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Article

Potential Spatial Accessibility to Primary Percutaneous Coronary Intervention (pPCI) Facilities in the Republic of Serbia for the Year 2030

by
Sreten Jevremović
1,*,
Filip Arnaut
1,
Nataša Mickovski Katalina
2,
Aleksandra Kolarski
1,
Zorana Vasiljević
3 and
Aleksandar Medarević
2
1
Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
2
Institute of Public Health of Serbia “Dr Milan Jovanović Batut”, Dr Subotića 5, 11000 Belgrade, Serbia
3
Faculty of Medicine, University of Belgrade, Dr Subotića Starijeg 8, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Urban Sci. 2025, 9(9), 355; https://doi.org/10.3390/urbansci9090355
Submission received: 18 July 2025 / Revised: 25 August 2025 / Accepted: 2 September 2025 / Published: 5 September 2025
(This article belongs to the Special Issue GIS in Urban Planning and Spatial Analysis)
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Abstract

This cross-sectional study evaluates the potential spatial accessibility of primary percutaneous coronary intervention (pPCI) facilities in the Republic of Serbia (RS) for the year 2030. Cardiovascular diseases, specifically acute myocardial infarction (AMI), are major contributors to mortality, requiring immediate intervention to reestablish blood circulation to the heart. This research employs travel time isochrone analysis to assess the percentage of the population residing within three specific time intervals (30, 60, and 90 min) from a pPCI facility. We project the percentage of the population residing within a 30 min travel time interval to be 50% in 2030. Additionally, the percentage of the population residing within the 90 min travel time interval from a pPCI facility, i.e., known as the “golden hour” travel time distance, is around 96%, with some weekly variations that equate to 1%. We utilized additional spatial analysis to identify population clusters that reside beyond the 90 min travel time from a pPCI facility. These results point to specific regions where either additional pPCI facilities or better road connections would most effectively reduce treatment delays. Additionally, the study highlighted the optimal location for a novel pPCI facility, which is the city of Vršac. Our findings underline the need for careful planning in the health system, where location and transport data can directly guide measures to improve access and lower cardiovascular disease (CVD) mortality.

1. Introduction

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, but in Serbia, they represent an especially serious challenge, both in terms of mortality and strain on the health system [1,2,3]. More than three-quarters of CVD deaths occur in low- and middle-income countries [4]. Within CVDs, ischemic heart disease, particularly acute myocardial infarction (AMI), is the leading single cause of death, responsible for both premature mortality and disability [1,5,6]. Eastern European countries, including the Republic of Serbia (RS), are especially affected, with substantially higher CVD mortality compared to Western Europe, largely due to population aging and high prevalence of risk factors [7]. In RS, ischemic heart disease accounted for 18% of cardiovascular deaths in 2022, despite an overall decline in mortality rates [8,9].
For patients with ST-elevation myocardial infarction (STEMI), rapid reperfusion is critical to reduce ischemic time and limit infarct size [10,11,12]. Among reperfusion strategies, primary percutaneous coronary intervention (pPCI) achieves better outcomes than thrombolysis and is regarded as the gold standard [13,14,15,16]. Clinical guidelines emphasize that intervention should occur within 120 min of symptom onset, ideally within the first 60 min, to maximize survival benefit [11,15,16].
Hospital accessibility can be characterized along two axes. Potential vs. revealed accessibility distinguishes between the opportunity to reach care based on service location and transport networks (potential), and the services actually reached and used (revealed) [17,18,19]. Spatial vs. non-spatial accessibility differentiates geographic frictions, distance, travel time, and congestion from organizational or socioeconomic barriers such as cost or insurance status [20,21,22]. This study focuses on potential spatial accessibility, quantified as travel time to the nearest pPCI-capable facility.
In this study, we evaluate potential spatial accessibility to pPCI facilities across Serbia for the year 2030 using travel time isochrones (30/60/90 min). This study differs from earlier work in several ways:
  • It incorporates the collected weekly working hours of pPCI facilities to account for high peak and low peak periods.
  • It uses gridded 2030 population projections to quantify the share of residents within each interval.
  • It applies cluster analysis and a what-if scenario to identify underserved populations and evaluate the effect of adding a facility at the most suitable location. These steps extend earlier studies, which typically assumed static 24/7 operation or simple distance buffers [23]. In addition, new pPCI capacities were established after the Mickovski-Katalina [23] study was conducted. Thus, we believe our findings will be instrumental in assessing the extent to which accessibility has changed, identifying areas for improvement, and predicting future changes according to demographic processes, changes in traffic, and the construction of new roads.
We framed this research within access-to-care theory [24], in which physical infrastructure and transport networks determine who can potentially reach a service, while organization and population patterns influence whether that access is equitable. Looking at accessibility like this makes it easier to translate the results into concrete policy steps, such as identifying the best sites for new facilities or the road links where upgrades would matter most. Consistent with behavioral models of health services use, we treat travel time and facility operating hours as enabling resources that condition the translation of clinical need into realized care. Accordingly, our outcome is potential spatial accessibility rather than utilization [25,26].

