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Background Black, Asian and minority ethnicity groups may experience better health outcomes when living in areas of high own-group ethnic density – the so-called ‘ethnic density’ hypothesis. We tested this hypothesis for the treatment outcome of compulsory admission. Methods Data from the 2010–2011 Mental Health Minimum Dataset (N = 1 053 617) was linked to the 2011 Census and 2010 Index of Multiple Deprivation. Own-group ethnic density was calculated by dividing the number of residents per ethnic group for each lower layer super output area (LSOA) in the Census by the LSOA total population. Multilevel modelling estimated the effect of own-group ethnic density on the risk of compulsory admission by ethnic group (White British, White other, Black, Asian and mixed), accounting for patient characteristics (age and gender), area-level deprivation and population density. Results Asian and White British patients experienced a reduced risk of compulsory admission when living in the areas of high own-group ethnic density [odds ratios (OR) 0.97, 95% credible interval (CI) 0.95–0.99 and 0.94, 95% CI 0.93–0.95, respectively], whereas White minority patients were at increased risk when living in neighbourhoods of higher own-group ethnic concentration (OR 1.18, 95% CI 1.11–1.26). Higher levels of own-group ethnic density were associated with an increased risk of compulsory admission for mixed-ethnicity patients, but only when deprivation and population density were excluded from the model. Neighbourhood-level concentration of own-group ethnicity for Black patients did not influence the risk of compulsory admission. Conclusions We found only minimal support for the ethnic density hypothesis for the treatment outcome of compulsory admission to under the Mental Health Act.

Introduction - There are concerns about young people's increasing use of social media and the effects this has on overall life satisfaction. Establishing the significance of social media use requires researchers to take simultaneous account of other factors that might be influential and it is essential to adopt a longitudinal perspective to investigate temporal patterns.Method - Measures of happiness for children aged 10–15 from 7 waves of the UK Household Longitudinal Study were examined (n = 7596). Multilevel models were used to assess the relative association between these measures, children's social media use and individual, household and community characteristics.Results - High use of social media was found to be significantly associated with change in happiness scores but was not associated with worsening life satisfaction trajectories. The most consistent factor was gender, with girls experiencing the largest decline in happiness between two time points (0.18 points) and being more likely to have a worsening trajectory over time (OR 1.77, 95% CI 1.36–2.32). Parental mental health, household support and household income were also important.Conclusion - Moderate use of social media does not play an important role in shaping children's life satisfaction. Higher levels of use is associated with lower levels of happiness, especially for girls but more research is needed to understand how this technology is being used. As well as focusing on high levels of social media use, policy makers should also concentrate on particular demographic groupings and factors affecting the social fabric of the households in which children grow up.

Compulsory community treatment for people with severe mental illness remains controversial due to conflicting research evidence. Recently, there have been challenges to the conventional view that trial‐based evidence should take precedence. This paper adds to these challenges in three ways. First, it emphasizes the need for critiques of trials to engage with conceptual and not just technical issues. Second, it develops a critique of trials centred on both how we can have knowledge and what it is we can have knowledge of. Third, it uses this critique to develop a research strategy that capitalizes on the information in large‐scale datasets.\ud \ud

IntroductionSupervised community treatment (SCT) for people with serious mental disorders has become accepted practice in many countries around the world. In England, SCT was adopted in 2008 in the form of community treatment orders (CTOs). CTOs have been used more than expected, with significant variations between people and places. There is conflicting evidence about the effectiveness of SCT; studies based on randomised controlled trials (RCTs) have suggested few positive impacts, while those employing observational designs have been more favourable. Robust population-based studies are needed, because of the ethical challenges of undertaking further RCTs and because variation across previous studies may reflect the effects of sociospatial context on SCT outcomes. We aim to examine spatial and temporal variation in the use, effectiveness and cost of CTOs in England through the analysis of routine administrative data.Methods and analysisFour years of data from the Mental Health Services Dataset (MHSDS) will be analysed using multilevel models. Models based on all patients eligible for CTOs will be used to explore variation in their use. A subset of CTO-eligible patients comprising a treatment group (CTO patients) and a matched control group (non-CTO patients) will be used to examine variation in the association between CTO use and study outcomes. Primary outcome will be total time in hospital. Secondary outcomes will include time to first readmission and mortality. Outputs from these models will be used to populate predictive models of healthcare resource use.Ethics and disseminationEthical approval has been granted by the National Health Service Data Access and Advisory Group and Warwick University. To ensure patient confidentiality and to meet data governance requirements, analyses will be carried out in a secure microdata laboratory using de-identified data. Study findings will be disseminated through academic channels and shared with mental health policy-makers and other stakeholders.

