This study aimed to estimate the proportion of families with five-to-fifteen-year-olds experiencing inadequate dental health support as a consequence of their parents' socioeconomic conditions and deprivation in their neighbourhood across small geographies in England using synthetic estimation technique and explore its association with area-level proportion of children experiencing dental caries. Secondary analyses were performed using Children's Dental Health Survey (CDHS) 2013, National Dental Epidemiological Programme (NDEP) 2018-2019, and Census 2011. Multilevel logistic regression using CDHS (five-to-fifteen-year-olds; n = 2177) derived odds of a child experiencing inadequate dental health support (child's poor dental attendance, parents not accompanying a child to dental visits, not using sugar-free/dental chewing gum) based on parents' family status, employment, and neighbourhood deprivation. These odds and census-informed population counts were used to derive the indicator of 'estimated percentage of families with children experiencing inadequate dental health support in a census-based geography of ∼7500 people (n = 6791)'. Unadjusted linear regression was undertaken to examine the relationship between the indicator and NDEP-based percentage of five-year-olds experiencing dental caries in larger geographies (n = 397). Results indicated that inadequate child dental health support [CDHS mean = 8% (SD:0.27%)] was significantly associated with higher neighbourhood deprivation and an interaction effect from being both a lone and an unemployed parent. Increased area-level percentage of families with children experiencing inadequate dental health support (range:3.7%-15.2%) was associated with 1.59 (95%CI:1.14-2.04) rise in area-level percentage of five-year-olds experiencing dental caries. This study underscores the need for targeted support for lone and unemployed parents and families living in the most deprived areas to promote children's dental health.

Recently there has been a resurgence among epidemiologists, sociologists, public health practitioners, and geographers in attempts to understand what it is about the place that influences health outcomes. Where a person lives is as important as who they are when it comes to explaining their health status. This entry focuses on the main debates which have dominated this topic over the last few decades, illustrating how early studies were concerned with estimating how much spatial variation in health outcomes could be ascribed to individual characteristics (composition) and how much was due to place effects (context). Attention then shifted to more theoretical arguments that challenged the simple composition versus context dualism. Here workers began to acknowledge the continual recursive interplay between people and places. Theoretical debates continue to focus on causal pathways within a complex framework which recognizes that social practice is undertaken recursively within social structures and within places constituted at various scales in various ways at different times.

OBJECTIVES:To investigate the relationship between socioenvironmental sugar promotion and geographical inequalities in the prevalence of dental caries amongst 5-year-olds living across small areas within England. METHODS:Ecological data from the National Dental Epidemiology Programme (NDEP) 2018-2019, comprising information on the percentage of 5-year-olds with tooth decay (≥1 teeth that are decayed into dentine, missing due to decay, or filled), and untreated tooth decay (≥1 decayed but untreated teeth), in lower-tier local authorities (LAs) of England. These were analysed for association with a newly developed Index of Sugar-Promoting Environments Affecting Child Dental Health (ISPE-ACDH). The index quantifies sugar-promoting determinants within a child's environment and provides standardized scores for the index, and its component domains that is, neighbourhood-, school- and family-environment, with the highest scores representing the highest levels of sugar promotion in lower-tier LAs (N = 317) of England. Linear regressions, including unadjusted models separately using index and each domain, and models adjusted for domains were built for each dental outcome. RESULTS:Participants lived across 272 of 317 lower-tier LAs measured within the index. The average percentage of children with tooth decay and untreated tooth decay was 22.5 (SD: 8.5) and 19.6 (SD: 8.3), respectively. The mean index score was (0.1 [SD: 1.01]). Mean domain scores were: neighbourhood (0.02 [SD: 1.03]), school (0.1 [SD: 1.0]), and family (0.1 [SD: 0.9]). Unadjusted linear regressions indicated that the LA-level percentage of children with tooth decay increased by 5.04, 3.71, 4.78 and 5.24 with increased scores of the index, and neighbourhood, school and family domains, respectively. An additional model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.37, and by family domain it increased to 6.33. Furthermore, unadjusted models indicated that the LA-level percentage of children with untreated tooth decay increased by 4.72, 3.42, 4.45 and 4.97 with increased scores of the index, and neighbourhood, school, and family domains, respectively. The model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.24 and by family domain rose to 6.47. School-domain was not significantly associated with either outcome in adjusted models. CONCLUSIONS:This study reveals that socioenvironmental sugar promotion, particularly within neighbourhood- and family-environments, may contribute to geographical inequalities in dental caries in children. Further research involving data on individual-level dental outcomes and confounders is required.

Abstract 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.

