Publications Summary

Document Type
Working Papers
Publication Topic(s)
Child Health and Development, Nutrition
Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Burkina Faso, Burundi, Cambodia, Cameroon, Chad, Colombia, Comoros, Congo, Congo Democratic Republic, Cote d'Ivoire, Dominican Republic, Egypt, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Jordan, Kenya, Kyrgyz Republic, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Peru, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Eswatini, Tajikistan, Tanzania, Timor-Leste, Togo, Uganda, Yemen, Zambia, Zimbabwe
Recommended Citation
Aimone, Ashley, Diego G. Bassani, Huma Qamar, Nandita Perumal, Sorrel M.L. Namaste, and Daniel E. Roth. 2021. Alternative and Complementary Metrics of Linear Growth for Tracking Global Progress in Child Nutritional Status. DHS Working Papers No. 153. Rockville, Maryland, USA: ICF
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Publication Date
September 2021
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Stunting prevalence is a core indicator of child health that is conventionally estimated as the proportion of children with height- for-age z-score (HAZ) values below -2 standard deviations (SDs), based on the World Health Organization (WHO) growth standards. Despite its widespread use in public health, stunting prevalence is conceptually problematic because it is often used to identify a subgroup of the population that is affected by undernutrition, rather than being correctly interpreted as a characteristic of the entire population. From a statistical perspective, stunting prevalence is a measure of location (MoL) of the HAZ distribution of an observed population relative to the distribution of a healthy standard population. A higher stunting prevalence usually represents a downward whole- population shift of the HAZ distribution, whereby even the tallest children in the population are shorter than expected. Moreover, stunting prevalence is based on cut-off values at the tails of the HAZ distribution and may therefore be more sensitive to imprecise data on height and date of birth than MoLs of central tendency (e.g., means, medians). We hypothesized that other linear growth metrics based on child height data may be less sensitive (i.e., more robust) to distribution asymmetry or the presence of influential outliers and therefore suitable alternatives or complementary approaches for describing the location of an observed HAZ distribution relative to international standards. The objectives of this study were to (1) identify and describe a range of candidate linear growth metrics that could be used as alternative or complementary indicators for assessing population childhood linear growth and nutritional status and (2) assess and compare these potential metrics of child linear growth based on the relative strengths of their associations with other key population indicators and on the robustness of these associations against variations in anthropometric data quality. Height and date of birth data of children under 5 years of age from Demographic and Health Surveys (DHS) from 64 countries (2000 to 2018) were used to generate two types of linear growth metrics: estimates of descriptive statistics based on observed distributions (e.g., MoLs such as means and stunting prevalence) and regression model-derived estimates (e.g., predicted means at discrete ages or slopes of decline within a defined age range). DHS data were also used to generate indices for anthropometric data quality based on principal component analysis. Correlations between each candidate linear growth metric and stunting prevalence among children under 5 were compared using the absolute value of Pearson correlation coefficients. Absolute values of Spearman rank correlations were used to compare pairwise associations between each linear growth metric and each of six population indicators of health and well-being (e.g., under-5 mortality [U5M], gross domestic product). Data quality was measured using indices composed of either three or six individual anthropometric data quality indicators (referred to as the 3Q index and 6Q index, respectively). Relationships between the metrics and population indicators were assessed using Spearman rank correlations (for a subset of three indicators) and linear mixed effects models (for all six indicators) to test their robustness against variations in anthropometric data quality. All analyses were performed using four approaches for identifying (i.e., flagging) and excluding HAZ outliers: (1) no flagging, and therefore no exclusions; (2) less restrictive flagging, which excluded HAZ values <-9 SDs and >+9 SDs from the age/sex-specific WHO standard median; (3) WHO flagging, which excluded HAZ values <-6 SDs and >+6 SDs from the age/sex- specific WHO standard median; and (4) Standardized Monitoring and Assessment of Relief and Transitions (SMART) flagging, which excluded HAZ values <-3 SDs and >+3 SDs from the observed sample mean. Results showed that descriptive linear growth metrics and model-derived predicted HAZ values at 2 years, 3 years, and 5 years were strongly correlated with stunting prevalence, with absolute values of the Pearson correlation coefficients exceeding 0.90 across all flagging approaches. Pearson correlations for model-derived slopes of HAZ and height-for-age difference (HAD) from birth to 3 years of age ranged from moderate to strong (absolute values =0.46 and =0.77, respectively). Predicted HAZ at birth had one of the weakest coefficients and the largest range of correlations across flagging approaches (-0.35 to -0.68). Spearman rank correlation coefficients for relationships between linear growth metrics and population indicators (i.e., absolute values) ranged from 0.01 to 0.73 with little variation across flagging approaches for each metric. Correlation strengths of the descriptive metrics tended to cluster near the midpoint of this range, with stunting prevalence generally ranking higher than other descriptive metrics. Conversely, correlations with model-derived metrics varied more widely; for example, predicted HAZ at birth had the weakest correlations and HAD slope from 0-3 years of age often had the strongest. In linear mixed effects models, the associations between descriptive linear growth metrics and any of the population indicators were not significantly modified by the 3Q index. When data quality was defined using the 6Q index, significant modifying effects were observed for associations between stunting and the percentage of the population with an improved source of drinking water. The 3Q index had a significant modifying effect on the associations between U5M and four of the model-derived linear growth metrics when the WHO or SMART flagging approaches were used; however, these interactions were not significant when using the 6Q index. With few exceptions, the 3Q index had no observable modifying effect on the associations between the model-derived linear growth metrics and other population indicators; these findings were similar for analyses using the 6Q index. Correlations between linear growth metrics and U5M were generally weaker with high (versus low) data quality, defined using the 3Q index; results were similar when data quality categorization was based on the 6Q index. However, differences between high- and low-quality strata were usually minor. Patterns for the other population indicators were inconsistent with the findings observed for U5M: For gross domestic product, the correlation strengths were greater at high (versus low) quality for half of the candidate metrics when using the 6Q index; for maternal education, correlation strengths were generally lower at high (versus low) quality using the 3Q index (similar to U5M), but this pattern was not consistent for many linear growth metrics when using the 6Q index. In conclusion, numerous alternative linear growth metrics may be derived from the same population-level child height data that are widely used to estimate stunting prevalence. The majority of these metrics correlate strongly with stunting prevalence and may therefore have no substantial empirical advantages. Findings to date suggest that some of the alternative metrics may outperform stunting prevalence in terms of the strength of their correlations with other important population indicators. However, it was methodologically challenging to assess the sensitivity of metric performance to variations in anthropometric survey data quality. Therefore, findings so far are insufficient to lead to a recommendation to adopt one or more of the alternative linear growth metrics for use in tracking country or regional improvements in child growth. In further research, we will apply a revised methodological approach with a more comprehensive set of alternative metrics to further our understanding of the potential application of alternative linear growth metrics for assessing and tracking child health and nutritional status in low-and middle-income countries.


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