Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique

Cassy, Sheyla Rodrigues; Manda, Samuel; Marques, Filipe J.; Martins, Maria do Rosário Oliveira

doi:10.3390/ijerph19106319

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Studies recommended that sampling weights in complex survey need to be taken into account in spatial modelling as it adjusts over/lower representation of sampling units (Vandendijck et al, 2016;Chen et al, 2014;Watjou et al, 2017). Failure to account sampling weights in spatial modelling could result in biased estimates (Chen et al, 2014;Mercer et al, 2014;Cassy et al, 2022). Spatial modelling approaches that considers sampling weights produce stable estimates with narrow confidence interval (Cassy et al, 2022;Mercer et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Failure to account sampling weights in spatial modelling could result in biased estimates (Chen et al, 2014;Mercer et al, 2014;Cassy et al, 2022). Spatial modelling approaches that considers sampling weights produce stable estimates with narrow confidence interval (Cassy et al, 2022;Mercer et al, 2014). In this dissertation we used complex survey 6.2 future work and limitations data to show the application of our approach to a real data set.…”

Section: Discussionmentioning

confidence: 99%

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Flexible Bayesian hierarchical spatial modeling in disease mapping.

Ayalew

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The Gaussian Intrinsic Conditional Autoregressive (ICAR) spatial model, which usually has two components, namely an ICAR for spatial smoothing and standard random effects for non-spatial heterogeneity, is used to estimate spatial distributions of disease risks. The normality assumption in this model may not always be correct and misspecification of the distribution of random effects could result in biased estimation of the spatial distribution of disease risk, which could lead to misleading conclusions and policy recommendations. Limited research studies have been done where the estimation of the spatial distributions of diseases under the ICAR-normal model were compared to those obtained from fitting ICAR-nonnormal model. The results from these studies indicated that the ICAR-nonnormal models performed better than the ICAR-normal in terms of accuracy, efficiency and predictive capacity. However, these efforts have not fully addressed the effect on the estimation of spatial distributions under flexible specification of ICAR models in disease mapping. The overall aim of this PhD thesis was to develop approaches that relax the normality assumption that is often used in modeling and fitting of ICAR models in the estimation of spatial patterns of diseases. In particular, the thesis considered the skewnormal and skew-Laplace distributions under the univariate, and skew-normal for the multivariate specifications to estimate the spatial distributions of either univariable or multivariable areal data. The thesis also considered non-parametric specification of the multivariate spatial effects in the ICAR model, which is a novel extension of an earlier work. The estimation of the models was done using Bayesian statistical approaches. The performances of our suggested alternatives to the ICAR-normal model were evaluated by simulating studies as well as with practical application to the estimation of district-level distribution of HIV prevalence and treatment coverage using health survey data in South Africa. Results from the simulation studies and analysis of real data demonstrated that our approaches performed better in the prediction of spatial distributions for univariable and multivariable areal data in disease mapping approaches. This PhD has shown the limitations of relying on the ICAR-normal model for the estimations of spatial distributions for all spatial analyses, even when the data could be asymmetric and non-normal. In such scenarios, skewed-ICAR and nonparametric ICAR approaches could provide better and unbiased estimation of the spatial pattern of diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Flexible Bayesian hierarchical spatial modeling in disease mapping.

Ayalew

View full text Add to dashboard Cite

show abstract

“…Studies recommended that sampling weights in complex survey need to be taken into account in spatial modelling as it adjusts higher or lower representation of sampling units (Vandendijck et al, 2016;Chen et al, 2014;Watjou et al, 2017). Failure to account sampling weights in spatial modelling could result in biased estimates (Chen et al, 2014;Mercer et al, 2014;Cassy et al, 2022). Spatial modelling approaches that considers sampling weights produce stable estimates with narrow confidence interval (Cassy et al, 2022;Mercer et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Failure to account sampling weights in spatial modelling could result in biased estimates (Chen et al, 2014;Mercer et al, 2014;Cassy et al, 2022). Spatial modelling approaches that considers sampling weights produce stable estimates with narrow confidence interval (Cassy et al, 2022;Mercer et al, 2014). In this thesis we have used complex survey data to show the application of our approach to a real data set.…”

Section: Discussionmentioning

confidence: 99%

Flexible Bayesian hierarchical spatial modelling in disease mapping.

Ayalew

View full text Add to dashboard Cite

The Gaussian Intrinsic Conditional Autoregressive (ICAR) spatial model, which usually has two components, namely an ICAR for spatial smoothing and standard random effects for non-spatial heterogeneity, is used to estimate spatial distributions of disease risks. The normality assumption in this model may not always be correct and misspecification of the distribution of random effects could result in biased estimation of the spatial distribution of disease risk, which could lead to misleading conclusions and policy recommendations. Limited research studies have been done where the estimation of the spatial distributions of diseases under the ICAR-normal model were compared to those obtained from fitting ICAR-nonnormal model. The results from these studies indicated that the ICAR-nonnormal models performed better than the ICAR-normal in terms of accuracy, efficiency and predictive capacity. However, these efforts have not fully addressed the effect on the estimation of spatial distributions under flexible specification of ICAR models in disease mapping. The overall aim of this PhD thesis is to develop approaches that relax the normality assumption that is often used in modeling and fitting of ICAR models in the estimation of spatial patterns of diseases. In particular, the thesis considers the skew-normal and skew-Laplace distributions under the univariate, and skew-normal for the multivariate specifications to estimate the spatial distributions of either univariable or multivariable areal data. The thesis also considers non-parametric specification of the multivariate spatial effects in the ICAR model, which is a novel extension of an earlier work. The estimation of the models was done using Bayesian statistical approaches. The performances of our suggested alternatives to the ICAR-normal model are evaluated by simulating studies as well as with practical application to the estimation of district-level distribution of HIV prevalence and treatment coverage using health survey data in South Africa. Results from the simulation studies and analysis of real data demonstrated that our approaches performed better in the prediction of spatial distributions for univariable and multivariable areal data in disease mapping approaches. This PhD work shows the limitations of relying on the ICAR-normal model for the estimations of spatial distributions for all spatial analyses, even when the data could be asymmetric and non-normal. In such scenarios, skewed-ICAR and nonparametric ICAR approaches could provide better and unbiased estimation of the spatial pattern of diseases.

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A Two‐stage Bayesian Small Area Estimation Approach for Proportions

Hogg,

Cameron,

Cramb

et al. 2024

Int Statistical Rev

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SummaryWith the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two‐stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.

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Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique

Cited by 4 publications

References 22 publications

Flexible Bayesian hierarchical spatial modeling in disease mapping.

Flexible Bayesian hierarchical spatial modeling in disease mapping.

Flexible Bayesian hierarchical spatial modelling in disease mapping.

A Two‐stage Bayesian Small Area Estimation Approach for Proportions

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