Having timely access to emergency health care (EHC) depends largely on where you live. In this Scandinavian case study, we investigate how accessibility to EHC varies spatially in order to reveal potential socio-spatial disparities in access. Distinct measures of EHC accessibility were calculated for southern Sweden in a network analysis using a Geographical Information System (GIS) based on data from 2018. An ANOVA test was carried out to investigate how accessibility vary for different measures between urban and rural areas, and negative binominal regression modelling was then carried out to assess potential disparities in accessibility between socioeconomic and demographic groups. Areas with high shares of older adults show poor access to EHC, especially those in the most remote, rural areas. However, rurality alone does not preclude poor access to EHC. Education, income and proximity to ambulance stations were also associated with EHC accessibility, but not always in expected ways. Despite indications of a well-functioning EHC, with most areas served within one hour, socio-spatial disparities in access to EHC were detected both between places and population groups.

This study investigates the spatiotemporal variations in ambulancedemand in southern Sweden, and how land use factors andsociodemographic factors are associated with variations in demand. Astandardized ambulance demand ratio (SADR) indicating whether therisk of requiring an ambulance exceeded the expected was calculatedusing ambulance dispatch data in southern Sweden in 2018. Spatialanalysis using a geographic information system (GIS) and spatialstatistical methods underlie the methodology. Findings show thatalthough rural areas are associated with a higher risk of requiring anambulance (especially in daytime), this risk may be obscured by themore numerous dispatches occurring in urban areas. The increased riskin areas with bars and nightclubs, schools, industrial areas or roadcrossings reflects temporal variations in people’s routine activities. Bydictating how and when population’s reshuffle, land use factors canhelp explain variations in risk for requiring an ambulance. Confirmingprevious research, areas with a higher share of older adults areassociated with a higher ambulance demand while areas with a highmedian income are associated with lower demand. Population size maybe an inadequate indicator of ambulance demand. Planning shouldaccount for daily changes in population sizes and compositions as thisimpacts ambulance demand.

Lowering the response times of fire and rescue services (FRS) can reduce costs and the risk of fatalities in emergencies. Ensuring everyone can be reached by the FRS as quickly as possible is therefore a key objective for planners, who often rely on estimated response times to know where response times are long. To be reliable, such estimations need to be validated against real response times. Therefore, this study investigates the spatial patterns of FRS in Sweden in 2018, and compares how estimated and real response times correspond, as well as what factors explain under- or overestimation of the real times. Network analysis performed in a Geographical Information System (GIS) and regression modelling underpin the methodology. In most FRS events (81.3%), estimated response times are shorter than real response times with an average of 83.6 s. Both real and estimated response times are highest in rural municipalities. However, on average, underestimations are larger in urban municipalities. Response times also tend to be underestimated in municipalities with formalized cooperation agreements. Joint organization of FRS resources is thus not necessarily an effective way to reduce response times, with implications for the way FRS is delivered. This study highlights the heterogeneous spatial patterns of difference between estimated and real response times, and shows the importance of being event and area specific when planning the FRS system.

Background: Assuring that emergency health care is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC.

Method:  Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. 

Results:  Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED’s catchment areas compared to urban, larger ED’s – especially during vacation time in the summer. 

Conclusions: Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED’s in Sweden, which largely depends on population sizes in ED’s catchment areas.