2. Materials and Methods

2.1. Geographical Setting of Serbia

The Republic of Serbia (RS) is situated in southeastern Europe, specifically on the Balkan peninsula (Figure 1a). The RS is a landlocked nation with various terrains, including the relatively flat Vojvodina region and the mountainous southern part of the country. Belgrade is located on both banks of the Sava and Danube Rivers in central Serbia. It is the capital and largest city, with a population of approximately 1.6 million. Additional notable cities in the RS encompass Novi Sad, situated in the northern region, with an approximate population of 360 thousand people; Niš, located in southeastern RS, with a population of 250,000 people; and Kragujevac, situated in central RS, with a population of 170 thousand people [27].
Figure 1b highlights the RS’s major and secondary highways as well as other roads. The high-speed motorway and highway network is particularly important for longer travel time intervals, such as 60 and 90 min. This is because these roads have a higher flow rate, resulting in reduced travel times [28]. In our scenario, an increase in the possible travel distance would occur for a fixed travel time interval.

2.2. Research Workflow and Data

This cross-sectional study combined information on hospital availability, population distribution, and the transport network. Between February and May 2024, weekly working hours were collected for all Serbian hospitals performing pPCI. Facility administrations provided verified information on location, city, and operating hours. These data were then extrapolated to 2030 to reflect planned expansions of 24/7 coverage, such as those at Novi Pazar and Zrenjanin.
Population estimates were obtained from the Global Human Settlement Layer (GHSL) of the European Commission [29,30]. The GHSL provides gridded population data with a 100 m resolution in five-year epochs from 1975 to 2030 [31,32,33]. For this study, the 2030 projection was used and confined to Serbian administrative boundaries [34]. To model the transport network, we relied on OpenStreetMap data [35].
Facility schedules were analyzed to determine daily high peak periods (07:00–13:00) and low peak periods (00:00–07:00 and 17:00–24:00). Datasets were then prepared for each day of the week to reflect variation in availability across peak and off-peak hours. On certain days, there were two periods of low peak hours, while on the remaining days, there was only one period, from midnight to 07:00. This information generated datasets for each day of the week to calculate travel time areas during high and low peak hours. Travel time isochrones were generated using the QGIS TravelTime plug-in under driving mode, with intervals of 30, 60, and 90 min. Webmap as well as the other relevant information about pPCI accessibility in Serbia can be found in Supplementary Material for GitHub. Representative times were selected for the analysis: 03:00 (early morning low peak), 10:00 (high peak), and 20:00 (evening low peak).
The TravelTime plug-in generated a cropped shapefile restricted to Serbian administrative borders. This step was necessary because several Serbian cities, including Subotica, Sombor, and Zaječar, are located close to national borders. Without cropping, the travel time areas would extend into neighboring countries, particularly for longer intervals of 60 and 90 min. The cropped isochrones were then used to calculate the population residing within each interval, representing the potential spatial accessibility of pPCI facilities based on the GHSL population distribution raster.
Accessibility was summarized by calculating the mean, minimum, and maximum proportion of the population within each interval. Results are expressed as absolute numbers and percentages of the national population, along with estimates of those outside each threshold.