SummaryConventional approaches to evidence that prioritise randomised controlled trials appear increasingly inadequate for the evaluation of complex mental health interventions. By focusing on causal mechanisms and understanding the complex interactions between interventions, patients and contexts, realist approaches offer a productive alternative. Although the approaches might be combined, substantial barriers remain.Declaration of interestAll authors had financial support from the National Institute for Health Research Health Services and Delivery Research Programme while completing this work. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the National Health Service, the National Institute for Health Research, the Medical Research Council, Central Commissioning Facility, National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre, the Health Services and Delivery Research Programme or the Department of Health. S.P.S. is part funded by Collaboration for Leadership in Applied Health Research and Care West Midlands. K.B. is editor of the British Journal of Psychiatry.

An increasing number of countries across the world are planning for the eradication of the tobacco epidemic. The actions necessary to realise this ambition have been termed the tobacco endgame. The focus of this paper is on the intersection between the tobacco endgame with place, a neglected theme in recent academic and policy debates. We begin with an overview of the key themes in the literature on endgame strategies before detailing the international landscape of engame initiatives, paying particular attention to the opportunities and challenges of endgame strategies in low and middle income countries. Finally, we critically assess the current endgame debates and suggest a novel agenda for integrating geographical perspectives into research on the endgame that provides enhanced understanding of the challenges associated with this important global health vision.

Objectives This study aims to address, for the first time, the challenges of constructing small area estimates of health status using linked national surveys. The study also seeks to assess the concordance of these small area estimates with data from national censuses. Setting Population level health status in England, Scotland and Wales. Participants A linked integrated dataset of 23 374 survey respondents (16+ years) from the 2011 waves of the Health Survey for England (n=8603), the Scottish Health Survey (n=7537) and the Welsh Health Survey (n=7234). Primary and secondary outcome measures Population prevalence of poorer self-rated health and limiting long-term illness. A multilevel small area estimation modelling approach was used to estimate prevalence of these outcomes for middle super output areas in England and Wales and intermediate zones in Scotland. The estimates were then compared with matched measures from the contemporaneous 2011 UK Census. Results There was a strong positive association between the small area estimates and matched census measures for all three countries for both poorer self-rated health (r=0.828, 95% CI 0.821 to 0.834) and limiting long-term illness (r=0.831, 95% CI 0.824 to 0.837), although systematic differences were evident, and small area estimation tended to indicate higher prevalences than census data. Conclusions Despite strong concordance, variations in the small area prevalences of poorer self-rated health and limiting long-term illness evident in census data cannot be replicated perfectly using small area estimation with linked national surveys. This reflects a lack of harmonisation between surveys over question wording and design. The nature of small area estimates as ‘expected values’ also needs to be better understood.

Binge drinking has been linked to escalating costs of hospitalisation and to premature mortality, and implicated in a range of acute and chronic health problems as well as crime, violence and other negative aspects of the wider well-being agenda. Variously defined, it can be characterised as brief periods of heavy drinking (across one day or evening) within a longer time-frame of lower consumption or even abstinence (across a week or several weeks). In England the current binge drinking epidemic has become particularly salient in the past decade and has been seen largely in terms of excessive consumption by younger people, particularly women in urban centres. It has also been linked to the liberalisation of licencing laws and the promotion of 24 h club cultures. This paper presents an observational study of the regional development of binge drinking between 2001 and 2009 as evidenced in the Health Survey for England. We innovate by using two different definitions of binge drinking within a multivariate multilevel modelling framework, with a focus on the random effects attributable to the year of study and region. We control for age, sex, ethnicity, marital status and individual socio-economic status, and confounding by neighbourhood deprivation and urbanisation. The paper identifies pronounced regional geographies that persist in the face of controls and vary little over time, and strong spatio-temporal gender differences which reflect the definition of binge drinking.

