total household income||Yes|
|Expenditure|FIES|Total household expenditure||Yes|
||NNS Form 1.3|Government cash transfers||Yes|
|Social support|NNS Form 3.1|Assistance with caretaking from<br>social network||Yes|
||NNS Form 1.1|Mother is married||Yes|
|Food production|NNS Form 1.2|Household has ability to produce<br>food|Household member is the owner<br>or administrator of a farm,<br>livestock or fish pen|Yes|
|Food<br>preservation|NNS Form 1.2|Refrigerator|Household has a functional<br>refrigerator|Yes|
|Caregiver<br>behaviors|FIES|Smokes or drinks|A household member smokes or<br>drinks alcohol|Yes|
|Household<br>setting|NNS Form 1.1|Urban or rural location of household||Yes|
|Time use|Not in<br>questionnaire|Workload & time availability for<br>caregiver||No|
|Access to<br>services|Not in<br>questionnaire|Distance to closest health facility||No|

Measures that proxy for the level of social support and time available for caretaking are mixed (Figure 22). Nationally 55 percent of children under-2 have married mothers which implies a relatively high share of children who live with a single parent. Additional assistance for childcare is prevalent. Over 8 in 10 mothers report they get help with childcare duties, with little differences associated with family income, education or the urban/rural context of the household. Children in the poorest 20% of households are about half as likely to have mothers who work or run a business compared to children in the richest 20% of households. Children under-2 in poorer, rural households or whose mothers have little formal education are more likely to receive assistance from the government’s conditional cash transfer (CCT). Over 8 in 10 children have mothers with some high school or tertiary education. Knowledge of breastfeeding “best practices” is mixed. Nearly 1 in 5 children whose mothers have relatively little formal education believe children should be exclusively breastfed for less than 6 months and nationally, nearly 1 in 3 children have mothers who believe exclusive breastfeeding should extend beyond 6 months. Average household income per month in 2015 was PHP 20,047 (USD 440) 40
<!-- Start of picture text --> % of children under-2 years of age<br>All ee wae Mother's Urban/rural HH<br>individuals Income quintile education residence<br>Mother ;is married-. 55° 53co64 60fos)65 46co61<br>24 16 40 16 41 21 29<br>Mother has a job or business- ° oo o—o co<br>Mother gets assistance; with. childcare; - 88° 87co95 84co91 88é /89<br>Mother seeks advice. on child; feeding. - 60° 53ae)67 47oo67 57co65<br>Current or previousi participantici iin CCT/_ 27oO o_o8 38 ps10 46S 15oo35<br>4Ps program<br>Agriculture (Owner/admin.livestock, fishpen)of farm,| 3 8 3 ae MH 4<br>Mother has highi school or tertiaryi _ 81° 64° 970 100° 76oo89<br>education<br>Mother states childi should be_ 51° 50/52ra 46oO55 49©52<br>exclusively breastfed for 6 mos 33<br>Mother states child should be fed other_ 8 84y84 80 85 89/<br>foods btw 6-9 mos<br>0% 25 50 75 100 0% 25 50 75 100 0% 25 50 75 100 0% 25 50 75 100<br>© Poorest 20% © Some or complete college © Urban<br>© Richest 20% © Some or complete elementary; or no schooling © Rural<br>Source: Author's calculations using 2015 NNS.<br><!-- End of picture text -->
||% of children under 2 years ofage<br>re<br>wae<br>All individuals<br>Income quintile|Mother's<br>education|Urban/rural HH<br>residence|
|---|---|---|---|
|Adequate pre-natal conditions (maternal.<br>nutrition, birth outcomes)|if<br>a A|42 &|ee|
|.<br>.<br>;<br>Adequate HH food security& diversity-|15<br>6<br>38<br> | ©<br>oo|32<br>°|13<br>18<br>[oo]|
|Adequate HH water, sanitation<br>_<br>infrastructure & environment|S<br>.<br>=|2<br>7|. 7|
|Adequate post-natal maternal & child_<br>health care|2<br>28|7%<br>88|40 42|
|;<br>;<br>Adequate home care & feeding practices-|29<br>24<br>42<br>°<br>oo|20<br>38<br>Co—o|29,31<br>©|
|0 domains (Definition 3)-|6<br>16<br> |o<br>2)|16<br>2)|8|
|;<br>7<br>Any 1 domains (Definition 3)-||19<br>7<br>28<br>©<br>o—o|11<br>29<br>rom)|18 20<br>Q|
|;<br>7<br>Any2domains (Definition 3)-|31<br>22 29<br>°<br>[os|24<br>31<br>co|31/31<br>os|
|;<br>7<br>Any 3 domains (Definition 3)-|28<br>21<br>35<br>°<br>oo|20<br>33<br>oo|27<br>31<br>©|
|;<br>.<br>Any4 or 5 domains (Definition 3)-||16<br>7<br>35<br>©<br>oo|5<br>29<br>Fo)|4<br>18<br>©|
|0|%25<br>5075100%0%25<br>50<br>75100%|0%25<br>50<br>75100|%0%25<br>50<br>75100%|

