Back

Magnitude of wasting and its predictors among under-five children in Bakadawula Ari district, Southern Ethiopia

Tesfaye, G. A.; Wana, E. W.; Gebru, M. G.

2023-01-19 nutrition
10.1101/2023.01.18.23284726 medRxiv
Show abstract

BackgroundGlobally, wasting threatens the lives of 50 million children under-five. In Ethiopia, wasting is not decreasing at the intended rate, but the reason remains unclear. Moreover, Bakadawula Ari district lacks scientific information regarding wasting among children. ObjectiveThis study was conducted to determine the magnitude of wasting and its predictors among under-five children in the district. MethodsA community-based cross-sectional study was conducted from January to March 2022. A multistage sampling technique was used to select 421 children. The data were entered and analyzed by SPSS 26 (Statistical Package for the Social Sciences version 26). Logistic regression analyses were used and presented with crude odds ratio (COR) and adjusted odds ratio (AOR) with their 95% confidence intervals (CI). ResultsThe prevalence of wasting among children in the study area was 22.6% (95% CI: 18.5-26.8). Fathers with primary education (AOR= 4.48; 95% CI: 1.93-10.39), households with improper solid waste-disposal (AOR= 2.54; 95% CI: 1.11-5.82), not usually sleeping under insecticide-treated bed net (ITN) (AOR=1.79; 95% CI: 1.01-3.19), unacceptable children dietary diversity score (DDS) (AOR= 2.56; 95% CI: 1.28-5.14) and unacceptable household DDS (AOR= 2.26; 95% CI: 1.02-5.00) were predictors of wasting. ConclusionsThe prevalence of wasting among children was critically high. Upgrading educational status of fathers, encouraging safe solid waste disposal, ensuring consistent use of ITN, and improving both children and household DDS should be given a due emphasis to reduce wasting in the study area.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.