When does geography matter most? Age-specific geographical effects in the patterning of, and relationship between, mental wellbeing and mental illness.

Mental illness and mental wellbeing are related but distinct constructs. Despite this, geographical enquiry often references the two as interchangeable indicators of mental health and assumes the relationship between the two is consistent across different geographical scales. Furthermore, the importance of geography in such research is commonly assumed to be static for all age groups, despite the large body of evidence demonstrating contextual effects in age-specific populations. We leverage simultaneous measurement of a mental illness and mental wellbeing metric from Understanding Society, a UK population-based survey, and employ bivariate, cross-classified multilevel modelling to characterise the relationship between geographical context and mental health. Results provide strong evidence for contextual effects for both responses before and after covariate adjustment, with weaker evidence for area-classification and PSU-level contextual effects for the GHQ-12 after covariate adjustment. Results support a two-continua model of mental health at the individual level, but indicates that consensual benefit may be achieved across both dimensions by intervening at household and regional levels. There is also some evidence of a greater contextual effects for mental wellbeing than for mental illness. Results highlight the potential of the household as a target for intervention design for consensual benefit across both constructs. Results also suggest the increased importance of geographical context for older respondents across both responses. This research supports an area-based approach to improving both mental illness and mental wellbeing in older populations.

Ageing and cohort trajectories in mental ill-health: An exploration using multilevel models.

Analyses of health over time must consider the potential impacts of ageing as well as any effects relating to cohort differences. The British Household Panel Survey (BHPS) and Understanding Society longitudinal studies are employed to assess trends in mental ill-health over a 26-year period. This analysis uses cross-classified multilevel models in an exploratory, non-parametric approach to evaluate age and cohort effects net of each other. Mental ill-health evidences an initial worsening trend as people age which then reverses and exhibits improvement in late-middle-age, before declining again in the latter stages of life. There were less defined cohort trends. The modelling technique also reveals the relative importance of the temporal contexts in relation to inter- and intra-individual effects on mental ill-health, demonstrating that the ageing and cohort dimensions explain little variation compared to these more dominant within and between influences. Ultimately, we suggest that researchers would benefit from wider use of this exploratory modelling strategy when evaluating underlying health trends and more research is now needed to explore potential explanations of these baseline trajectories.

Understanding the population structure of the GHQ-12: Methodological considerations in dimensionally complex measurement outcomes.

BACKGROUND: Mental health and its complexity, measurement and social determinants are increasingly important avenues of research for social scientists. Quantitative social science commonly investigates mental health as captured by population screening metrics. One of the most common of these metrics is the 12-Item General Health Questionnaire (GHQ-12). Despite itscanonical use as an outcome of interest in social science, the traditional use of the summed scores of summed questionnaires carries empirical and substantive assumptions which are often not fully considered or justified in the research. We outline the implications of these assumptions and the restrictions imposed by traditional modelling techniques and advocate for a more nuanced approach to population mental health modelling and inference. DATA & METHODS: We use novel Exploratory Structural Equation Modelling (ESEM) on a large, representative UK sample taken from the first wave of the Understanding Society Survey, totalling 40,452 respondents. We use this to exemplify the potential of traditional, restrictive assumptions to bias conclusions and policy recommendations. RESULTS: ESEM analysis identifies a 4-factor structure for the GHQ-12, including a newly proposed "Emotional Coping" dimension. This structure is then tested against leading proposed factor structures from the literature and is demonstrated to perform better across all metrics, under both Maximum Likelihood and Bayesian estimation. Moreover, the proposed factors are more substantively dissimilar than those retrieved from previous literature. CONCLUSIONS: The results highlight the inferential limitations of using simple summed scores as population health outcomes. We advocate for the use of the highlighted methods, which in combination with population studies offer quantitative social scientists the opportunity to explore predictors and patterns of underlying processes of population mental health outcomes, explicitly addressing the complexity and measurement error inherent to mental health analysis.

Social exclusion, self-rated health and depression among older people in China: Evidence from a national survey of older persons.

BACKGROUND: It is well established that social exclusion is a key social determinant of health; however, such association between social exclusion and health outcomes among older people remain a relatively under-researched area. This paper explores the effects of four dimensions of social exclusion on self-rated health and depression among older people in China. METHODS: This paper includes 8038 individuals aged 60 and over from the first wave national multi-stage probability sample (2014) from the China Longitudinal Aging Social Survey (CLASS). Descriptive univariate information for individual variables and four dimensions of social exclusion are presented. Multinomial and binary logistic regression models are used to examine the associations between social exclusion and self-rated health and depression. RESULTS: Older people who were in the lower level of exclusion from social relationships or subjective feelings of exclusion were significantly less likely to report fair or poor self-rated health than people in the higher level of exclusion (lower level of exclusion from social activities was significantly associated with being less likely to report poor SRH only). Older people who were in the lower level of subjective feeling of exclusion or exclusion from financial products were significantly less likely to report depression. CONCLUSIONS: Different dimensions of social exclusion have different effects on self-rated health and depression. Social policies need to reflect this and efforts of services could usefully be oriented to prevent multi-dimensions of social exclusion. Ultimately, such policies should have the potential to enhance the health of older people in China.

