1539Australia’s worsening mental health – what’s next?

Abstract Background The population prevalence of mental illness over time, and by sociodemographic subgroups, are important benchmark data. Examining reliable population level data can highlight groups with greater mental-illness related symptom burden and inform policy and strategy. Methods Secondary analysis of Australian National Health Surveys (n = 78,204) from 2001-02 to 2017-18. Trends in the prevalence of very high scores on the Kessler-10 (K10), a measure of psychological distress capturing symptoms of affective and anxiety disorders, were examined by time, age, gender, and socioeconomic status. Data were standardised to the 2001 Australian census population on the strata of sex and age. Results In 2017-18, the rate of probable mental illness was estimated at 5.1%, a 1.5% increase (representing an additional 367,000 Australians) since 2007. In 2017-18, the subgroups with the highest rates were women aged 18-24 (8.01%, 95% CI = 5.9%-10.2%), and the poorest fifth of Australians (8.02%, 95% CI = 7.0%-9.0%). Women aged 55-64 demonstrated the greatest increase in rates (2001: 3.5%, 95% CI = 2.5%-4.6%; 2017: 7.2%, 95% CI = 5.9%-8.5%; z = 4.10, p ≤ 0.001). Conclusions Despite efforts to improve population mental health, rates of probable mental illness in Australia have increased since 2007. Findings will be discussed in conjunction to extant social and health policies, and potential gaps in the delivery of gold-standard mental health care. Key messages The rate of probable mental illness in Australia seem to be increasing, especially in women aged 55-64, and those from low-SES backgrounds.

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Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health

BMC Psychiatry ◽

10.1186/s12888-020-02952-y ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Mark Cherrie ◽

Sarah Curtis ◽

Gergő Baranyi ◽

Stuart McTaggart ◽

Niall Cunningham ◽

...

Keyword(s):

Mental Health ◽

Mental Illness ◽

Sequence Analysis ◽

Local Level ◽

Population Level ◽

The United States ◽

Individual Risk ◽

White Female ◽

Prescription Data ◽

Scottish Population

Abstract Background Over the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. Methods National Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N = 151,418). Antidepressant prescription status over the previous 6 months was recorded for every month for which data were available (January 2009–December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. Results Five distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to be in the group that started a new course of antidepressant prescriptions. Conclusions The use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

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Comparing the Health of Transgender Women, Transgender Men, and Gender Non-Conforming Individuals Using Population-Level Data

Annals of LGBTQ Public and Population Health ◽

10.1891/lgbtq.2019-0002 ◽

2020 ◽

Vol 1 (1) ◽

pp. 43-62 ◽

Cited By ~ 1

Author(s):

Arryn A. Guy ◽

Wren Yoder ◽

Kelly Manser ◽

Stephen D. Ramos ◽

Steve N. Du Bois

Keyword(s):

Mental Health ◽

Physical Health ◽

Healthcare Access ◽

Population Level ◽

Transgender Women ◽

Level Data ◽

Race Ethnicity ◽

And Gender ◽

Mental And Physical Health ◽

Transgender Men

Converging evidence indicates transgender and gender non-conforming (TGNC) individuals generally experience poorer health than their cisgender counterparts. Less is known about TGNC health across gender identity subgroups. Extant work has yielded mixed findings, precluding conclusions regarding the comparative health of transgender women, transgender men, and gender non-conforming individuals. Additionally, this work is limited methodologically, e.g., utilizing convenience samples and operationalizing “transgender” differently across studies. This study aims to improve upon these limitations, and more broadly add to the literature on within-group health differences among TGNC individuals. We used 2016 U.S. population-level data (N = 1,117), conducting MANCOVA (multivariate analysis of covariance) and logistic regression to compare the health of transgender women, transgender men, and GNC individuals. Health outcomes of mental and physical health, substance use, and healthcare access/utilization were selected based on empirical and theoretical support for their relevance to TGNC health. We also completed interaction analyses to test the intersectional effects on health of gender identity and emergent sociodemographic variables, e.g. race/ethnicity. Across TGNC subgroups, individuals reported similar alcohol use, mental health, and healthcare access/utilization. Transgender men reported worse physical health than their counterparts, and GNC individuals reported lower smoking prevalence than their counterparts. Interaction analyses by race/ethnicity indicated Hispanic transgender women reported worse physical health than other Hispanic TGNC individuals, while Black transgender men, Multiracial GNC individuals, and Hispanic transgender women reported worse mental health than some of their intra-racial/ethnic TGNC counterparts. Findings add to the growing literature on TGNC health and highlight TGNC subgroups that may be vulnerable regarding mental and physical health.

