Prevalence and associated factors of undernutrition among pregnant women visiting ANC clinics in Silte zone, Southern Ethiopia

Abstract

Background Maternal undernutrition is highly prevalent in underdeveloped countries. Hence, this study was intended to determine the prevalence and associated factors of undernutrition among pregnant women visiting ANC clinics in Silte Zone. Method Facility-based cross-sectional study was conducted from July to January 2019. Systematic random sampling technique was used to select 422 study participants from 11 randomly selected health facilities. Data was collected by using a structured-interviewer administered questionnaire. Mid upper arm circumference (MUAC) was measured by standard non stretchable MUAC tape. Data was entered into a computer using Epi data 3.1 and edited, cleaned, and analyzed using SPSS version 20. Both bivariate and multiple logistic regression analyses were employed to identify factors associated with maternal undernutrition. Result In this study, the overall prevalence of undernutrition among study subjects was 21.8%. Age greater than 31 years of women (AOR = 0.15; 95% CI: 0.03, 0.93), Birth intervals > 2 years (AOR = 0.18; 95% CI: 0.04, 0.76), good nutritional knowledge (AOR = 0.34; 95% CI: 0.17, 0.67), and having no dietary change as a result of current pregnancy AOR = 6.02; 95% CI: 2.99, 12.14) were significantly associated with undernutrition. Conclusions The prevalence of undernutrition among pregnant women was 21.8%. Current estimate is lower than previously reported in the study area but higher than reported in developed country. Age of women, Birth intervals, and Dietary change as a result of current pregnancy and Nutrition knowledge were important risk factors/ predictors of undernutrition (MUAC < 23 cm). Interventions targeting maternal nutrition education and child spacing with giving special emphasis to adolescent pregnant women are recommended.

Undernutrition and its associated factors among pregnant women at the public hospitals of Bench-Sheko and Kaffa zone, southwest Ethiopia

“…According to this study, the mother with the age groups of 16–24 years was 3.9 times more likely undernutrition than ≥35 years old. This study finding is consistent with the previous study findings [ 14 , 41 , 42 ]. This finding might be due to the increased nutritional needs of younger pregnant women for their growing body, which increases their vulnerability for undernutrition than their counterparts.…”

“…For example, the mother who had poor dietary knowledge was 3.25 times more likely undernourished than good dietary knowledge. This study finding is aligned with other study findings [ 14 , 28 ]. Therefore, the possible explanations might be that poor dietary knowledge about nutrition practice during pregnancy could result in inadequate dietary intake, which is one of the possible factors for the high prevalence of undernutrition during pregnancy.…”

“…This alignment might be because the measurement cut-off points used were similar to this study's cut-off points for undernutrition, which all used <23 cm, and the socio-economic status of the study participants. However, much higher than the study reported from Silte zone 21.8% [ 14 ] and Dessie town 19.5% [ 28 ] of Ethiopia. The possible explanation for this discrepancy might be the socioeconomic and cultural variation.…”

“…In this study, the sample size was calculated using a single population proportion formula with the following assumptions in mind: The prevalence of undernutrition (P) among pregnant women from the Silte zone study finding, 21.8% [ 14 ], 5% marginal error(d), 95% confidence level (Zα/2 = 1.96), none responses rate of 10% and the design effect of 2. As a result, the sample size was calculated as follows: n= ((1.96) 2 ∗0.218 (1-0.218))/(0.05) 2 = ∼ 262.…”

“…For further analysis, the data was exported to the Statistical Package for Social Science (SPSS) version 21 software. Undernutrition was classified and coded as 1 for "yes" if the MUAC was 23 cm and 0 for "no" if the MUAC was 23 cm [ [14] , [21] , [22] , [23] , [24] , [25] ]. The household food insecurity access score was calculated for each household by summing up the nine food insecurity frequencies in the previous 30 days.…”