Background:Malnutrition and malaria are both significant causes of morbidity and mortality in African ***,the extent of their spatial comorbidity remains unexplored and an understanding of their spatial correlation st...
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Background:Malnutrition and malaria are both significant causes of morbidity and mortality in African ***,the extent of their spatial comorbidity remains unexplored and an understanding of their spatial correlation structure would inform improvement of integrated *** 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 ***:Data were from 49227 children living in 888 villages between 2007 to *** 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 ***:The empirical correlations with malaria were 0.16 and 0.23 for wasting and low-MUAC *** spatial residual effects were statistically significant for both wasting and *** 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 ***:The findings demonstrate a relationship between nutritional status and falciparum malaria parasitaemia,and support the use of the relatively simpler MUAC measurement in *** spatial distribution and distinct hotspots present opportunities for targeted seasonal chemoprophylaxis and other forms of malaria prevention integrated within nutrition programmes.
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 *** temporal projections do not...
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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 *** temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover *** predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally *** 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 *** 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 *** used to fill data gaps may vary according to the local context and the objective of the *** 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.
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