Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary

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HDOs play a crucial role in providing preventive services (nutrition, physical activity, mental hygiene), a significant factor in the health preservation of the elderly. The geographical location and accessibility of these Offices are essential parameters as they influence individual participation willingness. Methods Leveraging advanced geospatial modelling techniques with QGIS 3.34.0 and MS Excel software, we mapped the locations of HDOs relative to population centres, employing statistical tools such as the Lorenz curve and Gini index, LQ index, and Herfindahl-Hirschman Index. These methods allowed for a nuanced analysis of service concentration and the identification of geographic disparities in service provision. The stochastic relationship between the population and the number of HDOs was analysed through linear regression. This spatial and demographic study was based on 2022 data. Results The number of HDOs did not indicate significant spatial concentration relative to the population, although the Entropy Index measured substantial diversity among the counties. Based on the measured LQ Index values, it can be stated that the presence of HDOs is underrepresented in the capital and its surroundings, as well as in several counties. Additionally, our regression analysis indicated that an increase in population size does not necessarily equate to an increase in the number of HDOs. Conclusion The examination of geocoordinates through scatter plots, indicated a broad spectrum of dispersion, and the placement of HDOs on the map revealed a star topology. From the findings of our research, it can be concluded that the Hungarian network of Health Development Offices (N = 108) can meet the preventive health needs of both the general and the elderly population. Enhancing the geographical spread of HDOs is crucial for improving the accessibility and effectiveness of health prevention strategies, especially among Hungary's aging population, thereby contributing to a more equitable health service landscape. gerontology sociology health prevention health geospatial modelling ageing accessibility statistical concentration Health Development Office Figures Figure 1 Figure 2 Figure 3 Introduction In our study, we aimed to examine the coverage of Health Development Offices (HDOs) in Hungary as providers of health prevention services among both the general and elderly populations. Our choice of topic was motivated by the increasing importance of health prevention today, which necessitates an active institutional system and communication [ 2 ]. Further effort and financial resources are required to expand the support network for health development. Short-term social rationality understandably finances disease treatment and rehabilitation over prevention, depending on available resources [ 5 ]. However, in the long term, a nationwide health prevention network is necessary to avoid a drastic increase in health financing costs [ 10 , 4 ]. Therefore, examining the geographical placement of Health Development Offices is crucial because health development can be defined as part of public health, and if we wish to see improvement in our morbidity and mortality rates by increasing the number of healthy life years, nationwide coverage of health prevention services is essential [ 28 ]. Investigating the evolution of Health Development Offices, it can be said that until 2010, Hungary primarily saw isolated and often independent health prevention efforts [ 24 ]. To improve health-related attitudes and value systems, the first office network was established in 2014, based on the Swiss Model [ 20 ]. With the aid of European Union funds, a network of 61 Health Development Offices was established in 2014 [ 16 ]. As a further step forward in development, the concept of Health Development and Health Development Offices appeared in the 1997 Act CLIV. of Hungarian law in 2016, prescribing active collaboration with municipalities in local health development [ 24 ]. Between 2014 and 2020, with additional European Union funds, the number of Health Development Offices increased to 113. However, the health prevention network can only be most effective if it appears with even coverage and low concentration values [ 16 , 7 , 25 ]. The primary goal of establishing the health development network is to positively develop health-related behaviour, including among the elderly population [ 19 ]. A crucial aspect of its development is the population's ability to easily access the nearest Health Development Office from their residence. Despite a high number of Health Development Offices, if the Offices are only accessible to a portion of the population, or if difficult access due to a lack of transportation infrastructure becomes a significant barrier, it's crucial to establish a well-covered, accessible, and barrier-free health prevention service network. Generally, the prevention programs of Health Development Offices are diverse and can dynamically adapt to the local population's needs. Their preventive activities fundamentally cover three main areas: nutrition, mental hygiene [ 29 ], and physical therapy [ 16 , 21 ]. Based on the local population composition, the Health Development Offices provide health prevention services for both the young and active, as well as the elderly age groups, potentially serving as a useful gerontological and health prevention location specifically for those over 64. For the elderly population, the proximity of the Offices and barrier-free accessibility and utilization of transportation infrastructure is especially important. Health development faces several societal barriers, including issues related to socialization, health-related attitudes, lack of information, and environmental factors, which is general problem [ 36 ]. It's essential that the Hungarian population internalizes the knowledge that impacts health-related thinking and actions for health, even in old age [ 18 ]. This requirement necessitates low concentration and even coverage from the side of the Health Development Office network. Is the coverage of Health Development Offices operating in Hungary proportional to the population of the counties, or does it show a high concentration among certain areas according to some principle? Answering this question requires geographic information system analysis with statistical concentration measurement, which is a prerequisite for a list of Health Development Offices' locations for further analyses based on various health indicators and transportation infrastructure. Our research uses geospatial modelling and the adaptation of statistical concentration, we aimed to describe a methodology through a specific public health example that can be applied in further similar research studies. Methods Study design and data collection Our cross-sectional and quantitative study sought to explore the spatial patterns formed by Health Development Offices (HDOs) across Hungary and their territorial concentration among the general population as well as among the elderly population aged 64 and over. Initially, our objective was to create an Excel database containing the contact information (postal code, municipality, precise address) and names of Health Development Offices. The addresses for these offices were obtained from the National Public Health and Medical Officer Service website ( https://www.nnk.gov.hu/index.php/efi ) (107 addresses) [ 23 ], to which we also added the contact information of the Health Development Centre at Semmelweis University. Utilizing the addresses of 108 Health Development Offices, we generated geo coordinates with the help of Google Maps online ( https://maps.google.com/ ) [ 14 ], following the WGS 84 standard for extracting latitude and longitude values. These geo-coordinate values were then recorded in the mentioned Excel database. These offices were still operational in 2022. For spatial representation, we used QGIS (Quantum Geographic Information System) version 3.34, an open-source software that includes the freely accessible world map provided by the OpenStreetMap Foundation, encompassing a complete map of Hungary [ 30 ]. The source from OpenStreetMap (2024) included the full Hungarian administrative map [ 27 ], detailing the boundaries of municipalities, counties, and districts, thus facilitating the creation of our spatial database for the study. Utilizing our database, which contained addresses and associated geo coordinates, we imported the data into the QGIS spatial information system. This allowed us to visualize the geographical distribution of Offices across the Hungarian administrative map, based on the latitude and longitude coordinates [ 31 ]. In addition to the geospatial representation, our analysis was mainly based on the examination of the statistical concentration of the county population size and the Health Development Offices. County-level population data by age group were obtained from the website of the Hungarian Central Statistical Office [ 17 ], which provides the total population for each county in 2022 and the population over 64 years of age. A further prerequisite for our statistical investigations was the aggregation of the number of operational Health Development Offices per county based on their available addresses for 2022, which was the basis for our concentration calculations. We used the 32-bit version of MS Excel 2019, version 1808, with the SOLVER plug-in for the statistical calculations. Our detailed computational results (dataset), along with a brief statistical description and the database, were uploaded to the CERN scientific data repository, Zenodo, on 7 March 2024. This upload aims to ensure transparency and the reproducibility of our calculations. [ 8 ] Analysis with standardized data Due to the territorial comparison involving Health Development Offices, it was necessary to apply the method of statistical standardization [ 11 ]. This process involved using the available county population data and the frequency data of Health Development Offices per county (f(x)) to calculate the indicator of the number of Health Development Offices per 100,000 inhabitants ( \(\frac{Number of Health Development Offices at the county level}{Population of county} x 100 000\) ) Our statistical analysis focused on the total population of each county and the population aged over 64. The purpose of standardization was to make counties with differing population sizes comparable in terms of the territorial distribution of operational Health Development Offices. Used statistical tools Descriptive statistics (mean; minimum value, maximum value; standard deviation, coefficient of variation) [ 37 ]. Our statistical calculations required the measurement of these indicators, providing