2.3. Results Validation Workflow Utilizing Google Maps

The results of the travel time analysis were validated using Google Maps to assess agreement between modeled and real-world travel estimates. Validation (Figure 2) involved selecting random points along the outer boundaries of the 30, 60, and 90 min isochrones for both high and low peak periods. Coordinates of each sampled point were entered into Google Maps as the origin, and the nearest pPCI facility was set as the destination. Reported travel times were then recorded and compared to the modeled outputs. If multiple alternative routes were provided, times were averaged unless one route was markedly different, in which case it was excluded.
To balance conditions across the week, two random points were sampled for each interval during high peak hours (07:00–13:00) and four points during low peak hours (17:00–24:00). Sampling times alternated daily to capture time dependence variations. In total, 78 validation points were collected. This dataset covered different days, times, and traffic conditions, and is consistent with validation approaches used in comparable GIS-based accessibility studies [18,22,36].

3. Results

3.1. GHSL Population Raster Verification for the Republic of Serbia

We evaluated the suitability of the GHSL population raster for Serbia by comparing GHSL totals with official projected national population figures at five checkpoints (2000–2020, five-year steps). The average absolute difference between GHSL and the projected national totals was ~450,000 persons (MAPE ≈ 6.5%), indicating moderate overestimation but consistent trends across time. Table 1 summarizes year-by-year discrepancies (AE, APE) and their averages (MAE, MAPE).
Despite the bias, the GHSL series follows the official declining trajectory and is deemed adequate for accessibility estimation in this study, with the acknowledged uncertainty noted in the limitations.

3.2. Spatiotemporal Analysis of pPCI Facilities for the Year 2030

Figure 3 presents working hours of pPCI facilities in the 2030 projection case, utilizing data obtained from pPCI facilities in 2024 with approximations for 2030 regarding the changes in working hours, transitioning from a shift-based schedule to a 24/7 regime. This represents the ideal situation regarding the selected pPCI facilities that will transition to a 24/7 working schedule. The major distinction for the 2030 projection was GH Novi Pazar and GH Zrenjanin to transition from a work week morning shift schedule in 2024 to a non-stop working regime. Considering these improvements, conducting an analysis using the GHSL population data for 2030 was considered advantageous.
Table 2 displays the outcomes of the travel time area analysis for the year 2030. On average, 50% of the population lives within a 30 min travel radius of a pPCI facility. Throughout the week, fluctuations ranging from 49% to 52% are observable. Within a 60 min travel time, approximately 82% of the population, totaling around 5.6 million individuals, resides within proximity to a pPCI facility. In the final and most extensive travel time interval, the population percentages range from 96% to 97% during the week, equating to between 6.5 and 6.6 million individuals out of the total 6.8 million residents of the Republic of Serbia for the 2030 estimate.
The weekly maximum and minimum values for the 30 min travel time area have remained consistent for the Monday and Friday morning shifts. However, there has been an increase from 49% to 52% (as shown in Table 2). The maximum 84% and 97% values can be observed during the 60 min and 90 min travel time intervals, respectively. These values are consistently found throughout the week during the early morning and night shift periods, considered low peak hours. The number of individuals covered by different travel time intervals is as follows:
  • A total of 3.5 million people for the 30 min interval during the Monday morning shift;
  • A total of 5.7 million people for the 60 min interval during the Sunday early morning shift;
  • A total of 6.6 million people for the 90 min interval during the Monday afternoon shift.
The disparity between low and high peak travel time zones in 2030 is clear (Figure 4) and is primarily driven by differences in traffic volume at 3 AM versus 10 AM. During low peak, lower congestion extends the drivable network reach even though fewer pPCI facilities are operating (13 vs. 16 on Saturdays), producing the largest gap in the 60 min band: 84% coverage at low peak versus 78% at high peak. A notable special case involves Sombor and Subotica in northern Serbia: when these facilities are closed during low peak, their local populations are not directly served, but the expanded drivable range partly compensates by extending coverage into surrounding areas. Conversely, when these facilities are open during high peak, heavier traffic can contract the reachable area. Moving Sombor and Subotica to a 24/7 schedule would broaden coverage if implemented.
Cluster analysis isolates populations within the 90 min threshold. In the best coverage window (Monday afternoon), national coverage reaches 97% (≈6.6 million), leaving ≈260,000 residents outside. In Figure 5, we excluded Cluster 0 (Sombor/Subotica), where facilities exist but were closed during the sampled low peak period. Three major clusters remain: C1 (≈37,000; Vršac), C2 (≈28,000; Kladovo), and C6 (≈24,000; Prijepolje). Large urban areas such as Vršac and Prijepolje are only partially covered, with adjacent settlements also uncovered. Across all clusters, ≈213,000 people are identified, of whom ≈100,000 live in areas with no local pPCI facility.
In a what-if scenario that adds a pPCI facility in Vršac, and assuming 24/7 operation in Subotica and Sombor, the uncovered population outside of the 90 min threshold falls to ≈68,000. National 90 min coverage approaches 99% under this configuration.

