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Studies on fracture incidence have mostly been based on retrospectively registered data from local hospital databases. The Swedish Fracture Register (SFR) is a national quality register collecting data prospectively on fractures, at the time of care-seeking. In the present study the incidence of all different fractures, regardless of location, in adults’ ≥ 16 years treated at the only care provider for patients with fractures within a catchment area of approximately 550,000 inhabitants, during 2015‒2018 are described. Age, gender, and fracture location (according to AO/OTA classification) was used for the analyses and presentation of fracture incidences. During the 4-year study period, 23,917 individuals sustained 27,169 fractures. The mean age at fracture was 57.9 years (range 16‒105 years) and 64.5% of the fractures occurred in women. The five most common fractures accounted for more than 50% of all fractures: distal radius, proximal femur, ankle, proximal humerus, and metacarpal fractures. Seven fracture incidence distribution groups were created based on age- and gender-specific incidence curves, providing visual and easily accessible information on fracture distribution. This paper reports on incidence of all fracture locations based on prospectively collected data in a quality register. The knowledge on fracture incidence related to age and gender may be of importance for the planning of orthopaedic care, involving both in- and out-patients as well as allocating surgical resources. Further, this might be useful for organizing preventive measures, especially in countries with similar socioeconomic structure and fracture burden.

Background It is necessary to ensure sufficient healthcare. The use of current, precise and realistic methods to model spatial accessibility to healthcare and thus improved decision-making is helping this process. Generally, these methods—which include the family of floating catchment area (FCA) methods—incorporate a number of criteria that address topics like access, efficiency, budget, equity and the overall system utilization. How can we measure spatial accessibility? This paper investigates a sophisticated approach for quantifying the spatial accessibility of general practitioners. (GPs). Our objective is the investigation and application of a spatial accessibility index by an improved Huff three-step floating catchment area (MH3SFCA) method. Methods We modify and implement the huff model three-step floating catchment area (MH3SFCA) method and exemplary calculation of the spatial accessibility indices for the test study area. The method is extended to incorporate a more realistic way to model the distance decay effect. To that end, instead of a binary approach, a continuous approach is employed. Therefore, each distance between a healthcare site and the population is incorporated individually. The study area includes Swabia and the city of Augsburg, Germany. The data for analysis is obtained from following data sources: (1) Acxiom Deutschland GmbH (2020) provided a test dataset for the locations of general practitioners (GPs); (2) OpenStreetMap (OSM) data is utilized for road networks; and (3) the Statistische Ämter des Bundes und der Länder (German official census 2011) provided a population distribution dataset stemming from the 2011 Census. Results The spatial accessibility indices are distributed in an inhomogeneous as well as polycentric pattern for the general practitioners (GPs). Differences in spatial accessibility are found mainly between urban and rural areas. The transitions from lower to higher values of accessibility or vice versa in general are smooth rather than abrupt. The results indicate that the MH3SFCA method is suited for comparing the spatial accessibility of GPs in different regions. The results of the MH3SFCA method can be used to indicate over- and undersupplied areas. However, the absolute values of the indices do not inherently define accessibility to be too low or too high. Instead, the indices compare the spatial relationships between each supply and demand location. As a result, the higher the value of the accessibility indices, the higher the opportunities for the respective population locations. The result for the study area are exemplary as the test input data has a high uncertainty. Depending on the objective, it might be necessary to further analyze the results of the method. Conclusions The application of the MH3SFCA method on small-scale data can provide an overview of accessibility for the whole study area. As many factors have to be taken into account, the outcomes are too complex for a direct and clear interpretation of why indices are low or high. The MH3SFCA method can be used to detect differences in accessibility on a small scale. In order to effectively detect over- or undersupply, further analysis must be conducted and/or different (legal) constraints must be applied. The methodology requires input data of high quality.

Proximity to a healthcare supplier does not necessarily equate to meaningful access to care. Traditional healthcare accessibility models, particularly the Enhanced Two-Step Floating Catchment Area (E2SFCA) method, rely heavily on assumptions of proximity-driven behavior, fixed catchment sizes, and uniform distance decay. These simplifications often overlook the complexities of real-world healthcare-seeking behavior. This study examines the assumptions of the E2SFCA framework by integrating large-scale human mobility data in 2023, which captures anonymized, real-world visitation patterns between neighborhoods and hospitals across Pennsylvania. We revise the E2SFCA model through two key innovations: 1) replacing static catchment thresholds with dynamic, visit-weighted boundaries derived from observed travel behavior, and 2) estimating hospital-specific distance decay functions that better reflect heterogeneous patterns of attraction. These refinements result in accessibility metrics that align more closely with empirical realities. Compared to the traditional model, the revised E2SFCA demonstrates a more meaningful relationship with real-world health outcomes. Specifically, the revised model shows a stronger and statistically significant correlation with household income (r = 0.31, p = 0.011, vs. r = 0.14 in the traditional model) and a more plausible negative association with poor or fair health status (r = −0.12 vs. r = 0.17), aligning with the expectation that better accessibility corresponds to better health outcomes. Additionally, the revised model reveals significantly greater inequality in access that exposes disparities in healthcare accessibility that distance centric approaches tend to obscure. By integrating human mobility data in spatial accessibility modeling, this study offers a more realistic, equitable, and policy-relevant framework for evaluating healthcare access.