Background The increasing rate of compulsory admission to psychiatric inpatient beds in England is worrying. Studying variation between places and services could be key to identifying targets for interventions to reverse this trend. We modelled spatial variation in compulsory admissions in England using national patient-level data and quantified the extent to which patient, local-area, and service-setting characteristics accounted for this variation. Methods This study is a cross-sectional, multilevel analysis of the 2010–11 Mental Health Minimum Data Set (MHMDS). Data from eight provider trusts were excluded, including three independent provider trusts that lacked spatial identification codes. We excluded patients detained under sections of the Mental Health Act concerned only with conveyance to, or assessment in, a registered Place of Safety, or for short-term (≤72 h) assessment only, as these do not in themselves necessarily mean that the person will be admitted to an inpatient mental health bed. MHMDS contained reasonably complete data for a limited number of patient characteristics, namely age, sex, and ethnicity; however, several patient-level variables could not be included in our analysis because of high levels of missing data. Multilevel models were applied with MLwiN to estimate variation in compulsory admission, starting with null (unconditional) models that partitioned total variance in compulsory admission between each level in the model. The primary outcome was compulsory admission to a psychiatric inpatient bed, compared with people admitted voluntarily or receiving only community-based care. Findings Data were available for 1 238 188 patients, covering 64 National Health Service provider trusts (93%) and 31 865 census lower super output areas (LSOAs; 98%). 7·5% and 5·6% of the variance in compulsory admission occurred at LSOA level and provider trust levels, respectively, after adjusting for patient characteristics. Black patients were almost three times more likely to be admitted compulsorily than were white patients (odds ratio [OR] 2·94, 95% CI 2·90–2·98). Compulsory admission was greater in more deprived areas (OR 1·22, 1·18–1·27) and in areas with more non-white residents (OR 1·51, 1·43–1·59), after adjusting for confounders. Interpretation Rates of compulsory admission to inpatient psychiatric beds vary significantly between local areas and services, independent of patient, area, and service characteristics. Compulsory admission rates seem to reflect local factors, especially socioeconomic and ethnic population composition. Understanding how these factors condition access to, and use of, mental health care is likely to be important for developing interventions to reduce compulsion. Funding National Institute for Health Research Health Services and Delivery Research Programme.

This paper uses multivariate multilevel models with data from the British Crime Survey to investigate individual and neighbourhood influences on perceptions of local and national crime trends. In response to debates about the negative consequences of immigration and ethnic diversity, we specifically investigate the influence of ethnic heterogeneity on such perceptions. Results indicate that a person's socio-demographic background and their newspaper readership have the strongest association with perceptions of national trends whilst the strongest association with pessimistic views on localized crime is whether the individual has been a recent crime victim. Results suggest no negative effects for ethnic diversity. Moreover, the findings indicate that living in a mixed neighbourhood is associated with a reduced likelihood of perceiving rising levels of national crime.

Recent debates have suggested that increasing social diversity within Western economies is associated with adverse social consequences such as loss of community and decline of civic society, including an erosion of collective efficacy (ie shared expectations of and mutual engagement by residents in social control). In the UK and US, these debates have been given impetus by concerns about the effects of growing ethnic heterogeneity on community life. Here there is an assumption that heterogeneity undermines social cohesion and makes the established population less willing to share resources, trust fellow citizens, so that it eventually ‘hunkers down’ and withdraws from collective life. To date there are few studies that have examined this in detail across England at the small-area level. The research presented here explores this terrain by exploiting information from the British Crime Survey on two recognised dimensions of collective efficacy: namely, social cohesion and trust, and informal social control. Multivariate, multilevel models were used to determine the importance of individual and area characteristics in the possible explanation of these outcomes, and particular attention was paid to the relative importance of neighbourhood disadvantage over and above neighbourhood diversity. Results suggest that both diversity and disadvantage are statistically associated with reduced levels of social cohesion and trust, and informal social control, but greater substantive importance is attached to neighbourhood disadvantage.

There is much concern in the UK about the effects on community cohesion of anti-social behaviour (ASB) but to date relatively little is known about the geography of such behaviour: for what sort of people, and in what sort of places, are high levels of ASB a problem? What are the links, if any, between such behaviour and local socio-economic conditions, and how do such perceptions relate to local crime rates? Using data from the British Crime Survey and other secondary datasets, we develop and extend previous work which has investigated links between individual socio-economic characteristics, neighbourhood characteristics and individual perceptions of ASB. A multilevel modelling approach is used to ensure that individual and area level effects are not conflated. Secondly we extend the substantive knowledge surrounding the relationship between neighbourhood ethnic heterogeneity and individual perceptions of ASB. In so doing we challenge recent contentions that heterogeneity is associated with declining social cohesion and trust. We conclude that at a small-area scale for England, the primary area-level determinants of high levels of ASB lie in material circumstances, and that ethnic heterogeneity has no discernible effect on perceptions of ASB.