3.3. Addressing Underserved/Inaccessible Regions in the Republic of Serbia

Considering the previous cluster findings in Section 3.2, two complementary measures can reduce time-to-care in underserved areas:
  • Adding or extending pPCI services;
  • Lowering effective travel times via transport upgrades.
Based on the 90 min threshold analysis, the largest uncovered concentrations occur around Vršac (≈37,000), Kladovo (≈28,000), and Prijepolje (≈24,000), with adjacent settlements also outside coverage. These patterns indicate where additional capacity or faster corridors would yield the greatest accessibility gains.
Figure 6 overlays planned or under-construction high-speed corridors on the identified clusters, showing where network upgrades could shorten travel times, especially in the 90 min band. Relevant links include the “smile of Vojvodina” (Sombor–Srbobran–Kikinda), the Danube Road (Požarevac–Golubac–Kladovo), the Belgrade ring extension (Bubanj Potok–Vršac), and the Kotroman–Požega–Ivanjica–Boljare corridor. These projects traverse or border the main uncovered clusters.
Quantifying accessibility gains from these corridors requires scenario-based network modeling and is planned for future work. Here, we wanted to point out and identify where such gains are most plausible given the current conditions.

3.4. Results Validation Utilizing Google Maps

Validation followed the workflow described in Section 2.3. Random points were sampled along the edges of the 30, 60, and 90 min isochrones during the week of 24 June 2024, with any missed peak periods completed in the week starting 1 July 2024. Sampling balanced conditions across the day: two points per interval during high peak hours and four points per interval during low peak hours, yielding 78 observations (42 high peak; 36 low peak).
Modeled versus observed travel times showed close agreement: overall MAE = 5.53 min, median AE = 4.5 min, max = 15 min, min = 0 min; MAE was 6.25 min in high peak and 4.69 min in low peak periods. These results support the reliability of the TravelTime isochrones against Google Maps under the sampled conditions.
Given the one-week validation window (with a small follow-up for missed peaks), month-to-month traffic variability was not captured; longer multi-week validation would better reflect seasonal dynamics. We noted this as a limitation and a direction for future research.

