The Economic Consequences of Undernutrition in the Philippines by Bianca Bueno

The Economic Consequences of Undernutrition in the Philippines A DAMAGE ASSESSMENT REPORT (DAR)

Foreword The Undernutrition Costing Study is an important milestone in addressing the multi-dimensional issue of undernutrition in the Philippines; as well as in improving the overall health and development of Filipino children. UNICEF wishes to acknowledge the strong support, cooperation and commitment of the Department of Health (DOH) and the National Nutrition Council (NNC) in tirelessly working toward these objectives. Child undernutrition in the Philippines is alarming: the most recent statistics reveal that one in three Filipino children under five years of age is stunted; and one in 10 is wasted. This means 700,000 children under 5 are at-risk of dying and more than 3.3 million children will be deprived of achieving their full potential later in life because of their undernutrition in the early years. Thousands of children’s lives are in danger and millions of them start life disadvantaged. Undernutrition also means economic loss in terms of serious human capital development deficits that result in increased cost of health care, lower work productivity and earnings, and loss of lives. Addressing the problem of undernutrition in the country requires a multi-level and cross-sectoral approach; as well as solid evidence to make a case for investment and policy-making. Evidence shows the link between nutrition and the quality of human capital; suggesting that lower rates of undernutrition achieves sustained national economic development. UNICEF, in partnership with the DOH and NNC, thus commissioned two costing studies that show the impact of undernutrition to the national economy and how much investment we need to address this issue. The first study, “The Economic Consequences of Undernutrition in the Philippines: A Damage Assessment Report (DAR),” shows that the Philippines is losing and will continue to lose around $4.5 billion per year if current rates of undernutrition are not mitigated. In 2015, this loss was equivalent to around 1.5% of the country’s GDP, which has made a significant dent in the national economy.

The second study, “Business Case for Nutrition Investment in the Philippines,” shows how effective implementation of affordable and equity-focused nutrition interventions can significantly decrease the annual economic burden of undernutrition. It identifies necessary nutrition interventions, and the investment that is required. It also presents that for every $1 invested to address undernutrition, there will be a $12 return to the overall economy. We hope these studies will be useful to gather the support of the leaders of our government – the decision-makers and development partners – to increase the resources and effectively implement the interventions needed to address undernutrition. Not only to improve the national economy, but more importantly because we have a duty to ensure that every Filipino child enjoys the right to good nutrition for shaping a healthier, brighter and better future generation of the Philippines. Mabuhay kayong lahat!

LOTTA SYLWANDER Representative UNICEF Philippines

Message

from the Cabinet Secretary

Nutrition is a smart investment that any country would pursue to help generations of children reach their full potential. Recognizing that undernutrition among children leads to diminished physical, mental, psychological and emotional development, it is our mutual commitment to ensure an enabling environment to mainstream nutrition and wellness. Advocacy and policy development play a key role in this transformational journey. When backed by solid and clearly presented evidence, advocacy can bring about tangible and lasting change. Meanwhile, policy development is crucial in reducing susceptibilities and risks to diseases, which results in poor learning and development, ultimately, less productivity in adult life. This study will provide a profound understanding of the economic impact of undernutrition, particularly stunting. The importance of the first 1000 days in a childâ&#x20AC;&#x2122;s life is underscored in the lens of critically looking into the window of opportunity to reach Ambisyon Natin 2040. Communities, individuals, governments, the media, academia, and private sector alike need to be mobilized and empowered to raise voices of concern and action for nutrition. Each of us needs to be part of a collective process to ensure mutual accountability in combating hunger and malnutrition. Although much has been done in this front, there will still be a great deal of advocacy, planning and policy intervention in the years to come. The effort of UNICEF, Department of Health, and National Nutrition Council in publishing this piece of evidence is beyond commendation. Indeed, it is time for all development sectors to have a common understanding of this condition for us to approach it in a holistic and integrated manner. As steward of participatory governance, we call on all agencies to align development plans to incorporate doable strategies to curtail economic loss brought about by undernutrition. Conscientiousness and purposiveness in addressing this condition are seen as beacon of light and hope of our future generation. For certain, this should be identified as one of the major goals for which all the different sectors will be held accountable.

LEONCIO B. EVASCO, JR. Cabinet Secretary Office of the President of the Philippines

Message

from the Department of Health

Undernutrition remains to be one of the biggest threats to Filipino children. Using available national data, it is estimated that there are more than 3.3 million children who are stunted, more than 600,000 pregnant women who are anemic, around 2.8 million children who are not optimally breastfed, and around 800,000 children who suffer from acute malnutrition and are at increased risk of dying. Together with other national government agencies, the Department of Health has always been at the forefront in the country’s fight against undernutrition. However, we also recognize that there are capacity and quality gaps in service delivery, standards not being met or implemented efficiently, and “go-to” interventions done at the LGU level that are neither sustainable nor effective. We thus welcome this collaborative study done by UNICEF, NNC, and DOH that provides the much needed evidence on undernutrition and the cost of doing nothing. It not only quantifies the enormous cost of stunting and undernutrition on the health care system and the Philippine economy but also outlines the needed interventions and their corresponding budgetary estimates. This will allow the DOH to revisit its policies and strategies and see if DOH is adequately investing on evidence-based and cost-effective nutrition-specific interventions. On behalf of the Department of Health, I thank all the partners and stakeholders who spent their time and effort in order to contribute to such an important study. It is time that we start investing in the health and nutrition of the Filipino people especially during the first 1000 days towards “Boosting Universal Health Care via FOURmula One Plus for Health.”

FRANCISCO T. DUQUE III, MD, MSC. Secretary Department of Health

vii

Message

from the National Nutrition Council

The year 2017 marks the first year of implementation of the Philippine Plan of Action for Nutrition (PPAN) for 2017-2022, which outlines the countryâ&#x20AC;&#x2122;s goals, targets, strategies and programs for the next six years for the nutritional well-being of the Filipino people. One of the most pressing problems that the PPAN aims to address is the high rates of undernutrition among Filipino children. Most recent studies show that more than 3 million children under-five years old are stunted, indicating compromised physical and cognitive development, consequences of which are felt even in adulthood through reduced economic productivity. In addition, around 700,000 children under-five years old suffer from wasting or being thin for height. These children, at their early age are already exposed to serious health risks that threaten their survival. These show that we need urgent actions to address the nutritional needs of children, and assist parents and caregivers in nurturing them. These costing studies developed through the efforts of UNICEF, NNC and DOH quantify the economic burden of undernutrition and present how much resources are needed to uplift the nutritional status of our children. These studies serve as a useful advocacy tool in rallying the support of our national and local government leaders, civil society and development partners to invest in the strategies, programs, and projects outlined in the PPAN. It is our hope that the study results can bring all of us to reflect on our roles and responsibiities as duty bearers in fulfilling our childrenâ&#x20AC;&#x2122;s right to the best start in life, and align our resources to scale up nutrition for a brighter future for Filipino children and for the Philippines. Let us work together in our pursuit to end all forms of malnutrition, invest in the Philippine Plan of Action for Nutrition 2017-2022!

MARIA-BERNARDITA T. FLORES Assistant Secretary of Health and Executive Director National Nutrition Council

viii

Acknowledgements Acknowledgement is due to the following organizations and individuals who provided support, shared their expertise, time and effort for the development of these studies, as well as to those who provided invaluable inputs through participation in the two consultation workshops held in August 2016: NATIONAL NUTRITION COUNCIL (NNC) Assistant Secretary of Health Maria-Bernardita T. Flores, Maria Lourdes Vega, Jovita Raval, Dianne Cornejo, Jasmine Tandingan, Strawberry Francia, Jana Culla and Benjamin Pretsch DEPARTMENT OF HEALTH (DOH) Dr. Joyce Ducusin, Dr. Anthony Calibo, Luz Tagunicar, Arlene Rivera, June Corpuz, Marissa Ortega, Lindsay Orsolino and Danica Galvan NATIONAL ECONOMIC AND DEVELOPMENT AUTHORITY (NEDA) Arlene Ruiz and Kevin Godoy DEPARTMENT OF BUDGET AND MANAGEMENT (DBM) Director Cristina Clasara and Nenita Cabral PHILIPPINES STATISTICS AUTHORITY (PSA) Mildred Addawe and Driesch Cortel FOOD AND NUTRITION RESEARCH INSTITUTE (FNRI) of the DEPARTMENT OF SCIENCE AND TECHNOLOGY (DOST) Dr. Imelda Angeles Agdeppa, Charmaine Duante, Apple Joy Ducay, Lilibeth Dasco and Dr. Eldridge Ferrer PHILIPPINE HEALTH INSURANCE CORPORATION (PHILHEALTH) Merla Rose Reyes, Dr. Robert Balaoing, Rodelyn Ang, Emylou Raymundo and Gemma Vecina COUNCIL OF THE WELFARE OF CHILDREN (CWC) Emmanuel Mapili NUTRITION CENTER OF THE PHILIPPINES (NCP) Dr. Mary Christine Castro WORLD VISION Gem Kathleen Macanan UNICEF Lotta Sylwander, Julia Rees, Dr. Willibald Zeck, Joris van Hees, Maria Evelyn Carpio, Dr. Rene Gerard Galera, Janice Datu-Sanguyo, Melvin Marzan, Ruth Francisco, Angelita Evidente, Dr. Raoul Bermejo, Manual Alexander Haasis, Dr. Pura Angela Wee-Co, Dr. Mariella Castillo, Dr. Andrew Bucu, Alvin Manalansan, Bianca Stella Bueno, Flora Sibanda-Mulder, and Christiane Rudert Recognition is also given to Mr. Jack Bagriansky, the international consultant whose technical expertise has made the completion of these studies possible.

Table of Contents Foreword

Message from the Cabinet Secretary

Message from the DOH

vii

Message from the NNC

viii

Acknowledgements

List of Tables

List of Figures

List of Annexes

xii

Acronyms and Abbreviations

xiii

Executive Summary

Background and Rationale

A. Current indicators of undernutrition in the Philippines

B. Introduction to the economic analysis of undernutrition

C. Summary of results

D. Caveats of the consequence model methodology

Pathway #1: Child Mortality Attributable to Undernutrition A. Estimating the value of workforce lost to child mortality

B. Perspectives on the attributions of child mortality

Pathway #2: Depressed Future Productivity of Children

A. Stunting or small nature

B. Anemia in children

C. Iodine deficiency disorders

D. Long-term disability from folic acid-related neural tube defects

Pathway #3: Depressed Current Productivity: Anemia in Adult Workers

Pathway #4: Additional Healthcare Expenditures due to Preventable Diseases

A. Cases of diarrhea and ARI from suboptimal breastfeeding, zinc deficiency and maternal hygiene

B. Low birthweight cases associated with 3 indicators of maternal nutrition status

The Economic Burden of Undernutrition in the Philippines

Annexes

List of Tables Table 1.1

Undernutrition prevalence rates and estimated number of cases by risk group for 14 indicators

Table 2.1

Estimated attributable child deaths by age group

Table 4.1

Relative risk of diarrhea and acute respiratory infection by breastfeeding status

Table 4.2

Estimated number of cases, in-facility contacts, and claims for incidences of diarrhea and acute respiratory infection among children under age 5

Table 4.3

Projecting cases of diarrhea and acute respiratory infection due to suboptimal breastfeeding, handwashing and zinc deficiency

Table 4.4

Unit costs for estimating the financial burden of diarrhea and ARI cases

Table 4.5

Annual costs associated with current prevalence of suboptimal breastfeeding, zinc deficiency and handwashing

Table 4.6

Projecting cases of low birthweight attributed to 3 indicators of maternal nutrition

Table 6.1

Annual economic burden of undernutrition by indicator

Table 6.2

Economic losses segmented by intervention and target population

Table 6.3

Economic losses segmented by intervention content

List of Figures Figure 1.1

Distribution of undernutrition by income quintile

Figure 1.2

Estimating the net present value of future economic losses from undernutrition by indicator using the logic model

Figure 2.1

Estimating mortality using indicators of undernutrition

Figure 2.2

Projecting the Future Economic Losses from child mortality attributable to undernutrition

Figure 2.3

Distribution of child mortality by undernutrition indicators

Figure 3.1

Cross-country comparison of child growth over the first 1,000 days

Figure 3.2

Projecting the future economic losses in agriculture and other manual labor sectors from stunting among children

Figure 3.3

Estimating the net present value of the Future Economic Losses in the services and white collar labor sectors among stunted children

Figure 3.4

Projecting the Future Economic Losses among anemic children

Figure 3.5

Estimating the Future Economic Losses from iodine deficiency disorders among pregnant women

Figure 4.1

Estimating the current economic losses from anemic adults engaged in manual labor

Figure 6.1

Distribution of economic losses by indicator

List of Annexes ANNEX 1 Philippine Demographic Data Annex Table 1.1

Population

Annex Table 1.2

Projected population by 5-year age group and sex

Computations and Assumptions for Wages or Earnings Rates

Annex Table 2.1

2012 Household Income

Annex Table 2.2

Correction for inflation

Annex Table 2.3

Estimates of income in the manual and service sectors

Annex Table 2.4

Other employment indicators

Adjusted and Extrapolated Prevalence

Annex Table 3.1

Breastfeeding, less than one month

Annex Table 3.2

Breastfeeding, 1-5 months

Annex Table 3.3

Prevalence of segmented severe and moderate wasting

Annex Table 3.4

Segmented prevalence of severe and moderate underweight

Annex Table 3.5

Anemia among children 6-24 months

Annex Table 3.6

Anemia among adults

Annex Table 3.7

Estimating segments of severe and moderate stunting

Annex Table 3.8

Maternal handwashing behaviors

ANNEX 4

Segmented Morality by Age

ANNEX 5

Mortality Calculations

Annex Table 5.1

Estimated child deaths due to suboptimal breastfeeding from birth to less than one month

Annex Table 5.2

Estimated child deaths due to suboptimal breastfeeding during 1-5 months

Annex Table 5.3

Estimated child deaths due to suboptimal breastfeeding during 6-24 months

Annex Table 5.4

Burden of neural tube defects (Spina Bifida and Anencephaly)

Annex Table 5.5

Child deaths attributed to low birthweight associated with maternal nutrition status

Annex Table 5.6

Low birthweight mortality related to 3 indicators of maternal nutrition

Annex Table 5.7

Disease-specific approach: Child deaths associated with weight-for-age (WAZ)

Annex Table 5.8

Disease-specific approach: Child deaths associated with wasting (WHZ)

Annex Table 5.9

Cause-specific approach: Child deaths associated with zinc deficiency, ARI and diarrhea

