Faculty of Bioscience Engineering Academic year 2012...

Faculty of Bioscience Engineering

Academic year 2012 – 2013

Assessment of nutritional status of underfive children and its determinants in Sri Lanka

Md. Nazmul Haque

Promoter: Prof. dr. Patrick Kolsteren

Tutor: Ir. Anne –Marie De Winter

Master’s dissertation submitted in partial fulfillm ent of the requirements for the degree of Master of Science in Nutrition and Rural Development

Main subject: Human Nutrition - Major: Public Healt h Nutrition

Certification and Declaration

I, Md. Nazmul Haque declare that this report is a result of my original work and has not been

presented to any other examination body in here and elsewhere. Acknowledgement for other

information sources used in this report has been properly referenced according to methods

that are accepted by Ghent University. Only author and promoter of this report deserve the

right to give permission for consulting and copying parts of this work for personal use. For

any other use is obliged by Copyright laws. Particularly when using results from this maser’s

dissertation it is mandatory to specify the sources after having obtained the written

permissions.

Ghent University, August 2013

Promoter Tutor

Prof. dr. Patrick Kolsteren Ir. Anne-Marie Remaut-De Winter

Email: [email protected] Email: [email protected]

Author

Md. Nazmul Haque

Email: [email protected]

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Abstract

Background: Malnutrition among underprivileged population is widespread mostly in low and middle income countries. Malnourished children are often suffering from longer and severe illnesses and have a higher risk of dying and also have delayed motor development, lower cognitive function, and poor school performance. According to Sri Lanka Demographic and Health Survey (2006/07) 15%, 18% and 22% underfive children were wasted, stunted and underweight respectively. Remarkable improvements have been seen on many sectors in Sri Lanka such as improvements in agriculture, education, health, economic and technological sectors. However, malnutrition among underfive children is still a serious public health problem in Sri Lanka.

Objectives: The aim of this study is to assess the nutritional status and factors that are related to malnutrition among underfive children in Sri Lanka.

Methods: A cross-sectional descriptive study was conducted in at least one district from each of the nine provinces of Sri Lanka in 2009. 617 households per district were included using cluster sampling methodology, assuming that averagely 0.4% underfive children were present per household. The interviewer administered a pretested questionnaire to the household head and mothers were interviewed when consent was given to obtain the information on child care practices and maternal nutrition. Anthropometric assessments were used to assess the nutritional status of children and mothers.

Results: Among 3366 studied children 22%, 72% and 6% were living in urban, rural and estate sectors respectively. The mean Z-scores height-for-age, weight-for-age and weight-for-height were -1.01, -1.18 and -0.87 respectively and the prevalence of stunting, underweight and wasting was 20.1%, 22.8% and 12.3% respectively. The Bivariate analysis, using chi square and one way ANOVA test revealed that age of children, birthweight, area of residence, wealth index, education of mother and household head, mother’s BMI, access to toilets and drinking water showed significant association with all three indicators of malnutrition.In multivariate analysis using multiple linear regressions model demonstrated that age and birth weight of children were a significant predictor of stunting, underweight and wasting keeping other influential variables at constant. Furthermore, mother’s BMI and access to toilets were found to be a significant predictor of underweight and wasting at controlled condition.

Conclusions: The results of this study indicate that undernutrition among underfive children is still problem in Sri Lanka. Child age, birthweight, area of residence, socio-economic conditions, mother’s BMI, education, occupation, practices of hygiene and sanitation, household head education were found to be significant risk factors of malnutrition.

Key words: Malnutrition, stunting, underweight, wasting, underfive children, Sri Lanka

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Acknowledgement

First of all, I would like to express my gratitude to almighty Allah for being my source of

strength throughout my stay in Belgium. I would like to express my specific appreciation to

Prof. dr. Patrick Kolsteren and Ir. Anne-Marie Remaut-De Winter for helping me to get the

dataset from Medical Research Institute, Sri Lanka and also sincere appreciation to dr. Patrick

Kolsteren for his whole-hearted inspiration, constructive criticism, endearing company and in

particular, for his scholastic guidance in phrasing and articulating my dissertation.

I would particularly like to express my sincere gratitude to Ir. Anne-Marie Remaut-De

Winter, my master dissertation tutor and Coordinator MSc Nutrition and Rural Development

for her tireless cooperation and inspiration throughout the study. Without her motivation,

dedication encouragement and guidelines, I would not be writing this page today.

I would like to extend my appreciation to Dr. SM Moazzem Hossain ( MPH MBA) and Dr.

Renuka Jayatissa for helping me to release the dataset from Medical Research Institute, Sri

Lanka.

I would like to extend my special thanks to Mohammad Mahbubur Rahman for his useful

guidelines during data analysis using SPSS software and also thanks to Elton Dube,

Mozumder Salatul and David for their useful guidelines during write up my master

dissertation.

Last but not the least; I would like to express utmost gratitude to my beloved wife, family

members, friends and well-wishers for their enthusiastic support, constant inspiration and

blessings during my study.

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Table of contents

CERTIFICATION AND DECLARATION .................................................................................................................. I

ABSTRACT ......................................................................................................................................................... I

ACKNOWLEDGEMENT ...................................................................................................................................... II

TABLE OF CONTENTS ....................................................................................................................................... III

LIST OF FIGURES ............................................................................................................................................... V

LIST OF TABLES ................................................................................................................................................ VI

LIST OF ABBREVIATIONS ................................................................................................................................. VII

GLOSSARY ..................................................................................................................................................... VIII

1. INTRODUCTION........................................................................................................................................ 1

1.1. BACKGROUND ................................................................................................................................. 1

1.2. JUSTIFICATION OF THE STUDY .............................................................................................................. 3

1.3. OBJECTIVES OF THE STUDY ................................................................................................................. 4

1.3.1. General objective .......................................................................................................................... 4

1.3.2. Specific objectives ......................................................................................................................... 4

1.4. RESEARCH QUESTIONS ...................................................................................................................... 4

1.5. LIMITATIONS OF THE STUDY ................................................................................................................ 5

1.6. ORGANIZATIONAL STRUCTURE OF THE DISSERTATION ............................................................................... 5

2. LITERATURE REVIEW ................................................................................................................................ 6

2.1. MEASUREMENT OF NUTRITIONAL STATUS ............................................................................................. 6

2.2. ANTHROPOMETRIC INDICATORS .......................................................................................................... 6

2.3. DETERMINANTS OF CHILD NUTRITIONAL STATUS ..................................................................................... 7

2.3.1. Socio-economic determinants ....................................................................................................... 7

2.3.2. Biological factors ........................................................................................................................... 9

2.3.3. Child individual factors ................................................................................................................ 10

2.3.4. Geographical factors ................................................................................................................... 11

2.3.5. Household level factors ............................................................................................................... 11

2.3.6. Family size and number of children ............................................................................................ 12

2.3.7. Maternal factors ......................................................................................................................... 12

2.3.8. Mother’s Body-Mass-Index ........................................................................................................ 14

2.3.9. Mother’s behaviour ..................................................................................................................... 14

2.3.10. Infant and young child feeding practices .................................................................................... 14

2.3.11. Inadequate food intake and improper health care ..................................................................... 15

2.3.12. Morbidity of children ................................................................................................................... 15

2.3.13. Natural factors ............................................................................................................................ 17

2.3.14. Environmental contamination factors ........................................................................................ 17

2.3.15. Possible remedial measures ........................................................................................................ 17

2.3.16. The Role of NGO’s and mass media ............................................................................................ 19

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3. MATERIALS AND METHODS.. ..........................................................................................20

3.1. SURVEY DESIGN ............................................................................................................................. 20

3.2. STUDY AREA AND PERIOD ................................................................................................................ 20

3.3. SAMPLE SIZE ................................................................................................................................. 20

3.4. SAMPLE FRAME AND CLUSTER SELECTION ............................................................................................ 21

3.5. HOUSEHOLD SELECTION .................................................................................................................. 21

3.6. DATA COLLECTION .......................................................................................................................... 22

3.6.1. Composition of the survey team ................................................................................................. 22

3.6.2. Training of survey teams ............................................................................................................. 22

3.7. METHODS OF DATA COLLECTION ....................................................................................................... 22

3.7.1. Household surveys ...................................................................................................................... 22

3.7.2. Anthropometric assessment ....................................................................................................... 22

3.7.3. Measurement of hemoglobin levels ............................................................................................ 23

3.7.4. Key informant interviews, community interviews, market surveys ............................................ 23

3.8. SUPERVISION AND QUALITY ASSURANCE .............................................................................................. 23

3.9. DATA PROCESSING AND ANALYSIS ...................................................................................................... 23

3.10. ETHICAL ISSUES .............................................................................................................................. 25

4. RESULTS ................................................................................................................................................. 26

4.1. CHARACTERISTICS OF THE STUDIED POPULATION ................................................................................... 26

4.2. CHILD NUTRITIONAL STATUS ............................................................................................................ 26

4.3. BACKGROUND CHARACTERISTICS RELATED TO CHILD MALNUTRITION ......................................................... 29

5. DISCUSSION ........................................................................................................................................... 45

5.1. PREVALENCE OF UNDERNUTRITION .................................................................................................... 45

5.1.1. Sex influence ............................................................................................................................... 45

5.1.2. Age of children ............................................................................................................................ 45

5.1.3. Area of residence ........................................................................................................................ 45

5.1.4. Wealth index ............................................................................................................................... 47

5.1.5. Mother’s education ..................................................................................................................... 47

5.1.6. Mother’s body-mass-index .......................................................................................................... 48

5.2. DETERMINANTS OF STUNTING, UNDERWEIGHT AND WASTING ................................................................. 48

6. CONCLUSION AND RECOMMENDATIONS ............................................................................................... 53

6.1. CONCLUSION................................................................................................................................. 53

6.2. RECOMMENDATIONS ...................................................................................................................... 54

APPENDICES ...................................................................................................................................................... I

APPENDIX 1 DESCRIPTIVE ANALYSIS ....................................................................................................................... I

APPENDIX 2 BACKGROUND CHARACTERISTICS RELATED TO CHILD MALNUTRITION ........................................................... II

APPENDIX 2.1 BIVARIATE ANALYSIS USING ONE WAY ANOVA ...................................................................................... II

APPENDIX 2.2 BIVARIATE ANALYSIS USING CHI SQUARE TEST .......................................................................................... V

APPENDIX 3 EFFECT OF BACKGROUND CHARACTERISTICS ON NUTRITIONAL STATUS OF UNDERFIVE CHILDREN (SPSS OUTPUT) . X

APPENDIX 4 A SEPARATE DOCUMENT ON CD CONTAINING THE DATASETS ................................................................... XV

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List of figures

FIGURE 4.1 THE PREVALENCE OF UNDERNUTRITION AMONG UNDERFIVE CHILDREN ............................................................ 27

FIGURE APP. 3.1 EFFECT OF WEALTH INDEX QUINTILES ON HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN ...................... X

FIGURE APP. 3.2 EFFECT OF CHILD AGE GROUP ON THEIR HEIGHT-FOR-AGE Z-SCORE .......................................................... X

FIGURE APP. 3.3 EFFECT OF CHILD AGE GROUP ON THEIR WEIGHT-FOR-AGE Z-SCORE ........................................................ XI

FIGURE APP. 3.4 EFFECT OF CHILD AGE GROUP ON THEIR WEIGHT-FOR-HEIGHT Z-SCORE .................................................... XI

FIGURE APP. 3.5 EFFECT OF PLACE OF LIVING DISTRICTS ON HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN ................... XII

FIGURE APP. 3.6 EFFECT OF PLACE OF LIVING DISTRICTS ON WEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN .................. XII

FIGURE APP. 3.7 EFFECT OF PLACE OF LIVING DISTRICTS ON WEIGHT-FOR-HEIGHT Z-SCORE OF UNDERFIVE CHILDREN ............. XIII

FIGURE APP. 3.8 EFFECT OF AREA OF RESIDENCE ON HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN ........................... XIII

FIGURE APP. 3.9 EFFECT OF AREA OF RESIDENCE ON WEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN .......................... XIV

FIGURE APP. 3.10 EFFECT OF AREA OF RESIDENCE ON WEIGHT-FOR-HEIGHT Z-SCORE OF UNDERFIVE CHILDREN ...................... XIV

FIGURE APP. 3.11 EFFECT OF MOTHER’S EDUCATION ON HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN ........................ XV

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List of Tables

TABLE 3-1 CHARACTERISTICS OF SAMPLE ................................................................................................................. 21

TABLE 4-1 BACKGROUND CHARACTERISTICS OF UNDERFIVE CHILDREN (CHILDREN AND MOTHERS IN THIS TABLE BELONG TO THE

SAME FAMILY. HOWEVER, A MOTHER MAY HAVE MORE THAN ONE CHILD AND DUE TO MISSING DATA,TOTAL NUMBER OF CHILD

AND MOTHER MAY DIFFER) .................................................................................................................................. 26

TABLE 4-2 DISTRIBUTION OF CHILDREN BY AREA OF RESIDENCE..................................................................................... 26

TABLE 4-3 PREVALENCE OF UNDERNUTRITION AMONG UNDERFIVE CHILDREN BY BACKGROUND CHARACTERISTICS (RESULTS FROM

DESCRIPTIVE ANALYSIS) ....................................................................................................................................... 27

TABLE 4-4 EFFECT OF BACKGROUND CHARACTERISTICS ON NUTRITIONAL STATUS OF UNDERFIVE CHILDREN (RESULTS FROM BIVARIATE

ANALYSIS: ONE WAY ANOVA+ POST HOC MULTIPLE COMPARISONS) ......................................................................... 30

TABLE 4-5 SUMMARY OF SIGNIFICANT CHARACTERISTICS FOR THE THREE NUTRITIONAL INDICATORS FOLLOWS THE RESULTS

OBTAINED THROUGH ONE WAY ANOVA ................................................................................................................ 38

TABLE 4-6 ASSOCIATION OF SELECTED VARIABLES AND HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN (RESULTS FROM

BIVARIATE ANALYSIS : PEARSON CHI SQUARE TEST) ................................................................................................... 39

TABLE 4-7 ASSOCIATION OF SELECTED VARIABLES AND WEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN (RESULTS FROM


TABLE 4-8 ASSOCIATION OF SELECTED VARIABLES AND WEIGHT-FOR-HEIGHT Z-SCORE OF UNDERFIVE CHILDREN (RESULTS FROM



OBTAINED THROUGH PEARSON CHI SQUARE TEST ..................................................................................................... 42

TABLE 4-10 ESTIMATES FROM LINEAR REGRESSION MODEL............................................................................................ 43




OBTAINED THROUGH MULTIPLE LINEAR REGRESSIONS MODEL ..................................................................................... 44

TABLE APP. 1 SEX DISTRIBUTION OF UNDERFIVE CHILDREN ............................................................................................. I

TABLE APP. 2 EFFECT OF BACKGROUND CHARACTERISTICS ON NUTRITIONAL STATUS OF UNDERFIVE CHILDREN (RESULTS FROM

BIVARIATE ANALYSIS: ONE WAY ANOVA) ................................................................................................................ II

TABLE APP. 3 EFFECT OF BACKGROUND CHARACTERISTICS ON HEIGHT-FOR-AGE Z-SCORE AND WEIGHT-FOR-HEIGHT Z-SCORE OF

UNDERFIVE CHILDREN (RESULTS FROM BIVARIATE ANALYSIS: ONE WAY ANOVA) ............................................................ IV

TABLE APP. 4 EFFECT OF THE INCOME QUINTILES ON WEIGHT-FOR-HEIGHT Z-SCORE OF UNDERFIVE CHILDREN (RESULTS FROM

BIVARIATE ANALYSIS: ONE WAY ANOVA) ............................................................................................................... IV

TABLE APP. 5 ASSOCIATION OF SELECTED VARIABLES AND HEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN (RESULTS FROM

BIVARIATE ANALYSIS : PEARSON CHI SQUARE TEST) ..................................................................................................... V

TABLE APP. 6 ASSOCIATION OF SELECTED VARIABLES AND WEIGHT-FOR-AGE Z-SCORE OF UNDERFIVE CHILDREN(RESULTS FROM

BIVARIATE ANALYSIS : PEARSON CHI SQUARE TEST) .................................................................................................... VI

TABLE APP. 7 ASSOCIATION OF SELECTED VARIABLES AND WEIGHT-FOR-HEIGHT Z-SCORE OF UNDERFIVE CHILDREN(RESULTS FROM

BIVARIATE ANALYSIS : PEARSON CHI SQUARE TEST ................................................................................................... VIII

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List of abbreviations

ACC/SCN Administrative Committee on Coordination, Sub-Committee on

Nutrition (of the United Nations)

ARI Acute Respiratory Infection

BMI Body Mass Index (kg/m2)

CI Confidence Interval

DCS Department of Census and Statistics

H/A Height/Length-for-Age

HAZ Height-for-Age Z-score

Hb conc. Haemoglobin Concentration

HH Household Head

IDA Iron Deficiency Anaemia

IFPRI International Food Policy Research Institute

LBW Low birth weight (i.e. < 2.5 kg)

MC Municipal Council

MRI Medical Research Institute

NCHS National Centre for Health Statistics

NGO’s Non-Governmental Organizations

OR Odds Ratio

ORS Oral Rehydration Solution

SD Standard Deviation

SES Socio-economic status

SL-DHS Sri Lanka, Demographic and Health Survey

UNICEF United Nations Children’s Fund

W/A Weight-for-Age

W/H Weight-for-Height

WAZ Weight-for-Age Z-score

WHO World Health Organization

WHZ Weight-for-Height Z-score

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Glossary

Anthropometry Human body measurements

Stunting Height-for-age below -2 SD from the National Centre for Health Statistics/WHO reference median value

Undernutrition A condition in which a body contains lower than normal amounts of one or more nutrients.