basic information for further analysis of our examined variables. Lorenz curve with standardized data The use of the Lorenz curve required statistical standardization based on the differing population sizes of counties. This curve was designed to assess the disparities in the allocation of Health Development Offices across counties, considering the proportions of the general and elderly populations. The Gini index (G) facilitated the validation of our Lorenz curve \(G=\frac{1}{2{n}^{2}\stackrel{-}{x}}{\varSigma }_{i=1}^{n}{\varSigma }_{j=1}^{n}|{x}_{i}-{x}_{j}|\) [ 34 ] n: number of Hungarian counties x: number of HDOs per 100,000 population in the examined county Location Quotient (LQ) Index The LQ index allowed for the measurement of the concentration of Health Development Offices by county, comparing these figures to national data. A value greater than 1 indicated a higher concentration in the county relative to national figures. This indicator did not require statistical standardization based on population size. (LQ) index \(LQ=\frac{\frac{Number of HDOs in an examined county}{HDOs number in Hungry}}{\frac{The population of the examined county}{Total population in Hungary}}\) [ 3 ] Herfindahl-Hirschman Index (HHI) The basis for using the HHI Index was the relative frequency value of Health Development Offices per county. (HHI) index $$HHI= \sum _{i=1}^{n}{\left(Si\right)}^{2}$$ n: number of counties, \({S}_{i}\) : Health Office distribution ratio [ 38 ] Entropy Index (E) The diversity and uniformity of distribution across the national sample were measured using the Entropy Index, which indicates the territorial diversification of the offices. The application of a base-10 logarithm facilitates easier interpretation of the results, commonly used in health and sociological research. Entropy Index \(E=-\sum _{i=1}^{n}{p}_{i}{\times log}_{10}\left({p}_{i}\right)\) [ 12 ] n: number of counties \({p}_{i}\) relative frequency of Health Development Offices in a selected country Regression analysis and correlation calculation In our research, to depict the stochastic relationship between the population size of counties and the number of Health Development Offices, we used a univariate linear correlation equation. This was derived from interpolating the available data (x: population of the county; y: number of Health Development Offices in a given county) and displayed on a scatter plot to illustrate the relationship between the two variables. Our findings were verified using the correlation coefficient and the coefficient of determination R 2 . The latter examined how much variability in the dependent variable (y) is explained by the independent variable (x) within a linear regression model framework. [ 11 , 12 ] Results Location Analysis of Health Development Offices Our spatial modelling covered 19 counties and the administrative area of Budapest, encompassing a total of 108 Health Development Offices. Our analysis revealed a strong diversification among the counties in terms of the number of operational Health Development Offices (see Table 1 ). Including Budapest, the average number of Health Development Offices per county was 5.68, with a range from 0 to 11 Health Development Offices in the examined territorial units. The spatial placement patterns of the Health Development Offices indicated that, with the exception of Komarom-Esztergom county, at least one Health Development Office operates in every county. From the spatial representation, we inferred that the Health Development Offices exhibit a varied distribution, and the 108 offices cover a significant part of Hungary. The examination of geocoordinates through scatter plots, based on latitude and longitude, indicated a broad spectrum of dispersion, and the placement of Health Development Offices on the map revealed a pattern radiating from a central hub, suggesting a star topology (see Fig. 1 ). The reason for this spatial arrangement is partly due to the pattern of Health Development Offices aligning with the population data of the various counties. The emergence of this spatial structure was influenced by the fact that 31.24% of the Hungarian population is concentrated in Budapest and its surrounding county (Pest) [ 17 ]. If we further examine the population's age composition, it can be stated that 21% of the population is over 64 years old, which, with an urn-shaped age distribution, indicates an aging society whose economic impact will become increasingly significant in the future [ 13 ]. For these reasons, the spatial placement and accessibility of Health Development Offices are important factors for the elderly population as well, if there is a desire to increase the number of individuals utilizing health prevention services. To map out county-level differences, our analysis performed a Location Quotient (LQ) calculation to determine which counties are underrepresented relative to the national average. Table 1. Location Quotient Index of Health Development Offices Based on the Population of Counties (source: own work) Based on Table 1 , it is evident that Health Development Offices are less concentrated in Nograd, Vas, Gyor-Moson-Sopron counties, as well as in Budapest and Pest County. The Location Quotient (LQ) index value is less than 1 indicates that the concentration of Health Development Offices is lower than the national average. It is also noteworthy that as of January 2024, there are no operating Health Development Offices in Komarom-Esztergom County, which could be attributed to the presumed passivity of the relevant municipalities and healthcare stakeholders or the lack of infrastructure necessary for establishing an Office. Excluding Nograd County, it is observable that counties with a higher GDP per capita generally have a lower concentration of Health Development Offices, a situation that should be investigated in the context of the grant calls for the establishment of these Offices. The distribution of grant resources primarily favoured the establishment of Health Development Offices in underdeveloped regions [ 16 ]. When examining the LQ Indexes for the total population and the elderly population, a similar pattern emerges in terms of location concentration, except Baranya County becoming underrepresented in terms of the elderly population size and the number of Health Development Offices. Statistical concentration analysis In addition to the Location Quotient Index, we used the Lorenz curve to examine the concentration resulting from the population of the counties and the number of Health Development Offices located within them, both for the total and the elderly population, from which conclusions about the Offices' territorial distribution can be drawn. The Lorenz curve, expressing the degree of concentration, contrasts the cumulative increase in population size with the growth in the number of Health Development Offices, the latter in ascending order serving as the basis for the analysis. In the Cartesian coordinate system, a 45-degree reference line symbolizes the absence of concentration, representing perfect distribution, and the area between this line and the Lorenz curve indicates the magnitude of concentration. In relation to this area, the Gini coefficient can be interpreted, which can take on values between 0 and 1, where 0 represents perfect equality and 1 represents complete inequality in terms of the variables examined [ 34 ]. The comparison required statistical standardization, hence the study was based on the number of Health Development Offices per 100,000 population. Figure 2 shows the concentration of Health Development Offices as a function of the total population and the over-64 population of the county, using the Lorenz curve. At higher concentrations, the Lorenz curve converges towards the lower right corner. Based on the shape of the Lorenz curve for both the total population and the (65-x) population, it can be concluded that the concentration of Health Development Offices in Hungary is low. For both the total population and the older generation, a Gini coefficient around 0.25 indicates a relatively low level of inequality in the distribution of the number of Health Development Offices across different counties. Therefore, the distribution of Health Development Offices is relatively even, though variations exist among the counties, caused by the underrepresented counties as measured by the Location Quotient Index (see Table 1 ). We validated our measurement results with the Herfindahl-Hirschman Index (HHI), aimed at checking our findings. The indicator can take values between 0 and 1, with the distribution becoming more uniform as it approaches zero. In our research, the HHI Index helps to understand the extent to which Health Development Offices are concentrated in each county. Based on the number of Health Development Offices in the counties, the Herfindahl-Hirschman Index value was 0.063, indicating a low concentration, thus supporting our previous measurement results. The value measured in our study signifies a low concentration of Health Development Offices in the examined counties, assuming an even distribution and confirming the values measured by the Lorenz Curve. The indicator supports that the Offices are widely distributed among the counties and are not concentrated in a few. Measure of diversification With the Entropy Index, we aimed to measure the diversity of the standardized indicator value of Health Development Offices per 100,000 people. This statistical indicator is also used in thermodynamics instead of public health research, but it is capable of expressing the degree of diversification or uncertainty, suitable for further evaluation of our study [ 12 ]. The Entropy Index, calculated based on the number of Health Development Offices per 100,000 inhabitants across counties, was 1.2431. This value signifies a considerable variation in the distribution of Health Development Offices relative to the total population, despite previous measures indicating a low statistical concentration that suggested uniform national distribution. From the maximum value of Entropy, \({log}_{10}\left(20\right)=1.30103\) , it follows that the Entropy value is high, which can be explained by the variations among the counties in terms of standardized values. Regarding the aging demographic, the Entropy Index value for the number of Health Development Offices per 100,000 elderly individuals (65 -x) was calculated at 1.2454. This figure is attributed to variations in the Location Quotient Indexes across counties, as detailed in Table 1 . The distribution of Health Development Offices indicates an even distribution at the county level for both the aging and the total population, yet the number of Health Development Offices per 100,000 elderly was changeable among the counties, creating higher diversity in the