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This paper examines trends in mental health among adults in England during the period of economic recovery and austerity following the 2008 ‘great recession’. We report analysis of data on 17,212 individuals living in England, from the longitudinal Understanding Society Survey (USS). We examined how individual's self-reported mental health over time (2011–2017), related to their changing socio-geographical status. Self-reported mental health is reported in the USS using version 2 of the SF12 Mental Component Summary. Trends in this score (across 5 observations per subject) were categorised into Mental Health Trajectory Groups (MHTGs) using Group Based Trajectory Modelling. We used maximum-likelihood multinomial logit models to estimate for individuals the relative likelihood of belonging to different Mental Health Trajectory categories as compared with a ‘base’ category, for whom mental health was good and stable throughout the period. We focus on likelihood of belonging to a group showing ‘declining’ mental health. Predictor variables included individuals' attributes and area conditions in their places of residence (including Office of National Statistics indicators of local employment deprivation and data on average income loss within districts due to welfare benefit reforms, published by the Centre of Regional Economic and Social Research at Sheffield Hallam University, UK). Our results emphasise the multiple socio-geographical ‘determinants’ likely to be operating on individual mental health. Declining mental health was associated both with conditions at the start of the study period and with social and socio-geographical mobility by the end of the study period. Risks of declining mental health were significantly greater for more deprived individuals and also (controlling for individual attributes) among those living in English neighbourhoods that were already economically disadvantaged at the beginning of the ‘great recession’ and located in districts where average incomes were most severely impacted by the effects of governmental austerity programmes on welfare benefits.

This paper reports research exploring how trends in local labour market conditions during the period 2007–2011 (early stages of the ‘great recession’) relate to reported mental illness for individuals. It contributes to research on spatio-temporal variation in the wider determinants of health, exploring how the lifecourse of places relates to socio-geographical inequalities in health outcomes for individuals. This study also contributes to the renewed research focus on the links between labour market trends and population health, prompted by the recent global economic recession. We report research using the Scottish Longitudinal Study (SLS), a 5.3% representative sample of the Scottish population, derived from census data (https://sls.lscs.ac.uk/). In Scotland, (2011) census data include self-reported mental health. SLS data were combined with non-disclosive information from other sources, including spatio-temporal trends in labour market conditions (calculated using trajectory modelling) in the 32 local authority areas in Scotland. We show that, for groups of local authorities in Scotland over the period 2007–2011, trends in employment varied. These geographically variable trends in employment rates were associated with inequalities in self-reported mental health across the country, after controlling for a number of other individual and neighbourhood risk factors. For residents of regions that had experienced relatively high and stable levels of employment the odds ratio for reporting a mental illness was significantly lower than for the ‘reference group’, living in areas with persistently low employment rates. In areas where employment declined markedly from higher levels, the odds ratio was similar to the reference group. The findings emphasise how changes in local economic conditions may influence people's health and wellbeing independently of their own employment status. We conclude that, during the recent recession, the economic life course of places across Scotland has been associated with individual mental health outcomes.

It has suited both sides of Ireland’s religious and political divide to portray the Great Famine that affected Ireland in the late 1840s as primarily affecting the Catholic population. However, while the geographies of the Famine have been explored in detail in recent years its religious dimensions have been largely ignored, albeit with a few exceptions. As a consequence, the assumption that Great Famine was a Catholic famine has not been sufficiently challenged. Drawing on a relatively untapped source, the 1834 Commission on ‘the state of religion and other instruction in Ireland,’ as well as census data, this paper explore the relationship between religion, poverty and population loss over the Famine period. It shows that Catholics were disproportionately affected by the Famine because the Famine was most severe in areas whose population was overwhelmingly Catholic. In more mixed areas, however, the Protestant population seems to have been at least as affected as Catholics if not more so. This conundrum explains why the Famine actually had a surprisingly small impact on Ireland’s religious demography.

As the need for Irish firms to move into knowledge economy increases knowledge management is being more important. Among its central aims is for knowledge creation and development to enhance knowledge and inspire innovations. One of the ways knowledge management achieves these aims is through the development of knowledge management tools for the firm. Among these tools is the option to use business intelligence to enhance knowledge in the firm by exposing hidden knowledge and building on it. In particular data mining has been highlighted as being effective within business intelligence and knowledge management in discovering hidden knowledge. This dissertation investigates these systems and looks at how visual data mining in this area is enhancing knowledge creation throughout the firm and can give firms a competitive advantage over rivals. It considers the difficult decision for knowledge management practitioners at selecting the correct tool and provides a framework which can be apply in different situations. The dissertation concludes with analysis of the results and future work in the area.