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BACKGROUND: Low Birth Weight (LBW) babies account for nearly 80% of neonatal deaths globally. In Ethiopia, only 5% of them are weighed at birth. This study analyzes the prevalence and key proximate determinants of reported infant size, and its validity to use as a proxy indicator for low birth weight inthe Ethiopian context.
Data source: This study used data from the third round Ethiopian Demographic and Health Survey (EDHS) conducted in 2011. The survey was conducted in all regions of the country with representative samples. The details of the sample design, including the sampling framework and sample implementation and response rates are provided in the respective EDHS reports (www.measuredhs.com). In the DHS, there are three core questionnaires (Household, Women and a Male questionnaires) and nine recode files. This way of recoding is done because of two outstanding reasons; to define a standardized file that would make cross-country analysis easier and to compare data with the World Fertility Surveys (WFS) to study trends. The recode files have five main and two additional digits. The first two digits of the file name correspond to the country code (e.g. ET for Ethiopia). The next two digits identify the unit of analysis ( IR-Women, KR-Children, …etc). The fourth digit identifies the DHS phase. The fifth digit identifies the data release number and the last two digits identify whether it is a rectangular (RT) or flat (FL) file; for the hierarchical file they are left blank. In the current analyses, we used ETKR61FL.SAV recode data files, whereby ET stands for Ethiopia, KR for Kids (children), 6 for the year 2011, FL for flat file) for the analyses of the prevalence and proximate determinants of LBW. This means, we used the 2011 file of children under five to describe the validity, prevalence and key proximate determinants of small size babies in Ethiopia. Study variables: The dependent variable is prevalence of small size babies at birth. This depends on subjective evaluation of the baby’s size at birth by the mother. These potential predicting variables are categorized into four groups: socio-demographic, household, child characteristics and maternal obstetric/reproductive characteristics. Socio-demographic variables: These groups of indicators consist of maternal socio-demographic characteristics. Among these, maternal age, educational status, literacy level, region, urban/rural residence, wealth status by quintiles are included for analyses. Household variables: In this group, we included presence or absence of key household goods like electricity, radio, refrigerator, telephone and television. Other variables included in this category are relationship of respondents to the household, access to improved toilet facilities and access to safe water supply. Child characteristics: In this category, we selected child health and related characteristics such as child age, sex, birth weight, level of anemia and birth interval. We also included whether the child is alive or not during the interview and singleton versus twin pregnancy. Maternal reproductive and obstetric variables: In this category, the following variables were included: level of maternal anemia, number of births last year/last five years, knowledge about the reproductive system indicated by awareness of the ovulary cycle. In addtion, other variables like number of living children, history of abortion, history of caesarean delivery, use of alcohol, cigarette/suret and addictive substances during pregnancy were also included. Data analysis: This study employed a three-stage analysis. Uni-variate and bi-variate analyses were made to calculate validity, prevalence and associations between variables using chi-square, ANOVA and student t-test. Multivariate logistic regression analysis was used for the identification of final predicting variables for small size babies in Ethiopia. STATA 10 and SPSS version 20 softwares were used in both stages of the analysis. Data quality assessment: The data quality assessment report highlighted its findings on misreporting, omission, and digit preference, which are common data quality problems observed in surveys and censuses in developing countries. Ethical issues: This is a secondary data analysiss requiring no direct data collection from human subjects. However, request to access datasets from measure DHS website was made, and the websites had allowed the same before analysis was made.
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