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...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.展开更多
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 c...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.展开更多
基金supported by the Belgian Science Policy(BELSPO)under the Research programme for Earth Obser-vation“STEREO III”[grant number SR/00/304]AJT is supported by a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z)+4 种基金AJT,AS,AEG and FRS are supported by funding from the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350][grant number OPP1134076]supported by the Well-come Trust,UK as an intermediate fellow[grant number 095127]RWS is supported by the Wellcome Trust as Prin-cipal Research Fellow[grant number 103602]that also supported CWK.CWK is also grateful to the KEMRI Wellcome Trust Overseas Programme Strategic Award[grant number 084538]for additional support.
文摘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.
基金AMN was supported by a Wellcome Trust grant(No.:095127)that also supported DKK.DKK was also supported by a Sustaining Health award from the Wellcome Trust(No.:103926)RWS is supported as a Wellcome Trust Principal Fellow(No.:10360)JAB is supported by the Bill&Melinda Gates Foundation(No.:OPP1131320).
文摘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.