Smoking prevalence in England continues to reduce but further reduction is increasingly difficult. Cessation policy has successfully targeted those who want to quit but further reduction will need to shift attention to more difficult 'core smoker' populations. Following the established 'stages of change' perspective, this paper considers the characteristics of people who do not intend to quit smoking, anticipate difficulties in quitting and have not received advice about quitting. We deploy multilevel models of data drawn from the Health Survey for England years 2002-2004, and the NHS Primary Care Trust Patient Surveys for 2004 and 2005. It was found that variations in intentionality and anticipated ease of quitting are associated with individual factors such as smoking intensity, parental smoking, age/length of time as a smoker and the nature of the advice-giving consultation. Household composition and household income are also implicated in the intention to quit and anticipated difficulties in quitting. Once individual and household factors are taken into account the only identifiable area-level variation is reduced intentionality towards quitting in rural areas. We conclude by arguing that further gains in smoking cessation must focus on understanding the characteristics of 'hard-to-engage' populations.

This paper explores associations between local socioeconomic context and perceptions of neighbourhood quality of life, using a multilevel modelling approach to analyse data from the 2002/03 Survey of English Housing. A range of individual, property and area variables is successively introduced, the aim being to determine which of these has the greatest influence on the probability of being dissatisfied with one's neighbourhood. Several variables relating to property type and characteristics were derived and a set of ecological independent variables was used that is different from those deployed in previous analyses. Using a multilevel approach, the analysis determines the spatial scale at which variance is most concentrated—namely, the primary sampling unit (PSU). The relationship between tenure and property type is broken down and it is shown that social renting per se is not necessarily associated with greater dissatisfaction, although there is an association between private renting and greater satisfaction, probably reflecting the age and social profile of private renters. In respect of ecological indicators, increased levels of deprivation were associated with a raised probability of expressing neighbourhood satisfaction, as was settlement size, which is consistent with earlier work on quality of life. Conclusions are drawn concerning the use of studies of this kind to monitor trends in neighbourhood satisfaction.

Background Complete and accurate data on maternal smoking prevalence during pregnancy are not available at a local geographical scale in England. We employ a synthetic estimation approach to predict the expected prevalence of smoking during pregnancy and smoking at delivery by Primary Care Trust (PCT). Methods Multilevel logistic regression models were used with data from the 2010 Infant Feeding Survey and 2011 Census to predict the probability of mothers (a) smoking at any point during pregnancy and (b) smoking at delivery, according to age, deprivation, and the ethnic profile of the home area. These probabilities were applied to demographic information on mothers giving birth from 2010/11 Hospital Episode Statistics data to produce expected counts, and prevalence figures, of smokers by PCT, with Bayesian 95 % credible intervals. The expected prevalence of smoking at delivery by PCT was compared with midwife-collected Smoking at the Time of Delivery (SATOD) data using a Bland-Altman plot. Results The expected prevalence of smoking during pregnancy by PCT ranged from 8.1 % (95 % CI 5.6–1.0) to 31.6 % (27.5–34.8). The expected prevalence of smoking at delivery ranged from 2.5 % (1.4–4.0) to 17.1 % (13.7–20.4). Figures for expected smoking prevalence at delivery showed some agreement with SATOD, though SATOD data were generally higher than the synthetic estimates (mean difference 2.99 %). Conclusions It is possible to derive good estimates of expected smoking prevalence during pregnancy for small areas, potentially at much lower cost than conducting large surveys. Such data may be useful to help plan and commission smoking cessation services and monitor their effectiveness. Electronic supplementary material The online version of this article (doi:10.1186/s12963-015-0067-8) contains supplementary material, which is available to authorized users.

BackgroundSome UK studies have reported an urban excess in the prevalence of the most common mental disorders of anxiety and depression.AimsTo investigate rural/non-rural differences in the onset and maintenance of episodes of common mental disorders, after adjusting for the characteristics of respondents and their households.MethodA 12-month cohort study of 7659 adults aged 16–74 years living in 4338 private households, nested within 626 electoral wards in England, Wales and Scotland. Common mental disorders were assessed using the General Health Questionnaire (GHQ). Electoral wards were characterised by Office for National Statistics classification and by population density. Data were analysed using multilevel statistical modelling.ResultsRural residents had slightly better mental health than non-rural counterparts. The effects of geographical location on the mental health of participants were neither significantly confounded nor modified by socioeconomic status, employment status or household income.ConclusionsThere are small but statistically significant differences in rates of common mental disorders between urban and rural residents. Quantifying between-place differences using population density alone risks missing important contextual effects on mental health.