4. Discussion

4.1. General Discussion

This study estimates potential spatial accessibility to pPCI facilities in Serbia for 2030 by combining time-of-day travel time isochrones, facility-level operating hours, and gridded population projections. Three main findings emerge from the analysis.
  • Accessibility is time-sensitive, meaning lower congestion at night extends reachable areas even when fewer facilities are open, while daytime congestion contracts them despite broader availability.
  • Populations beyond the 90 min threshold are spatially clustered, with the largest concentrations around Vršac, Kladovo, and Prijepolje, and adjacent settlements are also outside of coverage.
  • Targeted “what-if” analysis shows that incremental capacity and extended hours can materially reduce the non-covered population and bring national 90 min coverage near “total” coverage. These results are consistent with our focused external validation against Google Maps (small absolute errors, slightly larger at high peak times).
Considering the behavior aspect, the observed differences between low and high peak coverage reflect variation in enabling resources (network speed/congestion and facility availability), not changes in predisposing or need factors. The most practical way to shorten delays is to adjust these factors directly, for example, by reducing congestion or extending facility hours [25,26].
International benchmarks help contextualize this impact. Prior studies report ~23% of Romania’s population within one hour of a cardiovascular hospital and ~62% of the Russian Federation within one hour after adding centers. In the 30 min access region is 43–69% of the population of Beijing’s Shijingshan District. In Kerala, about two-thirds of the population is within 30 min of a PCI-capable hospital. On the other hand, around 54% and 72% of the South African population is within 60 and 120 min, while ~70% of Canadian adults are within 90 min and ~84% of Americans are within 60 min. If we look at the Australian continent, more than 70% of Australians are within one hour of catheterization services [36,37,38,39,40,41,42,43]. Although results vary widely across countries, the comparisons show that even relatively small reductions in travel time could be important for Serbia in the case of time-sensitive emergencies.
Translating accessibility into outcomes depends on organization as much as infrastructure. Equipped EMS transport, constant communication with pPCI centers, and routing that avoids inter-facility detours shorten end-to-end times [44,45]. The European Society of Cardiology (ESC) recommends continuous 24/7 pPCI service [46], which directly addresses the confusion and delay that arise when centers operate only in shifts; delays also increase when small teams in interior centers face staffing gaps, prolonging total ischemic time and worsening myocardial injury [47]. Serbia has already operationalized parts of this agenda through the “Stent-Save a Life initiative” and national prehospital guidance [48,49], with Belgrade’s STEMI network a key early step [50], median call to hospital time being ~53 min across three years of data [49]. Local networks have since expanded across Serbia with demonstrated resilience even during the pandemic [51,52].
Comparable systems provide practical templates for network management and staffing. Recommended organizational practices for acute coronary syndrome networks include structured coordination and center rotation [53]; Vienna’s network illustrates balanced staffing and workload [54], and further operational exemplars exist in the Netherlands and North Carolina’s RACE project [55]. The STEMI-India model integrates primary PCI and pharmacoinvasive therapy in a hub and spoke design suitable for LMICs, but it depends on partnerships with local government and universal health insurance [56]. Regional evidence of effectiveness includes reduced AMI mortality coincident with STEMI network development in Bulgaria [57]. These international experiences are consistent with what we observed in Serbia and point to practical options such as expanding operating hours, designing networks more evenly, and better integrating EMS.
Equity and financing frameworks in Serbia support these operational choices: equity is a legal principle in health care [58]; users have the right to equal access without discrimination, and insurance coverage is near-universal with emergency care available to everyone (including vulnerable groups and foreigners) [59]; coronary stents are covered for insured persons under national (Republic Health Insurance Fund) implant regulations [60]. Within that enabling environment, prioritizing accessibility (even with trade-offs) remains warranted, especially where targeted transport upgrades can deliver short-term gains in the 90 min band.

