Shahnila Naz

M Phil Health Economics

Supervisors Dr Ahsan ul Haq

Dr Khalid Mehmood

PIDE

Dated: 9th October, 2015

Structural Equation Model for

determinants of child malnutrition in

Pakistan

Introduction

Overweight-excess of energy and nutritious food

Underweight- shortage of food and energy

Vulnerable age group- children under five

Influenced by many factors- environmental,

behavior, biological and maternal.

Global Situation of Child Malnutrition

More than 150 million of children under five of

age are malnourished in the world. 54% of which

are in the developing countries. NNS 2011.

Pakistan, India and Bangladesh have higher rate of

child malnutrition than other countries of sub

Saharan Africa

Situation of child malnutrition in

Pakistan 43.7% children under five were stunted

rural areas stunting 46 percent - urban areas 39.6%

Wasting 15.1% in Pakistan

urban areas 12.7%- Rural areas 16.1%.

31.5 % under weight

rural areas 31.1%

Malnutrition rate was higher in rural areas then urban areas

(NNS, 2011).

Objectives

To find the small set of unobserved variables that

can implicate the covariance among a large set of

observed variables

The effect of factors constructed in the first

objective

To decompose the total effect of maternal factors

as well as environmental factors into direct and

indirect effects

Hypothesis

Significant inter correlation exists between different indicators/item of the exogenous variable which affects malnutrition.

There is role of different aspects like biological, behavioral, environmental etc. on child malnutrition

Maternal factors play the role of mediation between biological aspects and child malnutrition.

Rationale of the study Contribution of individual, maternal,

environmental and behavior are important for

the policy makers to design targeted

interventions.

Determinants of malnutrition are documented

but they have not investigated the inter

correlation of all components.

This study will attempt to use the structural

equation model in order to test a regression

equation in the model.

Data

Pakistan Demographic and health survey, 2013.(PDHS)

Complete data available on child height and weight.

Data available on environmental aspects.

Data available on biological aspects

Data available on maternal aspects.

Data available on behavior aspects.

Methodology

Measure of prevalence of child malnutrition

1. Weight for age(WAZ)

2. Height for age(HAZ)

3. Weight for height(WHZ)

Unit of Analysis: Children under five age (0-59

months).

Differentials in Child malnutrition Dependent variables:

1. stunting

2. wasting

3.underweight

Independent Variables

1. Environmental Factors

2. Maternal Factors

3. Biological Factors

4. Behavior Factors

Environmental Factors Type of toilet facility

Wealth index

Source of drinking water

Household possessions

Household housing type

Presence of LHW

Maternal Factors

Mothers height and weight,

Mother’s marital status,

Mothers age when first birth, and

Occupation

Education of mother.

Behavioral factors Mother's breast feeding practice

Place of delivery

Mother’s family planning

Vaccination of the child

Antenatal visits of mothers during pregnancy.

Biological Factors

Age of the child,

Child is twin,

Interval between births and

The size and weight of the child at birth.

Statistic Analysis

CFA is used to evaluate the degree to which their

measurement hypotheses are consistent with actual data

produced by the respondents using the scale.

By examine the parameters estimates, fit indices and

potentially modification test we formally test the

measurement hypothesis and can modify hypothesis to be

more consistent with the actual structure of participant’s

responses to the scale.

Path model

Path analysis is an expansion of multiple regressions

It involves various multiple regression model or equations

that are estimated all together.

Provides a more effective and direct way to find out the

modeling mediation, indirect effects and other complex

relationship among variables.

Measurement Model

Unobserved latent variables cannot be measured directly but

are indicated or inferred by responses to a number of

observable variables.

Statistical techniques such as factor analysis, explanatory or

confirmatory have been widely used to examine the number

of latent constructs underlying the observed respondents and

to evaluate the adequacy if individual item or variables as

indicator for the latent constructs they are supported to

measure.

