Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina

1 Introduction

Cardiovascular diseases (CVDs) pose a major public health challenge, significantly contributing to the global disease burden across both high- and low-income countries [1]. From the perspective of health geography, CVDs are widely recognised as one of the leading causes of death in Europe [2]. The World Health Organization (WHO) estimates that complications arising from hypertension are responsible for approximately 9.4 million deaths each year [3]. Alarmingly, without immediate and effective interventions, this figure is expected to rise significantly in the coming years. Furthermore, projections suggest that by 2030, nearly a quarter of all global deaths will be attributed to CVDs, underscoring the urgent need for comprehensive strategies to address this growing public health crisis [3].

Among the key risk factors, high blood pressure ranks as one of the leading contributors to death and disability worldwide [4,5]. People’s health is greatly affected by environmental conditions and their impacts [6]. Various factors, such as smoking, poor dietary habits, lack of physical activity, stress, and environmental pollution, play a significant role in the development of noncommunicable diseases [6].

The global prevalence of hypertension – defined as a systolic blood pressure of ≥140 mmHg, diastolic blood pressure of ≥90 mmHg, or the use of antihypertensive medication – doubled from 650 million people in 1990 to 1.3 billion in 2019 [4,5]. Many individuals live with hypertension for extended periods before receiving a diagnosis [4,7].

The NCD Risk Factor Collaboration analysed global hypertension trends from 1990 to 2019 across 200 countries [8]. In 2019, the age-standardised prevalence of hypertension was lowest in Canada and Peru for both men and women (Figure 1). For women, low prevalence was also observed in Taiwan, South Korea, Japan, and several Western European countries, including Switzerland, Spain, and the United Kingdom [8]. Among men, low rates were noted in various low- and middle-income countries, such as Eritrea, Bangladesh, Ethiopia, and the Solomon Islands [8]. In 2019, South Korea, Canada, and Iceland recorded the highest rates of hypertension treatment and control, with treatment exceeding 70% and control surpassing 50% [8]. These were followed by countries like the United States, Costa Rica, Germany, Portugal, and Taiwan. In contrast, treatment rates were below 25% for women and under 20% for men in Nepal, Indonesia, and several nations in sub-Saharan Africa and Oceania [8]. Control rates were less than 10% for both women and men in these regions, as well as for men in parts of North Africa, Central and South Asia, and Eastern Europe [8].

Figure 1

Number of data sources by country for the hypertension [8].

Various studies have applied geographic information systems (GIS) and spatial statistical methods to analyse hypertension patterns and prevalence. For example, Murad and Khashoggi employed the Getis-Ord Gi* statistic to explore spatial clustering of diabetes, asthma, and hypertension in Jeddah, Saudi Arabia [9]. Similarly, Laohasiriwong et al. used local indicators of spatial association statistics to study hypertension distribution across Thailand’s 76 provinces [10]. In the United States, White et al. utilised Getis-Ord Gi* hotspot mapping to visualise clusters of hypertension prevalence in South Carolina [11].

In South Africa, Rudasingwa et al. conducted spatial scans and hotspot analyses using ArcGIS to identify areas with particularly high hypertension prevalence [1]. Leroux and Cloutier analysed geographic patterns of hypertension in Canada, demonstrating the value of health geography and GIS in understanding hypertension distribution and risk factors [12]. These tools help identify spatial clusters and map hypertension prevalence effectively [12]. Kamath et al. explored the spatial distribution of hypertension among men and women in India and examined its association with health insurance coverage [13]. Similarly, Xu et al. analysed the spatio-temporal variations in gender-specific hypertension risk across China [14].

Kang et al. conducted a study to evaluate temporal trends and regional variations in blood pressure across China, while Gupta and Gupta et al. analysed trends in hypertension epidemiology in India [15–17]. Heaton et al. investigated contemporary hypertension trends’ identification and management following the 2017 American College of Cardiology/American Heart Association guidelines [18]. Aekplakorn et al. analysed temporal trends in hypertension prevalence, awareness, treatment, and control in Thailand [19].

