Referral transit time between sending and first-line receiving health facilities: a geographical analysis in Tanzania

Schmitz, Michelle; Serbanescu, Florina; Arnott, George E; Dynes, Michelle; Chaote, Paul; Msuya, Abdulaziz Ally; Chen, Yi No

doi:10.1136/bmjgh-2019-001568

Cited by 24 publications

(25 citation statements)

References 27 publications

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“…In a Tanzanian study, excluding time to first facility, modelled median travel time estimates of actual facility referrals from periurban to urban area were 156.4 min (IQR: 7.9–356.6 min). 44 As per our findings, real travel time will be longer. Even after getting to these apex hospitals, evidence shows that women could still face an additional 60 min (IQR: 21–215 min) delay between diagnosis and receiving life-saving interventions.…”

Section: Discussionsupporting

confidence: 66%

“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

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 66%

“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

et al. 2021

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“…None of the three countries have national referral guidelines, despite national health policies emphasizing the importance of strong referral systems [ 37 – 39 ].…”

Section: Methodsmentioning

confidence: 99%

Surgical ambulance referrals in sub-Saharan Africa – financial costs and coping strategies at district hospitals in Tanzania, Malawi and Zambia

Ifeanyichi¹,

Broekhuizen²,

Cheelo

et al. 2021

BMC Health Serv Res

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Background An estimated nine out of ten persons in sub-Saharan Africa (SSA) are unable to access timely, safe and affordable surgery. District hospitals (DHs) which are strategically located to provide basic (non-specialist) surgical care for rural populations have in many instances been compromised by resource inadequacies, resulting in unduly frequent patient referrals to specialist hospitals. This study aimed to quantify the financial burdens of surgical ambulance referrals on DHs and explore the coping strategies employed by these facilities in navigating the challenges. Methods We employed a multi-methods descriptive case study approach, across a total of 14 purposively selected DHs; seven, three, and four in Tanzania, Malawi and Zambia, respectively. Three recurrent cost elements were identified: fuel, ambulance maintenance and staff allowances. Qualitative data related to coping mechanisms were obtained through in-depth interviews of hospital managers while quantitative data related to costs of surgical referrals were obtained from existing records (such as referral registers, ward registers, annual financial reports, and other administrative records) and expert estimates. Interview notes were analysed by manual thematic coding while referral statistics and finance data were processed and analysed using Microsoft Office Excel 2016. Results At all but one of the hospitals, respondents reported inadequacies in numbers and functional states of the ambulances: four centres indicated employing non-ambulance vehicles to convey patients occassionally. No statistically significant correlation was found between referral trip distances and total annual numbers of referral trips, but hospital managers reported considering costs in referral practices. For instance, ten of the study hospitals reported combining patients to minimize trip frequencies. The total cost of ambulance use for patient transportation ranged from I$2 k to I$58 k per year. Between 34% and 79% of all patient referrals were surgical, with total costs ranging from I$1 k to I$32 k per year. Conclusion Cost considerations strongly influence referral decisions and practices, indicating a need for increases in budgetary allocations for referral services. High volumes of potentially avoidable surgical referrals provide an economic case – besides equitable access to healthcare – for scaling up surgery capacity at the district level as savings from decreased referrals could be reinvested in referral systems strengthening.

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“… 21 37 AccessMod models geographical accessibility using terrain-based least-cost path distance calculation. This open-source software has been successfully applied in many different settings, among which accessibility and referral assessments of health facility networks, optimisation modelling of health programmes in obstetric and neonatal care (EmONC), 38 primary health care, 39 emergency care, 40 referral times 41 and treatment of fever cases. 42 …”

Section: Methodsmentioning

confidence: 99%

Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: an impact analysis of Cyclones Idai and Kenneth in Mozambique

Hierink

Rodrigues

Muñiz³

et al. 2020

BMJ Open

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ObjectivesModelling and assessing the loss of geographical accessibility is key to support disaster response and rehabilitation of the healthcare system. The aim of this study was therefore to estimate postdisaster travel times to functional health facilities and analyse losses in accessibility coverage after Cyclones Idai and Kenneth in Mozambique in 2019.SettingWe modelled travel time of vulnerable population to the nearest functional health facility in two cyclone-affected regions in Mozambique. Modelling was done using AccessMod V.5.6.30, where roads, rivers, lakes, flood extent, topography and land cover datasets were overlaid with health facility coordinates and high-resolution population data to obtain accessibility coverage estimates under different travel scenarios.Outcome measuresTravel time to functional health facilities and accessibility coverage estimates were used to identify spatial differences between predisaster and postdisaster geographical accessibility.ResultsWe found that accessibility coverage decreased in the cyclone-affected districts, as a result of reduced travel speeds, barriers to movement, road constraints and non-functional health facilities. In Idai-affected districts, accessibility coverage decreased from 78.8% to 52.5%, implying that 136 941 children under 5 years of age were no longer able to reach the nearest facility within 2 hours travel time. In Kenneth-affected districts, accessibility coverage decreased from 82.2% to 71.5%, corresponding to 14 330 children under 5 years of age having to travel >2 hours to reach the nearest facility. Damage to transport networks and reduced travel speeds resulted in the most substantial accessibility coverage losses in both Idai-affected and Kenneth-affected districts.ConclusionsPostdisaster accessibility modelling can increase our understanding of spatial differences in geographical access to care in the direct aftermath of a disaster and can inform targeting and prioritisation of limited resources. Our results reflect opportunities for integrating accessibility modelling in early disaster response, and to inform discussions on health system recovery, mitigation and preparedness.

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Referral transit time between sending and first-line receiving health facilities: a geographical analysis in Tanzania

Cited by 24 publications

References 27 publications

“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

“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

Surgical ambulance referrals in sub-Saharan Africa – financial costs and coping strategies at district hospitals in Tanzania, Malawi and Zambia

Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: an impact analysis of Cyclones Idai and Kenneth in Mozambique

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