distribution. Based on the number of Health Development Offices per 100,000 people in each county, the Entropy Index value for the total population was 1.2431, indicating high variability in Health Development Offices concerning the total population, despite the low statistical concentration measured earlier suggesting national coverage. From the statistical results, it can be concluded that the accessibility of Health Development Offices varies across counties for both the total and elderly populations, but does not show significant concentration or isolation in the representation of the HDOs, as also supported by the low Gini coefficient value. Examining the Regression Relationship Between Population and Health Development Offices (HDOs) Analyzing the stochastic relationship between county population and the number of Health Development Offices, it is an expected requirement that the number of operating Health Development Offices should increase with population growth. For this task, we applied correlation and linear regression calculations. A correlation coefficient value of 0.377 was measured between the total population and the number of operating Health Development Offices, indicating a weak stochastic relationship between the total county population size and the number of Health Development Offices (for the population over 64, the correlation coefficient value indicated a similar value of 0.374). Based on the interpolation of the data, the positive slope of the linear regression line suggests a trend-like relationship, where, in general, a higher population size is associated with a higher frequency of Health Development Office occurrences in the examined counties. If we exclude two outlier values (marked with a red) generated by Budapest and Pest County from our analysis, the correlation coefficient value increased to 0.7967, indicating a strong trend-like relationship between population size and the number of Health Development Offices outside of Budapest and its surrounding area. With this refinement, for the elderly population over 64, the correlation value was 0.86 in rural areas, indicating that population size was a significant factor in the establishment of rural Health Development Offices. In Budapest and Pest counties (marked with a red on the plotter chart), the number of Health Development Offices is underrepresented relative to the population size, as can also be seen in Fig. 3 , because the increase in the number of Health Development Offices is barely measurable in these two outlier areas and far from having the most Health Development Offices in the capital and its surrounding areas. Discussion The spatial placement and concentration of Health Development Offices are critically important for influencing the health behaviour of the elderly [ 5 ]. A health prevention service can achieve long-term results if the network of Health Development Offices has national coverage and is easily accessible to the population, especially the elderly [ 1 ]. Besides spatial placement, however, factors influencing service uptake, including transportation options and other sociological health factors, should not be overlooked [ 6 , 33 ]. The availability of transportation infrastructure can significantly improve, or its absence can worsen, the absorption of interested parties from the Office's catchment area. However, the health value attitudes and knowledge of those living in the area of a Health Development Office can also vary significantly, affecting both the establishment and utilization of the Office [ 9 ]. It must not be forgotten that the purpose of Health Development Offices is to improve morbidity indicators that lead to leading causes of death. Significant progress can be made in reducing health risks through nutrition, physical activity, and mental health improvements, which are worth considering in old age [ 15 ]. From the degree of concentration, it can be concluded, that despite inequalities, the network of Health Development Offices is suitable for serving the needs of the total and elderly populations. However, coverage is not yet complete, and the location concentration also highlights that there are areas in need of network development, as well as considering capacity development of existing providers in light of demands. Health Development Offices work with similar infrastructure and human resources regardless of the size of the affected district, while significant differences exist between their territorial service areas. Based on the descriptions, we must see that numerous factors influence the formation of the existing network of Health Development Offices [ 32 ]. The star topology of the Health Development Office is partly explainable by the population's territorial distribution, with transportation, communication, and infrastructure factors also cited as further explanatory reasons. Central organizational processes, with efficiency and effectiveness in mind, also facilitated the formation of the star topology; however, based on the county differences in the number of Health Development Offices per 100,000 population, it's evident that besides the mentioned factors, numerous factors influence the spatial placement of Health Development Offices. It's not coincidental that the capital and its immediate surroundings became underweight based on Location Quotient Indexes. As Kornyicki [ 16 ] revealed, those European Union funds and grants that established the Health Development Offices contributed to the formation of their territorial structure and preferred the following factors in Hungary: The formation and improvement of individual behaviour patterns serving health among the domestic population, especially improving the health attitudes of high-risk target groups. Preferring small regions over the capital. Upgrading underdeveloped areas, part of which is influencing health-related attitudes in a positive direction. Strengthening public health with systematic steps. Approaching health development with an integrated perspective. Ensuring the quality of health prevention services and reducing the quality heterogeneity of provided services. Enhancing the “gatekeeper” role of general practitioners. Improving the cooperation between preventive service providers and the social and economic actors in the affected areas. Improving morbidity and mortality indicators by prioritizing primary and secondary prevention. An additional important aspect in the establishment of Health Development Offices was that they operate integrally and play a significant role in the implementation of the region's health development strategy. This function represents an active link, a bridge between the region's health service providers, the local government, and civil organizations, which is critically important for preserving the health of the elderly, according to Molnár and colleagues and VG Janson & Elisabeth [ 26 , 35 ]. Facilitated by EU grant funding, 2014 saw the establishment of 20 Health Development Offices in the most socioeconomically disadvantaged districts and an additional 18 in districts categorized as disadvantaged, out of a total of 61 offices [ 16 ]. The data shows that the first health development offices served to catch up with underdeveloped areas to improve positive health attitudes and health preservation. Approaching from a health sociology perspective, we can expect significantly worse morbidity and mortality indicators in underdeveloped areas, which can be attributed to socialization, social and geographical environment, and individual values, warranting increased social attention [ 20 ]. After 2014, Health Development Offices inaugurated under the EFOP and VEKOP programs expanded their roles to include mental hygiene and mental health services, marking a notable advancement in the field [ 15 ]. Remarkable, it was the VEKOP grants that facilitated the inclusion of the capital city into the Health Development Offices network, thereby playing a crucial role in establishing a star topology in the distribution of these services where the population was a crucial influence factor. Limitations In addition to the applied statistical methods, it is worth noting the limitations of our research. Our study calculated quantitative concentration but did not examine the quantity and quality of services provided by the Offices. Another limitation is that the spatial placement of the Offices could have been influenced by various other health sociological factors, necessitating further analysis. Factors affecting the spatial pattern of Health Development Offices include the economic development of counties, educational attainment of the population, and other public health indicators. The low value of the measured R 2 coefficient of determination between population and number of HDOs, partially explains how the independent variable (total and elderly population), influences the number of Health Development Offices (dependent variable), on a national level. Based on the distribution of the scatter plot, a multivariate regression function could provide a better fit, meaning that other factors influenced the territorial placement based on population size, which requires further research. Conclusion The Hungarian Network of Health Development Offices, comprising 108 facilities, effectively meets the preventative healthcare needs of both the general population and individuals over 64 years of age, as indicated by low concentration metrics such as the Lorenz curve, Gini Index, and Herfindahl-Hirschman Index. Notwithstanding, disparities in the availability of Health Development Offices per 100,000 inhabitants are evident across various counties, including Budapest. This uneven distribution is described by the Location Quotient Index and the values of the Entropy Index. Therefore, expanding Health Development Offices in underrepresented areas is essential for reducing these disparities and achieving a more balanced county-wide distribution. The Health Development Offices offer a range of preventative services tailored to the elderly population [ 16 ], positioning the network as a key resource for gerontological care in addition to serving the broader active population. However, it was the VEKOP grants that facilitated the inclusion of the capital city into the Health Development Offices network, thereby playing a crucial role in establishing a star topology in the distribution of these services where the population was one of the crucial influence factors. The network's star topology infrastructure is particularly effective in fostering health value attitudes, catering especially well to the elderly. In the longer term, enhancing the Health Development Office network necessitates deliberate health communication strategies. These strategies should not only aim to expand the network but also to stimulate demand for