Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations

Submitted: 22 January 2024
Accepted: 1 May 2024
Published: 27 May 2024
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Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API’s in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

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Aday LA, Andersen R, 1981. The Concept of Access: Definition and Relationship to Consumer Satisfaction. Medical Care 19;127–140.
Ahmed S, Adams AM, Islam R, Hasan SM, Panciera R, 2019. Impact of traffic variability on geographic accessibility to 24/7 emergency healthcare for the urban poor: A GIS study in Dhaka, Bangladesh. PLoS ONE 14;0222488 DOI: https://doi.org/10.1371/journal.pone.0222488
Allam Z, Nieuwenhuijsen M, Chabaud D, Moreno C, 2022. The 15-minute city offers a new framework for sustainability, liveability, and health. Lancet Planet Health 6:e181-3. DOI: https://doi.org/10.1016/S2542-5196(22)00014-6
Avoka CK, Banke-Thomas A, Beňová L, Radovich E, Campbell OMR, 2022. Use of motorised transport and pathways to childbirth care in health facilities: Evidence from the 2018 Nigeria Demographic and Health Survey. PLOS Global Public Health 2:868 DOI: https://doi.org/10.1371/journal.pgph.0000868
Banke-Thomas A, Avoka CKO, Gwacham-Anisiobi U, Benova L, 2021. Influence of travel time and distance to the hospital of care on stillbirths: A retrospective facility-based cross-sectional study in Lagos, Nigeria. BMJ Global Health 6;007052 DOI: https://doi.org/10.1136/bmjgh-2021-007052
Banke-Thomas A, Avoka CKO, Gwacham-Anisiobi U, Omololu O, Balogun M, Wright K, Fasesin TT, Olusi A, Afolabi BB, Ameh C, 2022. Travel of pregnant women in emergency situations to hospital and maternal mortality in Lagos, Nigeria: A retrospective cohort study. BMJ Global Health 7;008604 DOI: https://doi.org/10.1136/bmjgh-2022-008604
Banke-Thomas A, Macharia PM, Makanga PT, Beňová L, Wong KLM, Gwacham-Anisiobi U, Wang J, Olubodun T, Ogunyemi O, Afolabi BB, Ebenso B, Omolade Abejirinde I-O, 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. Front Public Health 10;931401. DOI: https://doi.org/10.3389/fpubh.2022.931401
Banke-Thomas A, Wong KLM, Ayomoh FI, Giwa-Ayedun RO, Benova L, 2021. ‘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. BMJ Global Health 6;004318 DOI: https://doi.org/10.1136/bmjgh-2020-004318
Banke-Thomas A, Wong KLM, Collins L, Olaniran A, Balogun M, Wright O, Babajide O, Ajayi B, Afolabi BB, Abayomi A, Benova L, 2021. An assessment of geographical access and factors influencing travel time to emergency obstetric care in the urban state of Lagos, Nigeria. Health Policy Plann 36;1384-1396. DOI: https://doi.org/10.1093/heapol/czab099
Banke-Thomas A, Wong KLM, Olubodun T, Macharia PM, Sundararajan N, Shah Y, Prasad G, Kansal M, Vispute S, Shekel T, Ogunyemi O, Gwacham-Anisiobi U, Wang J, Abejirinde I-OO, Makanga PT, Azodoh N, Nzelu C, Afolabi BB, Stanton C, Beňová L, 2024. Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis. Lancet Global Health 12:e84858. DOI: https://doi.org/10.1016/S2214-109X(24)00045-7
Barth D, 2009, August 25. The bright side of sitting in traffic: Crowdsourcing road congestion data. https://googleblog.blogspot.com/2009/08/bright-side-of-sitting-in-traffic.html
Blanford JI, Kumar S, Luo W, MacEachren AM, 2012. It’s a long, long walk: accessibility to hospitals, maternity and integrated health centers in Niger. Int J Health Geograph 11;24 DOI: https://doi.org/10.1186/1476-072X-11-24
Bondarenko M, Tejedor Garavito N, Priyatikanto R, Sorichetta A, Tatem A, 2022. Interim: Unconstrained and constrained estimates of 2021-2022 total number of people per grid square, adjusted to match the corresponding UNPD 2022 estimates and broken down by gender and age groups (1km resolution), version 1.0. https://doi.org/10.5258/SOTON/WP00743