Undernutrition Proportion of underfive with height-for-age Z-score <-2.00; Underfive with weight-for-age Z-score <-2.00; plus underfive with weight-for-height Z-score <-2.00

Underweight Weight-for-age below -2 SD from the National Centre for Health Statistics/WHO reference median value

Wasting Weight-for-height below -2 SD from the National Centre for Health Statistics/WHO reference median value

Z-Score The deviation of an individual’s value from the median value of a reference population, divided by the standard deviation of the reference population.

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1. Introduction

1.1. Background

‘Burden of diseases’ is an emerging problem in the world, especially in the developing world

and malnutrition is the major cause for the ‘Burden of disease’ globally (WHO, 2002).

Childhood undernutrition still remains a serious public health problem in many low and

middle income countries and it has been estimated that annual death of children from low and

middle income countries is 1.7 to 3.6 million as a result of severe acute (SAM) and moderate

acute malnutrition (MAM) respectively (Ashraf H. et al., 2012).

Malnutrition is highly associated (54%) with the death of children mainly whose age is below

five. Inadequate nutrition can interrupt the normal growth and development of children,

particularly during first years of life. In a study WHO (2010) reported that undernutrition was

responsible for the 35% child mortality globally.

Nutritional status of children is considered to be a good indicator to measure the overall well-

being of a society. It reflects the existing socio-economic and environmental conditions,

healthcare system, and food security status (Bhutta Z., 2000; Oni’s M., 2000).

Underweight among the children is a useful tool to reflect the nutritional status of a

population. In the global context 26.7% of total children are estimated as to be underweight,

and 43.3%, the highest proportion of this prevails in South Center Asia followed by 28.9%

in South-East Asia, and 29.0% in overall Asia (ACC/SCN, 2000).

Wasting is an indicator that represents the acute shortage of food supply. Children are unable

to receive adequate nutrition and are suffering from acute episodes of illnesses such as

diarrhoea, pneumonia and so on. This is happening mainly during emergencies when the

supply of adequate food is deteriorated by emergency (SL-DHS, 2000). The overall wasting

prevalence in Asia is 10.4% with the highest prevalence (15.4%) in South Central Asia.

In South-East Asia, the wasting prevalence is 10.4%. The lowest prevalence (3.4%) can be

found in East Asia. In developing countries, the global prevalence of wasting is 9.4 %

(Allen L.H. and Gillespie S.R., 2001).Wasting prevalence is much lower compared to

underweight or stunting. In developing countries, 2-3% is the expected prevalence; if above

5% the mortality among children increases substantially (WHO, 1995).

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Stunting denotes, slowing down of the skeletal growth. The rate of growth may be reduced

from birth but the consequence of significant degrees of stunting are growth retardation.

Stunting is linked to poor economic conditions, chronic or repeated infections and inadequate

nutrient intake and it is frequently found to be in its associated condition (Prader A., 1977).

The consequences of chronic malnutrition (stunting and underweight) may lead to

irreversible cognitive deficits, poor school performance, school dropout and becoming a

poorer adult with low economic productivity. Girls growing up with undernutrition tend to

become short adults resulting likely to deliver small children with complexity of delivery

(Victora C.G. et al., 2008).

Maternal malnutrition during pregnancy and appropriate lactation practices are very

important for postnatal growth in breastfeed infants (Delgado H. et al., 1982). In the year

2005, 20% and 32% of underfive children in developing countries were underweight (weight

for age Z-score, <-2 SD) and stunted (height for age Z-score, <-2 SD) respectively, The

highest rate of prevalence of stunting and underweight was found in Africa and South-Central

Asia. About 2.1 million underfive children died worldwide due to stunting, wasting (weight

for height z-score <-2 SD) and/or intra uterine growth retardation. This age group comprises

21% of all deaths worldwide (Black R.E. et al., 2008).

Study area

Sri Lanka is, a beautiful island situated in south Asia, more precisely in the centre of the

Indian Ocean. It covers about 65,654 km2 with an estimated 19.9 million population (DCS,

2008a) and 1617 US$ per capita income (Ministry of Finance and Planning, 2007). In 2005,

the country’s crude birth and death rate was 18.1 and 6.5 per 1000 respectively (DCS,

2008c). Presently, a multi ethnic composition is seen in Sri Lanka. Major proportion (74%) of

the population is Sinhalese. Tamils and Muslims are 18 and 7 percent respectively and the

remaining one percent is composed of Burghers, Malays and other minor ethnic groups

(DCS, 2007).

Although it is an island it has a multidimensional landscape. South-Central region is

characterized by its mountainous area and the elevation is ranging from 300m to 2524m. The

rest of the island is occupied by its coastal plain nature, being narrower in the west as well as

in the south. However, the east and north region being broadening out. The tropical climate of

Sri Lanka is controlled by monsoon. It has two major types of monsoon which brings rain

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and the rainfall varies over the different regions of the island depending on the time period of

the year (SL-DHS, 2000).

1.2. Justification of the study

In Sri Lanka, the persistence of high prevalence of undernutrition among underfive children

is a serious public health problem, despite improvements in some health indicators. SL-DHS

(2006/07) reported that nearly 17% babies are born with a low birthweight (birthweight <2.5

kg). The prevalence of undernutrition in the form of acute undernutrition (wasting), chronic

undernutrition (stunting) and underweight among the children below five years was 15%,

18% and 22% respectively

Remarkable wide differences are observed in terms of geographical region and income

segments of the population. For instance, stunting prevalence on estate children was three

times higher than urban children. Inter districts variation on undernutrition was also observed.

As a wide range of factors are directly or indirectly influencing the nutritional status of

underfive children, conducting a comprehensive descriptive cross-sectional study where

information on basic underlying and immediate causes are available can be considered a need

at the present time.

The study in this research is conducted to assess the nutritional situation and the factors that

influence on nutritional status of underfive children in 9 districts in the country (one

district/province).

Finally, the study will provide useful information that are directly or indirectly related with

child nutrition and help the ministry of Health, other ministries and all other stake holders to

identify most vulnerable population, help to formulate interventions, targeting mechanisms

and serve as a baseline that can be compared with subsequent data collection.

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1.3. Objectives of the study

1.3.1. General objective

The general objective of this study is to assess the nutritional status of the underfive children

and to identify the determinants of undernutrition among underfive children in Sri Lanka in

order to deliver adequate informations to different stakeholders involved in formulating

interventions for this vulnerable groups.

1.3.2. Specific objectives

� To assess the prevalence of stunting, underweight and wasting of underfive children;

� To identify the relevant demographic, socioeconomic and environmental factors

influencing the nutritional status of the studied children;

� To identify biological variable and geographical factors influencing child nutrition;

� To access the relation between maternal factors and child nutrition;

� To estimate the morbidity pattern of the underfive children;

� To evaluate the feeding pattern of the underfive children;

� To analyse the links between household factors and child nutrition;

� To identify the relation between household hygiene and sanitation practices and child

nutrition;

� To find out the relationship of anaemia among child and mother with nutritional

status of the children.

1.4. Research questions

1) Is there any relation of the demographic, socio-economic, environmental, biological and geographical variations with the variations in the nutritional status of underfive children?

2) Is there any influence of maternal factors on the nutritional status of the underfive children?

3) Is there an impact of child feeding pattern on the differences of nutritional status of underfive children?

4) Does child nutrition depend on morbidity patterns?

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1.5. Limitations of the study

The limitations that were experienced in this study are:

i. The study was conducted in nine different districts in Sri Lanka. The infra

structure of some districts are quite poor and some respondent places of

residence were quite remote, hence facing some difficulties to communicate;

ii. Some of the households were not very co-operative during data collection

periods. So, it needed extra time and explanations to convince them;

iii. Managing time to interview the mothers was one of the difficult tasks which

was overcome by repeated motivation;

iv. While collecting the age of the children and mothers in the selected

population, some difficulties were faced as few of them had birth cards or

immunization cards and they could not even remember the exact year.

Various referral questions related to remarked incidents were used for

calculating the approximate age. So, there was a chance of recall bias;

v. As, the data used in this research are secondary, some desired information is

not available. For instance, there is no sufficient information on the feeding

patterns of the children;

vi. A part of dataset from the whole survey is obtained from ‘Nutrition and Food

Security Assessment in Sri Lanka, 2009’; Some relevant information is

critically absent.

1.6. Organizational structure of the dissertation

The master’s dissertation has been organized as follows:

The Chapter 2 consists of a literature review which focuses on finding out some evidence

based studies to explore the relationship of different factors and child and maternal nutrition

and possible evidence based interventions to address the malnutrition problem among

underfive children. Methodology for data collection and statistical analysis is described in

Chapter 3. Chapter 4 describes the results of this study in order to address the specific

research questions. In Chapter 5, the general discussion is presented based on the results and

some relevant studies. Conclusion and recommendations can be found in Chapter 6, followed

by the list of references and other supporting documents in the appendix section. A separate

document on CD containing the data sets is provided at the back of this research report.

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2. Literature Review

2.1. Measurement of nutritional status

There are various indicators that are used for the assessment of the nutritional status of

underfive children. Clinical signs, biochemical and anthropometric measurements are the

usual indicators to assess the nutritional status of underfive children. For practical purposes,

anthropometry is the most widely used tool (WHO Working Group, 1986) and it has more

advantages compared to other indicators (De Onis M., 2000) as clinical signs and

biochemical parameters are only more useful at extreme cases of malnutrition. At the

beginning when developing malnutrition, anthropometric indicator is also sensitive. Besides,

anthropometry is the most useful method to assess the nutritional status of individuals at the

population level. It is a convenient method in terms of being non-invasive, portable,

inexpensive and universally applicable and readily available to assess the size, proportion and

composition of the human body (De Onis M. and Habicht J.P., 1996).

There are three statistical terms of expressing anthropometric indices for comparing a child or

a group of children in relation to the reference population: Z-score (standard deviation

scores), percentiles and percent-of-median. Among them, the WHO recommended Z-score is

widely accepted for evaluating anthropometric data obtained from the survey (De Onis M.,

2000).

2.2. Anthropometric indicators

To evaluate the nutritional status of children in low and middle income countries, an

anthropometric indicator such as height-for-age (stunting), weight-for-age (underweight) and

weight-for-height (wasting) is an important tool. Malnutrition is still a serious public health

problem in many low and middle income countries. The global trends of undernutrition from

1980 to 2000 showed that there is no remarkable improvement in the prevalence of wasting,

but limited improvement in stunting as well as underweight. The stunting declined by 14%

and underweight by 7% (Dewey and Adu-Afarwuah, 2008).

Wasting and underweight are significantly associated with proximal factors of morbidity such

as feeding practices and child health status. Socioeconomic factors such as mother’s

education, presence of a latrine, de-worming status, environmental factors such as

7

maternal/reproductive and health delivery, morbidity like history of fever, child health status

are significantly associated with stunting (Henry Wamani et al., 2005).

In the United States on October 1, 2007 “Child Health Day’’ was proclaimed to show their

strong commitment in child health to increase global wealth and advanced medical

technologies. But the worse health outcomes among the children are still a great concern for

the world (Brooks-Gunn J and Duncan G.J., 1997).

2.3. Determinants of child nutritional status

Mosley W.H and Chen L. (1984) developed a framework of proximate determinants of

childhood morbidity and mortality. They incorporated social, economic, biological and

environmental factors in this study to identify the the determinants of childhood morbidity

and mortality. All social and economic determinants of childhood morbidity and mortality are

operating through a set of intermediate biological variables. The socio-economic

determinants were put into three broad Categories including (Mosley W.H and Chen L.,

1984):

1. Factors at individual level- measured by age, sex, religion, fathers and mothers level of

education, occupation, income;

2. Factors at household level- measured by household income and expenditure, wealth

index, housing condition, water and electricity supply, hygiene and access to information;

3. Factors at community level-measured by ecological settings. For example: system of

waste disposal, infrastructures like transport and road system, health system and so on.

The proximate determinants comprise five different categories (Mosley W.H and Chen L.

1984):

1. Maternal factors: age, parity and interval between births;

2. Environmental contaminants: food, water, air and soil;

3. Deficiency of nutrients: energy, macro and micro nutrients;

4. Control of personal health status: use of measures to control personal health status

like immunization, medication, antenatal and postnatal care;

5. Injury: intentional and accident.

2.3.1. Socio-economic determinants

Socio- economic status is the most frequently assessed social determinant of child health for

which income is the most notably indicator. Most of the studies have shown that children in

8

low income households are more prone to experience more infectious diseases such as

diarrhoea, measles, acute respiratory infection (ARI ) and other poor health outcomes

(Brooks-Gunn J. and Duncan G.J. , 1997; Currie A. et al., 2007; Louise Se´guin et al., 2005 ;

Rosenbaum S.,1992 and Scholer SJ et al., 1999).

The association between child health and income can be explained by one major explanation.

Families with high socio-economic status may be able to provide better health care services,

goods, services, adequate diet and resources to their children that can lead to a positive effect

keeping their children away from them experiencing poor health outcomes (Brooks-Gunn J

and Duncan G.J., 1997).

Historically as well as globally, among many other determinants, socio-economic status is

one of the most powerful determinants to the whole life cycle particularly, young children

are more vulnerable to the effects of poor socio-economic status and poverty. Poor socio-

economic condition and poverty are highly associated with childhood morbidity and

mortality. Children of households who have a poor income and who have a low socio-

economic status have a higher risk of death at infancy and childhood and are suffering from

many acute and chronic illnesses (Spencer, 2000).

Income is a socio-economic variable that has a significant impact on the nutritional profiles

of the household’s members. A study, conducted in Pakistan revealed that the number of

income sources has a positive impacts on child nutritional status. It has been found that the

prevalence of wasting among children is more in the households with one income source.

Households have less wasted children with two or more income sources compared to

households having one income source. Whether health and health related practices are

causally associated with acute malnutrition of children a study revealed that drinking water

has a significant link with wasting. A higher proportion of wasted children is found when not

consuming treated water compared to their counterpart. Mode of hand washing has also an

influence on wasting. The rate of wasting children is higher for those who are not use soap or

ashes to wash their hands after they defecate or before eating. Pattern of breastfeeding, hours

of delay for receiving colostrum and morbidity pattern of child are significantly associated

with wasting (ACF- Pakistan, 2012).

Results from a study in Bangladesh revealed that access to electricity, room height, and type

of floor ( all home environmental factors) were associated with nutritional status of children

(Jane A. Pryer et al,. 2003). Economic status of the household is a significant determinant of

9

child health and nutritional status (Sunkanmi O. A., 2012). Some underlying factors such as

households food insecurity, unhygienic environmental condition, inadequate healthcare

services, inadequate care practices and poor socio-economic conditions can exacerbate the

level of stunting (Edward A. Frongillo et al,. 1997).

2.3.2. Biological factors

2.3.2.1. Birthweight

The infants who weigh less than 2.5 kg at birth are referred as having low birthweight

(LBW). More than 20 million LBW infants are born each year. This represents 16% of all

births at the global level. However, the proportion is not distributed uniformly. It differs from

nation to nation. For instance, at Scandinavia the rate is 4%, while around 50% in parts of

India and Bangladesh (WHO, 1980; WHO,1984).

In a social gradient scale, birthweight is remarkable. It has profound effects to the entire

lifecycle from infancy to adults (Barker D. J. P., 1993).

Foetal and neonatal health is influenced by birthweight. Hence, low birthweight is considered

as an important determinant of the foetal and neonatal health status. 14 percent of born

children with low birthweight accounts for about 60 to 80 percent death of neonatals

worldwide. Birthweight is causally associated with foetal, neonatal as well as post neonatal

mortality.