ANNEX 2

ANNEX 3

xii

Annex Table 5.10

Child deaths associated with vitamin A deficiency

Annex Table 5.11

Child deaths associated with suboptimal maternal handwashing behaviors

Background Calculations for the Cost of Treating Diarrhea and ARI Cases

Annex Table 6.1

Diarrhea cases

Annex Table 6.2

Consult cost

ANNEX 6

Acronyms and Abbreviations ASEAN

Association of Southeast Asian Nations

NDHS

National Demographic Health Survey

ARI

acute respiratory infection

NNS

National Nutrition Survey

BMI

Body Mass Index

NPV

net present value

CPH

Census of Population and Housing

NTD

neural tube defect

DAR

Damage Assessment Report

odds ratio

DOH

Department of Health

PAR

population attributable risk

DOST

Department of Science and Technology

PHC

primary health care

EBF

exclusive breastfeeding

PhilHealth

Philippine Health Insurance Corporation

FAD

folic acid deficiency

PSA

Philippine Statistics Authority

FEL

Future Economic Losses

relative risk

FNRI

Food and Nutrition Research Institute

SAM

severe acute malnutrition

GDP

gross domestic product

standard deviation

HAZ

height-for-age Z score

SGA

small for gestational age

IDD

iodine deficiency disorders

UIC

urinary iodine concentration

intelligence quotient

VAD

vitamin A deficiency

IUGR

intrauterine growth retardation

WAZ

weight-for-age Z score

LFPR

labor force participation rate

WHO

World Health Organization

LFS

Labor Force Survey

WHZ

weight-for-height Z score

xiii

The Economic Consequences of Undernutrition in the Philippines A DAMAGE ASSESSMENT REPORT (DAR)

Executive Summary The global scientific evidence linking nutrition with the quality of human capital indicates that lowering rates of undernutrition will be an indispensable component of promoting national economic development. In recent years, the Philippine gross domestic product (GDP) has been growing by more than 6% annually, among the highest economic growth rates in Southeast Asia. However, the nationâ&#x20AC;&#x2122;s nutrition indicators continue to lag behind several countries in the region. Sustaining and expanding its robust economic growth require investments to lower the currently high prevalence of undernutrition in order to protect and fully develop the nationâ&#x20AC;&#x2122;s human capital. Fourteen indicators of undernutrition indicate over 28 million cases of stunting, wasting, micronutrient deficiencies and other undernutrition issues. For each indicator, scientific evidence suggests serious survival and health risks. Evidence also points to deficits in cognitive development, physical growth, learning capacity and student performance, and ultimately, lower work productivity and earnings. The economic burden emerging from various cases of undernutrition ripple across the Philippine economy, eroding the human capital that lays the foundation of economic growth: peoplesâ&#x20AC;&#x2122; strength and energy, creative and analytical capacity, and initiative and entrepreneurial drive. Based on the current prevalence rates for these 14 indicators of undernutrition, along with the globally established coefficients of risk and deficit, this study estimates the cost of doing nothing or the Future Economic Losses (FEL) from current rates of undernutrition using national demographic, health, economic and labor statistics.

EXECUTIVE SUMMARY

The cost of doing nothing for 2015 is measured across the following four discrete pathways: PATHWAY #1 The net present value (NPV) of the forgone future workforce lost due to child mortality attributable to undernutrition is measured across five indicators of undernutrition: i) poor maternal nutrition status, ii) maternal underweight, iii) suboptimal breastfeeding, iv) zinc deficiency, and v) vitamin A deficiency. About 38% of child mortality can be attributed to these indicators. The NPV of the FEL due to the 29,561 lives lost in 2015 is estimated at around $667 million per year. PATHWAY #2 The NPV of depressed future adult productivity due to deficits in child growth and cognition is measured across several indicators of undernutrition, including childhood stunting, anemia and iodine deficiency disorders. The NPV of this future productive potential lost due to undernutrition in 2015 is projected at around $3.1 billion per year. PATHWAY #3 The current value of depressed productivity among anemic adults working in agriculture, industry and other employment manual labor is estimated at $233 million per year. PATHWAY #4 The current value of excess and preventable healthcare utilization due to zinc deficiencies, suboptimal breastfeeding and low birthweight is around $379 million per year.

Of these pathways, PATHWAY #2 (future productivity deficits) entails the greatest economic losses from undernutrition, reaching over $3 billion per year. The economic losses from undernutrition are estimated at around $4.5 billion per year or equivalent to around 1.5% of the Philippine GDP in 2015.

Four pathways to measuring the economic losses from undernutrition

Maternal status and hygiene Weight-for-age Z score Weight-for-height Z score Exclusive breastfeeding Vitamin A deficiency Zinc deficiency

HEALTH ISSUES

LOSSES

VALUE OF LOSSES

CHILD M O R TA L I T Y

Lost future workforce

$667M/y

Future productivity deficit

$3.1B/y

Work performance deficit

$233M/y

Additional health utilization cost

$379M/y

S O L

Iodine deficiency Anemia Childhood stunting

CHILD COGNITIVE AND GROWTH DEFECTS

G 5

Adult anemia

A D U LT WORK DEFICIT

PAT H W AY 4

PAT H W AY 3

PAT H W AY 2

PAT H W AY 1

NUTRITION INDICATORS

CHILD MORBIDITY Maternal status and hygiene Zinc Exclusive breastfeeding

COST OF DOING NOTHING

~ $4.5B/y

National economic losses of around $4.5 billion per year for doing nothing to address undernutrition (1.5% of GDP in 2015)

EXECUTIVE SUMMARY

@UNICEF Philippines/2014/JoeyReyna

Background and Rationale Poverty and undernutrition are locked in a vicious cycle that includes increased mortality and morbidity, poor health, retarded cognitive development and physical growth, diminished learning capacity and school performance, and ultimately, lower labor productivity and earnings. The negative impacts of undernutrition ripple across economies, eroding the human capital that lays the foundation of economic growth: peoples’ strength and energy, creative and analytical capacity, and initiative and entrepreneurial drive. The stark, tragic and visible conditions of undernutrition represent only a small tip of the iceberg, that is only 1%–5% of the burden of undernutrition.1 The predominant burden of undernutrition emerges from widespread subclinical indices of undernutrition. ‘Hidden hunger’ is characterized by a handful of biological, anthropometric and other indicators. These indicators are associated not only with child health and survival, but also with physical and intellectual growth, school performance and adult productivity. When prevalence is widespread, individual risks and deficits emerging from these indicators can aggregate into a substantial drag on national economic growth. Consequently, achieving reductions in the prevalence of undernutrition can substantially lower its national burden, as well as generate greater human and social capital to fuel economic development.

The global scientific evidence linking nutrition with the quality of human capital strongly suggests that lowering rates of undernutrition will be an indispensable component to achieving greater national economic development. In recent years, the gross domestic product (GDP) of Philippines has expanded at a rate of more than 6% annually, among the highest economic growth rates in Southeast Asia. However, the nation’s nutrition indicators continue to lag behind most countries in the Association of Southeast Asian Nations (ASEAN) region. Sustaining and expanding recently robust economic growth require investments to lower the currently high undernutrition prevalence in the country in order to protect and fully develop its human capital.

Latham, M., 1997, ‘Human Nutrition in the Developing World,’ Food and Nutrition Series No. 29, Rome, Food and Agriculture Organization (FAO).

Investing in nutrition programs is traditionally motivated by the moral imperative that improved nutrition status is basic to the protection of human rights and the promotion of good governance. Based on the evidence linking nutrition to productivity, job performance and the overall quality of human resources, this study seeks to augment this traditional rationale for investing in nutrition with a purely economic case.

B A C KG R O U N D A N D R AT I O N A L E

@UNICEF Philippines/2016/JeoffreyMaitem

A. CURRENT INDICATORS OF UNDERNUTRITION IN THE PHILIPPINES Reducing the prevalence of undernutrition, indicator by indicator, can substantially lower its national burden and the associated costs. Fourteen nutrition indicators documented in national surveys in the Philippines—including the 2013 National Demographic and Health Survey (NDHS), 2013 National Nutrition Survey (NNS) and 2015 National Nutrition Update—suggest a significant burden on national human, social and economic development. Table 1.1 summarizes the estimated prevalence rates and number of cases for the 14 key indicators of undernutrition. The commonly held perspective on undernutrition focuses on severe acute malnutrition (SAM), which is defined by a very low weight for height, visible severe muscle wasting, or the presence of nutritional oedema. In the Philippines, SAM represents about 253,000 cases.2 Although these cases require urgent attention, they however represent only a minute fraction of the total national burden of undernutrition.

TABLE 1.1 Undernutrition prevalence rates and estimated number of cases by risk group for 14 indicators

RISK GROUP AND INDICATOR

PREVALENCE RATE (%)

SOURCE

ESTIMATED PREVALENCE (CASES)

PREGNANT WOMEN AND NEWBORNS 1. Low body mass index (BMI)

24.8

2015 NNS 3

619,611

2. Stunting

42.7

Addo et al 2013 4

3. Anemia

25.2

2013 NNS 5

4. Folic acid deficiency

53.6

2013 NNS

1,339,160

5. Iodine deficiency

14.4

2013 NNS

1,451,244

6a. Non-exclusive breastfeeding, 0–5 months

51.2

2015 NNS

1,208,215

6b. Non-continued breastfeeding, 6–23 months

46.8

2015 NNS

1,623,830

7. Maternal hygiene

10.2

2013 NNS

1,150,420

8. Underweight

21.5

2015 NNS Update 6

2,166,788

1,066,831 629,605

CHILDREN UNDER AGE 5

9. Wasting

7.1

2015 NNS Update

715,544

10. Stunting

33.4

2015 NNS Update

3,366,080

11. Vitamin A deficiency

20.4

2013 NNS

2,055,929

12. Zinc deficiency

21.6

Capanzana et al. 2008 7

2,176,866

13. Anemia

21.0

2013 NNS

2,113,038

14.5

2013 NNS

4,628,285

6.0

2013 NNS

1,958,005

ADULT 14a. Anemia, women 14b. Anemia, men

B A C KG R O U N D A N D R AT I O N A L E

@UNICEF Philippines/2013/AdamFerguson

The current prevalence of undernutrition in the Philippines reflects not only the concerns of many poor people. The threat of malnutrition expands throughout the population regardless of income. Figure 1.1 shows the results for four nutrition indicators by income quintile. It indicates that while lower income groups are generally worse off than the more affluent, the nutrition status of the more affluent segments is considered as a moderate to severe public threat by the World Health Organization (WHO). While rates of stunting and underweight are closely related to income, rates of low weight for height or wasting are generally the same across all income groups. Among 6–11 month old children, anemia is over 40% across the less affluent segments, but remains around 30% for the more affluent groups.8 Stunting levels are very high among the first three poorer income quintiles, and lower among the two richer quintiles (Figure 1.1).

FIGURE 1.1 Distribution of undernutrition by income quintile

Anemia (6-11 months) Stunting (6-59 months) Underweight (6-59 months) Wasting (6-59 months)

49.2

48.5

47.6

44.9 40

39.5 32.4

31.5

29.2

31.6

22.1

26.0 20

20.9 14.8

13.3

8.4

7.7

8.8

7.1

5.8

6.3

Poorest 20%

Quintile 2

Quintile 3

Quintile 4

Richest 20%

B. INTRODUCTION TO THE ECONOMIC ANALYSIS OF UNDERNUTRITION This damage assessment report (DAR) uses the consequence modeling methodology to estimate the economic impact of undernutrition in the Philippines. It starts with a computer-modeling scenario describing the negative outcomes of the status quo, defined as the current prevalence of each of the 14 indicators of undernutrition shown in Table 1.1. For each indicator included, 2

the scientific literature provides substantial evidence on their negative consequences in terms of higher risks of mortality or morbidity, and deficits in mental development, physical performance, or on-the-job productivity. The literature expresses these negative outcomes quantitatively as coefficients of relative risk (RR) or proportional deficit (%).9

The prevalence of 253,000 indicates an annual incidence of more than 400,000 SAM cases. This was obtained using the following conversion formula: Population of children 6-59 months x SAM Prevalence x 1.6 correction factor.

Food and Nutrition Research Institute-Department of Science and Technology (FNRI-DOST), 2013, ‘8th National Nutrition Survey’, Taguig City: FNRI-DOST.

Addo, O.Y., et al, 2013, on behalf of the Consortium on Health Orientated Research in Transitional Societies (COHORTS) Group, “Maternal height and child growth patterns,” Journal of Pediatrics, 163(2):549-554.

FNRI-DOST, 2013, ‘8th National Nutrition Survey,’ Taguig City: FNRI-DOST.

FNRI-DOST, 2016, ‘MDGS by 2015: Did Juan Hit National Targets (NNS 2015 update).’

Capanzana, et al., 2015, “Zinc status of Filipinos by serum zinc level: 7th National Nutrition Survey, Philippine 2008,” European Journal of Nutrition & Food Safety, 5(5):565566.

FNRI-DOST, 2015, ‘8th National Nutrition Survey,’ Taguig City: FNRI-DOST.

According to the Farlex Partner Medical Dictionary (Farlex 2012), the relative risk (RR) for a disease, death, or other outcome is expressed as the ratio of the incidence (frequency) rate among individuals with a given risk factor to the incidence rate among those without it. The Mosby’s Medical Dictionary (9th edition, 2009) describes it as the ratio of the chance of a disease developing among members of a population exposed to a factor compared with a similar population not exposed to the factor.

B A C KG R O U N D A N D R AT I O N A L E

However, while these coefficients reflect universal biological or psychological processes, many determinants of economic impact are measured at the national level. The national context determines the scale of consequences: prevalence of undernutrition, birth and mortality rates, incidence and type of infections, and access to health care. Moreover, economic consequences are measured in financial units and therefore are very sensitive to national economic and labor context: average income, labor participation rates, share of manual employment in the agriculture and industry sectors versus service sector, and other factors.