4.2. Policy and Implementation Measures

Organizing the accessibility findings into action calls for a layered approach that targets the mechanisms driving delay, service availability, EMS operations, and network speed, while staying proportional to resources. The comprehensive aims are to shorten the time to reperfusion, reduce geographic inequities, and ensure accountability for results. Accordingly, the measures below are organized from low-cost organizational changes to medium-term transport upgrades, with monitoring steps to verify impact and guide scale-up.
Service organization (low-cost)
  • One clear improvement would be to extend operating hours at Subotica and Sombor. This would eliminate the repeated low peak gaps we observed in northern Serbia.
  • Pilot a shared on-call pPCI shift across neighboring facilities to smooth hour-by-hour availability without duplicating capacity.
  • Introduce dynamic scheduling: align lab staffing with empirically busier daytime windows while preserving a minimal night schedule to capitalize on lower congestion.
Emergency medical services (EMS) and operations:
  • Preposition EMS units closer to the three major clusters (Vršac, Kladovo, Prijepolje) during high-risk periods; evaluate temporary satellite bases.
  • Deploy real-time navigation and hospital load information to route patients to the fastest effective facility, not only the nearest by distance.
  • Standardize prehospital triage and tele-ECG to shorten decision-to-balloon time.
Transport and corridors (medium-term):
  • Prioritize segments of the “smile of Vojvodina”, Danube Road (Požarevac–Golubac–Kladovo), Belgrade ring extension (Bubanj Potok–Vršac), and Kotroman–Požega–Ivanjica–Boljare, where they intersect with uncovered clusters; design EMS-focused access treatments (ramps, turning pockets, shoulder hardening).
  • Incorporate accessibility key performance indicators (e.g., residents within 60/90 min) into corridor appraisal alongside traffic and economic metrics.
Monitoring and evaluation:
  • Establish an annual accessibility dashboard tracking the share of the population within 30/60/90 min by period (low/high peak), district, and facility availability pattern.
  • Use before/after evaluations when hours are extended or corridors open (e.g., Subotica/Sombor 24/7) to measure realized gains.
Evidence-based planning remains central: shortest travel time analyses and related GIS methods can guide siting, hours, and network upgrades, and should be paired with disease-specific resource mapping and, where possible, local outcome tracking to test whether improved access translates into reduced CVD mortality.

4.3. Limitations

This study approximates average traffic conditions using time-of-day scenarios; it cannot predict real-time congestion. The validation of Google Maps covered one week (with a short follow-up) and a sample size (n = 78) balanced across peak periods, so seasonal/atypical traffic patterns were not captured. As a result, the estimates represent average conditions, not the day-to-day fluctuations that occur in real traffic.
We modeled private vehicle driving rather than full EMS operations. Ambulances can use alternative routes, limited exemptions from traffic rules, and professional driving protocols; consequently, reachable areas may be somewhat larger under true EMS conditions than our estimates imply.
Population inputs introduce known bias. The GHSL raster overestimates Serbia’s population relative to official statistics (e.g., 7.4 million vs. 6.8 million in 2020), with an average absolute difference of ~450,000 (MAPE ≈ 6.5%). We therefore treat percent coverage as robust and read absolute headcounts with caution. For reference, the Statistical Office of the Republic of Serbia reports 6.6 M residents on 1 January 2024 [61,62].
Population is also dynamic: internal mobility and cross-border movement vary hourly to annually. This inherent variability reinforces the decision to emphasize relative shares (within 30/60/90 min) over precise counts.
Analyses are cross-sectional (inputs collected February to May 2024). Scenario results reflect the planning horizon to 2030, including the assumption that facilities such as GH Zrenjanin and GH Novi Pazar operate 24/7 by that time. If operating schedules or siting change materially, a reanalysis is required. For example, the December 2024 announcement of a new pPCI center at GH Loznica would necessitate updating accessibility estimates once operational details are confirmed.
Two further modeling choices need interpretation. First, isochrones were clipped to Serbia’s borders to avoid counting foreign facilities; this improves comparability but may understate options for some border communities. Second, corridor effects were not quantified; estimating their contribution requires scenario-based network modeling with updated speeds and connections. More broadly, the results represent potential spatial access, not full care timelines (dispatch, stabilization, door-to-balloon, and dynamic hospital load balancing were outside of our scope).
The research was limited to infrastructure and service availability. We did not examine patient characteristics or actual use of services, which would be important to connect potential access with real outcomes. Future work integrating patient flows and clinical outcomes would complete this perspective [25,26].
Future research should focus on this by running longer validation periods, modeling EMS-specific travel, testing sensitivity to speed and population assumptions, and linking potential access with data on actual patient flows.

