The association between county-level mental health provider shortage areas and suicide rates in the United States during the COVID-19 pandemic.

OBJECTIVE: Prior literature has shown that mental health provider Health Professional Shortage Areas (MHPSAs) experienced a greater increase in suicide rates compared to non-shortage areas from 2010 to 2018. Although suicide rates have been on the rise, rates have slightly decreased during the COVID-19 pandemic. This study sought to characterize the differences in suicide rate trends during the pandemic by MHPSA status. METHOD: We used generalized estimating equation regression to test the associations between MHPSA status and suicide rates from 2018 to 2021. Suicide deaths were obtained from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research. RESULTS: MHPSA status was associated with higher suicide rates (adjusted IRR:1.088 [95% CI, 1.024-1.156]). Furthermore, there was a significant interaction between MHPSA status and year (adjusted IRR:1.056 [95% CI, 1.022-1.091]), such that suicide rates did not significantly change among MHPSAs but slightly decreased among non-MHPSAs from 2018 to 2021. CONCLUSIONS: During the COVID-19 pandemic, there was a slight decrease in suicide rates among non-MHPSAs, while those with shortages experienced no significant changes in suicide rates. It will be important to closely monitor MHPSAs as continued at-risk regions for suicide as trendlines return to their pre-pandemic patterns.

Psychosis prevalence in London neighbourhoods; A case study in spatial confounding.

Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.

The ethnic density effect as a contextual influence in ecological disease models: Establishing its quantitative expression.

Research suggests higher neighbourhood ethnic minority density to be associated with lessened chances of ethnic group illness. We focus on the density effect on psychosis, arguing that (at higher ethnic concentrations) it acts as a contextual influence attenuating the compositional influence whereby minority ethnicity is associated with higher psychosis risk. In terms of ecological disease regression, the ethnic density effect will then be apparent in nonlinear impacts of minority concentration. Contextual effects may also be evident in spatially varying regression coefficient models for psychosis. Nonlinearity or heterogeneity may be associated with other contextual processes where geography modifies demography (e.g. deprivation amplification). We illustrate these issues with an analysis of psychosis prevalence in 4835 London neighbourhoods. The data are collected in primary care (during 2019/20) using clinical diagnosis (e.g. based on referrals to specialists or psychosis hospitalisation), and refer to patients currently under care: such care may extend retrospectively over several years. The data offer a complete population perspective in contrast to survey data, which typically offer limited geographic perspectives. We consider impacts on psychosis prevalence of non-white ethnicity, as well as those of deprivation, social fragmentation and urbanicity. We find evidence suggesting nonlinear impacts of non-white ethnicity on psychosis (essentially flat risk above a threshold concentration), but find no evidence for deprivation amplification.

Measuring Obesogenicity and Assessing Its Impact on Child Obesity: A Cross-Sectional Ecological Study for England Neighbourhoods.

Both major influences on changing obesity levels (diet and physical activity) may be mediated by the environment, with environments that promote higher weight being denoted obesogenic. However, while many conceptual descriptions and definitions of obesogenic environments are available, relatively few attempts have been made to quantify obesogenic environments (obesogenicity). The current study is an ecological study (using area units as observations) which has as its main objective to propose a methodology for obtaining a numeric index of obesogenic neighbourhoods, and assess this methodology in an application to a major national dataset. One challenge in such a task is that obesogenicity is a latent aspect, proxied by observed environment features, such as poor access to healthy food and recreation, as well as socio-demographic neighbourhood characteristics. Another is that obesogenicity is potentially spatially clustered, and this feature should be included in the methodology. Two alternative forms of measurement model (i.e., models representing a latent quantity using observed indicators) are considered in developing the obesogenic environment index, and under both approaches we find that both food and activity indicators are pertinent to measuring obesogenic environments (though with varying relevance), and that obesogenic environments are spatially clustered. We then consider the role of the obesogenic environment index in explaining obesity and overweight rates for children at ages 10-11 in English neighbourhoods, along with area deprivation, population ethnicity, crime levels, and a measure of urban-rural status. We find the index of obesogenic environments to have a significant effect in elevating rates of child obesity and overweight. As a major conclusion, we establish that obesogenic environments can be measured using appropriate methods, and that they play a part in explaining variations in child weight indicators; in short, area context is relevant.

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.

Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.

A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates.

The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases-linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.

Development of the Feedback Quality Instrument: a guide for health professional educators in fostering learner-centred discussions.

