A comparison of methods for calculating general practice level socioeconomic deprivation

Background: A measure of the socioeconomic deprivation experienced by the registered patient population of a general practice is of interest because it can be used to explore the association between deprivation and a wide range of other variables measured at practice level. If patient level geographical data are available a population weighted mean area-based deprivation score can be calculated for each practice. In the absence of these data, an area-based deprivation score linked to the practice postcode can be used as an estimate of the socioeconomic deprivation of the practice population. This study explores the correlation between Index of Multiple Deprivation 2004 (IMD) scores linked to general practice postcodes (main surgery address alone and main surgery plus any branch surgeries), practice population weighted mean IMD scores, and practice level mortality (aged 1 to 75 years, all causes) for 38 practices in Rotherham UK. Results: Population weighted deprivation scores correlated with practice postcode based scores (main surgery only, Pearson r = 0.74, 95% CI 0.54 to 0.85; main plus branch surgeries, r = 0.79, 95% CI 0.63 to 0.89). All cause mortality aged 1 to 75 correlated with deprivation (main surgery postcode based measure, r = 0.50, 95% CI 0.22 to 0.71; main plus branch surgery based score, r = 0.55, 95% CI 0.28 to 0.74); population weighted measure, r = 0.66, 95% CI 0.43 to 0.81). Conclusion: Practice postcode linked IMD scores provide a valid proxy for a population weighted measure in the absence of patient level data. However, by using them, the strength of association between mortality and deprivation may be underestimated

Modifying a Geodemographic Classification of the e-Society using public feedback

The e-Society geodemographic classification (Longley et al., 2008) categories neighbourhoods based on their engagement with new information communication technologies. This classification was launched online in 2006, and allowed users to both view and comment on the accuracy of their assigned neighbourhood Type. This paper utilises the user generated feedback on the accuracy of the e-Society classification and through external validation calculates their accuracy. The pilot methodology developed in this paper is scalable and could be repeated for any classification. We believe that this methodology gives the recipients of these classification procedures a voice that their concerns of classification accuracy can be heard

Booms, busts and retirement timing

Cyclical fluctuations - which affect both asset and labour markets - can have an ambiguous effect on retirement. We explore this empirically using data from the British Household Panel Survey, exploiting small area geographic identifers to match local house prices, earnings and unemployment to respondents. We match stock prices via the date of interview. Our results show little evidence of any positive wealth effects despite large spatial and temporal variations in asset prices over the period analysed. We find more response to local labour market conditions - increases in unemployment are associated with earlier retirement while increases in wages delay retirement

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

East Kent maternity services review

Background to Report: This document presents the results of an analysis of the responses to the formal public consultation on east Kent maternity services which took place over a 14 week period from 14th October 2011 to 20th January 2012. The consultation was conducted by NHS Kent and Medway, and the data gathered was analysed independently by the Centre for Nursing and Healthcare Research at the University of Greenwich

Dropping off the edge 2015: persistent communal disadvantage in Australia

This report shows that complex and entrenched disadvantage is experienced by a small but persistent number of locations in each state and territory across Australia. Foreword In 2007, Jesuit Social Services and Catholic Social Services Australia commissioned ground-breaking research into place-based disadvantage across the nation. The resulting report, Dropping off the edge, built on previous work that Jesuit Social Services had engaged Professor Tony Vinson to undertake on its behalf and quickly became a critical resource for governments, service providers and communities attempting to address the challenge of entrenched and often complex geographical disadvantage. That report received over 284 scholarly citations and supported the establishment of the Australian Social Inclusion Board – a body charged with identifying long-term strategies to end poverty in Australia. Since the publication of Dropping off the edge, our organisations have received many requests to update the findings and produce a new report tracking the wellbeing of communities in Australia over the intervening time. Sadly, the current report drives home the enormous challenge that lies in front of our policy makers and service providers, as many communities identified as disadvantaged in 2007 once again head the list in each state and territory. As a society we cannot, and should not, turn away from the challenge of persistent and entrenched locational disadvantage, no matter how difficult it may be to solve the problem. We call on government, community and business to come together to work alongside these communities to ensure long term sustainable change. We hold hope that the young people and future generations in these communities will have a better outlook and life opportunities than is currently available to them. It is our belief that every Australian should have access to the opportunities in life that will enable them to flourish – to complete their education, to get a job, to access safe and affordable housing, to raise their children in safe communities and to see the next generation thrive. Jesuit Social Services and Catholic Social Services Australia are indebted to the dedication and perseverance of Professor Tony Vinson in leading this important research and analysis over the past 15 years. Julie Edwards Chief Executive Officer Jesuit Social Services Marcelle Mogg Chief Executive Officer Catholic Social Services Australi

Social fragmentation, deprivation and urbanicity: relation to first-admission rates for psychoses

<i>Declaration</i> <i>of</i> <i>interest</i>: None. <i>Background</i>: Social disorganisation, fragmentation and isolation have long been posited as influencing the rate of psychoses at area level. Measuring such societal constructsis difficult. A census-based index measuring social fragmentation has been proposed. <i>Aims</i>: To investigate the association between first-admission rates for psychosis and area-based measures of social fragmentation, deprivation and urban/rural index. <i>Method</i>: We used indirect standardisation methods and logistic regression models to examine associations of social fragmentation, deprivation and urban/rural categories with first admissions for psychoses in Scotland for the 5-year period 1989–1993. <i>Results</i>: Areas characterised by high social fragmentation had higher first-ever admission rates for psychosis independent of deprivation and urban/rural status. There was a dose–response relationship between social fragmentation category and first-ever admission rates for psychosis. There was no statistically significant interaction between social fragmentation, deprivation and urban/rural index. <i>Conclusions</i>: First-admission rates are strongly associated with measures of social fragmentation, independent of material deprivation and urban/rural category

Geographically intelligent disclosure control for flexible aggregation of census data

This paper describes a geographically intelligent approach to disclosure control for protecting flexibly aggregated census data. Increased analytical power has stimulated user demand for more detailed information for smaller geographical areas and customized boundaries. Consequently it is vital that improved methods of statistical disclosure control are developed to protect against the increased disclosure risk. Traditionally methods of statistical disclosure control have been aspatial in nature. Here we present a geographically intelligent approach that takes into account the spatial distribution of risk. We describe empirical work illustrating how the flexibility of this new method, called local density swapping, is an improved alternative to random record swapping in terms of risk-utility