Divorce and subsequent increase in uptake of antidepressant medication: a Finnish registry-based study on couple versus individual effects

Prevalence and Associated Factors of Depression among Adults Suffering from Migraine in Spain

Considering the significance of migraine and the limited amount of research conducted on its association with depression in population-based studies in Spain, this study aimed to determine the prevalence and change of depression from 2017 to 2020 among adults with migraine in Spain and to analyze the sociodemographic and health-related variables linked to depression in migraine sufferers. A cross-sectional study on 5329 adults with migraine from the Spanish National Health Survey 2017 and the European Health Survey in Spain 2020 was performed. Binary logistic regression was used to examine the factors related to depression. A total of 26.32% of people with migraine suffered from depression. No significant changes in that prevalence between 2017 and 2020 were observed. The risk factors associated with depression included being between the ages of 25 and 44 (p = 0.018), being separated or divorced (p = 0.033), being unemployed (p < 0.001), not engaging in recreational physical activity (p = 0.016), perceiving one's health as average, poor, or very poor (p < 0.001), experiencing moderate to severe pain in daily activities (p = 0.011, p = 0.004, p < 0.001, respectively), and having 1-2 or ≥3 chronic conditions (p = 0.003, p < 0.001, respectively). Conversely, being married (p = 0.001) and alcohol consumption (p = 0.007) were identified as protective factors.

Socioeconomic status and public health in Australia: A wastewater-based study

Analysis of untreated municipal wastewater is recognized as an innovative approach to assess population exposure to or consumption of various substances. Currently, there are no published wastewater-based studies investigating the relationships between catchment social, demographic, and economic characteristics with chemicals using advanced non-targeted techniques. In this study, fifteen wastewater samples covering 27% of the Australian population were collected during a population Census. The samples were analysed with a workflow employing liquid chromatography high-resolution mass spectrometry and chemometric tools for non-target analysis. Socioeconomic characteristics of catchment areas were generated using Geospatial Information Systems software. Potential correlations were explored between pseudo-mass loads of the identified compounds and socioeconomic and demographic descriptors of the wastewater catchments derived from Census data. Markers of public health (e.g., cardiac arrhythmia, cardiovascular disease, anxiety disorder and type 2 diabetes) were identified in the wastewater samples by the proposed workflow. They were positively correlated with descriptors of disadvantage in education, occupation, marital status and income, and negatively correlated with descriptors of advantage in education and occupation. In addition, markers of polypropylene glycol (PPG) and polyethylene glycol (PEG) related compounds were positively correlated with housing and occupation disadvantage. High positive correlations were found between separated and divorced people and specific drugs used to treat cardiac arrhythmia, cardiovascular disease, and depression. Our robust non-targeted methodology in combination with Census data can identify relationships between biomarkers of public health, human behaviour and lifestyle and socio-demographics of whole populations. Furthermore, it can identify specific areas and socioeconomic groups that may need more assistance than others for public health issues. This approach complements important public health information and enables large-scale national coverage with a relatively small number of samples.

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.

"Divorce burnout" among recently divorced individuals

Traditionally, the concept of burnout has been applied to the workspace but recent research suggests its applicability to more general contexts such as the family sphere. The present study applies burnout to the divorce context and (a) proposes and statistically tests the Divorce Burnout Model (DBM), (b) investigates the contribution of sociodemographic variables and divorce characteristics to burnout scores, and (c) investigates the relationship between burnout scores and the number of sick days and days of absence from work. Using a cross-sectional design and a sample of 1,856 recently divorced Danish citizens, multigroup confirmatory factor analysis verified the DBM´s good fit to data. Using this model, for men, it was found that fewer previous divorces, former spouse initiation of divorce, not having a new partner and a higher degree of conflict significantly predicted higher levels of divorce burnout. For women, lower income, former spouse divorce initiation, not having a new partner, and a higher level of conflict significantly predicted higher levels of divorce burnout. Across gender, burnout scores were found to significantly predict number of sick days and days of absence from work in the past 3 months over and above sociodemographic variables, divorce related characteristics, and depressive symptoms.

Anxiety, depression and associated factors among recently divorced individuals

Background: In divorce research, studies using large samples, very recently divorced individuals and validated measures of depression and anxiety with available background populations for comparison are missing.Aims: This study aimed to investigate symptoms of depression and anxiety among recently divorced Danes and assess the explanatory power of relevant sociodemographic- and divorce-related variables on these symptoms.Methods: The study utilized an online cross-sectional design and a total of 1856 Danish citizens recruited through the Danish State Administration. Average scores for depression and anxiety were compared to the Danish background population and regression analyses were conducted to assess the explanatory power of sociodemographic- and divorce characteristics on symptoms of depression and anxiety.Results: Divorcees reported significantly higher levels of both depressive and anxiety symptoms than the background population with a large proportion of the sample scoring equal to or higher than generally recommended cut-off values for risk of suffering from a psychiatric diagnosable case of depression or anxiety. Both sociodemographic- and divorce characteristics were predictive of symptoms of depression and anxiety.Conclusion: The findings underline the relevance of public health intervention targeting symptoms of depression and anxiety among recently divorced individuals.

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

Objectives

The rate of antidepressant use in the United Kingdom has outpaced diagnostic increases in the prevalence of depression. Research has suggested that personal and socioeconomic risk factors may be contributing to antidepressant use. To date, few studies have addressed these possible contributions. Thus, this study aimed to assess the relative strength of personal, socioeconomic and trauma-related risk factors in predicting antidepressant use.

Methods

Data were derived from the Adult Psychiatric Morbidity Survey (n=7403), a nationally representative household sample of adults residing in England in 2007. A multivariate binary logistic regression model was developed to assess the associations between personal, socioeconomic and trauma-related risk factors and current antidepressant use.

Results

The strongest predictor of current antidepressant use was meeting the criteria for an ICD-10 depressive episode [odds ratio (OR)=9.04]. Other significant predictors of antidepressant use in this analysis included English as first language (OR=3.45), female gender (OR=1.98), unemployment (OR=1.82) and childhood sexual abuse (OR=1.53).

Conclusions

Several personal, socioeconomic and trauma-related factors significantly contributed to antidepressant use in the multivariate model specified. These findings aid our understanding of the broader context of antidepressant use in the United Kingdom.