par Tatem, Andrew J;Gaughan, Andrea E;Stevens, Forrest R;Patel, Nirav N;Jia, Peng;Pandey, Aman;Linard, Catherine 
Référence The Lancet (North American Edition), 381, Supplement 2, page (S142)
Publication Publié, 2013
Référence The Lancet (North American Edition), 381, Supplement 2, page (S142)
Publication Publié, 2013
Article révisé par les pairs
| Résumé : | AbstractBackgroundThe Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress towards their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, prompting the development of sophisticated cartographic techniques for mapping and modelling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition are lacking, prompting many health metric studies to overlook the substantial demographic variations that exist subnationally and to merely apply national-level adjustments.MethodsHere, we describe the development of high-resolution age and sex structured spatial population datasets for Africa, Asia, and Latin America in 2000â15, built from millions of measurements mapped to more than 200â000 subnational units, and originating from censuses, census microdata, and household surveys.FindingsWe analysed the substantial variations seen within countries, by settlement type, and across the continents for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and found that substantial differences in various MDG-related health and development indicators can result through using only national-level statistics compared with accounting for subnational variation.InterpretationProgress towards meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on health metrics. Further details and data are available through the project websites: www.afripop.org, www.asiapop.org, and www.ameripop.org.FundingAJT acknowledges funding support from the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and is also supported by grants from NIH/NIAID (U19AI089674) and the Bill ANDamp; Melinda Gates Foundation (#49446 and #1032350). |