Background Spatial accessibility to healthcare facilities has drawn much attention in health geography. In China, central and local governments have aimed to develop a well-organized hierarchical system of healthcare facilities in recent years. However, few studies have focused on the measurement of healthcare accessibility in a hierarchical service delivery system, which is crucial for the assessment and implementation of such strategies. Methods Based on recent improvements in 2SFCA (two-step floating catchment area) method, this study aims to propose a Hierarchical 2SFCA (H2SFCA) method for measuring spatial accessibility to hierarchical facilities. The method considers the varied catchment area sizes, distance decay effects, and transport modes for facilities at various levels. Moreover, both the relative and absolute distance effects are incorporated into the accessibility measurement. Results The method is applied and tested in a case study of hierarchical healthcare facilities in Shenzhen, China. The results reveal that the general spatial accessibility to hierarchical healthcare facilities in Shenzhen is unevenly distributed and concentrated. The disparity of general accessibility is largely caused by the concentrated distribution of tertiary hospitals. For facilities at higher levels, average accessibility of demanders is higher, but there are also larger disparities in spatial accessibility. The comparison between H2SFCA and traditional methods reveals that traditional methods underestimate the spatial disparity of accessibility, which may lead to biased suggestions for policy making. Conclusions The results suggest that the supply of healthcare resources at primary facilities is far from sufficient. To improve the spatial equity in spatial accessibility to hierarchical healthcare facilities, various actions are needed at different levels. The proposed H2SFCA method contributes to the modelling of spatial accessibility to hierarchical healthcare facilities in China and similar environments where the referral system has not been well designed. It can also act as the foundation for developing more comprehensive measures in future studies.

Public medical service facilities are among the most basic needs of the public and are directly related to residents’ health. The balanced development of medical service facilities is of great significance. Public medical service facilities can be divided into different levels according to their medical equipment, service catchment, and medical quality, which is very important but has been ignored for a long time in accessibility evaluations. In this research, based on the hospital and population datasets of Shenzhen, we propose a hierarchical two-step floating catchment area (H2SFCA) method to evaluate the spatial accessibility of public medical resources considering the factors at different levels of medical resources. In the proposed method, the spatial accessibility of each level of public medical service facilities are evaluated using different distance attenuation functions according to the medical service’s scope. In addition, a measurement is proposed to evaluate the equity of medical service facilities based on accessibility and population density distributions. To synthesize the general spatial accessibility and the distribution balance of public medical service facilities, we standardize the spatial accessibility of public medical service facilities at each level and then calculate the weighted sums of the accessibility of each level. The general spatial equity of public medical service facilities is also evaluated. The results show that the accessibility and distribution balance of medical resources performs dissimilarly at the three levels and can be discriminated within different regions of the city. The accessibility of citywide medical facilities in Shenzhen decreases from the city center to the suburban area in a radial pattern and the accessibility and distribution balance in the suburban areas needs improvement.

Growing physician maldistribution and population demographic shifts have contributed to large geographic variation in healthcare access and the emergence of advanced practice providers as contributors to the healthcare workforce. Current estimates of geographic accessibility of physicians and advanced practice providers rely on outdated \"provider per capita\" estimates that have shortcomings. To apply state of the art methods to estimate spatial accessibility of physician and non-physician clinician groups and to examine factors associated with higher accessibility. We used a combination of provider location, medical claims, and U.S. Census data to perform a national study of health provider accessibility. The National Plan and Provider Enumeration System was used along with Medicare claims to identify providers actively caring for patients in 2014 including: primary care physicians (i.e., internal medicine and family medicine), specialists, nurse practitioners, and chiropractors. For each U.S. ZIP code tabulation area, we estimated provider accessibility using the Variable-distance Enhanced 2 step Floating Catchment Area method and performed a Getis-Ord Gi* analysis for each provider group. Generalized linear models were used to examine associations between population characteristics and provider accessibility. National spatial patterns of the provider groups differed considerably. Accessibility of internal medicine most resembled specialists with high accessibility in urban locales, whereas relative higher accessibility of family medicine physicians was concentrated in the upper Midwest. In our adjusted analyses independent factors associated with higher accessibility were very similar between internal medicine physicians and specialists-presence of a medical school in the county was associated with approximately 70% higher accessibility and higher accessibility was associated with urban locales. Nurse practitioners were similar to family medicine physicians with both having higher accessibility in rural locales. The Variable-distance Enhanced 2 step Floating Catchment Area method is a viable approach to measure spatial accessibility at the national scale.

