Dataset Information

Spatial Accessibility of Primary Care in England: A Cross-Sectional Study Using a Floating Catchment Area Method.

ABSTRACT:

Objective

To analyze the general practitioners (GPs) with regard to the degree of urbanization, social deprivation, general health, and disability.

Data sources

Small area population data and GP practice data in England.

Study design

We used a floating catchment area method to measure spatial GP accessibility with regard to the degree of urbanization, social deprivation, general health, and disability.

Data collection

Data were collected from the Office for National Statistics and the general practice census and analyzed using a geographic information system.

Principal findings

In all, 25.8 percent of the population in England lived in areas with a significant low GP accessibility (mean z-score: -4.2); 27.6 percent lived in areas with a significant high GP accessibility (mean z-score: 7.7); 97.8 percent of high GP accessibility areas represented urban areas, and 31.1 percent of low GP accessibility areas represented rural areas (correlation of accessibility and urbanity: r = 0.59; p<.001). Furthermore, a minor negative correlation with social deprivation was present (r = -0.19; p<.001). Results were confirmed by a multivariate analysis.

Conclusion

This study showed substantially differing GP accessibility throughout England. However, socially deprived areas did not have poorer spatial access to GPs.

SUBMITTER: Bauer J

PROVIDER: S-EPMC5980177 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatial Accessibility of Primary Care in England: A Cross-Sectional Study Using a Floating Catchment Area Method.

Bauer Jan J Müller Ruth R Brüggmann Dörthe D Groneberg David A DA

Health services research 20170707 3

<h4>Objective</h4>To analyze the general practitioners (GPs) with regard to the degree of urbanization, social deprivation, general health, and disability.<h4>Data sources</h4>Small area population data and GP practice data in England.<h4>Study design</h4>We used a floating catchment area method to measure spatial GP accessibility with regard to the degree of urbanization, social deprivation, general health, and disability.<h4>Data collection</h4>Data were collected from the Office for National ...[more]

PMID: 28685827

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Project description:Background: Geographic weights are vital in the floating catchment area (FCA) method of accessibility measurements due to their simulation of spatial barriers in various ways. When modelling population demand, geographical weights with different distance decay coefficients can reflect diverse distance tolerances in facility utilization and could lead to erratic accessibility results. Quantifying accessibility as the sum of weighted supply-demand ratios can alleviate the distance decay coefficient's influence and generate stable geographic patterns. However, the effects of weighted ratios on different FCA models and resources have not been investigated. Methods: To identify impacts of weighted ratios on various FCA variants, this study contrasted the accessibility calculated from the sum of ratios (access) and the sum of weighted ratios (access ratios) within three prevalent FCA models: enhanced two-step FCA (E2SFCA), modified two-step FCA (M2SFCA), and three-step FCA (3SFCA). In addition, the accessibilities of various resources evaluate the stability of the weighted ratios' effect. This study therefore examined the accessibilities to primary schools, job opportunities, and major hospitals in Shanghai. Shanghai is a case study that provides lessons on using big data to measure accessibility in metropolitan areas. Results: Geographic weights can not only mitigate the impact of the distance decay coefficients, but can also eliminate model features, which reduces the performance of the M2SFCA's supply decay and the 3SFCA's population demand adjustment in accessibility results. Moreover, weighted ratios tend to overestimate accessibility in marginal communities that lie within fewer catchments, regardless of the resource type. This tendency can lead to an epistemological trap that creates an inaccurate and counter-intuitive perception of resource distribution in a given area. Conclusions: The results identify a gap between the methodological logic and the empirical perception in accessibility measurements. This study concludes that the use of geographic weights needs to be cautious and epistemologically consistent.

Project description:BackgroundThere are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran.MethodsThe 2021 age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different access scenarios: only public hospitals, only public healthcare centres and both (either hospitals or healthcare centres) as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process, based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres and distance to main roads), were used to choose potential vaccination centres looking for the highest suitability for residents. Global Moran's index (GMI) was used to measure the spatial autocorrelation of the accessibility index in different scenarios and the proposed model.ResultsThere were 26 public hospitals and 271 public healthcare centres in the study area. Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area, our findings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. The value of GMI for the proposed model (accessibility to selected vaccination centres) was calculated as 0.53 (Z = 162.42, P < 0.01). Both GMI and Z-score values decreased in the proposed model, suggesting an enhancement in accessibility to COVID-19 vaccination services.ConclusionsThe periphery and poor areas of the city had the least access to COVID-19 vaccination centres. Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems, provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available.

Project description:BACKGROUND:Primary care provides the foundation for most modern health-care systems, and in the interests of equity, it should be resourced according to local need. We aimed to describe spatially the burden of chronic conditions and primary medical care funding in England at a low geographical level, and to measure how much variation in funding is explained by chronic condition prevalence and other patient and regional factors. METHODS:We used multiple administrative data sets including chronic condition prevalence and management data (2014/15), funding for primary-care practices (2015-16), and geographical and area deprivation data (2015). Data were assigned to a low geographical level (average 1500 residents). We investigated the overall morbidity burden across 19 chronic conditions and its regional variation, spatial clustering and association with funding and area deprivation. A linear regression model was used to explain local variation in spending using patient demographics, morbidity, deprivation and regional characteristics. RESULTS:Levels of morbidity varied within and between regions, with several clusters of very high morbidity identified. At the regional level, morbidity was modestly associated with practice funding, with the North East and North West appearing underfunded. The regression model explained 39% of the variability in practice funding, but even after adjusting for covariates, a large amount of variability in funding existed across regions. High morbidity and, especially, rural location were very strongly associated with higher practice funding, while associations were more modest for high deprivation and older age. CONCLUSIONS:Primary care funding in England does not adequately reflect the contemporary morbidity burden. More equitable resource allocation could be achieved by making better use of routinely available information and big data resources. Similar methods could be deployed in other countries where comparable data are collected, to identify morbidity clusters and to target funding to areas of greater need.

Dataset Information

Spatial Accessibility of Primary Care in England: A Cross-Sectional Study Using a Floating Catchment Area Method.

Objective

Data sources

Study design

Data collection

Principal findings

Conclusion

Publications

Spatial Accessibility of Primary Care in England: A Cross-Sectional Study Using a Floating Catchment Area Method.

Similar Datasets

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