Customize Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

WorldPop Projects

High resolution gender-disaggregated mapping of stunting in children

Project leads: Andy Tatem, Claudio Bosco

Collaborators/funders: UN Foundation, Data2x program

Improved understanding of geographic variation and inequity in health status, wealth, and access to resources within countries is increasingly being recognized as central to meeting development goals. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure.

Demographic and Health surveys (DHS) data measuring the rate of stunting in children under age 5 were used to predict high spatial resolution gender disaggregated maps, at unobserved location in Nigeria, Kenya and Bangladesh, using predictive modelling techniques. Bayesian geostatistical and machine learning modelling methods (Artificial neural networks) are used to take advantage of the fact that many indicators related to population health and development are correlated to environmental or sociological factors, many of which are available nowadays as gridded spatial datasets.

The outputs consist of high-resolution maps (1×1 km) of stunting in boys and girls in Nigeria, Kenya and Bangladesh together with estimates of mapping uncertainty. Quantify the distribution of gender disaggregated childhood stunting in low- or medium-low- income countries is valuable to adequately inform policy-makers and decision-makers for promoting any initiative aimed at making advances towards reducing stunting and achieving gender equality.

Fig. 1. Visualisation of approach and outputs for mapping stunting in girls in Nigeria in 2013