Estimating the health burden of road traffic injuries in Malawi using an individual-based model

Abstract

Background: Road traffic injuries are a significant cause of death and disability globally. However, in some countries the exact health burden caused by road traffic injuries is unknown. In Malawi, there is no central reporting mechanism for road traffic injuries and so the exact extent of the health burden caused by road traffic injuries is hard to determine. A limited number of models predict the incidence of mortality due to road traffic injury in Malawi. These estimates vary greatly, owing to differences in assumptions, and so the health burden caused on the population by road traffic injuries remains unclear.

Methods: We use an individual-based model and combine an epidemiological model of road traffic injuries with a health seeking behaviour and health system model. We provide a detailed representation of road traffic injuries in Malawi, from the onset of the injury through to the final health outcome. We also investigate the effects of an assumption made by other models that multiple injuries do not contribute to health burden caused by road accidents.

Results: Our model estimates an overall average incidence of mortality between 23.5 and 29.8 per 100,000 person years due to road traffic injuries and an average of 180,000 to 225,000 disability-adjusted life years (DALYs) per year between 2010 and 2020 in an estimated average population size of 1,364,000 over the 10-year period. Our estimated incidence of mortality falls within the range of other estimates currently available for Malawi, whereas our estimated number of DALYs is greater than the only other estimate available for Malawi, the GBD estimate predicting and average of 126,200 DALYs per year over the same time period. Our estimates, which account for multiple injuries, predict a 22-58% increase in overall health burden compared to the model ran as a single injury model.

Conclusions: Road traffic injuries are difficult to model with conventional modelling methods, owing to the numerous types of injuries that occur. Using an individual-based model framework, we can provide a detailed representation of road traffic injuries. Our results indicate a higher health burden caused by road traffic injuries than previously estimated.

Keywords: Health burden; Individual-based model; Malawi; Road traffic injuries.

Fig. 1

Road traffic injury model diagram, Q1 through 6 related to the questions asked by the model to determine the health burden and corresponding health system usage caused by RTIs in Malawi. Owing to the large number parameters used to determine the answer to these questions, we have listed the parameters in ‘Appendix’ in Table 2, organised by the question asked

Fig. 2

Model calibration. Panel a shows the calibration of the model’s incidence of RTIs to the mean incidence of RTIs predicted by the GBD study between 2010 and 2019 in Malawi. Panel b shows the calibration of the model’s predicted proportion of RTIs involving males to the average proportion of males from the GBD estimates 2010–2019. Panel c shows the age distribution of those involved in RTIs. Panel d shows the proportion of RTIs involving alcohol. Panel e shows the calibration of the model’s predicted incidence of on-scene mortality to an estimate derived from Malawi’s police data (Schlottmann et al. 2017). Panel f shows the model’s predicted average number of injuries per person calibrated to results from Kamuzu Central Hospital (KCH) (Sundet et al. 2018). Panel g shows the calibration effort to produce HSB falling within the bounds reported in other SSA countries (Zafar et al. 2018). Finally, panel h shows the calibration of the model’s overall predicted mortality of those who have received treatment, taking the relationship between ISS scores and mortality reported by Kuwabara et al. (2010) and scaling this to the results from a national-scale study on mortality with treatment in Tanzania (Sawe et al. 2021)

Fig. 3

The multiple injury model’s predicted incidences of death for different values of rt_emergency_care_ISS_score_cut_off. We find that the values 5 to 9 of the parameter rt_emergency_care_ISS_score_cut_off produce levels of HSB which fall within the bounds reported by Zafar et al. (2018) and conclude the model’s estimated incidence of mortality ranges from 23.5 to 29.8 per 100,000 person years

Fig. 4

The model's predicted total number of DALYs caused by RTIs between 2010 and 2019 for varying values of rt_emergency_care_ISS_score_cut_off. The values of rt_emergency_care_ISS_score_cut_off between 5 and 9 produce an overall percentage of HSB which falls within the ranges reported by Zafar et al. (2018). From these runs, we conclude that the model predicts roughly 1.8 to 2.3 million DALYs occurring as a result of RTIs

Fig. 5

A comparison of the GBD RTI model, the single injury model and the multiple injury model. Both the single and multiple injury model predicts a higher health burden caused by RTIs than the GBD study, with both predicting a higher incidence of mortality and a greater number of DALYs. By comparing the single and multiple injury forms of the model, we see that accounting for multiple injuries led to a roughly 45% increase in health burden caused by RTIs

Fig. 6

A comparison of the health burden predicted by the model with and without the health system, a compares the predicted number of DALYs and b compares the predicted incidence of mortality

Fig. 7

An example distribution of number of injuries assigned to each per person and the resulting average number of injuries per person resulting from the distribution compared to the results of Sundet et al. (2018)

Fig. 8

The effect on the number of model runs on the average incidence of road traffic injuries in the model. For each number of samples (x-axis), we sampled N runs from the 20 model runs 100 times. We then plotted the average incidence of road traffic injuries between the N model runs as circles, associated with the number of runs sampled per time. We then plotted the 95% uncertainty interval of the incidence of road traffic injuries between 2010 and 2019 taken from the GBD study (shown in blue). We find that all of the 20 model runs fell within the 95% uncertainty interval of incidence of road traffic injuries within the GBD study