© Poorest 20% © Some or complete college © Urban © Richest 20% © Some or complete elementary; or no schooling 9 Rural log(m(x )) = a+ Bx
All models account for the clustering of children by primary sampling units. Results are presented as changes in the risk of stunting (relative to 1) given by . Models are estimated using R’s “geeglm” package and expected conditional probabilities (predicted probabilities) are computed using the “Zelig” package. ee<sup>ββ</sup>

# **6 Results**

Model 1a draws on data from 693 infants between 0 and 5 completed months of whom 11.7 percent are stunted (Figure 24). The direction and magnitude of the association for each of the predictors is consistent in both the unadjusted and adjusted model. Infants with low birth weight (LBW) have 3.3 times the risk (adjusted RR) of stunting compared to infants with a normal birth weight, LBW infants have a predicted probability (PP)<sup>47</sup> of stunting of 21.3 percent compared to 6.6 percent among normal birth weight infants. To a lesser degree, maternal short stature and a low body mass index are also associated with an increased risk of stunting (adjusted RR of 1.7 and 1.9, respectively). Infants whose mothers attended at least 4 antenatal care visits are almost half as likely to be stunted compared to infants whose mothers did not attend at least 4 visits (adjusted PP of stunting of 7.3 vs 12.8 percent, respectively). Only one postnatal factor is associated with stunting: Infants with access to improved drinking water are 0.44 times as likely to be stunted compared to infants without access to improved drinking water (adjusted PP of stunting of 7.5 vs 17.2 percent, respectively). Only one socio-economic predictor - whether the mother is married - is significantly associated stunting. Married women are almost half as likely to have infants who are stunted than single women, all else equal.

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Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant stunting (0-6 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g) | = 3. ——342_____<br>Mother has short stature (height < 145cm)} 9 ie —i_§_<br>Mother has low body mass index (BMI < 18.5) $ —2___ —12____<br>Attended at least 4 antenatal visits } 9 Ops<br>Skilled birth delivery} = =—*——— Ce<br>Child is currently breastfed} =—--—— —-—_—_<br>Child is currently fed solid or semi-solid foods 9 a a<br>Child is currently fed other milk & 7 -—<br>Mother reads & plays with child] <= 7 a<br>Mother handwashes regularly 12 —— —-<br>Household is food secure; —*— —+—<br>HH consumed protein rich foods in past week] 9" —— ——<br>HH consumed vitamin A rich foods in past week a2 7 Tr<br>Underwent neonatal screening 2 —.— st<br>SkillAg e d post-natappropri a lte carevaccine for “restatus; .= a>_—e >—-_—e———-<br>Improved drinking water] 9" -*—— og4<br>Improved sanitation (toilet is water sealed) | 7 —-—- —-—-<br>>75% of HHs in municipality have improved sanitation s. —-— —-—<br>Improved cooking fuel & no garbage burn} $ —_ ><br>HH member smokes}3 ~*~ —<br>Functioning refrigerator in household} 3 —— ae<br>Female —- +<br>Single birth ——— ——<br>Urban —_<br>Total HH size (N)jo +<br>Total children under five in HH (N)j.Q ——<br>Total HH income per month (1 = 1000 PHP) (log) s ><br>In poorest quintile of total HH income per capita] 6 ——<br>Mother has high school or tertiary education} % ——<br>Mother is married oe<br>Mom wasa teenager at time of birth ——<br>Mother hasa job or business >t<br>0 1 2 3 4 50 1 2 3 4 5<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. Unadjusted regression models do not control for socio-economic factors.
Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant and early childhood stunting (6-23 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g) | = —92_ a<br>Mother has short stature (height < 145cm)} 9 ro —<br>Mother has low body mass index (BMI < 18.5) $ 144 _ —44_<br>Attended at least 4 antenatal visits 7 2 —+— ——_<br>Skilled birth delivery | = ee ee<br>Child is currently breastfed | ® —— 3<br>Child is currently fed solid or semi-solid foods 9 —-——- ———_<br>Child is currently fed other milk & oe oe<br>Mother reads & plays with child] <= —— ——<br>Mother handwashes regularly 12 —— ——<br>Household is food secure |” —- —<br>HH consumed protein rich foods in past week] ? 1°<br>HH consumed vitamin A rich foods in past week | & _ ~~<br>Underwent neonatal screening 2 —— ——<br>SkillAg e d post-natappropri a lte carevaccine for “restatus; .= ao— a_<br>Improved drinking water 4%" ——- a<br>Improved sanitation (toilet is water sealed) | 7 ——- ———<br>>75% of HHs in municipality have improved sanitation s. —— ——<br>Improved cooking fuel & no garbage burn} $ —— ——-<br>HH member smokesj 3 ee —<br>Functioning refrigerator in household} 3 oe Ost<br>Female oa ope<br>Single birth} © —2#—— 2<br>Urban ——<br>Total HH size (N)j a +<br>Total children under five in HH (N) ° 43<br>Total HH income per month (1 = 1000 PHP) (log) s ——<br>In poorest quintile of total HH income per capita] 6 a<br>Mother has high school or tertiary education} % ——<br>Mother is married —+—<br>Mom wasa teenager at time of birth —_—+—<br>Mother hasa job or business Te<br>0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. The unadjusted regression model does not control for socio-economic factors.
stunting persists for this older age group (adjusted RR of 1.8), the association of LBW and maternal low BMI diminishes (adjusted RR of 1.34 and 1.14, respectively). Several postnatal factors are associated with a reduced risk of stunting. Children in food secure households and with access to animal sourced foods (ASFs) are around 10 percent less likely to be stunted compared to children with weaker food security and access to ASFs (unadjusted RRs of 0.88), though the inclusion of socioeconomic factors in the adjusted model attenuates these associations, likely reflecting the role of income in mediating access to food. Neonatal screening is associated with a 14 percent reduction in the risk of stunting (adjusted RR of 0.86) and use of improved cooking fuel and access to a functional refrigerator are associated with reductions in stunting on the order of 15 and 25 percent (adjusted RR of 0.84 and 0.77, respectively). Children whose mothers have high school or tertiary education are 11 percent less likely to be stunted (RR of 0.89) compared to children whose mothers have lower educational attainment.

