Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Congdon, Peter

doi:10.1007/s00477-016-1292-9

Cited by 13 publications

(20 citation statements)

References 45 publications

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“…Estimating the joint spatial distribution of multiple diseases will better outperform the underlying risks than it would be obtained from the univariate analysis. For example, recent developments of theory and applications of multivariate joint modelling can be found in the literature [39][40][41] and a scale mixture approach for spatial dependence was proposed by 42 as a recent contribution in this field.…”

Section: Joint Disease Modelingmentioning

confidence: 99%

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

Adeyemi¹,

Zewotir²,

Ramroop³

2019

Afr H. Sci.

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Background: In epidemiological studies, several diseases share common risk factors or co-exist in their spatial prevalence. Disease mapping allows the health practitioners and epidemiologists to hypothesize the disease aetiology and gain better understanding of the geographical prevalence of the disease risks.Objective: This paper investigates the differences in small scale geographical variations and the underlying risk factors of child’s health outcomes (anemia, stunting and wasting) in Sub-Saharan Africa using spatial epidemiology. Method: The study first carried out an independent univariate analysis on each malnutrition indicator to identify underlying risk factors. A multivariate conditional autoregressive prior was explored to jointly model the spatial correlation between the undernutrition indicators and the small area-geographical disparities at sub-national levels in two sub-Saharan African countries.Results: The approach was implemented on data from National cross-sectional household- based demographic and health surveys conducted in 17,307 under-five children in Burkina Faso and Mozambique in 2010-2012. Out of these children, 31.8% are found to be stunted, 15.5% wasted and 30.9% had anemia among Burkina Faso children, while 42.5% of Mozambican children were stunted, 5.9% wasted and 30.9% suffered from iron-deficiency anemia. The multivariate analysis revealed that the spatial prevalence existed across regions in Burkina Faso with geographical variations in stunting estimated as: 0.7549, CI (0.4693, 1.264); wasting 0.9197; (95%CI : 0.535, 1.591)and anemia : 0.734; (0.4606, 1.214). In additin, the spatial correlation between stunting and wasting was negatively correlated: -0.998; 95% CI (-1.000, -0.984), and a perfect negative correlation;(-1) between stunting and anemia, and positive for wasting and anemia: 0.997; (0.978, 1.000). The spatial occurrence across provinces in Mozambique indicated that there was strong positive correlation between stunting and wasting; 0.986; (0.899, 1.000) and a significant negative correlation between stunting and anemia: -0.720, (-0.934, -0.308) and wasting and anemia: -0.640; (-0.903 -0.174) with individual geographical variability in child stunting: 1427, (913.6, 2268); wasting:1751, (1117, 2803) and anemia: 556, (279.5, 978.9). These extra random effect parameters computed in our multivariate approach would outperform a univariate analysis in similar studies. Our model further detected high prevalent of malnutrition and anemia in the northern Burkina Faso, but high anemia prevalent found in central Mozambique, and high stunting and wasting identified Southern Mozambique. In addition, the risk factors of malnutrition and iron deficiency anemia included household poverty, morbidity, short birth interval (less 18 months), breast feeding, antenatal attendance and maternal literacy.Conclusion: The statistical relevance of the identified risk factors in this study is useful to target specific individual interventions and the maps of the geographical inequalities in sub-national region can be used for designing nutrition interventions and allocation of scarce health resources.Keywords: Joint spatial mapping, childhood anemia, malnutrition, sub-Saharan Africa.

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Section: Joint Disease Modelingmentioning

confidence: 99%

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

Adeyemi¹,

Zewotir²,

Ramroop³

2019

Afr H. Sci.

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“…The most common way of carrying out statistical spatial smoothing is to specifically include terms to account for spatial autocorrelation and sampling variability so as to satisfy model assumptions and reduce uncertainty of the estimates. This approach models the observed data using a Bayesian generalized linear mixed model (GLMM) [44,45] to augment the linear predictor with a set of spatially autocorrelated random effects. This spatial correlation between areas is readily incorporated through the prior information, and recognizing that neighbouring geographical areas are more likely to share similar characteristics (i.e.…”

Section: Spatial Smoothingmentioning

confidence: 99%

Bayesian spatial modelling of early childhood development in Australian regions

Baffour

2020

Int J Health Geogr

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Background Children’s early development plays a vital role for maintaining healthy lives and influences future outcomes. It is also heavily affected by community factors which vary geographically. Direct methods do not provide a comprehensive picture of this variation, especially for areas with sparse populations and low data coverage. In the context of Australia, the Australian Early Development Census (AEDC) provides a measure of early child development upon school entry. There are two primary aims of this study: (i) provide improved prevalence estimates of children who are considered as developmentally vulnerable in regions across Australia; (ii) ascertain how social-economic disadvantage partly explains the spatial variation. Methods We used Bayesian spatial hierarchical models with the Socio-economic Indexes for Areas (SEIFA) as a covariate to provide improved estimates of all 335 SA3 regions in Australia. The study included 308,953 children involved in the 2018 AEDC where 21.7% of them were considered to be developmentally vulnerable in at least one domain. There are five domains of developmental vulnerability—physical health and wellbeing; social competence; emotional maturity; language and cognitive skills; and communication and general knowledge. Results There are significant improvements in estimation of the prevalence of developmental vulnerability through incorporating the socio-economic disadvantage in an area. These improvements persist in all five domains—the largest improvements occurred in the Language and Cognitive Skills domain. In addition, our results reveal that there is an important geographical dimension to developmental vulnerability in Australia. Conclusion Sparsely populated areas in sample surveys lead to unreliable direct estimates of the relatively small prevalence of child vulnerability. Bayesian spatial modelling can account for the spatial patterns in childhood vulnerability while including the impact of socio-economic disadvantage on geographic variation. Further investigation, using a broader range of covariates, could shed more light on explaining this spatial variation.

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“…These rules have been studied by Ugarte et al. 15,16 Other approaches based on hierarchical modelling include Lee and Mitchell, 17 Wakefield and Kim, 18 Charras-Garrido et al., 19 Hossain and Lawson 20 and Congdon, 21 but these are beyond the reach of most practitioners and health researchers and are difficult to apply if specific software or code is not provided. Recently, Anderson et al.…”

Section: Introductionmentioning

confidence: 99%

A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters

Adin

Lee

Goicoa

et al. 2018

Stat Methods Med Res

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Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

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Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Cited by 13 publications

References 45 publications

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities

Bayesian spatial modelling of early childhood development in Australian regions

A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters

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