The kriging method has been widely applied in public health and environmental research [20–30]. Matheron, in Principles of Geostatistics, established the foundations of geostatistics by developing a methodological framework for more precise estimation of ore reserves and mineral concentrations in mining [20]. Oliver and Webster, in Kriging: A Method of Interpolation for Geographic Information Systems, explored the integration of the kriging method into GIS, highlighting that traditional interpolation techniques often rely on deterministic models that fail to capture the inherent spatial variability of geographic data [21]. The application of kriging in epidemiological research was demonstrated by Carrat and Valleron, who employed this approach to map an influenza epidemic in France [31]. Oliver et al. analysed the spatial distribution of childhood cancer cases in England, using ordinary and conditionally unbiased cokriging to estimate cancer risk [30]. Jerrett et al. applied universal kriging alongside a multiquadric interpolator to generate a smoothed air pollution surface for Los Angeles, linking it to mortality rates from various causes [24]. Goovaerts and Gebreab compared Bayesian spatial models with Poisson kriging using lung and cervical cancer mortality data from 118 counties, as well as simulated datasets [26]. Mercer et al. assessed the effectiveness of universal kriging and land-use regression in predicting nitrogen oxide (NO _x ) concentrations within the Multi-Ethnic Study of Atherosclerosis and Air Pollution [28].

In Serbia, undiagnosed and untreated hypertension remains widespread among the adult population [7]. As reported by Timmis et al., Serbia had an incidence rate of 920 per 100,000 population in 2019, ranking 18th among 54 countries in terms of CVD incidence rate [6,32]. In Serbia and the territory of Autonomous Province (AP) Vojvodina, medical studies analysing hypertension were primarily conducted. Grujić et al. evaluated the prevalence of hypertension and assessed awareness, treatment, and control levels within the population in Serbia [7]. Jocić-Savić et al. examined the prevalence of hypertension among the elderly and its treatment, considering various factors such as regions of Serbia, type of settlement, gender, age, educational background, and socioeconomic status [33]. Vulin et al. conducted a retrospective analysis of data from the outpatient department at the Institute of Cardiovascular Diseases of Vojvodina for the year 2020 [34]. The study identified a link between short-term blood pressure variability and left ventricular diastolic dysfunction in patients with arterial hypertension [34]. A small number of studies in the territory of AP Vojvodina used a geographical approach in the study of CVDs and hypertension [6,35–39]. Kričković et al. applied the Getis-Ord G* method to identify CVD hotspots in Vojvodina, while Tomasevic et al. explored the growing public health issues of obesity and hypertension in the province [6,35]. Furthermore, Petrović and Čanković investigated obesity prevalence and the influence of socio-demographic factors among adolescents aged 15–19 in Vojvodina [36]. Maksimović et al. studied the connection between magnesium and calcium levels in drinking water and CVDs across 65 municipalities in Serbia, while Poloniecki et al. and Jevtić et al. explored the link between air pollution and the incidence of CVDs in the same region [37–39].

This study will focus on the distribution and trends of hypertension within the AP Vojvodina region, specifically among patients treated at the Institute for Cardiovascular Diseases of Vojvodina (ICDV), spanning from 2000 to 2023. While it will not delve into the underlying causes of hypertension, it aims to provide valuable insights into its geographic prevalence and patterns over the past two decades. By integrating spatial analysis with public health research, this study underscores the critical role of geographic perspectives in understanding and addressing the growing burden of hypertension and other non-communicable diseases. Such research is essential for developing targeted interventions and policies that account for regional health disparities and the unique challenges faced by different populations in managing chronic conditions like hypertension. Although hypertension is a significant risk factor for CVDs and one of the most prevalent chronic conditions worldwide, there is a lack of detailed spatial and temporal data on its distribution in Serbia. In the AP Vojvodina region, limited insight into regional trends and patterns hampers public health protective measures. This gap inhibits effective resource allocation and the implementation of tailored preventive actions. By analysing 23 years of hypertension data from the ICDV, this study aims to provide spatial-temporal insights that support evidence-based planning and promote regional health equity.

2 Methods

2.1 Study area

The AP Vojvodina, situated in the northern part of Serbia, boasts a strategically significant geographical location [6,40,41]. It lies between 44°38′ and 46°10′ north latitude and 18°10′ and 21°15′ east longitude in south-eastern Europe, within the Balkans, and covers the southern portion of the Pannonian Basin (Figure 2) [6,40,41]. To the north, it borders Hungary, to the east Romania, to the west Croatia, and to the southeast Bosnia and Herzegovina [6,40]. To the south, it is bounded by the Danube and Sava rivers [6,40,42]. The total area of the research region is 21,506 km² [6,40]. In 2022, the Republic Institute of Statistics recorded 1,740,230 inhabitants in the AP Vojvodina [40,43]. The research area has 467 settlements. The modern network of urban areas consists of 52 settlements, 45 local self-governments, and 37 settlements represent municipal centres [44,45]. The largest number of settlements belongs to the category of 5,000–10,000 inhabitants, and only Novi Sad belongs to the category of over 100,000 inhabitants [44,45].

Figure 2

Position of the study area in the South-eastern Europe and settlements by population number.