Furthermore, birthweight is a strong determinant of infant and childhood morbidity. Some

chronic diseases and long-term growth and development are also associated with birthweight

(McCormick and Marie C., 1985; Barker D. J. P., 1995; WHO, 1995) and a study in Vietnam

revealed that low birthweight leads children to all forms of undernutrition like underweight,

stunting and wasting. On average LBW babies have six times or even more risk to become

underweight, stunted and wasted compared to their respective counterparts (Hien N.N. and

Kam S., 2008).

Multiple factors that are affecting mortality are interrelated. Limited studies have been

conducted to determine the effect of individual factors while controling the factors that are

related one to another. In Baltimore, A cohort study was conducted during 1960-64. A

known birth weight of 108852 single live births was followed till one year after the birth. The

individual effects of birthweight were determined by controlling the interrelated factors such

as race, socio-economic status, maternal age, birth order and parental care and so on. It was

10

found that the birthweight was the most significant factor for neonatal mortality and the

factors like race, socio-economic status, maternal age, birth order and parental care were also

important for their contributing role on birthweight (Shah F.K and Abbey H., 1971).

There is an association between birthweight and mortality rates of children aged 1-4 years.

For example, a study in Gambia showed that the mortality of children was higher among the

children who were born in the wet season (the incidence of LBW is high in that time)

compared to children that were born in the dry season (Prentice A. M., 1980).

2.3.2.2. Birth interval

Birth interval is a significant determinant of child nutritional status. Studies revealed that

duration between births was inversely associated with child low weight-for-age. As the

duration of subsequent birth interval increased, the proportion of low weight-for-age also

decreased. Longer birth interval made a chance for parents for paying more attention on child

care and made a better option for getting more share among living siblings by reducing

sharing problems (Rayhan M. I. and Khan M. S. H., 2006).

2.3.3. Child individual factors

2.3.3.1. Sex of child

Sex is an important determinant for childhood undernutrition. Boys are more vulnerable to be

stunted than girls (Kandala N.B. et al., 2011). However, a study conducted in India, revealed

that girls suffered more from stunting than their counterparts. But generally boys are more

prone to underweight than girls and the association is statistically significant (Kumar D. et al,

2006).

2.3.3.2. Age of child

Age is a factor influencing the variation on undernutrition among children. Literature studies

showed that second year of life is vulnerable for stunting and underweight.The highest

prevalence of stunting and underweight was found within the age group among underfive

children. The rate of stunting declined after two years of age. However, wasting was found at

higher level among the age group between 37-48 months (Kumar D. et al., 2006).

A study finding from Mozambique has showed that two to five year of children are more

vulnerable in terms of their health and nutritional status compared to the younger children. At

that stage, they are more dependent on complementary foods along with breastmilk. Their

mobile nature requires high energy to maintain growth and development and becoming used

to the variety of family foods. Hence, increasing the exposure to environmental contaminants

11

might lead them to a higher risk of infectious diseases and poor health. Thus, food safety as

well as factors of environmental hygiene become more crucial for their optimum growth and

development (Garrett and Ruel, 1999).

The higher prevalence of wasting was found among the age group between 48-59 months old

children (Hien N.N. and Kam S., 2008). Children aged 6-11 months and 24-35 months are

more vulnerable to undernutrition (Pongou R. et al., 2006).

Another study, conducted in the Democratic Republic of Congo found that age is a strong

determinant of stunting among the age of underfive children. There is an inverse linear

association between age group and stunting. As the age of the children is increasing, the risk

of being stunted also increases. Among the stunting prevalence, the highest prevalence was

found among the age group of five years while the lowest prevalence of stunting was in the

age group of one year old children (Kandala N.B. et al., 2011).

2.3.4. Geographical factors

Area of residence has a significant impact on child nutritional status. Based on an

observational study, it has been found that rural and mountainous children have a higher risk

to become undernourished for all three forms of anthropometric indices (underweight,

stunting and wasting) compared to urban children. Children of mountainous areas are the

most vulnerable among the three area of residence (Hien N.N. and Kam S., 2008).

Also a study in Democratic Republic of Congo depicted that the area of residence is an

influential factor of stunting. Rural children are more stunted than urban children. The rate of

stunting among children is differing from their place of birth. More stunted children were

found among the ones not born at hospitals (Kandala, N. B. et al., 2011).

A research report from Indonesia revealed that resources like public services and information

resources are less accessible for especially the rural people due to distance. Hence, the lower

the accessibility to these services, worse the community nutritional condition. Kusumayati

A. and Gross R., (1998) and Sunkanmi O. A., (2012) also supported the above findings.

2.3.5. Household level factors

2.3.5.1. Sex of household head

A study conducted in Bangladesh showed that a better nutritional status was seen among

female headed household members, particularly better nourished children. The fact that

12

women who are in charge of the budget, were more child and nutrition inputs-centered, hence

could prefer to buy better foods and seek better medical care for their children (Jane A. Pryer

et al,. 2003).

It is assumed that women are much more involved in child healthcare and the older the

mother, the more important the care for her child. (Sunkanmi O. A., 2012).

2.3.6. Family size and number of children

Family size has been positively associated with the nutritional status of underfive children.

However, the number of underfive children in a household negatively associated with the

nutritional status. In families having more than three children, the children were on average

four or even more times are more likely to be underweight, stunted, wasted compared to

those having less than three children (Hien N.N. and Kam S., 2008).

A study showed that with a ten percent increase of the number of underfive children within

the family, the chance of being stunted increased by 3.7 percent. Several studies revealed that

households with a small number of children have more chance to consume adequate energy.

Intra-household food distribution does not favour the food intake of young children,

especially female children (Garrett and Ruel, 1999).

2.3.7. Maternal factors

2.3.7.1. Mother’s age

Conception at young age has to be considered as an additional risk factor which can lead to a

poor pregnancy outcome (Allen L. and Gillespie S., 2001)

2.3.7.2. Mother’s education

Several studies have been conducted over the past decade and found that mother’s schooling

level and child survival has a nearly universal and positive association. This association has

been persisting even when the other variables like socio-economic factors have been kept

constant (Govindasamy P. and Ramesh B.M., 1997).

A study in India showed that for a better use of healthcare services , maternal education

presume to be a more significant, powerful and positive predictor when considering alo other

background factors such as maternal residence, work status , caste and religion, age, sex and

birth order of the children. Mother’s who have at least middle school education are 40 to 60

percent more likely to use in a better way the health care facilities for the treatment of their

children suffering from infectious diseases (ARI and diarrhoea and so on) compared to

13

illiterate mothers. Women educated with at least some years of primary school education are

two times more likely to be utilizing modern healthcare services compared to illiterate

women (Govindasamy P. and Ramesh B.M., 1997).

Mother’s who are educated may have a strengthened capacity to substitute with less

expensive sources of nutrients at the periods of recessions, thereby mitigating the risk of

undernutrition of their children. However, those mothers who have limited education and low

economic status have less chance for such type of substitution (Weil et al., 1990l).

Mother education is a crucial determinant of child malnutrition. It has been found that mother

education has a linear association with child’s stunting. The highest prevalence of stunting

was found among the mothers with no schooling and followed by mothers with primary

education. The lowest prevalence was found among mothers with secondary education,

followed by higher education. Households socio-economic status is one of the strongest

determinants of child undernutrition. It has a great influence on stunting among the children

of underfive years and is linearly associated with stunting. The highest rate of stunting was

found among the poorest group, followed by children whose household economic status was

poor at middle class. Lowest stunting was found among children of households with high

economic status considered as richest (Kandala, N. B. et al., 2011).

Women who are educated have more chance to take independent decisions. Educated

mothers can change the traditional familiar rules. They can make an independent decision to

take their sick child to modern healthcare facilities. Such a decision leading her to greater

utilization of modern healthcare facilities (Caldwell, 1979)

There are 3 major pathways of influence, linking between maternal schooling and child

mortality. Firstly, educated women can take the decision to break away the tradition. For

better results they can seek the modern means of safeguarding for their own health as well as

for their children. Educated mothers can take the challenges of neglecting childhood

morbidities as they are concerned on the remedy for childhood morbidities. They can use the

modern means to keep safe their children from illnesses (Caldwell J. C., 1988;Cleland J.,

1990)

2.3.7.3. Mother’s occupation

Mother’s occupation has a positive association with the nutritional status of children

younger than five years. A study found that mothers engaged with office work has

14

significantly less underweight and wasted children compared to them who are occupied as a

laborer, farmer and housewife. Mother’s occupation is also significantly associated with child

stunting. But the risk for being stunted by the type of occupation is not as high compared to

underweight and wasting (Hien N.N. and Kam S., 2008).

2.3.8. Mother’s Body-Mass-Index

Studies have found that there is a significant association between maternal Body-Mass-Index

(BMI) and child nutritional status. Children of malnourished mothers had a higher risk of

being underweight compared with children of wellnourished mothers. The reason behind is

that mother with poor nutritional status may not be able to provide sufficient breastmilk due

to their inadequate intake of nutrients. Maternal malnutrition could be a hindrance for her

child’s growth and development (Rayhan M. I. and Khan M. S. H., 2006).

2.3.9. Mother’s behaviour

Child haemoglobin status and maternal body weight had positive impacts on the weight-for-

age of children. There is an inverse relationship between maternal employment and cost,

time, delayed health care seeking behaviour with child’s weight-for-age. Women

empowerment enhances engagement for employment, their participation during decision

making and then choice for private healthcare services. There is a positive association with

child’s weight-for-age along with mother’s physical morbidity. Children who are not being

offered enough energy or nutrient dense foods and missing appropriate immunization have a

higher risk on low weight-for-age (Sethuraman K. et al., 2006).

2.3.10. Infant and young child feeding practices

Infant feeding practices is a strong determinant of stunting as well as of underweight among

the underfive year’s children. Studies have shown that the proportion of underweight and

stunted children are significantly lower whose mothers initiated breastfeeding within six

hours of birth. A higher prevalence of both stunting and underweight is found among

underfive children who are deprived from colostrums. Improper complementary feeding is a

risk factor for underweight among children. More underweight children were found among

the groups who did not get proper complementary foods follows their needs (Kumar D. et al.,

2006).

Duration of exclusive breastfeeding is an important determinant of child nutritional status.

Children of mother’s who were exclusively breastfeeding their infants for less than six

15

months, had a higher risk on being underweight, stunted and wasted compared to the

children whose duration of exclusive breastfeeding is up to six months (Hien N.N. and Kam

S., 2008).

Inadequate dietary intake by the children due to frequent infectious diseases are considered to

be an immediate cause of stunting (Frongillo E. A. et al., 1997).

2.3.11. Inadequate food intake and improper health care

Poor maternal nutritional status at conception and undernutrition in utero, inappropriate or

inadequate breastfeeding, delayed and inadequate complementary feeding, poor absorption of

nutrients due to parasites or intestinal infection or the combination of these factors are

considered the most common immediate causes of poor growth among children in developing

countries (Allen H.S. and Gillespie R.S., 2001).

Child malnutrition is a complex phenomenon. Nutritional status of children is influenced by

multiple factors. Inadequate food intake and improper health care are the most important

immediate factors causing childhood’s undernutrition. However, both factors are highly

related with household socio-economic status. Allocation of households resources on food

and healthcare and the households decision maker is attitudes on children are the influential

factors on child nutritional status. Studies showed that better nourished children are found in

households with higher land resources (Bhuiya A. et al., 2001).

2.3.12. Morbidity of children

2.3.12.1. Malnutrition and infection

It is estimated that each year for about 12 million underfive children who die, a large

proportion of those children are from developing countries. Attributable factors of death like

diarrhoea, measles, ARI and malaria which are manageable with low cost interventions are

responsible for more than 50 % of these deaths ( UNICEF, 1998).

Diseases and dietary inadequacies, the immediate causes of undernutrition are mutually

interacting in a reinforcing manner. Undernutrition leads to impaired immune competences.

Hence, the risks are increased for infections. Without the effect of diseases, inadequate

dietary intake may cause death. Even diseases such as malaria and measles may lead to the

cause of death among wellnourished children. But, the combination of both factors is the

most frequent cause of child mortality (Allen L. H. and Gillespie S. R., 2001).

16

A well known mutual synergistic relation exist between malnutrition and infection. Infectious

diseases such as diarrhoea, measles, ARI, gastro-enteritis, pneumonia may lead to poor health

status. Suffering from infectious diseases with high energy cost may lead to nutrient

deficiency by loosing appetite, hence worsening the nutritional status of children. Risk of

expose to infectious diseases and its severity is higher among children with poor nutritional

status. A clear synergism has be seen with gastro-enteritis or pneumonia and poor nutritional

status. Literature showed that the severity of these diseases is increased by worsening the

nutritional status (Tupasi T. E. et al., 1988).

2.3.12.2. Diarrhoea

Briend A. (1990) revealed that diarrhoeal effects on permanent stunting was small as the

velocity of growth can be faster compared to the average for age during illness episodes

resulting in catch-up growth. For this reason, the causal effects on childhood undernutrition

and consequently, of the diarrhoea control measures (intervention on sanitation, hygienic

practices) and its potential effect they might have on growth are still on unresolved. It was

estimated by the Lancet of Maternal and Child Undernutrition series that with 99% coverage

of implemented sanitation and hygienic practices the intervention would reduce the incidence

of diarrhoea by 30%, resulting in a prevalence of stunting also decreases by 2.4% (Bhutta Z.

A. et al., 2009).

Another study in Vietnam demonstrated that children experiencing diarrhoea during the two

weeks before the survey have a higher risk for being underweight, stunted and wasted. Those

are suffering from cough had even more risk being underweight and wasted (Hien N.N. and

Kam S., 2008).

2.3.12.3. Iron deficiency

Infants with low birthweight have very low iron reserves. Within 2 to 3 months after birth the

stored iron is depleted. Iron supplementation should be started at 2 months of age because

breastmilk cannot provide sufficient iron for their requirements (Allen L. H. and Gillespie S.

R., 2001).

Anaemia as well as low haemoglobin concentration is more prevalent in stunted children. So,

significantly higher prevalence of both anaemia and iron deficiency anaemia was found in

stunted children. The prevalence of iron deficiency anaemia is significantly higher in boys

than in girls. Age is a risk factor for iron deficiency anaemia. Less than 7 years children are

more vulnerable compared to older children (Stoltzfus R. J. et al., 1997).

17

2.3.13. Natural factors

Findings from developed countries revealed that the downturned households economic status

due to recession of the national economy (Paxson C. and Schady N., 2005) at natural

calamity (for instance flood, shock on rainfall, drought stress and so on) might have a

negative impact on health and mortality among children (Foster A. D., 1995; Jensen R., 2000;

Yamano T. et al., 2003).

2.3.14. Environmental contamination factors

A study in Bangladesh demonstrated that the better the use of appropriate sanitary means to

defecate was significantly associated with better child nutrition (Jane A. Pryer et al,.2003).

Unhygienic environment caused by unavailability or poor accessibility of clean and safe

water and defecates in an open place, increases the probability of infectious diseases that

leads the children to certain types of undernutrition (UNICEF, 1990; Engle P.L., 1992).

2.3.15. Possible remedial measures

2.3.15.1. Interventions

Many factors are associated with poorer growth of children. Probable assumptions are

inadequate intake of the right foods. Dietary solution is a key focus area for many

researchers.Several studies have been done about impaired nutrient dense foods, fortification,

supplementation, strategies on behavioural changes in infant-young child-feeding practices

and nutrition education interventions. The recent results of 38 of those studies have shown

that children (12-24 months) gained extra weight upto 760 g or even more (0.0-0.76 W/A Z-

score) and increased length by 1.7 cm extra (0.0-0.64 H/A Z-score) less than one year

compared to control children though one or more of those interventions. However, normal

growth was not achievable by none of this interventions (Dewey K.G and Adu-Afarwuah S.,

2008).

Mother’s milk is not sufficient after six months to maintain optimal growth. The reason

behind is that after six months of age, the nutritional requirement increases; hence maternal

supplied milk is not sufficient for optimum growth. So, complementary foods have to be

introduced around that time. Insufficient maternal supplied milk and delayed supplementation

may lead the children into slowed growth. Provision of adequate nutrients by early

supplementation leads them to a higher risk of diarrhoea. This scenario has been seen if

breastfeeding was early stopped due to economic or social pressure. Healthy practices and

nutrition education are considered as a core feature for most of the mother and child health

18

programs. The aim of the undertaken program is to change maternal practices which harmful

for their children’s health and to adopt practices which likely will be beneficial. Educational

campaigns have been launched with provision of simple messages providing knowledge to

mothers to understand the negative effect of their practices.