Figure 1.2 outlines how the logic model is used to estimate the net present value (NPV) of future productivity deficits or Future Economic Losses (FEL) from current rates of undernutrition using national demographic, health, economic and labor statistics. The report uses the same approach when estimating current value of depressed productivity and cost of health utilization due to preventable diseases except that no discounting is required. The gross value of FEL per year that the relevant population group is working is computed as the product of the following: (i) the estimated

FIGURE 1.2 Estimating the net present value of future economic losses from undernutrition by indicator using the logic model

PREVALENCE prevalence rate x risk group

AVERAGE ANNUAL EARNINGS

LABOR FORCE PARTICIPATION RATE

$ per year

COEFFICIENT OF RELATIVE RISK OR DEFICIT

GROSS VALUE OF ANNUAL FUTURE ECONOMIC LOSSES DUE TO UNDERNUTRITION FEL, $ per year

t 0 +50

∑(

1 1+r

)

* FEL

t=t 0

Note: t is time or number of years after today, t0 is the number of years after today that individuals start working, and r is the social discount rate

B A C KG R O U N D A N D R AT I O N A L E

NET PRESENT VALUE OF TOTAL FUTURE ECONOMIC LOSSES DUE TO UNDERNUTRITION $

The wage or income parameters used in model include: •

$2,472/ year per employed person (based on Philippine Statistical Authority [PSA] reports);10

•

$2,830/year for employment in the service sectors (based on PSA reports);

•

$1,752/year for employment in the manual labor sectors, including agriculture and industry derived from several sources;

•

a national minimum wage of PhP 330 based on the average of highest and lowest reported for various national regions; and

•

PhP to $ exchange rate at 0.0212358.

@UNICEF Philippines/2016/ShehzadNoorani

prevalence of a specific undernutrition indicator, (ii) the average annual income per individual, (iii) the rate of labor participation, and (iv) the coefficient of relative risk. The number of cases or prevalence of each nutrition indicator is derived using the prevalence rate from official national surveys conducted between 2008 and 2015 (see Table 1.1), the population estimates of the various risk groups from the 2010 Philippine Census of Population and Housing (CPH), and finally, the number of births based on the reported birthrate of 24.6 per 1,000 population.11 The negative impacts of undernutrition are only applied to individuals who are actually in the labor force, as indicated by labor participation rate of 63.6% (78.4% for males and 50.3% for females) as reported in the 2015 Philippine Labor Force Survey (LFS).12 This however underestimates the actual consequences of undernutrition, particularly among women (whose labor participation outside the household is low). The 2015 LFS estimates that 16% of the labor force is employed in the industry sector, 29.6% in the agriculture sector, while 54.5% is in the services sector. The logic model in this report applies estimates of global coefficients of risks and deficits from the scientific literature to national health, demographic, labor and economic data. Valuing the future productivity of children is complex. For a child born, say in 2016, the earnings stream may not begin until 15 years later when the child enters the workforce, say in 2031, and may last around 50 years into the future. In order to estimate the economic impact of undernutrition, the model assumes that the workers should work for a total of 50 years. The literature from psychology and economics confirms that people place higher value on the present than the future—and the further off in the future, the less the perceived value is. Following the literature, it applies a social

discount rate, r, of 3% to calculate the NPV of lost future earnings due to child mortality or growth deficits.13 The social discount rate reflects the subjective preference for current over future consumption or savings.14,15 As shown in Figure 1.2, the model computes the net present value of total FEL by adding the discounted gross value of FEL per year, (1/(1+r))t × FEL (where r is the social discount rate and t is time or number of years after today), throughout the working age of the relevant population group (i.e., from the time they started working, t = t0 , until the time they retire, t0 + 50).

10 The PSA published an updated average income in October 2016. 11 Annexes 1 and 2 provides a full list of demographic and health indicators and sources. 12 Labor force participation rates (LFPRs) are estimated based on quarterly population surveys implemented by the PSA rather than on a compilation of data for officially registered workers. While an error band is recognized, the LFPR estimate captures self-employed, informal employed, employment with payment kind and other unofficially registered work. For more details, visit https://psa.gov.ph/statistics/survey/labor-force. 13 For instance, the World Bank uses the same discount rate in its World Development Report 1993: Investing in Health. Similarly, Horton, et al use the same in their Copenhagen Consensus Challenge Paper 2008. To illustrate discounting, consider the following examples: (i) applying a 3% annual discount rate to a 50-year gross earnings of around $124,000 amounts to an NPV of around $23 per year; and (ii) applying a 7% discount rate values the same lifetime of earnings at only about $10 per year. 14 Ross, J., et al., 2003, ‘Calculating the Consequences of Micronutrient Undernutrition on Economic Productivity,’ Health and Survival, AED. Applying the discount rate over the value of the future earnings forgone due to losses in productivity yield an estimate of the net present value of annualized economic losses. A higher discount rate implies a higher preference for current consumption over future consumption. 15 Harvard Business Review, ‘A Refresher on Net Present Value,’ https://hbr.org/2014/11/a-refresher-on-net-present-value.

B A C KG R O U N D A N D R AT I O N A L E

C. SUMMARY OF RESULTS The logic model outlined above is applied to each of the 14 nutrition indicators listed in Table 1.1 to generate projections that paint a picture of the magnitude of human and economic consequences emerging from the status quo, as described by these nutrition indicators. These economic losses or depressed economic activity is measured across four discrete pathways, which are summarized and discussed in more details in the succeeding sections of this report. PATHWAY #1 PAT H W AY 1

CHILD M O R TA L I T Y

Lost future workforce

$667M/y

The NPV of the future workforce lost due to child mortality attributable to undernutrition is measured across five undernutrition indicators: maternal nutrition and underweight, suboptimal breastfeeding, and zinc and vitamin A deficiencies. The NPV of the future workforce lost to child mortality from undernutrition in 2015 is estimated at $667 million per year.

CHILD COGNITIVE AND GROWTH DEFECTS

PAT H W AY 4

PAT H W AY 3

PAT H W AY 2

PATHWAY #2

A D U LT WORK DEFICIT

CHILD MORBIDITY

Future productivity deficit

$3.1B/y

The NPV of depressed adult productivity due to deficits in child growth and cognition is measured across several indicators of undernutrition, including childhood stunting, anemia and iodine deficiency disorders. The NPV of this future productive potential lost to undernutrition in 2015 is projected at $3.1 billion per year. PATHWAY #3

Work performance deficit

$233M/y

The current value of depressed productivity among anemic adults working in agriculture, industry and other manual labor is projected at $233 million per year. PATHWAY #4

Additional health utilization cost

$379M/y

The current value of excess and preventable healthcare utilization due to zinc deficiencies, suboptimal breastfeeding and low birthweight is around $379 million per year.

The total economic burden of undernutrition emerging from the 14 indicators of undernutrition amounts to around $4.5 billion per year or equivalent to 1.5% of Philippine GDP in 2015.16 A companion document to this assessment report, entitled Business Case for Nutrition Investment in the Philippines,17 outlines how implementing nutrition interventions that are effective, available, affordable and equity-focused can significantly diminish the annual economic burden of undernutrition, thereby yielding high returns and an attractive benefit-cost ratio.

B A C KG R O U N D A N D R AT I O N A L E

16 According to the World Development Indicators Database Online, the Philippine GDP in 2015 is $291,965,336,391. For more details, visit http://databank.worldbank.org/data/ reports.aspx?source=2&country=PHL. 17 Department of Health/National Nutrition Council/UNICEF, 2017, â&#x20AC;&#x2DC;Business Case for Nutrition Investment in the Philippinesâ&#x20AC;&#x2122;. UNICEF Philippines.

D. CAVEATS OF THE CONSEQUENCE MODEL METHODOLOGY Converting indicators of undernutrition to forgone economic activities and output and attaching a monetary value to it entail a complicated process. The following describes the caveats associated with using the consequence model methodology to estimate the economic impact of undernutrition. •

Monetizing the consequences of undernutrition is often dependent on a relatively limited evidence base, complex methodologies, and the quality of national health, demographic and socioeconomic statistics.

•

Many factors beyond individual physical and intellectual potentials determine work performance and earnings. Workplace incentives and opportunities, capital and technology affect how realizing human capital potential actually translates into actual improved labor productivity.

•

Benefits of improved nutrition extend beyond the workplace to a range of entrepreneurial pursuits and unpaid activities, such as parenting, household activities, educational improvement, and community participation. In a world where improved productivity will emerge mainly from individual choices and behaviors, the significance of these activities cannot be overstated.

•

Estimating the value of a life saved or improved as the sum NPV of lost potential earnings clearly underestimates the value of human life. The forgone income due to undernutrition is also insufficient to evaluate the impact of lives lost to undernutrition. Likewise, applying losses only to economically active populations underestimates further the negative impact of undernutrition, particularly on women, many of whom do most of the housework and are economically inactive.

•

Conditions of malnutrition often coexist in the same individual. Data on the number of individuals suffering multiple conditions is not available. However, it is reasonable to conclude that simply adding the number of cases recorded for each of the 14 indicators of undernutrition may overestimate the number of cases, and therefore inflate the projected losses.

The consequence model applies a statistical adjustment factor to account for multiple population attributable risks (PAR), and arrive at a conservative projection for total deaths attributable to different undernutrition indicators.18 The adjustment discounts infant mortality by around 12% among children age six months and below, and by around 21% among those age 6-59 months. It discounts the impacts on cognitive growth and development by 20% as outlined in Pathway #2. Although the consequence model accounts for overlapping conditions of undernutrition, it does not account for economic losses outside the workplace. Moreover, it aggregates the NPV of losses in future productivity and earnings of the next generation of workers (Pathways #1 and #2) and the current productivity losses and financial costs of preventable diseases (Pathways #3 and #4). Hence, the estimates generated by the model should be considered only as an order of magnitude. By painting a general picture of the magnitude of the economic cost of doing nothing, these estimates can help in directing and facilitating policy discussion on national investments to lower the prevalence of undernutrition. Finally, it should be noted that this assessment report focuses only on the economic costs of undernutrition. Humanitarian, moral and good governance rationales for investing in nutrition are not reflected here as they are beyond the scope of this report. 18 Rockhill, B., et al., 1998, “Use and misuse of population attributable fractions,” American Journal of Public Health, 88(1):15–19.

B A C KG R O U N D A N D R AT I O N A L E

PAT H W AY 1

NUTRITION INDICATORS

HEALTH ISSUES

LOSSES

VALUE OF LOSSES

CHILD M O R TA L I T Y

Maternal status and hygiene Weight-for-age Z score Weight-for-height Z score Exclusive breastfeeding Vitamin A deficiency Zinc deficiency

Lost future workforce

PATHWAY #1

Child Mortality Attributable to Undernutrition

$667M/y

National surveys suggest that each year, around 77,500 Filipino children die before their fifth birthday. Of these, about 42% die in their first month of life and about three-fourths die before their first birthday. Although undernutrition is widely recognized in the literature as the underlying cause of up to 45% of all child deaths, it is rarely reported as the cause of death, except for a few cases of SAM and rare cases of kwashiorkor or marasmus.19 The negative synergy between undernutrition and infectious disease masks the proportion of child mortality emerging from undernutrition. To estimate the proportion of child mortality attributable to each of the indicators of undernutrition, this damage assessment report considers the following coefficients of relative risk (RR), which are taken from the literature strongly associating a range of nutrition indicators with higher mortality:

Maternal nutrition. The higher risk of low birthweight deliveries and subsequent increased mortality is projected over three maternal risk factors. Evidence suggesting that a 20% reduction in the risk of low birthweight is associated with antenatal iron supplementation indicates an RR of 1.25.20 Infants of mothers with BMI lower than 18.5 kg/m are 1.71 times more likely to be small for gestational age (SGA) at births than those of mothers with normal BMI.21 Stunted mothers, with height of less than 145 cm, face increased likelihood of SGA births with RR of 2.2.22,23 Underweight (weight-for-age Z score or WAZ) and wasting (weightfor-height Z score or WHZ). A pooled analysis covering more than 55,000 child-years of follow-up and 1,315 child death finds that severe WAZ or WHZ, defined as less than 3 standard deviations (SD) below an international reference, brings a significant risk of death from acute respiratory infection (ARI) and diarrhea and other infections with an RR of 11.6 for WHZ and an RR of 9.4 for WAZ. The RR is lower, but remains significant, for moderate cases (i.e., between -2 SD to -3 SD).24

19 Black, R.E., et al., 2013, “Maternal and child undernutrition and overweight in low-income and middle-income countries,” The Lancet, 382(9890):427–451. 20 Imdad, A., and Z.A. Bhutta, 2012, “Routine iron/folate supplementation during pregnancy: effect on maternal anemia and birth outcomes,” Paediatric Perinatal Epidemiology, 26(s1):168–77. 21 Supplement to: Black, R.E., et al., 2013, “Maternal and child undernutrition and overweight in low-income and middle-income countries,” The Lancet, 382(9890):427–451. 22 Supplement to: Black, R.E., et al., 2013, “Maternal and child undernutrition and overweight in low-income and middle-income countries,” The Lancet, 382(9890):427–451. 23 Given the lack of data, this report uses the risk of low birthweight as an indicator of the risk of SGA. Data collection systems in the Philippines do not differentiate low birthweight from either SGA and intrauterine growth retardation (IUGR), which have distinct clinical conditions. Although most of the recent literature focuses on SGA, evidence for negative outcomes emerge from all three conditions. 24 Olofin I., et al, 2013, “Associations of suboptimal growth with all-cause and cause-specific mortality in children under age 5: a pooled analysis of ten prospective studies,” PloS One 8(5):e64636.

PAT H WAY # 1: C H I L D M O R TA L I T Y AT T R I B U TA B L E T O U N D E R N U T R I T I O N

Maternal behavior and suboptimal breastfeeding. Increased mortality risk for nonbreastfed versus exclusively breastfed infants ranges from an RR of 10.53 for diarrhea and an RR of 15.13 for pneumonia.25 Compared to exclusively breasted infants, the risks were lower, but still significant for predominant and partial breastfeeding—with an RR ranging from 1.48 to 2.28. Discontinuing breastfeeding after the first six months of life and until 24 months of age also brings nearly twice the mortality risk compared to babies benefiting from continued breastfeeding.26 Hygiene interventions to improve hand-washing behaviors during childcare and feeding suggest an RR of 2.08.27 Micronutrient deficiencies. A recent Cochrane Review found a 24% reduction in mortality associated with vitamin A interventions. This protective effect indicates an RR of 1.32.28 The literature widely observes an association between zinc deficiency and increased mortality due to infectious disease. A recent meta-analysis finds an RR of mortality from diarrhea of 2.01 and an RR of 1.96 for pneumonia.29 A Cochrane Review including five folic acid supplementation trials identified a 72% reduction in the risk of neural tube defects, such as spina bifida and anencephaly.30 To estimate the PAR, the consequence model applies the RR of mortality from the global scientific literature to the national prevalence of the appropriate indicator as shown in Figure 2.1.31 The PAR, expressed as a percentage, is then applied to the mortality in the relevant age group to project the proportion of deaths associated with a particular indicator of undernutrition. The following illustrates how the logic model estimates the deaths attributable per nutrition indicator.