Bouanchaud P, Macharia PM, Demise EG, Nakimuli D, 2022. Comparing modelled with self-reported travel time and the used versus the nearest facility: modelling geographic accessibility to family planning outlets in Kenya. BMJ Global Health 7;e008366. DOI: https://doi.org/10.1136/bmjgh-2021-008366
Cambridge Dictionary. (2023). Conurbation. https://dictionary.cambridge.org/dictionary/english/conurbation
Chavane LA, Bailey P, Loquiha O, Dgedge M, Aerts M, Temmerman M, 2018. Maternal death and delays in accessing emergency obstetric care in Mozambique. BMC Pregnancy Childbirth 18:71. DOI: https://doi.org/10.1186/s12884-018-1699-z
Cuervo LG, Martinez-Herrera E, Osorio L, Hatcher-Roberts J, Cuervo D, Bula MO, Pinilla LF, Piquero F, Jaramillo C, 2022. Dynamic accessibility by car to tertiary care emergency services in Cali, Colombia, in 2020: Cross-sectional equity analyses using travel time big data from a Google API. BMJ Open 12;062178 DOI: https://doi.org/10.1136/bmjopen-2022-062178
Curtis A, Monet JP, Brun M, Bindaoudou IA, Daoudou I, Schaaf M, Agbigbi Y, Ray N, 2021. National optimisation of accessibility to emergency obstetrical and neonatal care in Togo: A geospatial analysis. BMJ Open 11;e045891 DOI: https://doi.org/10.1136/bmjopen-2020-045891
Delamater PL, Messina JP, Shortridge AM, Grady SC, 2012. Measuring geographic access to health care: raster and network-based methods. Int J Health Geograph 11:15 DOI: https://doi.org/10.1186/1476-072X-11-15
Diaz Olvera L, Plat D, Pochet P, 2013. The puzzle of mobility and access to the city in Sub-Saharan Africa. J Transport Geograph 32;9. DOI: https://doi.org/10.1016/j.jtrangeo.2013.08.009
Ekpenyong, M. S., Matheson, D., & Serrant, L. (2022). The role of distance and transportation in decision making to seek emergency obstetric care among women of reproductive age in south–South Nigeria: A mixed methods study. International Journal of Gynecology and Obstetrics, 159(1). https://doi.org/10.1002/ijgo.14103 DOI: https://doi.org/10.1002/ijgo.14103
EPMM Working Group. (2015). Strategies toward ending preventable maternal mortality (EPMM). https://www.who.int/reproductivehealth/topics/maternal_perinatal/epmm/en/
Esri. (2022). Routing and directions. Data Coverage. https://developers.arcgis.com/rest/network/api-reference/network-coverage.htm
Geldsetzer P, Reinmuth M, Ouma, PO, Lautenbach S, Okiro EA, Bärnighausen T, Zipf A, 2020. Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis. Lancet Healthy Longevity 1:e32–e42. DOI: https://doi.org/10.1016/S2666-7568(20)30010-6
Geofabrik GmbH. (2023). Download OpenStreetMap data for this region: Nigeria. https://download.geofabrik.de/africa/nigeria.html
Giorgi E, Fronterrè C, Macharia PM, Alegana VA, Snow RW, Diggle PJ, 2021. Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: To explain and to predict. J R Soc Interface 18:20210104. DOI: https://doi.org/10.1098/rsif.2021.0104
Gligorić K, Kamath C, Weiss DJ, Bavadekar S, Liu Y, Shekel T, Schulman K, Gabrilovich E, 2023. Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic. Comm Med 3;157. DOI: https://doi.org/10.1038/s43856-023-00384-9
Guagliardo MF, 2004. Spatial accessibility of primary care: Concepts, methods and challenges. In Int J Health Geograph 3;3.
Hierink, F., Boo, G., Macharia, P. M., Ouma, P. O., Timoner, P., Levy, M., Tschirhart, K., Leyk, S., Oliphant, N., Tatem, A. J., & Ray, N. (2022). Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa. Communications Medicine, 2(1). https://doi.org/10.1038/s43856-022-00179-4 DOI: https://doi.org/10.1038/s43856-022-00179-4
Hierink F, Rodrigues N, Muñiz M, Panciera R, Ray N, 2020. Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: An impact analysis of Cyclones Idai and Kenneth in Mozambique. BMJ Open, 10:039138 DOI: https://doi.org/10.1136/bmjopen-2020-039138
Juran S, Broer PN, Klug SJ, Snow RC, Okiro EA, Ouma PO, Snow RW, Tatem AJ, Meara JG, Alegana VA, 2018. Geospatial mapping of access to timely essential surgery in sub-Saharan Africa. BMJ Global Health 3;e000875. DOI: https://doi.org/10.1136/bmjgh-2018-000875
