Predictors of antidepressant use in the English population: analysis of the Adult Psychiatric Morbidity Survey

Which Non-Pharmaceutical Primary Care Interventions Reduce Inequalities in Common Mental Health Disorders? A Protocol for a Systematic Review of Quantitative and Qualitative Studies

Common mental health disorders (CMDs) represent a major public health concern and are particularly prevalent in people experiencing disadvantage or marginalisation. Primary care is the first point of contact for people with CMDs. Pharmaceutical interventions, such as antidepressants, are commonly used in the treatment of CMDs; however, there is concern that these treatments are over-prescribed and ineffective for treating mental distress related to social conditions. Non-pharmaceutical primary care interventions, such as psychological therapies and "social prescribing", provide alternatives for CMDs. Little is known, however, about which such interventions reduce social inequalities in CMD-related outcomes, and which may, unintentionally, increase them. The aim of this protocol (PROSPERO registration number CRD42021281166) is to describe how we will undertake a systematic review to assess the effects of non-pharmaceutical primary care interventions on CMD-related outcomes and social inequalities. A systematic review of quantitative, qualitative and mixed-methods primary studies will be undertaken and reported according to the PRISMA-Equity guidance. The following databases will be searched: Assia, CINAHL, Embase, Medline, PsycInfo and Scopus. Retrieved records will be screened according to pre-defined eligibility criteria and synthesised using a narrative approach, with meta-analysis if feasible. The findings of this review will guide efforts to commission more equitable mental health services.

ICD-11 'mixed depressive and anxiety disorder' is clinical rather than sub-clinical and more common than anxiety and depression in the general population

Background

The new International Classification of Diseases was published in 2018 (ICD‐11; World Health Organization, 2018) and now includes ‘Mixed depressive and anxiety disorder’ (6A73: MDAD) designated as a mood disorder. This disorder is defined by symptoms of both anxiety and depression occurring more days than not, for a period of two weeks, and neither set of symptoms considered separately reaches a diagnostic threshold for either disorder. However, to date no study has examined the validity of these guidelines in a general population sample.

Methods

Using Goldberg et al.’s (2017) guidelines regarding measurement of depression and anxiety, this study used factor mixture modelling (FMM) to examine the validity of the ICD‐11 criteria of MDAD. Symptom endorsement rates are provided as well as demographic predictors and somatization outcomes.

Results

Fit indices suggested the two‐factor four‐class solution was the best balance between model complexity and model fit. The results did not support a class that is subsyndromal to both anxiety and depression. On the contrary, we suggest that there exists a ‘Comorbid’ class that represents endorsement of both anxiety and depression symptoms at a higher level when compared to both ‘anxiety’ and ‘depression’ groups. Demographic predictors, as well as somatization and functional impairment outcomes, provided support for this FMM solution.

Conclusions

The ‘Comorbid’ group was the largest symptomatic group and had the highest levels of both anxiety and depression symptoms. Importantly, this group was larger than either the ‘anxiety’ or ‘depression’ group and was associated with high levels of functional impairment and somatization.

Rates and predictors of uptake of mental health support during the COVID-19 pandemic: an analysis of 26,720 adults in the UK in lockdown

PURPOSE

The coronavirus disease 2019 (COVID-19) pandemic has put a great strain on people's mental health. A growing number of studies have shown worsening mental health measures globally during the pandemic. However, there is a lack of empirical study on how people support their mental health during the COVID-19 pandemic. This study aimed to examine a number of formal and informal mental health support. Further, it explored factors that might be associated with the use of different types mental health support.

METHODS

Data from 26,720 adults in the UCL COVID-19 Social Study were analysed between 13th April 2020 and 3rd July 2020. Data were analysed using logistic and Poisson regression models.

RESULTS

About 45% of people reported talking to friends or family members to support their mental health, 43% engaging in self-care activities, 20% taking medication, 9% speaking to mental health professionals, 8% talking to a GP or other health professional, and another 8% using helpline or online services. Gender, education, living status, loneliness, pre-existing mental health conditions, general depression and anxiety, coping and personality were found to be associated with the use of mental health support.

CONCLUSION

While the negative impacts caused by the COVID-19 pandemic are inevitable, people can play an active role in managing their mental health. Understanding the patterns and predictors of various kinds of mental health support during the pandemic is crucial for future service planning and delivery through recognising potential barriers to mental health care faced by certain groups.

Social and Psychophysiological Consequences of the COVID-19 Pandemic: An Extensive Literature Review

The Coronavirus Disease 2019 (COVID-19) pandemic, now a global health crisis, has surprised health authorities around the world. Recent studies suggest that the measures taken to curb the spread of the COVID-19 outbreak have generated issues throughout the population. Thus, it is necessary to establish and identify the possible risk factors related to the psychosocial and psychophysiological strain during the COVID-19 outbreak. The present extensive literature review assesses the social, psychological, and physiological consequences of COVID-19, reviewing the impact of quarantine measures, isolation, vast human loss, social and financial consequences in the family's economies, and its impact on the psychological health of the population. We also discussed the effect of psychophysiological factors, considering the impact of physical inactivity and modifications in nutritional habits, at psychological and physiological levels. The present review includes an actualized to date bibliography, articles for which were methodologically analyzed to verify they met the standards of quality and scientific accuracy. Authors understand the pandemic as a multifactorial event for which only a profound and extensive analysis would lead to better compression and efficient intervention in the near future.

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

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.

Prevalence and Factors Associated With Psychiatric Medication Use in Bariatric Surgery Candidates

OBJECTIVE

We aimed to describe the rates of psychiatric medication use in bariatric surgery candidates and factors associated with psychiatric medication use.

METHODS

Patients from the Toronto Western Hospital Bariatric Surgery Program were recruited from 2011 to 2014. Data extracted included demographics, clinical factors (e.g., mood disorder, anxiety disorder, eating disorder, Patient Health Questionnaire-9, Generalized Anxiety Disorder 7), and psychiatric medication use. Logistic regression analyses were used to examine the relationship between demographic variables, clinical factors, and psychiatric medication use. Multiple logistic regression was conducted to determine the predictors of clinical factors from demographic variables with psychiatric medication use.

RESULTS

A total of 262 (35.1%) patients were taking at least 1 psychiatric medication and 105 patients (14.1%) were taking more than 1 psychiatric medication. Antidepressants were the most common psychiatric medication reported. The majority of patients taking a psychiatric medication had a psychiatric illness, with 16.0% not having a lifetime diagnosis of a mental illness. Being male and being employed significantly predicted lower odds of being on a psychiatric medication. Older age significantly predicted higher odds of being on a psychiatric medication. Psychiatric disorders were significantly associated with psychiatric medication use independent of demographic variables.

CONCLUSION

Our study provides insights into clinical and demographic factors related to psychiatric medication use in bariatric surgery patients. The findings support careful screening and clarification of psychiatric medications, especially in patients without a formal psychiatric diagnosis.