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results.

Background Implementation of the Healthy China Strategy and the hierarchical diagnosis and treatment system has injected new vitality into medical services. Given the insufficient supply of medical services and increasing demand for medical treatment, exploring the supply-demand pattern of medical services has become an urgent theoretical and practical problem to be solved. The equity of healthcare facilities has received widespread attention, but due to limited data, there is little research on the supply-demand pattern of medical services. This study focuses on evaluating the supply-demand matching pattern of medical services at different levels in Haikou City with big geographic data and promoting the realization of a balance between medical supply and demand. Methods This study utilizes spatial data of medical institutions, Didi Chuxing Data, and population density data. Firstly, use the two-step floating catchment area method and GIS spatial analysis to explore characteristics of the supply-demand patterns of medical services at different levels in Haikou. Secondly, we mine residents’ demand for medical treatment based on Didi Chuxing Data. Then combined with population density data, divide supply-demand matching of medical institutions into four types. Finally, propose optimization strategies for the problems. Results The accessibility pattern of high-level medical institutions in Haikou presents high in the north and low in the south. The accessibility pattern of low-level medical institutions is the opposite. High-level medical institutions have a strong demand for medical treatment, which is less hampered by distance. The healthcare demand of low-level medical institutions is small, and they mainly are medium- and short-distance medical travel. The types of medical services at different levels are mainly “low supply - low demand” and “high supply - low demand” types. Conclusions Medical services at different levels in Haikou are mainly in supply-demand imbalance. Therefore, we put forward optimization strategies to promote the equity of primary medical services, such as propelling the establishment and improvement of the hierarchical diagnosis and treatment system, building a new model of medical and health service supply, and strengthening balanced coverage of primary medical institutions. The mining of big geographic data is beneficial to alleviate the mismatch between medical supply and demand, although the data and methods need to be improved.

Background Evidence indicating persistent geographic inequalities in health outcomes signifies a need for routine subnational monitoring of health-related Sustainable Development Goal targets in sub-Saharan Africa. Health facilities may be an appropriate subnational unit for monitoring purposes, but a lack of suitable demographic data complicates the production of baseline facility-level population denominators against which progress can be reliably measured. This scoping review aimed to map the methods and data sources used to estimate health facility catchment areas and translate them to population denominators for child health indicators in the region. Methods Peer-reviewed research publications and grey literature reports were identified by searching bibliographic databases and relevant organisational websites. The inclusion criteria required that studies were conducted in sub-Saharan Africa since January 2000, described quantitative method(s) for estimating health facility catchment areas and/or population denominators, and focussed on children as the population of interest. Following title/abstract then full text screening of search results, relevant data were extracted using a standard form. Thematic analysis was undertaken to extract themes and present a narrative synthesis. Results Overall, 33 research publications and 3 grey literature reports were included. Of these, only 7 research studies and 1 technical guidance document outlined aims explicitly framed around methods development and/or evaluation. Studies increasingly estimated catchment areas using complex geostatistical or travel time-based modelling approaches rather than simpler proximity metrics, and produced denominators by intersecting catchment boundaries with gridded population surfaces rather than aggregating area-based administrative counts. Few studies used data produced by or describing health facilities to link estimation methods to service utilisation patterns, inter-facility competition or facility characteristics. Conclusion There is a need for catchment population estimation methods that can be scaled to national-level facility networks and replicated across the region. This could be achieved by leveraging routinely collected health data and other readily available and nationally consistent data sources. Future methodological development should emphasise modern geostatistical approaches drawing upon the relative strengths of multiple data sources and capturing the range of spatial, supply-side, individual-level and environmental factors with potential to influence catchments’ extent, shape and demographic composition.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Method The rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The E2SFCA has two major steps. First, it calculates a bed-to-population ratio for each hospital location. Second, it sums these ratios for residential locations where hospital locations overlap. Results The comparison of the spatial accessibility measures for COVID-19 patients to those of population at risk identifies which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. This study also identified vulnerable population residing in the areas with low spatial accessibility in Chicago. Conclusion Rapidly measuring spatial accessibility of healthcare resources provides an improved understanding of how well the healthcare infrastructure is equipped to save people’s lives during the COVID-19 pandemic. The findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.