25 Black, R.E., et al., 2008, “Maternal and child undernutrition: global and regional exposures and health consequences,” The Lancet, 371(9608):243–260. 26 Black, R.E., et al., 2008, “Maternal and child undernutrition: global and regional exposures and health consequences,” The Lancet, 371(9608):243–260. 27 This is extrapolated from Bhutta, Z., 2014, ‘Lives Saved Tool (LiST) Analysis for Global Nutrition Report

FIGURE 2.1 Estimating mortality using indicators of undernutrition

Independent Expert Group,’ http://www. globalnutritionreport.org. The protective

PREVALENCE prevalence rate x risk group

RELATIVE RISK OF MORTALITY $ per year

POPULATION ATTRIBUTABLE RISK

effect of intervention (RR 0.48) is converted to threat of negative outcome (1/0.48). 28 Black, R.E., et al., 2013, “Maternal and child undernutrition and overweight

(PAR)

in low-income and middle-income countries,” The Lancet, 382(9890):427– 451. 29 Supplement to: Black, R.E., et al., 2013, “Maternal and child undernutrition and overweight in low-income and middle-income countries,” The Lancet,

ATTRIBUTABLE DEATHS PER YEAR

POPULATION ATTRIBUTABLE RISK

MORTALITY RATE IN RISK GROUP

(PAR)

% and number of cases

382(9890):427–451. 30 De-Regil L.M., et al., 2010, “Effects and safety of periconceptional folate supplementation for preventing birth defects,” Cochrane Database of Systematic Reviews, 2010(10):CD007950. 31 The PAR indicates the number (or proportion) of cases in a population. It depends on the prevalence of the risk

Mortality projections were estimated for each of the indicators of undernutrition listed earlier (see Annex 4 for more details). Based on the current prevalence among pregnant women and children of the various undernutrition conditions (see Table 1.1) and the RR of mortality associated with each indicator, cases of child mortality is estimated for the appropriate risk group (see Annex 3 for more details). Because these nutritional deficiencies and risk factors often coexist and affect the same child, simply adding the estimates for each indicator inflates the aggregate estimate. Hence, this report applies statistical adjustments to theoretically correct for overlapping risks and arrive at a more conservative and realistic aggregate estimate of total child deaths from undernutrition.32

PAT H WAY # 1: C H I L D M O R TA L I T Y AT T R I B U TA B L E T O U N D E R N U T R I T I O N

factor and the strength of its association (relative risk) with the disease. The formula is: PAR = Prevalence (RR-1)/[1 + Prevalence(R-1)]. For more details visit the website of the University of Ottawa School of Medicine at http://www.med. uottawa.ca/SIM/data/PAR_e.htm. 32

Rockhill, B., et al., 1998, “Use and misuse of population attributable fractions,” American Journal of Public Health, 88(1):15–19.

Table 2.1 shows that 29,561 deaths per year or 38% of annual child deaths in the Philippines are caused by undernutrition. Nearly 60% of all child deaths occur during the first six months of life, when multiple threats to survival are most acute. About a quarter of these infant death cases is associated with maternal nutrition status, nonexclusive breastfeeding, and birth defects related to folic acid. Furthermore, undernutrition becomes a relatively greater threat to survival in the subsequent 6–59 month period. This report estimates that about half of all child deaths are associated with the following six indicators of undernutrition: (i) suboptimal breastfeeding, (ii) maternal hygiene behaviors, (iii) underweight, (iv) wasting, (v) vitamin A deficiency, and (vi) zinc deficiency. ©UNICEF Philippines/2016/CherylGagalac

TABLE 2.1 Estimated attributable child deaths by age group

* Statistically adjusted to account for overlapping cases of undernutrition.

AGE GROUP AND INDICATOR

NUMBER OF DEATHS

ADJUSTED NUMBER OF DEATHS*

RISK GROUP (%)

1. Maternal nutrition status

6,187

5,462

2. Maternal folic acid deficiency (FAD) neural tube defect (NTD)

3,774

3,331

3. Suboptimal breastfeeding, less than 1 month

1,353

1,195

4. Suboptimal breastfeeding, 1–5 months

5,376

4,746

16,690

14,734

1,900

1,506

404

320

7. Underweight (weight-for-age Z score)

7,279

5,771

8. Wasting (weight-for-height Z score)

5,057

4,009

9. Vitamin A deficiency (VAD)

1,815

1,438

10. Zinc deficiency

2,249

1,783

18,704

14,827

35,395

29,561

A. CHILDREN, 0-5 MONTHS

Subtotal (1 + 2 + 3 + 4) B. CHILDREN, 6-59 MONTHS 5. Suboptimal breastfeeding, 6-24 months 6. Handwashing

Subtotal (5 + 6 + 7 + 8 + 9 + 10) C. CHILDREN UNDER AGE 5 (A+B)

PAT H WAY # 1: C H I L D M O R TA L I T Y AT T R I B U TA B L E T O U N D E R N U T R I T I O N

@UNICEF Philippines/2017/ATorralba

A. ESTIMATING THE VALUE OF WORKFORCE LOST TO CHILD MORTALITY The value of life lost is immeasurable. However, from an economic perspective, child mortality implies a forgone future workforce. The NPV of their forgone lifetime earnings (i.e., 50 years) can be estimated. The present value of the forgone future workforce can be used as an indicator of the economic consequences of mortality attributed to undernutrition. Figure 2.2 shows how the NPV of the projected FEL from child mortality is estimated. The NPV of FEL is estimated at around $667 million per year, derived using a 3% rate over the projected future lifetime earnings of children under age 5 whose deaths were attributable to undernutrition, and then correcting for a 14- to 15-year delay in the beginning of the earnings stream. A 14-year delay is imposed for cases of deaths among children between 1-5 years old, and a 15-year delay for cases of infant deaths. This discounting approach reduces the total lifetime earnings to around $22,500 per child. Clearly, although this methodology is accepted as a financial measure, it is inadequate to measure the value of human life. FIGURE 2.2 Projecting the Future Economic Losses from child mortality attributable to undernutrition

ATTRIBUTED DEATHS

AVERAGE ANNUAL WAGE

LABOR FORCE PARTICIPATION RATE

GROSS VALUE OF FUTURE INCOME OR FUTURE ECONOMIC LOSSES

29,561

$2,472 per year

63.3%

FEL, $ per year

t 0 +50

â&#x2C6;&#x2018;(

1 1+r

)

* FEL

NET PRESENT VALUE OF TOTAL FEL FROM CHILD MORTALITY ATTRIBUTABLE TO UNDERNUTRITION

t=t 0

PAT H WAY # 1: C H I L D M O R TA L I T Y AT T R I B U TA B L E T O U N D E R N U T R I T I O N

$667 million per year

B. PERSPECTIVES ON THE ATTRIBUTIONS OF CHILD MORTALITY

Child micronutrient deficiencies (vitamin A, zinc)

11% Anthropometry (WAZ, WHZ)

33% Mother’s behavior (breastfeeding, hygiene)

26%

Mother’s nutrition status (BMI, HAZ, anemia, FAD)

30%

The following figure shows the distribution of child mortality attributable to undernutrition by indicator. Child underweight and wasting, the traditional indicators of undernutrition, account for the largest share (33%). The remaining two-thirds of child mortality cases is associated with maternal nutrition status and behavior, along with micronutrient deficiencies. This has several important policy implications, particularly for planning and targeting interventions. Significant reductions in child mortality attributed to undernutrition will require a comprehensive set of interventions, including micronutrient supplementation, dietary education and behavior change, targeting young women, mothers, as well as malnourished children.

FIGURE 2.3 Distribution of child mortality by undernutrition indicators WAZ WHZ BMI HAZ FAD

— weight-for-age Z score — weight-for-height Z score — body mass index — height-for-age — folic acid deficiency

33%

Anthropometry

30%

Mother’s nutrition status

26%

Mother’s behavior

11%

Child micronutrient deficiencies

@UNICEF Philippines/2013/VJVillafranca

PAT H WAY # 1: C H I L D M O R TA L I T Y AT T R I B U TA B L E T O U N D E R N U T R I T I O N

PAT H W AY 2

NUTRITION INDICATORS

HEALTH ISSUES

CHILD COGNITIVE AND GROWTH DEFECTS

Iodine deficiency Anemia Childhood stunting

PATHWAY #2

Depressed Future Productivity of Children

LOSSES

Future productivity deficit

VALUE OF LOSSES

$3.1B/y

Undernutrition coincides with many health and economic deprivations, which affect child growth and development. Distinguishing between the ‘nutrition’ and ‘child development’ factors is complicated by countless interactions between nutrition, nature and nurture. However, substantial evidence suggests that after correcting for poverty and related threats, nutrition status has independent and additive impacts on child growth, cognition and development.33 Undernutrition diminishes children’s cognitive development through physiological changes, as well as reducing learning ability. Compared to their well-nourished peers, even mild or moderately undernourished children score poorly on tests of cognitive function, psychomotor development and fine motor skills.34 Studies show that undernourished children have less interaction with their environment and consequently fail to acquire physical and intellectual skills at normal rates. In turn, these early childhood deficits determine children’s ability to capitalize on educational opportunities and later employment prospects, resulting in adult productivity deficits.35 This report focuses on childhood anemia, stunting and iodine deficiency disorders (IDD), which are all strongly associated with depressed cognition, as well as suboptimal school performance and subsequent reduced adult earnings. While many children concurrently suffer IDD, anemia and stunting, data describing the extent to which these deficiencies overlap is not available. As in Pathway #1, estimates here are adjusted statistically.36

A. STUNTING OR SMALL STATURE Stunting or low height-for-age is a general marker of the cumulative effects of chronic child undernutrition. It is a complex indicator, reflecting not only nutrition deficiency but also a combination of inadequate diet, infection, and suboptimal childcare. Children falling more than two standard deviations below the international reference population developed by the WHO are considered low height-for-age (HAZ) or stunted.37 A cross-country study involving wellnourished populations and multiple ethnic groups consistently shows that 33 Grantham-McGregor, S., et al., 2007, “Developmental potential in the first 5 years for children in developing countries,” The Lancet, 369(9555):60–70. 34 Grantham-McGregor, S., et al., 2007, “Developmental potential in the first 5 years for children in developing countries,” The Lancet, 369(9555):60–70. 35 For instance, see Behrman (1993), Behrman and Deolalikar (1989), Deolalikar (1988), Foster and Rosenzweig (1993), Glick and Sahn (1997), Haddad and Bouis (1991), Schultz (1996), Strauss and Thomas (1998) and Thomas and Strauss (1997) Behrman (1993), Behrman and Deolalikar (1989), Deolalikar (1988), Foster and Rosenzweig (1993), Glick and Sahn (1997), Haddad and Bouis (1991), Schultz (1996), and Thomas and Strauss (1997). 36 Rockhill, B., et al., 1998, “Use and misuse of population attributable fractions,” American Journal of Public Health, 88(1):15–19. 37 WHO, ‘Global Database on Child Growth and Malnutrition,’ http://www.who.int/nutgrowthdb/about/introduction/en/index5.html.

PAT H WAY # 2 :

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

children tend to grow around the same trajectory. As indicated in Figure 3.1 from the WHO, an assessment of linear growth in well-nourished children from birth to 1,000 days in Brazil, Ghana, India, Norway, Oman and USA shows that despite some minor variation, the growth curves among these populations are very similar.38 There is no significant difference among these well-nourished populations.

FIGURE 3.1 Cross-country comparison of child growth over the first 1,000 days

Mean of length (cm)

Brazil Ghana India Norway Oman USA

Birth

6 mo

12 mo

18 mo

24 mo

Being “short” only has negative effects when there is undernutrition. Stunted children suffer low physical activity, impaired motor and mental development, lowered immune competence, greater severity of infections and increased mortality.39 Numerous studies directly associate stunting with lower test scores for childhood cognition. A number of studies have established an association between stunting and future economic productivity via two general pathways.

Suboptimal school achievement. Stunted children start school later, progress through school less rapidly and show lower over-all grade attainment. A Lancet review of evidence from 79 countries concluded that “for every 10% increase in stunting, the proportion of children reaching the final grade of primary school dropped by 7.9%.”40 The authors concluded that stunted children suffer a combined grade attainment and performance deficit of 2.91 years suggesting a “total percentage loss of adult yearly income” of 19.8%.” Reduced earnings in manual labor. Several studies controlling for a variety of characteristics, document a direct association between lower adult height and reduced earnings in physically demanding jobs.41 Among sugar cane workers in the Philippines, Haddad et al found productivity rose 1.38% for every 1% increase in height.42 Since severe stunting (> -3 SD) represents a 6.25% reduction in height and moderate stunting (-2 to -3 SD) represents a 4.375% deficit, this finding associates severely stunted children with a productivity loss of 8.6% while moderate stunting results in about a 6% future deficit.43

PAT H WAY # 2 :

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

@UNICEF Philippines/2013/VJVillafranca

Since the evidence shows stunting impacts productivity through these two very distinct pathways, schooling and agricultural work performance, the DAR applies differing coefficients of deficit to the appropriate employment sectors. The 6-8.6% productivity deficit found among sugar workers are applied in the DAR modeling to 16% of the Filipino labor force employed in industry and 29.6% working in agriculture and other manual jobs. The Lancet findings of a 19.8% deficit due to school attainment and performance deficit are applied to the 54.5% of the labor force employed in the growing service sector: in jobs requiring a range of numeracy, literacy and other intellectual skill. As shown in Figure 3.2, the model estimates that the NPV of future productivity losses in manual labor sectors attributed to the 22.7% moderately stunted and 10.7% severely stunted children age 6-59 months amounts to $283.3 million for moderately stunted, and $190.5 million for severely stunted children per one-year cohort.44 This implies an NPV of $473.7 million productivity losses for all moderately and severely stunted children age 6-59 months who are expected to be employed in the manual labor sector in the future. The model applies a 6% deficit to around 1 million moderately stunted children age 6-59 months, and an 8.4% deficit to 491,000 severely stunted children age 6-59 months who are expected to be later employed in the manual labor sectors when they reach around age 15. The model applies an average delay of 12.5 years until these children enter the workforce and start earning an average income of $1,752 per year (see Annex 2). A prevalence of 33.4% stunting suggests that over 5.4 million children age 6-59 months, who will later become white

collar workers in the services sector, will suffer schooling and subsequent intellectual and future work performance deficits associated with 2.91 “grade equivalents.” Literacy, numeracy, communication and analytical skills are key to productivity in the country’s growing services sector. As shown in Figure 3.3, the model applies a 19.6% productivity deficit to the average annual earnings potential of $2,830 per worker in the services sector and estimates an NPV of over $2 billion in depressed future productivity per one-year cohort.45 Unlike in the case of manual labor, the model applies a delay in the earnings stream of services workers of 15 years to account for higher mean years of schooling. Projected losses per child are relatively small, $308 per lifetime for those in manual labor and $1,307 for service sector jobs. However, as a consequence of high stunting prevalence, these relatively small individual deficits aggregate to burden the entire economy. For example, annual unadjusted losses for stunted children in service jobs are estimated at $2.4 billion annually. After statistical correction for productivity deficits emerging from possibly co-existing anemia and IDD, losses in future productivity from stunting are projected at around 2.29 billion annually.