BACKGROUND: Face-to-face feedback plays an important role in health professionals' workplace learning. The literature describes guiding principles regarding effective feedback but it is not clear how to enact these. We aimed to create a Feedback Quality Instrument (FQI), underpinned by a social constructivist perspective, to assist educators in collaborating with learners to support learner-centred feedback interactions. In earlier research, we developed a set of observable educator behaviours designed to promote beneficial learner outcomes, supported by published research and expert consensus. This research focused on analysing and refining this provisional instrument, to create the FQI ready-to-use. METHODS: We collected videos of authentic face-to-face feedback discussions, involving educators (senior clinicians) and learners (clinicians or students), during routine clinical practice across a major metropolitan hospital network. Quantitative and qualitative analyses of the video data were used to refine the provisional instrument. Raters administered the provisional instrument to systematically analyse educators' feedback practice seen in the videos. This enabled usability testing and resulted in ratings data for psychometric analysis involving multifaceted Rasch model analysis and exploratory factor analysis. Parallel qualitative research of the video transcripts focused on two under-researched areas, psychological safety and evaluative judgement, to provide practical insights for item refinement. The provisional instrument was revised, using an iterative process, incorporating findings from usability testing, psychometric testing and parallel qualitative research and foundational research. RESULTS: Thirty-six videos involved diverse health professionals across medicine, nursing and physiotherapy. Administering the provisional instrument generated 174 data sets. Following refinements, the FQI contained 25 items, clustered into five domains characterising core concepts underpinning quality feedback: set the scene, analyse performance, plan improvements, foster learner agency, and foster psychological safety. CONCLUSIONS: The FQI describes practical, empirically-informed ways for educators to foster quality, learner-centred feedback discussions. The explicit descriptions offer guidance for educators and provide a foundation for the systematic analysis of the influence of specific educator behaviours on learner outcomes.

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK.

BACKGROUND: The evolution of the COVID-19 epidemic has been accompanied by efforts to provide comparable international data on new cases and deaths. There is also accumulating evidence on the epidemiological parameters underlying COVID-19. Hence, there is potential for epidemic models providing mid-term forecasts of the epidemic trajectory using such information. The effectiveness of lockdown or lockdown relaxation can also be assessed by modelling later epidemic stages, possibly using a multiphase epidemic model. METHODS: Commonly applied methods to analyse epidemic trajectories or make forecasts include phenomenological growth models (e.g., the Richards family of densities) and variants of the susceptible-infected-recovered (SIR) compartment model. Here, we focus on a practical forecasting approach, applied to interim UK COVID data, using a bivariate Reynolds model (for cases and deaths), with implementation based on Bayesian inference. We show the utility of informative priors in developing and estimating the model and compare error densities (Poisson-gamma, Poisson-lognormal, and Poisson-log-Student) for overdispersed data on new cases and deaths. We use cross validation to assess medium-term forecasts. We also consider the longer-term postlockdown epidemic profile to assess epidemic containment, using a two-phase model. RESULTS: Fit to interim mid-epidemic data show better fit to training data and better cross-validation performance for a Poisson-log-Student model. Estimation of longer-term epidemic data after lockdown relaxation, characterised by protracted slow downturn and then upturn in cases, casts doubt on effective containment. CONCLUSIONS: Many applications of phenomenological models have been to complete epidemics. However, evaluation of such models based simply on their fit to observed data may give only a partial picture, and cross validation against actual trends is also valuable. Similarly, it may be preferable to model incidence rather than cumulative data, although this raises questions about suitable error densities for modelling often erratic fluctuations. Hence, there may be utility in evaluating alternative error assumptions.