The utility of geodemographic indicators in small area estimates of limiting long-term illness.

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.

Occupied with classification: Which occupational classification scheme better predicts health outcomes?

Health inequalities continue to grow despite continuous policy intervention. Work, one domain of health inequalities, is often included as a component of social class rather than as a determinant in its own right. Many social class classifications are derived from occupation types, but there are other components within them that mean they may not be useful as proxies for occupation. This paper develops the exposome, a life-course exposure model developed by Wild (2005), into the worksome, allowing for the explicit consideration of both physical and psychosocial exposures and effects derived from work and working conditions. The interactions between and within temporal and geographical scales are strongly emphasised, and the interwoven nature of both psychosocial and physical exposures is highlighted. Individuals within an occupational type can be both affected by and effect upon occupation level characteristics and health measures. By using the worksome, occupation types are separated from value-laden social classifications. This paper will empirically examine whether occupation better predicts health measures from the European Working Conditions Survey (EWCS). Logistic regression models using Bayesian MCMC estimation were run for each classification system, for each health measure. Health measures included, for example, whether the respondent felt their work affected their health, their self-rated health, pain in upper or lower limbs, and headaches. Using the Deviance Information Criterion (DIC), a measure of predictive accuracy penalised for model complexity, the models were assessed against one another. The DIC shows empirically which classification system is most suitable for use in modelling. The 2-digit International Standard Classification of Occupations showed the best predictive accuracy for all measures. Therefore, examining the relationship between health and work should be done with classifications specific to occupation or industry rather than socio-economic class classifications. This justifies the worksome, allowing for a conceptual framework to link many forms of work-health research.

Understanding and misunderstanding group mean centering: a commentary on Kelley et al.'s dangerous practice.

Kelley et al. argue that group-mean-centering covariates in multilevel models is dangerous, since-they claim-it generates results that are biased and misleading. We argue instead that what is dangerous is Kelley et al.'s unjustified assault on a simple statistical procedure that is enormously helpful, if not vital, in analyses of multilevel data. Kelley et al.'s arguments appear to be based on a faulty algebraic operation, and on a simplistic argument that parameter estimates from models with mean-centered covariates must be wrong merely because they are different than those from models with uncentered covariates. They also fail to explain why researchers should dispense with mean-centering when it is central to the estimation of fixed effects models-a common alternative approach to the analysis of clustered data, albeit one increasingly incorporated within a random effects framework. Group-mean-centering is, in short, no more dangerous than any other statistical procedure, and should remain a normal part of multilevel data analyses where it can be judiciously employed to good effect.

Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour.

Many ecological- and individual-level analyses of voting behaviour use multiple regressions with a considerable number of independent variables but few discussions of their results pay any attention to the potential impact of inter-relationships among those independent variables-do they confound the regression parameters and hence their interpretation? Three empirical examples are deployed to address that question, with results which suggest considerable problems. Inter-relationships between variables, even if not approaching high collinearity, can have a substantial impact on regression model results and how they are interpreted in the light of prior expectations. Confounded relationships could be the norm and interpretations open to doubt, unless considerable care is applied in the analyses and an extended principal components method for doing that is introduced and exemplified.

Stressed out? An investigation of whether allostatic load mediates associations between neighbourhood deprivation and health.

Deprived neighbourhoods have long been associated with poorer health outcomes. However, many quantitative studies have not evidenced the mechanisms through which place 'gets under the skin' to influence health. The increasing prevalence of biosocial data provides new opportunities to explore these mechanisms and incorporate them into models of contextual effects. The stress pathway is a key biosocial mechanism; however, few studies have explicitly tested it in neighbourhood associations. This paper addresses this gap by investigating whether allostatic load, a biological response to chronic stress, mediates relationships of neighbourhood deprivation to physical and mental health. Data from UK Understanding Society is used to undertaken a multilevel mediation analysis. Allostatic load is found to mediate the association between neighbourhood deprivation and health, substantiating the biological mechanism of the stress pathway. More deprived areas are associated with higher allostatic load, and in turn worse allostatic load relates to poorer physical and mental health. Allostatic load is a stronger mediator of physical health than mental health, suggesting the stress pathway is more pertinent to explaining physical health gradients. Heterogeneity in the results between physical and mental health suggests more research is needed to disentangle the biosocial processes that could be important to health and place relationships.

The hierarchical age-period-cohort model: Why does it find the results that it finds?

