International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, 400088, India.
Department of Fertility Studies, International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, 400088, India.
BMC Public Health. 2018 Aug 17;18(1):1027. doi: 10.1186/s12889-018-5873-z.
Despite sustained economic growth and reduction in money metric poverty in last two decades, prevalence of malnutrition remained high in India. During 1992-2016, the prevalence of underweight among children had declined from 53% to 36%, stunting had declined from 52% to 38% while that of wasting had increased from 17% to 21% in India. The national average in the level of malnutrition conceals large variation across districts of India. Using data from the recent round of National Family Health Survey (NFHS), 2015-16 this paper examined the spatial heterogeneity and meso-scale correlates of child malnutrition across 640 districts of India.
Moran's I statistics and bivariate LISA maps were used to understand spatial dependence and clustering of child malnutrition. Multiple regression, spatial lag and error models were used to examine the correlates of malnutrition. Poverty, body mass index (BMI) of mother, breastfeeding practices, full immunization, institutional births, improved sanitation and electrification in the household were used as meso scale correlates of malnutrition.
The univariate Moran's I statistics was 0.65, 0.51 and 0.74 for stunting, wasting and underweight respectively suggesting spatial heterogeneity of malnutrition in India. Bivariate Moran's I statistics of stunting with BMI of mother was 0.52, 0.46 with poverty and - 0.52 with sanitation. The pattern was similar with respect to wasting and underweight suggesting spatial clustering of malnutrition against the meso scale correlates in the geographical hotspots of India. Results of spatial error model suggested that the coefficient of BMI of mother and poverty of household were strong and significant predictors of stunting, wasting and underweight. The coefficient of BMI in spatial error model was largest found for underweight (β = 0.38, 95% CI: 0.29-0.48) followed by stunting (β = 0.23, 95% CI: 0.14-0.33) and wasting (β = 0.11, 95% CI: 0.01-0.22). Women's educational attainment and breastfeeding practices were also found significant for stunting and underweight.
Malnutrition across the districts of India is spatially clustered. Reduction of poverty, improving women's education and health, sanitation and child feeding knowledge can reduce the prevalence of malnutrition across India. Multisectoral and targeted intervention in the geographical hotspots of malnutrition can reduce malnutrition in India.
尽管在过去二十年中经济持续增长且货币贫困率有所降低,但印度的营养不良问题仍然很严重。1992 年至 2016 年期间,儿童体重不足的比例从 53%下降到 36%,发育迟缓的比例从 52%下降到 38%,而消瘦的比例从 17%上升到 21%。印度全国平均营养不良水平掩盖了各地区之间的巨大差异。本研究利用最近一轮全国家庭健康调查(NFHS)2015-16 年的数据,研究了印度 640 个地区儿童营养不良的空间异质性和中尺度相关性。
采用 Moran's I 统计量和双变量局部空间自相关(LISA)地图来理解儿童营养不良的空间依赖性和聚类。采用多元回归、空间滞后和误差模型来检验营养不良的相关性。将贫困、母亲的体重指数(BMI)、母乳喂养实践、完全免疫、机构分娩、家庭卫生改善和电气化等作为营养不良的中尺度相关性因素。
单变量 Moran's I 统计量分别为 0.65、0.51 和 0.74,表明印度存在儿童营养不良的空间异质性。与母亲 BMI 的双变量 Moran's I 统计量分别为 0.52、0.46 和-0.52,与贫困有关,与卫生条件有关。消瘦和体重不足的情况类似,表明在印度的地理热点地区,存在与中尺度相关性因素相关的营养不良空间聚类。空间误差模型的结果表明,母亲 BMI 和家庭贫困的系数是发育迟缓、消瘦和体重不足的重要和显著预测因素。在空间误差模型中,母亲 BMI 的系数对体重不足的影响最大(β=0.38,95%置信区间:0.29-0.48),其次是发育迟缓(β=0.23,95%置信区间:0.14-0.33)和消瘦(β=0.11,95%置信区间:0.01-0.22)。妇女教育程度和母乳喂养实践也对发育迟缓与体重不足有显著影响。
印度各地区的营养不良呈空间聚集性。减少贫困、提高妇女教育和健康水平、改善卫生条件和儿童喂养知识,可以降低印度的营养不良发生率。在营养不良的地理热点地区采取多部门和有针对性的干预措施,可以减少印度的营养不良问题。