By acquiring knowledge they can change their KAP (Knowledge, Attitude and Practices)

towards beneficial actions on child health. This approach makes them confident. They

promote use of oral rehydration solution (ORS) during diarrhoea, increased number of times

of infant feeding, use of locally available foods as a weaning food, use of high density energy

foods (particularly after illness) etc. However, often the well communicated messages were

understood by the mothers but change does not still occur due to different reasons (for

example the cultural hindrance). Intensive efforts need to overcome cultural barriers for

making a educational program more successful (Nabarro D., 1984).

In developing countries, the factors like maternal poor nutrition, pre-eclampsia, short birth

intervals, teenage pregnancy, certain infections and arduous work during mid pregnancy are

likely to be found as a causal association with LBW. Consumption of alcohol and smoking

are additional risk factors. To reduce the prevalence of LBW, a cost-effective intervention

can reduce the causally associated factors and their relative risk of LBW. In literature,

maternal food supplementation has been found to be the most studied one. If it is targeted to

mothers who are nutritionally at risk and the provided food is consumed along with their

usual diet, then it can be expected that the prevalence of LBW will reduce (Ashworth and

Feachem, 1985)

Children are considered to be the most vulnerable section of a population for undernutrition.

Children are bounded on particular requirements by intake of nutrients and type of food they

can consume. They are very susceptible to infectious diseases during the weaning age. From

the moment of conception. One should consider this as the starting time of child nutrition.

Intrauterine growth solely depends on maternal nutrition during pregnancy. Inadequate

supply of energy, iron and being exposed to malaria during pregnancy may lead the mother to

deliver a LBW baby with consequently a higher risk on morbidity and mortality. During

pregnancy, supplementation of energy rich foods and iron and treatment of malaria are the

most effective interventions to increase the birthweight (Nabarro, D. 1984).

19

2.3.16. The Role of NGO’s and mass media

NGO’s and mass media played a remarkable role on child health status by providing

necessary health care information as well as nutrition education with large coverage (Rayhan

M. I. and Khan M. S. H., 2006).

20

3. Materials and Methods

A cross-sectional household survey was carried out in nine provinces in Sri Lanka using

multiple methods of data collection.

3.1. Survey design

To represent each of the nine provinces, one district was randomly selected from each

province. An interviewer-administered questionnaire was used to collect data at household

level. Furthermore, the household level questionnaires were complemented by community

interviews, key informant interviews and market surveys.

3.2. Study Area and Period

The study was conducted in at least one district from each of the nine provinces in Sri Lanka

and was carried out from January to April 2009. The included 10 districts were:

1.Northern Province: Jaffina

2.Eastern Province: Trincomalee

3.Western Province: (1) Colombo municipal council (MC) area (2) other area in Colombo

4.North Western Province: Kurunegala

5.North Central Province: Anuradhapura

6.Central Province: Nuwara Eliya

7.Uva Province: Badulla

8.Sabaragmuwa Province: Ratnapura

9.Southern Province: Hambantota

3.3. Sample size

The target variable for the study was acute undernutrition (wasting) in children underfive

years of age. Therefore, the sample size of the study was calculated on the basis of the

prevalence of wasting in children younger than five years.

According the SL-DHS (2006/07) report, the moderate acute malnutrition of a prevalence of

15 percent. With a 1.5 design effect and 5.6 percent precision, a total of 247 underfive

children had to be recruited per district and this required 617 households per district . It was

assumed that only 0.4 % underfive children would be present per household.

21

Table 3-1 Characteristics of Sample

Target group and

indicator

Estimated

prevalence

Dessign

effect

Desired

precision

Sample

size

5% non

response

rate

Households

necessary

Underfive

children(acute

undernutrition)N=3366

15%

1.5

±5.6%

234

247

617

3.4. Sample frame and cluster selection

Population data from the 2001 census conducted by Sri Lanka. Department of Census and

Statistics was used for selection of clusters. The clusters were selected on the basis of Grama

Niladhari (GN) division. Probability proportional ‘size’ techniques were used to identify the

cluster.Population data were used to calculate sampling interval and the first cluster was

identified randomly, followed by a total 30 clusters were identified from each district by

using the sampling interval.

3.5. Household selection

A household was defined as all who are living in the same compound and/or physical

location and sharing food that is cooked in the same pot. So, other people than relatives or

members by blood connection or by marriage can be considered as member of the same

household.

A list of the households was provided by the Grama Niladhari (GN). In case of his absence,

the list of households was obtained from a mid-wife or other official representative.

Approximately 63 households were grouped from the listed household and then one group

was selected randomly, from each third household within that group for interview, thus

enabling to 21 households from each cluster to be selected.

All households respective to presence of child underfive years were included in the selection.

To identify the missing household members, every household was visited three times unless

logistic constrains or security issues were hindering the team to do so. If members of the

households were absent during the study period, the households were replaced by other

households with similar characteristics in the same cluster.

22

3.6. Data collection

3.6.1. Composition of the survey team

One team leader along with three interviewers made up a survey team. The interviewers

composed newly graduate students. During the study period, the same interviewers were

used for interviewing the respondents. Interviewers fluent in the Tamil language were

recruited particularly for Tamil speaking areas (Northern and Eastern provinces and some

area in the estate sector). A coordinator was recruited from each district to take the overall

charge of the survey in that district. Trained staff of Medical Research Institute (MRI) with

experience from past surveys was recruited as team leaders and coordinators.

3.6.2. Training of survey teams

The training session was conducted by MRI over a four day period to train the interviewers

on basic nutrition, use of the questionnaire, interview techniques, data collection and record

keeping. Training on the use of the questionnaire at the field level and quality assurance

techniques was given to the team leaders as well as coordinators. Team leaders, recruited

from MRI pool of public health inspectors who also had both training and experience on

appropriate measurement techniques conducted the anthropometric measurements. A pilot

study was done by the interviewers to pretest the questionnaire prior to leaving for field work.

3.7. Methods of data collection

3.7.1. Household surveys

Administration of the questionnaire: The interviewers introduced the pretested

questionnaire to the household head. Mothers were interviewed to obtain the information on

child care practices and maternal nutrition. If consent was obtained, a mother should be

minimum 15 years old to be an inclusion criteria. Houses were revisited when actual

information was not completely obtained. High consideration was taken into account on

household members to be confined to their houses during the interview.

3.7.2. Anthropometric assessment

All underfive children (N= 3366) along with their mother within the household were selected

for anthropometric measurements. All measurements (length, weight and height) were done

by team leaders and standardized procedures were used (WHO, 1995).

23

Uniscales and Unicef measuring boards were used for anthropometric measurements.

3.7.3. Measurement of hemoglobin levels

Measurements were taken using the hemocue method and using capillary blood for all

selected individuals except for children less than six months of age.

3.7.4. Key informant interviews, community interviews, market surveys

To obtain information on prices of selected food items, team leaders conducted the market

survey in each of the 30 clusters at local level. Team leader used a pre designed market

survey sheet to get the information on price of commodities on the day of survey and six

months prior by visiting the local stores. To get a reliable estimate of the price level, highest

and lowest prices were obtained in a given food item in a local market.

In depth interviews with key informants and small community groups were conducted in

one of three clusters by a coordinator. The aim of these interviews were to obtain in depth

information to support the data from household questionnaires.

Key informant interviews were conducted on selected participants who included public

health midwives, Grama Niladharis (village headmen), Divisional Secretaries and plantation

welfare directors.

3.8. Supervision and quality assurance

All activities at the field level were conducted under constant supervision and monitoring.

Interviewers were monitored by the team leader, while coordinator monitored team leaders

along with interviewers. Field-editing of all questionnaires by team leaders was done

routinely. Data cleaning was performed by the team leader at the end of each day. The team

coordinator randomly took ten percent of the questionnaires and checked them to ensure the

quality of collected information.

3.9. Data processing and analysis

Data management and entry were performed using EPI info 6.0 software package. Range and

checks of consistency and skips to minimize entry of erroneous data were performed during

data entry. MS access was used to clean the data and extreme values were filtered. SQL

queries were used to check logical errors. Detection and correction of errors were made

during data entry and performed by consistency check. WHO Anthro version 3.0 was used

24

to analyze the anthropometric data by calculating Z-scores of underfive children based on the

anthropometric measurements, WHO standards were used as reference values. Furthermore,

the provided secondary data (data obtained from Nutrition and Food Security Assessment in

Sri Lanka, 2009) that was already entered into SPSS software package was verified for its

consistency using descriptive statistics, for example frequency and cross tabulation was

performed for outliers and missing values. If identified errors, appropriate corrective

measures were taken. Finally, SPSS 17.00 software packages were used to analyze the

secondary data. Descriptive statistics were performed to explore the background

characteristics of children and calculate the prevalence of undernutrition among them by their

background characteristics.

Bivariate analysis, Pearson chi square test and one way ANOVA were performed to see the

association between dependent variables and independent (explanatory) variables and one

way ANOVA was performed to determine whether the Z-score mean of height-for-age,

weight-for-age and weight-for-height of any selected variable is statistically different for

more than two groups within the studied population.

For performing one way ANOVA, homogeneity of variances test was performed by using

Levene test. Post Hoc Multiple Comparisons: Tukey test was performed when equal

variances are assumed and Games-Howell test was performed when the variances are not

assumed to be equal and the mean difference was significant at the 5% level.

2×2 contingency table were used to calculate odds ratio that represented the increment in

risk. P-value <0.05 was considered as an indicative of a statistical significant difference. Then

all variables that showed significant association in Bivariate analysis were included into the

final model,the multiple linear regressions model to see whether factors that were

significantly associated in Bivariate model were still significantly or not related to the

dependent variable when other factors were included in the relationship.

Finally multivariate analysis (multiple linear regressions model) were performed to see the

predictor of dependent variable ( height-for-age Z-score; weight-for-age Z-score and weight-

for-height Z-score ) from the selected explanatory variables by controlling the effect of all

others variables in the model. When a P<0.05 was obtained, the co-efficient was significantly

different to zero.

25

3.10. Ethical issues

Written ethical approval was obtained from the ethics review committee of the MRI, Ministry

of Health. Paper questionnaires were used to collect the data. A secure location was used to

keep the original documents in the field. By ensuring confidentiality, only members of the

survey team were allowed to access the original documents. The purpose of the study was

communicated to the participants and they were told that at any time, they could refuse their

children to be measured. Informed verbal consent was obtained ‘in writing’ prior to taking

the blood sample for assessment of haemoglobin levels.

26

4. Results

4.1. Characteristics of the studied population

Table 4-1 Background Characteristics of underfive children (children and mothers inthis table belong to the same family. However, a mother may have more thanone child and due to missing data,total number of child and mother may differ)

Background Characteristics N° Mean± SD Minimum Maximum Child age in months 3366 28.97±16.69 0.03 59.93 Family Size 3366 5.16±1.77 2 17 Birth weight in kg 3254 2.90±0.50 1.00 8.00 Age of mother in years 3035 30.41±6.00 10 56 Age of household head in years 3350 37.72±10.80 19 85 BMI of mother 2914 22.90±4.51 9.29 56.08 Height-for-age Z-score 3226 -1.01±1.73 -11.17 20.55 Weight-for-age Z-score 3224 -1.18±1.23 -7.34 10.68 Weight-for-height Z-score 3200 -0.87±1.15 -8.94 9.93

Source:compiled from Nutrition and Food Security Assessment in Sri Lanka, 2009

Table 4-2 Distribution of children by area of residence

Sex of child Area of residence Male

N°(%) Female N°(%

Total N°(%)

Urban 380(22.6) 350(20.8) 730(21.7) Rural 1203(71.4) 1231(73.2) 2434(72.3) Estate 102(6.1) 100(5.9) 202(6.0) Total 1685(100.0) 1681(100.0) 3366(100.0)

Source:compiled from Nutrition and Food Security Assessment in Sri Lanka, 2009

The mean age of the children, family size, birthweight , age of mother, age of household head

and BMI of mother was 28.97, 5.16, 2.90, 30.41, 37.72 and 22.90 respectively.Furthermore,

the mean Z-score of all three indicators of malnutrition also presented in the above table.

Among 3366 studied children 22%, 72% and 6% lived in urban, rural and estate sectors

respectively (Table 4-1; 4-2)

4.2. Child Nutritional Status

Figure 4.1 shows that the prevalence of chronic malnutrition (stunting), underweight and

acute malnutrition (wasting) is 20.1%, 22.8% and 12.3% respectively.

Table 4-3 repsesented that the prevalence of stunting among male children is marginally

higher than female children but the prevalence of underweight and wasting among male and

female children are the nearly same. The prevalence of stunting and underweight was low at

the first year of life but showed an increasing trend from 12 to 47 months, while declining in

27

the fifth year of life but during first six months more underweight children were found than

the second six months of life (Table 4-3).

Figure 4.1 The prevalence of undernutrition among underfive children

Table 4-3 Prevalence of undernutrition among underfive children by background characteristics (Results from descriptive analysis)

Nutritional Indicators Variables Stunting

N°(%) N° Underweight

N° (%) N° Wasting

N° (%) N°

Sex of the children Male 325(20.2) 1611 351(21.8) 1609 194(12.2) 1593

Female 297(18.4) 1615 363(22.5) 1615 195(12.1) 1607 Total 622(19.3) 3226 714(22.1) 3224 389(12.2) 3200

Child age in months 0-5 31(13.5) 230 38(16.5) 230 28(12.2) 229

6-11 35(12.8) 274 31(11.2) 276 18(6.6) 273

12-17 54(18.8) 288 56(19.4) 288 29(10.1) 287

18-23 72(24.0) 300 59(19.6) 301 25(8.4) 298 24-35 132(20.3) 649 166(25.7) 646 83(13.0) 640 36-47 132(23.1) 571 153(27.0) 566 80(14.2) 562 48-59 91(17.7) 513 125(24.4) 513 81(15.9) 511 Total 547(19.4) 2825 628(22.3) 2820 344(12.3) 2800

Area of residence Urban 107(15.4) 696 126(18.1) 698 76(11.0) 693 Rural 432(18.4) 2345 518(22.1) 2342 287(12.3) 2324 Estate 83(44.9) 185 70(38.0) 184 26(14.2) 183 Total 622(19.3) 3226 714(22.1) 3224 389(12.2) 3200

stunting

20.1%

wasting

12.3%

underweight

22.8%

28

Continue table 4-3

Nutritional Indicators Variables Stunting

N°(%) N° Underweight

N° (%) N° Wasting

N° (%) N°

Name of Districts Colombo MC 34(13.3) 255 40(15.6) 256 20(7.8) 255 Kurunagala 25(12.7) 197 37(18.8) 197 27(13.8) 195 Colombo 36(14.5) 248 58(23.5) 247 45(18.3) 246

Jaffna 38(15.7) 242 34(14.0) 242 21(8.7) 242 Anuradhapura 32(15.4) 208 39(19.2) 203 26(12.9) 202 Trincomale 48(17.6) 272 50(18.4) 272 29(10.7) 270 Monaragala 58(19.9) 291 71(24.4) 291 36(12.5) 289

Hambanthota 38(14.4) 263 57(21.5) 265 34(13.0) 262 Vavuniya 43(19.9) 216 52(24.2) 245 22(10.4) 212 Ampara 56(19.7) 284 66(23.2) 284 40(14.2) 282

Ratnapura 55(22.4) 246 62(25.0) 248 34(13.9) 245 Badulla 62(24.6) 252 61(24.3) 251 25(10.0) 250

Nuwara Eliya 97(38.5) 252 87(34.4) 253 30(12.0) 250 Total 622(19.3) 3226 714(22.1) 3224 389(12.2) 3200

Mother’s education

Higher 75(12.8) 586 103(17.5) 587 72(12.4) 582 O’ level 195(19.0) 1027 221(21.5) 1026 110(10.8) 1019

Secondary 205(21.9) 934 209(22.4) 933 108(11.7) 925 Primary 55(25.2) 218 67(30.6) 219 30(13.8) 217 Illiterate 24(31.6) 76 21(27.6) 76 12(15.8) 76

Total 554(19.5) 2841 621(21.9) 2841 332(11.8) 2819 BMI of mother

< 16(severely thin) 16(24.6) 65 26(40.0) 65 14(21.5) 65

16.0-16.99 (moderately thin)

29(24.6) 118 42(35.3) 119 30(25.6) 117

17.00-18.49(mildly thin)

61(21.6) 282 80(28.6) 280 44(15.8) 279

18.5-24.99(normal) 302(19.9) 1521 335(22.0) 1522 182(12.0) 1513 ≥25 (over weight) 138(16.4) 843 135(16.0) 844 64(7.7) 834

Total 546(19.3) 2829 618(21.8) 2830 334(11.9) 2808 Wealth index

quintiles

Poorest 201(26.8) 751 226(30.2) 749 113(15.2) 745 Second 145(22.0) 659 147(22.4) 656 75(11.5) 653 Middle 115(19.3) 597 127(21.2) 598 64(10.8) 591 Forth 92(15.8) 581 133(22.7) 586 79(13.6) 580

Richest 69(10.8) 638 81(12.8) 635 58(9.2) 631 Total 622(19.3) 3226 714(22.1) 3224 389(12.2) 3200

Legend: Anthropometric indicators;<-2SD considered as stunting, underweight, wasting; N°= Total no. of children; Due to missing data total no. of children may differ

29

The proportion of wasting was not showing any consistent pattern with the age of children. A

higher proportion was found in two year old children and above compared to less than two

year old children. The prevalence of wasting was almost double during the first six months

compared to second six months of life. All of the three indicators of malnutrition (stunting,

underweight and wasting) had higher prevalence in estate sectors than rural and urban area.

