Comparison of Different Anthropometric Indicators for Assessment of Nutritional Status Among Adolescent Girls in an Urban Resettlement Colony in New Delhi: A Cross-Sectional Study.

Background In India, 44.8% of adolescent girls are under-nourished, while about 8%-13% of girls are overweight. Though several studies have been done regarding the nutritional status of adolescent girls over the years, there have been no significant changes. Also, there are several different anthropometric indicators for nutritional status assessment, due to which there are huge variations in the prevalence of malnutrition across different studies. So the objectives of this study were to determine the prevalence of malnutrition using different anthropometric indicators and compare them. Methods A random sample of 426 girls was taken from Health Management Information Systems (HMIS) of the Centre for Community Medicine (CCM), All India Institute of Medical Sciences (AIIMS), and a semi-structured questionnaire was administered among 386 of them to determine associated factors. Height, weight, and mid-upper arm circumference (MUAC) were measured for 386 girls, and BMI for age and height for age z scores were calculated using WHO AnthroPlus. Mid-upper arm circumference for age z scores (MUAC for age) were calculated using Lambda-Mu-Sigma (LMS) charts by the CDC for girls aged one year to 20 years. Results It was found in this study that using BMI for age z scores (BAZ), 33.4% of the adolescent girls were malnourished; 18.9% (95% CI 15.1-23.2) being underweight, 10.6% (95% CI: 7.7-14.1) being overweight, and 3.9% (95% CI: 2.2-6.3) were obese. While using BMI solely as an indicator, the prevalence of thinness was 51.8% (95% CI: 46.9-56.9), while that of overweight and obesity was 10.6% (95% CI: 5.7-11.5) and 1.0% (95% CI: 0.2-2.6), respectively. The prevalence of under-nutrition by MUAC for age z scores was 53.4% (95% CI: 48.2-58.4), and that of over-nutrition was 1.8% (95% CI: 1.0-3.7). BMI for age z scores positively and strongly correlated with both MUAC and MUAC for age z scores and had a significant association with both on univariable linear regression. Though there was a negative correlation between BMI for age z scores and height-for-age z scores, it was not significant. Height-for-age z scores, even though positively correlated with MUAC for age z scores, the correlation was not that strong.