We describe a method of producing small-area estimates of characteristics of the neighbourhood social environment, such as participation in associational life. The method may be characterised as ‘modelling nationally, and predicting locally’ because the initial models are estimated using multilevel data derived from national datasets, calibrated to allow for within-region as well as between-region variation. These models produce coefficients which can then be applied to socioeconomic data at the local scale. As an example we outline a model of the determinants of volunteering. This indicates significant variation between places in the relationship between individual and area characteristics which influence the probability of volunteering. Then we proceed to show how this can be used to generate predictions of the variations between places in a number of aspects of the social environment. The estimates we produce are validated against survey data from other surveys, and a satisfactory degree of correlation is obtained, giving us confidence in the estimates we have produced.

There has recently been much debate about the influence of social capital on health outcomes. In particular it has been suggested that levels of social capital vary from place to place and that such variations may account for previously unexplained between-place variations in health outcomes. As yet few studies exist of the influence of small-area variations in social capital on health outcomes. One reason for this is the difficulty of obtaining indicators for small areas such as electoral wards in England, and we describe a method used to derive what we term 'synthetic estimates' of aspects of social capital by linking coefficients produced from multi-level analyses of national survey datasets to census data. We produce estimates for electoral wards in England and apply these in multi-level models of our response variable, the probability of survival of individuals surveyed in the Health and Lifestyle Survey of England. We report various combinations of models incorporating individual attributes, health-related behaviours, area measures of deprivation, and area measures of social capital. Our overall conclusion is that we find little support, at this spatial scale, for the proposition that area measures of social capital exert a beneficial effect on health outcomes.

Study objective: To test hypotheses about associations between area level exposures and the prevalence of the most common mental disorders (CMD) in Britain. A statistically significant urban-rural gradient was predicted, but not a socioeconomic gradient, in the prevalence of CMD after adjusting for characteristics of individual respondents. The study tested the hypothesis that the effects of area level exposures would be greatest among those not in paid employment. Design: Cross sectional survey, analysed using multilevel logistic and linear regression. CMD were assessed using the General Health Questionnaire (GHQ). Electoral wards were characterised using the Carstairs index, the Office of National Statistics (ONS) Classification of Wards, and population density. Setting: England, Wales, and Scotland. Participants: Nearly 9000 adults aged 16–74 living in 4904 private households, nested in 642 electoral wards. Main results: Little evidence was found of statistically significant variance in the prevalence of CMD between wards, which ranged from 18.8% to 29.5% (variance 0.035, SE 0.026) (p=0.11). Associations between CMD and characteristics of wards, such as the Carstairs index, only reached statistical significance among those who were economically inactive (adjusted odds ratio for top v bottom Carstairs score quintile 1.58, 95% CI 1.08 to 2.31) (p

It is still not known whether the places that people live affect their mental health. The principal aim of this 1991 study was to quantify simultaneously variance in the prevalence of the most common mental disorders, anxiety and depression, in Britain at the individual, household, and electoral ward levels. Data from a cross-sectional, nationally representative survey of 8,979 adults aged 16-74 years living in private households nested within 642 electoral wards in England, Wales, and Scotland were analyzed by using multilevel logistic and linear regression. Common mental disorders were assessed by using the General Health Questionnaire. Less than 1% of the total variance in General Health Questionnaire scores occurred at the ward level. This variance was further reduced and was no longer statistically significant after adjustment for characteristics of persons. By contrast, the proportion of total variance at the household level (14.4%, 95% confidence interval: 11.4, 17.5 in the null model) (p < 0.001) was statistically significant and remained so after adjustment for individual- and household-level exposures. While these findings suggest that future interventions should target persons and households rather than places, further research is first required to establish whether other (particularly smaller) areas lead to similar results.