2.2 Data set

For this study, a reference database from the ICDV in Sremska Kamenica was used [46]. The database was organised according to the International Classification of Diseases (ICD), as defined by the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems, published by the WHO [47]. Although the WHO released the 11th Revision of the ICD at the 72nd World Health Assembly in 2019, which came into effect on January 1, 2022 [48], the 10th Revision remains in use in the Republic of Serbia. Following this classification, the group of diseases related to hypertension (I10-I15) was analysed, and the results are presented cumulatively. Table 1 provides the hypertension classification used in this study.

Table 1

International classification of hypertension according to 10th revision by the WHO used in the research [47,49]

Code	Diagnosis
I10	Essential (primary) hypertension
I11	Hypertensive heart disease
I11.0	Hypertensive heart disease with (congestive) heart failure
I11.9	Hypertensive heart disease without (congestive) heart failure
I12	Hypertensive renal disease
I12.0	Hypertensive renal disease with renal failure
I12.9	Hypertensive renal disease without renal failure
I13	Hypertensive heart and renal disease
I13.0	Hypertensive heart and renal disease with (congestive) heart failure
I13.1	Hypertensive heart and renal disease with renal failure
I13.2	Hypertensive heart and renal disease with both (congestive) heart failure and renal failure
I13.9	Hypertensive heart and renal disease, unspecified
I15	Secondary hypertension
I15.0	Renovascular hypertension
I15.1	Hypertension secondary to other renal disorders
I15.2	Hypertension secondary to endocrine disorders
I15.8	Other secondary hypertension
I15.9	Secondary hypertension, unspecified

The study examines the period from 2000 to 2023. Data on admission diagnoses of patients treated for hypertension, based on their place of residence, were used. Data on the number of deaths due to hypertension at ICDV were not included, as the number of deaths was small. Table 2 provides the total number of hypertension cases by diagnosis. The majority of cases were classified as essential (primary) hypertension. Conversely, only one case of hypertensive heart and renal disease with renal failure was recorded, making it the least frequent diagnosis.

Table 2

The type of admission diagnosis and its frequency [46]

Diagnosis	Count
I10	140,975
I11.9	5,979
I11	602
I11.0	151
I13	15
I13.0	11
I12.0	7
I13.9	6
I13.2	5
I12	4
I13.1	1
Sum	147,756

This table shows that the total number of recorded hypertension cases in the database is 147,756. However, the analysis included only patients from the AP Vojvodina region who were treated at the specified institute. Consequently, the total number of hypertension cases used for this study was 146,606. Data on patients from outside the AP Vojvodina region were not analysed. Additionally, patients treated at other healthcare institutions within the AP Vojvodina region were excluded from the analysis due to the lack of access to their data. Figure 3 presents the settlements in the AP Vojvodina region by the total number of hypertension cases. Most cases are concentrated near the largest city in the study area, Novi Sad.

Figure 3

Settlements in the AP Vojvodina by the sum of hypertension cases.

From this figure, it is evident that no hypertension cases were recorded in some settlements. In 136 out of 467 settlements, there were no recorded cases of hypertension. Conversely, hypertension cases were reported in 331 settlements.

From 1995 to 2005, the data are quite limited, as these were the early years of the information system at the ICDV, and data entry was optional during that period [46]. Starting in 2005, data entry became mandatory. If data from the early period of implementing the information system were used, it would create a mistaken insight that some settlements have increased trends or are hot spots. Figure 4 depicts the research framework, outlining the workflow from data acquisition and processing through the use of software for geospatial analysis to the visualization of results.

Figure 4

The initial stage of the research process.

These maps with visualised data were used for subsequent analysis to compare the results of the two methods. Clusters from the Emerging Hot Spot Analysis (HSA) and settlement polygons from the Mann-Kendall (MK) analysis were used as inputs in the Kriging procedure to present the results of both analyses in a consistent manner. Figure 5 illustrates the schematic process of this procedure.

Figure 5

The second stage of the research process.

First, the polygon features from the aforementioned analyses were converted into point features, as the Kriging method in ArcGIS Pro requires point features as input. The next step involved creating a Kriging raster, which was subsequently used to generate contour lines. This approach allowed for a uniform comparison of the two analyses.

3 Methods

4 Results

4.1 Results of MK analysis

Table 3 reveals increasing trends in hypertension in 36 settlements. Only four settlements recorded decreasing trends in hypertension cases. Overall, in 40 of 467 settlements significant trend was detected. In 296 settlements, the MK analysis was not conducted either due to the absence of recorded hypertension cases or insufficient parameters. Only settlements with a recorded period of at least 10 years were included in the analysis. If hypertension cases were recorded in a settlement for a shorter period, that settlement was excluded from the analysis. This is because the MK analysis yields reliable results only for periods of 10 years or longer. For shorter timeframes, the test may lack statistical significance. Given that 136 settlements had no recorded hypertension cases, this means that in 160 settlements, some cases were recorded, but not in a sufficient number to be included in the MK analysis. Due to the large number of 131 settlements where some MK parameters were available but no significant trend was detected, the parameters for these settlements are not presented in this study.