Karra K, Kontgis C, Statman-Weil Z, Joseph Mazzariello J, Mathis M, Brumby S; 2021. Global land use/land cover with Sentinel-2 and deep learning. IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. Available form: https://igarss2021.com/view_paper.php?PaperNum=3500 DOI: https://doi.org/10.1109/IGARSS47720.2021.9553499
Keyes EB, Parker C, Zissette S, Bailey PE, Augusto O, 2019. Geographic access to emergency obstetric services: A model incorporating patient bypassing using data from Mozambique. BMJ Global Health 4:000772. DOI: https://doi.org/10.1136/bmjgh-2018-000772
Kruk ME, Mbaruku G, McCord CW, Moran M, Rockers PC, Galea S, 2009. Bypassing primary care facilities for childbirth: A population-based study in rural Tanzania. Health Pol Plann 24:czp011. DOI: https://doi.org/10.1093/heapol/czp011
Lau J, 2020. Google Maps 101: How AI helps predict traffic and determine routes. Maps101. https://blog.google/products/maps/google-maps-101-how-ai-helps-predict-traffic-and-determine-routes/
Lin Y, Lippitt C, Beene D, Hoover J, 2021. Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources. Cartogr Geogr Inf Sci 48:471–90. DOI: https://doi.org/10.1080/15230406.2021.1960609
Macharia PM, Banke-Thomas A, Beňová L, 2023. Advancing the frontiers of geographic accessibility to healthcare services. Comm Med 3;158. DOI: https://doi.org/10.1038/s43856-023-00391-w
Macharia PM, Moturi AK, Mumo E, Giorgi E, Okiro EA, Snow RW, Ray N, 2022. Modelling geographic access and school catchment areas across public primary schools to support subnational planning in Kenya. Children’s Geograph 21:832–48. DOI: https://doi.org/10.1080/14733285.2022.2137388
Macharia PM, Mumo E, Okiro EA, 2021. Modelling geographical accessibility to urban centres in Kenya in 2019. PLoS ONE, 16:0251624 DOI: https://doi.org/10.1371/journal.pone.0251624
Macharia PM, Ray N, Giorgi E, Okiro EA, Snow RW, 2021. Defining service catchment areas in low-resource settings. BMJ Global Health 6;6381. DOI: https://doi.org/10.1136/bmjgh-2021-006381
Macharia PM, Ray N, Gitonga CW, Snow RW, Giorg E, 2022. Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations. Spatial Stat 51;100679. DOI: https://doi.org/10.1016/j.spasta.2022.100679
Macharia PM, Wong KLM, Olubodun T, Beňová L, Stanton C, Sundararajan N, Shah Y, Prasad G, Kansal M, Vispute S, Shekel T, Gwacham-Anisiobi U, Ogunyemi O, Wang J, Abejirinde I-OO, Makanga PT, Afolabi BB, Banke-Thomas A, 2023. A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations. Sci Data 10;736. DOI: https://doi.org/10.1038/s41597-023-02651-9
Makacha L, Makanga PT, Dube YP, Bone J, Munguambe K, Katageri G, Sharma S, Vidler M, Sevene E, Ramadurg U, Charantimath U, Revankar A, Von Dadelszen P, 2020. Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan. Int J Health Geograph 19:1. DOI: https://doi.org/10.1186/s12942-020-0197-5
Makanga PT, Schuurman N, Sacoor C, Boene HE, Vilanculo F, Vidler M, Magee L, Dadelszen P, Sevene E, Munguambe K, Firoz T, 2017. Seasonal variation in geographical access to maternal health services in regions of southern Mozambique. Int J Health Geograph 16;1. DOI: https://doi.org/10.1186/s12942-016-0074-4
Milusheva S, Björkegren D, Viotti L, 2021. Assessing Bias in Smartphone Mobility Estimates in Low Income Countries. ACM SIGCAS Conference on Computing and Sustainable Societies. DOI: https://doi.org/10.1145/3460112.3471968
Molenaar L, Hierink F, Brun M, Monet J-P, Ray N, 2023. Travel scenario workshops for geographical accessibility modeling of health services: A transdisciplinary evaluation study. Front Public Health 10;1051522 DOI: https://doi.org/10.3389/fpubh.2022.1051522
Moturi AK, Suiyanka L, Mumo E, Snow RW, Okiro EA, Macharia PM, 2022. Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis. Front Public Health 10;1002975 DOI: https://doi.org/10.3389/fpubh.2022.1002975
Mukaka MM, 2012. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69-71.
