Total: 139
Exploring the high-resolution mapping of gender-disaggregated development indicators.
Journal of the Royal Society Interface (2017).
Author(s): C. Bosco, V. Alegana, T. Bird, C. Pezzulo, L. Bengtsson, A. Sorichetta, J. Steele, G. Hornby, C. Ruktanonchai, N. Ruktanonchai, E. Wetter, A. J. Tatem
Type: method. Year: 2017
DOI: 10.1098/rsif.2016.0825.

Abstract: Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.
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Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model
BMC Medical Research Methodology. 2017 17:67 .
Author(s): Victor A. Alegana author, Jim Wright, Carla Pezzulo, Andrew J. Tatem and Peter M. Atkinson
Type: method. Year: 2017
DOI: 10.1186/s12874-017-0346-0.

Abstract: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).
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Sub-national mapping of population pyramids and dependency ratios in Africa and Asia
Scientific Data 4, Article number: 170089 (2017).
Author(s): Carla Pezzulo, Graeme M. Hornby, Alessandro Sorichetta, Andrea E. Gaughan, Catherine Linard, Tomas J. Bird, David Kerr, Christopher T. Lloyd & Andrew J. Tatem.
Type: method. Year: 2017
DOI: 10.1038/sdata.2017.89.

Abstract: The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.
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GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data.
International Journal of Health Geographics (2017) 16:25.
Author(s): Dana R. Thomson author, Forrest R. Stevens, Nick W. Ruktanonchai, Andrew J. Tatem and Marcia C. Castro.
Type: method. Year: 2017
DOI: 10.1186/s12942-017-0098-4.

Abstract: Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample “seed” cells with probability proportionate to estimated population size, then “grows” PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.
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