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

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.

Neonatal and perinatal mortality in the urban continuum: a geospatial analysis of the household survey, satellite imagery and travel time data in Tanzania

“… 44 45 Further, we made assumptions about travel speed, which may not hold true in all places and might have a larger margin of error within cities due to, for example, variability in traffic and weather, and waiting time. 46 However, this was necessary due to lack of observational data. 47 The exact location of the household of residence for each woman is obscured by provision of one cluster location and by cluster displacement in DHS due to reasons of anonymity.…”

“…Travel time was based on the nearest public hospital, whereas in reality, women often bypass the nearest facility 44 45. Further, we made assumptions about travel speed, which may not hold true in all places and might have a larger margin of error within cities due to, for example, variability in traffic and weather, and waiting time 46. However, this was necessary due to lack of observational data 47.…”

Neonatal and perinatal mortality in the urban continuum: A geospatial analysis of the household survey, satellite imagery and travel time data in Tanzania

“…61 62 Further, we made assumptions about travel speed, which may not hold true in all places and might have a larger margin of error within cities due to, for example, variability in traffic and weather, and waiting time. 63 However, this was necessary due to lack of observational data. 64 .…”

Approaches to defining health facility catchment areas in sub-Saharan Africa

“…This will also aid in testing model assumptions, and deal with the perennial incompleteness of spatial data, poor record keeping and geocoding inadequacies [92]. With the advancement in data science (such as machine learning) and data collection techniques (such as remote sensing), a range of climatic and environmental data (e.g., land use, rainfall patterns), road networks and traffic patterns can now be easily collected [93][94][95]. Increasingly available household surveys and routine data will be valuable for tracking utilization rates in the population to derive better thresholds for different health outcomes and contexts.…”