Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings

Banke-Thomas, Aduragbemi and Macharia, Peter M. and Makanga, Prestige Tatenda and Beňová, Lenka and Wong, Kerry L. M. and Gwacham-Anisiobi, Uchenna and Wang, Jia and Olubodun, Tope and Ogunyemi, Olakunmi and Afolabi, Bosede B. and Ebenso, Bassey and Omolade Abejirinde, Ibukun-Oluwa (2022) Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings. Frontiers in Public Health, 10: 931401. ISSN 2296-2565

Abstract

Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called “urban advantage” is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project.