preventative health services, leveraging the existing infrastructure. Nevertheless, regional development efforts must be supported by further research focused on more effectively enhancing health value attitudes among both the general and elderly populations through strategic adaptations. Optimizing the Health Development Office network by taking into account the current network topology and spatial distribution is recommended to achieve a decrease in statistical concentration and ensure more equitable coverage. Although the network of Health Development Offices has been established, coverage remains incomplete, with disparities in the availability of Offices per 100,000 population across different counties, a finding corroborated by the Entropy Index values we observed. Future initiatives should prioritize development in regions where the presence of operational Health Development Offices is notably sparse. Crucially, despite intentions to expand, the effectiveness of these Offices may be compromised if the absence or inaccessibility of Health Development Offices impedes the utilization of preventive services. Given their availability, older people are likely to make greater use of these health preventive services. From a gerontological perspective, the physical location and accessibility of these offices are critical, as ease of access in old age is fundamental to service utilisation. Abbreviations E Entropy Index EFOP Human Resources Development Operational Programme (EU grant at state member level in Hungary) G Gini Index HDO Health Development Office HHI Herfindahl-Hirschman Index KSH Hungarian Central Statistical Office LQ Location Quotient index MS Microsoft NNK National Public Health Centre R 2 coefficient of determination QGIS Quantum Geographic Information System VEKOP Economic Development and Innovation Operational Programme (EU grant at state member level in Hungary) Declarations Availability of data and materials The datasets generated and/or analysed during the current study are available in the Zenodo repository, https://zenodo.org/records/10730741. (DOI: 10.5281/zenodo.10730741) Detailed descriptions of the files uploaded to the Zenodo repository and their sources are available, aiming to enhance transparency and reproducibility. All data generated and analyzed during this study are available in our repository, along with a brief statistical summary and any requests, please contact the corresponding author. Acknowledgments Not applicable. Funding This research was conducted with the institutional support of Semmelweis University, which includes PhD scholarships (PD and VA) and faculty salary (IV). No specific external funding was provided for this study. Author information Authors and Affiliations Semmelweis University, School of PhD Studies, Health Sciences Division, Interdisciplinary Applied Health Sciences Program, Vas street 17., 1088 Budapest, Hungary Peter Domjan Semmelweis University, School of PhD Studies, Health Sciences Division Institute of Digital Health Sciences, Ferenc Square 15., 1094 Budapest Hungary Viola Angyal Semmelweis University, Faculty of Health Sciences, Department of Social Sciences, Vas street 17., 1088 Budapest, Hungary Istvan Vingender References Antal ZL. Egészségszociológia holisztikus megközelítésben. [Health Sociology: A Holistic Approach.] L. Harmattan Könyvkidó Kft. [Harmattan Publishing Ltd.]; 2020. Bodkin A, Hakimi S. Sustainable by design: a systematic review of factors for health promotion program sustainability. BMC Public Health. 2020. 10.1186/s12889-020-09091-9 . Benassi F, Crisci M, Rimoldi S. (2022). Location quotient as a local index of residental segregation. 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(2019) A gazdaság és a társadalom statisztikája, [Statistics of the Economy and Society. ] Pécsi Tudományegyetem, Közgazdaságtudományi Kar, [University of Pécs, Faculty of Business and Economics] https://pea.lib.pte.hu/bitstream/handle/pea/23142/galambosne-tiszberger-monika-a-gazdasag-es-a-tarsadalom-statisztikaja-pte-ktk-pecs-2019.pdf?sequence=1&isAllowed=y of subordinate document. Accessed 19 Jan 2024. Geocoordinate of examined lacation. US Google Maps, California. https://maps.google.com/ Accessed 2 Jan 2024. Kaposvári Cs, Vitrai J. Hogyan fejlesszük egy ország egészségkultúráját? A RAND Corporation jelentésének ismertetése. [How to Develop a Country's Health Culture? Describe RAND Corporation Riport]. Egészségfejlesztés [Health Promotion]. 2017. 10.24365/ef.v58i3.179 . Kornyicki Á. Egészségfejlesztési irodák működése: Múlt, jelen és a vízionált jövő. [Operation of Health Development Offices: Past, Present, and Envisioned Future]. Egészségfejlesztés [Health Promotion]. 2022;63:4. 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Cross-cultural adaptation and validation of the rapid assessment of physical activity questionnaire (RAPA) in Hungarian elderly over 50 years. BMC Sports Science, Medicine and Rehabilitation. 2022; 14(1):131. Nagy Z. Közlekedésstatisztika, [Transport Statistics. ] Akadémiai Kiadó. [Akadémiai Publishing House]. 2018. 10.1556/9789634542797 . Nemzeti Népegészségügyi Központ [National Public Health. Center] Egészségfejlesztési Irodák elérhetőségei, [Accessibility of Health Development Offices. ] 2023. https://www.nnk.gov.hu/index.php/efi of subordiante document. Accessed 5 Dec 2023. Nemzeti Népegészségügyi Központ Egészségvonal [National Public Health Center Health. Line] Egészségfejlesztési irodák hálózata, [Network of Health Development Offices. ] 2023. https://egeszsegvonal.gov.hu/maradj-egeszseges/egeszsegfejlesztesi-irodak.html of subordinate document. Accessed 4 Dec 2023. NNK [National Public Health Center] Az egészségfejlesztési irodák hálózata, [Network of Health Development Offices. ] 2024. https://www.nnk.gov.hu/index.php/nepegeszsegugyi-strategiai-egeszsegfejlesztesi-es-egeszsegmonitorozasi-foosztaly/egeszsegfejlesztesi-osztaly/egeszsegfejlesztesi-irodak/feladatok of subordinate document. Accessed 4 Jan 2024. Molnár T, Scharle Á, Tóth E, Váradi B. Mit tehetnek a települési önkormányzatok az idősekért? [What Can Local Governments Do for the Elderly? ]. Megújuló Magyarországért Alapítvány; [Foundation for a Renewing Hungary]; 2019. OpenStreetMap A szabad világtérkép, [The Free World Map. ] 2024. https://www.openstreetmap.org/ of subordinate document. Accessed 2 Jan 2024. Pakai A. Az idősek egészségi állapota, biológiai változások és a prevenció szerepe a mortalitási és morbiditási adatok tükrében. 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Népegészségügy az egészségügyben – Az Egészségfejlesztési Irodák fennmaradásának kérdései. [Public Health in Healthcare – The Sustainability of Health Development Offices. ]. IME – Interdiszciplináris Magyar Egészségügy, [IME – The Interdisciplinary Hungarian Health Care]. 2016; 15(1) p. 36–40. Saidla K. Health promotion by stealth: active transportation success in Helsinki. Finland Health Promotion Int. 2017. 10.1093/heapro/daw110 . Sitthiyot T, Holasut. K A simple method for estimating the Lorenz curve. Humanit Social Sci Commun. 2021. 10.1057/s41599-021-00948-x . Janson EVG, Tillgren PE. Health promotion at local level: a case study of content, organization and development in four Swedish municipalities. BMC Public Health. 2010. 10.1186/1471-2458-10-455 . Vingender I. (2004). Egészségszociológia. [Health Sociology. Semmelweis Egyetem Egészségtudományi Kar. [Semmelweis University, Faculty of Health Sciences] 2004. Walters SJ. (2021). Medical Statistics. Blackwell's, 2021. Werden GJ. Using the Herfindahl-Hirschman index. Appl Industrial Econ. 1998. 10.1017/CBO9780511522048.021 . Additional Declarations No competing interests reported. Supplementary Files Dataofdemography.xlsx EFI.qgz EntropyIndex.xlsx GeocoordinatesandnamesofHungarianHealthDevelopmentOffices.csv GiniIndex.xlsx HerfindahlHirschmanIndex.xlsx LQIndex.xlsx Lorenzcurve.xlsx NumberofHDOs.xlsx README.md Regressioncorrelation.xlsx ShortDescriptionofDataAnalysis.pdf Standardizeddata.xlsx Statisticalformulas.pdf Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Reviews received at journal 15 Jul, 2024 Reviews received at journal 29 Jun, 2024 Reviewers agreed at journal 29 Jun, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers invited by journal 19 Jun, 2024 Editor invited by journal 25 Mar, 2024 Submission checks completed at journal 21 Mar, 2024 Editor assigned by journal 21 Mar, 2024 First submitted to journal 10 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4066239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282234437,"identity":"c19e1621-8258-4381-8623-d1869c203c92","order_by":0,"name":"Peter 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1","display":"","copyAsset":false,"role":"figure","size":1089487,"visible":true,"origin":"","legend":"\u003cp\u003ePattern of Spatial Placement of Health Development Offices in 2022, in Quantum GIS Software \u003cbr\u003e\n(source: own work)\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4066239/v1/86f267a94ac43d722003c115.png"},{"id":53419181,"identity":"92700df9-439d-4da4-b932-ac34686196f5","added_by":"auto","created_at":"2024-03-25 18:11:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128370,"visible":true,"origin":"","legend":"\u003cp\u003eLorenz Curve of Health Development Offices per 100,000 People Based on County Population \u003cbr\u003e\n(source: own 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Our choice of topic was motivated by the increasing importance of health prevention today, which necessitates an active institutional system and communication [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Further effort and financial resources are required to expand the support network for health development. Short-term social rationality understandably finances disease treatment and rehabilitation over prevention, depending on available resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, in the long term, a nationwide health prevention network is necessary to avoid a drastic increase in health financing costs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, examining the geographical placement of Health Development Offices is crucial because health development can be defined as part of public health, and if we wish to see improvement in our morbidity and mortality rates by increasing the number of healthy life years, nationwide coverage of health prevention services is essential [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInvestigating the evolution of Health Development Offices, it can be said that until 2010, Hungary primarily saw isolated and often independent health prevention efforts [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To improve health-related attitudes and value systems, the first office network was established in 2014, based on the Swiss Model [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. With the aid of European Union funds, a network of 61 Health Development Offices was established in 2014 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a further step forward in development, the concept of Health Development and Health Development Offices appeared in the 1997 Act CLIV. of Hungarian law in 2016, prescribing active collaboration with municipalities in local health development [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Between 2014 and 2020, with additional European Union funds, the number of Health Development Offices increased to 113. However, the health prevention network can only be most effective if it appears with even coverage and low concentration values [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary goal of establishing the health development network is to positively develop health-related behaviour, including among the elderly population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A crucial aspect of its development is the population's ability to easily access the nearest Health Development Office from their residence. Despite a high number of Health Development Offices, if the Offices are only accessible to a portion of the population, or if difficult access due to a lack of transportation infrastructure becomes a significant barrier, it's crucial to establish a well-covered, accessible, and barrier-free health prevention service network.