38 Onis, M., 2006, “Assessment of differences in linear growth among populations in the WHO Multicentre Growth Reference Study,” Acta Paediatrica, 95(S450):56–65. 39

Martorell, R., 1996, “The role of nutrition in economic development,” Nutrition Reviews, 54(4):S66–S71.

40 Grantham-McGregor, S., et al., 2007, “Developmental potential in the first 5 years for children in developing countries,” The Lancet, 369(9555):60–70. 41

For example, see Behrman (1993), Behrman and Deolalikar (1989), Deolalikar (1988), Foster and Rosenzweig (1993), Glick and Sahn (1997), Haddad and Bouis (1991), Schultz (1996), and Strauss and Thomas (1998 and 1997).

42 Haddad, L.J., and H.E. Bouis, 1991, “The impact of nutritional status on agricultural productivity: wage evidence from the Philippines,” Oxford Bulletin of Economics and Statistics, 53(1):45–68. 43 Burkhalter, B.R., et al., 1998, ‘PROFILES: A Data-Based Approach to Nutrition Advocacy and Policy Development,’ Virginia, Partnership for Child Health Care, Basic Support for Institutionalizing Child survival [BASICS]. 44

Stunting figures based on FNRI-DOST, 2015, ‘8th National Nutrition Survey,’ Taguig City: FNRI-DOST.

See Annex 2 for more details on the wage segmentation methodology.

PAT H WAY # 2 :

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

FIGURE 3.2 Projecting the future economic losses in agriculture and other manual labor sectors from stunting among children

CHILDREN WITH DEFICIT OR RISK

AVERAGE ANNUAL WAGE OR EARNINGS

LABOR FORCE PARTICIPATION RATE

COEFFICIENT OF DEFICIT OR RISK

Moderate: 22.7% 1,043,551

$1,752 per year

63.6%

Moderate: 6.04%

Severe: 10.7% 491,381

Severe: 8.6%

GROSS VALUE OF FUTURE INCOME OR FUTURE ECONOMIC LOSSES FEL, $ per year

61.5

∑(

1 1+r

)

NET PRESENT VALUE OF TOTAL FEL DUE TO STUNTING

* FEL

t=12.5

$283.2 million per one-year cohort (moderate) $190.5 million per one-year cohort (severe)

FIGURE 3.3 Estimating the net present value of the Future Economic Losses in the services and white collar labor sectors among stunted children

CHILDREN WITH DEFICIT OR RISK

AVERAGE ANNUAL WAGE OR EARNINGS

LABOR FORCE PARTICIPATION RATE

COEFFICIENT OF DEFICIT OR RISK

Stunting: 33.4% 5,482,478

$2,830 per year

63.6%

19.8%

GROSS VALUE OF FUTURE INCOME OR FUTURE ECONOMIC LOSSES FEL, $ per year

∑(

1 1+r

)

* FEL

t=15

PAT H WAY # 2 :

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

NET PRESENT VALUE OF TOTAL FEL DUE TO STUNTING $2.3945 billion per one-year cohort

B. ANEMIA IN CHILDREN A range of evidence links anemia and iron deficiency in young children to cognitive and development delays. A review published in the Journal of Nutrition observed a consistently positive impact of iron interventions on cognitive scores, generally ranging from 0.5 to 1 SD and concluded “available evidence satisfies all of the conditions needed to conclude that iron deficiency causes cognitive deficits and developmental delays.”46 There is a consensus in the literature from child psychology, nutrition and economics that development deficits related to iron status in children less than 5 years old are associated with a 4% drop in earnings.47 In general, studies show that iron supplementation in children under age 5 led to cognitive improvements that were sustained into adolescence with a correlation coefficient 0.62.48 Applied to the 4% earnings deficit, the correlation coefficient suggests 2.5% lower future earnings.49 Using data from the 2013 NNS, the share of anemic children among children 6-24 months is estimated at 20.1%.50 Figure 3.4 shows that applying a 2.5% future productivity loss to a cohort of 785,000 anemic children age 6-59 months yields a total NPV of $353 million per one-year cohort in terms of future productivity losses due to childhood anemia. After correcting for overlapping cases of anemia and stunting the model attributes nearly $282 million losses per one-year cohort. 46 Haas, J. and T. Brownlie, 2001 “Iron deficiency and reduced work capacity: a critical review of the research to determine a causal relationship,” Journal of Nutrition, 131(2):676S–690S. 47 Horton, S., and J. Ross, 2003, “The economics of iron deficiency,” Food Policy, 28(1):51–75. 48 For instance, see Pollitt et al. 1995 and Jensen1980 (cited in Horton and Ross 2003). 49 Horton, S., and J. Ross, 2003, “The economics of iron deficiency,” Food Policy, 28(1):51–75. 50 See Annex 3.

FIGURE 3.4 Projecting the Future Economic Losses among anemic children

CHILDREN WITH DEFICIT OR RISK

AVERAGE ANNUAL WAGE OR EARNINGS

LABOR FORCE PARTICIPATION RATE

COEFFICIENT OF DEFICIT OR RISK

Anemia: 20.1% 785,757

$2,472 per year

63.6%

2.5%

GROSS VALUE OF FUTURE INCOME OR FUTURE ECONOMIC LOSSES FEL, $ per year

∑(

1 1+r

)

* FEL

t=15

NET PRESENT VALUE OF TOTAL FEL DUE TO CHILDHOOD ANEMIA $353 million per one-year cohort

PAT H WAY # 2 :

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

C. IODINE DEFICIENCY DISORDERS A substantial body of literature links iodine deficiency disorders or IDD with impaired brain development and subsequent deficits in productivity. A systematic review by the WHO in 2015 concludes that “children exposed to iodized salt during gestation, infancy and early childhood had higher intelligence quotient (IQ) and reduced risk of low intelligence compared to unexposed children”. 51 The review based on 18 quasi-experimental studies with 31 comparisons finds that with exposure to iodized salt, there is a median improvement of 8.18 IQ points or more than half a standard deviation from the median.52 A recent literature review found a 17.7% increase in wages associated with each standard deviation increase in cognitive scores, suggesting a 1.18% earnings deficit for each IQ point lost.53 This parameter, along with the WHO findings of 8.18 IQ point losses, suggests newborns suffering from IDD may lose 9.6% of future productive potential.

for individual iodine status, it is not possible to establish the number of children currently suffering the impacts of IDD. However, median urinary iodine concentration (UIC) of 105 ug/L found in pregnant women in the 2013 NNS is well below the recommended median UIC of 150 ug/L for pregnant women, suggesting that IDD represents a public health threat to newborns in the Philippines. Moreover, the 2013 NNS finds that 64.4% of individual samples taken from pregnant women were below the threshold of 150 ug/L. This report conservatively assumes that the proportion of samples with over 150 ug/L in excess of the expected 50% (the median) represents the prevalence of IDD among newborns (i.e., 14.4%). Based on this rough estimate of IDD prevalence among newborns, the following figure suggests $683 million in annual depressed productivity per oneyear cohort because of IDD in pregnant women. After correcting for overlapping cases of IDD, anemia and stunting, the model attributes nearly $546 million losses per one-year cohort.

Since the standard markers of iodine nutrition are based on a population median of 100 microgram per liter (ug/L) rather than a specific cut-off or threshold

FIGURE 3.5 Estimating the Future Economic Losses from iodine deficiency disorders among pregnant women

CHILDREN WITH DEFICIT OR RISK

AVERAGE ANNUAL WAGE OR EARNINGS

LABOR FORCE PARTICIPATION RATE

COEFFICIENT OF DEFICIT OR RISK

IDD: 14.4% of newborns or 358,774

$2,472 per year

63.6%

9.6%

GROSS VALUE OF FUTURE INCOME OR FUTURE ECONOMIC LOSSES FEL, $ per year

∑(

1 1+r

t=15

PAT H WAY # 2 :

)

* FEL

NET PRESENT VALUE OF TOTAL FEL DUE TO IDD AMONG PREGNANT WOMEN $683 million per one-year cohort

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

@UNICEF Philippines/2013/AdamFerguson

D. LONG-TERM DISABILITY FROM FOLIC ACID-RELATED NEURAL TUBE DEFECTS In a developing country like the Philippines, where nearly 40% of births are not delivered in medical facilities, and where pediatric neurosurgery is not widely available, infants born with serious neural tube defects (NTD) (e.g., spina bifida and anencephaly) face serious survival risks (see discussion on child mortality under Pathway #1).54 A speculated 80% fatality rate suggests 943 NTD survivors with lifelong moderate or severe disability. While no data was identified, this report assumes that 25% of these survivors will be severely disabled and unable to work in any way representing 100% productivity loss. The remaining three-fourths are assumed moderately disabled and may be able to work at some level of employment. Using a parameter of 50% of potential productivity, we estimate that the disability associated with survivors of folic acid-related NTDs will result in annual earning loss of NPV of $12.8 million annually. In all probability, there are additional medical, rehabilitation and other costs associated with these NTD cases. However, data is not available and no estimates are ventured.

PAT H WAY # 2 :

51 Aburto, N., et al., 2014, ‘Effect and Safety of Salt Iodization to Prevent Iodine Deficiency Disorders: A Systematic Review with Meta-analyses.’ Geneva: WHO. 52 Aburto, N., et al., 2014, ‘Effect and Safety of Salt Iodization to Prevent Iodine Deficiency Disorders: A Systematic Review with Meta-analyses.’ Geneva: WHO. 53 Bagriansky, J. 2015. ‘Economic Impact of Iodine Deficiency,’ Report submitted to the UNICEF. 54 The analysis excludes the impact of Spina bifida.

DEPRESSED FUTURE PRODUCTIVIT Y OF CHILDREN

PAT H W AY 3

NUTRITION INDICATORS

HEALTH ISSUES

A D U LT WORK DEFICIT Adult anemia

PATHWAY #3

Depressed Current Productivity: Anemia in Adult Workers

LOSSES

Work performance deficit

VALUE OF LOSSES

$233M/y

Although this assessment report focuses mainly on undernutrition among pregnant women and children, widespread anemia among current adults results in substantial work performance deficits, and therefore losses to the national economy. In addition, anemia among women of reproductive age is a key component of maternal undernutrition during early pregnancy—a key driver of poor birth outcomes and therefore, a key factor behind the intergenerational burden of undernutrition. Weakness, fatigue and lethargy due to anemia results in measurable productivity deficits in manual labor. Evidence shows that anemia results in productivity deficits of about 12% among workers performing heavy manual labor (e.g., agricultural and construction workers) and about 5% among blue-collar workers involved in manual but less physically-demanding work (e.g. manufacturing workers).55 56 57 58 In Indonesia, the output of iron supplemented rubber tree tappers was 17% higher than that of their non-supplemented co-workers.59 An additional 12% deficit is applied for the 15% of workers assumed to be engaged in heavy manual labor. Since these are current annual losses, no discounting is required. Projected losses averaging $119 per anemic worker add up to a national burden of around $232.8 million in depressed productivity per year (see Figure 4.1).

FIGURE 4.1 Estimating the current economic losses from anemic adults engaged in manual labor

INDIVIDUALS WITH DEFICIT OR RISK

AVERAGE ANNUAL WAGE OR EARNINGS

LABOR FORCE PARTICIPATION RATE

COEFFICIENT OF DEFICIT OR RISK

ANEMIC 15-64 YEARS OLD

$1,752 per year

Female: 50.3% Male: 78.4%

Manual labor: 45.6% Heavy: +15%

Females: 14.5% Males: 6%

ANNUAL LOSSES Manual labor: $126.5 million per year Heavy: $106.4 million per year

55 Horton, S., and J. Ross, 2003, “The economics of iron deficiency,” Food Policy, 28(1):51–75. 56 Li R., et al., 1994, “Functional consequences of iron supplementation in iron-deficient female cotton workers in Beijing, China,” The American Journal of Clinical Nutrition, 59(4):908–913. 57 Scholz B.D., et al., 1997, “Anaemia is associated with reduced productivity of women workers in even less-physically-strenuous tasks,” British Journal of Nutrition, 77(1):47–57. 58 Unturo J., et al., 1998, “Association between BMI and hemoglobin and work productivity among Indonesian female factory workers,” European Journal of Clinical Nutrition, 52(2):131–135. 59 Basta S.S., et al., 1979, “Iron deficiency anemia and the productivity of adult males in Indonesia,” The American Journal of Clinical Nutrition, 32(4):916–925.

PAT H WAY # 3 : D E P R E S S E D C U R R E N T P R O D U C T I V I T Y: A N E M I A I N A D U LT W O R K E R S

PAT H W AY 4

NUTRITION INDICATORS

HEALTH ISSUES

CHILD MORBIDITY Maternal status and hygiene Zinc Exclusive breastfeeding

LOSSES

Additional health utilization cost

VALUE OF LOSSES

$ 379 M/y

PATHWAY #4

Additional Healthcare Expenditures due to Preventable Diseases

Undernutrition among pregnant women contributes to low birthweight among infants, while child undernutrition contributes to impaired immunity and infectious diseases among them. Both cases of undernutrition translate into increased utilization of health services, generating additional financial burden on families and the health care system. To estimate the economic impact of preventable diseases attributable to undernutrition, this paper considers the consequences of suboptimal breastfeeding, maternal hygiene and zinc deficiency. Both cases of undernutrition are associated with increased risks of diarrhea and ARI. Maternal stunting, low BMI and anemia are considered as contributors to low birthweight deliveries and subsequent health care costs for those delivering in health facilities.