Framework of the study

BbF

AGe111

BWe21

DIe31

TWe41

AYSe51

BeF

NHV e6

1

1

MFP e71

DBF e81

CV e91

PCD e101

CF

WIe11

11

TFe121

De131

HTe14 1

MA

BMI e15

1

1

EM e161

MS e171

DME e181

MAAC e191

MA e201

MN

ST

e21

1

1

WS

e22

1

UW

e23

1

DA

A

e24

1

G

e25

1

HS

e26

1

M

e27

1

RSe291

LHWe301

1

SEM The measurement model in SEM is evaluated through

confirmatory factor analysis.

CFA differs from explanatory factor analysis in that factor structures are hypothesized a priori and empirically verified rather than derived from the data

CFA allows indicators to load on multiple factors. It also allows residuals or errors to correlate.

When the measurement model has been specified structural relations of the latent factors are then modeled essentially the same way as they are in the path model.

The combination of CFA model with structural path model on the latent constructs represents the general SEM framework in analyzing covariance structures.

Child malnutrition by age of the child

CAM HAZ WAZ WHZ

not stunted

severely

stunted

moderately

stunted

not

underweig

ht

severely

underweig

ht

moderately

underweig

ht not wasted

severely

wasted

moderately

wasted

0-5 77.1 10.7 12.1 76.4 5.7 17.9 85.4 5.4 9.3

6-8 76.6 12.1 11.3 76.6 9.9 13.5 83.7 5.7 10.6

9-11 69.8 17.0 13.2 74.8 10.1 15.1 84.9 5.7 9.4

12-17 57.1 24.4 18.5 70.7 12.0 17.3 81.5 4.6 13.9

18-23 52.0 26.4 21.6 72.2 7.9 19.8 88.5 2.6 8.8

24-35 46.0 32.8 21.2 71.8 10.3 17.9 91.9 2.0 6.1

36-47 50.1 28.9 21.0 74.9 9.8 15.3 92.3 3.6 4.1

48-59 52.1 28.7 19.2 74.0 6.7 19.2 93.7 2.0 4.3

N 1696 798 577 2261 276 534 2754 102 215

total 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Child is twin and child malnutrition

HAZ WAZ WHZ

not

stunted

severely

stunted

moderate

ly

stunted

not

underwei

ght

severely

underwei

ght

moderate

ly

underwei

ght

not

wasted

severely

wasted

moderate

ly

wasted

single 55.2 26.0 18.7 73.8 9.0 17.2 89.7 3.3 7.1

twin 56.1 22.8 21.1 64.9 8.8 26.3 89.5 7.0 3.5

total 1696 798 577 2261 276 534 2754 102 215

N 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Differential in child malnutrition and

gender of the child

Gender of

child HAZ WAZ WHZ

not stuntd

severely

stunted

moderately

stunted

not

underweight

severely

underweight

moderately

underweight not wasted

severely

wasted

moderately

wasted

Male 53.5 27.4 19.1 72.0 9.4 18.5 88.5 3.7 7.8

Female 57.0 24.5 18.5 75.3 8.5 16.2 90.9 2.9 6.2

N 1696 798 577 2261 276 534 2754 102 215

Total 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Differentials in child malnutrition and

wealth index

wealth

Index not stunted

severely

stunted

moderately

stunted

not

underweig

ht

severely

underweig

ht

moderately

underweig

ht not wasted

severely

wasted

moderately

wasted

Poorest 37.9 43.4 18.8 62.1 15.8 22.1 87.2 4.3 8.5

Poorer 45.2 31.6 23.2 67.4 11.1 21.5 89.2 3.8 7.0

Middle 56.1 24.4 19.5 74.5 7.6 17.9 89.4 4.3 6.3

Richer 61.4 18.4 20.2 79.8 6.8 13.4 91.2 2.1 6.7

Richest 75.8 11.9 12.3 84.3 3.4 12.3 91.3 2.3 6.4

total 1696 798 577 2261 276 534 2754 102 215

N 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Differentials in child malnutrition and