\u003c/p\u003e \u003cp\u003eGenerally, the prevention programs of Health Development Offices are diverse and can dynamically adapt to the local population's needs. Their preventive activities fundamentally cover three main areas: nutrition, mental hygiene [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and physical therapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the local population composition, the Health Development Offices provide health prevention services for both the young and active, as well as the elderly age groups, potentially serving as a useful gerontological and health prevention location specifically for those over 64. For the elderly population, the proximity of the Offices and barrier-free accessibility and utilization of transportation infrastructure is especially important.\u003c/p\u003e \u003cp\u003eHealth development faces several societal barriers, including issues related to socialization, health-related attitudes, lack of information, and environmental factors, which is general problem [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It's essential that the Hungarian population internalizes the knowledge that impacts health-related thinking and actions for health, even in old age [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This requirement necessitates low concentration and even coverage from the side of the Health Development Office network.\u003c/p\u003e \u003cp\u003eIs the coverage of Health Development Offices operating in Hungary proportional to the population of the counties, or does it show a high concentration among certain areas according to some principle? Answering this question requires geographic information system analysis with statistical concentration measurement, which is a prerequisite for a list of Health Development Offices' locations for further analyses based on various health indicators and transportation infrastructure.\u003c/p\u003e \u003cp\u003eOur research uses geospatial modelling and the adaptation of statistical concentration, we aimed to describe a methodology through a specific public health example that can be applied in further similar research studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cp\u003eOur cross-sectional and quantitative study sought to explore the spatial patterns formed by Health Development Offices (HDOs) across Hungary and their territorial concentration among the general population as well as among the elderly population aged 64 and over.\u003c/p\u003e \u003cp\u003eInitially, our objective was to create an Excel database containing the contact information (postal code, municipality, precise address) and names of Health Development Offices. The addresses for these offices were obtained from the National Public Health and Medical Officer Service website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nnk.gov.hu/index.php/efi\u003c/span\u003e\u003cspan address=\"https://www.nnk.gov.hu/index.php/efi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (107 addresses) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], to which we also added the contact information of the Health Development Centre at Semmelweis University. Utilizing the addresses of 108 Health Development Offices, we generated geo coordinates with the help of Google Maps online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maps.google.com/\u003c/span\u003e\u003cspan address=\"https://maps.google.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], following the WGS 84 standard for extracting latitude and longitude values. These geo-coordinate values were then recorded in the mentioned Excel database. These offices were still operational in 2022.\u003c/p\u003e \u003cp\u003eFor spatial representation, we used QGIS (Quantum Geographic Information System) version 3.34, an open-source software that includes the freely accessible world map provided by the OpenStreetMap Foundation, encompassing a complete map of Hungary [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The source from OpenStreetMap (2024) included the full Hungarian administrative map [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], detailing the boundaries of municipalities, counties, and districts, thus facilitating the creation of our spatial database for the study.\u003c/p\u003e \u003cp\u003eUtilizing our database, which contained addresses and associated geo coordinates, we imported the data into the QGIS spatial information system. This allowed us to visualize the geographical distribution of Offices across the Hungarian administrative map, based on the latitude and longitude coordinates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to the geospatial representation, our analysis was mainly based on the examination of the statistical concentration of the county population size and the Health Development Offices. County-level population data by age group were obtained from the website of the Hungarian Central Statistical Office [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which provides the total population for each county in 2022 and the population over 64 years of age. A further prerequisite for our statistical investigations was the aggregation of the number of operational Health Development Offices per county based on their available addresses for 2022, which was the basis for our concentration calculations.\u003c/p\u003e \u003cp\u003eWe used the 32-bit version of MS Excel 2019, version 1808, with the SOLVER plug-in for the statistical calculations.\u003c/p\u003e \u003cp\u003eOur detailed computational results (dataset), along with a brief statistical description and the database, were uploaded to the CERN scientific data repository, Zenodo, on 7 March 2024. This upload aims to ensure transparency and the reproducibility of our calculations. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis with standardized data\u003c/h2\u003e \u003cp\u003eDue to the territorial comparison involving Health Development Offices, it was necessary to apply the method of statistical standardization [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This process involved using the available county population data and the frequency data of Health Development Offices per county (f(x)) to calculate the indicator of the number of Health Development Offices per 100,000 inhabitants ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Number of Health Development Offices at the county level}{Population of county} x 100 000\\)\u003c/span\u003e\u003c/span\u003e) Our statistical analysis focused on the total population of each county and the population aged over 64. The purpose of standardization was to make counties with differing population sizes comparable in terms of the territorial distribution of operational Health Development Offices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUsed statistical tools\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDescriptive statistics\u003c/b\u003e (mean; minimum value, maximum value; standard deviation, coefficient of variation) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our statistical calculations required the measurement of these indicators, providing basic information for further analysis of our examined variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLorenz curve with standardized data\u003c/h2\u003e \u003cp\u003eThe use of the Lorenz curve required statistical standardization based on the differing population sizes of counties. This curve was designed to assess the disparities in the allocation of Health Development Offices across counties, considering the proportions of the general and elderly populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eThe Gini index (G) facilitated the validation of our Lorenz curve\u003c/h2\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(G=\\frac{1}{2{n}^{2}\\stackrel{-}{x}}{\\varSigma }_{i=1}^{n}{\\varSigma }_{j=1}^{n}|{x}_{i}-{x}_{j}|\\)\u003c/span\u003e \u003c/span\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003en: number of Hungarian counties\u003c/p\u003e\u003cp\u003ex: number of HDOs per 100,000 population in the examined county\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLocation Quotient (LQ) Index\u003c/h2\u003e \u003cp\u003eThe LQ index allowed for the measurement of the concentration of Health Development Offices by county, comparing these figures to national data. A value greater than 1 indicated a higher concentration in the county relative to national figures. This indicator did not require statistical standardization based on population size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e(LQ) index\u003c/h2\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(LQ=\\frac{\\frac{Number of HDOs in an examined county}{HDOs number in Hungry}}{\\frac{The population of the examined county}{Total population in Hungary}}\\)\u003c/span\u003e \u003c/span\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHerfindahl-Hirschman Index (HHI)\u003c/h2\u003e \u003cp\u003eThe basis for using the HHI Index was the relative frequency value of Health Development Offices per county.