A. CASES OF DIARRHEA AND ARI FROM SUBOPTIMAL BREASTFEEDING, ZINC DEFICIENCY AND MATERNAL HYGIENE

Evidence suggests the association between suboptimal breastfeeding and increased morbidity from acute respiratory infection and diarrhea. A recent Lancet review suggests that the RRs for various suboptimal breastfeeding behaviors range from 1.17 to 2.65, as shown in Table 4.1. The literature linking zinc deficiency with incidences of diarrhea and ARI in children 6–59 months suggests an RR of 2.85 for diarrhea and RR of 2.07 for ARI.60 A recent review in Africa and South Asia suggests that adequate hand washing could reduce diarrhea morbidity by 48% (RR: 0.52, 95% CI: 0.37–0.76).61 This report uses an RR of 2.08 to reflect higher risk of diarrhea from lack of optimal hand washing behaviors.62 60 Supplement to: Black, R.E., et al., 2013, “Maternal and child undernutrition and

TABLE 4.1 Relative risk of diarrhea and acute respiratory infection by breastfeeding status AGE GROUP AND BREASTFEEDING BEHAVIOR

DIARRHEA

ARI

None

2.65

2.48

Partial

1.68

2.07

Predominant

1.26

1.79

B. CHILDREN, 6-23 MONTHS

2.07

1.17

A. CHILDREN, 0-6 MONTHS

Source: Black, R.E., et al., 2013, “Maternal and child undernutrition and

overweight in low-income and middle-income countries,” The Lancet, 382(9890):427–

overweight in low-income and middle-income countries,” The Lancet,

451.

382(9890):427–451.

61 Bhutta, Z., 2014, ‘Lives Saved Tool (LiST) Analysis for Global Nutrition Report Independent Expert Group,’ http://www.globalnutritionreport.org. 62 This is extrapolated from Bhutta, Z., 2014, ‘Lives Saved Tool (LiST) Analysis for Global Nutrition Report Independent Expert Group,’ http://www.globalnutritionreport.org. The protective effect of intervention (RR 0.48) is converted to threat of negative outcome (1/0.48).

PAT H WAY # 4 : A D D I T I O N A L H E A LT H C A R E E X P E N D I T U R E S D U E T O P R E V E N TA B L E D I S E A S E S

The 2013 Philippine NDHS indicates that 8% of Filipino mothers reported diarrhea and 6% reported ARI in their children over the previous 2 weeks. This translates to around two cases of diarrhea and 1.5 cases of ARI per child annually. This suggests a national annual burden of more than 41 million cases across the population of children under age 5. The same survey also indicated that 42% of diarrhea and 64% of ARI cases resulted in visits to health providers, suggesting 21 million cases seen in public and private health facilities annually (see Table 4.2). @UNICEF Philippines/2013/VJVillafranca

TABLE 4.2 Estimated number of cases, in-facility contacts, and claims for incidences of diarrhea and acute respiratory infection among children under age 5

DIARRHEA

ARI

2.08

1.56

23,560,784

17,670,588

9,895,529

11,309,176

380,807

676,094

342,726

473,266

3.46

4.18

A. Estimated number of cases per child B. Total number of cases (Children under age 5 x A) C. Share of those seeking facility care (%) D. Total in-facility contacts (B x C) E. Total claims for acute gastro and pneumonia F. Assumption: share of children under age 5 (%) G. Total claims for acute gastro and pneumonia among children under age 5 (E x F) Referral from primary to higher levels of care (%)

Additionally, the 20 million cases not resulting in visits to health facilities also burden families with opportunity and cases costs. Based on the reports by the Philippine Health Insurance Corporation (PhilHealth) on claims for acute gastroenteritis and pneumonia (and assumptions regarding childrenâ&#x20AC;&#x2122;s share of these claims), the model assumes 3.46% of diarrhea cases and 4.18% of ARI cases result in referral from primary health care facilities to higher levels of care. Based on the estimated number of ARI and diarrhea cases and global evidence of higher risks of diarrhea and ARI outlined in the global literature, Table 4.3 outlines the parameters used to project cases of diarrhea and ARI that can be attributed to the current prevalence of suboptimal breastfeeding, handwashing behaviors, and zinc deficiency. @UNICEF Philippines/2013/VJVillafranca

PAT H WAY # 4 : A D D I T I O N A L H E A LT H C A R E E X P E N D I T U R E S D U E T O P R E V E N TA B L E D I S E A S E S

TOTAL

41,231,372

21,204,706

815,992

PAR — population attributable risk ARI — acute respiratory infection

TABLE 4.3 Projecting cases of diarrhea and acute respiratory infection due to suboptimal breastfeeding, handwashing and zinc deficiency

INDICATOR AND AGE GROUP

RELATIVE RISK

PREVALENCE (%)

PAR (%)

ANNUAL CASES (MILLION)

CASES ATTRIBUTED TO SUBOPTIMAL BREASTFEEDING (THOUSAND)

(A)

(B)

(A) X (B)

(C)

(A) X (B) X (C)

None

2.65

23.9

28.3

238

Partial

1.68

17.0

10.4

87.7

Predominant

1.26

6.6

2.0

239

A. SUBOPTIMAL BREASTFEEDING BEHAVIOR 1. Diarrhea among children age 0-6 months

Subtotal

0.8

340

2. ARI among children age 0-6 months None

2.07

23.9

26.0

279

Partial

2.48

17.0

15.0

165

Predominant

1.79

6.6

5.0

Subtotal

1.1

498

3. Diarrhea among children age 6-24 months None

2.07

14.1

4.4 million

1.17

6.8

0.4 million

2.08

10.16 63

8.6

20.9

2.1 million

Diarrhea

2.85

21.6

28.6

20.9

5.9 million

ARI

2.07

21.6

18.8

15.7

2.9 million

4. ARI among children 6-24 months None B. MATERNAL HANDWASHING Diarrhea C. ZINC DEFICIENCY

63 See Table 26 in FNRI-DOST, 2015, ‘8th National Nutrition Survey,’ Taguig City: FNRI-DOST.

These cases of diarrhea and ARI attributed to suboptimal breastfeeding, handwashing behaviors and zinc deficiency burden the national economy in 2 ways: i) cost of medical care at health facilities and 2) the cost of care to families. The table below shows the various unit costs applied to these parameters (background to these estimates detailed in Annex 6). Based on unit costs from Table 4.4 and the attributed cases of diarrhea and ARI in Table 4.3, Table 4.5 summarizes annual national financial burden on the health system and families.

PAT H WAY # 4 : A D D I T I O N A L H E A LT H C A R E E X P E N D I T U R E S D U E T O P R E V E N TA B L E D I S E A S E S

TABLE 4.4 Unit costs for estimating the financial burden of diarrhea and ARI cases

COST ITEM

DIARRHEA

ARI

6.30

6.17

Based on PhilHealth Claims

119

306

Based on PhilHealth Claims

Family transport to PHC ($)

1.47

2013 NDHS

Opportunity cost of visit ($)

3.71

Based on the minimum wage

12.26

7.01

ASSOCIATED WITH THE HEALTH CARE SYSTEM (%) Primary health care (PHC) contact ($) Hospitalization ($)

HOME TREATMENT (%) Opportunity cost ($) Home treatments ($)

TABLE 4.5 Annual costs associated with current prevalence of suboptimal breastfeeding, zinc deficiency and handwashing

TOTAL BURDEN

SOURCE 2013 NDHS

2013 NDHS Based on Larsen et al. Assumption

ARI

DIARRHEA

TOTAL

1,372,689

6,046,797

7,421,486

2. Non-continued breastfeeding, 6–24 months

19,448,927

5,610,184

25,059,111

3. Zinc deficiency

26,254,395

35,949,322

62,203,718

A. COST OF CONSULTATIONS 1. Non-exclusive breastfeeding, 0–6 months

4. Maternal hygiene

9,000,342

Subtotal (A1 + A2 + A3 + A4)

9,000,342

55,815,229

47,650,061

103,465,290

3,453,565

3,087,166

6,540,731

2. Non-continued breastfeeding, 6–24 months

23,969,742

2,383,838

25,167,110

3. Zinc deficiency

59,061,502

18,347,700

77,409,201

4. Maternal hygiene

20,504,178

B. FAMILY CASH AND OPPORTUNITY COST 1. Non-exclusive breastfeeding, 0–6 months

Subtotal (B1 + B2 + B3 + B4)

106,988,987

20,504,178 23,818,704

129,621,221

4,826,654

9,135,963

13,962,216

2. Non-continued breastfeeding, 6–24 months

43,418,668

7,994,022

50,226,221

3. Zinc deficiency

85,315,897

54,297,022

139,612,919

4. Maternal hygiene

29,504,520

C. COST OF CONSULTATIONS, FAMILY CASH AND OPPORTUNITY COST (A + B) 1. Non-exclusive breastfeeding, 0–6 months

Total (C1 + C2 + C3 + C4) ($)

163,065,739

29,504,520 71,427,007

64 Christian, P., 2015, ‘Maternal Nutritional Status and Micronutrient Deficiencies: Impact of Interventions on Birth Outcomes,’ Powerpoint presented on 8-11 November in Dohad Capetown. 65 Kuzuki, N., et al, 2015, “Short maternal stature increases risk of small-for-gestational-age and preterm births in low-and middle-income countries,” Journal of Nutrition 145(11):2542–2550. 66 Addo, O.Y., et al, 2013, “Maternal height and child growth patterns,” The Journal of Pediatrics, 163(2):549–554.

PAT H WAY # 4 : A D D I T I O N A L H E A LT H C A R E E X P E N D I T U R E S D U E T O P R E V E N TA B L E D I S E A S E S

233,305,876

B. LOW BIRTHWEIGHT CASES ASSOCIATED WITH 3 INDICATORS OF MATERNAL NUTRITION STATUS The 2013 NDHS found annual incidence of low birthweight in the Philippines at 21.4%, suggesting an estimated 534,665 cases. While all face threats to health and survival, the 61% of low birthweight cases delivered in medical facilities represent a significant financial burden on the Filipino health care system. A recent review of the literature projected the association of several indicators of maternal nutrition status with small for gestational age or SGA.64 •

A meta-analysis based on 12 population studies and the WHO Global Survey on Maternal and Perinatal Health found RR of 2.03 for women less than 145 cm in height delivering term SGA babies.65 While no official national data on maternal height was identified, Addo et al surveyed nearly 2,000 Philippine expectant mothers classifying 42.7% with low height.66

•

Based on 12 studies reporting associations between maternal anemia and SGA, Kozuki et al found association with moderate and severe anemia with odds ratio (OR) of 1.53 (95% CI: 1.24–1.87). Since anemia data segmented for severity is not available, this DAR applies the lower end of the confidence interval RR 1.24.67 The 2013 NNS found 25% of pregnant women are anemic.

•

Based on a review of eight cohorts including 19,124 mothers, Anne et al found RR of 1.41 for SGA among women with BMI less than 18.5.68 This DAR uses the 2013 NNS findings of 24.8% “nutritionally at risk pregnant women” based on low weight for height measurements, indicating low BMI.

It has to be noted as a shortcoming, however, that health data collection systems in the Philippines do not differentiate between prematurity and low birthweight, and SGA and intrauterine growth retardation (IUGR), which are distinct clinical conditions. Evidence for negative outcomes emerge from all three conditions, although much of the recent literature focuses on SGA. However, given lack of differentiating data in the Philippines and in order to include these critical maternal indicators in the DAR, the established national prevalence of low birthweight is used as a rough surrogate for term SGA deliveries. Based on global coefficients risk and roughly estimated national data, the following table develops a series of PARs for these three maternal risk factors listed above (anemia, and low height and BMI), which are applied to the estimated annual cohort of low birthweight babies. After statistically adjusting for multiple conditions in the same pregnancy, the model estimates around 216,000 low birthweight deliveries attributable to these indicators of maternal nutrition status in the Philippines. PhilHealth data indicates a case cost averaging around $1,098 for each low birthweight delivery in a medical facility. Since 61% of deliveries are in health facilities, this paper applies this cost to 52,275 cases and estimates an annual financial burden of $145.2 million to the health system.

TABLE 4.6 Projecting cases of low birthweight attributed to 3 indicators of maternal nutrition INDICATOR

PAR — population attributable risk

RELATIVE RISK OF DELIVERY

PREVALENCE (%)

PAR (%)

ANNUAL CASES OF LOW BIRTHWEIGHT

CASES ATTRIBUTED TO MATERNAL NUTRITION STATUS (THOUSAND)

(A)

(B)

(A) X (B)

(C)

(A) X (B) X (C)

1. Low body mass index

1.41

24.8

9.2

534,665

49.3

2. Low height

1.98

42.7

29.5

534,665

157,731

3. Anemia

1.24

25.2

5.7

534,665

30.5

67 Kozuki, N., et al., 2012, “Moderate to severe, but not mild, maternal anemia is associated with increased risk of small-for-gestational-age outcomes.” The Journal of Nutrition, 142(2):358–362. 68 Christian, P., 2015, ‘Maternal Nutritional Status and Micronutrient Deficiencies: Impact of Interventions on Birth Outcomes,’ Powerpoint presented on 8-11 November in Dohad Capetown.

PAT H WAY # 4 : A D D I T I O N A L H E A LT H C A R E E X P E N D I T U R E S D U E T O P R E V E N TA B L E D I S E A S E S

@UNICEF Philippines/2016/ShehzadNoorani

The Economic Burden of Undernutrition in the Philippines Aggregating the estimates for the four pathways and correcting for overlapping risks suggest that the total net present value of the economic burden of undernutrition in the Philippines is around 4.5 billion per year. This figure represents the economic cost of doing nothing to address undernutrition in the country. This economic burden is comprised of the different types of losses measured across the four pathways presented in the previous sections of this report.

PATHWAY #1 The estimated NPV of the future workforce lost from over 29,400 annual deaths amounts to $667 million per year. This represents 15% of the national economic burden of undernutrition.

PATHWAY #2 The estimated NPV of future productivity losses due to cognitive and other deficits arising from childhood anemia, IDD and stunting amounts to $3.1 billion per year which is more than two-thirds of the total national economic burden of undernutrition.

PATHWAY #3 The current annual losses due to work performance deficits among anemic adults engaged in the manual labor sector are estimated at $233 million per year. This represents 5% of the total annual economic burden of undernutrition.

THE ECONOMIC BURDEN OF UNDERNUTRITION IN THE PHILIPPINES

PATHWAY #4 The cost of treating preventable cases of low birthweight, diarrhea or respiratory diseases due to zinc deficiency, suboptimal breastfeeding and maternal undernutrition is around $379 million per year. This is 9% of the total economic burden of undernutrition.

Any delay in the implementation of interventions to address undernutrition also implies economic costs. Table 6.1 shows that for each year of delay, the country incurs around $4.5 billion in terms of current and future productivity losses, as well as additional health expenditures. This is equivalent to 1.5% of the Philippine GDP in 2015.