It is claimed the hierarchical-age-period-cohort (HAPC) model solves the age-period-cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure-that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439-1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125-128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.

Determinants of access to health care for depression in 49 countries: A multilevel analysis.

BACKGROUND: The relative importance of individual and country-level factors influencing access to diagnosis and treatment for depression across the world is fairly unknown. METHODS: We analysed cross-national data from the WHO World Health Surveys. Depression diagnosis and access to health care were ascertained using a structured interview. Logistic Bayesian Multilevel analyses were performed to establish individual and country level factors associated with: (1) receiving a diagnosis and (2) accessing treatment for depression if a diagnosis was ascertained. RESULTS: The sample included 7870 individuals from 49 countries who met ICD-10 criteria for depressive episode in the past 12 months. A third (32%) of these individuals had ever been diagnosed with depression in their lifetime. Among those diagnosed with depression, 66% reported to have ever received treatment for depression. Although individual factors were more important determinants of access to treatment for depression, country-level factors explained 27.6% of the variance in access to diagnosis and 24.1% in access to treatment. Access to treatment for depression improved with increasing country income. Female gender, better education, the presence of physical co-morbidity, more material assets, and living in urban areas were individual level determinants of better access. LIMITATIONS: Data on other contextual factors was not available. Unmet need was likely underestimated, since only lifetime treatment data was available. CONCLUSION: This study highlights major inequalities in access to a diagnosis and treatment of depression. Unlike the prevalence of depression, where contextual factors have shown to have less importance, a significant proportion of the variance in access to depression care was explained by country-level income.

Does neighbourhood deprivation affect the genetic influence on body mass?

Most research into the role of gene-environment interactions in the etiology of obesity has taken environment to mean behaviours such as exercise and diet. While interesting, this is somewhat at odds with research into the social determinants of obesity, in which the focus has shifted away from individuals and behaviours to the types of wider obesogenic environments in which individuals live, which influence and produce these behaviours. This study combines these two strands of research by investigating how the genetic influence on body mass index (BMI), used as a proxy for obesity, changes across different neighbourhood environments measured by levels of deprivation. Genetics are incorporated using a classical twin design with data from Twins UK, a longitudinal study of UK twins running since 1992. A multilevel modelling approach is taken to decompose variation between individuals into genetic, shared environmental, and non-shared environmental components. Neighbourhood deprivation is found to be a statistically significant predictor of BMI after conditioning on individual characteristics, and a heritability of 0.75 is estimated for the entire sample. This heritability estimate is shown, however, to be higher in more deprived neighbourhoods and lower in less deprived ones, and this relationship is statistically significant. While this research cannot say anything directly about the mechanisms behind the relationship, it does highlight how the relative importance of genetic factors can vary across different social environments, and therefore the value of considering both genetic and social determinants of health simultaneously.

Social trust, interpersonal trust and self-rated health in China: a multi-level study.

BACKGROUND: Trust is important for health at both the individual and societal level. Previous research using Western concepts of trust has shown that a high level of trust in society can positively affect individuals' health; however, it has been found that the concepts and culture of trust in China are different from those in Western countries and research on the relationship between trust and health in China is scarce. METHOD: The analyses use data from the national scale China General Social Survey (CGSS) on adults aged above 18 in 2005 and 2010. Two concepts of trust ("out-group" and "in-group" trust) are used to examine the relationship between trust and self-rated health in China. Multilevel logistical models are applied, examining the trust at the individual and societal level on individuals' self-rated health. RESULTS: In terms of interpersonal trust, both "out-group" and "in-group" trust are positively associated with good health in 2005 and 2010. At the societal level, the relationships between the two concepts of trust and health are different. In 2005, higher "out-group" social trust (derived from trust in strangers) is positively associated with better health; however, higher "in-group" social trust (derived from trust in most people) is negatively associated with good health in 2010. The cross-level interactions show that lower educated individuals (no education or only primary level), rural residents and those on lower incomes are the most affected groups in societies with higher "out-group" social trust; whereas people with lower levels of educational attainment, a lower income, and those who think that most people can be trusted are the most affected groups in societies with higher "in-group" social trust. CONCLUSION: High levels of interpersonal trust are of benefit to health. Higher "out-group" social trust is positively associated with better health; while higher "in-group" social trust is negatively associated with good health. Individuals with different levels of educational attainment are affected by trust differently.

Ethnic Residential Segregation: A Multilevel, Multigroup, Multiscale Approach Exemplified by London in 2011.