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Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study (Preprint)

10.2196/preprints.10194 ◽

2018 ◽

Author(s):

Niclas Palmius ◽

Kate E A Saunders ◽

Oliver Carr ◽

John R Geddes ◽

Guy M Goodwin ◽

...

Keyword(s):

Mental Health ◽

Mental Illness ◽

Interindividual Variability ◽

Depressive Symptomatology ◽

Population Level ◽

Mental States ◽

Training Data ◽

Mobile Phone Data ◽

Dirichlet Process Prior ◽

Behavioral Features

BACKGROUND Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. OBJECTIVE The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, “group-personalized” models can be built for individuals based on other individuals to whom they are most similar. METHODS We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants’ smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. RESULTS The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P<.001). Analysis of subgroups showed that different groups were characterized by different features derived from raw geolocation data. CONCLUSIONS This study demonstrates the importance of handling interindividual variability when developing models of mental illness. Population-level models do not capture nuances in how different individuals respond to illness, and the group-personalized model demonstrates a potential way to overcome these limitations when estimating mental state from objective behavioral features.

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What does self rated mental health represent

Journal of Public Health Research ◽

10.4081/jphr.2014.287 ◽

2014 ◽

Vol 3 (3) ◽

Cited By ~ 22

Author(s):

Daphna Levinson ◽

Giora Kaplan

Keyword(s):

Mental Health ◽

Mental Illness ◽

Physical Health ◽

Multiple Logistic Regression Analysis ◽

Secondary Analysis ◽

The Self ◽

Computer Assisted ◽

Physical And Mental Health ◽

Physical Conditions ◽

Design And Methods

Background. Unlike the widely used self rated health, the self rated mental health was found unsuitable as a proxy for mental illness. This paper analyses the relationships between the self ratings of physical health, mental health and overall health, and their association of with the objective indicators for physical and mental health. Design and methods. The study is a secondary analysis of data from a nationwide representative sample of the non-institutionalized adult residents of Israel in 2003 that was collected via computer-assisted personal interview methods [n=4859]. Results. The self rated physical health and the self rated mental health were strongly related to each other yet the self rated mental health was not related to chronic physical conditions and the self rated physical health was not related to mental disorders. In a multiple logistic regression analysis, those with positive self rated mental health had 93 times the odds of reporting positive overall health whereas those with positive self rated physical health had 40 times the odds of reporting positive overall health. Conclusions. The self rating of mental health presents a qualitatively different dimension from mental illness. The self rated mental health is two times more important than the self rated physical health in predicting the self rated overall health

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On the Need for Digital Phenotyping to Obtain Insights into Mental States in the COVID-19 Pandemic

Digital Psychology ◽

10.24989/dp.v1i2.1857 ◽

2020 ◽

Vol 1 (2) ◽

pp. 40-42

Author(s):

Christian Montag ◽

Paul Dagum ◽

Jon D. Elhai

Keyword(s):

Mental Health ◽

Population Level ◽

Mental States ◽

User Privacy ◽

Policy Makers ◽

Building Trust ◽

Digital Phenotyping ◽

Level Information ◽

Population Mental Health ◽

Insight Into

Highlights Digital phenotyping provides real-time insight into population mental health in a crisis such as COVID-19. Digital phenotyping empowers policy makers with population level information to help fight a pandemic like COVID-19. User privacy and informed consent is paramount in building trust with digital phenotyping.