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e(HHI) index\u003c/h2\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$HHI= \\sum _{i=1}^{n}{\\left(Si\\right)}^{2}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003en: number of counties, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{i}\\)\u003c/span\u003e\u003c/span\u003e: Health Office distribution ratio [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEntropy Index (E)\u003c/h2\u003e \u003cp\u003eThe diversity and uniformity of distribution across the national sample were measured using the Entropy Index, which indicates the territorial diversification of the offices. The application of a base-10 logarithm facilitates easier interpretation of the results, commonly used in health and sociological research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEntropy Index\u003c/h2\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(E=-\\sum _{i=1}^{n}{p}_{i}{\\times log}_{10}\\left({p}_{i}\\right)\\)\u003c/span\u003e \u003c/span\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003en: number of counties\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({p}_{i}\\)\u003c/span\u003e \u003c/span\u003e relative frequency of Health Development Offices in a selected country\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegression analysis and correlation calculation\u003c/h2\u003e \u003cp\u003eIn our research, to depict the stochastic relationship between the population size of counties and the number of Health Development Offices, we used a univariate linear correlation equation. This was derived from interpolating the available data (x: population of the county; y: number of Health Development Offices in a given county) and displayed on a scatter plot to illustrate the relationship between the two variables. Our findings were verified using the correlation coefficient and the coefficient of determination R\u003csup\u003e2\u003c/sup\u003e. The latter examined how much variability in the dependent variable (y) is explained by the independent variable (x) within a linear regression model framework. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eLocation Analysis of Health Development Offices\u003c/h2\u003e\n\u003cp\u003eOur spatial modelling covered 19 counties and the administrative area of Budapest, encompassing a total of 108 Health Development Offices. Our analysis revealed a strong diversification among the counties in terms of the number of operational Health Development Offices (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Including Budapest, the average number of Health Development Offices per county was 5.68, with a range from 0 to 11 Health Development Offices in the examined territorial units. The spatial placement patterns of the Health Development Offices indicated that, with the exception of Komarom-Esztergom county, at least one Health Development Office operates in every county. From the spatial representation, we inferred that the Health Development Offices exhibit a varied distribution, and the 108 offices cover a significant part of Hungary. The examination of geocoordinates through scatter plots, based on latitude and longitude, indicated a broad spectrum of dispersion, and the placement of Health Development Offices on the map revealed a pattern radiating from a central hub, suggesting a star topology (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The reason for this spatial arrangement is partly due to the pattern of Health Development Offices aligning with the population data of the various counties. The emergence of this spatial structure was influenced by the fact that 31.24% of the Hungarian population is concentrated in Budapest and its surrounding county (Pest) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003cp\u003eIf we further examine the population's age composition, it can be stated that 21% of the population is over 64 years old, which, with an urn-shaped age distribution, indicates an aging society whose economic impact will become increasingly significant in the future [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. For these reasons, the spatial placement and accessibility of Health Development Offices are important factors for the elderly population as well, if there is a desire to increase the number of individuals utilizing health prevention services. To map out county-level differences, our analysis performed a Location Quotient (LQ) calculation to determine which counties are underrepresented relative to the national average.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cp\u003eTable 1. Location Quotient Index of Health Development Offices Based on the Population of Counties \u003cbr /\u003e (source: own work)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003cp\u003eBased on Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, it is evident that Health Development Offices are less concentrated in Nograd, Vas, Gyor-Moson-Sopron counties, as well as in Budapest and Pest County. The Location Quotient (LQ) index value is less than 1 indicates that the concentration of Health Development Offices is lower than the national average. It is also noteworthy that as of January 2024, there are no operating Health Development Offices in Komarom-Esztergom County, which could be attributed to the presumed passivity of the relevant municipalities and healthcare stakeholders or the lack of infrastructure necessary for establishing an Office. Excluding Nograd County, it is observable that counties with a higher GDP per capita generally have a lower concentration of Health Development Offices, a situation that should be investigated in the context of the grant calls for the establishment of these Offices. The distribution of grant resources primarily favoured the establishment of Health Development Offices in underdeveloped regions [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. When examining the LQ Indexes for the total population and the elderly population, a similar pattern emerges in terms of location concentration, except Baranya County becoming underrepresented in terms of the elderly population size and the number of Health Development Offices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical concentration analysis\u003c/h2\u003e\n\u003cp\u003eIn addition to the Location Quotient Index, we used the Lorenz curve to examine the concentration resulting from the population of the counties and the number of Health Development Offices located within them, both for the total and the elderly population, from which conclusions about the Offices' territorial distribution can be drawn.\u003c/p\u003e\n\u003cp\u003eThe Lorenz curve, expressing the degree of concentration, contrasts the cumulative increase in population size with the growth in the number of Health Development Offices, the latter in ascending order serving as the basis for the analysis. In the Cartesian coordinate system, a 45-degree reference line symbolizes the absence of concentration, representing perfect distribution, and the area between this line and the Lorenz curve indicates the magnitude of concentration. In relation to this area, the Gini coefficient can be interpreted, which can take on values between 0 and 1, where 0 represents perfect equality and 1 represents complete inequality in terms of the variables examined [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe comparison required statistical standardization, hence the study was based on the number of Health Development Offices per 100,000 population.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the concentration of Health Development Offices as a function of the total population and the over-64 population of the county, using the Lorenz curve.\u003c/p\u003e\n\u003cp\u003eAt higher concentrations, the Lorenz curve converges towards the lower right corner. Based on the shape of the Lorenz curve for both the total population and the (65-x) population, it can be concluded that the concentration of Health Development Offices in Hungary is low. For both the total population and the older generation, a Gini coefficient around 0.25 indicates a relatively low level of inequality in the distribution of the number of Health Development Offices across different counties. Therefore, the distribution of Health Development Offices is relatively even, though variations exist among the counties, caused by the underrepresented counties as measured by the Location Quotient Index (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe validated our measurement results with the Herfindahl-Hirschman Index (HHI), aimed at checking our findings. The indicator can take values between 0 and 1, with the distribution becoming more uniform as it approaches zero.\u003c/p\u003e\n\u003cp\u003eIn our research, the HHI Index helps to understand the extent to which Health Development Offices are concentrated in each county. Based on the number of Health Development Offices in the counties, the Herfindahl-Hirschman Index value was 0.063, indicating a low concentration, thus supporting our previous measurement results. The value measured in our study signifies a low concentration of Health Development Offices in the examined counties, assuming an even distribution and confirming the values measured by the Lorenz Curve. The indicator supports that the Offices are widely distributed among the counties and are not concentrated in a few.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eMeasure of diversification\u003c/h2\u003e\n\u003cp\u003eWith the Entropy Index, we aimed to measure the diversity of the standardized indicator value of Health Development Offices per 100,000 people. This statistical indicator is also used in thermodynamics instead of public health research, but it is capable of expressing the degree of diversification or uncertainty, suitable for further evaluation of our study [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Entropy Index, calculated based on the number of Health Development Offices per 100,000 inhabitants across counties, was 1.2431. This value signifies a considerable variation in the distribution of Health Development Offices relative to the total population, despite previous measures indicating a low statistical concentration that suggested uniform national distribution. From the maximum value of Entropy, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({log}_{10}\\left(20\\right)=1.30103\\)\u003c/span\u003e\u003c/span\u003e, it follows that the Entropy value is high, which can be explained by the variations among the counties in terms of standardized values.\u003c/p\u003e\n\u003cp\u003eRegarding the aging demographic, the Entropy Index value for the number of Health Development Offices per 100,000 elderly individuals (65 -x) was calculated at 1.2454. This figure is attributed to variations in the Location Quotient Indexes across counties, as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The distribution of Health Development Offices indicates an even distribution at the county level for both the aging and the total population, yet the number of Health Development Offices per 100,000 elderly was changeable among the counties, creating higher diversity in the distribution. Based on the number of Health Development Offices per 100,000 people in each county, the Entropy Index value for the total population was 1.2431, indicating high variability in Health Development Offices concerning the total population, despite the low statistical concentration measured earlier suggesting national coverage.