To investigate the relationship between neighbourhood school environment and dental care needs of 5-to-11-year-olds attending a local dental facility in Portsmouth, South-East England. Secondary analyses were undertaken using three cross-sectional, open-access datasets. Data included 4 years of children's electronic dental records from the University of Portsmouth Dental Academy comprising age, gender, tooth extraction history and residential postcodes converted to Index of Multiple Deprivation (IMD) quintiles for census-based geographies called middle-layer super output areas (MSOAs). Additionally, overall effectiveness scores (OES) (1=Outstanding to 4=Inadequate) from Office for Standards in Education, Children's Services and Skills of neighbouring schools were computed to 'population-weighted mean OES'. Descriptive, univariate logistic regression and multilevel-modelling analyses investigated contextual-level influence of school-OES on tooth extraction. There were 429-patients [mean-age 7.78 years (SD 1.97 years), female 50.1%] living across 23-MSOAs. Seventy had undergone tooth extraction treatment. Population-weighted mean OES range was 1.74–3.00, while 3.5% and 48% of patients belonged to the most and least deprived IMD-quintiles, respectively. Univariate models revealed age and population-weighted mean OES as statistically significant predictors of tooth extraction (p < 0.05). Multilevel modelling, controlling for age and population-weighted mean OES indicated likelihood of tooth extraction increased by 15% with increasing age and by 161% for patients living in MSOAs with higher population-weighted mean OES (i.e. poor school-performance). School effectiveness scores remained a significant predictor of tooth extraction in our study when controlling for individual predictors of dental health. Further research is required to assess the role of neighbourhood school environment in predicting child dental health at the national level.

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.

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.

Background: Community treatment orders are widely used in England. It is unclear whether their use varies between patients, places and services, or if they are associated with better patient outcomes. Objectives: To examine variation in the use of community treatment orders and their associations with patient outcomes and health-care costs. Design: Secondary analysis using multilevel statistical modelling. Setting: England, including 61 NHS mental health provider trusts. Participants: A total of 69,832 patients eligible to be subject to a community treatment order. Main outcome measures: Use of community treatment orders and time subject to community treatment order; re-admission and total time in hospital after the start of a community treatment order; and mortality. Data sources: The primary data source was the Mental Health Services Data Set. Mental Health Services Data Set data were linked to mortality records and local area deprivation statistics for England. Results: There was significant variation in community treatment order use between patients, provider trusts and local areas. Most variation arose from substantially different practice in a small number of providers. Community treatment order patients were more likely to be in the ‘severe psychotic’ care cluster grouping, male or black. There was also significant variation between service providers and local areas in the time patients remained on community treatment orders. Although slightly more community treatment order patients were re-admitted than non-community treatment order patients during the study period (36.9% vs. 35.6%), there was no significant difference in time to first re-admission (around 32 months on average for both). There was some evidence that the rate of re-admission differed between community treatment order and non-community treatment order patients according to care cluster grouping. Community treatment order patients spent 7.5 days longer, on average, in admission than non-community treatment order patients over the study period. This difference remained when other patient and local area characteristics were taken into account. There was no evidence of significant variation between service providers in the effect of community treatment order on total time in admission. Community treatment order patients were less likely to die than non-community treatment order patients, after taking account of other patient and local area characteristics (odds ratio 0.69, 95% credible interval 0.60 to 0.81). Limitations: Confounding by indication and potential bias arising from missing data within the Mental Health Services Data Set. Data quality issues precluded inclusion of patients who were subject to community treatment orders more than once. Conclusions: Community treatment order use varied between patients, provider trusts and local areas. Community treatment order use was not associated with shorter time to re-admission or reduced time in hospital to a statistically significant degree. We found no evidence that the effectiveness of community treatment orders varied to a significant degree between provider trusts, nor that community treatment orders were associated with reduced mental health treatment costs. Our findings support the view that community treatment orders in England are not effective in reducing future admissions or time spent in hospital. We provide preliminary evidence of an association between community treatment order use and reduced rate of death. Future work: These findings need to be replicated among patients who are subject to community treatment order more than once. The association between community treatment order use and reduced mortality requires further investigation. Study registration: The study was approved by the University of Warwick’s Biomedical and Scientific Research Ethics Committee (REGO-2015-1623). Funding: This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 9. See the NIHR Journals Library website for further project information.

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

This chapter outlines the ways in which medical and health geographers have contributed to the understanding of mental health. It discusses quantitative modeling of mental-health outcomes, detailing how aspects of individual, household and neighborhood socioeconomic context interact to influence risk. The chapter considers the use of qualitative approaches in elucidating the way in which the identities of those experiencing poor mental health are associated with the features and social meaning of places. Geographers have approached the nexus of place and mental health from several perspectives. Emphasis has been placed on determining the geographical scale at which place effects become important, with a focus often centered on socioeconomic inequalities and relative (income) disadvantage. The chapter provides a brief overview of the role of place in understanding mental-health outcomes and care. Wilkinson's well-established income-inequality thesis stakes a claim in the explanation of mental-health inequalities.

