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Background: Emergency medical services play a key role in the healthcare system. An ageing population, economic challenges, and technological advances require emergency prehospital care to be flexible, efficient, and equal. We investigated emergency ambulance services in Västerbotten County, a sparsely populated region in northern Sweden, with the aim of understanding the spatio-temporal distribution of alarms and identifying factors that explain the response times of priority-1 alarms.

Methods: We analysed 11,764 priority-1 alarms between 2022 and 2023 in Västerbotten County. The spatial and temporal distributions of the alarms were examined at various levels of aggregation, with a particular focus on age and gender. Response times were investigated together with its three components: dispatch time, preparation time, and travel time. Multivariate regression was used to analyse the components, considering six binary factors: age, gender, alarm location, season, time of the day, and day of the week.

Results: The alarm incidence increased with age, with a sharp increase around 60–70 years of age, where the increase in alarm incidence was 13% (p 0.001) higher among men than women. The alarm incidence varied significantly over time, with the highest frequencies observed during daytime, weekdays, and winter season. The county’s median response time for priority-1 (MRT1) alarms was 14.6 minutes, with high variation between municipalities and even larger differences across rural, suburban, and urban districts. Elderly patients (60+) had 10% (p 0.001) longer MRT1 than younger patients (0–59). This resulted from elderly patients having longer dispatch and travel times, which could be attributed to the location of alarms. Interestingly, regardless of age, women had approximately 8% (p 0.001) longer dispatch times than men.

Conclusion: From the age of 60, alarm incidence increases substantially, with a greater rise among men than women. A central finding of this study is that the EMS process times, including response times, are associated with the age and gender of the patients. These results are partly attributed to the spatial distribution of the alarms, but for the dispatch time the gender and age differences are arguably causal. Hence, an ageing population will result in more alarms and potentially longer missions, which will demand flexible and efficient EMS systems.

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.

In order to optimally utilise the resources of a country’s prehospital care system, i.e. ambulance service(s),it is crucial that one is able to spatio-temporally forecast hot-spots, i.e. spatial regions and periods with anincreased risk of seeing a call to the emergency number 112 which results in the dispatch of an ambulance.Such forecasts allow the dispatcher to make strategic decisions regarding e.g. the fleet size and where todirect unoccupied ambulances. In addition, simulations based on forecasts may serve as the startingpoint for different optimal routing strategies. Although the associated data typically comes in the form ofspatio-temporal point patterns, point process based modelling attempts in the literature has been scarce.In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consists ofthe spatial (gps) locations of the dispatch addresses and the associated days of occurrence of the calls.The spatial study region is given by the four northernmost regions of Sweden and the study period isJanuary 1, 2014 to December 31, 2018. Motivated by the non-infectious disease nature of the data, wehere employ log-Gaussian Cox processes (LGCPs) for the spatio-temporal modelling and forecasting ofthe calls. To this end, we propose a K-means based bandwidth selection method for the kernel estimationof the spatial component of the separable spatio-temporal intensity function. The temporal componentof the intensity function is modelled by means of Poisson regression, using different calendar covariates,and the spatio-temporal random field component of the random intensity of the LGCP is fitted usingsimulation via the Metropolis-adjusted Langevin algorithm. A study of the spatio-temporal dynamics ofthe data shows that a hot-spot can be found in the south eastern part of the study region, where mostpeople in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Thefitted temporal component of the intensity functions reveals that there is a significant association betweenthe expected number of calls and the day of the week as well as the season of the year. In addition,non-parametric second-order spatio-temporal summary statistic estimates indicate that LGCPs seem tobe reasonable models for the data. Finally, we find that the fitted forecasts generate simulated futurespatial event patterns which quite well resemble the actual future data.