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modelling changing population distributions:an example of the Kenyan Coast,1979–2009

International Journal of Digital Earth 2017年第10期10卷 1017-1029页

作者： Catherine Linard Caroline W.Kabaria marius Gilbert Andrew J.Tatem Andrea E.Gaughan Forrest R.Stevens Alessandro Sorichetta Abdisalan M.Noor Robert W.Snow Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium Spatial Health metrics Group KEMRI Wellcome Trust Research ProgrammeNairobiKenya Fonds National de la Recherche Scientifique(F.R.S.-FNRS) BrusselsBelgium WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Fogarty International Center National Institutes of HealthBethesdaMDUSA Flowminder Foundation StockholmSweden Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA Nuffield Department of Clinical medicine Centre for Tropical Medicine and Global HealthUniversity of OxfordOxfordUK

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.

关键词： Human population distribution modelling gridded population datasets temporal change Kenya

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Co-morbidity of malnutrition with falciparum malaria parasitaemia among children under the aged 6-59 months in Somalia:a geostatistical analysis

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Infectious Diseases of Poverty 2018年第1期7卷 714-722页

作者： Damaris K.Kinyoki Grainne m.moloney Olalekan A.Uthman Elijah O.Odundo Ngianga-Bakwin Kandala Abdisalan M.Noor Robert W.Snow James A.Berkley Spatial Health metrics Group INFORM ProjectKenya Medical Research Institute/Wellcome Trust Research ProgrammeNairobiKenya Nutrition Section United Nations Children’s Fund(UNICEF)Kenya Country OfficeUN Complex GigiriNairobiKenya Warwick medical School Health Sciences Research InstituteWarwick EvidenceUniversity of WarwickGibbet HillCoventry CV47ALUK Food Security and Nutrition Analysis Unit(FSNAU)-Somalia Food and Agriculture Organization of the United NationsNgecha Road CampusNairobiKenya Department of mathematics and Information sciences Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle upon TyneUK Faculty of Health and Sport Sciences University of AgderKristiansandNorway Division of Epidemiology and Biostatistics School of Public HealthUniversity of the WitwatersrandJohannesburgSouth Africa Kenya medical Research Institute/Wellcome Trust Research Programme Centre for Geographic Medicine Research(coast)KilifiKenya Centre for Tropical medicine and Global Health Nuffield Department of Clinical MedicineUniversity of OxfordCCVTMOxford OX37LJUK

Background:malnutrition and malaria are both significant causes of morbidity and mortality in African children.However,the extent of their spatial comorbidity remains unexplored and an understanding of their spatial correlation structure would inform improvement of integrated interventions.We aimed to determine the spatial correlation between both wasting and low mid upper arm circumference(mUAC)and falciparum malaria among Somalian children aged 6-59 months.methods:Data were from 49227 children living in 888 villages between 2007 to 2010.We developed a Bayesian geostatistical shared component model in order to determine the common spatial distributions of wasting and falciparum malaria;and low-mUAC and falciparum malaria at 1×1 km spatial resolution.Results:The empirical correlations with malaria were 0.16 and 0.23 for wasting and low-mUAC respectively.Shared spatial residual effects were statistically significant for both wasting and low-mUAC.The posterior spatial relative risk was highest for low-mUAC and malaria(range:0.19 to 5.40)and relatively lower between wasting and malaria(range:0.11 to 3.55).Hotspots for both wasting and low-mUAC with malaria occurred in the South Central region in Somalia.Conclusions:The findings demonstrate a relationship between nutritional status and falciparum malaria parasitaemia,and support the use of the relatively simpler mUAC measurement in surveys.Shared spatial distribution and distinct hotspots present opportunities for targeted seasonal chemoprophylaxis and other forms of malaria prevention integrated within nutrition programmes.

关键词： malnutrition Wasting Low-mUAC malaria Comorbidity Somalia

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