Santu Ghosh1, Anura V Kurpad2, Harshpal S Sachdev3, Tinku Thomas1. 1. Department of Biostatistics, St John's Medical College, St. John's National Academy of Health Sciences, Bangalore, India. 2. Department of Physiology, St John's Medical College, St. John's National Academy of Health Sciences, Bangalore, India. 3. Sitaram Bhartia Institute of Science and Research, New Delhi, India.
Abstract
BACKGROUND: Biomarkers of micronutrient status vary with inflammation, and can be corrected by a regression-based approach [Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA)] using measured concentrations of inflammation biomarkers, e.g., C-reactive protein (CRP) and/or α1-acid-glycoprotein (AGP). However, this is confounded when inflammation is measured with multiple assays with variable limits of detection (LOD) and lower limits of quantification (LLOQ). OBJECTIVES: We aimed to develop a probability approach for the estimation of prevalence of micronutrient deficiency using the distribution of true serum/plasma micronutrient concentrations in the population. METHODS: Left-censoring of an inflammation biomarker due to varying values of LOD or LLOQ was addressed by estimating the distribution of the inflammation biomarker at concentrations lower than the LOD and using this for the probability estimation of prevalence of nutrient deficiency. This method was evaluated using 2 publicly available data sets for children <5 y old: BRINDA and the Indian Comprehensive National Nutrition Survey. Each data set included measures of serum ferritin (SF), vitamin A, zinc, and CRP measured using different assays with variable LLOQs. RESULTS: The empirical distribution of SF after correction for CRP and AGP by the BRINDA method was comparable with the estimated probability distribution of SF, yielding similar estimates of iron deficiency prevalence when evaluated in the BRINDA data (17.4%; 95% CI: 15.2%, 19.7% compared with 16.8%; 95% CI: 13.9%, 20.0%; BRINDA compared with the probability method). The BRINDA method-adjusted iron deficiency prevalence was linearly associated with the proportion of left-censored CRP data, whereas these were not associated in the probability method. In the Indian survey data, estimates of prevalence of iron and zinc deficiency were comparable but vitamin A deficiency was lower by the probability method (17.6%; 95% CI: 16.7%, 20.2% compared with 15.7%; 95% CI: 15.2%, 16.3%; BRINDA compared with the probability method). CONCLUSIONS: The proposed probability method is a robust alternate approach to the estimation of the prevalence of nutrient deficiency with left-censored inflammation biomarker data.
BACKGROUND: Biomarkers of micronutrient status vary with inflammation, and can be corrected by a regression-based approach [Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA)] using measured concentrations of inflammation biomarkers, e.g., C-reactive protein (CRP) and/or α1-acid-glycoprotein (AGP). However, this is confounded when inflammation is measured with multiple assays with variable limits of detection (LOD) and lower limits of quantification (LLOQ). OBJECTIVES: We aimed to develop a probability approach for the estimation of prevalence of micronutrient deficiency using the distribution of true serum/plasma micronutrient concentrations in the population. METHODS: Left-censoring of an inflammation biomarker due to varying values of LOD or LLOQ was addressed by estimating the distribution of the inflammation biomarker at concentrations lower than the LOD and using this for the probability estimation of prevalence of nutrient deficiency. This method was evaluated using 2 publicly available data sets for children <5 y old: BRINDA and the Indian Comprehensive National Nutrition Survey. Each data set included measures of serum ferritin (SF), vitamin A, zinc, and CRP measured using different assays with variable LLOQs. RESULTS: The empirical distribution of SF after correction for CRP and AGP by the BRINDA method was comparable with the estimated probability distribution of SF, yielding similar estimates of iron deficiency prevalence when evaluated in the BRINDA data (17.4%; 95% CI: 15.2%, 19.7% compared with 16.8%; 95% CI: 13.9%, 20.0%; BRINDA compared with the probability method). The BRINDA method-adjusted iron deficiency prevalence was linearly associated with the proportion of left-censored CRP data, whereas these were not associated in the probability method. In the Indian survey data, estimates of prevalence of iron and zinc deficiency were comparable but vitamin A deficiency was lower by the probability method (17.6%; 95% CI: 16.7%, 20.2% compared with 15.7%; 95% CI: 15.2%, 16.3%; BRINDA compared with the probability method). CONCLUSIONS: The proposed probability method is a robust alternate approach to the estimation of the prevalence of nutrient deficiency with left-censored inflammation biomarker data.