Adjusted for multiple risks, in $ million per year

TABLE 6.1 Annual economic burden of undernutrition by indicator INDICATOR

PATHWAY #1: FUTURE WORKFORCE LOST

PATHWAY #2: FUTURE PRODUCTIVITY LOSSES

1. Maternal nutrition

188.3

12.7

2. Suboptimal breastfeeding 3. Maternal hygiene

PATHWAY #4: CURRENT HEALTH COSTS

TOTAL

145.2

346.2

176.6

64.2

240.8

7.3

29.5

36.8

4. Stunting 5. Underweight or wasting

2,291.9

221.9

6. Iodine deficiency

221.9 545.7

7. Zinc deficiency

40.5

8. Vitamin A deficiency

32.7

9. Childhood anemia

545.7 139.6

282.2

282.2 232.8

667

180.1 32.7

10. Adult anemia TOTAL

PATHWAY #3: CURRENT PRODUCTIVITY LOSSES

3,133

233

232.8 379

4,411

Global evidence shows that on average, nutrition status responds rather slowly to economic growth. If the living conditions of Filipino household will continue to improve with economic growth, especially among the poorer ones, the national prevalence of child undernutrition is likely to improve over the next decades. Sustaining and accelerating both the reductions in the current prevalence of undernutrition and the improvements in human capital in the Philippines require programs that are effective, affordable and strategic. A recent World Bank analysis covering 79 countries concluded that although income growth can help reduce poverty and undernutrition, greater and more effective direct nutrition interventions are necessary to meet nutrition improvement goals.69 The Business Case for Nutrition Investment in the Philippines outlines a portfolio of nutrition interventions, estimates impact and projects costs. The analysis concludes that applying effective, available, affordable nutrition interventions will yield high returns and an attractive benefit-cost ratio. Considering the relative contribution of the various nutrition indicators to the national burden can provide a perspective on developing and prioritizing interventions.

@UNICEF Philippines/2013/JeoffreyMaitem

69 Alderman, H., et al., 2001, â&#x20AC;&#x2DC;Reducing Child Undernutrition: How Far Does Income Growth Take Us?â&#x20AC;&#x2122; Centre for Research in Economic Development and International Trade, University of Nottingham.

THE ECONOMIC BURDEN OF UNDERNUTRITION IN THE PHILIPPINES

As shown in Figure 6.1, stunting and micronutrient deficiencies represent majority of the economic losses due to undernutrition (over 80% of total losses). Cases of undernutrition as indicated by traditional anthropometric measures of undernutrition (i.e., underweight and wasting) represent only about 5% of the total economic burden of undernutrition in the Philippines. Although important in addressing mortality and morbidity, focusing nutrition interventions towards reducing the prevalence of the traditional cases will likely fail to address majority of the national economic burden of undernutrition. Moreover, this assessment report only focuses on the economic costs of undernutrition and does not reflect the humanitarian, moral and good governance rationale for investing in nutrition. The data also highlights that interventions solely targeting children may miss a significant share of the burden of undernutrition. Table 6.2 shows that more than a third of the losses emerge from adult anemia; maternal behaviors like non-exclusive and noncontinued breastfeeding; and maternal nutrition status including BMI, stunting and micronutrient deficiencies. While all programs to improve child nutrition target mothers as a channel to reach children, there is a need to target the adult population independently, particularly women before and during pregnancy.

TABLE 6.2 Economic losses segmented by intervention and target population

Maternal nutrition and behavior

Wasting and underweight

14%

Micronutrient deficiencies

Stunting

52%

29%

FIGURE 6.1 Distribution of economic losses by indicator

STRATEGY AND INDICATOR

52%

Stunting

29%

Micronutrient deficiencies

14%

Maternal nutrition and behavior

LOSS ($)

Wasting and underweight

% OF TOTAL

A. TARGETING MATERNAL BEHAVIOR AND NUTRITION STATUS TOWARDS IMPROVING CHILD NUTRITION STATUS 1. Maternal nutrition, Low Birth Weight, Neural Tube Defects

346.3

2. Breastfeeding, Diarrhea, Acute Respiratory Infection

240.8

3. Iodine deficiency disorders

545.7

36.8

232.8

1,402.4

4. Maternal hygiene 5. Adult anemia Subtotal (1 + 2 + 3 + 4 + 5)

B. IMPROVING CHILD NUTRITION STATUS THROUGH DIRECT INTERVENTIONS AND IMPROVED MATERNAL BEHAVIORS AND NUTRITION 6. Stunting

2,291.9

7-8. Weight-for-age z score (WAZ) and weight-for-height z score (WHZ)

221.9

9. Zinc deficiency

180.1

32.6

282.2

3,008.7

10. Vitamin A deficiency 11. Child anemia Subtotal (6 + 7 + 8 + 9 + 10 + 11)

THE ECONOMIC BURDEN OF UNDERNUTRITION IN THE PHILIPPINES

@UNICEF Philippines/2016/ShehzadNoorani

Table 6.3 considers losses by potential content of program interventions: communication and education for behavior change; vitamin and mineral supplementation or improved diet quality; and/or providing protein, calories and other macronutrients via additional targeted staple and/or therapeutic food products. Each has an appropriate and strategic program response. •

•

Breastfeeding and maternal handwashing behaviors account for 6% of the projected national burden. Addressing these maternal behaviors requires a mix of communications and education including targeted face-to-face and/or peer-group counseling, community and national promotions and appropriate national regulatory changes. Thirty-one percent (31%) of the national burden emerges from micronutrient deficiencies including folic acid, IDD, vitamin A and zinc deficiency as well as anemia in women and children - associated with quality of the diet rather than quantity of food or calories. Improving dietary quality includes education to optimize nutrition from available foods; fortified foods; and pharmaceutical supplements including iron, zinc and vitamin A capsules as well as multiple micronutrient supplements and multiple micronutrient powders. A third set of indicators, representing 64% of the burden, includes maternal height and BMI along with child anthropometric indicators. These indicators are influenced by deficits in quantity of food, food quality, as well as maternal behavior. This suggests that in addition to behavior change and providing opportunities to improve the quality of the diet, investment to improve access to quality food, including targeted distribution of food supplements.

TABLE 6.3 Economic losses segmented by intervention content INTERVENTION AND NUTRITION INDICATORS

SHARE TO TOTAL ECONOMIC LOSSES (%)*

A. BEHAVIOR CHANGE 1. Breastfeeding

2. Maternal hygiene

Subtotal (1 + 2)

B. FOOD QUALITY, MICRONUTRIENT SUPPLEMENT IN COMBINATION WITH BEHAVIOR CHANGE 3. FAD birth defects 4. Iodine deficiency disorders (IDD)

2 12

5. Zinc deficiency

6. Vitamin A deficiency

7. Child anemia

8. Adult anemia

Subtotal (3 + 4 + 5 + 6 + 7 + 8)

C. FOOD QUANTITY FOR HIGH RISK IN RIGHT QUALITY IN COMBINATION WITH BEHAVIOR CHANGE 9. Maternal nutrition 10. Stunting 11. Underweight Subtotal (9 + 10 + 11)

7 52 5 64

* Total exceeds 100% due to rounding off

THE ECONOMIC BURDEN OF UNDERNUTRITION IN THE PHILIPPINES

ANNEX 1 Philippine Demographic Data

ANNEX TABLE 1.1 Population

INDICATOR

VALUE

Total population

101,562,300

Birthrate (per 1,000 population)

ANNEX TABLE 1.2 Projected population by 5-year age group and sex (â&#x20AC;&#x2DC;000)

ANNEXES

Projected from 2010 Census UNICEF 2012

Annual births

2,498,433

Calculated

Population, below 6 months

1,249,216

Annual number of births/2

Population, 6-24 months

3,747,649

Annual number of births x 3

Population, 0-59 months

11,327,300

Projected from 2010 Census

Population, 6-59 months

10,078,084

PSA, children under age 5

Population, 15-65 years

64,406,300

Projected from 2010 Census

Adult women, 15-65 years

31,835,300

Projected from 2010 Census

AGE GROUP

MALE

FEMALE

15-19

5,212.1

4,924.8

20-24

4,904.0

4,739.4

25-29

4,223.3

4,109.2

30-34

3,702.3

3,639.7

35-39

3,391.3

3,294.0

40-44

2,997.0

2,919.4

45-49

2,702.9

2,648.3

50-54

2,271.4

2,258.6

55-59

1,830.9

1,872.2

60-64

1,335.8

1,429.7

32,571.0

31,835.3

TOTAL

24.6

DATA SOURCE OR FORMULA

ANNEX 2 Computations and Assumptions for Wages or Earnings Rates

ANNEX TABLE 2.1 2012 Household Income*

INDICATOR

VALUE

Nominal Monthly Wage (PhP)

8,280

Annual wage (PhP)

99,360

Annual wage ($)

2,110

Annual family income (PhP)

530,349

Annual family income ($)

11,262

Average Income ($)

2,048

* Average family size = 5.5 Source: 2012 Income & Expenditure Survey. PSA Website (http://psa.gov.ph/)

ANNEX TABLE 2.2 Correction for inflation

INDICATOR

2010

GDP per worker (%)

2011

2012

2013

2014

2015

2016

4.6

3.2

4.1

1.4

1.3

Nominal PSA 2011 ($)

2,110

2,207

2,278

2,346

2,442

2,476

2,509

2012 Family income & expenditure ($)

2,048

2,142

2,210

2,277

2,370

2,403

2,435

2010-16*

2,472

* Average for the period

ANNEX TABLE 2.3 Estimates of income in the manual and service sectors Source: Philippine Statistics

INDUSTRY

BASIC DAILY WAGE (PHP)

% WEIGHTING

IMPLIED INCOME ($)

Agriculture

191.61

1,245

Industry

347.37

2,258

Manual labor

269.49

1,752

Services

435.35

114

2,830

ALL SECTORS

380.23

100

2,472

Authority.

ANNEXES

ANNEX TABLE 2.4

Other employment indicators

INDICATOR

VALUE

SOURCE

Labor participation rate (%)

63.6

https://psa.gov.ph/statistics/survey/labor-force

Labor participation rate, male (%)

78.4

http://www.nscb.gov.ph/gender/PSA-NSCB_2015%20Factsheet%20on%20WAM.pdf

Labor participation rate, female (%)

50.3

http://www.nscb.gov.ph/gender/PSA-NSCB_2015%20Factsheet%20on%20WAM.pdf

Employed population

48,584,156

Calculated

Employed, male

32,571,000

Calculated

Employed, female

16,013,156

Calculated

Work force entry

Work force exit

Income scenarios Average overall wage ($)

2,472

Average wage, services sector ($)

2,830

Average wage, manual labor employed in the industry and agriculture sectors ($)

1,752

Minimum wage, PhP/day Minimum wage, $/day Discount rate (%) Exchange rate ($:PhP)

ANNEXES

330.00

Minimum Wage Rates by Sector and Region, Philippines: As of January 11, 2016

7.01 3 0.0212358

http://www.xe.com/currencyconverter/convert/?From=USD&To=PHP AUG 4, 2016

ANNEX 3 Adjusted and Extrapolated Prevalence

ANNEX TABLE 3.1 Breastfeeding, less than one month

BREASTFEEDING

SHARE TO TOTAL

Exclusive (Table 36)

65.5

Non-exclusive breastfeeding*

34.5

Predominant (Table 37)

Source: 2013 NNS Appendix 2a.

5.1

No breastmilk (Estimated)**

10.1

Partial*

19.3

FEEDING PRACTICE WITHOUT ANY BREASTMILK

SHARE TO TOTAL (% of less than 2 months)

Pure other milk

9.4%

Other milk and foods

0.7%

No milk

TOTAL (No breastmilk, less than 2 months)

ANNEX TABLE 3.2 Breastfeeding, 1-5 months

MONTH

Source: 2013 NNS and 2015 NNS Update. 1

7.1% reported in 2015 NNS Update.

10.1%

EXCLUSIVE

NON-EXCLUSIVE

PREDOMINANT

PARTIAL

NONE

64.3%

35.7%

5.8%

19.8%

10.1%

54.4%

45.6%

5.5%

15.7%

24.4%

58.8%

41.2%

4.7%

12.1%

24.4%

44.2%

55.8%

7.9%

17.7%

30.2%

28.3%

71.7%

10.5%

31.0%

30.2%

AVERAGE

50.0%

6.9%

19.3%

23.9%

2013 NNS Table 36

Calculated

2013 NNS Table 37

Calculated

2013 NNS Appendix 2a

Sources:

ANNEX TABLE 3.3 Prevalence of segmented severe and moderate wasting

* Calculated. Source: 2013 NNS. **Figure used is for children less than 2 months. See computation below.

(% of less than 1 month)

WASTING (WHZ)

PREVALENCE (%)

REPORTED

Adjusted severe based on 2013

2.5

Weighted: 35.4%

Adjusted moderate based on 2013

4.6

Weighted: 64.6%

Severe

2.8

35.4%

Moderate

5.1

64.6%

TOTAL1

7.9

ANNEXES

ANNEX TABLE 3.4 Segmented prevalence of severe and moderate underweight UNDERWEIGHT (WAZ)

ANNEX TABLE 3.5 Anemia among children 6-24 months AGE GROUP

PREVALENCE (%)

REPORTED

6 months-1 year

40.5

14%

1-2 years

11.2

PREVALENCE (%)

NOTES

5.0

Weighted: 23%

17.0

Weighted: 77%

4.6

23%

6-59 months

Moderate

15.4

77%

Source: 2013 NNS.

TOTAL2

20.0

Adjusted severe Adjusted moderate Severe

20.97%

Source: 2013 NNS. 2

21.5% prevalence reported in 2015 NNS.

ANNEX TABLE 3.6 Anemia among adults

SEX AND AGE GROUP

Source: 2013 NNS.

Male

SAMPLE SIZE OR POPULATION

ANEMIA (%)

IMPLIED ANEMIC POPULATION

DERIVED PREVALENCE (%)

622.19

10,350

13-19

2,898

5.3

153.594

20-39

3,746

4.1

153.586

40-59

3,706

8.5

315.01

Female

53,745,200

Below 20 Over 20

7,813,594.4

4,779,200

19.8

946,281.6

22,093,400

13.4

2,960,515.6

26,872,600

ANNEX TABLE 3.7 Estimating segments of severe and moderate stunting STUNTING

3,906,797.2

ANNEX TABLE 3.8 Maternal handwashing behaviors

PREVALENCE (%)

REPORTED

Severe Adjusted

10.7

Weighted: 32%

Moderate Adjusted

22.7

Weighted 68%

HANDWASHING BEHAVIORS

2013 Prevalence Segmented Severe Moderate

RURAL

URBAN

Before food preparation

87.3

89.2

Before feeding

86.5

88.3

After defecating

92.2

96.8

9.7

32%

After child defecation

88.8

90.6

20.6

68%

AVERAGE

88.7

91.2

30.3

100%

54.7%4

45.3%

48.525

41.325

Source: 2013 NNS and 2015 NNS Update. 3

33.4% prevalence rate reported in the 2015 NNS Update.

