Limitations

This work should be assessed in full acknowledgement of the data and methodological limitations. While our present study is informed by 209 sources (totalling 1.29 million measured children), areas of greatest uncertainty (Figs 1f, ​,2f2f and Extended Data Fig. 2f) usually correspond to those in need of newer and/or updated information (Extended Data Figs 5, ​,66 and Supplementary Figs 2, 3). Expansion to additional countries and indicators underscores the need for enhanced data collection (and equally importantly, retrospective data retrieval) as we iteratively update the measurement of progress towards global targets. While not a focus of this study, a combination of the magnitude of CGF indicator prevalence (Figs 1c, ​,2c2c and Extended Data Fig. 2c), the uncertainty in its estimation (Figs 1f, ​,2f2f and Extended Data Fig. 2f), and our knowledge of national survey coverage (Extended Data Figs 5, ​,66 and Supplementary Figs 2, 3) can be used to help to identify countries and vulnerable sub-populations that would benefit from further survey enumeration.

There are limitations to the data used in this analysis and thus areas for future refinement. For example, the height or weight of children may have been measured or recorded incorrectly due to equipment calibration or user error, or based on difficulties originating from measuring younger children lying down rather than standing up65. Levels of ‘missingness’ in these survey data may also be high due to recall error of a child’s birthday. Given that growth standards are age- and sex-specific, children without detailed age information were excluded from the analysis (see Supplementary Information). In addition, a child must have been present in the home in order for the survey taker to record measurements. Given that only children alive at the time of the survey could be counted, children under 5 who died due to undernourishment or other causes before the survey was taken would not have been measured. Conflict zones in select countries or regions may also have been excluded from surveying because of security and safety issues. The direction of all of these biases is towards an underestimation of CGF.

Moreover, our estimates are not stratified by sex, wealth or any other socio-economic indicators. This may mask higher rates of CGF present in sub-populations within the areas measured and while this work presents a very fine scale for comprehensive geospatial estimates of child growth failure, the 5 × 5-km resolution is still too coarse to account for urban slums and other hyperspecific spatial disparities. Similarly, relatively coarse AROCs taken across time may obscure higher-frequency changes within the time series, and more research in studying and summarizing spatially correlated temporal trends should be pursued. Although comprehensive, due to a lack of high-resolution spatial data, our set of included covariates does not cover all CGF drivers and confounders. On the modelling side, we have attempted to propagate as much uncertainty through the various modelling stages, but there are still some propagations, such as incorporating uncertainty from the child model ensemble fits, that proved computationally infeasible. Future research is also ongoing to develop computational methods for better geostatistical integration of point and areal data to continental-scale mapping studies for a variety of indicators. These geostatistical tools are driven primarily by infrequently reported national survey data and are thus well-positioned for monitoring and evaluating progress across years, but are not suited for day-to-day assessments of CGF vulnerability. We show, however, that there is considerable room for exciting harmonization with such efforts, for example, by focusing attention of early warning efforts on populations that are the most vulnerable and least resilient66.