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

This paper examines trends in mental health among adults in England during the period of economic recovery and austerity following the 2008 'great recession'. We report analysis of data on 17,212 individuals living in England, from the longitudinal Understanding Society Survey (USS). We examined how individual's self-reported mental health over time (2011-2017), related to their changing socio-geographical status. Self-reported mental health is reported in the USS using version 2 of the SF12 Mental Component Summary. Trends in this score (across 5 observations per subject) were categorised into Mental Health Trajectory Groups (MHTGs) using Group Based Trajectory Modelling. We used maximum-likelihood multinomial logit models to estimate for individuals the relative likelihood of belonging to different Mental Health Trajectory categories as compared with a 'base' category, for whom mental health was good and stable throughout the period. We focus on likelihood of belonging to a group showing 'declining' mental health. Predictor variables included individuals' attributes and area conditions in their places of residence (including Office of National Statistics indicators of local employment deprivation and data on average income loss within districts due to welfare benefit reforms, published by the Centre of Regional Economic and Social Research at Sheffield Hallam University, UK). Our results emphasise the multiple socio-geographical 'determinants' likely to be operating on individual mental health. Declining mental health was associated both with conditions at the start of the study period and with social and socio-geographical mobility by the end of the study period. Risks of declining mental health were significantly greater for more deprived individuals and also (controlling for individual attributes) among those living in English neighbourhoods that were already economically disadvantaged at the beginning of the 'great recession' and located in districts where average incomes were most severely impacted by the effects of governmental austerity programmes on welfare benefits.

Geographical Aspects of Recent Trends in Drug-Related Deaths, with a Focus on Intra-National Contextual Variation.

BACKGROUND: Recent worldwide estimates are of 53 million users of opioids annually, and of 585,000 drug-related deaths, of which two thirds are due to opioids. There are considerable international differences in levels of drug death rates and substance abuse. However, there are also considerable variations within countries in drug misuse, overdose rates, and in drug death rates particularly. Wide intra-national variations characterize countries where drug deaths have risen fastest in recent years, such as the US and UK. Drug deaths are an outcome of drug misuse, which can ideally be studied at a relatively low spatial scale (e.g., US counties). The research literature suggests that small area variations in drug deaths to a considerable degree reflect contextual (place-related) factors as well as individual risk factors. METHODS: We consider the role of area social status, social cohesion, segregation, urbanicity, and drug supply in an ecological regression analysis of county differences in drug deaths in the US during 2015-2017. RESULTS: The analysis of US small area data highlights a range of factors which are statistically significant in explaining differences in drug deaths, but with no risk factor having a predominant role. Comparisons with other countries where small area drug mortality data have been analyzed show differences between countries in the impact of different contextual factors, but some common themes. CONCLUSIONS: Intra-national differences in drug-related deaths are considerable, but there are significant research gaps in the evidence base for small area analysis of such deaths.

A diabetes risk index for small areas in England.

UK and international studies point to significant area variation in diabetes risk, and summary indices of diabetic risk are potentially of value in effective targeting of health interventions and healthcare resources. This paper aims to develop a summary measure of the diabetic risk environment which can act as an index for targeting health care resources. The diabetes risk index is for 6791 English small areas (which provide entire coverage of England) and has advantages in incorporating evidence from both diabetes outcomes and area risk factors, and in including spatial correlation in its construction. The analysis underlying the risk index shows that area socio-economic status, social fragmentation and south Asian ethnic concentration are all positive risk factors for diabetes risk. However, urban-rural and regional differences in risk intersect with these socio-demographic influences.

Geographical Patterns in Drug-Related Mortality and Suicide: Investigating Commonalities in English Small Areas.

There are increasing concerns regarding upward trends in drug-related deaths in a number of developed societies. In some countries, these have been paralleled by upward trends in suicide. Of frequent concern to public health policy are local variations in these outcomes, and the factors underlying them. In this paper, we consider the geographic pattern of drug-related deaths and suicide for 2012-2016 across 6791 small areas in England. The aim is to establish the extent of commonalities in area risk factors between the two outcomes, with a particular focus on impacts of deprivation, fragmentation and rurality.

Development of the Australian neighborhood social fragmentation index and its association with spatial variation in depression across communities.

PURPOSE: We know little about how community structures influence the risk of common mental illnesses. This study presents a new way to establish links between depression and social fragmentation, thereby identifying pathways to better target mental health services and prevention programs to the right people in the right place. METHOD: A principal components analysis (PCA) was conducted to develop the proposed Australian neighborhood social fragmentation index (ANSFI). General practice clinical data were used to identify cases of diagnosed depression. The association between ANSFI and depression was explored using multilevel logistic regression. Spatial hot spots (clusters) of depression prevalence and social fragmentation at the statistical area level 1 (SA1) were examined. RESULTS: Two components of social fragmentation emerged, reflecting fragmentation related to family structure and mobility. Individuals treated for depression in primary care were more likely to live in neighborhoods with lower socioeconomic status and with higher social fragmentation related to family structure. A 1-SD increase in social fragmentation was associated with a 16% higher depression prevalence (95% CI 11%, 20%). However, the association attenuated with adjustment for neighborhood socio-economic status. Considerable spatial variation in social fragmentation and depression patterns across communities was observed. CONCLUSIONS: Developing a social fragmentation index for the first time in Australia at a small area level generates a new line of knowledge on the impact of community structures on health risks. Findings may extend our understanding of the mechanisms that drive geographical variation in the incidence of common mental disorders and mental health care.

