Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach.

BACKGROUND: Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators. METHODS: The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS). RESULTS: The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated. CONCLUSIONS: In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

A data linkage study of the effects of the Great Recession and austerity on antidepressant prescription usage.

BACKGROUND: International literature shows unemployment and income loss during the Great Recession worsened population mental health. This individual-level longitudinal study examines how regional economic trends and austerity related to depression using administrative prescription data for a large and representative population sample. METHODS: Records from a sample of the Scottish Longitudinal Study (N=86 500) were linked to monthly primary care antidepressant prescriptions (2009-15). Regional economic trends were characterized by annual full-time employment data (2004-14). Economic impact of austerity was measured via annual income lost per working age adult due to welfare reforms (2010-15). Sequence analysis identified new cases of antidepressant use, and group-based trajectory modelling classified regions into similar economic trajectories. Multi-level logistic regression examined relationships between regional economic trends and new antidepressant prescriptions. Structural equation mediation analysis assessed the contributory role of welfare reforms. RESULTS: Employed individuals living in regions not recovering post-recession had the highest risk of beginning a new course of antidepressants (AOR 1.23; 95% CI 1.08-1.38). Individuals living in areas with better recovery trajectories had the lowest risk. Mediation analyses showed that 50% (95% CI 7-61%) of this association was explained by the impact of welfare benefit reforms on average incomes. CONCLUSIONS: Following the Great Recession, local labour market decline and austerity measures were associated with growing antidepressant usage, increasing regional inequalities in mental health. The study evidences the impact of austerity on health inequalities and suggests that economic conditions and welfare policies impact on population health. Reducing the burden of mental ill-health primarily requires action on the social determinants.

Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health.

BACKGROUND: Over the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. METHODS: National Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N = 151,418). Antidepressant prescription status over the previous 6 months was recorded for every month for which data were available (January 2009-December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. RESULTS: Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to be in the group that started a new course of antidepressant prescriptions. CONCLUSIONS: The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

Trajectories in mental health and socio-spatial conditions in a time of economic recovery and austerity: A longitudinal study in England 2011-17.

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.

Changing labour market conditions during the 'great recession' and mental health in Scotland 2007-2011: an example using the Scottish Longitudinal Study and data for local areas in Scotland.

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.

The secret garden? Elite metropolitan geographies in the contemporary UK.

There is an enduring, indeed increasing awareness of the role of spatial location in defining and reinforcing inequality in this country and beyond. In the UK, much of the debate around these issues has focussed on the established trope of a long-standing 'north-south divide', a divide which appears to have deepened in recent decades with the inexorable de-industrialisation of northern Britain presented in stark counterpoint to the burgeoning concentration of wealth in London and the south-east, driven by the financial and ancillary services sectors. Due to a lack of available data, such debates have tended to focus solely on economic inequalities between places, and until now there was little understanding of how these disparities played out in the social and cultural domains. This paper significantly advances our understanding of the true meaning of spatial inequality in the UK by broadening that definition to encompass not only the economic, but also the social and cultural arenas, using data available from the BBC's Great British Class Survey experiment. We argue that these data shine a light not only on the economic inequalities between different parts of the country which existing debates have already uncovered but to understand how these are both reinforced and mediated across the social and cultural dimensions. Fundamentally, we concur with a great many others in seeing London and the south-east as a vortex for economic accumulation but it is also much more than that; it is a space where the coming together of intense economic, social and cultural resources enables the crystallisation of particular and nuanced forms of elite social class formations, formations in which place is not incidental but integral to their very existence.