“In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

Abstract

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

An assessment of geographical access and factors influencing travel time to emergency obstetric care in the urban state of Lagos, Nigeria

“…First, while we have leveraged actual patient data to map the travel paths of the pregnant women, we have not captured the travel time based on their actual ‘experienced’ travel time. However, Google Maps has been shown to be 85% (IQR = 69–98%) accurate in reflecting actual travel time ( Banke-Thomas et al. , 2021 ).…”

“…, 2016 ). A recent study showed that compared to estimates by GIS-modelled platforms, Google Maps allowed the best-case estimate of reality in an urban LMIC setting ( Banke-Thomas et al. , 2021 ).…”

Influence of travel time and distance to the hospital of care on stillbirths: a retrospective facility-based cross-sectional study in Lagos, Nigeria

“…The study used estimates of distance and travel time from Google Maps, which are closer to reality. 26 However, our study also has some limitations that should be considered in interpreting our findings. First, we used reported clinical data, as such, there are some data that are not routinely recorded in patient notes and others not properly recorded.…”

“…11 Evidence shows that estimates using Google Maps, like we have done in our study, are more reflective of reality compared with models. 26 On the other hand, self-reported travel time are usually higher than modelled estimates. 11 While, methodological differences may explain the difference, it might also relate with road transport quality.…”

“…[22][23][24][25] Compared with spatial model estimates, distance and travel time estimates using global positioning satellite navigation software like Google Maps (Alphabet, Mountain View, California, USA) have been shown to be closer to reality in an LMIC urban setting. 26 Building on this evidence, we georeferenced the place of residence, referral points and destination facility for each woman in Google Maps, based on the data extracted from their clinical records. For addresses that were not discoverable on Google Maps, we contacted local persons who were well acquainted with the neighbourhoods to check for any spelling errors and reattempted to locate the street.…”