Obesity and Urban Environments.

Obesity is a major public health issue, affecting both developed and developing societies. Obesity increases the risk for heart disease, stroke, some cancers, and type II diabetes. While individual behaviours are important risk factors, impacts on obesity and overweight of the urban physical and social environment have figured large in the recent epidemiological literature, though evidence is incomplete and from a limited range of countries. Prominent among identified environmental influences are urban layout and sprawl, healthy food access, exercise access, and the neighbourhood social environment. This paper reviews the literature and highlights the special issue contributions within that literature.

Variations in Obesity Rates between US Counties: Impacts of Activity Access, Food Environments, and Settlement Patterns.

There is much ongoing research about the effect of the urban environment as compared with individual behaviour on growing obesity levels, including food environment, settlement patterns (e.g., sprawl, walkability, commuting patterns), and activity access. This paper considers obesity variations between US counties, and delineates the main dimensions of geographic variation in obesity between counties: by urban-rural status, by region, by area poverty status, and by majority ethnic group. Available measures of activity access, food environment, and settlement patterns are then assessed in terms of how far they can account for geographic variation. A county level regression analysis uses a Bayesian methodology that controls for spatial correlation in unmeasured area risk factors. It is found that environmental measures do play a significant role in explaining geographic contrasts in obesity.

The effect of regional politics on regional life expectancy in Italy (1980-2010).

BACKGROUND: The evidence on the association between politics and health is scarce considering the importance of this topic for population health. Studies that investigated the effect of different political regimes on health outcomes show inconsistent results. METHODS: Bayesian time-series cross-section analyses are used to examine the overall impact of regional politics on variations in Italian regional life expectancy (LE) at birth during the period 1980-2010. Our analyses control for trends in and unobserved determinants of regional LE, correct for temporal as well as spatial autocorrelation, and employ a flexible specification for the timing of the political effects. RESULTS: In the period from 1980 to 1995, we find no evidence that the communist, left-oriented coalitions and Christian Democratic, centre-oriented coalitions have had an effect on regional LE. In the period from 1995 onwards, after a major reconfiguration of Italy's political regimes and a major healthcare reform, we again find no evidence that the Centre-Left and Centre-Right coalitions have had a significant impact on regional LE. CONCLUSION: The presented results provide no support for the notion that different regional political regimes in Italy have had a differential effect on regional LE, even though Italian regions have had considerable and increasing autonomy over healthcare and health-related policies and expenditures.

Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology.

Emerging infectious diseases, and the resurgence of previously controlled infectious disease (e.g., malaria, tuberculosis), are a major focus for public health concern, as well as providing challenges for establishing aetiology and transmission. [...].

Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes.

BACKGROUND: Enhanced quality of care and improved access are central to effective primary care management of long term conditions. However, research evidence is inconclusive in establishing a link between quality of primary care, or access, and adverse outcomes, such as unplanned hospitalisation. METHODS: This paper proposes a structural equation model for quality and access as latent variables affecting adverse outcomes, such as unplanned hospitalisations. In a case study application, quality of care (QOC) is defined in relation to diabetes, and the aim is to assess impacts of care quality and access on unplanned hospital admissions for diabetes, while allowing also for socio-economic deprivation, diabetes morbidity, and supply effects. The study involves 90 general practitioner (GP) practices in two London Clinical Commissioning Groups, using clinical quality of care indicators, and patient survey data on perceived access. RESULTS: As a single predictor, quality of care has a significant negative impact on emergency admissions, and this significant effect remains when socio-economic deprivation and morbidity are allowed. In a full structural equation model including access, the probability that QOC negatively impacts on unplanned admissions exceeds 0.9. Furthermore, poor access is linked to deprivation, diminished QOC, and larger list sizes. CONCLUSIONS: Using a Bayesian inference methodology, the evidence from the analysis is weighted towards negative impacts of higher primary care quality and improved access on unplanned admissions. The methodology of the paper is potentially applicable to other long term conditions, and relevant when care quality and access cannot be measured directly and are better regarded as latent variables.