The nutrition transition is underway in India
The nutrition transition is underway in India
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DOI：
10.1093/jn/131.10.2692
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发表时间：
2001-10-01
影响因子：
4.2
通讯作者：
Bentley, ME
中科院分区：
文献类型：
--
作者：
Griffiths, PL;Bentley, ME
印度的营养研究此前主要关注与营养缺乏和高感染率相关的严重营养不良问题。然而，1998/99 年全国家庭健康调查 (NFHS 2) 的最新数据表明，相当大比例的印度妇女体重超标，且营养不良率很高。本文探讨了印度安得拉邦农村和城市社区妇女正在出现的营养转型。 NFHS 2 提供了具有全国代表性的女性体重和身高数据。在本文中，我们研究了安得拉邦的代表性数据（n = 4032 名女性）。对数据进行逻辑回归分析，以确定超重和瘦弱的社会经济、区域和人口决定因素。女性面临的主要营养问题仍然是营养不良，37%的女性体重指数较低[(BMI) < 18.5 kg/m(2)]；其中 8% 的女性严重营养不良（BMI < 16 kg/m(2)）。然而，12% 的女性可归类为超重（BMI > 25 kg/m(2)），2% 为肥胖（BMI > 30 kg/m(2)）。此外，在 4% 的样本居住的州大城市中，37% 的女性超重或肥胖，而在 74% 的样本居住的农村地区，43% 的女性体重指数较低。来自社会经济地位较低群体的女性的体重指数也明显较低。逻辑回归模型的结果表明，与居住地相比，社会经济地位是体重超重和体重不足的更重要的预测因素。
Nutrition research in India has previously focused on the serious problem of undernutrition related to nutrient deficit and high rates of infection. Recent data from the National Family Health Survey 1998/99 (NFHS 2), however, identified a significant proportion of Indian women as overweight, coexisting with high rates of malnutrition. This paper examines the emerging nutrition transition for women living in rural and urban communities of Andhra Pradesh, India. NFHS 2 provides nationally representative data on women's weight and height. In this paper, we examine representative data from the state of Andhra Pradesh (n = 4032 women). Logistic regression analyses are applied to the data to identify socioeconomic, regional and demographic determinants of overweight and thinness. The major nutrition problem facing women continues to be undernutrition, with 37% having a low body mass index [(BMI) < 18.5 kg/m(2)]; 8% of these women are severely malnourished (BMI < 16 kg/m(2)). However, 12% of the women can be classified as overweight (BMI > 25 kg/m(2)) and 2% are obese (BMI > 30 kg/m(2)). Furthermore, in the large cities of the state in which 4% of the sample live, 37% of women are overweight or obese, whereas in the rural areas in which 74% reside, 43% have a low BMI. Women from lower socioeconomic groups are also significantly more likely to have a low BMI. Findings from the logistic regression models reveal socioeconomic status to be a more important predictor of both over- and underweight than location of residence.