14.5

Source: 2013 NNS. 4

For more details, visit Philippine Statistical Authority website at https://psa. gov.ph/tags/urban-rural-classification.

ANNEXES

Average used in Damage Assessment Report (DAR) = 89.8%.

ANNEX 4 Segmented Morality by Age

DHS 2013 (2008-2012).

Calculated.

INDICATOR AND AGE GROUP

VALUE

Child mortality rate Under age 51 1-5 years2

31 8

Infants (‘000)1

Less than one month1

1-12 months2

Deaths2 Under age 5

77,451

Infants (‘000)

57,464

1-5 years

19,987

Less than one month

32,480

Less than six months

52,467

1-11 months

24,984

1-5 months

14,991

Less than six months

47,470

6-11 months Less than six months 6-11 months

9,994 47,470 9,994

6-24 months (6-11 months, one-third of 6-59 months)

14,991

6-59 months

29,981

ANNEXES

ANNEX 5 Mortality Calculations

ANNEX TABLE 5.1 Estimated child deaths due to suboptimal breastfeeding from birth to less than one month

INDICATOR

DIARRHEA

ARI

Attributed neonatal deaths (%)

0.6

5.40

Attributed neonatal deaths

195

1,754

No breastfeeding (%)

10.1

Partial breastfeeding (%)

19.3

5.1

10.53

15.13

Partial breastfeeding

4.62

2.49

Predominant breastfeeding

2.28

1.75

No breastfeeding (%)

49.0

58.8

Partial breastfeeding (%)

41.1

22.3

6.1

3.7

No breastfeeding

1,031

Partial breastfeeding

392

Predominant breastfeeding

188

1,488

NEONATAL DEATHS (less than one month): 32,480

Prevalence of suboptimal breastfeeding behavior

Predominant breastfeeding (%) Relative risk of mortality (Lancet) No breastfeeding

Population attributable risk mortality

Predominant breastfeeding (%) Unadjusted attributed deaths by behavior

Unadjusted attributed deaths by disease Unadjusted total mortality

1,675

Adjusted projected mortality (%)

71.8

Adjusted mortality

140

1,213

Unadjusted total Overall population attributable risk neonatal mortality (%)

1,353 4.17

LOSS OF PRODUCTIVE POTENTIAL Annual wage ($) Effective employment rate (%) Net Present Value (NPV) of losses with 15 as the workforce entry age ($)

ANNEXES

2,471.55 63.6 29,339,860

ANNEX TABLE 5.2 Estimated child deaths due to suboptimal breastfeeding during 1-5 months

INDICATOR

DIARRHEA

ARI

13.6

29.5

2,039

4,422

No breastfeeding (%)

23.9

Partial breastfeeding (%)

19.3

6.9

10.53

15.13

Partial breastfeeding

4.62

2.49

Predominant breastfeeding

2.28

1.75

No breastfeeding (%)

77.1

Partial breastfeeding (%)

22.3

4.9

1,416

3,411

Partial breastfeeding

838

986

Predominant breastfeeding

165

217

Unadjusted attributed deaths by disease

2,419

4,614

Unadjusted total mortality

7,032

CHILD DEATHS (1-5 months): 14,991 Attributed neonatal deaths (%) Attributed neonatal deaths Prevalence of suboptimal breastfeeding behavior

Predominant breastfeeding (%) Relative risk of mortality No breastfeeding

PAR Mortality

Predominant breastfeeding (%) Unadjusted attributed deaths by behavior No breastfeeding

Adjusted projected mortality

83.5%

83.1%

Adjusted mortality

1,702

3,675

Unadjusted total

5,376

Overall PAR neonatal mortality

35.9%

LOSS OF PRODUCTIVE POTENTIAL Annual wage ($) Effective employment rate (%) NPV with 15 entry until entry in to workforce ($)

2,471.55 63.6 116,554,258

ANNEXES

ANNEX TABLE 5.3 Estimated child deaths due to suboptimal breastfeeding during 6-24 months

INDICATOR

DIARRHEA

ARI

13.6%

29.5%

2,039

4,422

Suboptimal breastfeeding (%)

42.7

Relative risk of mortality (Lancet)

2.10

1.92

PAR mortality (%)

32.0

28.2

Unadjusted attributed deaths

652

1,248

CHILD DEATHS (6-24 months): 14,991 Proportional mortality (WHO) Total attributed deaths by disease

Unadjusted attributed deaths by disease

1,900

Overall PAR

13%

LOSS OF PRODUCTIVE POTENTIAL Annual wage ($) Effective employment rate (%) NPV with average 14 years entry until entry into workforce ($)

ANNEX TABLE 5.4 Burden of neural tube defects (Spina Bifida and Anencephaly)

INDICATOR Annual Births NTD Rate/10,000 (Maceda et al)

2,471.55 64 43,108,583

VALUE 2,498,433 23.88

Rate in absence of folic acid deficiency (WHO/FFI)

5.00

Projected NTDs

4717

Fatality Rate (Assume)

80%

Total Projected Deaths from NTDs

3,774

Proportion Neonatal Mortality

11.6%

Number Disabled

943

Severe

236

Moderate

708

LOSS OF PRODUCTIVITY FROM MORTALITY Average Annual Wage All Sectors Employed (all sexes) Mortality: NPV with average entry until entry into workforce

ANNEXES

$2,471.55 64% $81,810,305

Lost productivity severe disability

$5,113,144

Lost productivity moderate Disability

$7,669,716

Total Disability

$12,782,860

TOTAL NTD

$94,593,165

ANNEX TABLE 5.5 Child deaths attributed to low birthweight associated with maternal nutrition status

INDICATOR Births Prevalence rate of low birthweight

VALUE

SOURCE

2,498,433 21.4%

DHS 2013 Table 10.1

Prevalence (cases)

534,665

Calculated

Estimated 2,000-2,500 grams

472,345

WHO 80% Preterms 1500-2500g

Estimated less than 2,000 grams Rate 2,000-2,500 grams as % of all births

62,320

WHO 20% Preterms < 1500g

18.91%

Calculated

Rate less than 2,000 grams

2.49%

Calculated

Total number of neonatal deaths less than 1 year

32,480

Estimated 2,000-2,500 grams (Black 2008) Prevalence RR PAR Attributed deaths

18.9% 2.8 25.4% 8,247

Estimated less than 2,000 grams Prevalence RR PAR Attributed deaths Total attributed low birthweight mortality per year

2.49% 8.1 15.05% 4,887 13,133

ANNEXES

ANNEX TABLE 5.6 Low birthweight mortality related to 3 indicators of maternal nutrition

INDICATOR

VALUE

SOURCE

LBW Attributed to Mothers Low BMI RR LBW BMI < 18.5

1.71

Prevalence of Low BMI

24.8%

PAR Chances of Mortality

15.0%

LBW Mortality Attributed to Mothers Low BMI

Black, Low BMI Term SGA 2013 NNS Low WHZ "Nutritionally At Risk"

1,966

LBW Mortality Attributed to Mother Stunting RR HAZ < 145 cm Prevalence of Low HAZ PAR Chances of Mortality LBW Mortality Attributed to Mothers Low HAZ

2.2 42.70%

Black, Low HAZ Term SGA Addo Maternal Height Child Growth Patterns, J of Pediatrics 2013

33.9% 4,450

LBW Mortality Attributed to Mothers Anemia Anemia

1.25

Prevalence of Anemia PAR Chances of Mortality LBW Mortality Attributed to Mothers Anemia

25.2% 5.9% 778

LBW Due Birth Spacing < 1 yr Unadjusted Total Mortality Adjustment Adjusted Attributed Mortality PAR Proportion Neonatal Deaths

7,194.17 47% 6,187 19.05%

Loss of Productive Potential Annual wage Effective employment rate

ANNEXES

$2,471.55 $0.64

In Black et via Dibley 2013 NNS

ANNEX TABLE 5.7 Disease-specific approach: Child deaths associated with weight-for-age (WAZ) Estimated mortality among children 6-59 months: 29,981

INDICATOR

DIARRHEA

ARI

OTHER INFECTIONS

MEASLES

13.6%

29.5%

8.50%

0.60%

4,077

8,844

2,548

180

4.9%

16.6%

11.6

10.10

8.28

7.73

2.9

3.10

1.58

3.12

Severe

34.4%

31.0%

26.5%

25.0%

Moderate

23.9%

25.8%

8.8%

26.0%

1,402.25

2,745

675

975.65

2,282

223

2,377.91

5,026

898

50.1%

48.8%

32.9%

44.5%

Projected mortality

2,042

4,318

839

Total deaths

7,279

Attributed deaths

Prevalence of underweight Severe Moderate Relative risk of mortality Severe Moderate PAR mortality

Unadjusted death Severe Moderate Unadjusted sum PAR adjusted for multiple risks

Combined PAR 6-59 months

24%

LOSS OF PRODUCTIVE POTENTIAL Annual wage Effective employment rate NPV with average 14 years entry until entry into workforce

$2,471.55 63.6% $165,158,479

ANNEXES

ANNEX TABLE 5.8 Disease-specific approach: Child deaths associated with wasting (WHZ) Estimated mortality among children 6-59 months: 29,981

INDICATOR

DIARRHEA

ARI

OTHER INFECTIONS

MEASLES

13.6%

30%

8.5%

0.60%

4,077

8,844

2,548

180

Severe

2.5%

Moderate

4.6%

12.30

9.70

11.2

6.01

3.40

4.70

11.20

2.79

22.1%

18.0%

20.4%

11.2%

9.9%

14.5%

31.9%

7.6%

Severe

903

1,589

521

Moderate

404

1,282

812

1,307

2,871

1,332

29.9%

45.8%

17.9%

Adjusted deaths by cause

1,217

2,641

1,167

Adjusted total deaths

5,057

Attributed deaths

Prevalence of wasting

Relative risk of mortality Severe Moderate PAR mortality Severe Moderate Unadjusted death

Unadjusted sum PAR adjusted for multiple risks

Overall PAR

17%

LOSS OF PRODUCTIVE POTENTIAL Annual wage Effective employment rate

$2,471.55 63.6%

NPV with average 14 years entry until entry into workforce

$114,731,631

NPV with average 15 years entry until entry into workforce

$131,476,167

ANNEXES

ANNEX TABLE 5.9 Cause-specific approach: Child deaths associated with zinc deficiency, ARI and diarrhea

INDICATOR

VALUE

6-59 Month Mortality

29,981

Diarrhea % Mortality

13.6%

Attributed Deaths ARI Mortality

SOURCE

WHO

4,077 30% 8,844

Zinc Deficiency Prevalence RR of Mortality Diarrhea PAR Diarrhea Mortality

21.60% 2.01

Lancet 2013 Pooled Estimate Web Table 14

17.9%

Attributed Diarrhea Mortality

730

RR Mortality ARI

1.96

PAR ARI Mortality

17.2%

Attributed ARI Mortality

1,519

Zinc Attributed Mortality

2,249

Over-all PAR

FNRI 7th NNS 2008

Lancet 2013 Pooled Estimate Web Table 14

Loss of Productive Potential Annual wage Effective employment rate NPV with average 14 years entry until entry into workforce

$2,471.55 63.6% $51,031,501

ANNEXES

ANNEX TABLE 5.10 Child deaths associated with vitamin A deficiency

INDICATOR

VALUE

Children 6-59 months

SOURCE

11,327,300

Deaths of Children < 6-59 months

29,981

Prevalence of vitamin A deficiency

20.4%

Total with VAD

NNS 2008

2,310,769

Coefficient of Loss Relative risk of death due to vitamin A deficiency

1.32

Population attributable risk

6.1%

The number of deaths due to vitamin a deficiency

1,815

Lancet

LOSS OF PRODUCTIVE POTENTIAL Annual wage

$2,472

Effective employment rate

63.6%

NPV average of 14 years until entry to workforce

$41,168,702

ANNEX TABLE 5.11 Child deaths associated with suboptimal maternal handwashing behaviors

INDICATOR

VALUE

Mortality, 6-59 months1 Diarrhea, mortality rate

Attributed deaths

29,981 13.6% 4,077

Suboptimal handwashing behaviors3

10.16%

Relative risk of death due to diarrhea4

2.08

Population at-risk, diarrhea mortality

9.9%

Attributed diarrhea mortality

404

Over-all PAR

LOSS OF PRODUCTIVE POTENTIAL Annual wage ($) Effective employment rate NPV with average 14 years entry until entry into workforce ($)

ANNEXES

2,471.55 63.6% 9,169,616

Based on national statistics.

WHO year.

2013 NNS.

Converted from LIST.

ANNEX 6 Background Calculations for the Cost of Treating Diarrhea and ARI Cases ANNEX TABLE 6.1 Diarrhea cases

ANNEX TABLE 6.2 Consult cost 1

Unit cost derived by adding estimated cost of nurse (PhP 46 per contact, 20

DIARRHEA CASES

LAMERTI SYSTEMATIC REVIEW (%)

WEIGHTED CASELOAD for 42% taken to PHC

Mild

22.8

39.3%

Moderate

34.7

59.8%

Severe

0.5

0.9%

TOTAL

58.0

100.0%

CASES AND COST ITEM

UNIT COST

SHARE PER SEGMENT (%)

DURATION

COST

(minutes)

A. DIARRHEA Personnel and facility overhead cost per case w/ 2 contacts1

102

204.0

minutes), physician (PhP 10 per contact, 2

Other costs2

minutes), and facility overhead (PhP 46 per

Mild, ORS+Zn

65.00

39.3

25.55

110.00

59.8

65.81

Severe, lactated ringer solution plus IV set

50.00

0.9

0.43

Tests

71.00

0.71

contact). These are computed based on the monthly salaries of nurses (PhP 24,315 per month) and physicians (PhP 52,578 per month). 2

Weighted cost/case.

Moderate, ORS+Zn

Subtotal

92.50

Average cost per case (PhP)

296.52

Average cost per case ($)

6.30

B. ACUTE RESPIRATORY INFECTION (ARI) Personnel and facility overhead cost per case w/ 2 contacts1

102

204.00

59.8

110.00

59.8

Amoxicillin

37.00

29.60

Cotrimoxazole

13.85

1.39

TB medicine

2500.00

25.00

Blood count

155.00

7.75

Urinalysis

101.00

5.05

Stool Exam

71.00

3.55

Chest X-ray

200.00

4.00

Purified protein derivative (PPD)

500.00

10.00

Other costs3

Subtotal

86.34

Total cost per case (PhP)

290.35

Total cost per case ($)

6.17

ANNEXES