General practitioner fundholding is often represented as one of the more successful elements of the 1989/90 Conservative reforms of the UK National Health Service (NHS). Successive annual 'waves' of fundholding practices were approved from 1990 through to 1997 and, over time, the initiative came to involve some 50% of UK general practitioners. Fundholding is known to have had a strong regional geography that changed with evolving fundholding eligibility criteria. Further, there have been persistent allegations that fundholding tended to occur disproportionately in areas of higher social status. Past studies of fundholding have tended to consider single waves or the overall impact of the initiative rather than its development over time. They have also tended to work at a single geographic scale or through single-region case studies when exploring the statistical regularities underlying the uptake of fundholding. Using multilevel analysis, this paper seeks to enhance understanding of fundholding through an examination of the interaction of district health authority and practice characteristics across all implemented waves for all general medical practices in England and Wales. We conclude that wave mattered on a national scale, that deprivation was relatively unimportant and that there were certain types of area that exhibited persistent but unexpected high uptake.

This paper examines the secondary data requirements for multilevel small area synthetic estimation (ML-SASE). This research method uses secondary survey data sets as source data for statistical models. The parameters of these models are used to generate data for small areas. The paper assesses the impact of knowing the geographical location of survey respondents on the accuracy of estimates, moving beyond debating the generic merits of geocoded social survey datasets to examine quantitatively the hypothesis that knowing the approximate location of respondents can improve the accuracy of the resultant estimates. Four sets of synthetic estimates are generated to predict expected levels of limiting long term illnesses using different levels of knowledge about respondent location. The estimates were compared to comprehensive census data on limiting long term illness (LLTI). Estimates based on fully geocoded data were more accurate than estimates based on data that did not include geocodes.

Censuses have traditionally been a key source of localised information on the state of a nation's health. Many countries are now adopting alternative approaches to the traditional census, placing such information at risk. The purpose of this paper is to inform debate about whether existing social surveys could provide an adequate ‘base’ for alternative model-based small area estimates of health data in a post traditional census era. Using a case study of 2011 UK Census questions on self-assessed health and limiting long term illness, we examine the extent to which the results from three large-scale surveys – the Health Survey for England, the Crime Survey for England and Wales and the Integrated Household Survey – conform to census output. Particularly in the case of limiting long term illness, the question wording renders comparisons difficult. However, with the exception of the general health question from the Health Survey for England all three surveys meet tests for convergent validity

Using data from the 1994 Health Survey for England, logistic multivariate multilevel modelling techniques are used to investigate the simultaneous effect of individual demographic characteristics and socio-structural factors on self-reported problem drinking as revealed by CAGE scores and 'unsafe' levels of alcohol consumption. Whilst the influence of key socio-structural variables is broadly similar for both unsafe alcohol consumption and high CAGE scores, there are notable exceptions when results are examined by tenure group: those in the rented sector are more likely to be problem drinkers as revealed by CAGE, but less likely to consume 'unsafe' amounts of alcohol. Both dimensions of drinking behaviour are influenced by the consumption patterns of others in the household, with both likelihoods increasing as the average consumption of others in the household rises. After taking into account individual compositional variables, the research indicates that there is very little evidence for geographical variation remaining in these two dimensions of drinking behaviour. It is found that the proportion of the population whose drinking behaviour may be classed as (potentially) problematic via the CAGE responses is substantially less than the proportion consuming above recommended 'safe' levels. The research concludes, however, that the two measures are broadly similar in their relationship to social and structural variables. Tenure provides an exception to this conclusion and indicates a continuing need to take account of housing circumstances in developing an understanding of drinking behaviour.

Background Smoking behaviour is of central concern to enhanced population health and there is a recognized need for detailed information concerning many different aspects of this behaviour. This paper assesses the utility of three large-scale national surveys according to their ability to provide such information. These are the General Household Survey (GHS), the British Household Panel Survey (BHPS) and the Health Survey for England (HSE). Methods A number of important data items are identified and a comparative content analysis of the surveys is undertaken to indicate whether these items are present or absent in each source. Results Although current smoking status and consumption are covered in all three data sets, the GHS and the HSE provide information on many other aspects of the behaviour. Information on the major contextual variables can be found in each of the surveys. All sources can be used within a repeated crosssectional analytical framework, and limited population-based longitudinal analyses can be undertaken with the GHS and the HSE. The BHPS theoretically opens up potential for a true longitudinal analysis of the cohort dynamics of individual smoking behaviour. Issues of place are best explored using the HSE, where the geographical information is most detailed. Conclusions The paper highlights the importance of all three data sets as statistical modelling resources for investigating patterns of smoking prevalence. Although the advantages of the panel approach are noted for cohort-based longitudinal studies, there still remain some problems in undertaking these analyses because of the small number of BHPS waves carried out to date.