Table 3

Parameters of the MK test for the hypertension at the significance level of 0.05 in the study area (p – p-value of the significance test, Z – standardised test statistics, s – so-called Sen’s slope, B – intercept)

Settlement	Trend	P	Z	s	B
Bačka Palanka	Increasing	0.02	2.28	6.43	103.14
Bački Brestovac	Increasing	0.03	2.22	0.18	−0.64
Bački Gračac	Increasing	0.01	2.51	0.14	−0.21
Begeč	Increasing	0.01	2.53	1	24
Budisava	Increasing	0.02	2.39	1	38
Bukovac	Increasing	4.07 × 10−4	3.54	3.50	45.50
Čelarevo	Increasing	1.60 × 10−3	3.16	1.55	16.09
Ečka	Increasing	0.02	2.32	0.14	−0.07
Futog	Increasing	2.69 × 10−5	4.20	12.33	149
Gajdobra	Increasing	7.41 × 10−4	3.37	1	9
Jarak	Increasing	0.02	2.32	0.11	0.17
Karavukovo	Increasing	2.60 × 10−3	3.01	0.43	0.14
Kikinda	Increasing	2.74 × 10−3	3	0.50	2.50
Krčedin	Decreasing	0.04	−2.04	−0.60	27.40
Kumane	Decreasing	0.02	−2.40	−0.25	3.63
Ledinci	Increasing	0.02	2.24	1.55	48.09
Ljuba	Decreasing	3.11 × 10−3	−2.96	−0.14	2.07
Manđelos	Increasing	0.02	2.41	0.20	0.40
Mileševo	Increasing	0.04	2.02	0.12	0.94
Mladenovo	Increasing	0.02	2.40	0.50	6.50
Neštin	Increasing	0.03	2.21	0.45	4.91
Novi Bečej	Increasing	6.83 × 10−4	3.40	0.50	2.50
Novi Kneževac	Increasing	0.01	2.60	0.33	−1.50
Novi Sad	Increasing	1.68 × 10−3	3.14	110	2,379
Odžaci	Increasing	2.22 × 10−3	3.06	0.71	5.65
Petrovaradin	Increasing	9.61 × 10−7	4.90	11.67	68.33
Platičevo	Increasing	0.01	2.43	0.15	0.69
Prigrevica	Increasing	3.07 × 10−3	2.96	0.18	1.45
Ratkovo	Decreasing	0.02	−2.30	−0.33	9
Ravno Selo	Increasing	3.62 × 10−3	2.91	0.65	6.18
Ruma	Increasing	0.05	2	1.21	33.07
Rumenka	Increasing	4.51 × 10−3	2.84	3.17	65.50
Senta	Increasing	0.05	1.99	0.29	2.07
Sombor	Increasing	0.05	1.96	0.63	9.38
Sremska Mitrovica	Increasing	0.01	2.46	1.33	14
Srpski Miletić	Increasing	0.01	2.48	0.10	0.10
Stari Ledinci	Increasing	4.07 × 10−4	3.54	0.67	−1.33
Titel	Increasing	0.01	2.46	2.17	58.50
Tovariševo	Increasing	1.57 × 10−3	3.16	1.07	6.36
Veternik	Increasing	1.36 × 10−4	3.82	9	113

In most settlements with an increasing trend in hypertension cases, the MK analysis recorded in South Bačka county. In 19 settlement, an increasing trend was detected – Bačka Palanka (p = 0.02, Z = 2.28), Begeč (p = 0.01, Z = 2.53), Budisava (p = 0.02, Z = 2.39), Bukovac (p = 4.07 × 10⁻⁴, Z = 3.54), Čelarevo (p = 1.60 × 10⁻³, Z = 3.16), Futog (p = 2,69 × 10⁻⁵, Z = 4.20), Gajdobra (p = 7,41 × 10⁻⁴, Z = 3.37), Ledinci (p = 0.02, Z = 2.24), and Mileševo (p = 0.04, Z = 2.02). Figure 6 shows the graph of the MK analysis for these settlements.

Figure 6

MK analysis for: Bačka Palanka, Begeč, Budisava, Bukovac, Čelarevo, Futog, Gajdobra, Ledinci, and Mileševo (South Bačka county).