\u003c/p\u003e\n\u003cp\u003eFrom the statistical results, it can be concluded that the accessibility of Health Development Offices varies across counties for both the total and elderly populations, but does not show significant concentration or isolation in the representation of the HDOs, as also supported by the low Gini coefficient value.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eExamining the Regression Relationship Between Population and Health Development Offices (HDOs)\u003c/h2\u003e\n\u003cp\u003eAnalyzing the stochastic relationship between county population and the number of Health Development Offices, it is an expected requirement that the number of operating Health Development Offices should increase with population growth. For this task, we applied correlation and linear regression calculations. A correlation coefficient value of 0.377 was measured between the total population and the number of operating Health Development Offices, indicating a weak stochastic relationship between the total county population size and the number of Health Development Offices (for the population over 64, the correlation coefficient value indicated a similar value of 0.374).\u003c/p\u003e\n\u003cp\u003eBased on the interpolation of the data, the positive slope of the linear regression line suggests a trend-like relationship, where, in general, a higher population size is associated with a higher frequency of Health Development Office occurrences in the examined counties. If we exclude two outlier values (marked with a red) generated by Budapest and Pest County from our analysis, the correlation coefficient value increased to 0.7967, indicating a strong trend-like relationship between population size and the number of Health Development Offices outside of Budapest and its surrounding area. With this refinement, for the elderly population over 64, the correlation value was 0.86 in rural areas, indicating that population size was a significant factor in the establishment of rural Health Development Offices.\u003c/p\u003e\n\u003cp\u003eIn Budapest and Pest counties (marked with a red on the plotter chart), the number of Health Development Offices is underrepresented relative to the population size, as can also be seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, because the increase in the number of Health Development Offices is barely measurable in these two outlier areas and far from having the most Health Development Offices in the capital and its surrounding areas.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe spatial placement and concentration of Health Development Offices are critically important for influencing the health behaviour of the elderly [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A health prevention service can achieve long-term results if the network of Health Development Offices has national coverage and is easily accessible to the population, especially the elderly [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Besides spatial placement, however, factors influencing service uptake, including transportation options and other sociological health factors, should not be overlooked [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe availability of transportation infrastructure can significantly improve, or its absence can worsen, the absorption of interested parties from the Office's catchment area. However, the health value attitudes and knowledge of those living in the area of a Health Development Office can also vary significantly, affecting both the establishment and utilization of the Office [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It must not be forgotten that the purpose of Health Development Offices is to improve morbidity indicators that lead to leading causes of death.\u003c/p\u003e \u003cp\u003eSignificant progress can be made in reducing health risks through nutrition, physical activity, and mental health improvements, which are worth considering in old age [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom the degree of concentration, it can be concluded, that despite inequalities, the network of Health Development Offices is suitable for serving the needs of the total and elderly populations. However, coverage is not yet complete, and the location concentration also highlights that there are areas in need of network development, as well as considering capacity development of existing providers in light of demands. Health Development Offices work with similar infrastructure and human resources regardless of the size of the affected district, while significant differences exist between their territorial service areas.\u003c/p\u003e \u003cp\u003eBased on the descriptions, we must see that numerous factors influence the formation of the existing network of Health Development Offices [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The star topology of the Health Development Office is partly explainable by the population's territorial distribution, with transportation, communication, and infrastructure factors also cited as further explanatory reasons.\u003c/p\u003e \u003cp\u003eCentral organizational processes, with efficiency and effectiveness in mind, also facilitated the formation of the star topology; however, based on the county differences in the number of Health Development Offices per 100,000 population, it's evident that besides the mentioned factors, numerous factors influence the spatial placement of Health Development Offices. It's not coincidental that the capital and its immediate surroundings became underweight based on Location Quotient Indexes.\u003c/p\u003e \u003cp\u003eAs Kornyicki [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] revealed, those European Union funds and grants that established the Health Development Offices contributed to the formation of their territorial structure and preferred the following factors in Hungary:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe formation and improvement of individual behaviour patterns serving health among the domestic population, especially improving the health attitudes of high-risk target groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePreferring small regions over the capital.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUpgrading underdeveloped areas, part of which is influencing health-related attitudes in a positive direction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrengthening public health with systematic steps.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eApproaching health development with an integrated perspective.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnsuring the quality of health prevention services and reducing the quality heterogeneity of provided services.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnhancing the “gatekeeper” role of general practitioners.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImproving the cooperation between preventive service providers and the social and economic actors in the affected areas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImproving morbidity and mortality indicators by prioritizing primary and secondary prevention.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAn additional important aspect in the establishment of Health Development Offices was that they operate integrally and play a significant role in the implementation of the region's health development strategy. This function represents an active link, a bridge between the region's health service providers, the local government, and civil organizations, which is critically important for preserving the health of the elderly, according to Molnár and colleagues and VG Janson \u0026amp; Elisabeth [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFacilitated by EU grant funding, 2014 saw the establishment of 20 Health Development Offices in the most socioeconomically disadvantaged districts and an additional 18 in districts categorized as disadvantaged, out of a total of 61 offices [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The data shows that the first health development offices served to catch up with underdeveloped areas to improve positive health attitudes and health preservation. Approaching from a health sociology perspective, we can expect significantly worse morbidity and mortality indicators in underdeveloped areas, which can be attributed to socialization, social and geographical environment, and individual values, warranting increased social attention [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter 2014, Health Development Offices inaugurated under the EFOP and VEKOP programs expanded their roles to include mental hygiene and mental health services, marking a notable advancement in the field [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Remarkable, it was the VEKOP grants that facilitated the inclusion of the capital city into the Health Development Offices network, thereby playing a crucial role in establishing a star topology in the distribution of these services where the population was a crucial influence factor.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eIn addition to the applied statistical methods, it is worth noting the limitations of our research. Our study calculated quantitative concentration but did not examine the quantity and quality of services provided by the Offices. Another limitation is that the spatial placement of the Offices could have been influenced by various other health sociological factors, necessitating further analysis. Factors affecting the spatial pattern of Health Development Offices include the economic development of counties, educational attainment of the population, and other public health indicators.