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Objectives: Current standards for comparing stunting across human populations assume a universal model of child growth. Such comparisons ignore population differences that are independent of deprivation and health outcomes. This paper partitions variation in height-for-age that is specifically associated with deprivation and health outcomes to provide a basis for cross-population comparisons. Materials & Methods: Using a multi-level model with a sigmoid relationship of resources and growth, we partition variation in height-for-age z-scores (HAZ) from 1,522,564 children across 70 countries into two components: 1) accrued HAZ shaped by environmental inputs (e.g., undernutrition, infectious disease, inadequate sanitation, poverty), and 2) a country-specific basal HAZ independent of such inputs. We validate these components against population-level infant mortality rates, and assess how these basal differences may affect cross-population comparisons of stunting. Results: Basal HAZ differs reliably across countries (range of 1.5 SD) and is independent of measures of infant mortality. By contrast, accrued HAZ captures stunting as impaired growth due to deprivation and is more closely associated with infant mortality than observed HAZ. Ranking populations by accrued HAZ suggest that populations in West Africa and the Caribbean suffer much greater levels of stunting than suggested by observed HAZ. Discussion: Current universal standards may dramatically underestimate stunting in populations with taller basal HAZ. Relying on observed HAZ rather than accrued HAZ may also lead to inappropriate cross-population comparisons, such as concluding that Haitian children enjoy better conditions for growth than do Indian or Guatemalan children.
The canine lymphoma blood test detects the levels of two biomarkers, the acute phase proteins (C-Reactive Protein and Haptoglobin). This test can be used for diagnostics, for screening, and for remission monitoring as well. We analyze clinical data,
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity and mechanisms underlying human health and disease. Large-scale metabolomics data, gen
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using
Background. Emerging technologies now allow for mass spectrometry based profiling of up to thousands of small molecule metabolites (metabolomics) in an increasing number of biosamples. While offering great promise for revealing insight into the patho
More advanced visualization tools are needed to assist with the analyses and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity. Using a dataset of several hundred bioactive lipid metabolites profiled i