The stunting prevalence (44.9%) was three times higher in the estate children compared to

urban children (15.4%) (Table 4-3).

Inter districts comparison, the highest prevalence of stunting was found in Nuwara Eliya,

Badulla, Ratnapura and it was 38.5%, 24.6%, 22.4% respectively. Almost the similar trend

was seen for the prevalence of underweight. However, the highest (18.3%) and lowest

proportion (7.8%) of wasting was found in Colombo and Colombo (Municipal Council) MC

respectively. The higher prevalence of stunting, underweight, wasting was seen among the

children whose mothers had no education or with primary education. The declining pattern of

all three indicators was shown with improvement in the mother’s education. The declining

pattern of stunting and underweight were associated with increasing wealth quintiles. But, a

similar pattern of wasting was not related to wealth quintiles (Table 4-3).

4.3. Background characteristics related to child malnutrition

4.3.1 Bivariate analysis

One way ANOVA test was performed to determine the significant effect of background

characteristics of studied children on their mean Z-score height-for-age (H/A), weight-for-age

(W/A) and weight-for-height (W/H).

Age of the children showed a significant difference of all H/A; W/A and W/H mean Z-

score.

Post Hoc Multiple Comparisons indicated that the mean Z-score H/A, W/A and W/H of

children by age group was significantly different from one group to another. A mean Z-score

significant difference for H/A was found between (0-5) and all other age groups, exactly the

(6-11) also a significant difference was found between(6-11) and all other age groups except

(0-5) (table 4-4).

Table 4-4 represented that the mean Z-score W/A differed significantly between the age

group (0-5) months and (18-23), (24-35), (36-47),(48-59) months.Besides; the significant

difference was found between (6-11) and (12-17), (18-23),(24-35),(36-47), (48-59).

Furthermore,The age group (12-17) was significantly different from (36-47) and (48-59).

30

Table 4-4 Effect of background characteristics on nutritional status of underfive children

(Results from Bivariate analysis: one way ANOVA+ Post Hoc Multiple Comparisons)

Height-for-Age Z-score Weight-for-Age Z-score Weight-for-Height Z-score Variables Mean Z-

scores±SD Significant difference

Mean Z-scores±SD

Significant difference

Mean Z-scores±SD


Child age in months 0-5 -0.17± 3.21 (0-5)-(12-17) -0.74±1.78 (0-5)-(18-23) -0.61±1.42 (0-5)- (24-35) 6-11 -0.41±2.46 (0-5)-(18-23) -0.66±1.39 (0-5)-(24-35) -0.43±1.40 (0-5)-(36-47) 12-17 -1.04±1.44 (0-5)-(24-35 -1.10±1.16 (0-5)-(36-47) -0.83±1.08 (0-5)-(48-59) 18-23 -1.18±1.44 (0-5)-(36-47 -1.22±1.11 (0-5)-(48-59) -0.89±1.01 (6-11)-(12-17) 24-35 -1.25±1.28 (0-5)-(48-59 -1.31±1.08 (6-11)-(12-17) -0.95±1.08 (6-11)-(18-23) 36-47 -1.29±1.41 (6-11)-(12-17) -1.45±1.104 (6-11)-(18-23) -1.03±0.96 (6-11)-( 24-35) 48-59 -1.18±1.15 (6-11)-(18-23) -1.36±1.03 (6-11)-(24-35) -1.00±1.05 (6-11)-(36-47)

(6-11)-(24-35) (6-11)-(36-47) (6-11)-(36-47) (6-11)-(48-59) (6-11)-(48-59) (12-17)-(36-47) (12-17)-(48-59)

Name of Districts Colombo MC -0.79±2.02 Colombo-

Nuwara Eliya -0.89±1.32 Colombo-

Nuwara Eliya -0.60±1.08 Hambanthota-

Colombo MC Kurunagala -0.90±1.33 Nuwara Eliya-

Hambanthota -1.18±1.02 Nuwara Eliya-

Jaffna -1.01±1.10

Colombo -0.89±1.59 Nuwara Eliya-Jaffna

-1.12±1.24 Nuwara Eliya-Vavuniya

-0.90±1.16 Colombo MC- Kurunagala

Jaffna -0.76±1.70 Nuwara Eliya-Vavuniya

-0.98±1.07 Nuwara Eliya-Ampara

-0.72±0.98

Anuradhapura -0.93±1.79 Nuwara Eliya- Ampara

-1.16±1.36 Nuwara Eliya-Trincomale

-0.81±1.197

Trincomale -0.89±2.01 Nuwara Eliya-Trincomale

-1.08±1.33 Nuwara Eliya-Kurunagala

-0.75±1.273

Monaragala -1.08±1.95 Nuwara Eliya-Kurunagala

-1.26±1.28 Nuwara Eliya-Anuradhapura

-0.92±1.11

Hambanthota -0.94±1.82 Nuwara Eliya-Anuradhapura

-1.24±1.20 Nuwara Eliya-Colombo MC

-1.06±0.991

Vavuniya -0.89±1.90 Nuwara Eliya-Monaragala

-1.17±1.31 Jaffna- Badulla -0.89±1.097

Ampara -1.08±1.39 Nuwara Eliya- Ratnapura

-1.22±1.13 Badulla-Colombo MC

-0.85±1.14

Ratnapura -1.15±1.24 Nuwara Eliya-Colombo MC

-1.27±1.00 Colombo MC- Monaragala

-0.95±1.00

Badulla -1.36±1.58 Jaffna-Badulla -1.38±1.11 -0.83±1.12 Nuwara Eliya -1.66±1.81 Badulla-

Colombo MC -1.59±1.26 -0.93±1.43

31

Continue table 4-4



Mean Z-scores±SD


Mean Z-scores±SD


Area of residence Urban -0.89±1.84 Urban-Estate -1.04±1.30 Urban-Rural -0.77±1.14 Urban-Estate Rural -1.00±1.69 Rural- Estate -1.191.20 Urban-Estate -0.88±1.14 Estate -1.91±1.71 -1.791.12 Rural- Estate -1.02±1.35 Mother’s education

Higher -0.75±1.78 Higher-Primary -0.96±1.32 Higher-Illiterate -0.77±1.15 Higher-Illiterate

O’level -1.08±1.50 Higher-Secondary

-1.20±1.18 Higher-Primary -0.84±1.13

Secondary -1.10±1.94 Higher-O’ level -1.25±1.21 Higher-Secondary

-0.87±1.15

Primary -1.33±1.37 -1.44±1.15 Higher-O’ level -0.95±1.03

Illiterate -1.11±2.01 -1.52±0.95 O’level- Primary

-1.16±1.24

Income quintiles based on HIES

≥32406 -0.70±1.14 ≤8983-(13839 – 20229)

-0.92±1.05 ≤8983-(13839 – 20229)

20230 - 32405

-1.01±1.35 (≤8983)-(≥32406)

-1.15±1.22 (≤8983)- (≥32406)

13839 - 20229

-0.93±1.78 -1.11±1.27 (8984-13838)- (≥32406)

8984 - 13838

-1.12±1.93 (8984-13838)- (≥32406)

-1.23±1.29

≤8983 -1.18±1.69 -1.31±1.15

Wealth index quintiles

Poorest -1.23±1.84 Poorest-Fourth -1.49±1.17 Poorest-Second -1.07±1.14 Poorest-Second

Second -1.17±1.55 Poorest-Richest -1.26±1.11 Poorest-Middle -0.82±1.20 Poorest-Middle

Middle -1.04±1.68 Second-Fourth -1.18±1.20 Poorest-Fourth -0.85±1.09 Poorest-Fourth

Fourth -0.82±2.20 Second-Richest -1.07±1.41 Poorest-Richest -0.83±1.10 Poorest-Richest

Richest -0.80±1.32 Middle-Richest -0.92±1.14 Second-Richest -0.70±1.11 Middle- Richest

32

Continue table 4-4



Mean Z-scores±SD


Mean Z-scores±SD


BMI of mother < 16 (severely

thin)

-0.91±2.93 Moderately

thin-Over

weight

-1.50±1.52 Severe thinness-Overweight

-1.39±1.01 Severe thinness- Normal

16.0-16.99

(moderately

thin)

-1.34±1.28 -1.72±0.98 Moderate thinness-Normal

-1.36±1.02 Severe thinness- Over

weight

17.00-18.49

(mildly thin)

-1.09±2.07 -1.41±1.28 Moderate thinness-

Overweight

-1.14±1.21 Moderate thinness-Normal

18.5-

24.99(normal)

-1.08±1.49 -1.23±1.11 Mild thinness-Overweight

-0.88±1.07 Moderate thinness-Weight

≥25 (over weight)

-.90±2.01 -.96±1.33 Normal-Overweight

-0.64±1.20 Mild thinness-Normal

Mild thinness- Over weight

Normal-Over weight

Mother’s occupation Managerial -0.22±1.12 Professional -

Elementary

occupation

-0.63±1.06 Managerial-Elementary occupation

-0.74±1.04 Professional-Elementary occupation

Professional -0.53±1.09 -0.71±1.01 Professional-Elementary occupation

-0.60±0.93 Housewife-Elementary occupation

Clerical -0.99±1.82 -0.98±1.49 -0.48±1.38

Sales & related

occupation

-1.13±1.06 Housewife-

Elementary

occupation

-1.23±1.13 Professional-Skilled agri

worker

-0.88±1.11

Skilled agri

worker

-1.15±1.02 -1.37±1.03 Professional-Housewife

-1.03±1.00

Elementary

occupation

-1.54±1.82 -1.63±1.23 Professional-Others

-1.13±1.18

Housewife -1.02±1.75 -1.17±1.24 Housewife-Elementary occupation

-0.85±1.13

Others -1.33±2.03 -1.40±0.98 -0.85±1.56

33

Continue table 4-4



Mean Z-scores±SD


Mean Z-scores±SD


Source of drinking water Piped water

dwelling

-0.90±1.96 Spring unprotected-Piped water

dwelling

-1.01±1.30 Piped water dwelling-Dug

well unprotected

-0.73±1.21 Piped water

dwelling- Dug

well

unprotected Piped water

yard

-1.02±1.37 Spring unprotected-

Piped water yard

-1.27±1.19 Piped water dwelling-Spring

unprotected

-0.96±1.18

Public

tap/standpipe

-1.18±1.46 Spring unprotected-

Tube well/borehole

-1.26±1.18 Spring unprotected-Dug well protected

-0.93±0.99

Tube

well/borehole

-0.80±1.65 Spring unprotected-Dug well protected

-1.14±1.14 -0.83±1.34

Dug well

protected

-0.91±1.79 Spring unprotected-Dug well unprotected

-1.14±1.27 -0.88±1.11

Dug well

unprotected

-1.16±1.62 -1.33±1.24 -0.97±1.17

Spring

protected

-1.23±2.16 -1.33±1.17 -0.82±1.50

Spring

unprotected

-1.73±1.73 -1.60±1.01 -1.06±1.04

Surface water -1.21±1.37 -1.31±0.97 -0.90±0.94

Other -1.24±1.19 -1.37±0.99 -1.02±0.96

Toilet facilities Flush toilet -1.00±1.75 Flush toilet-No

toilet

-1.15±1.22 Flush toilet- No

toilet

-0.84±1.14 Flush toilet- No

toilet Pit latrine -1.08±1.80 -1.28±1.25 -0.96±1.13 Temporary

toilet -1.29±1.32 -1.50±1.06 -1.05±1.00

No toilet -1.39±1.74 -1.59±1.35 -1.13±1.42

34

Continue table 4-4



Mean Z-scores±SD


Mean Z-scores±SD


Religion of the household head Buddhist -1.04±1.65 Hindu-Islam -1.24±1.20 Buddhist- Islam -0.94±1.13 Buddhist-Hindu

Hindu -1.15±1.58 -1.21±1.14 -0.80±1.13

Islam -0.86±2.31 -1.02±1.46 Hindu-Islam -0.71±1.29 Buddhist-Islam Catholic -1.06±1.42 -1.20±1.06 -0.84±1.04 HH education Higher -0.79±1.88 Higher-Primary -0.97±1.31 Higher-Illiterate -0.75±1.16 Higher-Primary O’level -0.97±1.68 Higher-

Secondary -1.15±1.26 Higher-Primary -0.84±1.16

Secondary -1.14±1.68 O’ level-Primary

-1.26±1.18 Higher-Secondary

-0.88±1.14

Primary -1.25±1.69 -1.37±1.11 Primary-O’level -0.97±1.06 Illiterate -1.17±1.56 -1.36±1.11 -0.98±1.18 Household head occupation category Armed forces -1.19±0.93 Managerial-

Elementary

occupation

Managerial -0.68±1.06

Professional -0.90±1.20

Clerical -1.12±1.22

Sales & related occupation -1.11±1.28

Skilled agri worker -1.27±1.17

Mechanical worker -1.06±1.31

Elementary occupation -1.28±1.25

Housewife -1.26±1.29

Unemployed -1.11±1.11 Others -1.21±1.25

Legend: The mean difference is significant at 5% level

Table 4-4 depicted that the mean Z-score for W/H showed significant difference between (0-

5) and (24-35), (36-47), (48-59). Moreover, the significant difference was found between (6-

11) and (12-17), (18-23), (24-35), (36-47) and (48-59).

The mean Z-score H/A and W/A of estate children was significantly different from rural and

urban children. The mean Z-scores W/A of urban children were significantly different from

rural children and the mean Z scores W/H were significantly differed between urban children

and estate children.

The mean Z-scores H/A, W/A and W/H of the children by living districts significantly

differed between different districts. The mean Z-score H/A of Nuwara Eliya children

significantly differed from Colombo, Hambanthota, Jaffna, Vavuniya, Ampara, Trincomale,

35

Kurunagala, Anuradhapura, Monaragala, Ratnapura, Colombo MC. Also the mean Z-score

H/A was significantly different between Jaffna against Badulla and between Badulla and

Colombo MC.

The mean Z-score W/A by districts significantly differed fromNuwara Eliya compared with

Colombo, Jaffna, Vavuniya, Ampara, Trincomale, Kurunagala, Anuradhapura, Colombo MC

and between Jaffna and Badulla and between Colombo MC and Badulla, Monaragala. The

mean Z-score W/H was significantly differed between Hambanthota and Colombo MC and

between Colombo MC and Kurunagala (Table 4-4).

The mean Z-score H/A, W/A and W/H of children by mother’s education was significantly

different from one group to another. The mean Z-score H/A differed between higher and

primary, secondary and O’level. The mean Z-score W/A differed between higher and

illiterate, primary, secondary and O’level and between O’level and primary education. The

mean Z-score W/H differed between higher education and illiterate mothers.

The mean Z-score H/A and W/A of children by household monthly income done by using

income and expenditure survey showed significant differences from one category to another.

The mean Z-score H/A by income quintiles was significantly different between ≤8983 and

(13839–20229), ≥32406 and between (8984-13838) and ≥32406.. The mean Z-scores W/A

was different between ≤8983 and (13839–20229), ≥32406 and between (8984-13838) and

≥32406. However, the mean Z-score W/H of the children by income quintiles based on

households income expenditure survey (HIES) was not significantly different from one group

to another.

The mean Z-score H/A, W/A and W/H of the children by wealth index quintiles was

significantly different between different wealth index category. The mean Z-score H/A

difference were found between poorest and fourth group as well as richest, between second

and fourth, richest and between middle and richest. For the mean Z-score W/A differences

were found between poorest and second, middle, fourth as well as the richest group, also the

mean difference was found between second compared with richest and middle versus richest

and the mean Z score W/H differences were significantly different between the poorest and

second, middle, fourth a well as the richest (Table4-4).

