Search

The increasing significance of the role of the general practitioner (GP) in the British National Health Service, evolving from a provider to purchaser and now a key player in the organisation of Primary Care Groups, suggests the need for GPs to possess more and more information about their registered population. GP catchment areas, though an essential basis for providing GPs with important information such as levels of accessibility to surgery, are rarely clearly or accurately defined. Previous approaches towards the definition of GP catchments have been confined to single regionalisation methods, such as mean distance measures, and are prone to problems of either overestimating or underestimating medical service areas. This problem is compounded by a lack of acknowledgement that the application of contrasting catchment methodologies to a common service population has the potential to yield vastly different results which can have serious implications for health care planning and resource allocation. The lack of sophistication in the definition of medical service areas calls for a new methodology to be considered. In this paper, attention is given to the adaptation of multidimensional regional analytical techniques developed outside the health domain and applied in a Regional Health Authority in Northern Ireland. The technique involves the creation of a Synthetic Data Matrix (SDM) which compares patient to GP flow (affiliation) information aggregated at the Census Enumeration District level across a number of catchment areas created using different methodologies. The SDM is then analysed using a modified version of the European Regionalisation Algorithm to create an optimal set of nonoverlapping regions according to pre-defined population size and self-containment criteria. The results, a set of compact, robust and highly self-contained catchments, are extremely encouraging. The paper considers the future potential use of such a methodology for health care planning and highlights areas for further research in this field. Keywords: GP Catchments; Regionalisation; Medieal service areas; GIS; UK

Introduction: To review the geographic exposure measures used to characterize the tobacco environment in terms of density of tobacco outlets and proximity to tobacco outlets, and its association with smoking-related outcomes.Methods: We used PubMed and Google Scholar to find articles published until December 2019. The search was restricted to studies that (1) measured the density of and/or proximity to tobacco outlets and (2) included associations with smoking outcomes. The extraction was coordinated by several observers. We gathered data on the place of exposure, methodological approaches, and smoking outcomes.Results: Forty articles were eligible out of 3002 screened papers. Different density and proximity measures were described. 47.4% density calculations were based on simple counts (number of outlets within an area). Kernel density estimations and other measures weighted by the size of the area (outlets per square kilometer), population, and road length were identified. 81.3% of the articles which assessed proximity to tobacco outlets used length distances estimated through the street network. Higher density values were mostly associated with higher smoking prevalence (76.2%), greater tobacco use and smoking initiation (64.3%), and lower cessation outcomes (84.6%). Proximity measures were not associated with any smoking outcome except with cessation (62.5%).Conclusion: Associations between the density of tobacco outlets and smoking outcomes were found regardless of the exposure measure applied. Further research is warranted to better understand how proximity to tobacco outlets may influence the smoking outcomes. This systematic review discusses methodological gaps in the literature and provides insights for future studies exploring the tobacco environment.Implications: Our findings pose some methodological lessons to improve the exposure measures on the tobacco outlet environment. Solving these methodological gaps is crucial to understand the influence of the tobacco environment on the smoking outcomes. Activity spaces should be considered in further analyses because individuals are exposed to tobacco beyond their residence or school neighborhood. Further studies in this research area demand density estimations weighted by the size of the area, population, or road length, or measured using Kernel density estimations. Proximity calculations should be measured through the street network and should consider travel times apart from the length distance. [ABSTRACT FROM AUTHOR]

Background: Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data.Objective: To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels.Methods: Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking.Results: Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11-70 years (β = -0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70-79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05).Conclusion: The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health. [ABSTRACT FROM AUTHOR]