Assessing inequalities in geographical access to emergency medical services in metropolitan Lisbon: a cross-sectional and ecological study

Abstract

ObjectivesStudies have suggested that material deprivation is strongly associated with negative health outcomes, and lower usage of various levels of healthcare. We aim to analyse geographical access to emergency medical services (EMSs) and hospital emergency units by EMS in relation to deprivation in the Lisbon Metropolitan Area (LMA), Portugal.DesignThis study estimates road network-based access times from the centroids of statistical sections (census block groups equivalent) to locations of EMS and hospital emergency services. Each statistical section has been linked to a Material Deprivation Index (MDI). A non-parametric analysis of variance (ANOVA) was undertaken to compare MDI-linked statistical sections in terms of access to emergency care. Geographical access analysis was conducted for 2018.Primary outcome measureRoad network-based access time (in minutes) for EMSs to statistical sections and then on to emergency units in hospitals.ResultsOverall, 82.4% of the LMA population is located less than a 10 min drive from an EMS without transport, and 99.1% from an EMS with transport. Travel time from EMS with transport to hospital is potentially less than 20 min for 95.2% of the population. However, 63.1% of residents living beyond a 30 min threshold (total time from emergency call to hospital arrival) are in areas with very high MDI (18.8% in high MDI, 13.3% in medium MDI, 4.7% in low MDI, 0% in very low MDI). Kruskal-Wallis ANOVA confirms discrepancies in access times between better-off and poorer areas.ConclusionPoorer areas experience worse geographical access to EMS and hospital emergency units. More research is needed to explore the quality of services and their outcomes, and to refine the analysis by focusing on specific vulnerable groups.

Locating emergency medical services to reduce urban-rural inequalities

“…Unlike Objective (2) which only focuses on rural population, Objective (3) explicitly considers the disparity between urban and rural areas, represented by the difference in achieved service levels (i.e., the proportions of covered population). As patient survival rate in rural areas is often lower than that in urban areas [2,4], a common practice in EMS location optimization when focusing on reducing urban-rural inequality is to increase the service provision to rural areas (e.g., Refs.…”

“…Objective (3) is to minimize urban-rural inequality in service coverage. Constraints (4) indicate that demand i is either covered by a station j (j ∈ Ω i ) or assigned to an open station j (j ∕ ∈ Ω i ). If ∑ j∈Ωi X j ≥ 1, demand i will be covered by at least one station and the associated Y ij (j ∕ ∈ Ω i ) will be zero due to the preference of ∑ j∕ ∈Ωi Y ij = 0 in objective (1) and the minimization function in (2).…”

“…Fair access to public services like EMS is crucial to overall social equity and is one of the sustainable development goals set by the United Nations (i.e., Goal 10: Reduce inequality within and among countries) [1]. However, inequalities in EMS within/across regions and between population groups are common worldwide and are often attributed to limited resources, including funding or physical barriers such as distance and road infrastructure [2][3][4][5]. For example, patients experiencing out-of-hospital cardiac arrest in urban areas had higher survival rates than those in rural districts in Victoria, Australia, where a key influential factor was proximity to the nearest ambulance station [2].…”

“…For example, patients experiencing out-of-hospital cardiac arrest in urban areas had higher survival rates than those in rural districts in Victoria, Australia, where a key influential factor was proximity to the nearest ambulance station [2]. In Lisbon, it was found that deprived areas generally had worse geographic access to EMS than affluent areas [4]. Such inequalities and their implications for care, have been exacerbated by the outbreak of Coronavirus Disease-2019 (COVID- 19).…”

Socio-spatial disparities in access to emergency health care—A Scandinavian case study

“…Disparities in levels of accessibility has also been observed between demographic and socioeconomic groups. For example, in the urban setting, foreign born individuals [31] and populations living in materially deprived areas [32] have been shown to have poorer accessibility to trauma centers. Residential segregation may also facilitate inequitable levels of access to health care for minority groups [33,34] and between ethnic groups [31,35].…”