Also, in the same county, this analysis recorded an increasing trend in hypertension cases in Mladenovo (p = 0.02, Z = 2.40), Neštin (p = 0.03, Z = 2.21), Novi Sad (p = 1.68 × 10⁻³, Z = 3.14), Petrovaradin (p = 9.61 × 10⁻⁷, Z = 4.90), Ravno Selo (p = 3.62 × 10⁻³, Z = 2.91), Rumenka (p = 4.51 × 10⁻³, Z = 2.84), Stari Ledinci (p = 0.01, Z = 3.54), Titel (p = 0.01, Z = 2.46), Tovariševo (p = 1.57 × 10⁻³, Z = 3.16), and Veternik (p = 1.36 × 10⁻⁴, Z = 3.82). Figure 7 shows the graph of the MK analysis for these settlements.

Figure 7

MK analysis for: Mladenovo, Neštin, Novi Sad, Petrovaradin, Ravno Selo, Rumenka, Stari Ledinci, Titel, Tovariševo, and Veternik (South Bačka county).

In West Bačka County, seven settlements recorded an increasing trend in hypertension cases – Bački Brestovac (p = 0.03, Z = 2.22), Bački Gračac (p = 0.01, Z = 2.51), Karavukovo (p = 2.60 × 10⁻³, Z = 3.01), Odžaci (p = 2.22 × 10⁻³, Z = 3.06), Prigrevica (p = 3.07 × 10⁻³, Z = 2.96), Sombor (p = 0.05, Z = 1.96), and Srpski Miletić (p = 0.01, Z = 2.48). Figure 8 shows the graph of the MK analysis for these settlements.

Figure 8

MK analysis for: Bački Brestovac, Bački Gračac, Karavukovo, Odžaci, Prigrevica, Sombor, and Srpski Miletić (West Bačka County).

This analysis recorded an increasing trend in five settlements – Jarak (p = 0.02, Z = 2.32), Manđelos (p = 0.02, Z = 2.41), Platičevo (p = 0.01, Z = 2.43), Ruma (p = 0.05, Z = 2), and Sremska Mitrovica (p = 0.01, Z = 2.46). Figure 9 shows a graph for these settlements.

Figure 9

MK analysis for: Jarak, Manđelos, Platičevo, Ruma, and Sremska Mitrovica (Srem County).

In three settlements in North Banat county, the MK analysis recorded an increasing trend in hypertension cases – Kikinda (p = 2.74 × 10⁻³, Z = 3), Novi Kneževac (p = 0.01, Z = 2.60), and Senta (p = 0.05, Z = 1.99). Figure 10 presents a graph for these settlements. No settlements with an increasing trend were recorded in North Bačka and South Banat counties.

Figure 10

MK analysis for: Kikinda, Novi Kneževac, and Senta (North Banat County).

Figure 11 presents two settlements in Middle Banat county with a recorded increasing trend in hypertension cases – Ečka (p = 0.02, Z = 2.32) and Novi Bečej (p = 6.83 × 10⁻⁴, Z = 3.40).

Figure 11

MK analysis for: Ečka and Novi Bečej.

Conversely, in only four counties in the study area, the MK analysis showed a decreasing trend in hypertension cases. Those settlements are as follows: Krčedin (p = 0.04, Z = −2.04), Kumane (p = 0.02, Z = −2.40), Ljuba (p = 3.11 × 10⁻³, Z = −2.96), and Ratkovo (p = 0.02, Z = −2.30). Figure 12 presents the MK analysis for these settlements.

Figure 12

MK analysis for: Krčedin, Kumane, Ljuba, and Ratkovo.

In contrast, the remaining 427 settlements did not exhibit any notable trends. Figure 13 shows the MK map of trends at the settlement level within the territory of AP Vojvodina. The map highlights settlements with increasing trends, decreasing trends, and those with no observable trends.

Figure 13

Map of MK analysis at the settlement level in AP Vojvodina.

4.2 Results of Emerging HSA

The Emerging HSA for hypertension is presented using the aforementioned parameters in Table 4. The graphical representation of this analysis is shown in Figure 14.