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Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant and early childhood stunting (24-60 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g)| > 132 14<br>Mother has short stature (height < 145cm) ee —<br>Mother has low body mass index (BMI < 18.5) FA nd Ait<br>Skilled birth delivery} & —— ——<br>Household is food secure} & Ogs —-<br>HH consumed protein rich foods in past week 3 og8 ><br>HH consumed vitamin A rich foods in past week} 2 - 7<br>Underwent neonatal screening] , on a<br>Age appropriate vaccine status x — a<br>In Vitamin A supplementation program | 2 + +<br>Participated in OPT plus} > a +e<br>Improved drinking water | ., — —+—<br>Improved sanitation (toilet is water sealed) m — —<br>>75% of HHs in municipality have improved sanitation = — —<br>Improved cooking fuel & no garbage burn S oe a<br>Functioning refrigeratorHH memberin household] smokes 3TM —_07 + —O77 +<br>Female +> ><br>Single birth 2 ot<br>Urban ><br>Total HH size (N)} 5 194<br>Total children under five in HH (N)]6 *<br>Total HH income per month (1 = 1000 PHP) (log) S ogs<br>In poorest quintile of total HH income per capita ry i<br>Mother has high school or tertiary education ° oge<br>Mother is married re<br>Mom wasa teenager at time of birth —-<br>Mother hasa job or business +<br>0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. The unadjusted regression model does not control for socio-economic factors.
For children between the ages of 6 and 23 completed months (model 2b, Figure 28), an adequate prenatal environment is associated with a nearly 40 percent reduction in stunting risk (adjusted RR of 0.62) and adequate household food security and diversity is associated with a 25 percent reduction in stunting risk (adjusted RR of 0.75).

Results for children over the age of 23 completed months that exclude measures of adequate caretaking at home (since these data were not collected for older children) show comparable reductions in stunting risk associated with prenatal conditions and food security as well as reductions in stunting risk associated with water and sanitation infrastructure and postnatal child health care (model 2c, Figure 29). To the extent that linear growth retardation by- and extending beyond- the age of 2 reflects the accumulated impact of environmental insults during the child’s first 1,000 days, this model may provide the most comprehensive general description of the relative importance of the drivers of stunting in the Philippines. The relatively large attenuation of the relative risks of stunting associated with food security, water and sanitation and health care in the unadjusted versus adjusted model also points to the importance of current income as a key mediating factor enabling households to secure favorable environments in those domains.

