埃塞俄比亚两岁以下儿童发育迟缓决定因素分析及发育迟缓风险特征识别。潜类分析。

IF 1.5 Q3 HEALTH POLICY & SERVICES
Health Services Research and Managerial Epidemiology Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.1177/23333928241271921
Anteneh Fikrie, Berhanu Adula, Jitu Beka, Dejene Hailu, Cheru Atsmegiorgis Kitabo, Mark Spigt
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引用次数: 0

摘要

背景:儿童发育迟缓会对认知发展和整体福祉产生长期影响。了解不同的发育迟缓状况对于采取有针对性的干预措施和制定有效的政策至关重要。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特征:我们对 395 对母子进行了横断面研究,这些母子都曾到选定的公共卫生中心接受生长监测和促进 5 岁以下儿童门诊和免疫接种服务。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 版输入,并导出到 STATA 16 和 Jamovi 2.3.28 版进行分析。贝叶斯逻辑回归分析用于确定导致发育迟缓的潜在因素。同样,生命周期评估分析(LCA)也用于研究发育迟缓程度的异质性:结果:24 个月以下儿童发育迟缓的总发生率为 47.34%(95% 置信区间:42.44-52.29%)。LCA 确定了三种不同的风险状况。第一类被称为低风险,占儿童总数的 23.8%,发育迟缓发生率最低(23.4%)。这组儿童发育迟缓的风险较低。第二类是高风险的第 2 类,占 47.1%,发育迟缓发生率高(66.7%),表明与第 1 类相比,发育迟缓的易感性更高。第三种情况是第 3 类,被归类为混合风险,发育迟缓发生率为 35.7%,属于中等水平,表明导致发育迟缓的各种因素之间存在复杂的相互作用:我们的研究发现了幼儿发育迟缓的三种不同风险状况。相当一部分(近一半)属于高风险类别,发育迟缓在这一类别中更为常见。要确定发育迟缓的特征,就必须在干预措施中考虑风险因素的异质性。医疗保健从业人员应进行筛查、提供营养咨询并推广母乳喂养。政策制定者应加强社会安全网并支持初等教育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Determinants of Stunting and Identifications of Stunting Risk Profiles Among Under 2-Year-Old Children in Ethiopia. A Latent Class Analysis.

Background: 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.

Methods and materials: 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.

Results: 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 Class 1, 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 Class 2, 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 Class 3, which is categorized as mixed-risk and had a moderate stunting prevalence of 35.7%, indicating a complex interplay of factors contributing to stunting.

Conclusion: 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.

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来源期刊
CiteScore
1.60
自引率
6.20%
发文量
32
审稿时长
12 weeks
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