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How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique
How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique
Background Existence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals.

Methods The analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models’ prediction errors at different administrative unit levels.

Results The models’ prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels.

Conclusion The validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
2059-7908
1-12
Dube, Yolisa Prudence
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Ruktanonchai, Corrine W
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Sacoor, Charfudin
6a16c348-99e3-4da8-9b33-e0dfe1a8266a
Tatem, Andrew
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Munguambe, Khatia
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Boene, Helena
4962ac7b-ed12-4ac7-bfb4-489d13ca236e
Vilanculo, Faustino Carlos
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Sevene, Esperanca
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Matthews, Zoe
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von Dadelszen, Peter
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Makanga, Prestige T
a8ff41d0-5ac9-401c-9be3-f5721b4ea04b
Dube, Yolisa Prudence
adad57d2-f146-484e-8459-2bcf216a3bf2
Ruktanonchai, Corrine W
44e6fcd0-246b-480e-8940-9557dbb7c0cc
Sacoor, Charfudin
6a16c348-99e3-4da8-9b33-e0dfe1a8266a
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Munguambe, Khatia
3abc17e4-6aae-4d3c-bb3a-4a5bf4d8bf54
Boene, Helena
4962ac7b-ed12-4ac7-bfb4-489d13ca236e
Vilanculo, Faustino Carlos
67837824-5eac-4220-9713-a83d28016d1e
Sevene, Esperanca
3808ff46-2e2f-4648-b33a-5d7246d3d3ad
Matthews, Zoe
ebaee878-8cb8-415f-8aa1-3af2c3856f55
von Dadelszen, Peter
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Makanga, Prestige T
a8ff41d0-5ac9-401c-9be3-f5721b4ea04b

Dube, Yolisa Prudence, Ruktanonchai, Corrine W, Sacoor, Charfudin, Tatem, Andrew, Munguambe, Khatia, Boene, Helena, Vilanculo, Faustino Carlos, Sevene, Esperanca, Matthews, Zoe, von Dadelszen, Peter and Makanga, Prestige T (2019) How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique. BMJ Global Health, 4 (5), 1-12. (doi:10.1136/bmjgh-2018-000894).

Record type: Article

Abstract

Background Existence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals.

Methods The analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models’ prediction errors at different administrative unit levels.

Results The models’ prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels.

Conclusion The validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

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Accepted/In Press date: 13 July 2018
e-pub ahead of print date: 1 July 2019

Identifiers

Local EPrints ID: 432849
URI: http://eprints.soton.ac.uk/id/eprint/432849
ISSN: 2059-7908
PURE UUID: 146582a7-e301-41df-9c0b-6683c6701efc
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 30 Jul 2019 16:30
Last modified: 29 Aug 2019 00:36

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