Table 4

Parameters of the HSA for hypertension in the study area (Z – z-score, p – p-value)

Settlement	Pattern	Z	p
Kisač	Consecutive Hot Spot	4.37	1.26 × 10−5
Čenej	Consecutive Hot Spot	4.17	3.07 × 10−5
Bački Jarak	Consecutive Hot Spot	4.12	3.81 × 10−5
Rumenka	Consecutive Hot Spot	4.14	3.40 × 10−5
Kać	Consecutive Hot Spot	4.14	3.40 × 10−5
Đurđevo	Consecutive Hot Spot	3.97	7.19 × 10−5
Futog	Consecutive Hot Spot	4.19	2.73 × 10−5
Veternik	Consecutive Hot Spot	4.19	2.73 × 10−5
Novi Sad	Consecutive Hot Spot	4.19	2.73 × 10−5
Budisava	Consecutive Hot Spot	3.95	7.94 × 10−5
Ledinci	Consecutive Hot Spot	4.14	3.40 × 10−5
Petrovaradin	Consecutive Hot Spot	4.10	4.22 × 10−5
Kovilj	Consecutive Hot Spot	3.90	9.75 × 10−5
Beočin	Consecutive Hot Spot	4.24	2.19 × 10−5
Rakovac, Stari Ledinci	Consecutive Hot Spot	4.14	3.40 × 10−5
Sremska Kamenica	Consecutive Hot Spot	4.14	3.40 × 10−5
Bukovac	Consecutive Hot Spot	3.90	9.75 × 10−5
Sremski Karlovci	Consecutive Hot Spot	3.95	7.94 × 10−5
Vrdnik	Consecutive Hot Spot	4.19	2.73 × 10−5

Figure 14

Map of Emerging HSA.

The Emerging HSA detected 19 clusters – Kisač, Čenej, Bački Jarak, Rumenka, Kać, Đurđevo, Futog, Veternik, Novi Sad, Budisava, Ledinci, Petrovaradin, Kovilj, Beočin, Rakovac, Stari Ledinci, Sremska Kamenica, Bukovac, Sremski Karlovac, and Vrdnik – that were classified as “Consecutive Hot Spots” among 316 analysed clusters. All the detected settlements in this category are located within the territory of the South Bačka county. No discernible patterns were observed in the remaining clusters.

4.3 The results of Kriging procedures

As previously mentioned, this procedure required converting polygon features into points before applying the Kriging method. The resulting raster was then used to generate contour lines. In Figure 15a, points with graduated z-values from the MK analysis, ranging from −3 to 4.9, are presented. Figure 15b displays the Kriging raster, with values ranging from −0.653 to 2.846, while Figure 15c shows the final contour lines, with values ranging from −0.6 to 2.7. These maps indicate that the highest concentration of values is found in most settlements within South Bačka County, with some exceptions in other counties. The ordinary Kriging method was used, with a spherical model applied for the semi-variogram model. The output cell size was 500 m, while the contour interval was set to 0.2, as the values are small and vary within a limited numerical range, as mentioned earlier.

Figure 15

MK analysis z-score presented by: (a) points, (b) Kriging raster, and (c) contours.

The same procedure was applied to the Emerging HSA. The results are similar, as shown in Figure 16a, where points representing z-values of trends range from −0.61 to 5.4. Figure 16b displays the Kriging raster, with values ranging from −0.175 to 4.857, while Figure 16c presents the final contour lines, with values ranging from 0 to 4.8. Similar to the previous analysis, this one also highlights settlements in South Bačka County as the area with the highest values. The same parameters were used in this procedure as in the previous analysis – ordinary Kriging with a spherical model, a 500-m cell size, and a 0.2 contour interval. Analyses indicate that settlements in South Banat County have the lowest number of hypertension cases.

Figure 16

Emerging HSA trend z-score (count) presented by: (a) points, (b) Kriging raster, and (c) contours.

5 Discussion

Research has shown that the MK analysis identified significant trends in 40 of the 467 settlements in the study area. Of these, 36 settlements exhibited an increasing trend in hypertension cases, while a decreasing trend was observed in only four settlements. The Emerging HSA detected 19 clusters in the South Bačka county classified as “Consecutive Hot Spots” among 316 analysed clusters, with no discernible patterns observed in the remaining clusters. Figure 17 presents a synthesis map of hypertension, overlaying the trends from the MK analysis and clusters identified by the Emerging HSA. Settlements in the South Bačka county exhibited an increasing trend and were classified as Consecutive Hot Spots, indicating that targeted prevention measures should be prioritised in this county. Krčedin, Kumane, Ljuba, and Ratkovo represent positive examples, as they exhibit a decrease in the growth of hypertension patients. In these settlements, studies could be conducted to examine the lifestyle and health-related behaviours of residents to identify the factors contributing to the reduction of this condition. These positive examples may serve as a foundation for identifying the factors contributing to reducing hypertension and guiding protective measures, particularly in high-risk areas. The findings could also form part of broader population health protection strategies and offer valuable insights for improving public health.

Figure 17

Synthesis map of hypertension, overlaying the trends from the MK analysis and clusters identified by the Emerging HSA.