There are extensive debates about the measurement and extent of ethnic heterogeneity and segregation, which have formed the basis of much research in the UPTAP programme and elsewhere. However, the focus of this chapter is on debates about the consequences of heterogeneity specifically, whether or not ethnic heterogeneity has an identifiable effect on perceptions of neighbourhood antisocial behaviour and two dimensions of collective efficacy (social cohesion and trust and informal social control). A multilevel modelling approach is used to analyse these aspects of neighbourhood using data from the British Crime Survey which has been linked to external data sources, including the UK Census and the Index of Multiple Deprivation. After allowing for socio-economic characteristics of individuals and areas we find very little evidence for any negative effects of ethnic heterogeneity on perceptions of social cohesion and trust, informal social control or antisocial behaviour. Instead we highlight the importance of neighbourhood disadvantage, rather than diversity, in understanding these aspects of neighbourhood disorder.

Geographical variations in health status and health outcomes have long posed a challenge to geographers and epidemiologists. Explanations have moved from a focus on the environmental determinants of ill-health to an emphasis on material circumstances and lifestyle. The realization that even after allowing for these factors, there remains unexplained variation has led to a shift in analytical attention to the notion of social capital in determining health outcomes. There are important conceptual debates about social capital, sparked off recently by the influential work of Robert Putnam. Several types of social capital – bonding, bridging, and linking – have been distinguished, and approaches to measurement focus on various structural and cognitive components of the concept. The specific issue raised by geographical approaches to this question is whether social capital is a true contextual construct (a property of places or contexts) or merely a compositional feature (i.e., the sum of the properties of the individuals who make up the population of a place). The article briefly outlines a range of research, using both quantitative and qualitative methodologies, which bears on the question of whether levels of social capital in a place affect health outcomes, and it also summarizes some key points of dispute in the social capital debates.

Background. Well-being is an important determinant of health and social outcomes. Measures of positive mental health states are needed for population-based research. The 12-item General Health Questionnaire (GHQ-12) has been widely used in many settings and languages, and includes positively and negatively worded items. Our aim was to test the hypothesis that the GHQ-12 assesses both positive and negative mental health and that these domains are independent of one another.Method. Exploratory (EFA) and confirmatory (CFA) factor analyses were conducted using data from the British Household Panel Survey (BHPS) and the Health Survey for England (HSE). Regression models were used to assess whether associations with individual and household characteristics varied across positive and negative mental health dimensions. We also explored higher-level variance in these measures, between electoral wards.Results. We found a consistent, replicable factor structure in both datasets. EFA results indicated a two-factor solution, and CFA demonstrated that this was superior to a one-factor model. These factors correspond to ‘symptoms of mental disorder’ and ‘positive mental health’. Further analyses demonstrated independence of these factors in associations with age, gender, employment status, poor housing and household composition. Statistically significant ward-level variance was found for symptoms of mental disorder but not positive mental health.Conclusions. The GHQ-12 measures both positive and negative aspects of mental health, and although correlated, these dimensions have some independence. The GHQ-12 could be used to measure positive mental health in population-based research.

BackgroundThere is little geographical variation in the prevalence of the common mental disorders. However, there is little longitudinal research.AimsTo estimate variance in rates of common mental disorders at individual, household and electoral ward levels prospectively.MethodA 12-month cohort study of 7659 adults aged 16–74 years in 4338 private households, in 626 electoral wards. Data were collected as part of the British Household Panel Survey. Common mental disorders were assessed using the 12-item General Health Questionnaire (GHQ). Ward-level socio-economic deprivation was measured using the Carstairs index.ResultsLess than 1% of total variance, in onset and maintenance of common mental disorders and change in GHQ score between waves, occurred at ward level. However, 12% of variance, which is a statistically significant difference, was found at household level (a much smaller geographical unit) and this difference remained after further analyses.ConclusionsWard level socio-economic deprivation does not influence the onset and maintenance of common mental disorders in Britain but local factors at the household level do. Reasons for this remain unclear.

A recent paper in Social Science and Medicine (Twigg et al. 50 (2000) 1109) outlined an approach to the estimation of prevalences of small-area health-related behaviour using multilevel models. This paper compares results from the application of the multilevel approach with those derived using the more traditional strategy of the local 'lifestyle' survey. Estimations of smoking prevalence and high alcohol consumption are examined and critical assessments made of both estimation approaches. It is concluded that the alternative method is more suited towards the prediction of smoking rates as opposed to unsafe alcohol consumption.

