Stunting at birth: linear growth failure at an early age among newborns in Hawassa city public health hospitals, Sidama region, Ethiopia: a facility-based cross-sectional study.
Haileyesus EjiguZelalem TafesePublished in: Journal of nutritional science (2023)
On a global basis, 144 million people are stunted, and in Ethiopia, it remains a major public health problem. A limited number of studies have been conducted at the national level and in the study area to generate information on stunting at birth. The present study investigated the magnitude and predictors of stunting among newborns delivered at the Public Hospitals of Hawassa City, Ethiopia. A facility-based cross-sectional study was conducted between August and September 2021 among mothers and newborns ( N 371). Data were collected through face-to-face interviews with the mother in a waiting room after the delivery of the child at the hospital. Newborn length and weight were measured and converted to length-for-age Z -score using WHO standards. The prevalence of stunting at birth (35⋅6 %) and low birth weight (24⋅6 %) were high. In the adjusted model, factors significantly associated with stunting were birth interval <2 years, low birth weight, inadequate dietary diversity and food insecurity ( P < 0⋅01) mid-upper arm circumference (MUAC) of mother <23 cm ( P < 0⋅05). The high magnitude of stunting and low birth weight calls all stakeholders and nutrition actors to work on preventing maternal undernutrition and improving their dietary practice through nutrition education. It is also recommended to mitigate food insecurity with evidence-based interventions using a combination of measures. Additionally improving maternal health services including family spacing was recommended to reduce stunting and low birth weight among newborns in the study area.
Keyphrases
- low birth weight
- preterm infants
- human milk
- preterm birth
- public health
- gestational age
- healthcare
- birth weight
- body mass index
- physical activity
- primary care
- mental health
- pregnant women
- pregnancy outcomes
- quality improvement
- high resolution
- risk factors
- weight loss
- machine learning
- mass spectrometry
- drug induced
- artificial intelligence
- deep learning