The mean Z-score H/A; W/A and W/H of children by mother’s BMI was significantly

different between varying BMI category. The mean Z-score H/A significantly differed

36

between moderate thinness and overweight. The mean Z score W/A was differed between

severe thinness and overweight, moderate thinness and normal, moderate thinness and

overweight, mild thinness and overweight and between normal and overweight. The mean Z

score W/H differed between severe thinness and normal, overweight, between moderate

thinness and normal, moderate thinness and overweight, between mild thinness and normal

and overweight and between normal and overweight.

The mean Z-score H/A, W/A and W/H of children by mother’s occupation was significantly

different between different depending on occupation category. The mean Z-score H/A was

different between elementary occupation and professional occupation, housewife. The mean

Z-scores W/A differed between managerial and elementary occupations. Furthermore, the

mean difference was found between professional and elementary occupation, skilled agri

worker, others and housewife and between housewife and elementary work. The mean Z-

score W/H differed between professional and elementary and between housewives and

elementary.

The mean Z-score H/A; W/A and W/H of children by source of drinking water significantly

differed by source. The mean Z-score H/A was differed between spring unprotected and

piped water dwelling, piped water yard, tube well/borehole, dug well protected as well as dug

well unprotected. For W/A the mean difference was found between piped water dwelling and

dug well unprotected, spring unprotected and between springs unprotected and dug well

protected. The mean Z-score difference for W/H was between piped water dwellings and dug

well unprotected.

The mean Z-score difference was found in all the three indicators H/A, W/A and W/H by

access to toilet facilities. For all three indicators the mean difference was between flush

toilet users with those who do not use toilet. However, access to drinking water and toilet use

is a proxy determinant of child stunting because it is highly dependent on degree of wealth.

Better off households wealth showed a greater access to safe water as well as better toilet

facilities (Table4-4).

In all three indicators, the mean Z-score difference was seen by different religion of

household head. For H/A the mean difference was seen between Hindu and Muslim. The

mean Z-score W/A differed between Buddhist and Muslim and between Hindu and Muslim

and for W/H the mean difference was seen between Buddhist’s and Hindu as well as Muslim.

37

By household head education, the three indicators showed the significant difference on

varying education level. For H/A, the mean Z-score differed between higher and primary,

secondary and between O’level and primary. For W/A the mean difference was between

higher and illiterate, primary, secondary and between primary levels and O’level. The mean

Z-score difference for W/H was found between higher and primary.

Only the mean Z-scores W/A showed significant mean Z-score difference by household

head occupation and the difference was found between managerial occupations and

elementary occupations (Table 4-4).

However, the mean Z-score of all three indicators H/A, W/A and W/H of the children by

family size, mothers’ age in years, initiation of breastmilk after birth , household head

age in years and type of family income was not significantly different between different

groups (Table App. 2).

38

Table 4-5 Summary of significant characteristics for the three nutritional indicators follows the results obtained through one way ANOVA

characteristics Height-for-age

Z-score

Weight-for-

age Z-score

Weight-for-

height Z-score

Child age in months + + +

Name of districts + + +

Area of residence + + +

Mother’s education + + +

Income quintiles based on HIES + + -

Wealth index quintiles + + +

BMI of Mother + + +

Mother’s occupation + + +

Source of drinking water + + +

Toilet facilities + + +

Religion of household head + + +

Household head education + + +

Household head occupation - + -

Legend: + = significant association; - = not significant association

4.3.2 Bivariate analysis

A Pearson chi square test was performed to assess the significant association between two

variables. A p-value was used to measure the strength of association and odds ratio was used

to measure the risk.

Table 4-6;4-7;4-8 showed that birthweight of the children showed significant association with

all of three indicators of malnutrition-height-for-age Z-score (HAZ), weight-for-age Z-score

(WAZ) and weight-for-height Z-score (WHZ). Low birthweight (<2.5kg) babies had almost

2.5, 3.0 and 2.15 fold higher risk of being stunted, underweight and wasted respectively

compared to normal children (birthweight ≥2.5 kg).

39

Table 4-6 Association of selected variables and height-for-age Z-score of underfive children (Results from Bivariate analysis : Pearson chi square test)

Height-for-Age Characteristics Nutritional Status

Total N°(%)

Pearson χ2

value

p-value

OR

(95%CI) <-2SD N°(%)

≥-2SD N°(%)

Child’s birthweight( kg) <2.5 202(30.8) 416(15.4) 618(18.5) 83.01 0.000 2.44

(2.00, 2.97) ≥2.5 453(69.2) 2278(84.6) 2731(81.5) Total 655(100.0) 2694(100.0) 3349(100.0)

Mother’s BMI <18.5(thinness) 122(20.2) 391(16.2) 513(17.0)

5.40

0.020 1.30

(1.04, 1.64) ≥18.5 (normal) 482(79.8) 2019 (83.8) 2501(83.0) Total 604(100.0) 2410(100.0) 3014(100.00)

Anaemia among mother Present 143(27.4) 464(21.1) 607(22.3)

9.80

0.002 1.42

(1.14, 1.76) Absent 379(72.6) 1740(78.9) 2119(77.7) Total 522(100.0) 2204(100.0) 2726(100.0)

Initiation of breastmilk within 1 day

&above 36(13.3) 92(8.4) 128(9.4)

6.04

0.014 1.67

(1.11, 2.51) < 1 hour 235(86.7) 1001(91.6) 1236(90.6)

Total 271(100.0) 1093(100.0) 1364(100.0) Income quintile based on HIES

≤13838 178(65.7) 596(54.4) 774(56.6) 11.30

0.001

1.61 (1.22, 2.12) >13838 93(34.3) 500(45.6) 593(43.4)

Total 271(100.0) 1096(100.0) 1367(100.0) Anaemia among children

Present 177(31.3) 610(26.0) 787(27.1) 6.43

0.011

1.30 (1.06, 1.58) Absent 388(68.7) 1732(74.0) 2120(72.9)

Total 565(100.0) 2342(100.0) 2907(100.0) Area of residence

Urban 117(16.9) 614(22.1) 731(21.1) 113.10

0.000 Rural 470(67.7) 2039(73.5) 2509(72.3)

Estate 107(15.4) 121(4.4) 228(6.6) Total 694(100.0) 2774(100.0) 3468(100.0)

40

Table 4-7 Association of selected variables and weight-for-age Z-score of underfive children (Results from Bivariate analysis : Pearson chi square test)

Characteristics Weight-for-Age Nutritional Status Total

N° (%) Pearson χ2

value

p-value OR (95%CI) <-2SD

N°(%) ≥-2SD N° (%)

Child’s birthweight(kg) <2.5 254(33.6) 363(14.0) 617(18.4) 149.82 0.000 3.11

(2.58, 3.75) ≥2.5 501(66.4) 2228(86.0) 2729(81.6) Total 755(100.0) 2591(100.0) 33346(100.0)

Sex of HH Male 734(92.9) 2551(95.4) 3285(94.8)

7.45

0.006 0.63

(0.46, 0.88) Female 56(7.1) 124(4.6) 180(5.2) Total 790(100.0) 2675(100.0) 3465(100.0)


32.22

0.000 1.83

(1.48, 2.25) ≥18.5 (normal) 511(75.8) 1992(85.1) 2503(83.0) Total 674(100.0) 2340(100.0) 3014(100.0)

Income quintile based on HIES ≤13838 441(62.6) 1395(56.6) 1836(58.0)

8.16

0.004 1.29

(1.08, 1.53) >13838 263(37.4) 1069(43.4) 1332(42.0) Total 704(100.0) 2464(100.0) 3168(100.0)

Membership of microcredit Yes 155(22.3) 461(18.9) 616(19.7)

3.95

0.047 1.23

(1.00, 1.51) No 539(77.7) 1974(81.1) 2513(80.3) Total 694(100.0) 2435(100.0) 3129(100.0)

Toilet facilities Unsanitary toilet 155(21.9) 397(15.9) 552(17.2)

13.88

0.000 1.48

(1.21, 1.83) Sanitary toilet 552(78.1) 2096(84.1) 2648(82.8) Total 707(100.0) 2493(100.0) 3200(100.0)

Area of residence Urban 124(17.2) 523(22.4) 647(21.2)

33.05

0.000

Rural 516(71.8) 1689(72.3) 2205(72.2) Estate 79(11.0) 125(5.3) 204(6.7) Total 719(100.0) 2337(100.0) 3056(100.0)

41

Table 4-8 Association of selected variables and weight-for-height Z-score of underfive children (Results from Bivariate analysis : Pearson chi square test)

Characteristics Weight-for-Height Nutritional Status

Total N°(%)

Pearson χ2 value

p-value

OR

(95%CI) <-2SD N°(%)

≥-2SD N°(%)

Child’s birthweight(kg) <2.5 124(30.2) 489(16.8) 613(18.5) 43.16

0.000 2.14

(1.70, 2.70) ≥2.5 286(69.8) 2422(83.2) 2708(81.5) Total 410(100.0) 2911(100.0) 3321(100.0)


26.28

0.000 1.94

(1.50, 2.51) ≥18.5 (normal) 263(73.5) 2219(84.3) 2482(83.0) Total 358(100.0) 2632(100.0) 2990(100.0)

Toilet facilities Unsanitary toilet 82(21.3) 464(16.6) 546(17.2)

5.19

0.023 1.357


Child suffered from cough in last two weeks Yes 177(46.2) 1119(40.7) 1296(41.4)

4.25

0.039 1.25

(1.01, 1.55) No 206(53.8) 1632(59.3) 1838(58.6) Total 383(100.) 2751(100.0) 3134(100.0)

Legend: <-2SD consider be stunting, underweight and wasting based on NCHS reference standards; OR=Odds Ratio; SD=Standard Deviation; χ2= chi square; Unsanitary toilet included pit latrine, temporary & no toilet

BMI of mother also significantly associated with all three indicators of malnutrition.

Children of malnourished mother (BMI<18.5) had 1.3, 1.83 and 1.94 times higher odds of

being stunted, underweight and wasted respectively compared to children of normal mother

(BMI≥18.5). Family monthly income (based on HIES) showed significant association with

HAZ and WAZ. Children whose family monthly income were ≤13838 LKR (Sri Lankan

currency) were 1.61 and 1.3 times higher risk being stunted and underweight compare to

whose income were >13838LKR. Anaemia among mother as well as children was

significantly associated with child HAZ. Children of anaemic mothers as well as anaemic

children had 1.4 and 1.3 times higher risk of being stunted respectively compared to their

counterparts. Sex of household head showed significant association only with WAZ. Children

of male headed household had 37% less odds being underweight compared to female headed

household head. Child suffering from cough in preceding two weeks showed significant

association with acute malnutrition and had 1.25 odds of being wasted compared to their

counterparts. Initiation of breastmilk after birth showed significant association only with

HAZ. Children who were initiated with breastmilk within one day and above had 1.67 times

higher risk of being stunted compare to whom initiated breastmilk less than one hour. Access

to toilets showed significant association with WAZ. Unsanitary means of defecation had 1.4

42

times higher risk for underweight than their counterparts. Membership of microcredit was

also significantly associated with WAZ and Children had 1.2 odds of being underweight

compared to the children whose family were not membership of microcredit. Area of

residence showed significant association with HAZ and WAZ (Table 4-6;4-7;4-8).

However, Sex of child, receiving advice on child nutritional status and child growth, child

having diarrhoea in preceding two weeks, child having fast/difficult breath during cough

were not statistically associated with all three indicators of malnutrition (Table App.5;6;7). Table 4-9 Summary of significant characteristics for the three nutritional indicators

follows the results obtained through Pearson chi square test


Z-score

Weight-for-

age Z-score

Weight-for-

height Z-score

Child’s birthweight in kg + + +

Mother’s BMI + + +

Anaemia among mother + - -

Initiation of breastmilk + - -

Income quintile based on HIES + + -

Anaemia among children + - -

Area of residence + + -

Sex of household head - + -

Membership of microcredit - + -

Toiletfacilities - + +

Child suffer from cough in last two

weeks

- - +


4.3.3 Multivariate analysis

Multiple linear regressions model was performed to predict the score of indicator of

nutritional status of the children from the selected explanatory variable by controlling the

effect of other factors. When P<0.05 was considered, the co-efficient were significantly

different to zero.

43

Table 4-10 Estimates from linear regression model

Explanatory variables B

Std. error

p-value 95% CI for B Lower bound

Upper bound

Child age in months -0.021 0.005 0.000 -0.030 -0.012 Child’s birthweight in kg 0.483 0.127 0.000 0.235 0.731 BMI of mother 0.012 0.015 0.406 -0.017 0.042 Income quintile based on HIES 0.175 0.132 0.186 -0.084 0.434

Initiation of breast-milk -0.083 0.227 0.716 -0.529 0.363 Anaemia among children -.040 0.145 0.782 -0.325 0.244 Anaemia among mother -0.158 0.161 0.326 -0.474 0.157

Legend: Dependent variable: height-for-age Z-score; B=Co-efficient; CI= Confidence Interval; Income quintile based on HIES: 0=≤13838, 1= >13838, Initiation of breast-milk: 0= within 1 day & above; 1=<1hour, Anemia among children; 0=No; 1=Yes, Anemia among mother: 0=No, 1=Yes,



Std. error


Upper bound

Child age in months -0.013 0.001 0.000 -0.016 -0.010 Child’s birthweight in kg 0.640 0.045 0.000 0.551 0.729 BMI of mother 0.030 0.005 0.000 0.020 0.040 Sex of household head-female -0.122 0.110 0.267 -0.337 0.093

Income quintile based on HIES 0.049 0.046 0.295 -0.042 0.140

Membership of microcredit 0.032 0.057 0.577 -0.080 0.143 Toilet facilities 0.249 0.060 0.000 0.131 0.367

Legend: Dependent variable: weight-for-age Z-score; B= Co-efficient; CI= Confidence Interval; Toilet facilities: 0=Unsanitary latrine; 1=Sanitary latrine, Sex of hh: 0=male, 1=female; Income quintile based on HIES: 0=≤13838, 1= >13838, Membership of microcredit: 0=Yes, 1=No



Std. error


Upper bound

Child age in months -0.009 0.001 0.000 -0.012 -0.007 Child’s birthweight in kg 0.372 0.044 0.000 0.286 0.458 BMI of mother 0.042 0.005 0.000 0.032 0.051 Toilet facilities 0.150 0.058 0.010 0.036 0.264 Child suffered from cough in last two weeks

-0.064 0.043 0.139 -0.149 0.021

Legend: Dependent variable: weight-for-height Z-score; B= Co-efficient; CI= Confidence Interval; Toilet facilities: 0=Unsanitary latrine; 1=Sanitary latrine; Suffered from cough in last two weeks: 0=No, 1=Yes

44

The mean HAZ, WAZ and WHZ of the children will decrease with an estimated 0.021 unit,

0.013 unit and 0.009 unit respectively if the age of the children will increases with 1 month

and the other explanatory variables kept fixed. The mean HAZ, WAZ and WHZ of the

children will increase with an estimated 0.503 unit, 0.638 unit and 0.354 unit respectively if

the birth weight of the children increases with 1kg and the other explanatory variables were

kept fixed. The mean WAZ and WHZ also will increase with an estimated 0.030 unit and

0.041 unit respectively if the mother’s BMI increases with 1 kg/m2 and the other explanatory

variables were kept constant. However, not enough evidence could be found to reject the

hypothesis that there was no linear relationship between the HAZ and BMI of mother and

BMI of mother was not useful for predicting HAZ of the children by controlling other factors.

Sanitary means of defecation had on average 0.249 and 0.155 points higher score on WAZ

and WHZ respectively than unsanitary means of defecation. Income quintile based on HIES

showed no linear relationship with HAZ and WAZ. Similarly, Initiation of breastmilk,

Anaemia among mother and children showed no linear association with HAZ, sex of

household head and membership of microcredit did not show linear association with WAZ.

Similarly, child suffering from coughs in the preceding two weeks showed no linear

association with acute malnutrition of children (Table 4-10;4-11;4-12).

Table 4-13 Summary of significant characteristics for the three nutritional indicators follows the results obtained through multiple linear regressions model


Z-score

Weight-for-

age Z-score

Weight-for-

height Z-score

Child age in months + + + Child’s birthweight in kg + + + BMI of Mother - + + Income quintiles based on HIES - - - Initiation of breast-milk - - - Anaemia among children - - - Anaemia among mother - - - Sex of household head-female - - - Membership of microcredit - - - Toilet facilities - + + Child suffered from cough in last two weeks

- - -


45

5. Discussion

5.1. Prevalence of undernutrition

5.1.1. Sex influence

This study revealed that the prevalence of stunting among male children was slightly higher

than female children. but the prevalence of underweight and wasting was nearly same on

male and female children. According to Sri Lanka, Demographic and Health Survey of

2006/07 (SL-DHS, 2006/07); all indicators of malnutrition showed a higher prevalence in

male as compared to female children. Another study in Sri Lanka revealed that male children

were at higher risk as well as more vulnerable to malnutrition (Jayastissa R. et al., 2006).

