Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania
Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania
Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off
between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.
epidemiology, geographic information systems, maternal health
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Ruktanonchai, Corrine, Warren
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Nieves, Jeremiah, Joseph
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Ruktanonchai, Nick
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Nilsen, Kristine
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Steele, Jessica
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Matthews, Zoe
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Tatem, Andrew
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1 July 2020
Ruktanonchai, Corrine, Warren
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Nieves, Jeremiah, Joseph
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Ruktanonchai, Nick
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Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Steele, Jessica
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Matthews, Zoe
ebaee878-8cb8-415f-8aa1-3af2c3856f55
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Ruktanonchai, Corrine, Warren, Nieves, Jeremiah, Joseph, Ruktanonchai, Nick, Nilsen, Kristine, Steele, Jessica, Matthews, Zoe and Tatem, Andrew
(2020)
Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania.
BMJ Global Health, 4 (e002092), , [e002092].
(doi:10.1136/bmjgh-2019-002092).
Abstract
Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off
between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.
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e002092.full
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e002092.full
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More information
Accepted/In Press date: 9 January 2020
e-pub ahead of print date: 10 February 2020
Published date: 1 July 2020
Additional Information:
Funding Information:
Funding The authors would like to acknowledge the support provided by the Economic and Social Research Council’s Doctoral Training Programme, which funds CWR.
Publisher Copyright:
© 2020 Author(s) (or their employer(s)). Re-use permitted under CC BY. Published by BMJ.
Keywords:
epidemiology, geographic information systems, maternal health
Identifiers
Local EPrints ID: 438136
URI: http://eprints.soton.ac.uk/id/eprint/438136
ISSN: 2059-7908
PURE UUID: d35ab86f-a98d-4225-8482-3af348d4bdae
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Date deposited: 03 Mar 2020 17:30
Last modified: 17 Mar 2024 03:35
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Contributors
Author:
Corrine, Warren Ruktanonchai
Author:
Jeremiah, Joseph Nieves
Author:
Nick Ruktanonchai
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