PT  - JOURNAL ARTICLE
AU  - Dube, Yolisa Prudence
AU  - Ruktanonchai, Corrine Warren
AU  - Sacoor, Charfudin
AU  - Tatem, Andrew J
AU  - Munguambe, Khatia
AU  - Boene, Helena
AU  - Vilanculo, Faustino Carlos
AU  - Sevene, Esperanca
AU  - Matthews, Zoe
AU  - von Dadelszen, Peter
AU  - Makanga, Prestige Tatenda
ED  - ,
TI  - How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique
AID  - 10.1136/bmjgh-2018-000894
DP  - 2019 Jun 01
TA  - BMJ Global Health
PG  - e000894
VI  - 4
IP  - Suppl 5
4099  - http://gh.bmj.com/content/4/Suppl_5/e000894.short
4100  - http://gh.bmj.com/content/4/Suppl_5/e000894.full
SO  - BMJ Global Health2019 Jun 01; 4
AB  - 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.