The reason behind this could be that male children are more likely to fall ill in the first few

years of their lives compare to female children (Agnihotri, 1999).

5.1.2. Age of children

This study also revealed that malnutrition among underfive children showed variation by

age. The prevalence of stunting, underweight and wasting was 20.1%, 22.8% and 12.3%

respectively and the prevalence of stunting increased with increasing age. The highest

percentage was found in the age category of 18-23 months and started to decline from 24

months onwards. For underweight, the highest percentage (27.0%) was found in the age

category of 36-47 months and started to decline from 48 month onwards. The wasting

prevalence was almost double during the first 6 months compared to second six months of

life and highest percentage (15.9%) was found in the age category of 48-59 months.

SL-DHS (2006/07) reported that at national level the prevalence of stunting, underweight and

wasting among underfive children was 21%, 25% and 18% respectively. The prevalence of

acute undernutrition showed a remarkable decreasing pattern compared to SL-DHS

(2006/07). The reason behind this could be due to the influences of the survey period

(January to April). Probably this is the period of high production. However,The prevalence of

all indicators decreased since 2006.

5.1.3. Area of residence

This study demonstrated that the prevalence of stunting among rural children (18.4%) was

slightly higher than urban (15.4%), but in estate sectors it was almost three times higher than

urban and the percentage of underweight was higher in rural and estate children than their

46

counterparts. However, the prevalence of wasting was slightly higher in rural and urban areas

compared to estate sectors. Similar findings were demonstrated by SL-DHS (2006/07). But

the prevalence of chronic undernutrition showed decreasing trend compared to SL-DHS

(2006/07) over time. Similar findings were reported by Kandala N.B. et al., (2011).

Variation in malnutrition were seen in administrative districts. The highest percentage of

stunting was found in Nuwara Eliya (38.5%) and Badulla (24.6%). These two districts are

located in estate sectors in Sri Lanka characterized by hilly areas consisting of large tea

plantations.

The reason behind this could be that people in estate sectors are rarely allowed to go out and

hence they are deprived from the opportunities to mix with outsiders and interact with

neighbouring communities.Outsiders also are not allowed to enter the land without the

permission of estate management. Such geographical isolation, socio-cultural differences of

Indian origin Tamils from the rest of the people of the country, problem with citizenship

rights and ethnic conflict related security considerations influenced them to live in isolated

conditions.

The highest percentage of underweight was found in Nuwara Eliya (34.4%) and Ratnapura

(25.0%). The lowest percentage of stunting was in Kurunagala (12.7%).

Ratnapura is characterized by its rich gem industry with large tea plantations, rubber

surrounded along with few rice cultivations. Farmers are switching towards more profitable

gem mining and is located in south-western part (wet zone) of Sri Lanka.

Kurunagala is the capital city of North Western Province and the junction of the several main

roads linking to the other parts of the country with a feature of tropical rainforest climate.

Nearly 75% people are Sinhalese and it is a commercial center of rice, coconut and rubber

plantations.

The lowest prevalence of all three indicators was found in Colombo municipal council (MC)

and Jaffna.

The reason behind this might Colombo Municipal Council is the largest city and financial

center in Sri Lanka. Usually mothers living in city area have better accessibility to modern

healthcare facilities, nutritional information and better food and They are more educated

compared to mothers living in rural areas and show a higher tendency to take their child

having fever, diarrhoea and related morbidities to a healthcare canter and have more cash

income opportunities that may positively affects the better health and nutritional status of

underfive children. Jaffna is located in Northern province which is an agriculture dominant

47

province. Households of that district are may be more secured on their food security satus

which can positively influences the better nutritional status of underfive children.

According to SL-DHS (2006/07), the highest prevalence of stunting was also found in the

same districts and the higher prevalence of underweight and wasting was found in

Trincomale, Monaragala, and Hambanthota. The overall prevalence of chronic and acute

undernutrition showed decreasing trend since 2006.

5.1.4. Wealth index

Results of this study presents that the higher prevalence of stunting, underweight and wasting

was found among children with poor wealth index (WI) and showed a decreasing pattern

from poorest to richest. For example, stunting prevalence were nearly 2.5 fold higher among

the children with poor WI. This was consistent with the findings of SL-DHS (2006/07).

This may be due to stress of food insecurity and also that children with poor WI families have

limited range of food sources and hence are exposed to food insecurity that negatively affects

the child’s nutritional status. A Study revealed that better nutritional status was found among

underfive children in the richest quintiles compared to the poorest (Barun Kanjilal et al.,

2010). Similar conclusion were drown by Sunkanmi (2012).

5.1.5. Mother’s education

The present study also revealed that higher prevalence of chronic undernutrition among

underfive children were found in children whose mothers had primary education or no

schooling. A pattern of decreasing stunting and underweight was associated with increasing

mother’s education level. However, wasting did not shown any consistent pattern but the

higher prevalence was seen on children with illiterate mother. This findings were also

supported by SL-DHS (2006/07).

A study by Kumar D. et al.,( 2006) showed that higher proportions of undernourished

children were found among illiterate mother compared to literate mother.

Another study also reported that higher proportion of stunted children were found among

mothers who are illiterate or with primary education but the lower proportion was found

among mothers with secondary followed by higher education ( Kandala N.B. et al., 2011).

Access to education is often associated with degree of socioeconomic status (Gobotswang,

1998); meaning that mothers from poor families do not get proper access to schooling.

48

5.1.6. Mother’s Body-Mass-Index

This study showed that malnutrition among underfive children showed an inverse

relationship with mother’s body-mass-index (BMI). Higher percentages of stunted,

underweight and wasted children had mothers who were moderately as well as severely thin.

A decreasing pattern with increasing BMI cut-off was also shown, which corresponds to the

SL-DHS (2006/07) findings.

5.2. Determinants of stunting, underweight and wasting

In this study the determinants of stunting, underweight and wasting were determined using

Bivariate analysis ( Pearson chi square and one way ANOVA) and all the factors that show

significant association in the Bivariate analysis were put into the final multivariate model

(multiple linear regression model) to see wheather the variables that are significantly

associated in the Bivariate model are still significant or not associated with the dependent

variables height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), weight-for-height Z-

score (WHZ) when other factors are included in the model.

Bivariate analysis using chi square test shows no significant difference regarding malnutrition

between male and female children for any of the three indicators of malnutrition and which

corresponds to the findings by Genebo T. and Girma W. (2002); Kamiya (2011).

Bivariate analysis using one way ANOVA test reveals that age of children is significantly

associated with all three indicators of malnutrition and multivariate analysis using multiple

linear regressions shows that age of the children shows an inverse significant linear

association with all of the three indicators of malnutrition (stunting, underweight and

wasting). These findings may be due to the fact that the attention on child care can decrease

with increasing age. Thus older children were more likely to be vulnerable to undernutrition

than younger children. Another reason might be as the child grows older, he/she starts to

move more, hence there is more demand for food. Weaning and less breastmilk also make

them more vulnerable to acute and chronic malnutrition.

A study by Kamiya, (2011) also agreed with these findings. Another study in a neighbour

country of Sri Lanka showed that the highest prevalence of stunting and underweight was

found in children 13-24 months and showed declining trend of stunting after 24 months and

the highest prevalence of wasted children were found between 37-48 months (Kumar D. et

al., 2006). A study in Bangladesh, however revealed the opposite findings that age of the

children was inversely associated with HAZ (Jane A. Pryer et al., 2003). A study in Kenya

49

using DHS data demonstrated that after 2 years of age, cessation of breastfeeding was

common, the more mobile nature of children, resulting in demand for nutrients was higher

than in the tender age and hence a need for more nutrients from variety of foodstuffs and

distribution of child Z-socres by their age are followed an inverted ‘U’shape (Jane Kabubo-

Mariaraa et al., 2008).

Bivariate analysis using chi square test reveals that birth weight of children show a significant

association with all of the three indicators of malnutrition and LBW children showed 2 to 3

fold higher risk of being stunted, underweight and wasted compared to normal birth weight

children. In the multivariate analysis, birthweight shows a significant positive linear relation

to all three indicators of malnutrition. A study in Vietnam found that LBW babies had 5, 6

and 8 times higher risk of being stunted, underweight and wasted respectively (Hien N.N. and

Kam S., 2008).

Bivariate analysis using one way ANOVA shows that mean Z-scores of HAZ, WAZ and

WHZ of studied children significantly differed with their degree of wealth index (WI).

Similar findings were also reported by Islam M.M. et al., (2013). A study conducted in rural

Bangladesh unveiled that children from household with upperclasss assets holder had almost

double the chance of being healthy compared to those in lower class assets holdings (Rahman

M. et al., 2009).

In Bivariate analysis, prevalence of stunting, underweight and wasting of children

significantly differ with mother’s BMI in all of three Z-scores and in multivariate analyses

revealed that mother’s BMI showed a significant positive linear relationship with WAZ and

WHZ. However, BMI of mother was not significantly associated with stunting.

Stunting among children show a decreasing trend while increasing BMI of mother and shows

significant association with stunting but other study results are not support, BMI of mother as

a predictor of stunting (Rahman A. and Chowdhury S., 2007; Rayhan M.I. and Khan M. S.

H., 2006). Rahman M et al., (1993) reported that mothers who are undernourished are mostly

from poor socio-economic background and normally tend to have little or even no education.

Probably undernourished mothers are not capable of supplying adequate breastmilk to their

children which makes them vulnerable to poor nutrition and in the long run usually deliver

low birthweight babies.

The ANOVA results demonstrate that education of mother showed significant association

with all three indicators of malnutrition. Several studies proved that mother’s education is

significantly associated with child nutritional status. An educated mother is capable of

efficient management on household limited resources, has a better health promoting behavior,

50

greater utilizes more the healthcare services, has lower fertility rates, better child centered

caring practices is conscious about child health status, more income generating activities and

allocates higher proportion of income to child care; all of the mentioned factors are

significantly associated with child health and nutritional status ( Felice WF, 1999; Shah SM

et al., 2003).

Educated mothers may have expanded capacities to manage well their resources. For

instance, during the household economic recessions, mothers can choose comparatively

cheap and locally available substitute sources of nutrients, hence able to tackle the risk of

malnutrition in their children. However, the management ability of such type of substitution

could be positively related with level of mother’s education and degree of economic status

(Weil et al., 1990).

In Bivariate analysis, education of the household head of the family is significantly associated

with all three indicators of malnutrition. Similar findings were also reported by Gobotswang,

(1998). Normally the household head is the principal earner and decision maker of the family.

So, his/her level of education may play a role on child nutritional status. But access to

education is often depending on degree of wealth. Households with poor socio-economic

status have less access to education and also engage their children for income generating

activities instead of schooling.

Bivariate analysis - chi square test results show that area (rural, urban and estate) of residence

was significantly associated with HAZ and WAZ but not with WHZ. But in the ANOVA

findings, area of residence was associated with all three indicators of malnutrition. Genebo T.

and Girma W. (2002) unveiled that a significantly a higher proportion of stunted children

was found in rural areas compared to urban areas. A study (using data from DHS in Malawi,

Tanzania and Zimbabwe) demonstrated that area of residence (rural and urban) showed

significant association with HAZ and similar findings regarding wasting were shown. An

exception for WAZ was shown in Malawi. The effect of regional variation could be higher

for chronic malnutrition rather than for acute malnutrition of underfive children (Makoka,

2013). A study in 36 countries using DHS data (Kothari and Abderrahim, 2010) revealed that

better nourished children were found in urban areas than in rural areas. The possible

explanation could be that mothers in urban area have better availability and accessibility to

health, nutritional information and better food and are more educated compared to the rural

areas and show a higher tendency to take their child having fever, diarrhoea and related

morbidities to a healthcare center.

51

Bivariate analyses show that mean Z-scores of children significantly differed with the

administrative districts. Rahman A. and Chowdhury S., (2007) reported that significant

variation on stunting prevalence was seen by residential place. Thus community inequality

regarding residential place seems to be a significant determinant of stunting among underfive

children (Michel et al,. 1989).

Bivariate analysis ,chi square test results show that toilet facilities is significantly associated

with underweight and wasting but not with stunting but the ANOVA test reveals that the

toilet facilities were significantly associated with all three indicators of malnutrition.

Furthermore, in multivariate analysis, toilet facilities were shown to be a significant predictor

of underweight and wasting.

Toilet facilities are considered a proxy determinant of malnutrition because access to sanitary

toilets are positively related with degree of wealth. A similar conclusion was drown by Islam

M.M. et al.,(2013) from a study on Bangladesh. Another study on a Nigeria sample

demonstrated that toilet facilities were negatively associated with stunting and wasting

(Babatunde R.O. et al., 2011).

One way ANOVA results show a significant association between source of drinking water

and all three indicators of malnutrition. Poor mean Z-scores are found among the children

who were exposed to unprotected drinking water than their fortune counterpart.but this

variable also relates to degree of wealth. Usually, households with better economic status

have a greater accessibility to safe water. It is said that water is considered as a vehicle to

transmit waterborne diseases. So, provision of clean water prevents the spread of waterborne

diseases resulting positively in a better health and nutrition status of children. Joyce K.

Kikafunda et al,. (1998) ; Genebo T. and Girma W. (2002) agree and reported that access to

safe water is a significant determinant of both chronic and acute malnutrition in children

(Babatunde R.O. et al., 2011).

Chi square test results reveal that anaemia of children shows a significant association with

HAZ but not with WAZ and WHZ. A study on Tanzanian children demonstrated that

hemoglobin concentration (Hb) of underfive chidren was significantly associated with all of

the three indicators of malnutrition. Low blood Hb was found among the children with low Z-

score and also higher proportion of anaemic children were found among severe stunted,

underweight and wasted children. Worsening trend of nutritional indicators was seen with

decreasing of Hb concentration and viceversa (Schellenberg D. et al., 2003).

52

Mothers occupation shows a significant association with all the three indicators of

malnutrition. Mean Z-score variation is seen with mother’s occupation category and

supported by the report of Joshi HS et al., (2011). More healthy children is seen whose

mother worked for cash and the probability of being healthy is nearly 2.5 fold higher

(Rahman M. et al., 2009). Women employment contributes to raising household income

resulting in remarkable benefits to household nutrition. It promotes her confidence, improves

her status and power and expands the power to take decisions how to spend her earnings.

Normally women are more child centered, hence, able to spend their earnings on her child’s

health and nutritional status resulting in a better health and nutritional status.

53

6. Conclusion and Recommendations

6.1. Conclusion

This study’s results show that the prevalence of undernutrition among children are

significantly affected by geographical locations and income segments. The survey identifies

multiple factors: socio-economic, demographic, geographic, environmental, biological,

feeding behaviour, health status of childand maternal factors as having direct and indirect

effects on health and nutritional status of underfive children.

Bivariate and multivariate analyses reveal the potential determinants of malnutrition. Among

the several factors, age of the children and birthweight are found to be a significant predictor

of all three indicators of malnutrition. Birthweight of children is positively associated with

nutritional status, while age is negatively associated with their nutritional status Maternal

factors such as mother’s BMI, education, occupation were shown to be significant

determinants of all three indicators of malnutrition.

Higher proportions of undernourished children are found in the poorest wealth index

quintiles. Wealth index quintiles and proxy determinants of assets such as access to toilets

and source of potable water and geographical factors (area of residence and administrative

districts) are found to be detrimental factors causing undernutrition. Household head’s

education and religion are found as determinants of all of three indicators of malnutrition. But

sex and occupation of the household heads are found only to be determinants of underweight,

while and family monthly income is the determinant only for stunting and underweight.

Initiation of breastfeeding is found to be a determinant of chronic undernutrition.

Finally one can conclude that older children who were born with low birth weight, living in

the rural and estate area of some particular districts are most vulnerable to undernutrition.

Low socio-economic status, poor maternal health status and poor maternal schooling,

unhygienic and poor sanitation practices and delayed initiation of breastmilk were found to

be the most attributable factors of undernutrition among underfive children.

54

6.2. Recommendations

• This study explores the potential determinants and multi sectoral issues that are

directly or indirectly related to undernutrition among underfive children. Based on the

results cohesive multi-sectoral approaches need to develop to reduce malnutrition in

children.

• Regarding the prevalence of undernutrition, inter districts differentials are observed. It

is necessary to develop and implement short term and long term plans and

programmes at national as well as sub national (districts) levels to eliminate the

factors that are responsible for undernutrition in children. Monitoring of the activities

has to be ensured. The key focus has to be given to vulnerable groups, on the basis of

geographical area and socioeconomic criteria.

