Multilevel Time Series Modelling of Mobility Trends in the Netherlands for Small Domains

Boonstra, Harm Jan; Brakel, Jaap Van; Das, Sumonkanti

doi:10.1111/rssa.12700

Cited by 11 publications

(21 citation statements)

References 37 publications

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“…S.1 ). Since these variance estimates are treated as fixed and known in the hierarchical Bayesian model as the Fay-Herriot area-level model [ 39 ], they are smoothed using Generalized Variance Functions (GVF) method ([ 40 ], Chapter 7) following the approach described in [ 38 ]. Figure 1 shows that the direct SEs are positively correlated with the estimates for all indicators and also the direct input estimates are very noisy for urban domains.…”

Section: Methodsmentioning

confidence: 99%

“…We observe that the square-root ( sqrt ) transformation reduces this correlations and also stabilizes the variance estimates. For the sqrt transformation case, the GVF model is developed under sqrt scale instead of original scale following [ 38 ]. The GVF models are given in Supplementary Tables S.1-S.3 .…”

Section: Methodsmentioning

confidence: 99%

“…To define the multilevel Bayesian model, let denote vector of dimension D =64×2×5×2=1280, where denotes the direct estimate of any childhood undernutrition indicator for domain d . Following [ 38 ], a multilevel Bayesian model for can be written as …”

Section: Methodsmentioning

confidence: 99%

“…A number of models structured as ( 1 ) were fitted using Markov Chain Monte Carlo simulation method for each of the undernutrition indicators in R [ 42 ] using package mcmcsae [ 43 ]. The details of model formulation are given in the Supplementary sub-section S.3 based on [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…These multilevel models are known to reduce uncertainty by including variability in the outcome variables at various aggregation levels and hence provide consistent estimates for the domains with very small samples in the survey data [ 36 ]. This is accomplished through borrowing strength by accounting for cross-sectional correlation from similar domains, and spatial correlation from the neighbouring domains [ 37 , 38 ]. Spatio-demographic district level estimates are obtained directly from the developed multilevel models and then these disaggregated estimates are aggregated to obtain district, division and national level estimates, which provides numerically consistent estimates for the disaggregated level domains.…”

Section: Introductionmentioning

confidence: 99%

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Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

Das

Baffour

2022

BMC Public Health

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Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

Das

Baffour

2022

BMC Public Health

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Improved estimates of child malnutrition trends in Bangladesh using remote-sensed data

Das,

Basher,

Baffour

et al. 2024

J Popul Econ

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This study investigates the trends in chronic malnutrition (stunting) among young children across Bangladesh’s 64 districts and 544 sub-districts from 2000 to 2018. We utilized remote-sensed data–nighttime light intensity to indicate urbanization, and environmental factors like precipitation and vegetation levels–to examine patterns of stunting. Our primary data source was the Bangladesh Demographic and Health Survey, conducted six times within the study period. Using Bayesian multilevel time-series models, we integrated cross-sectional, temporal, and spatial data to estimate stunting rates for years not covered by the direct survey information. This approach, enhanced by remote-sensed data, allowed for greater prediction accuracy by incorporating information from neighboring areas. Our findings show a significant reduction in national stunting rates, from nearly 50% in 2000 to about 30% in 2018. Despite this overall progress, some districts have consistently high levels of stunting, while others show fluctuating levels. Our model gives more precise sub-district estimates than previous methods, which were limited by data gaps. The study highlights Bangladesh’s advancements in reducing child stunting, highlighting the value of integrating remote-sensed data for more precise and credible analysis.

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The Utility of Socioeconomic and Remoteness Indicators in Understanding the Geographical Variation in the Regional Prevalence of Early Childhood Vulnerability in Australia

Baffour,

Das,

et al. 2024

Child Ind Res

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The family lives of children and their early childhood development outcomes are attributable to the level of socio-economic disadvantage and relative isolation. This study aims to investigate how the disadvantage of the local area (i.e., socio-economic indexes for areas (SEIFA)) and the remoteness (i.e., accessibility/remoteness index of Australia (ARIA)) contribute to improved prevalence estimates of child development vulnerability in statistical areas level 3 (SA3) and 4 (SA4) across Australia. Data from the 2018 Australian Early Development Census (AEDC) has been used. The study included 308,953 children involved in the AEDC 2018 where one-in-ten of them were considered to be developmentally vulnerable, nationally. We developed models in a hierarchical Bayesian framework at the SA3 level using SEIFA and ARIA indices as covariates to account for spatial and unobserved heterogeneity. The performances of developed models are examined based on the consistency at SA3, SA4, and state level. The results reveal that SEIFA makes a significant contribution to explaining the spatial variation in childhood development vulnerability across small domains in Australia. Further, the inclusion of the ARIA score improves the model performance and provides better accuracy, particularly in remote and very remote regions. In these regions, the spatial model fails to distinguish the remoteness characteristics. The chosen non-spatial model accounting for heterogeneity at higher hierarchies performs best. The utilization of socio-economic disadvantage and geographic remoteness of the finer level domains helps to explain the geographic variation in child development vulnerability, particularly in sparsely populated remote regions in Australia.

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Multilevel Time Series Modelling of Mobility Trends in the Netherlands for Small Domains

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 11 publications

References 37 publications

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

Improved estimates of child malnutrition trends in Bangladesh using remote-sensed data

The Utility of Socioeconomic and Remoteness Indicators in Understanding the Geographical Variation in the Regional Prevalence of Early Childhood Vulnerability in Australia

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