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Unadjusted and adjusted changes in risk associated with underlying pre-natal and postnatal risk factors for infant and early childhood stunting (0-5 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Adequate pre-natal conditionssg: (maternal nutrition,a birth. outcomes)} 0.39-*— 0.37_<br>Adequate home care & feeding practices —_ an<br>Adequate HH food security & diversity ———- ——_—<br>Adequate post-natal maternal & child health care —_-—1.62 —_-——1.78<br>Adequate HH water, sanitation infrastructure & environment am fe<br>Female ° *<br>Single birth} +2— oe<br>Urban 7°<br>Total HH size (N) +<br>Total children under five in HH (N) ih<br>Total HH income per month (1 = 1000 PHP) (log) 2:<br>In poorest quintile of total HH income per capita —_—-—-<br>Mother has high school or tertiary education —_———<br>Mother is married oe<br>Mom wasa teenager at time of birth o—<br>Mother has a job or business a<br>1 2 1 2<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
<!-- Start of picture text --> Predicted probabilities of stunting during infancy (< 6 months of age) for<br>select conditions<br><!-- End of picture text --> <!-- Start of picture text --> Predicted. baseline,risk —o—8.8<br>Pre-natal environment:. Inadequate —o——_16.1<br>Pre-natal environment:. Adequate —_o—5.9<br>Post-natal health care environment:: . 6.36<br>Inadequate<br>j<br>Post-natal- health care environment:: 11.36<br>Adequate<br>Post-natal water/sanitation environment: 138<br>Inadequate<br>Post-natal water/sanitationitati environment:i . 7.96<br>Adequate<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2a (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
Unadjusted and adjusted changes in risk associated with underlying pre-natal and postnatal risk factors for infant and early childhood stunting (6-23 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>pre-natal conditions (maternal nutrition, birth outcomes) ae oe<br>Adequate home care & feeding practices _—- —_—<br>Adequate HH food security. & diversity. . ——0.69 +0.74<br>Adequate post-natal maternal & child health care —+— —+—<br>Adequate HH water, sanitation infrastructure & environment oe J<br>Female —_0.66 —0.65<br>Single birth} —?s*—— +1<br>Urban a<br>Total HH size (N) +<br>Total children under five in HH (N) 3.<br>Total HH income per month (1 = 1000 PHP) (log) ——<br>In poorest quintile of total HH income per capita °<br>Mother has high school or tertiary education —_———<br>Mother is married —_——<br>Mom wasa teenager at time of birth o<br>Mother has a job or business a<br>0.4 0.6 0.8 1.0 1.2 04 06 08 1.0 1.2<br>Relative Risk (95% Cl)<br><!-- End of picture text --> <!-- Start of picture text --> Adequate pre-natal conditions (maternal nutrition, birth outcomes)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
Predicted probabilities of stunting during infancy (6-23 months of age) for select conditions <!-- Start of picture text --> 23<br>Predicted baseline risk —_o—<br>29.9<br>Pre-natal environment: Inadequate —_o—<br>18.6<br>Pre-natal environment: Adequate —_o—<br>Food security/diversitytas . environment:. . 24.1O<br>Inadequate<br>Food security/diversity environment: Ad<br>Adequate<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2b (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
Unadjusted and adjusted changes in risk associated with underlying pre-natal and postnatal risk factors for infant and early childhood stunting (24-60 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Adequate pre-natal conditionsa: (maternal nutrition,a birth: outcomes) —0.71 —0.72<br>Adequate HH food security & diversity} —*—0.6 —_——0.74<br>Adequate HH water, sanitationPeerinfrastructure & environment. —_0.76 —0.89<br>Adequate child. health care _0.84 ——0.91<br>Female > ——<br>Single birth —_—_0.85 —_—0.8<br>Urban —e<br>Total HH size (N) 1.03.<br>Total children under five in HH (N) 14<br>Total HH income per month (1 = 1000 PHP) (log) —0.78<br>In poorest quintile of total HH income per capita —_——1.12<br>Mother has high school or tertiary education _0.87<br>Mother is married ae<br>Mom wasa teenager at time of birth a<br>Mother has a job. or business. —0.93<br>0.7 0.9 1.1 0.7 0.9 1.1<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
<!-- Start of picture text --> Predicted probabilities of stunting during late childhood (24-60 months of age)<br>for select conditions<br><!-- End of picture text --> <!-- Start of picture text --> : ar 35.8<br>Predicted baseline risk —oO-<br>Pre-natal environment: Inadequate =e40.3<br>Pre-natal environment:. Adequate —o—29.2<br>Food security/diversity environment: 344<br>Inadequate<br>Food security/diversity environment: 3°<br>Adequate<br>Water/sanitation environment: Inadequate —o—38.7<br>Water/sanitation environment: Adequate 2634.5<br>Post-natal health care environment: y<br>Inadequate<br>Post-natal health care environment: 336<br>Adequate<br>0 10 20 30 40 50%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2c (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown
Unadjusted and adjusted changes in risk associated with simultaneous access to nutrition drivers for infant and early childhood stunting (0-23 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Any 1 domains (Definition 3) — ——_—_<br>Any 2 domains (Definition 3) aoe —.—-<br>Any 3 domains (Definition 3)} 2s a<br>Any 4 or 5 domains (Definition 3) ose a<br>Female oe oe<br>Female, 0-6 mo ——8_ ___ ><br>Single birth] —-s+— —<br>Urban +7<br>Total HH size (N) +<br>Total children under five in HH (N) tit<br>Total HH income per month (1 = 1000 PHP) (log) —<br>In poorest quintile of total HH income per capita —e<br>Mother has high school or tertiary education o—<br>Mother is married ——-<br>Mom wasa teenager at time of birth —_——<br>Mother has a job or business ><br>0.5 1:0 15 05 10 #15 |<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
Predicted probability of early childhood stunting (< 24 months of age) for simultaneous access to nutrition driver domains <!-- Start of picture text --> 19.2<br>Predicted baseline risk —o—<br>29.2<br>Adequate access to 0 domains ——. 0H<br>Adequate access to any 1 domain. —o—24.3<br>Adequate access to any 2 domains. —o——23.5<br>Adequate access to any 3 domains. —_o—19.5<br>Adequate access to any 4 or 5 domains. —o—13.3<br>0 10 20 30 40%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 3a (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
# Unadjusted and adjusted changes in risk associated with simultaneous access to nutrition drivers for infant and early childhood stunting (24-60 months of age)

<!-- Start of picture text --> Unadjusted Adjusted<br>Any 1 domains (Definition 5) 0.78> _0.9<br>Any 2 domains (Definition 5) 0.57~~ —0.73<br>Any 3 domains. (Definitiona 5) 0.42a —_0.56<br>Any 4 or 5 domains (Definition 5)}_ —*—0.27 —_——0.43<br>Female P +<br>Single birth aa a<br>Urban —<br>Total HH size (N) 1.03°<br>Total children under five in HH (N) +<br>Total HH income per month (1 = 1000 PHP) (log) 0.78~~<br>In poorest quintile of total HH income per capita _——1.11<br>Mother has high school or tertiary education ost<br>Mother is married r-°<br>Mom wasa teenager at time of birth aa<br>Mother has a job or business eal0.93<br>0.25 0.50 0.75 1.00 1.25 0.25 0.50 0.75 1.00 1.25<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). statistically significant predictors are labeled.

