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巴西圣保罗棚户区学龄前儿童蛋白质-能量营养不良的风险因素。

Risk factors for protein-energy malnutrition in pre-school shantytown children in São Paulo, Brazil.

作者信息

Ferrari A A, Solymos G M, Castillo R M, Sigulem D M

机构信息

Departament of Pediatrics and Psychiatry, Universidade Federal de São Paulo, EPM, Brazil.

出版信息

Sao Paulo Med J. 1998 Mar-Apr;116(2):1654-60. doi: 10.1590/s1516-31801998000200003.

Abstract

OBJECTIVES

To investigate the health and nutritional conditions of people living in a shantytown in the city of São Paulo in order to identify risk factors for infant malnutrition.

DESIGN

A retrospective cohort study.

PARTICIPANTS

Children living in a shantytown was conducted among children less than 72 months of age.

METHODS

Home visits were made and information was collected regarding the risk factors for malnutrition.

RESULTS

The prevalence of chronic malnutrition was 41.6% according to Gomez, 36.6% according to Waterlow, and 17.6% according to WHO. Risk factors for malnutrition, according to the weight-for-age index, included birthweight, presence of upper respiratory tract infections, number of pregnancies, number of births, maternal body mass index, birthplace of father, and home building material; according to the weight-for-height index, they included birthweight and maternal age at the time of birth; and according to the height-for-age index, they included the number of prenatal medical visits, birthweight, maternal height, maternal body mass index, father's employment being unregistered, and maternal birthplace. An instrument for identifying children at risk of malnutrition was devised from these major risk factors for future malnutrition, which may then be applied to newly-born children.

摘要

目的

调查圣保罗市一个棚户区居民的健康和营养状况,以确定婴儿营养不良的风险因素。

设计

一项回顾性队列研究。

参与者

对居住在棚户区的72个月以下儿童进行研究。

方法

进行家访并收集有关营养不良风险因素的信息。

结果

根据戈麦斯标准,慢性营养不良患病率为41.6%;根据沃特洛标准为36.6%;根据世界卫生组织标准为17.6%。根据年龄别体重指数,营养不良的风险因素包括出生体重、上呼吸道感染情况、怀孕次数、生育次数、母亲体重指数、父亲出生地和房屋建筑材料;根据身高别体重指数,风险因素包括出生体重和母亲分娩时的年龄;根据年龄别身高指数,风险因素包括产前检查次数、出生体重、母亲身高、母亲体重指数、父亲未登记就业情况和母亲出生地。根据这些未来营养不良的主要风险因素设计了一种识别营养不良风险儿童的工具,该工具随后可应用于新生儿。

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