Height was measured to the nearest 01 cm in standing

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Height was measured to the nearest 0.1 cm in standing position at Frankfurt plane with the occipi- tal, shoulder and the buttock touches the vertical stand using a stadiometer seca (Germany). Weight was measured to the nearest 0.1 kg using electronic weighing scale with wearing light clothes and with no shoes. The Percentile values for BMI-for-age (BAZ) of children were generated from WHO AnthroPlus ver- sion 1.0.3 software [30]. Overweight/obesity in school aged children were con- sidered as dependant variable. BMI for age greater than or equal to 85th but less than 95th percentile was con- sidered as overweight, while obesity was considered when BMI for age was greater than or equal to the 95th percentile [31]. Mekonnen et al. Italian Journal of Pediatrics (2018) 44:17 Page 2 of 8
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Data quality control For data quality control a pre-test on 5% of the samples was performed and regular supervision during data col- lection was also carried out. The completeness of the questionnaire was checked before data entry too. Data processing and analysis The data were first coded and entered using EpiData statistical software version 3.1 and then exported into SPSS statistical software version 20 for data management and analysis. Descriptive statistical analysis, such as sim- ple frequencies and measures of central tendency was used to describe the characteristics of participants. Mag- nitude of overweight/obesity were determined by export- ing age, sex, height, weight of the child to WHO AnthroPlus from SPSS software. Household wealth sta- tus was estimated using Household wealth index (HWI) constructed by using Principal Component Analysis (PCA) and the component score coefficient matrix was computed based on household assets then the house- holds were categorized into tertiles according to the HWI as poor, middle income, high income. Crud odds ratio with 95% CI was used to see the asso- ciation between each independent variable and the out- come variable by using Binary logistic regression. Those associations with p -value < 0.2 were entered into the multivariable logistic regression model to control the ef- fect of confounding. Variables with a p-value less than 0.05 were taken as statistically significant associated fac- tors. The adjusted odds ratio with 95% confidence inter- val was presented to show the strength and precision of the association. Results Socio-demographic and economic characteristics A total of 616 children participated, with a response rate of 97.2%. Of the total, 483 (78.4%) were attending public schools. 339 (55%) children were female. The mean age of the children was 9.7 ± 1.4 years. 519(84.3%) mothers were married and 475(88%) attended formal education. 321 (52.1%) and 224(36.4%) of fathers and mothers were self employed, respectively (Table 1). Type of school the students enrolled by the household s wealth status A substantial variation in the type of school enrollment was observed in the lowest and highest household s wealth status category. Accordingly, about 41.4 and 4.51% of children from the lowest wealth status attended public and private schools, respectively. On the other hand,
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