presence of LHW

LHW HAZ WAZ WHZ

not stunted

severely

stunted

moderately

stunted

not

underweight

severely

underweight

moderately

underweight not wasted

severely

wasted

moderately

wasted

No 50.4 33.2 16.4 72.5 10.8 16.8 89.1 3.6 7.2

Yes 57.4 22.7 19.9 74.2 8.2 17.7 89.9 3.2 6.9

Total 1696 798 577 2261 276 534 2754 102 215

N 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Differentials in child malnutrition and

age of mother at first birth

HAZ WAZ WHZ

AORFB not stunted

severely

stunted

moderately

stunted

not

underweigt

severely

underweigh

t

moderately

underweigh

t not wasted

severely

wasted

moderately

wasted

12-18 48.8 30.4 20.8 70.3 10.2 19.5 89.5 3.5 7.0

19-25 56.3 25.4 18.4 73.5 9.2 17.3 89.4 3.3 7.3

26-32 64.5 19.6 16.0 81.0 5.2 13.8 90.9 3.0 6.1

33-39 78.3 4.3 17.4 100.0 0.0 0.0 100.0 0.0 0.0

total 1696 798 577 2261 276 534 2754 102 215

N 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Mother's work status and child

malnutrition

HAZ WAZ WHZ

work status not stunted

severely

stunted

moderately

stunted

not

underweigh

t

severely

underweigh

t

moderately

underweigh

t not wasted

severely

wasted

moderately

wasted

No 57.2 25.2 17.6 75.4 8.4 16.2 89.6 3.4 7.0

Yes 47.4 29.2 23.4 66.6 11.2 22.2 89.9 3.1 7.0

Total

N

1689

55.1

798

26.0

577

18.8

2254

73.6

276

9.0

534

17.4

2747

89.7

102

3.3

215

7.0

Child malnutrition and breast feeding

practices

HAZ WAZ WHZ

Breast

feeding not stunted

severely

stunted

moderately

stunted

not

underweig

ht

severely

underweig

ht

moderately

underweig

ht not wasted

severely

wasted

moderately

wasted

No 66.7 21.4 11.9 81.0 10.7 8.3 91.7 1.2 7.1

Yes 54.9 26.1 19.0 73.4 8.9 17.7 89.7 3.3 7.0

N 1692 797 577 2257 275 534 2751 100 215

Total 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Child malnutrition and place of delivery

HAZ WAZ WHZ

place of

delivery not stunted

severely

stunted

moderately

stunted

not

underweight

severely

underweight

moderately

underweight not wasted

severely

wasted

moderately

wasted

Home 44.7 34.8 20.4 68.9 12.0 19.2 88.9 3.9 7.2

hospital 64.2 18.4 17.4 77.7 6.4 15.9 90.4 2.8 6.8

Total 1696 798 577 2261 276 534 2754 102 215

N 55.2 26.0 18.8 73.6 9.0 17.4 89.7 3.3 7.0

Child vaccination and child

malnutrition

HAZ WAZ WHZ

vaccination not stunted

severely

stunted

moderately

stunted

not

underweight

severely

underweight

moderately

underweight not wasted

severely

wasted

moderately

wasted

No 44.8 43.3 11.9 65.5 13.9 20.6 84.5 6.7 8.7

Yes 50.9 28.8 20.3 71.7 10.1 18.3 90.1 3.0 6.9

total 1128 684 434 1594 236 416 2010 76 160

N 50.2 30.5 19.3 71.0 10.5 18.5 89.5 3.4 7.1

Framework of the study

BbF

AGe111

BWe21

DIe31

TWe41

AYSe51

BeF

NHV e6

1

1

MFP e71

DBF e81

CV e91

PCD e101

CF

WIe11

11

TFe121

De131

HTe14 1

MA

BMI e15

1

1

EM e161

MS e171

DME e181

MAAC e191

MA e201

MN

ST

e21

1

1

WS

e22

1

UW

e23

1

DA

A

e24

1

G

e25

1

HS

e26

1

M

e27

1

RSe291

LHWe301

1

Econometric Model

Measurment Model

Table 5.1 Regression Weights: (Default model)