Notes: Only the relative risk estimates for
Predicted probability of early childhood stunting (24-60 months of age) for simultaneous access to nutrition driver domains <!-- Start of picture text --> 35.8<br>Predicted baseline risk —-oO-<br>46.2<br>Adequate access to 0 domains —_o—<br>Adequate access to any 1 domain; —o—417<br>Adequate access to any 2 domains. —o—33.6<br>Adequate access to any 3 domains. —_o—.26<br>Adequate access to any 4 domains. —_—o——_—_-19.7<br>0 10 20 30 40 50%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 3b (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown.
**Table 9. Sub-optimal environments and stunting, Infancy & early childhood: 0-23 months (2015 Stunting prevalence = 25%)**

||Prevalence<br>(% of<br>children<br>exposed)|Relative<br>risk of<br>stunting<br>(adjusted<sup>1</sup>)|Predicted increase in<br>absolute probability of<br>stunting<sup>2</sup>associated<br>with sub-optimal<br>conditions in domain<br>(percentage points)|Population<br>attributable<br>fraction<sup>3</sup>|
|---|---|---|---|---|
|Sub-optimal prenatal environment|43.0%|1.75|10.20|24%|
|Sub-optimal home care & feeding practices|29.2%|1.02|0|--|
|Sub-optimal household food security & diversity|84.6%|1.35|6.10|23%|
|Sub-optimal postnatal maternal & child health care|59.6%|0.93|0|--|
|Sub-optimal HH water, sanitation infrastructure &<br>environment|26.2%|1.16|1.50|4%|

1Adjusting for socioeconomic characteristics of the household including household size, number of children household income, mother's education, occupation status, marital status and mother's age.<sup>2</sup> This is calculated as the difference in the predicted probability of stunting in the sub-optimal condition and the predicted probability of stunting in the optimal condition.<sup>3</sup> The population attributable fraction is the proportional reduction in population stunting that would occur if exposure to risk factor were changed to an adequate scenario.

# **Table 10. Sub-optimal environments and stunting, late childhood: 24-60 months (2015 Stunting prevalence = 37.6%)**

||Prevalence<br>(% of<br>children<br>exposed)|Relative<br>risk of<br>stunting<br>(adjusted)|Predicted increase in<br>absolute probability of<br>stunting<sup>2</sup>associated<br>with sub-optimal<br>conditions in domain<br>(percentage points)|Population<br>attributable<br>fraction|
|---|---|---|---|---|
|Sub-optimal prenatal environment<sup>1</sup>|67.4%|1.38|11.10|20%|
|Sub-optimal home care & feeding practices|||No data||
|Sub-optimal household food security & diversity|84.1%|1.34|11.20|22%|
|Sub-optimal postnatal maternal & child health care<sup>2</sup>|64.8%|1.10|3.80|6%|
|Sub-optimal HH water, sanitation infrastructure &<br>environment|27.9%|1.12|3.50|3%|

1 Excludes recommended use of at least 4 antenatal care visits (not asked in relation to the pregnancies of children above the age of 2).<sup>2</sup> Excludes access by mother to skilled postnatal check-up (not asked in relation to the pregnancies of children above the age of 2) Unlike the stunting risk associated with low birth weight and maternal low BMI which diminishes with the child’s age, the risk of stunting associated with maternal short stature persists from infancy through childhood. This finding could suggest that different mechanisms link stunting with nutritional shocks faced during pregnancy (that contribute to low birth weight and maternal low BMI) and intergenerational or chronic nutritional shocks (that contribute to the stunted growth of mothers during their own childhood and adolescence). The fact that maternal short stature is associated with child nutrition outcomes independently of an association with low birth weight suggests that adolescent and reproductive health for girls, not just nutrition during pregnancy, is important.

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As the stunting risk associated with the nutritional status of the mother and child during pregnancy diminishes for older children, the stunting risk associated with the family’s ability to adequately supply nutrition to the child postnatally increases. Children in households that consume eggs, meat and milk or have tools that improve their capability to safely consume them are less likely to be stunted. The presence of a refrigerator and use of improved cooking fuels are both associated with lower stunting risk. Several possibilities might explain this. Both tools protect against illness (by lowering the degree of indoor air pollution and by preventing food spoilage and food borne illness) and facilitate care and feeding at home (a refrigerator enables households to purchase nutrient-rich perishable foods, cookstoves that use cleaner fuel can ease meal preparation and reduce cooking times).

Nearly 85 percent of children under-5 years of age in the Philippines reside in moderately or severely food insecure households that do not regularly consume protein-rich foods (as measured by consumption of ASFs at least once in the past week) (Tables 9 and 10). These children have 1.35 times the risk of stunting compared to children with a more favorable food security and diversity profile, controlling for socio-economic factors and other prenatal and postnatal stunting determinants (Tables 9 and 10). Around 23 percent of cases of stunting among children under-5 are attributable to inadequate household food security and diversity (Tables 9 and 10). Without measures of the actual frequency, quantity and quality of children’s diets in the NNS data it is unclear to what degree food security and diversity at the household level translates to child-level food consumption, but it is plausible that this relationship in part reflects the benefits of a higher quality diet enabled by food security and access to ASFs high in protein content. More research would help shed light on both the supply-side (such as the availability and price of ASFs in markets) and demand-side (such as knowledge, traditions and preferences) constraints to diversity in children’s diets.