Mutono N, Wright JA, Mutunga M, Mutembei H, Thumb SM, 2022) Impact of traffic congestion on spatial access to healthcare services in Nairobi. Front Health Serv 2;788173 DOI: https://doi.org/10.3389/frhs.2022.788173
National Population Commission Nigeria, & The DHS Program ICF. (2019). Nigeria Demographic and Health Survey 2018. https://dhsprogram.com/pubs/pdf/FR359/FR359.pdf
Olubodun T, Macharia PM, Wong KL, Gwacham-Anisiobi U, Ogunyemi O, Beňová L, Abejirinde I-OO, Makanga PT, Wang J, Afolabi BB, Banke-Thomas A, 2023. Geocoded database of health facilities with verified capacity for caesarean section in urban Nigeria. figshare. Dataset. https://doi.org/10.6084/m9.figshare.22689667
OSRM. (2023). Open source routing machine. http://project-osrm.org/
Ouma PO, Macharia PM, Okiro E, Alegana V, 2021. Methods of Measuring Spatial Accessibility to Health Care in Uganda. P. T. Makanga, Ed.; pp. 77–90. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-63471-1_6
Ouma PO, Maina J, Thuranira PN, Macharia PM, Alegana VA, English M, Okiro EA, Snow RW, 2018. Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis. The Lancet Global Health, 6;e342–e350. DOI: https://doi.org/10.1016/S2214-109X(17)30488-6
Paxton A, Maine D, Freedman L, Fry D, Lobis S, 2005. The evidence for emergency obstetric care. Int J Gynecol Obstetr 88;26. DOI: https://doi.org/10.1016/j.ijgo.2004.11.026
Penchansky R, Thomas JW, 1981. The Concept of Access: Definition and relationship to consumer satisfaction. Medical Care 19;127–140. DOI: https://doi.org/10.1097/00005650-198102000-00001
QGIS Development Team. (2023). QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org
R Core Team. (2021). R: A language and environment for statistical computing. https://www.R-project.org/.
Ray N, Ebener S, 2008. AccessMod 3.0: Computing geographic coverage and accessibility to health care services using anisotropic movement of patients. Int J Health Geograph 7;63. DOI: https://doi.org/10.1186/1476-072X-7-63
Rudolfson N, Gruendl M, Nkurunziza T, Kateera F, Sonderman K, Nihiwacu E, Ramadhan B, Riviello R, Hedt-Gauthier B, 2020. Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times. World J Surg 44;2123–30. DOI: https://doi.org/10.1007/s00268-020-05480-8
Schiavina M, Melchiorri M, Pesaresi M, 2022. GHS-SMOD R2022A - GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2022A and GHS-BUILT-S R2022A, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/4606D58A-DC08-463C-86A9-D49EF461C47F
Shashidharan S. (2023, October 16). How AI and imagery keep speed limits on Google Maps updated. Maps 101. https://blog.google/products/maps/how-ai-and-imagery-keep-speed-limits-on-google-maps-updated/
Stewart BT, Tansley G, Gyedu A, Ofosu A, Donkor P, Appiah-Denkyira E, Quansah R, Clarke DL, Volmink J, Mock C, 2016. Mapping population-level spatial access to essential surgical care in Ghana using availability of bellwether procedures. JAMA Surg 151:1239 DOI: https://doi.org/10.1001/jamasurg.2016.1239
Tatem AJ, 2017. WorldPop, open data for spatial demography. Sci Data 4:170004. DOI: https://doi.org/10.1038/sdata.2017.4
The s2geometry.io. (2022). S2 Cells geometry. https://s2geometry.io/devguide/s2cell_hierarchy.html
Tobler W, 1993. Three presentations on geographical analysis and modeling: Non-isotropic geographic modeling speculations on the geometry of geography global spatial analysis. Technical Report (National Center for Geographic Information and Analysis), February.
United Nations. (2015). Sustainable Development Goals (SDGs). https://sdgs.un.org/goals
University of Geneva/GeoHealth group, World Health Organization, & MORU/Health GeoLab Group. (2023). AccessMod 5. https://www.accessmod.org/
van Etten J, 2017. R package gdistance: Distances and routes on geographical grids. J Stat Softw 76;i13 DOI: https://doi.org/10.18637/jss.v076.i13
Van Zyl JJ, 2001. The shuttle radar topography mission (SRTM): A breakthrough in remote sensing of topography. Acta Astronautica 48:5–12. DOI: https://doi.org/10.1016/S0094-5765(01)00020-0
Wong KLM, Banke-Thomas A, Olubodun T, Macharia PM, Stanton C, Sundararajan N, Shah Y, Prasad G, Kansal M, Vispute S, Shekel T, Ogunyemi O, Gwacham-Anisiobi U, Wang J, Abejirinde I-OO, Makanga PT, Afolabi BB, Beňová L, 2024. Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria. Comm Med 4;34. DOI: https://doi.org/10.1038/s43856-024-00458-2
Wood SN, 2017. Generalized additive models: An introduction with R, second edition. In Generalized Additive Models: An Introduction with R, Second Edition. https://doi.org/10.1201/9781315370279 DOI: https://doi.org/10.1201/9781315370279
Yao J, & Agadjanian V, 2018. Bypassing health facilities in rural Mozambique: Spatial, institutional, and individual determinants. BMC Health Serv Res 18:1006. DOI: https://doi.org/10.1186/s12913-018-3834-y

How to Cite

Macharia, P. M., Wong, K. L., Beňová, L., Wang, J., Makanga, P. T., Ray, N., & Banke-Thomas, A. (2024). Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1266