Int J Health Geogr - A high resolution spatial population database of Somalia for disease risk mapping.

Tópicos

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{ clinic(1479) use(1117) guidelin(835) }
{ method(1969) cluster(1462) data(1082) }
{ data(3008) multipl(1320) sourc(1022) }
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{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ analysi(2126) use(1163) compon(1037) }
{ can(774) often(719) complex(702) }
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{ ehr(2073) health(1662) electron(1139) }
{ data(2317) use(1299) case(1017) }
{ time(1939) patient(1703) rate(768) }
{ high(1669) rate(1365) level(1280) }
{ estim(2440) model(1874) function(577) }
{ imag(2830) propos(1344) filter(1198) }
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{ age(1611) year(1155) adult(843) }
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{ health(1844) social(1437) communiti(874) }
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{ cancer(2502) breast(956) screen(824) }
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Resumo

CKGROUND: Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.RESULTS: Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 ? 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach.CONCLUSIONS: The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.

Resumo Limpo

ckground million somali depriv basic health servic due unstabl polit situat countri attempt made reconstruct health sector particular estim extent infecti diseas burden howev approach requir use model diseas rate requir reason inform popul distribut lowincom countri somalia popul data lack poor qualiti becom outdat rapid model method therefor need product contemporari spatial detail popul dataresult land cover inform deriv satellit imageri exist settlement point dataset use spatial realloc popul within census unit use simpl semiautom method can implement free imag process softwar produc easili updat grid popul dataset meter spatial resolut popul dataset match administr popul total project un comparison test new dataset exist popul dataset reveal import differ popul size distribut popul risk malaria estim differ particular import dens popul area strong depend settlement data use model approachconclus result show possibl produc detail contemporari easili updat settlement popul distribut dataset somalia use exist data popul dataset produc freeli avail product afripop project can download httpwwwafripoporg

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