• For short term plans and programmes it is recommended that dissemination of health

and nutrition information to the targeted groups by collaborating with community

health center, health workers, agriculture extension worker, community leader, local

political and religious leader and monitoring of the activities has to be ensured for

acquiring the knowledge followed by attitude and practices.

• For long term plans and programmes it is recommended that to compare the

prevalence of undernutrition since 2006 to 2009 and calculate the desired outcome

through existing programmes to achieve the objectives and then modify the

programes by talking into considerations the determinants obtained by this research.

• To identifying wealth, it is highly recommended that simple and easily understandable

method of identifying wealth (even modified from wealth index criteria) can be used

to identify the beneficiary households to be considered for poverty alleviation and

food supplementation programmes.

• Strictly defined criteria has to be applied for selection of targeted individuals to

benefit from food supplementation/poverty alleviation programmes and

comprehensive ‘packages’ of inputs need to be availed to the targeted beneficiaries

with necessary follow-up.

• Special attention needs to be paid to reduce low birthweight. It is recommended that

during pregnancy maternal food supplementation along with iron and treatment of

malaria are the most effective interventions to increase birthweight.

55

• Sustainable, locally available, socially and culturally acceptable and effective food

and nutrient supplementation programmes have to be ensured at regular and

continuous basis with proper monitoring.

• Furthermore, to find out the specific set of determinants of child and maternal

nutrition (since the child and maternal nutrition are strongly linked) a prospective

study needs to be conducted.

• Furthermore, a prospective study is needed to identify the relevant specific set of

determinants of child and maternal nutrition (since the child and maternal nutrition are

strongly linked)

• Investment on education, particularly on women education is crucial.

• Need to pay more attention on older children (2-5 years).

• Clear and appropriate guidelines for the community to adopt with cheap, easily

available and accessible and appropriate home foods for their young children.

56

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AI

APPENDICES

Appendix 1: Descriptive analysis

Table App. 1 Sex distribution of underfive children

Sex of the child Number Percentage Male 1685 50.1

Female 1681 49.9 Total 3366 100.0

AII

Appendix 2: Background characteristics related to child malnutrition

Appendix 2.1 Bivariate analysis using one way ANOVA

Table App. 2 Effect of background characteristics on nutritional status of underfive children (Results from Bivariate analysis: one way ANOVA)

Characteristics

Height-for-Age Z-score

Weight-for-Age Z-score

Weight-for-Height Z-score

Mean Z-scores

SD Mean Z-scores

SD Mean Z-scores

SD

*Mother age in years

≤19 -0.93 1.97 -1.07 1.19 -0.71 1.04 20-29 -1.08 1.80 -1.21 1.24 -0.86 1.16 30-39 -1.00 1.75 -1.18 1.24 -0.89 1.18 40-49 -1.00 1.42 -1.18 1.12 -0.88 1.02 Total -1.03 1.75 -1.19 1.23 -0.87 1.15

*Family size 1-3 -0.97 1.48 -1.17 1.16 -0.90 1.13 4-6 -1.05 1.71 -1.20 1.22 -0.86 1.17 7-9 -1.00 2.12 -1.22 1.38 -0.91 1.17 ≥10 -1.13 1.05 -1.29 .98 -0.95 1.07

Total -1.03 1.75 -1.20 1.24 -0.88 1.16 *Initiation of breastmilk

<1 hour -1.03 1.92 -1.16 1.27 -0.80 1.25 Within 1 day -1.08 1.63 -1.19 1.34 -0.89 1.02

>1 day -1.17 4.42 -1.89 2.55 -1.43 1.43 Total -1.04 1.95 -1.17 1.30 -0.82 1.23

AIII

Continue Table App. 2

Height-for-Age Z-score

Weight-for-Age Z-score

Weight-for-Height Z-score

Characteristics Mean Z-scores

SD Mean Z-scores

SD Mean Z-scores

SD

*HH age in years

19-29 -1.18 1.73 -1.27 1.25 -0.87 1.20 30-39 -0.95 1.75 -1.13 1.23 -0.84 1.17 40-49 -0.97 1.68 -1.19 1.18 -0.91 1.05 50-59 -1.10 1.85 -1.22 1.28 -0.88 1.07 60-69 -0.87 1.65 -1.14 1.23 -0.94 1.17

70 and above -1.12 1.75 -1.30 1.42 -0.93 1.34 Total -1.01 1.740 -1.18 1.23 -0.87 1.15

*Type of income Daily regular -1.15 1.76 -1.25 1.22 -0.83 1.27

Daily irregular -1.04 1.89 -1.25 1.27 -0.94 1.11 Weekly -1.06 1.75 -1.23 1.03 -0.87 1.01 Monthly -0.98 1.70 -1.14 1.27 -0.83 1.19 Seasonal -1.08 1.46 -1.25 1.11 -0.91 1.10

Total -1.04 1.74 -1.20 1.24 -0.88 1.162 Legend: *P-value >0.05, for Height-for-Age Z-score; Weight-for-Age Z-score; Weight-for-Height Z-score; HH= household head

AIV

Table App. 3 Effect of background characteristics on height-for-age Z-score and weight-for-height Z-score of underfive children (Results from Bivariate analysis: one way ANOVA)

Height-for-Age Z-score Weight-for-Height Z-score Characteristics Mean Z-

scores SD Mean Z-

scores SD

*Household head occupation

Armed forces -1.05 1.20 -0.89 1.11 Managerial -0.65 1.23 -0.50 1.13 Professional -0.71 1.48 -0.68 1.00 Clerical -1.05 1.84 -0.72 0.93 Sales & related occupation -0.98 1.94 -0.79 1.11 Skilled agri worker -1.09 1.59 -0.91 1.13 Mechanical worker -0.90 1.99 -0.78 1.01 Elementary occupation -1.14 1.78 -0.92 1.11 Housewife -0.97 2.21 -0.99 1.01 Unemployed -0.94 1.37 -0.81 1.17 Others -1.07 1.54 -0.85 1.40 Total -1.05 1.73 -0.86 1.14

Legend: *P-value >0.05, for Height-for-Age Z-score; Weight-for-Height Z-score

Table App. 4 Effect of the income quintiles on weight-for-height Z-score of underfive children (Results from Bivariate analysis: one way ANOVA)

Characteristics No Mean Z-score

SD p-value

Income quintiles based on HIES

32406+ 190 -0.76 1.10

0.28 20230 - 32405 294 -0.84 1.23 13839 - 20229 684 -0.82 1.11 8984 - 13838 604 -0.87 1.22 ≤8983 1130 -0.92 1.11 Total 2902 -0.87 1.14

Legend: *P-value >0.05, for Weight-for-Height Z-score;Exchange rate as of August 2013: US $ 1 = LKR 126.45

AV

Appendix 2.2: Bivariate analysis using Pearson chi square test

Table App. 5 Association of selected variables and height-for-age Z-score of underfive children (Results from Bivariate analysis : Pearson chi square test)

Height-for-Age

Characteristics Nutritional Status Total

N°(%) Pearson

χ2 value


N°(%) ≥-2SD N°(%)

Sex of children Male 325(52.3) 1286(49.4) 1611(49.9)

1.64

0.199

1.12 (0.94, 1.34)

Female 297(47.7) 1318(50.6) 1615(50.1) Total 622(100.0) 2604(100.0) 3226(100.0)

Sex of HH Male 650(93.7) 2638(95.1) 3288(94.8)

2.33

0.127 0.76

(0.54, 1.08) Female 44(6.3) 136(4.9) 180(5.2) Total 694(100.0) 2774(100.0) 3468(100.0)

Received advice on child-

nutritional status

Yes 539(86.9) 2183(89.4) 2722(88.9) 2.93

0.087

0.79 (0.61, 1.04) No 81(13.1) 260(10.6) 341(11.1)

Total 620(100.0) 2443(100.0) 3063(100.0) Received advice on child growth

Yes 542(88.1) 2191(90.3) 2733(89.8) 2.47

0.116

0.80 (0.61, 1.06) No 73(11.9) 236(9.7) 309(10.2)

Total 615(100.0) 2427(100.0) 3042(100.0) Membership of

microcredit

Yes 53(20.0) 215(19.7) 268(19.8) 0.01

0.925

1.017 (0.73, 1.42) No 212(80.0) 874(80.3) 1086(80.2)

Total 265(100.0) 1089(100.0) 1354(100.0) Toilet facilities Unsanitary toilet 56(20.6) 175(15.8) 231(16.7) 3.67 0.055 1.02


Child suffered from cough in last two weeks Yes 237(38.5) 1065(41.9) 1302(41.2)

2.35

0.125 0.87

(0.73, 1.04) No 379(61.5) 1479(58.1) 1858(58.8) Total 616(100.0) 2544(100.0) 3160(100.0)

Child had diarrhea in last two weeks Yes 43(6.9) 174(6.7) 217(6.7)

0.03

0.849 1.04

(0.73, 1.46) No 579(93.1) 2423(93.3) 3002(93.3) Total 622(100.0) 2597(100.0) 3219(100.0)

Child had fast/difficult breath during cough Yes 100(43.3) 450(42.3) 550(42.5)

0.07

0.790 1.04

(0.78, 1.38) No 131(56.7) 613(57.7) 744(57.5) Total 231(100.0) 1063(100.0) 1294(100.0)

AVI

Table App. 6 Association of selected variables and weight-for-age Z-score of underfive children(Results from Bivariate analysis : Pearson chi square test)

Weight-for-Age Characteristics Nutritional Status Total

N°(%) Pearson χ2

value

p-value

OR (95%CI) <-2SD

N°(%) ≥-2SD N°(%)

Sex of the child Male 351(49.2) 1258(50.1) 1609(49.9) 0.20 0.651 0.96

(0.81, 1.13) Female 363(50.8) 1252(49.9) 1615(50.1) Total 714(100.0) 2510(100.0) 3224(100.0)

Anemia among mother

Present 159(24.9) 491(21.8) 650(22.5) 2.73

0.098

1.19 (0.97, 1.46)

Absent 479(75.1) 1759(78.2) 2238(77.5) Total 638(100.0) 2250(100.0) 2888(100.0)

AVII

Continue table App. 6

Characteristics Nutritional Status Total N°(%)

Pearson χ2

value


N°(%) ≥-2SD N°(%)

Received advice on child-

nutritional status

Yes 631(87.8) 2088(89.2) 2719(88.9) 1.13 0.286 0.87

(0.67, 1.13) No 88(12.2) 253(10.8) 341(11.1)


Yes 645(89.7) 2098(89.8) 2743(89.8) 0.00

0.960

0.99 (0.75, 1.31) No 74(10.3) 239(10.2) 313(10.2)

Total 719(100.0) 2337(100.0) 3056(100.0) Initiation of breast-milk

within 1 day &above

34(11.9) 92(8.5) 126(9.2) 3.06

0.080

1.45 (0.96, 2.20)

< 1 hour 252(88.1) 988(91.5) 1240(90.8) Total 286(100.0) 1080(100.0) 1366(100.0)

Anemia among children

Present 215(29.8) 631(26.4) 846(27.2) 3.21

0.073

1.18 (0.98, 1.42) Absent 507(70.2) 1760(73.6) 2267(72.8)

Total 722(100.0) 2391(100.0) 3113(100.0) Child suffered from cough in last two weeks

Yes 300(42.5) 1004(40.9) 1304(41.3) 0.54

0.462

1.07 (0.90,1.26) No 406(57.5) 1448(59.1) 1854(58.7)

Total 706(100.0) 2452(100.0) 3158(100.0) Child had

diarrhea in last two weeks

Yes 49(6.9) 168(6.7) 217(6.7) 0.03

0.860

1.03 (0.74, 1.43) No 662(93.1) 2338(93.3) 3000(93.3)

Total 711(100.0) 2506(100.0) 3217(100.0) Child had

fast/difficult breath during

cough

Yes 123(41.6) 430(43.0) 553(42.7) 0.19

0.659

0.94 (0.72, 1.22) No 173(58.4) 570(57.0) 743(57.3)

Total 296(100.0) 1000(100.0) 1296(100.0)

AVIII

Table App. 7 Association of selected variables and weight-for-height Z-score of underfive children(Results from Bivariate analysis : Pearson chi square test

Weight-for-Height Characteristics Nutritional Status Total

N°(%) Pearson χ2 value


N°(%) ≥-2SD N°(%)

Sex of the child Male 194(49.9) 1399(49.8) 1593(49.8)

0.01

0.970

1.00 (0.81, 1.24)

Female 195(50.1) 1412(50.2) 1607(50.2) Total 389(100.0) 2811(100.0) 3200(100.0)

Sex of HH Male 396(93.6) 2863(94.9) 3259(94.8)

1.28

0.257 0.78

(0.51, 1.20) Female 27(6.4) 153(5.1) 180(5.2) Total 423(100.0) 3016(100.0) 3439(100.0)

Anemia among mother

Present 71(21.1) 574(22.7) 645(22.5) 0.46

0.497

0.91 (0.69, 1.20) Absent 266(78.9) 1953(77.3) 2219(77.5)

Total 337(100.0) 2527(100.0) 2864(100.0) Received advice

on child-nutritional status

Yes 345(90.1) 2354(88.7) 2699(88.9) 0.64

0.421

1.16 (0.81, 1.65) No 38(9.9) 300(11.3) 338(11.1)


Yes 351(91.4) 2373(89.6) 2724(89.8) 1.22

0.269

1.24 (0.848,1.805) No 33(8.6) 276(10.4) 309(10.2)

Total 384(100.0) 2649(100.0) 3033(100.0) Initiation of breast-milk

within 1 day &above

14(8.9) 111(9.3) 125(9.3) 0.03

0.857

0.95 (0.53, 1.18)

< 1 hour 144(91.1) 1082(90.7) 1226(90.7) Total 158(100.0) 1193(100) 1351(100.0)

Income quintile based on HIES

≤13838 226(58.7) 1594(57.8) 1820(57.9) 0.11

0.730

1.04 (0.84, 1.29) >13838 159(41.3) 1165(42.2) 1324(42.1)

Total 385(100.0) 2759(1000) 3144(100.0) Microcredit membership

Yes 80(20.9) 534(19.6) 614(19.8) 0.34

0.557

1.08 (0.83, 1.41) No 303(79.1) 2189(80.4) 2492(80.2)

Total 383(100.0) 2723(100.0) 3106(100.0)

AIX

Continue table App. 7

Characteristics Nutritional Status Total N°(%)

Pearson χ2 value


N°(%) ≥-2SD N°(%)

Anemia among children

Present 101(26.8) 740(27.2) 841(27.2) 0.02

0.865

0.98

(0.77, 1.25) Absent 276(73.2) 1980(72.8) 2256(72.8) Total 377(100.0) 2720(100.0) 3097(100.0)

Child had diarrhea in last two weeks Yes 26(6.7) 189(6.7) 215(6.7)

0.00

0.998 1.00

(0.65, 1.53) No 360(93.3) 2618(93.3) 2978(93.3) Total 386(100.0) 2807(100.0) 3193(100.0)

Child had fast/difficult breath during cough Yes 68(39.1) 482(43.3) 550(42.7)

1.07

0.299 0.84

(0.61, 1.17) No 106(60.9) 632(56.7) 738(57.3) Total 174(100.0) 1114(100.0) 1288(100.0)

Area of residence Urban 71(18.5) 570(21.5) 641(21.1)

2.16

0.338 Rural 284(74.0) 1906(72.0) 2190(72.) Estate 29(7.6) 173(6.5) 202(6.7) Total 384(100.0) 2649(100.0) 3033(100.0)

AX

Appendix 3. Effect of background characteristics on nutritional status of underfive children SPSS output)

Figure App. 3.1 Effect of wealth index quintiles on height-for-age Z-score of underfive children

Figure App. 3.2 Effect of child age group on their height-for-age Z-score

AXI

Figure App. 3.3 Effect of child age group on their weight-for-age Z-score

Figure App. 3.4 Effect of child age group on their weight-for-height Z-score

AXII

Figure App. 3.5 Effect of place of living districts on height-for-age Z-score of underfive children

Figure App. 3.6 Effect of place of living districts on weight-for-age Z-score of underfive

children

AXIII

Figure App. 3.7 Effect of place of living districts on weight-for-height Z-score of underfive children

Figure App. 3.8 Effect of area of residence on height-for-age Z-score of underfive children

AXIV

Figure App. 3.9 Effect of area of residence on weight-for-age Z-score of underfive children

Figure App. 3.10 Effect of area of residence on weight-for-height Z-score of underfive children

AXV

Figure App. 3.11 Effect of mother’s education on height-for-age Z-score of underfive children

Appendix 4.: A separate document on CD containing the datasets

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