Correlation Between Maternal Smoking And Overweight Among School Aged Children

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The study by Mamudu, Wang, and Wu (2013) had two purposes. The first objective was to examine associations between maternal smoking and overweight among school-aged children. The second objective was to identify mothers and offspring characteristics that affect children’s weight. It is expected that children of mothers who smoked 1 year before birth were likely to be overweight during school ages than those of mothers who never smoked.
Participants included a total of 8,986 mothers that gave birth in the research hospitals during sampling periods in 1991, and 5,416 (60%) agreed to be telephoned in 2 weeks. The exclusion criteria included mothers younger than 18, those that did not speak English, did not agree with the 2 week
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They determined the BMI percentile of the children, which was calculated according to the U.S. Centers for Disease Control and Prevention’s (CDC) age and gender specific growth. A BMI less than the 85th percentile were normal weight and a BMI greater than the 85th percentile was overweight. The height and weight of the children were measured were recorded twice each time from birth until the children were in first grade. (Wang et al., 2013). Mother’s smoking status within1 year before the birth (1990) of the target child was assessed retrospectively with ‘The year before my child was born’ question. This consisted of two items measuring the mother’s smoking behavior shortly before and during pregnancy. The small sample size of 1041 was divided into two categories, never smoking and ever smoking within 1 year before the birth of the target. The main covariates were collected at the 1 month of child age interview. These included maternal age, education, living status, poverty, and breast feeding status. Other covariates included offspring characteristics, such as sex, ethnicity, and birth weight. Child sex and ethnicity were recorded at 1 month after birth. The sample sizes for ethnicity were not large enough to allow separate subgroups so they were categorized into Whites, Blacks, or Others. The data was analyzed using univariate, bivariate, and multivariate statistics. (Wang et al., 2013).
Results and
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