How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique. (1st July 2019)

• Record Type: Journal Article
• Authors: Dube, Yolisa Prudence; Ruktanonchai, Corrine Warren; Sacoor, Charfudin; Tatem, Andrew J; Munguambe, Khatia; Boene, Helena; Vilanculo, Faustino Carlos; Sevene, Esperanca; Matthews, Zoe; von Dadelszen, Peter; Makanga, Prestige Tatenda
• Other Names: Macete Eusébio author non-byline.; Vala Anifa author non-byline.; Amose Felizarda author non-byline.; Pires Rosa author non-byline.; Nhamirre Zefanias author non-byline.; Macamo Marta author non-byline.; Chiaú Rogério author non-byline.; Matavele Analisa author non-byline.; Nhancolo Ariel author non-byline.; Cutana Silvestre author non-byline.; Mandlate Ernesto author non-byline.; Macuacua Salésio author non-byline.; Bique Cassimo author non-byline.; Mocumbi Sibone author non-byline.; Gonçálves Emília author non-byline.; Maculuve Sónia author non-byline.; Biz Ana Ilda author non-byline.; Mulungo Dulce author non-byline.; Augusto Orvalho author non-byline.; Lee Tang author non-byline.; Filimone Paulo author non-byline.; Nobela Vivalde author non-byline.; Tchavana Corsino author non-byline.; Nkumbula Cláudio author non-byline.; Bone Jeffrey author non-byline.; Dunsmuir Dustin author non-byline.; Drebit Sharla K author non-byline.; Kariya Chirag author non-byline.; Kinshella Mai-Lei Woo author non-byline.; Li Jing author non-byline.; Lui Mansun author non-byline.; Payne Beth A author non-byline.; Khowaja Asif R author non-byline.; Sawchuck Diane author non-byline.; Sharma Sumedha author non-byline.; Tu Domena K author non-byline.; Ukah Ugochi V author non-byline.;
• Is Part Of: BMJ global health. Volume 4(2019)Supplement 5
• Publication Date: 2019-07-01
• Subjects: health geography -- demographic distribution -- comparison -- maternal health census -- live births and pregnancies; World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2018-000894
• Languages: English
• ISSNs: 2059-7908

Abstract

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.