Energy intake from milk - but not breastmilk - by children between 6 and 24 months of age is associated with a reduction in stunting risk even after controlling for income and mother’s education (use of breastmilk substitutes is strongly associated with family income and mother’s education, reflecting a preference among mothers with more financial resources and education not to breastfeed). More research and data are needed to untangle the behaviors and factors associated with milk feeding that might help explain why it is associated with better nutrition.

There is some evidence that factors associated with children’s postnatal health reduce stunting risk. Among infants, access to improved drinking water is associated with a reduction in the risk of stunting. This effect possibly links with the low prevalence of exclusive breastfeeding in the Philippines and the potential prevention of illness among infants whose formula is prepared with clean water. Assistance during birth by a skilled practitioner is negatively associated with stunting for children between the ages of 2 and 5 years. Given that other measures of child health care utilization are not available in the NNS data for children in this age group, this predictor may be capturing the benefits of health seeking behaviors more broadly (for example, mothers who delivered in hospital may be more likely to vaccinate their children or seek professional health care in other situations, such as when their child is ill).

Two socio-economic factors also emerge as important independent drivers of child nutrition in this analysis. Infants under the age of 6 months in single-mother households have an elevated stunting risk relative to children in two-parent households, possibly reflecting the greater burden of caretaking and housework that single mothers face. Children over the age of 2 with university educated mothers have a lower risk of stunting, possibly reflecting the role of formal education in facilitating the acquisition of knowledge - either directly or through expanded social networks - for more effective meal selection and feeding practices.

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This analysis also suggests that nutrition drivers across sectors are complementary in their impact on child nutrition. In other words, children’s nutrition stands to benefit more if public and private efforts work to create favorable environments for children across multiple domains, not just a single one. But as decision makers move towards defining how limited resources should be used to improve child nutrition, what initiatives should be prioritized?

Empirically, the results explored in this paper suggest that the greatest reduction in stunting prevalence would occur if significant progress was made on two fronts: improving prenatal conditions and improving food security and diversity. Combined, sub-optimal conditions in these two domains account for nearly 50 percent of the cases of stunting in children under the age of 2 years and 42 percent of the cases of stunting in children between the ages of 2 and 5 years. In the former case, this means greater focus on supporting the nutrition and health of expecting mothers and adolescents to improve birth outcomes, including access to contraception to give women greater ability to exercise their fertility preferences. In the latter case, this means supporting efforts to ensure that children are regularly consuming a range of foods that deliver key micronutrients, including protein-dense perishable foods such as meat or eggs and the adoption of technologies that facilitate their consumption (such as refrigeration). This is especially important when children reach the age of 6 months and their dietary intake needs to be significantly supplemented with energy and nutrients from solid or semi-solid foods. In areas where birth outcomes and food security are already generally favorable (e.g. areas with low prevalence of preterm or LBW newborns and diets that incorporate diverse food sources), stakeholders might examine ways to improve the safety and reliability of the drinking water supply and sanitation and wastewater systems. One aspect of the home care environment that is not associated with stunting per this analysis, but nevertheless is important for the cognitive development of children is stimulation through reading and play. Nationally, only 39 percent of children under the age of 2 are both read to and get play time with their caretaker, so more direct interventions to improve learning and schooling outcomes might include encouraging cognitive stimulation for children through outreach, education and investment in early childhood development.

# **References and Notes**

> 1 The Philippines Plan of Action for Nutrition, 2017-2022, National Nutrition Council: http://www.nnc.gov.ph/index.php/downloads/file/998-ppan2017-2022-executive-summary.html

> 2 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R, Maternal and Child Nutrition Study Group. Lancet. 2013 Aug 3; 382(9890):427-451.

> 3 Michelle A. Mendez, Linda S. Adair; Severity and Timing of Stunting in the First Two Years of Life Affect Performance on Cognitive Tests in Late Childhood, The Journal of Nutrition, Volume 129, Issue 8, 1 August 1999, Pages 1555–1562, https://doi.org/10.1093/jn/129.8.1555

> 4 Save the Children (2016). Cost of Hunger: - - Philippines. https://resourcecentre.savethechildren.net/library/cost <u>hunger philippines</u>

> 5 UNICEF (2017)

> 6 Harvard Univeristy Center on the Developing Child. The Science of Early Childhood Development. https://developingchild.harvard.edu/resources/inbrief-science-of-ecd/

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> 7 Elizabeth L Prado, Kathryn G Dewey; Nutrition and brain development in early life, Nutrition Reviews, Volume 72, Issue 4, 1 April 2014, Pages 267–284, https://doi.org/10.1111/nure.12102

> 8 <u>http://blogs.worldbank.org/developmenttalk/adversity-gets-brain</u>

> 9 WHO’s “igrowup” package for R was used to compute HAZ scores for all children in the NNS anthropometry dataset. https://www.who.int/childgrowth/software/en/

> 10 United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: key findings of the 2018 Edition of the Joint Child Malnutrition Estimates. Geneva: World Health Organization; 2018 Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf?ua=1

> 11 Since almost all stunting occurs within the first two years of life, the first “1,000 days” between conception and a child’s second birthday has become a leading organizing framework for nutrition interventions (https://thousanddays.org).

