Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/6117
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dc.contributor.authorKerry L. M. Wongen_US
dc.contributor.authorAduragbemi Banke-Thomasen_US
dc.contributor.authorTope Olubodunen_US
dc.contributor.authorPeter M. Machariaen_US
dc.contributor.authorCharlotte Stantonen_US
dc.contributor.authorNarayanan Sundararajanen_US
dc.contributor.authorYash Shahen_US
dc.contributor.authorGautam Prasaden_US
dc.contributor.authorMansi Kansalen_US
dc.contributor.authorSwapnil Visputeen_US
dc.contributor.authorTomer Shekelen_US
dc.contributor.authorOlakunmi Ogunyemien_US
dc.contributor.authorUchenna Gwacham-Anisiobien_US
dc.contributor.authorJia Wangen_US
dc.contributor.authorIbukun-Oluwa Omolade Abejirinde,en_US
dc.contributor.authorPrestige Tatenda Makangaen_US
dc.contributor.authorBosede B. Afolabien_US
dc.contributor.authorLenka Beňováen_US
dc.date.accessioned2024-05-10T06:28:05Z-
dc.date.available2024-05-10T06:28:05Z-
dc.date.issued2024-02-28-
dc.identifier.urihttps://cris.library.msu.ac.zw//handle/11408/6117-
dc.description.abstractBackground Better geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian cities. Methods We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta’s Relative Wealth Index (RWI). We used the Google Maps Platform’s internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was calculated. Results We show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC facilities. Conclusions Our findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settingsen_US
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.relation.ispartofcommunications medicineen_US
dc.subjectrelative wealth indexen_US
dc.subjectemergency obstetric careen_US
dc.subjectaccessibilityen_US
dc.subjectNigeriaen_US
dc.titleSocio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeriaen_US
dc.typeresearch articleen_US
dc.identifier.doihttps://doi.org/10.1038/s43856-024-00458-2-
dc.contributor.affiliationFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UKen_US
dc.contributor.affiliationFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK; School of Human Sciences, University of Greenwich, London, UK; Maternal and Reproductive Health Research Collective, Lagos, Nigeriaen_US
dc.contributor.affiliationDepartment of Community Medicine and Primary Care, Federal Medical Centre Abeokuta, Abeokuta, Ogun Nigeriaen_US
dc.contributor.affiliationDepartment of Public Health, Institute of Tropical Medicine, Antwerp, Belgium; Population & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UKen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationGoogle LLC, Mountain View, CA USAen_US
dc.contributor.affiliationLagos State Ministry of Health, Ikeja, Lagos Nigeriaen_US
dc.contributor.affiliationNuffield Department of Population Health, University of Oxford, Oxford, UKen_US
dc.contributor.affiliationSchool of Computing & Mathematical Sciences, University of Greenwich, London, UKen_US
dc.contributor.affiliationDalla Lana School of Public Health, University of Toronto, Toronto, Canada; Women’s College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Canadaen_US
dc.contributor.affiliationSurveying and Geomatics Department, Midlands State University Faculty of Science and Technology, Gweru, Midlands Zimbabwe; Climate and Health Division, Centre for Sexual Health and HIV/AIDS Research, Harare, Zimbabween_US
dc.contributor.affiliationMaternal and Reproductive Health Research Collective, Lagos, Nigeria; Department of Obstetrics and Gynaecology, College of Medicine of the University of Lagos, Lagos, Nigeriaen_US
dc.contributor.affiliationDepartment of Public Health, Institute of Tropical Medicine, Antwerp, Belgiumen_US
dc.relation.issn2730-664Xen_US
dc.description.volume4en_US
dc.description.startpage1en_US
dc.description.endpage10en_US
item.openairetyperesearch article-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
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