Background Localised information on health related behaviours and outcomes are seldom possible from surveys as they normally do not sample in all neighbourhoods. Furthermore, in those localities which are sampled, the sample sizes are rarely large enough to produce reliable estimates. However, the growing demand for localised health statistics has led to synthetic estimates being generated for alcohol consumption, smoking behaviour, psychiatric ill-health and other conditions. Generally these synthetic estimates have been based on models of binary outcomes. Self-assessed health measures tend to be recorded on an ordinal scale and dichotomising these loses potentially useful information. We set out to assess whether it would be possible to extend the multilevel synthetic estimation methodology to generate outcomes with more than two categories. Methods We developed multinomial multilevel models (in MLwiN) of limiting long term illness (LLTI) using data from the 2010/11 sweep of the Crime Survey (n=46,754). Synthetic estimates were calculated for the proportion of adults who stated that (i) they were severely limited by a long term illness and (ii) their illness limited their activities but not severely. Estimates were generated for every Middle Layer Super Output Area in England and Wales. Results To internally validate the synthetic estimates they were aggregated to a geography where robust direct estimates from the source survey were available. There was a strong, statistically significant, positive relationship between the synthetic and direct estimates (Spearman’s rank correlations were 0.90 and 0.84 for severe and not severe LLTI respectively, with both correlations being statistically significant at the 0.01 level). An alternative to internal validation is to focus on the estimates’ external validation by comparing the synthetic estimate against an alternative data source. In this instance the estimates were evaluated against the results from the 2011 Census with the Spearman’s rank correlations being 0.92 and 0.86 respectively (both significant at the 0.01 level). Conclusion This study has demonstrated that it is possible to extend the synthetic estimation methodology to produce small area estimates of health status based on multinomial multilevel models. Traditionally, national censuses have been one of the main sources of small-area health information, with numerous academic publications attesting to its importance as a data source for studies of health inequalities. However, the end of the traditional UK census raises potential challenges for the continued provision of very localised health data. Synthetic estimation, including the methodological extensions emanating from this study, could potentially help fill any future gaps in such health data.

Health-related behaviours are of central importance to health promotion and to the promotion of enhanced population health. In the UK, localised knowledge of the quantitative dimensions of health-related behaviours is traditionally attained by conducting a costly sample survey. Such surveys seldom generate reliable data at scales more local than that of the health authority, they also need to be repeated regularly. This paper outlines an alternative framework for generating statistics on small-area health related behaviours using routinely available data from the annual Health Survey for England (N = 17,000) and the decennial Population Census. Using a multilevel modelling approach nesting individuals within postcode sectors within health authorities, and focusing on the prevalence of smoking and 'problem' drinking, the paper comprises four sections: a consideration of the modelling strategy, a comparison of the smoking and drinking models, an outline of the estimation strategy, and the presentation and discussion of ward-level estimates of smoking and drinking behaviour for England. The paper concludes that the method is better at estimating smoking than drinking but that it offers a feasible, cheap and more informative alternative to the survey approach to the generation of information on smoking and drinking behaviour.

There is much potential for the use of geographical information systems (GIS) within the management and analysis of health and health care data. This paper addresses the problems of using routine data in the creation of geographical information systems for health. The numerous sources of British official routine health information however vary greatly in their geographical coverage and geographical detail. Particular attention therefore focuses upon the extent to which existing data sets can readily be used within GIS. Recommendations for changes in the data collection process are forwarded to improve the potential for GIS use. The overall utility of geographical health information systems within health service planning is illustrated using the example of community based activities.

The recent and rapid changes in the ways in which information can be gathered, processed, stored and transmitted have added a qualitatively new dimension to modern society and to the government — citizen relationship. In this chapter we shall examine some major elements of this transformation and ways in which the Conservative government has responded under Margaret Thatcher. After a general overview, we shall examine three case studies of information policy. Our thesis is that there is no ‘government information policy’ as such, but a series of ad hoc responses to particular issues and events. The main purpose of government policy has been to serve the interests of the government itself rather than to shape policy in accord with a particular philosophical or ideological perspective. Nonetheless, we can identify responses which are strongly in keeping with the authoritarian, centralist and cost-cutting tendencies of Thatcherism but which at the same time contradict Thatcherite emphases on individual citizen participation, responsibility and choice.