Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach.
Bayesian inference
combining misaligned areal units
geo-statistical approach
hierarchical space-time-age model
sub-national burden of disease study
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
20 02 2021
20 02 2021
Historique:
received:
06
10
2019
revised:
06
10
2020
accepted:
04
11
2020
pubmed:
8
12
2020
medline:
22
6
2021
entrez:
7
12
2020
Statut:
ppublish
Résumé
Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1021-1033Informations de copyright
© 2020 John Wiley & Sons, Ltd.
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