Structural Equation Model for determinants of child malnutrition in Pakistan

Transcription

1 Structural Equation Model for determinants of child malnutrition in Pakistan Shahnila Naz M Phil Health Economics Supervisors Dr Ahsan ul Haq Dr Khalid Mehmood PIDE Dated: 9 th October, 205

2 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.

3 Global Situation of Child Malnutrition More than 50 million of children under five of age are malnourished in the world. 54% of which are in the developing countries. NNS 20. Pakistan, India and Bangladesh have higher rate of child malnutrition than other countries of sub Saharan Africa

4 Situation of child malnutrition in Pakistan 43.7% children under five were stunted rural areas stunting 46 percent - urban areas 39.6% Wasting 5.% in Pakistan urban areas 2.7%- Rural areas 6.%. 3.5 % under weight rural areas 3.% Malnutrition rate was higher in rural areas then urban areas (NNS, 20).

5 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

6 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.

7 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.

8 Data Pakistan Demographic and health survey, 203.(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.

9 Methodology Measure of prevalence of child malnutrition. Weight for age(waz) 2. Height for age(haz) 3. Weight for height(whz) Unit of Analysis: Children under five age (0-59 months).

10 Differentials in Child malnutrition Dependent variables:. stunting 2. wasting 3.underweight Independent Variables. Environmental Factors 2. Maternal Factors 3. Biological Factors 4. Behavior Factors

11 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.

12 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.

13 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.

14 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.

15 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.

16 Framework of the study BbF AG e BW e2 DI e3 TW e4 AYS e5 BeF NHV e6 MFP e7 DBF e8 CV e9 PCD e0 CF WI e TF e2 D e3 HT e4 MA BMI e5 EM e6 MS e7 DME e8 MAAC e9 MA e20 MN ST e2 WS e22 UW e23 DA A e24 G e25 HS e26 M e27 RS e29 LHW e30

17 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.

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19 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 N total

20 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 twin total N

21 Differential in child malnutrition and gender of the child Gender of child HAZ WAZ WHZ severely moderately not severely moderately severely moderately not stuntd stunted stunted underweight underweight underweight not wasted wasted wasted Male Female N Total

22 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 Poorer Middle Richer Richest total N

23 Differentials in child malnutrition and presence of LHW LHW HAZ WAZ WHZ severely moderately not severely moderately severely moderately not stunted stunted stunted underweight underweight underweight not wasted wasted wasted No Yes Total N

24 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 total N

25 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 Yes Total N

26 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 Yes N Total

27 Child malnutrition and place of delivery HAZ WAZ WHZ place of severely moderately not severely moderately severely moderately delivery not stunted stunted stunted underweight underweight underweight not wasted wasted wasted Home hospital Total N

28 Child vaccination and child malnutrition HAZ WAZ WHZ severely moderately not severely moderately severely moderately vaccination not stunted stunted stunted underweight underweight underweight not wasted wasted wasted No Yes total N

29 Framework of the study BbF AG e BW e2 DI e3 TW e4 AYS e5 BeF NHV e6 MFP e7 DBF e8 CV e9 PCD e0 CF WI e TF e2 D e3 HT e4 MA BMI e5 EM e6 MS e7 DME e8 MAAC e9 MA e20 MN ST e2 WS e22 UW e23 DA A e24 G e25 HS e26 M e27 RS e29 LHW e30

30 Econometric Model

31 Table 5. Regression Weights: (Default model) Ent Mrt Bio Bev SODW <--- Envt TOTF <--- Envt.3* LHW <--- Envt -0.4* Measurment Model TOPR <--- Envt 0.63* MWM <--- Envt.5* MRM <--- Envt.34* MFM <--- Envt.50* WI <--- Envt -2.62* MO <--- MAT MAFB <--- MAT -2.5* RDM <--- MAT 3.0* TORP <--- MAT -0.6* ME <--- MAT -2* CS <--- Bio SOC <--- Bio 0.48* BW <--- Bio -873 PBI <--- Bio 0.58* CT <--- Bio 0.04* CAM <--- Bio -.3* DOB <--- Beh POD <--- Beh.03* ANV <--- Beh -.3* MFP <--- Beh 3.8* CV <--- Beh -0.*

32 Structural Regression Analysis MN Bio Beh Env Mart Beh Env Bio Mart

33 Structural Equation Analysis (Equation ) (Equation 2) (Equation 3) Variables Malnutrition Behavior Biological Env (0.336) (0.09) Bio (****) Beh (0.7) Mart (****) (****) CMIN/d.f= 3.60, AGFI = 0.7,RMR = *p-value are in parenthesis

34 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 (-0.75) and (0.406) respectively in standardized. Environment also have indirect effect through behavior which can be measured as (-0.75*0.065= ). So the total effect of environment on malnutrition is direct effect plus indirect effect (0.406) + (-0.046) which is 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*-.543) is The total effect of maternal factor on malnutrition is also sum of direct plus indirect effects (-8.406) + (-0.99).

35 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.

36 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

37 References Ahmad M. (200).Poverty across the agro-ecological zones in rural Pakistan. Ajieroh, V. (200). 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. (202). Child Malnutrition and Poverty: The Case of Pakistan (No. 202: 03). Pakistan Institute of Development Economics. Arif, G. M. (2004). Child health and poverty in Pakistan. The Pakistan Development Review, Babatunde, R. O., Olagunju, F. I., Fakayode, S. B., & Sola-Ojo, F. E. (20). Prevalence and determinants of malnutrition among under-five children of farming households in Kwara state, Nigeria. Journal of Agricultural Science,3(3), p73. Babar, N. F., Muzaffar, R., Khan, M. A., &Imdad, S. (200). Impact of socioeconomic factors on nutritional status in primary school children. J Ayub Med Coll Abbottabad, 22(4), 5-8. Borgen, H. (200). Child undernutrition in the Far-West Terai of Nepal. Cheah, W. L., Muda, W. W., &Zamh, Z. H. (200). A structural equation model of the det Family Dynamics, Lifestyles and Nutrition Study ( ). Malaysian Journal of Nutrition, 8(), 3-3. DHS, M. (202).Demographic and Health Survey.

38 QUESTIONS