Figure 18 presents a synthesis map of hypertension, overlaying the trends from the MK analysis and the calculated trends from the HSA. The identified increasing trends are similar in both analyses, with convergence towards the South Bačka County. Additionally, both analyses revealed a relatively small number of settlements with increasing trends in hypertension cases in the South Banat County. This synthesis map also highlights the importance of observing neighbouring settlements, as hypertension cases are not confined within settlement borders. Identifying trends for each settlement individually is insufficient; it is essential to consider neighbouring settlements as well, which is facilitated by the Emerging HSA.

Figure 18

Synthesis map of hypertension cases, overlaying the trends from the MK analysis and calculated trends in HSA.

The application of GIS techniques to map disease prevalence, density, and spatial diffusion provides an efficient way to identify the root causes of diseases and pinpoint sources of infection [9]. Therefore, the same approach to presenting results is applied in this study.

The Kriging method is most appropriate when there is a spatially correlated distance or directional bias in the data [63], as is the case with these hypertension occurrences. As shown in Figure 19, it is evident that hypertension cases are most concentrated in settlements within South Bačka County, as well as in North Banat County. In contrast, settlements in South Banat County recorded the lowest trends in hypertension cases.

Figure 19

z-values of hypertension case trends represented through contours and Kriging raster in: (a) MK analysis and (b) Emerging HSA.

Figure 19a presents z-values calculated in the MK analysis, while Figure 19b displays z-values from trends calculated in the Emerging HSA. Because of the use of neighbouring values instead of the complete dataset or individual settlements, the contours in the HSA appear smoother than those in the MK analysis, making them easier to read and interpret.

AP Vojvodina is a centre of agricultural production and has a well-developed food industry [64]. During the last decade of the 20th century, former Yugoslavia experienced wars, a decline in living standards, significant migrations, and a prolonged recession [64]. These factors contributed to changes in population structure, living conditions, and general lifestyle [64].

Pork meat and pork meat products are commonly consumed in AP Vojvodina by the local population [45]. Consequently, CVDs, diabetes, and obesity are more prevalent in the region [45,65]. A study conducted by Tomasevic et al. found that the prevalence of obesity and hypertension in Vojvodina is rising, with obese adults being twice as likely to develop hypertension [35].

Further research by Petrović and Čanković revealed that obesity and abdominal obesity are highly prevalent among adolescents in Vojvodina, with boys being more affected [36]. The study also confirmed that gender, community type, and the father’s level of education significantly influence both obesity and abdominal obesity [36].

In addition, a 2017 study conducted by the Institute of Public Health of Vojvodina examined the salt content in meals prepared for preschool children [66]. It found that the salt levels in all three daily meals generally exceeded the recommended daily intake, posing a risk for hypertension [6,45,66].

The Institute of Public Health of Vojvodina conducted an examination of the nutritional status and health risks of school children in 2020 [6,45,67]. An analysis of students’ knowledge and attitudes regarding the principles of proper nutrition revealed that 46.15% of students do not know how many meals they should have per day and 19.23% believe that eating puff pastry is healthier than whole meal bread [6,45]. Furthermore, 11.54% of students think eating chips is healthier than boiled potatoes, and drinking sweetened beverages is healthier than drinking water [45,67]. The analysis also determined that 46.15% of students do not drink milk daily, 23.08% do not eat fruit, and 46.15% do not consume salad. On the other hand, 61.54% of students consume sweets regularly, and 23.08% drink carbonated beverages daily [6,45,47]. It can be concluded that children’s nutrition is neither properly distributed nor balanced. Therefore, it is crucial to enhance education for children about the importance of proper nutrition [45]. This study recommends the establishment and implementation of continuous nutrition education programs across all schools and educational levels. Such initiatives would contribute to raising public awareness about the benefits of proper nutrition and its role in improving health outcomes throughout the education system. Incorporating such programs into the formal curriculum would not only promote healthy lifestyle habits from an early age but also contribute to the long-term prevention of diet-related diseases. Moreover, integrating nutritional education into various subjects and extracurricular activities can enhance students’ understanding and encourage the practical application of acquired knowledge in everyday life.

The study titled “Research on the Health of the Population of Serbia in 2019”, conducted by the Statistical Office of the Republic of Serbia (RZS) in collaboration with the Institute of Public Health of Serbia (IZJZS) and the Ministry of Health of the Republic of Serbia, included 1,324 households from the AP Vojvodina [6,45,68]. According to the study, 64.7% of the population assessed their health as good, while 46.1% reported having an illness lasting at least six months [45,68]. The analysis also revealed that women, particularly those with the lowest levels of education and poor financial status, reported the worst health conditions [6,45,68]. Additionally, the study recorded a significantly higher percentage of obese individuals (25.4%) in AP Vojvodina compared to the overall territory of the Republic of Serbia [6,45,68]. This difference is attributed to the frequent use of fatty and salty foods and similar dietary habits [6,45,48]. Future research should take into account more detailed information about alcohol consumption and sodium intake and further identify the potential gender-specific risk factors at regional levels [14].