5. Conclusions

Performing travel time estimations for critical services like pPCI facilities is demanding yet valuable. It yields vital data regarding the proportion of the population reached within a specific travel time duration, the spatial distribution, and the percentage of the population that falls outside a given travel time range.
The travel time intervals chosen for this study were 30, 60, and 90 min. The TravelTime QGIS plug-in was employed to create the travel time areas, which were analyzed afterward. The findings indicated that in the 2030 projection, it is anticipated that selected pPCI facilities will transition from operating only in the morning to operating 24/7.
Cluster analysis indicates that in 2030, the city of Vršac is expected to have the largest population cluster without access to a pPCI facility within a 90 min travel time. A what-if analysis showed that if the city of Vršac had a pPCI facility, only approximately 60 thousand people would be outside the travel time interval of 90 min.
While travel time modeling can only approximate real conditions, it still provides valuable guidance for planning new pPCI services and other essential facilities. Further research should continue in this direction to support decision-making in Serbia’s health system.

Supplementary Materials

The following supporting information can be accessed at: https://arnautf.github.io/PCI_Webmap_GH/. Accessed on 4 September 2025.

Author Contributions

Conceptualization—A.M. and F.A.; data curation—A.M. and N.M.K.; methodology—F.A., A.K. and S.J.; formal analysis—F.A., A.K. and S.J.; writing original draft—F.A., A.K. and S.J.; writing, review and editing—A.M., F.A., N.M.K., A.K., S.J. and Z.V.; writing, final draft—A.M., F.A., N.M.K., A.K., S.J. and Z.V. All authors have read and agreed to the published version of the manuscript.

Funding

(SJ: FA and AK)—This work was funded by the Institute of Physics Belgrade, University of Belgrade, through a grant by the Ministry of Science, Technological Development and Innovations of the Republic of Serbia.

Data Availability Statement

Data is available from the corresponding author upon reasonable request. Additionally, Supplementary Material can be accessed at: https://arnautf.github.io/PCI_Webmap_GH/. Accessed on 4 September 2025.

