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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
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.
2059-7908
1-8
Ruktanonchai, Corrine, Warren
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Nieves, Jeremiah, Joseph
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Ruktanonchai, Nick
fe68cb8d-3760-4955-99fa-47d43f86580a
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Steele, Jessica
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Matthews, Zoe
ebaee878-8cb8-415f-8aa1-3af2c3856f55
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Ruktanonchai, Corrine, Warren
a576fb11-a475-4d48-885a-85938b60a7a8
Nieves, Jeremiah, Joseph
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Ruktanonchai, Nick
fe68cb8d-3760-4955-99fa-47d43f86580a
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Steele, Jessica
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
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), 1-8, [e002092]. (doi:10.1136/bmjgh-2019-002092).

Record type: Article

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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Accepted/In Press date: 9 January 2020
e-pub ahead of print date: 10 February 2020

Identifiers

Local EPrints ID: 438136
URI: http://eprints.soton.ac.uk/id/eprint/438136
ISSN: 2059-7908
PURE UUID: d35ab86f-a98d-4225-8482-3af348d4bdae
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 03 Mar 2020 17:30
Last modified: 07 Oct 2020 02:01

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Contributors

Author: Corrine, Warren Ruktanonchai
Author: Kristine Nilsen
Author: Jessica Steele
Author: Zoe Matthews
Author: Andrew Tatem ORCID iD

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