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.

Conventional 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.

Aims and methodTo compare rates of admission for different types of severe mental illness between ethnic groups, and to test the hypothesis that larger and more clustered ethnic groups will have lower admission rates. This was a descriptive study of routinely collected data from the National Health Service in England.ResultsThere was an eightfold difference in admission rates between ethnic groups for schizophreniform and mania admissions, and a fivefold variation in depression admissions. On average, Black and minority ethnic (BME) groups had higher rates of admission for schizophreniform and mania admissions but not for depression. This increased rate was greatest in the teenage years and early adulthood. Larger ethnic group size was associated with lower admission rates. However, greater clustering was associated with higher admission rates.Clinical implicationsOur findings support the hypothesis that larger ethnic groups have lower rates of admission. This was a between-group comparison rather than within each group. Our findings do not support the hypothesis that more clustered groups have lower rates of admission. In fact, they suggest the opposite: groups with low clustering had lower admission rates. The BME population in the UK is increasing in size and becoming less clustered. Our results suggest that both of these factors should ameliorate the overrepresentation of BME groups among psychiatric in-patients. However, this overrepresentation continues, and our results suggest a possible explanation, namely, changes in the delivery of mental health services, particularly the marked reduction in admissions for depression.Declaration of interestNone.

Introduction Supervised 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 analysis Four 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 dissemination Ethical 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.

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 longterm 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 longterm 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.

Background: Rates of compulsory admission have increased in England in recent decades, and this trend is accelerating. Studying variation in rates between people and places can help identify modifiable causes. Objectives: To quantify and model variances in the rate of compulsory admission in England at different spatial levels and to assess the extent to which this was explained by characteristics of people and places. Design: Cross-sectional analysis using multilevel statistical modelling. Setting: England, including 98% of Census lower layer super output areas (LSOAs), 95% of primary care trusts (PCTs), 93% of general practices and all 69 NHS providers of specialist mental health services. Participants: 1,287,730 patients. Main outcome measure: The study outcome was compulsory admission, defined as time spent in an inpatient mental illness bed subject to the Mental Health Act (2007) in 2010/11. We excluded patients detained under sections applying to emergency assessment only (including those in places of safety), guardianship or supervision of community treatment. The control group comprised all other users of specialist mental health services during the same period. Data sources: The Mental Health Minimum Data Set (MHMDS). Data on explanatory variables, characterising each of the spatial levels in the data set, were obtained from a wide range of sources, and were linked using MHMDS identifiers. Results: A total of 3.5% of patients had at least one compulsory admission in 2010/11. Of (unexplained) variance in the null model, 84.5% occurred between individuals. Statistically significant variance occurred between LSOAs [6.7%, 95% confidence interval (CI) 6.2% to 7.2%] and provider trusts (6.9%, 95% CI 4.3% to 9.5%). Variances at these higher levels remained statistically significant even after adjusting for a large number of explanatory variables, which together explained only 10.2% of variance in the study outcome. The number of provider trusts whose observed rate of compulsory admission differed from the model average to a statistically significant extent fell from 45 in the null model to 20 in the fully adjusted model. We found statistically significant associations between compulsory admission and age, gender, ethnicity, local area deprivation and ethnic density. There was a small but statistically significant association between (higher) bed occupancy and compulsory admission, but this was subsequently confounded by other covariates. Adjusting for PCT investment in mental health services did not improve model fit in the fully adjusted models. Conclusions: This was the largest study of compulsory admissions in England. While 85% of the variance in this outcome occurred between individuals, statistically significant variance (around 7% each) occurred between places (LSOAs) and provider trusts. This higher-level variance in compulsory admission remained largely unchanged even after adjusting for a large number of explanatory variables. We were constrained by data available to us, and therefore our results must be interpreted with caution. We were also unable to consider many hypotheses suggested by the service users, carers and professionals who we consulted. There is an imperative to develop and evaluate interventions to reduce compulsory admission rates. This requires further research to extend our understanding of the reasons why these rates remain so high. Funding: The National Institute for Health Research Health Services and Delivery Research programme.

This article is based on original research in Vietnam and is focused on explaining the offending behaviour of young people on educational programmes incarcerated in educational institutions. The primary research includes a self-report survey of young people in custody (n = 2009) in the four national educational institutions as well as interviews with young people (n = 98) and staff (n = 34). The research concludes that the interaction of problematic family circumstances; community-based issues – specifically on-line gaming; and individual disposition, within a context of the problems created by rapid socio-economic change, explains the offending of these young people.