估计亚马逊农村和城市儿童营养不良的双重负担,使用贝叶斯潜伏模型
Jesem Douglas Yamall Orellana1, Luke Parry2,3, Francine Silva Dos Santos4
1Instituto Leônidas & Maria Deane (ILMD/Fiocruz Amazônia), Manaus, Amazonas, Brazil.
Frontiers in public health
|March 27, 2025
概括
营养不良的双重负担 (DBM) 影响着巴西亚马逊地区的儿童,尤特亚的农村地区的患病率更高. 贝叶斯模型准确地估计了偏远人口中的DBM,改善了公共卫生监测.
科学领域:
- 公共卫生 公共卫生
- 营养流行病学 营养流行病学
- 贝叶斯统计学贝叶斯统计学
背景情况:
- 营养不良的双重负担 (DBM),以同一个人内营养不良和营养过剩的共存为特征,是一个重要的公共卫生问题,特别是在低收入和中等收入国家 (LMICs).
- DBM与不良健康结果有关,包括非传染性疾病风险增加,分娩并发症以及与成人肥胖相关的医疗保健成本增加.
- 在小,偏远的人群中准确评估低患病率结果对传统方法构成统计挑战.
研究的目的:
- 在巴西亚马逊地区的小,偏远群体中,估计DBM在个体水平上的患病率.
- 应用贝叶斯潜伏模型,以更准确地估计难以到达社区的DBM患病率.
- 为监测和解决脆弱人群中DBM的公共卫生策略提供信息.
主要方法:
- 在巴西亚马逊地区的四个依赖河流的市镇中,在6-59个月的儿童中进行了横截面调查.
- DBM被定义为一个孩子同时出现衰减和超重的情况.
- 贝叶斯潜伏模型被用来估计DBM的流行率及其可信区间 (CI),超过概率用于人口水平量化.
主要成果:
- 与马乌斯和卡帕兰加相比,Jutai (3.3%;CI:1.5%6.7%) 的DBM农村患病率显著更高.
- 农村DBM患病率超过1%的概率在Jutai (99.7%) 和Ipixuna (63.2%) 非常高.
- 对于DBM流行率的超值概率在城市亚群中差异很大,从Maues的6.7%到Caapiranga的41.2%不等.
结论:
- 这些发现强调了在巴西亚马逊地区的弱势和边缘化人口中解决DBM的关键需求,那里的营养状况经常受到损害.
- 采用的贝叶斯分析方法可以在具有挑战性的环境中更精确地估计低患病率的健康结果.
- 这种方法可以加强难以接触的人群中DBM的监测,指导有针对性的公共卫生干预.
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