Alternative and Complementary Metrics of Linear Growth for Tracking Global Progress in Child Nutritional Status

The study aimed to investigate the correlations between candidate linear growth metrics and stunting prevalence, as well as their associations with population indicators and anthropometric data quality. Anthropometric data sources used in this study included Demographic and Health Surveys (DHS) conducted between 2000 and 2018 in 64 low- and middle-income countries. The data sources were used to identify, estimate, and predict candidate linear growth metrics. The study also employed a data quality index to assess the quality of anthropometric data. The study identified six candidate linear growth metrics, which were correlated with stunting prevalence among children under 5 years of age. The results showed that the correlations between linear growth metrics and stunting prevalence varied in strength, with some metrics exhibiting stronger correlations than others. The study also found that the correlations between linear growth metrics and population indicators, such as under-5 mortality and child stunting, varied in strength. A composite anthropometric data quality index was developed from a selected set of data quality indicators. The study found that the effects of variation in anthropometric data quality on the performance of linear growth metrics were inconclusive. However, the results suggested that the quality of anthropometric data may influence the associations between linear growth metrics and population indicators. The study's findings have implications for the development of alternative and complementary metrics of linear growth for tracking global progress in child nutritional status. The results suggest that a combination of linear growth metrics and anthropometric data quality indices may provide a more comprehensive understanding of child nutritional status. The study's findings also highlight the need for further research to develop and validate alternative and complementary metrics of linear growth. The study's results are based on an analysis of 145 DHS conducted between 2000 and 2018 in 64 low- and middle-income countries. The analysis included a total of 1.3 million children under 5 years of age. The study's findings are presented in a series of tables and figures, which provide a detailed overview of the correlations between linear growth metrics and stunting prevalence, as well as their associations with population indicators and anthropometric data quality. The study's conclusions and policy implications are presented in the final section of the report. The study's findings are summarized, and recommendations are made for the development and implementation of alternative and complementary metrics of linear growth for tracking global progress in child nutritional status. The study's conclusions highlight the need for further research to develop and validate alternative and complementary metrics of linear growth, as well as the importance of considering anthropometric data quality in the development and implementation of linear growth metrics. The study's results are presented in a series of tables and figures, which provide a detailed overview of the correlations between linear growth metrics and stunting prevalence, as well as their associations with population indicators and anthropometric data quality. The tables and figures include: * Table 1: Characteristics of the analytical samples of 145 DHS conducted between 2000 and 2018 in 64 low- and middle-income countries, by outlier flagging approach * Table 2: Definitions and methods of derivation of candidate linear growth metrics * Table 3: Population indicators selected for inclusion in validation analyses * Table 4: Individual data quality indicators included in the anthropometric data quality indices * Table 5: Summary statistics and factor loadings in principal component analyses of six indicators of anthropometric data quality used to develop anthropometric data quality indices for height-for-age in 145 DHS from 64 countries * Table 6: Pearson correlation coefficient matrix of data quality indices and six individual metrics of anthropometric data quality in 145 DHS from 64 countries * Table 7: Pearson correlation coefficients for the relationships between linear growth metrics and stunting prevalence among children <5 years of age in the most recent DHS from 64 countries * Table 8: Spearman rank correlations of under-5 mean height-for-age z-score, stunting prevalence, and data quality indices with population indicators from 49 countries * Table 9a: Spearman rank correlation coefficients for the relationships between linear growth metrics and the restricted (3Q) survey data quality index in the most recent DHS from 64 countries * Table 9b: Spearman rank correlation coefficients for the relationships between linear growth metrics and the extended composite (6Q) anthropometric data quality index in the most recent DHS from 64 countries * Table 10a: Spearman rank correlation coefficients for the relationships between linear growth metrics and under-5 mortality at high and low data quality (defined as above or below the 50th percentile of the restricted [3Q] anthropometric data quality index) in the most recent DHS from 64 countries * Table 10b: Spearman rank correlation coefficients for the relationships between linear growth metrics and under-5 mortality at high and low data quality (defined as above or