We develop and apply a multilevel modeling approach that is simultaneously capable of assessing multigroup and multiscale segregation in the presence of substantial stochastic variation that accompanies ethnicity rates based on small absolute counts. Bayesian MCMC estimation of a log-normal Poisson model allows the calculation of the variance estimates of the degree of segregation in a single overall model, and credible intervals are obtained to provide a measure of uncertainty around those estimates. The procedure partitions the variance at different levels and implicitly models the dependency (or autocorrelation) at each spatial scale below the topmost one. Substantively, we apply the model to 2011 census data for London, one of the world's most ethnically diverse cities. We find that the degree of segregation depends both on scale and group.

Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China.

This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.

Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land.

This commentary clarifies our original commentary (Bell and Jones, 2014c) and illustrates some concerns we have regarding the response article in this issue (Reither et al., 2015). In particular, we argue that (a) linear effects do not have to be produced by exact linear mathematical functions to behave as if they were linear, (b) linear effects by this wider definition are extremely common in real life social processes, and (c) in the presence of these effects, the Hierarchical Age Period Cohort (HAPC) model will often not work. Although Reither et al. do not define what a 'non-linear monotonic trend' is (instead, only stating that it isn't a linear effect) we show that the model often doesn't work in the presence of such effects, by using data generated as a 'non-linear monotonic trend' by Reither et al. themselves. We then question their discussion of fixed and random effects before finishing with a discussion of how we argue that theory should be used, in the context of the obesity epidemic.

An exploratory discrete-time multilevel analysis of the effect of social support on the survival of elderly people in China.

This study undertakes a survival analysis of elderly persons in China using Chinese Longitudinal Healthy Longevity Survey 2002-2008. Employing discrete-time multilevel models, we explored the effect of social support on the survival of elderly people in China. This study focuses on objective (living arrangements and received support) and subjective activities (perceived support) of social support, finding that the effect of different activities of social support on the survival of elderly people varies according to the availability of different support resources. Specifically, living with a spouse, financial independence, perceiving care support from any resource is associated with higher survival rates for elderly people. Separate analysis focusing on urban elderly and rural elderly revealed broadly similar results. There is a larger difference between those perceiving care support from family or social service and not perceiving care support in urban areas comparing to those in rural areas. Those who cannot pay medical expenses are the least likely to survive. The higher level of economic development in province has no significant effect on the survival of elderly people for the whole sample model and the elderly people in urban areas; however, there is a negative influence on the survival of the rural elderly people.

The role of the neighborhood, family and peers regarding Colombian adolescents' social context and aggressive behavior.

OBJECTIVE: Examining neighborhood conditions, parenting and peer affiliations' association with adolescents' aggressive behavior. Testing various mechanisms through which neighborhood conditions influence two adolescent outcomes, both directly and indirectly (via their impact on parenting and peer-affiliation): aggression and delinquency. METHOD: Data regarding adolescents was taken from a self-reporting survey of 1,686 Colombian adolescents living in 103 neighborhoods of Medellin. Neighborhood-related data was taken from official government datasets, as well as two separate community surveys. Both multilevel modeling and multilevel structural equation modeling were used in the analysis. RESULTS: The probability of an adolescent engaging in aggression in Medellin was 7.0 % and becoming involved in delinquency 0.3 %. There was also significant variation for both forms of aggressive behavior at neighborhood-level (7.0 % aggression and 14 % regarding the delinquency scale). No neighborhood condition had a direct association with adolescents' aggressive behavior; however; the neighborhood exerted an indirect influence on adolescent behavior which was mainly transmitted through families and the quality of friends within a particular community. CONCLUSIONS: Residing in disadvantaged neighborhoods did have an adverse effect on adolescents' aggressive behavior, mainly because of a lack of effective parenting strategies thereby facilitating affiliations being made with deviant peers. More efficient intervention for reducing adolescents' aggressive behavior should thus target areas having high odds of aggressive behavior and focus on improving community resources and, more importantly, on controlling adolescent peer groups, the lack of parental monitoring and inconsistent discipline.

Investigating neighbourhood effects on health: using community-survey data for developing neighbourhood-related constructs.

OBJECTIVE: Structural and social neighbourhood constructs have been developed for studying a neighbourhood's influence on a variety of health outcomes; community surveys are being increasingly used for capturing such information. This paper has proposed a six-fold approach which integrates existing methodologies (i.e. multilevel factor analysis, ecometrics, multilevel spatial multiple membership models and multilevel latent class analysis) for estimating reliable and valid measurement of neighbourhood conditions. METHODS: The proposed approach used seven demographic and socio-economic variables reported in a community survey by 20,413 individuals residing in 244 neighbourhoods in Medellin, Colombia, to measure structural neighbourhood conditions. RESULTS: The set of variables reliably measured one neighbourhood construct: the deprivation index; this showed significant variation between neighbourhoods as well as significant spatial clustering across the city. CONCLUSIONS: The approach presented here should enable public health researchers to better estimate neighbourhood indicators and may result in more accurate assessment of the relationship between neighbourhood characteristics and individual-level health outcomes.