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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
References Ahmad M. (2001).Poverty across the agro-ecological zones in rural Pakistan.
Ajieroh, V. (2010). A quantitative analysis of determinants of child and maternal malnutrition in Nigeria. International Food Policy Research Institute (IFPRI).
Arif, G. M., Shujaat F., Saman N., &Marium S. (2012). Child Malnutrition and Poverty: The Case of Pakistan (No. 2012: 03). Pakistan Institute of Development Economics.
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
Family Dynamics, Lifestyles and Nutrition Study (1997-2001). Malaysian Journal of Nutrition, 8(1), 13-31.
DHS, M. (2012).Demographic and Health Survey.
QUESTIONS