A large body of research has examined relationships between accessibility to green space and a variety of health outcomes with many researchers finding benefits in terms of levels of physical activity and relationships with levels of obesity, mental health, and other health conditions. Such studies often use spatial analytical techniques to examine relationships between distance to such spaces and health data collated at an individual survey respondent's home address or, more commonly, derived from area-based census measures summarised at a centroid. Generally, such measures are becoming more sophisticated and have moved on from the use of straightforward Euclidean-based measures to those based on network distance. However, few studies tend to use a combination of approaches or seek to establish the implications of incorporating alternative measures of accessibility on potential relationships. Using a database of green spaces (and associated attributes) and a detailed network dataset for the city of Cardiff, Wales, we examine the sensitivity of findings to the ways in which different metrics are calculated. This is illustrated by examining the variations in association between such metrics and a census-based deprivation index widely used in health studies to measure socioeconomic conditions. Our findings demonstrate that not only will the distances to green spaces vary according to the methodologies adopted but that any study that aims to investigate relationships with attributes of the nearest green space should acknowledge that matches may vary widely according to the techniques used. We conclude by warning against the use of inappropriate methodologies in examining access to green space which may directly influence directions (and levels) of association and hence may limit their relevance in wider geographical contexts.

Previous research has drawn attention to the importance of measuring accessibility to public transit services for transport planning and decision-making purposes and to the use of GIS to produce accessibility maps. Existing measures have been criticised for their lack of sophistication and reliance on simple operations such as Euclidean buffering. This article introduces an accessibility measure based on enhanced ‘floating catchment’ techniques, which have to date not been applied to measure access to public transport opportunities. We demonstrate that, after essential modifications to reflect the characteristics of transit service provision, this approach captures many detailed aspects of accessibility including proximity, the balance between service supply and demand, cumulative opportunity and temporal availability. We illustrate its application using a case study based in South Wales, integrating publicly available digital transport timetables and information on bus stop locations with geospatial analysis tools to provide a realistic appraisal of bus transit accessibility. Such measures could clearly form the basis of future studies of social exclusion and access to public transport opportunities. We conclude by drawing attention to the potential for including more intelligent demand-side and supply-side modelling inputs and by reiterating the policy importance of developing more accurate evaluations of the current and future state of the public transport network.

Background: frailty has major implications for health and social care services internationally. The development, validation and national implementation of the electronic Frailty Index (eFI) using routine primary care data has enabled change in the care of older people living with frailty in England. Aims: to externally validate the eFI in Wales and assess new frailty-related outcomes. Study design and setting: retrospective cohort study using the Secure Anonymised Information Linkage (SAIL) Databank, comprising 469,000 people aged 65–95, registered with a SAIL contributing general practice on 1 January 2010. Methods: four categories (fit; mild; moderate and severe) of frailty were constructed using recognised cut points from the eFI. We calculated adjusted hazard ratios (HRs) from Cox regression models for validation of existing outcomes: 1-, 3- and 5-year mortality, hospitalisation, and care home admission for validation. We also analysed, as novel outcomes, 1-year mortality following hospitalisation and frailty transition times. Results: HR trends for the validation outcomes in SAIL followed the original results from ResearchOne and THIN databases. Relative to the fit category, adjusted HRs in SAIL (95% CI) for 1-year mortality following hospitalisation were 1.05 (95% CI 1.03-1.08) for mild frailty, 1.24 (95% CI 1.21-1.28) for moderate frailty and 1.51 (95% CI 1.45-1.57) for severe frailty. The median time (lower and upper quartile) between frailty categories was 2,165 days (lower and upper quartiles: 1,510 and 2,831) from fit to mild, 1,155 days (lower and upper quartiles: 756 and 1,610) from mild to moderate and 898 days (lower and upper quartiles: 584 and 1,275) from moderate to severe. Conclusions: further validation of the eFI showed robust predictive validity and utility for new outcomes.