Research conducted by Jocić-Savić et al., using data from surveys carried out in 2000 and 2006 by the IZJZS, revealed that residents of Vojvodina are not the most frequently treated for hypertension when compared to the rest of the Republic of Serbia [33]. Additional efforts are needed to accelerate the detection and treatment of high blood pressure [7]. Emphasis should also be placed on educational programs to enhance patients’ knowledge, attitudes, and awareness of hypertension [7].

This study did not investigate the underlying causes of hypertension but instead focused on identifying the disease’s hotspots and examining its trends using data from the ICDV. Future research should expand this focus to include the geographic distribution of hypertension patients from other hospitals across the Vojvodina region, providing a more comprehensive understanding of the disease’s prevalence and patterns in the area. Such an undertaking is inherently complex and would require collaboration among experts from various disciplines, not just healthcare professionals. One key recommendation from this study is that data from other hospitals in the AP Vojvodina region be made publicly accessible, as this would offer a more complete and transparent view of the hypertension situation throughout the entire province. Hypertension imposes significant economic burdens on individuals, families, health systems, and national economies [5]. Patients face direct medical expenses and lost income, often during their prime working years, potentially impoverishing families [5]. Health systems bear the high costs of hospital and outpatient care for heart attacks and strokes caused by uncontrolled hypertension [5]. National economies suffer from lost tax revenue, reduced productivity, increased healthcare expenses, and greater societal support needs for survivors of cardiovascular events and their dependents [5]. However, improved hypertension treatment programs are highly cost-effective, with an estimated benefit-to-cost ratio of 18:1 [5].

Certain countries, including Canada, Costa Rica, South Korea, and Taiwan, have successfully attained low hypertension prevalence or high control rates by enhancing prevention measures and optimising every step of the treatment process [8,69,70].

Pharmacologic treatment of high blood pressure reduces the risk of CVDs, such as stroke, coronary heart disease, and renal insufficiency [5,7]. However, high blood pressure is one of several modifiable risk factors for CVDs [5,7]. A comprehensive approach is essential to address interrelated health risks, including tobacco use, obesity, physical inactivity, poor diet, and diabetes [5,7]. Effective strategies focus on promoting a healthy, low-sodium diet, maintaining a healthy weight, avoiding alcohol and tobacco, and engaging in regular physical activity, which benefits overall health as well [5,7].

Effective hypertension management provides significant health, well-being, and economic benefits [5,8]. It alleviates pressure on acute-care services, promotes integrated healthcare systems, and reduces deaths, suffering, and costs from complications like heart attack, stroke, and kidney failure [5,8]. Hypertension can be easily identified in primary healthcare settings, and affordable treatments are available for effective management [5,8].

6 Concluding remarks

This study offers valuable insights into the geographic distribution and trends of hypertension in the AP Vojvodina, employing spatial-temporal analysis to deepen our understanding of the disease’s prevalence and dynamics in the region. The findings underscore the need for targeted public health interventions, particularly in high-risk areas, and highlight the importance of exploring the underlying causes of hypertension to develop more effective prevention strategies.

Moreover, enhancing the accessibility and integration of health data from various healthcare institutions in the province could provide a more comprehensive view of the hypertension’s spread and its impact. This data-sharing approach would not only offer a clearer understanding of the disease’s current state but also inform more precise, region-specific interventions. This study recommends adopting specific strategies for regional data integration and fostering inter-institutional cooperation, which would significantly support future research on population public health.

A multi-disciplinary approach, involving collaboration between healthcare professionals, public health experts, data scientists, and policymakers, is essential for addressing this growing health challenge. This would facilitate the development of evidence-based strategies for prevention, early detection, and treatment of hypertension across diverse communities.

Future research should expand beyond the current scope, exploring additional factors such as socio-economic determinants, lifestyle behaviours, and environmental influences on hypertension trends. Longitudinal studies that track changes in hypertension rates over time, combined with interventions aimed at improving nutrition, physical activity, and healthcare access, could provide critical data to inform public health policy and practice. Additionally, research on the effectiveness of existing treatment protocols in different regions of Vojvodina could offer valuable insights into optimising care and outcomes. Ultimately, continued investigation and a holistic approach will be key to reducing the burden of hypertension and ensuring a healthier population in AP Vojvodina, north Serbia.