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Toward a Science of Mental Health

The Oxford Handbook of Positive Psychology ◽

10.1093/oxfordhb/9780195187243.013.0009 ◽

2009 ◽

pp. 88-96 ◽

Cited By ~ 9

Author(s):

Corey L. M. Keyes

Keyword(s):

Mental Health ◽

Mental Illness ◽

Mental Disorder ◽

Psychosocial Functioning ◽

Well Being ◽

Care Utilization ◽

Subjective Well Being ◽

Level Of Functioning ◽

Population Mental Health ◽

Better Than

This chapter summarizes the research on the dual-continua model of mental health and mental illness. Studies supported this model and therefore the view that the presence of mental health is more than the absence of mental illness. Mental health is conceived of as a constellation of dimensions of subjective well-being, specifically hedonic and eudaemonic measures of subjective well-being. Specifically, the mental health continuum ranges from languishing, moderate, to flourishing mental health. These classifications are important for distinguishing and predicting level of functioning for individuals with and without a current mental disorder. Among individuals free of a mental disorder, flourishing individuals report the fewest missed days of work, the fewest half-day or greater work cutbacks, the healthiest psychosocial functioning, high resilience, and high intimacy), the lowest risk of cardiovascular disease, the lowest number of chronic physical diseases at all ages, the fewest health limitations of activities of daily living, and lower health-care utilization. Even among adults with a mental disorder during the past 12 months, those who are flourishing functioned better than those with moderate mental health, who in turn functioned better than those who were languishing. The findings strongly support the adoption of a more positive paradigm to treatment, prevention, and promotion of population mental health.

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Use of sequence analysis for classifying individual antidepressant trajectories to monitor population mental health

10.21203/rs.3.rs-15826/v3 ◽

2020 ◽

Author(s):

Mark Cherrie ◽

Sarah Curtis ◽

Gergő Baranyi ◽

Stuart McTaggart ◽

Niall Cunningham ◽

...

Keyword(s):

Mental Health ◽

Mental Illness ◽

Sequence Analysis ◽

Local Level ◽

Population Level ◽

The United States ◽

Individual Risk ◽

White Female ◽

Prescription Data ◽

Scottish Population

Abstract BackgroundOver the past decade, antidepressant prescriptions have increased in European countries and the United States, partly due to an increase in the number of new cases of mental illness. This paper demonstrates an innovative approach to the classification of population level change in mental health status, using administrative data for a large sample of the Scottish population. We aimed to identify groups of individuals with similar patterns of change in pattern of prescribing, validate these groups by comparison with other indicators of mental illness, and characterise the population most at risk of increasing mental ill health. MethodsNational Health Service (NHS) prescription data were linked to the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population (N=151,418). Antidepressant prescription status over the previous six months was recorded for every month for which data were available (January 2009-December 2014), and sequence dissimilarity was computed by optimal matching. Hierarchical clustering was used to create groups of participants who had similar patterns of change, with multi-level logistic regression used to understand group membership. ResultsFive distinct prescription pattern groups were observed, indicating: no prescriptions (76%), occasional prescriptions (10%), continuation of prior use of prescriptions (8%), a new course of prescriptions started (4%) or ceased taking prescriptions (3%). Young, white, female participants, of low social grade, residing in socially deprived neighbourhoods, living alone, being separated/divorced or out of the labour force, were more likely to be in the group that started a new course of antidepressant prescriptions.ConclusionsThe use of sequence analysis for classifying individual antidepressant trajectories offers a novel approach for capturing population-level changes in mental health risk. By classifying individuals into groups based on their anti-depressant medication use we can better identify how over time, mental health is associated with individual risk factors and contextual factors at the local level and the macro political and economic scale.

