Fikrie Anteneh, Adula Berhanu, Beka Jitu, Hailu Dejene, Kitabo Cheru Atsmegiorgis, Spigt Mark
School of Public Health, Institute of Health, Bule Hora University, Bule Hora, Ethiopia.
School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia.
Health Serv Res Manag Epidemiol. 2024 Aug 16;11:23333928241271921. doi: 10.1177/23333928241271921. eCollection 2024 Jan-Dec.
Childhood stunting has a long-term impact on cognitive development and overall well-being. Understanding varying stunting profiles is crucial for targeted interventions and effective policy-making. Therefore, our study aimed to identify the determinants and stunting risk profiles among 2-year-old children in Ethiopia.
A cross-sectional study was conducted on 395 mother-child pairs attending selected public health centers for growth monitoring and promotion under 5 outpatient departments and immunization services. The data were collected by face-to-face interviews, with the anthropometric data collected using the procedure stipulated by the World Health Organization. The data were entered using Epi Data version 4.6 and exported to STATA 16 and Jamovi version 2.3.28 for analysis. Bayesian logistic regression analysis was conducted to identify potential factors of stunting. Likewise, lifecycle assessment analysis (LCA) was used to examine the heterogeneity of the magnitude of stunting.
The overall prevalence of stunting in children under 24 months was 47.34% (95% confidence interval (CI): 42.44-52.29%). The LCA identified 3 distinct risk profiles. The first profile is , which is labeled as low-risk, comprised 23.8% of the children, and had the lowest prevalence of stunting (23.4%). This group characterized as having a lower risk to stunting. The second profile is , which is identified as high-risk, comprised 47.1%, and had a high prevalence of stunting (66.7%), indicating a higher susceptibility to stunting compared to Class 1. The third profile is , which is categorized as mixed-risk and had a moderate stunting prevalence of 35.7%, indicating a complex interplay of factors contributing to stunting.
Our study identified 3 distinct risk profiles for stunting in young children. A substantial amount (almost half) is in the high-risk category, where stunting is far more common. The identification of stunting profiles necessitates considering heterogeneity in risk factors in interventions. Healthcare practitioners should screen, provide nutrition counseling, and promote breastfeeding. Policymakers should strengthen social safety nets and support primary education.
儿童发育迟缓对认知发展和整体健康具有长期影响。了解不同的发育迟缓情况对于有针对性的干预措施和有效的政策制定至关重要。因此，我们的研究旨在确定埃塞俄比亚2岁儿童发育迟缓的决定因素和风险状况。
对在5个门诊部门和免疫服务机构下选定的公共卫生中心参加生长监测和促进项目的395对母婴进行了横断面研究。数据通过面对面访谈收集，人体测量数据按照世界卫生组织规定的程序收集。数据使用Epi Data 4.6版本录入，并导出到STATA 16和Jamovi 2.3.28版本进行分析。进行贝叶斯逻辑回归分析以确定发育迟缓的潜在因素。同样，使用生命周期评估分析（LCA）来检查发育迟缓程度的异质性。
24个月以下儿童发育迟缓的总体患病率为47.34%（95%置信区间（CI）：42.44 - 52.29%）。LCA确定了3种不同的风险状况。第一种状况被标记为低风险，占儿童总数的23.8%，发育迟缓患病率最低（23.4%）。该组的特点是发育迟缓风险较低。第二种状况被确定为高风险，占47.1%，发育迟缓患病率较高（66.7%），表明与第一类相比，其发育迟缓易感性更高。第三种状况被归类为混合风险，发育迟缓患病率为35.7%，表明导致发育迟缓的因素相互作用复杂。
我们的研究确定了幼儿发育迟缓三种不同的风险状况。相当一部分（几乎一半）属于高风险类别，其中发育迟缓更为常见。确定发育迟缓状况需要在干预措施中考虑风险因素的异质性。医疗从业者应进行筛查、提供营养咨询并促进母乳喂养。政策制定者应加强社会安全网并支持小学教育。
