BackgroundDuring the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality—including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities.MethodsWe performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index.ResultsFour vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively.ConclusionVulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.
Health geographers have been long concerned with understanding how the accessibility of individuals to certain environmental features may influence health and wellbeing. Such insights are increasingly being adopted by policy makers for designing healthy neighbourhoods. To support and inform decision making, there is a need for small area national level data. This paper details the creation of a suite of open access health indicators, including a novel multidimensional index summarising 14 health-related features of neighbourhoods for Great Britain. We find no association of our overall index with physical health measures, but a significant association to mental wellbeing.
This paper examines how internal migration distance and its frictional effect vary between countries. Such comparisons are hampered by differences in the number and configuration MAUP W use the flexible aggregation routines embedded in the IMAGE Studio, a bespoke software platform which incorporates a spatial interaction model, to elucidate these scale and pattern effects in a set of countries for which finely grained origin-destination matrices are available.We identify an exponential relationship between mean migration distance and mean area size but show that the frictional effect of distance remains remarkably stable across spatial scale, except where zones have small populations and are poorly connected. This stability allows robust comparisons between countries even though zonal systems differ. We find that mean migration distances vary widely, being highest in large, low density countries and positively associated with urbanisation, HDI and GDP per capita. This suggests a positive link between development and migration distance, paralleling that between development and migration intensity. We find less variation in the beta parameter that measures distance friction but identify clear spatial divisions between more developed countries, with lower friction in larger, less dense countries undergoing rapid population growth. IntroductionMigration can be defined as changing residence from one geographical location to another.Whether this involves a permanent or a temporary relocation, travel occurs over a specific distance. As with many other forms of spatial interaction, migration conforms with the axiom following from ' proposition in the nineteenth century that (Ravenstein, 1885, p. 198). Implicit in this statement is that fewer migrants travel longer distances and that distance therefore exerts a frictional effect on migration behaviour. 3Bell et al. (2002) identified distance, along with intensity, connectivity and impact as the dimensions of internal migration that are important to consider when making crossnational comparisons. M residence, measured in the form of a rate or probability, whilst connectivity refers to the extent to which regions are linked by migration flows and can be measured using a simple index such as the proportion of the total flows between regions that are non-zero. Migration impact, on the other hand, indicates the extent to which migration transforms the pattern of population settlement and can be measured using a number of indicators such as migration effectiveness or aggregate net migration. Elsewhere we have examined the data available for making such comparisons (Bell et al., 2014b), developed software to compute comparative indicators and address key methodological issues (Stillwell et al., 2014), and assessed how countries differ with respect to overall migration intensities (Bell et al., 2015). In this paper, we turn to the distance dimension in order to examine how far people move and the frictional effect of distance on internal migration in countries around th...
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