Enablers and barriers of community initiated health emergency transport systems in the Upper West Region of Ghana

Haruna, Umar; Woods, Hannah; Kansanga, Moses Mosonsieyiri; Konkor, Irenius; Dixon, Jenna; Luginaah, Isaac

doi:10.1080/19376812.2021.1885458

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…In practice, many regions (including Northern Ghana) do have an existing level of service (which they may consider upgrading 14 ), but for simplicity, we will assume that no ambulance service currently exists. Similarly, we will also assume that the ambulance service is targeted, meaning it only attends to patients with a specific medical emergency.…”

Section: Applied Example Backgroundmentioning

confidence: 99%

Predicting the effects of introducing an emergency transport system in low-income and middle-income countries: a spatial-epidemiological modelling study

Scandrett,

Lilford,

Nepogodiev

et al. 2024

bmjph

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IntroductionMany low-income and middle-income countries lack an organised emergency transportation system, leaving people to arrange informal transport to hospital in the case of a medical emergency. Estimating the effect of implementing an emergency transport system is impractical and expensive, so there is a lack of evidence to support policy and investment decisions. Alternative modelling strategies may be able to fill this gap.MethodsWe have developed a spatial-epidemiological model of emergency transport for life-threatening conditions. The model incorporates components to both predict travel times across an area of interest under different scenarios and predict survival for emergency conditions as a function of time to receive care. We review potentially relevant data sources for different model parameters. We apply the model to the illustrative case study of providing emergency transport for postpartum haemorrhage in Northern Ghana.ResultsThe model predicts that the effects of an ambulance service are likely to be ephemeral, varying according to local circumstances such as population density and road networks. In our applied example, the introduction of the ambulance service may save 40 lives (95% credible interval 5 to 111), or up to 107 lives (95% credible interval −293 to –13) may be lost across the region in a year, dependent on various model assumptions and parameter specifications. Maps showing the probability of reduced transfer time with the ambulance service may be particularly useful and allow for resource allocation planning.ConclusionsAlthough there is scope for improvement in our model and in the data available to populate the model and inform parameter choices, we believe this work provides a foundation for pioneering methodology to predict the effect of introducing an ambulance system. Our spatial-epidemiological model includes much oppurtunity for flexibility and can be updated as required to best represent a chosen case study.

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Section: Applied Example Backgroundmentioning

confidence: 99%