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Child malnutrition is one of the most serious public
health problems in the developing world including
Ethiopia. National survey results often do not show
underlying variation of these nutritional indicators
by localities.
The main objective of this study was therefore to
assess the prevalence of child malnutrition and
identify these various causes in urban and rural
settings of Gimbi District, Western Ethiopia.
A comparative cross sectional study was conducted in
the study area on children of age 6-59 months in
2007. A multistage systematic sampling method was
employed to collect quantitative data using
structured questionnaire and anthropometric
measurements. The study variables include;
environmental, socio-economic and demographic, child
and maternal characteristics.
Data were processed using Epi-info soft ware and
analyzed using SPSS program. NCHS reference
population standard of WHO used to convert height
and weight measurements into Z-scores. Bivariate and
multivariate logistic regression analysis was used
to identify determinants of nutritional status and
to account for potential confounding factors.
health problems in the developing world including
Ethiopia. National survey results often do not show
underlying variation of these nutritional indicators
by localities.
The main objective of this study was therefore to
assess the prevalence of child malnutrition and
identify these various causes in urban and rural
settings of Gimbi District, Western Ethiopia.
A comparative cross sectional study was conducted in
the study area on children of age 6-59 months in
2007. A multistage systematic sampling method was
employed to collect quantitative data using
structured questionnaire and anthropometric
measurements. The study variables include;
environmental, socio-economic and demographic, child
and maternal characteristics.
Data were processed using Epi-info soft ware and
analyzed using SPSS program. NCHS reference
population standard of WHO used to convert height
and weight measurements into Z-scores. Bivariate and
multivariate logistic regression analysis was used
to identify determinants of nutritional status and
to account for potential confounding factors.