Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient‐Reported Travel Times

Abstract

Background Since long travel times to reach health facilities are associated with worse outcomes, geographic accessibility is one of the six core global surgery indicators; this corresponds to the second of the ''Three Delays Framework,'' namely ''delay in reaching a health facility.'' Most attempts to estimate this indicator have been based on geographical information systems (GIS) algorithms. The aim of our study was to compare GIS derived estimates to self-reported travel times for patients traveling to a district hospital in rural Rwanda for emergency obstetric care. Methods Our study includes 664 women who traveled to undergo a Cesarean delivery in Kirehe, Rwanda. We compared self-reported travel time from home to the hospital (excluding waiting time) with GIS estimated travel times, which were computed using the World Health Organization tool AccessMod, using linear regression. Results The majority of patients used multiple modes of transportation (walking = 48.5%, public transport = 74.2%, private transport = 2.9%, and ambulance 70.6%). Self-reported times were longer than GIS estimates by a factor of 1.49 (95% CI 1.40-1.57). Concordance was higher when the GIS model took into account that all patients in Rwanda are referred via their health center (b = 1.12; 95% CI 1.05-1.18). Conclusions To our knowledge, in this largest to date GIS validation study for geographical access to healthcare in low-and middle-income countries, a standard GIS model was found to significantly underestimate real travel time, which likely is in part because it does not model the actual route patients are travelling. Therefore, previous studies of 2-h access to surgery will need to be interpreted with caution, and future studies should take local travelling conditions into account. Niclas Rudolfson and Magdalena Gruendl are co-first authors.

Travel time and perinatal mortality after emergency caesarean sections: an evaluation of the 2-hour proximity indicator in Sierra Leone

“…This study endorses the previous finding that the geospatial travel time model described by Ouma et al 11 underestimates reported travel time. 16 However, we found that the more conservative model described by Huerta Munoz et al 12 provides estimates that are closer to patient-reported travel times. Possible reasons for the difference between patient-reported and modelled travel times are the fact that modelled travel time does not take into account the actual mode of transport and the actual route—including facilities that are visited before reaching the facility where the caesarean section is performed.…”

“…The first (model I) was based on the methods described by Ouma et al 11 , which overestimated geographical access compared with patientreported travel time in a recent study. 16 Several more conservative national models have been published from Rwanda, Ghana, Tanzania and Zambia. [12][13][14][15] As a sensitivity analysis, the model from Rwanda (Huerta Munoz et al 12 , walking and public transport scenario) was applied to our data set as it presented the most conservative travel time estimates.…”

“…A recent single-facility study from Rwanda comparing patient-reported travel time with geospatial modelled travel time concluded that the latter significantly underestimated real travel time. 16 Possible reasons for this deviation include applied assumptions about modes of transport, travel speeds and travel routes. In the present study, we extended the analysis to nine hospitals across all regions of Sierra Leone, and we compared reported travel times with estimates produced by two geospatial models.…”

“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

“…A publication in 2020 by Rudolfson and colleagues showed that even in rural settlements, self-reported times are longer than modelled estimates by a factor of 1.50 for women seeking CEmOC services. 32 We show that in a large sub-Saharan African megacity like Lagos, model-based methods are closest to actual travel time only over short distances (especially journeys <10 min), and underestimate travel time by an order of magnitude for longer journeys. This underestimation has significant impact on maternal and perinatal survival.…”

Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency

“…First, the geographical analysis used to estimate travel time relies heavily on assumptions regarding the mode of transportation and travel speeds and may underestimate the travel times for individual patients. 50 Analysts should take care to consider the appropriate spatial scale for their analysis, and use high-quality road network data and to the extent they are able, verify the speeds of transit and mode of transit of the population under study. A strength of geographical analytical estimates of travel time is objective measurement, incorporation of elevation and land cover, and ability to conduct cross-country, macrolevel analyses.…”