Acknowledgments

The authors would like to acknowledge employees at institutes and public health departments responsible for the acute coronary syndrome registry who helped in the data collection process.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Geographical position of the Republic of Serbia within Europe; (b) major highways, secondary highways, and other roads in the Republic of Serbia with the locations of all utilized pPCI facilities for the research; (c) pPCI facilities in the capital city of Belgrade.
Figure 1. (a) Geographical position of the Republic of Serbia within Europe; (b) major highways, secondary highways, and other roads in the Republic of Serbia with the locations of all utilized pPCI facilities for the research; (c) pPCI facilities in the capital city of Belgrade.
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Figure 2. Results validation workflow with an example sampling point.
Figure 2. Results validation workflow with an example sampling point.
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Figure 3. Working hours throughout the week for pPCI facilities in the Republic of Serbia for the year 2030; UCC—University Clinical Center; CHC—Clinical Hospital Center; GH—General Hospital; ICD—Institute for Cardiovascular Diseases; x—closed.
Figure 3. Working hours throughout the week for pPCI facilities in the Republic of Serbia for the year 2030; UCC—University Clinical Center; CHC—Clinical Hospital Center; GH—General Hospital; ICD—Institute for Cardiovascular Diseases; x—closed.
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Figure 4. The discrepancy between high and low peak travel time areas on Saturday: (a) 30 min travel time interval; (b) 60 min travel time interval; and (c) 90 min travel time interval.
Figure 4. The discrepancy between high and low peak travel time areas on Saturday: (a) 30 min travel time interval; (b) 60 min travel time interval; and (c) 90 min travel time interval.
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Figure 5. Cluster analysis of the population not residing within 90 min of a pPCI facility for the year 2030.
Figure 5. Cluster analysis of the population not residing within 90 min of a pPCI facility for the year 2030.
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Figure 6. Planned or under-construction high-speed motorways and highways in the Republic of Serbia.
Figure 6. Planned or under-construction high-speed motorways and highways in the Republic of Serbia.
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Table 1. Comparison of the calculated population of the Republic of Serbia based on the GHSL raster and the projected national data population; AE—absolute error; APE—absolute percentage error; MAE—mean absolute error; MAPE—mean absolute percentage error.
Table 1. Comparison of the calculated population of the Republic of Serbia based on the GHSL raster and the projected national data population; AE—absolute error; APE—absolute percentage error; MAE—mean absolute error; MAPE—mean absolute percentage error.
YearGHSL
Population		Projected National Data PopulationAE [/]APE [%]
20007,999,554 7,516,346 483,208 6.43
20057,892,178 7,440,769 451,409 6.07
20107,710,188 7,291,436 418,752 5.74
20157,571,148 7,095,383 475,765 6.71
20207,405,382 6,899,126 506,256 7.34
MAE/MAPE:467,078 6.46
Table 2. Percentage of the population living within a given travel time interval from a pPCI facility for the year 2030; A—high peak (10 AM); B—low peak (3 AM); C—low peak early in the afternoon (8 PM); D—low peak (5–7 PM, depending on facility schedules).
Table 2. Percentage of the population living within a given travel time interval from a pPCI facility for the year 2030; A—high peak (10 AM); B—low peak (3 AM); C—low peak early in the afternoon (8 PM); D—low peak (5–7 PM, depending on facility schedules).
Pop. Yr.
Pop. Tot.		TT Int.MonTueWedThuFriSatSun
ABABACDACDABABACD
2030
6,862,502		305250505150515050505052504950495051
608284798479848479848482847884798484
909697969796979696979696979697969796
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MDPI and ACS Style

Jevremović, S.; Arnaut, F.; Mickovski Katalina, N.; Kolarski, A.; Vasiljević, Z.; Medarević, A. Potential Spatial Accessibility to Primary Percutaneous Coronary Intervention (pPCI) Facilities in the Republic of Serbia for the Year 2030. Urban Sci. 2025, 9, 355. https://doi.org/10.3390/urbansci9090355

AMA Style

Jevremović S, Arnaut F, Mickovski Katalina N, Kolarski A, Vasiljević Z, Medarević A. Potential Spatial Accessibility to Primary Percutaneous Coronary Intervention (pPCI) Facilities in the Republic of Serbia for the Year 2030. Urban Science. 2025; 9(9):355. https://doi.org/10.3390/urbansci9090355

Chicago/Turabian Style

Jevremović, Sreten, Filip Arnaut, Nataša Mickovski Katalina, Aleksandra Kolarski, Zorana Vasiljević, and Aleksandar Medarević. 2025. "Potential Spatial Accessibility to Primary Percutaneous Coronary Intervention (pPCI) Facilities in the Republic of Serbia for the Year 2030" Urban Science 9, no. 9: 355. https://doi.org/10.3390/urbansci9090355

APA Style

Jevremović, S., Arnaut, F., Mickovski Katalina, N., Kolarski, A., Vasiljević, Z., & Medarević, A. (2025). Potential Spatial Accessibility to Primary Percutaneous Coronary Intervention (pPCI) Facilities in the Republic of Serbia for the Year 2030. Urban Science, 9(9), 355. https://doi.org/10.3390/urbansci9090355

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