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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.

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.

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.

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.

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

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.

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.

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.