> 12 WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-forage, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization, 2006. https://www.who.int/childgrowth/standards/technical_report/en/

> 13 United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: key findings of the 2018 Edition of the Joint Child Malnutrition Estimates. Geneva: World Health Organization; 2018 Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf?ua=1

> 14 This visual builds off UNICEF’s original conceptual framework, later adapted by the Lancet series for maternal and child health and nutrition in 2008 and 2013, and maps out potential, evidence-based mechanisms that can lead to stunting in children.

> 15 Small-for-gestational age is defined as lower than the 10th centile for fetal growth from the 1991 US national reference population.

> 16 National and regional estimates of term and preterm babies born small for gestational age in 138 lowincome and middle-income countries in 2010. https://doi.org/10.1016/S2214-109X(13)70006-8

> 17 Philippines National Demographic and Health Survey 2017. Quezon City, Philippines, and Rockville, Maryland, USA: PSA and ICF.

> 18 The INTERGROWTH-21st project established the first international, multiethnic standard including well-dated pregnancies from eight geographically defined populations, and enables a common single standard to describe optimal and aspirational fetal growth around the world.

> 19 Lee Anne CC, Kozuki Naoko, Cousens Simon, Stevens Gretchen A, Blencowe Hannah, SilveiraMariangela F et al. Estimates of burden and consequences of infants born small for gestational age in low and middle income countries with INTERGROWTH-21st standard: analysis of CHERG datasets BMJ 2017; 358 :j3677.

> 20 Chawanpaiboon S, Vogel JP[1], Moller AB, Lumbiganon P, Petzold M, Hogan D, Landoulsi S, Jampathong N, Kongwattanakul K, Laopaiboon M, Lewis C, Rattanakanokchai S, Teng DN, Thikhamthrop J, Watananirun K, Zhang J, Zhou W, Gülmezoglu AM. Global, regional and national estimates of levels of preterm birth in 2014: a systematic review and modelling nalysis. Lancet Glob Health. 2018. 30 Oct. doi.org/10.1016/S2214-109X(18)30451-0

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> 21 Preterm prevalence in 2000 was estimated at 6.6 percent. It is worth noting that these are modeled estimates, since availability of preterm birth events in civil registration and vital statistics in the Philippines is limited.

> 22 Christian P, Lee SE, Donahue Angel M, et al. Risk of childhood undernutrition related to small-forgestational age and preterm birth in low- and middle-income countries. Int J Epidemiol. 2013;42(5):134055. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3816349/

> 23 Özaltin E, Hill K, Subramanian SV. Association of Maternal Stature With Offspring Mortality, Underweight, and Stunting in Low- to Middle-Income Countries. JAMA. 2010;303(15):1507– 1516. doi:10.1001/jama.2010.450

> 24 Linda S. Adair, David K. Guilkey; Age-Specific Determinants of Stunting in Filipino Children, The Journal of Nutrition, Volume 127, Issue 2, 1 February 1997, Pages 314– 320, https://doi.org/10.1093/jn/127.2.314

> 25 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R, Maternal and Child Nutrition Study Group. Lancet. 2013 Aug 3; 382(9890):427-451.

> 26 Linda S. Adair, David K. Guilkey; Age-Specific Determinants of Stunting in Filipino Children, The Journal of Nutrition, Volume 127, Issue 2, 1 February 1997, Pages 314– 320, https://doi.org/10.1093/jn/127.2.314

> 27 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R, Maternal and Child Nutrition Study Group. Lancet. 2013 Aug 3; 382(9890):427-451.

> 28 The studies used the following intervention groups: (1) Water only (W): Advocated use of chlorine to treat water + chlorine dispensers at communal points of access + 1L bottle of chlorine for use at home, (2) Handwashing only (H): Advocated handwashing with soap + 2 permanent handwashing stations in the home, (3) Sanitation only (S): Advocated latrine use and safe disposal of child and animal feces into latrine + Upgrade of latrine facility (if necessary) + sani scoop + child potty, (4) WSH interventions in combination, (5) Nutrition counseling + lipid supplementation and (6) WSH + Nutrition in combination.

> 29 Derek Headey, Kalle Hirvonen, John Hoddinott, Animal Sourced Foods and Child Stunting, American Journal of Agricultural Economics, Volume 100, Issue 5, October 2018, Pages 1302– 1319, https://doi.org/10.1093/ajae/aay053

> 30 Eggs in Early Complementary Feeding and Child Growth: A Randomized Controlled Trial Lora L. Iannotti, Chessa K. Lutter, Christine P. Stewart, Carlos Andres Gallegos Riofrío, Carla Malo, Gregory Reinhart, Ana Palacios, Celia Karp, Melissa Chapnick, Katherine Cox, William F. Waters Pediatrics Jul 2017, 140 (1) e20163459; DOI: 10.1542/peds.2016-