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More severe mental illness is more concentrated in deprived areas

The British Journal of Psychiatry ◽

10.1192/bjp.175.6.544 ◽

1999 ◽

Vol 175 (6) ◽

pp. 544-548 ◽

Cited By ~ 22

Author(s):

Gyles R. Glover ◽

Morven Leese ◽

Paul McCrone

Keyword(s):

Mental Health ◽

Primary Care ◽

Mental Illness ◽

Secondary Care ◽

Mental Health Problems ◽

Population Level ◽

Mentally Disordered Offenders ◽

Health Care Need ◽

Mentally Disordered ◽

The Uk

BackgroundThe greater frequency of mental illness in deprived and inner-city populations is well recognised; allocation of funds in the UK health service makes some allowance for this. However, it is not clear whether the differences are similar for all levels of mental health care need.AimsTo study the range in prevalence of mental health problems and care at primary care, general secondary care and forensic care levels.MethodWe used mainly descriptive statistics to study evidence available from existing sources – some based on indicators of likely need, some on observed prevalance of treatment.ResultsAmong English health authority areas, the most morbid have about twice the prevalence of primary care level mental illness of the least morbid. For secondary care the ratio is between 2.5 and 4 to 1, while for services for mentally disordered offenders it is in excess of 20:1.ConclusionsWhere needs indices are used for resource allocation, responsible authorities should ensure that they produce ranges reflecting the full compass of services funded. For forensic services the range of morbidity levels may be so great that funding needs to rest at a larger population level than that of health authorities.

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Mental and physical health

Oxford Textbook of Public Mental Health ◽

10.1093/med/9780198792994.003.0021 ◽

2018 ◽

pp. 181-190

Author(s):

Mike McHugh

Keyword(s):

Mental Health ◽

Mental Illness ◽

Physical Health ◽

Well Being ◽

Positive Influence ◽

Western Society ◽

Physical And Mental Health ◽

Population Mental Health ◽

Mental And Physical Health ◽

Health And Well Being

Until recently the biomedical model dominated thinking about both physical health and mental health in Western society. It is now more useful to frame health as an integrated totality—one that includes physiological functioning, psychological and spiritual processes, and behaviour. This chapter explores this emerging agenda and focuses on well-being and prevention, particularly where well-being and prevention impact on both physical and mental illness. Evidence tells us that by strengthening mental health and well-being we not only reduce the risk of mental illness, but we also enhance physical health and population health more widely. Equally, improving physical health has a significantly positive influence on population mental health. We can increasingly exploit our understanding of these interconnections and release their potential to tackle some of the pressing health and well-being challenges we face. We have an opportunity to meaningfully draw physical and mental health together as a mutually dependent, integrated whole.

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Ethics in Mental Health Care: A Public Health Perspective

The Oxford Handbook of Public Health Ethics ◽

10.1093/oxfordhb/9780190245191.013.21 ◽

2019 ◽

pp. 204-218

Author(s):

Richard J. Bonnie ◽

Heather Zelle

Keyword(s):

Public Health ◽

Mental Health ◽

Health Care ◽

Mental Illness ◽

Health Policy ◽

Mental Health Care ◽

Mental Health Policy ◽

Population Level ◽

Public Health Perspective ◽

Ethical Discourse

This chapter explores ethical issues in mental health policy from a public health perspective, with a focus on the United States. Ethical discourse about mental health treatment has typically focused on paradigmatic concepts of individual autonomy, competence, paternalism, and appropriate justifications for overriding individual decision-making and restricting individual liberty. This chapter focuses on overarching ethical challenges in mental health policy at the population level—enhancing access of persons with mental illness to preventive services and community supports, and facilitating their successful community integration. Achieving these goals can reduce the need for coercion and ameliorate the social burden and stigma of mental illness. Shifting ethical discourse to the population level is an important step in the continuing transformation of mental health care and policy in the twenty-first century.

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