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埃塞俄比亚德布雷伯哈恩市青少年营养不良的双重负担及其相关因素:多项回归模型分析

Double burden of malnutrition and its associated factors among adolescents in Debre Berhan Regiopolitan City, Ethiopia: a multinomial regression model analysis.

作者信息

Getacher Lemma, Ademe Beyene Wondafrash, Belachew Tefera

机构信息

School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia.

Department of Nutrition and Dietetics, Faculty of Public Health, Institute of Health, Jimma University, Jimma, Ethiopia.

出版信息

Front Nutr. 2023 Jul 18;10:1187875. doi: 10.3389/fnut.2023.1187875. eCollection 2023.

DOI:10.3389/fnut.2023.1187875
PMID:37545577
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10400360/
Abstract

BACKGROUND

The double burden of malnutrition (DBM), contained both undernutrition and overnutrition, is a growing public health concern that presents a significant challenge to the food and nutrition policies of developing nations such as Ethiopia. However, the prevalence and contributing factors of DBM among adolescents in the study area have not been adequately investigated by Ethiopian researchers. Therefore, this study aims to determine the prevalence of DBM and contributing factors among secondary school students in Debre Berhan City, Ethiopia.

METHODS

A school-based cross-sectional study was conducted among 742 adolescents aged 10-19 years from October 13, 2022, to November 14, 2022, using a multi-stage sampling method. Data were collected using the online Kobo toolbox tool. A multinomial logistic regression model was used to analyze the data. The data were cleaned and analyzed in R software 4.2.2. Adolescents who had body mass index for age Z score (BAZ) < -2 SD, > +1 SD, and > +2 from the median value were considered thin, overweight, and obese, respectively.

RESULTS

The overall prevalence of DBM was 21.5% (14.8% thinness and 6.7% overweight/obesity). In the multivariable multinomial logistic regression analysis models factors such as age [AOR = 0.79, 95% CL: (0.67, 0.93)], sex [AOR = 3.86, 95% CL: (2.35, 6.32)], school type [AOR 5.03, 95% CL: (2.30, 10.99)], minimum dietary diversity score [AOR = 2.29, 95% CL: (1.27, 4.14)], frequency of meals [AOR = 2.09, 95% CL: (1.13, 3.89)], home gardening practice [AOR = 2.31, 95% CL: (1.44, 3.67)], history of illness [AOR = 0.57, 95% CL: (0.36, 0.93)], and knowledge of nutrition [AOR = 4.96, 95% CL: (1.61, 15.33)] were the significant predictors of either thinness or overweight/obesity (DBM).

CONCLUSION

More than one-fifth of adolescents were affected by DBM in the study area. This prevalence is higher compared with the national and regional prevalence that found to be a public health concern. Thus, interventions like double-duty interventions should consider the age, sex, school type, minimum dietary diversity score, frequency of meals, home gardening practice, history of illness, and nutritional knowledge of adolescents.

CLINICAL TRIAL REGISTRATION

clinicaltrial.gov, identifier NCT05574842.

摘要

背景

营养不良双重负担(DBM),包括营养不足和营养过剩,是一个日益严重的公共卫生问题,对埃塞俄比亚等发展中国家的食品和营养政策构成重大挑战。然而,埃塞俄比亚研究人员尚未充分调查研究地区青少年中DBM的患病率及其影响因素。因此,本研究旨在确定埃塞俄比亚德布雷伯汉市中学生中DBM的患病率及其影响因素。

方法

2022年10月13日至2022年11月14日,采用多阶段抽样方法,对742名10 - 19岁的青少年进行了一项基于学校的横断面研究。使用在线Kobo工具箱工具收集数据。采用多项逻辑回归模型分析数据。数据在R软件4.2.2中进行清理和分析。年龄别体重指数Z评分(BAZ)低于中位数-2标准差、高于+1标准差和高于+2标准差的青少年分别被视为消瘦、超重和肥胖。

结果

DBM的总体患病率为21.5%(消瘦率为14.8%,超重/肥胖率为6.7%)。在多变量多项逻辑回归分析模型中,年龄[AOR = 0.79,95%置信区间:(0.67,0.93)]、性别[AOR = 3.86,95%置信区间:(2.35,6.32)]、学校类型[AOR 5.03,95%置信区间:(2.30,10.99)]、最低饮食多样性评分[AOR = 2.29,95%置信区间:(1.27,4.14)]、进餐频率[AOR = 2.09,95%置信区间:(1.13,3.89)]、家庭园艺实践[AOR = 2.31,95%置信区间:(1.44,3.67)]、疾病史[AOR = 0.57,95%置信区间:(0.36,0.93)]以及营养知识[AOR = 4.96,95%置信区间:(1.61,15.33)]等因素是消瘦或超重/肥胖(DBM)的重要预测因素。

结论

研究地区超过五分之一的青少年受到DBM的影响。与已发现成为公共卫生问题的国家和地区患病率相比,这一患病率更高。因此,像双重任务干预等措施应考虑青少年的年龄、性别、学校类型、最低饮食多样性评分、进餐频率、家庭园艺实践、疾病史和营养知识。

临床试验注册

clinicaltrial.gov,标识符NCT05574842。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/96c003805b8b/fnut-10-1187875-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/5baa77ce11aa/fnut-10-1187875-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/2800fcc3a5db/fnut-10-1187875-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/13adb48d32cb/fnut-10-1187875-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/224969501b34/fnut-10-1187875-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/96c003805b8b/fnut-10-1187875-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/5baa77ce11aa/fnut-10-1187875-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/2800fcc3a5db/fnut-10-1187875-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/13adb48d32cb/fnut-10-1187875-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/224969501b34/fnut-10-1187875-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5891/10400360/96c003805b8b/fnut-10-1187875-g005.jpg

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