3459, https://pediatrics.aappublications.org/content/140/1/e20163459.long

> 31 <u>https://pantawid.dswd.gov.ph/</u>

> 32 Eeshani Kandpal, Harold Alderman, Jed Friedman, Deon Filmer, Junko Onishi, Jorge Avalos, A Conditional Cash Transfer Program in the Philippines Reduces Severe Stunting, The Journal of Nutrition, Volume 146, Issue 9, September 2016, Pages 1793–1800, https://doi.org/10.3945/jn.116.233684

> 33 Denney L, Angeles-Agdeppa I, Capanzana MV, Toledo MB, Donohue J, Carriquiry A. Nutrient Intakes and Food Sources of Filipino Infants, Toddlers and Young Children are Inadequate: Findings

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from the National Nutrition Survey 2013. Nutrients. 2018;10(11):1730. Published 2018 Nov 11. doi: https://10.3390/nu10111730

> 34 Derek Headey, Kalle Hirvonen, John Hoddinott, Animal Sourced Foods and Child Stunting, American Journal of Agricultural Economics, Volume 100, Issue 5, October 2018, Pages 1302– 1319, https://doi.org/10.1093/ajae/aay053

> 35 World Bank. 2018. Making growth work for the poor: a poverty assessment for the Philippines (English). Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/273631527594735491/Making-growth-work-for-the- <u>poor-a-poverty-assessment-for-the-Philippines</u>

> 36 Minimum dietary diversity (MDD) is defined as the percent of children 6-23 months of age who receive foods from 4 or more food groups during the previous day. Seven food groups are considered: (1) grains, roots and tubers, (2) legumes and nuts, (3) dairy products (milk, yogurt, cheese), (4) flesh foods (meat, fish, poultry, and liver/organ meats), (5) eggs, (6) vitamin-A rich fruits and vegetables, (7) other fruits and vegetables. A minimum acceptable diet (MAD) defined as children 6–23 months of age who receive a minimum acceptable diet (apart from breast milk). This indicator is comprised of two fractions: (1) The proportion of breastfed children 6-12 months of age who had at least the minimum dietary diversity and the minimum meal frequency during the previous day and (2) The proportion of nonbreastfed children 6-23 months of age who received at least 2 milk feedings and had at least the minimum dietary diversity not including milk feeds and the minimum meal frequency during the previous day.

> 37 Chawanpaiboon S, Vogel JP[1], Moller AB, Lumbiganon P, Petzold M, Hogan D, Landoulsi S, Jampathong N, Kongwattanakul K, Laopaiboon M, Lewis C, Rattanakanokchai S, Teng DN, Thikhamthrop J, Watananirun K, Zhang J, Zhou W, Gülmezoglu AM. Global, regional and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2018. 30 Oct. doi.org/10.1016/S2214-109X(18)30451-0

> 38 In contrast to the DHS survey, the NNS’s indicator of LBW prevalence uses the denominator of all living children (rather than all live births).

> 39 Katz J, Lee ACC, Kozuki N, et al. Born too small or too soon: a pooled analysis of mortality risk among preterm and small-for-gestational-age infants in low- and middle-income countries. Lancet. 2013

> 40 World Bank (2018) “Operationalizing a Multi-Sectoral Approach for the Reduction of Stunting in Indonesia: An application using the 2007 and 2013 Riskesdas” The World Bank, Washington D.C. http://documents.worldbank.org/curated/en/docsearch/report/114207

> 41 World Bank (2018) “All Hands on Deck: Reducing Stunting through Multisectoral Efforts in SubSaharan Africa.” The World Bank, Washington D.C. Report: http://documents.worldbank.org/curated/en/260571530132166786/ Annex: http://documents.worl <u>dbank.org/curated/en/467711529406437446/</u>

> 42 Skoufias, E., K. Vinha, and R. Sato (2019) “Reducing Stunting through Multisectoral Efforts in SubSaharan Africa.” Journal of African Economies (forthcoming)

> 43 Prendergast, Andrew J and Jean H Humphrey. “The stunting syndrome in developing countries” Paediatrics and international child health vol. 34,4 (2014): 250-65.

> 44 Children’s birthweights were recorded either as verbal reports from mothers or with reference to a written record.

> 45 This indicator allows for delayed vaccination.

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> 46 <mark>Guangyong Zou, A Modified Poisson Regression Approach to Prospective Studies with Binary Data,</mark> _<mark>A</mark> merican Journal of Epidemiology_ <mark>, Volume 159, Issue 7, 1 April 2004, Pages 702– 706, h</mark> ttps://doi.org/10.1093/aje/kwh090

> 47 Predicted probabilities are calculated at the means of all other predictors.

> 48 Thirty percent of children under-5 are either low birth weight, have mothers with a low BMI or have mothers with short stature. Under-5s exposed to any one of these risk factors have 1.6 times the risk of stunting compared to children who are not exposed, controlling for age, sex, socio-economic factors and other postnatal stunting determinants. Children with any two of these risk factors have 1.7 times the risk of stunting, and children with all three of these risk factors have 1.9 times the risk of stunting, controlling for other factors.

> 49 The contribution of a risk factor to a disease or a death is quantified using the population attributable fraction (PAF). PAF is the proportional reduction in population disease or mortality that would occur if exposure to a risk factor were reduced to an alternative ideal exposure scenario (From WHO: https://www.who.int/healthinfo/global_burden_disease/metrics_paf/en/).

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