\u003c/p\u003e\u003cp\u003eThe low value of the measured R\u003csup\u003e2\u003c/sup\u003e coefficient of determination between population and number of HDOs, partially explains how the independent variable (total and elderly population), influences the number of Health Development Offices (dependent variable), on a national level. Based on the distribution of the scatter plot, a multivariate regression function could provide a better fit, meaning that other factors influenced the territorial placement based on population size, which requires further research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Hungarian Network of Health Development Offices, comprising 108 facilities, effectively meets the preventative healthcare needs of both the general population and individuals over 64 years of age, as indicated by low concentration metrics such as the Lorenz curve, Gini Index, and Herfindahl-Hirschman Index. Notwithstanding, disparities in the availability of Health Development Offices per 100,000 inhabitants are evident across various counties, including Budapest. This uneven distribution is described by the Location Quotient Index and the values of the Entropy Index. Therefore, expanding Health Development Offices in underrepresented areas is essential for reducing these disparities and achieving a more balanced county-wide distribution.\u003c/p\u003e \u003cp\u003eThe Health Development Offices offer a range of preventative services tailored to the elderly population [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], positioning the network as a key resource for gerontological care in addition to serving the broader active population. However, it was the VEKOP grants that facilitated the inclusion of the capital city into the Health Development Offices network, thereby playing a crucial role in establishing a star topology in the distribution of these services where the population was one of the crucial influence factors. The network's star topology infrastructure is particularly effective in fostering health value attitudes, catering especially well to the elderly.\u003c/p\u003e \u003cp\u003eIn the longer term, enhancing the Health Development Office network necessitates deliberate health communication strategies. These strategies should not only aim to expand the network but also to stimulate demand for preventative health services, leveraging the existing infrastructure. Nevertheless, regional development efforts must be supported by further research focused on more effectively enhancing health value attitudes among both the general and elderly populations through strategic adaptations.\u003c/p\u003e \u003cp\u003eOptimizing the Health Development Office network by taking into account the current network topology and spatial distribution is recommended to achieve a decrease in statistical concentration and ensure more equitable coverage. Although the network of Health Development Offices has been established, coverage remains incomplete, with disparities in the availability of Offices per 100,000 population across different counties, a finding corroborated by the Entropy Index values we observed. Future initiatives should prioritize development in regions where the presence of operational Health Development Offices is notably sparse. Crucially, despite intentions to expand, the effectiveness of these Offices may be compromised if the absence or inaccessibility of Health Development Offices impedes the utilization of preventive services.\u003c/p\u003e \u003cp\u003eGiven their availability, older people are likely to make greater use of these health preventive services. From a gerontological perspective, the physical location and accessibility of these offices are critical, as ease of access in old age is fundamental to service utilisation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEntropy Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEFOP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Resources Development Operational Programme (EU grant at state member level in Hungary)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGini Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Development Office\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHHI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHerfindahl-Hirschman Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHungarian Central Statistical Office\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocation Quotient index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrosoft\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNNK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Public Health Centre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoefficient of determination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQGIS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantum Geographic Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVEKOP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEconomic Development and Innovation Operational Programme (EU grant at state member level in Hungary)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Zenodo repository, https://zenodo.org/records/10730741. (DOI: 10.5281/zenodo.10730741)\u003c/p\u003e\n\u003cp\u003eDetailed descriptions of the files uploaded to the Zenodo repository and their sources are available, aiming to enhance transparency and reproducibility.\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are available in our repository, along with a brief statistical summary and any requests, please contact the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted with the institutional support of Semmelweis University, which includes PhD scholarships (PD and VA) and faculty salary (IV). No specific external funding was provided for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemmelweis University, School of PhD Studies, Health Sciences Division,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Interdisciplinary Applied Health Sciences Program, Vas street 17., 1088 Budapest,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHungary\u003c/p\u003e\n\u003cp\u003ePeter Domjan\u003c/p\u003e\n\u003cp\u003eSemmelweis University, School of PhD Studies, Health Sciences Division\u003cbr\u003e\u0026nbsp;Institute of Digital Health Sciences, Ferenc Square 15., 1094 Budapest\u003c/p\u003e\n\u003cp\u003eHungary\u003c/p\u003e\n\u003cp\u003eViola Angyal\u003c/p\u003e\n\u003cp\u003eSemmelweis University, Faculty of Health Sciences, Department of Social Sciences,\u0026nbsp;\u003cbr\u003e\u0026nbsp;Vas street 17., 1088 Budapest,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHungary\u003c/p\u003e\n\u003cp\u003eIstvan Vingender\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntal ZL. 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Appl Industrial Econ. 1998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/CBO9780511522048.021\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511522048.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gerontology, sociology, health prevention, health geospatial modelling, ageing, accessibility, statistical concentration, Health Development Office","lastPublishedDoi":"10.21203/rs.3.rs-4066239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4066239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines the availability and national distribution of Health Development Offices (HDOs; N = 108) in Hungary, with an emphasis on their role in health prevention for the general and elderly population. HDOs play a crucial role in providing preventive services (nutrition, physical activity, mental hygiene), a significant factor in the health preservation of the elderly. The geographical location and accessibility of these Offices are essential parameters as they influence individual participation willingness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeveraging advanced geospatial modelling techniques with QGIS 3.34.0 and MS Excel software, we mapped the locations of HDOs relative to population centres, employing statistical tools such as the Lorenz curve and Gini index, LQ index, and Herfindahl-Hirschman Index. These methods allowed for a nuanced analysis of service concentration and the identification of geographic disparities in service provision. The stochastic relationship between the population and the number of HDOs was analysed through linear regression. This spatial and demographic study was based on 2022 data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of HDOs did not indicate significant spatial concentration relative to the population, although the Entropy Index measured substantial diversity among the counties. Based on the measured LQ Index values, it can be stated that the presence of HDOs is underrepresented in the capital and its surroundings, as well as in several counties. Additionally, our regression analysis indicated that an increase in population size does not necessarily equate to an increase in the number of HDOs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe examination of geocoordinates through scatter plots, indicated a broad spectrum of dispersion, and the placement of HDOs on the map revealed a star topology. From the findings of our research, it can be concluded that the Hungarian network of Health Development Offices (N = 108) can meet the preventive health needs of both the general and the elderly population. Enhancing the geographical spread of HDOs is crucial for improving the accessibility and effectiveness of health prevention strategies, especially among Hungary's aging population, thereby contributing to a more equitable health service landscape.\u003c/p\u003e","manuscriptTitle":"Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:11:15","doi":"10.21203/rs.3.rs-4066239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T07:35:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-15T13:07:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-29T09:00:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333076504203208562453495692894073124551","date":"2024-06-29T05:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T14:54:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20649636986100855328508383260988248386","date":"2024-06-28T10:23:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178650292531703542191322339613680439784","date":"2024-06-19T20:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59042068737126372875938142606799206831","date":"2024-06-19T16:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-19T13:19:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-25T08:40:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-21T11:30:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-21T11:30:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-10T15:11:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6e79a5b-5b0c-4631-90e7-b912e44246a0","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:03:27+00:00","versionOfRecord":{"articleIdentity":"rs-4066239","link":"https://doi.org/10.1186/s12889-025-22392-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-04-21 15:58:06","publishedOnDateReadable":"April 21st, 2025"},"versionCreatedAt":"2024-03-25 18:11:15","video":"","vorDoi":"10.1186/s12889-025-22392-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22392-1","workflowStages":[]},"version":"v1","identity":"rs-4066239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4066239","identity":"rs-4066239","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}