Ent Mrt Bio Bev

SODW <--- Envt 1

TOTF <--- Envt 1.13*

LHW <--- Envt -0.14*

TOPR <--- Envt 0.63*

MWM <--- Envt 1.51*

MRM <--- Envt 1.34*

MFM <--- Envt 1.50*

WI <--- Envt -2.62*

MO <--- MAT 1

MAFB <--- MAT -2.5*

RDM <--- MAT 3.0*

TORP <--- MAT -0.6*

ME <--- MAT -12*

CS <--- Bio 1

SOC <--- Bio 0.48*

BW <--- Bio -873

PBI <--- Bio 0.58*

CT <--- Bio 0.04*

CAM <--- Bio -1.3*

DOB1 <--- Beh 1

POD <--- Beh .03*

ANV <--- Beh -1.3*

MFP <--- Beh 3.8*

CV <--- Beh -0.1*

Structural Regression Analysis

3........................................

2......................................

1.....

31

21

14321

EnvBeh

MartBio

EnvBehMartBioMN

Structural Equation Analysis

Variables

(Equation 1)

Malnutrition

(Equation 2)

Behavior

(Equation 3)

Biological

Env 0.406

(0.336)

0.065

(0.019)

Bio 0.596

(****)

Beh -0.715

(0.17)

Mart -8.86

(****)

-1.543

(****)

CMIN/d.f= 3.60, AGFI = 0.71 ,RMR = 0.076

*p-value are in parenthesis

Direct and Indirect Effects The total effect is combination of indirect and direct effect so the

total effect can be decomposed into two parts.

The direct effect of behavior and Environment on malnutrition is (-0.715) and (0.406) respectively in standardized. Environment also have indirect effect through behavior which can be measured as (-0.715*0.065= -0.046). So the total effect of environment on malnutrition is direct effect plus indirect effect (0.406) + (-0.046) which is 0.359.

The direct effect of maternal factors and biological factors on malnutrition is (-8.86) and (0.0596). Now the indirect effect of maternal factors on malnutrition is (0.596*-1.543) is -0.919.

The total effect of maternal factor on malnutrition is also sum of direct plus indirect effects (-8.406) + (-0.919).

Conclusion Prevalence of Malnutrition exists more in male children

children living in a poorest household are severely stunted.

children are severely stunted when the delivery took place at home.

The first objective was to find the inter relationship exists between variables

Maternal factors can affect through biological factors and then biological factors affect malnutrition so biological factor is playing a mediating role in child malnutrition.

Environmental factors can affect indirectly through behavior and then behavior affect child malnutrition but environmental factors are not directly affecting child malnutrition.

Policy Implications Easy access to health facility should be provided to rural areas.

Special awareness sessions should be conducted by health specialist to change in the behavior of mother towards child care, food and hygiene.

There is a need to focus the poor segments of the population by introducing interventions in already existing poverty alleviation programmes.

There is a need to provide health facilities to rural communities living in far flung areas specially those of immunization and of growth monitoring on growth charts especially of all children under 5 years of age

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Ajieroh, V. (2010). A quantitative analysis of determinants of child and maternal malnutrition in Nigeria. International Food Policy Research Institute (IFPRI).

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Arif, G. M. (2004). Child health and poverty in Pakistan. The Pakistan Development Review, 211-238.

Babatunde, R. O., Olagunju, F. I., Fakayode, S. B., & Sola-Ojo, F. E. (2011). Prevalence and determinants of malnutrition among under-five children of farming households in Kwara state, Nigeria. Journal of Agricultural Science,3(3), p173.

Babar, N. F., Muzaffar, R., Khan, M. A., &Imdad, S. (2010). Impact of socioeconomic factors on nutritional status in primary school children. J Ayub Med Coll Abbottabad, 22(4), 15-8.

Borgen, H. (2010). Child undernutrition in the Far-West Terai of Nepal.

Cheah, W. L., Muda, W. W., &Zamh, Z. H. (2010). A structural equation model of the det

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QUESTIONS