Is recession bad for your mental health? The answer could be complex: evidence from the 2008 crisis in Spain

Direct and Indirect Management Models in Public Health in the Framework of Mental Health

This article analyzes the relationship between per capita expenditure and financial and macroeconomic variables in the framework of mental health, in regions where the prevailing system is public healthcare governed by the state and in regions where the prevailing system is that of public ownership. The period 2006-2017 was analyzed. A simple linear regression analysis was carried out to determine the relationship between the expenditure per inhabitant and a series of relevant variables such as asset turnover, cash flow, and expenditure as a percentage of gross domestic product (GDP), applying statistical tests to validate the study. In regions where public-private co-financing prevails in the health system, two crucial variables to measure per capita expenditure on mental health were GDP per capita and cash flow of mental health providers. In the regions where management is direct, the crucial variables were asset turnover of mental health providers and expenditure on mental health as a percentage of GDP per capita. These elements are key to determining how to develop public investment policies in hospital systems in the different regions of Europe and the world.

Impact of the Economic Crisis on Endocarditis Mortality in Spain: A Nationwide Study

Economic recession has dire consequences on overall health. None have explored the impact of economic crisis (EC) on infective endocarditis (IE) mortality. We conducted a retrospective, nationwide, temporal trend study analyzing mortality trends by age, sex, and adverse outcomes in patients diagnosed with IE in Spain from 1997 to 2014. Data were divided into two subperiods: pre-EC (January 1997-August 2008) and post-EC (September 2008-December 2014). A total of 25 952 patients presented with IE. The incidence increased from 301.4 to 365.1 per 10 000 000 habitants, and the mortality rate rose from 24.3% to 28.4%. Those aged >75 years experienced more adverse outcomes. Complications due to sepsis, shock, acute kidney injury requiring dialysis, and heart failure increased after the EC onset, and expenditures soared to €16 216. Expenditure per community was related to mortality (P < .001). The EC resulted as an independent predictor for mortality (hazard ratio 1.06; 95% confidence interval 1.01-1.11). Incidence and mortality rate in patients with IE after the onset of the EC have increased as a result of rising adverse outcomes despite an overall increased investment.

Socioeconomic inequalities in self-assessed health and mental health in Barcelona, 2001-2016

OBJECTIVE

Previous research has found persistent socioeconomic inequalities in health outcomes at the national level, with different patterns after the economic crisis. However, inequalities in urban areas are also important. This study analyses socioeconomic inequalities in self-assessed health and mental health in the city of Barcelona.

METHOD

Repeated cross-sectional design using quinquennial data from the Barcelona Health Surveys carried out in 2001, 2006, 2011 and 2016 for the population older than 22 years. Robust Poisson regressions models were used to compute socioeconomic gradients and relative (RII) and slope indexes of inequality (SII) by occupational social class, with stratification by sex. RII and SII were also obtained with further adjustment by employment situation.

RESULTS

A consistent socioeconomic gradient was found for all years except for 2011. Relative and absolute inequalities followed a V-shape, showing a drop during the economic crisis but widening thereafter to recover pre-crisis figures for self-assessed health and widening for mental health, in both relative and absolute terms in 2016. Adjustment for employment situation reduces inequalities but a large part of these inequalities remains, with variability across years.

CONCLUSIONS

The lasting effects of the 2008 economic crisis and the austerity programmes imposed since then may have contributed to the persistence of socioeconomic inequalities in self-assessed health and the widening of those for mental health.

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.

Environmental, Health and Sociodemographic Determinants Related to Common Mental Disorders in Adults: A Spanish Country-Wide Population-Based Study (2006-2017)

Common mental disorders (CMD) represent a serious, growing public health concern, especially in women. The aims of this study were to report the prevalence of CMD among the adult population in Spain, to analyze the time trends from 2006 to 2017 and to explore the associations between CMD and gender, in relation to the perceived environmental and sociodemographic problems and clinical factors. A nationwide cross-sectional study was conducted including 48,505 participants aged 16 to 64 years old who had participated in the Spanish National Health Surveys in 2006, 2011/2012 and 2017. A logistic regression analysis was performed to identify the variables associated with CMD by gender. The prevalence of CMD was 20.4% in 2006, 20.8% in 2011/2012 and 16.9% in 2017 (p = 0.36). In women, the probability of having a CMD was higher in widowed or separated/divorced compared with single individuals and as the perception of distressing noise levels from outside the home increased. The probability of CMD was lower as the level of education increased in men. Foreigners and those with limitations due to health problems, chronic conditions and worse perceived health were more likely to suffer from a CMD in both women and men.

The importance of financial recession for mental health among students: short- and long-term analyses from an ecosocial perspective

Background and aim: Referring to the ecosocial theory and utilising the ‘natural experiment’ setting provided by the global recession at the beginning of 1990s, the aim of our study was to analyse the short- and long-term associations between trade and mental health in young students followed until mid-adulthood.

Method: The study was based on two prospective cohort studies, the older and the younger Northern Swedish Cohort which both consisted of all pupils in a middle-sized industrial town in Northern Sweden. At age 21, the younger cohort entered the labour market during the deep recession of the early 1990s, while the older cohort entered the labour market during the boom of the 1980s. Both cohorts were followed up with a high response rate in mid adulthood. For this study, all students were selected at age 21.

Results: At age 21, those who studied during recession had more depressive and functional somatic symptoms than those who studied during boom. The cohort differences did not remain over age: by the follow-up in early middle age the differences between the cohorts were non-significant, most notably due to decreased depressive symptoms in the younger cohort and increase of functional somatic symptoms in the older cohort.

Conclusions: The short-term mental health consequences of the business cycle seem to be more extensive than limited only to those who are unemployed, even though the possible long-term consequences seem to be more complex. Thus, the macrolevel had a great short-term impact on the individual level in relation to the microlevel setting of university/school. The chronosystem was also of major importance. Future research would benefit from taking the context into account.

Preventing bias from selective non-response in population-based survey studies: findings from a Monte Carlo simulation study

Background

Health researchers often use survey studies to examine associations between risk factors at one time point and health outcomes later in life. Previous studies have shown that missing not at random (MNAR) may produce biased estimates in such studies. Medical researchers typically do not employ statistical methods for treating MNAR. Hence, there is a need to increase knowledge about how to prevent occurrence of such bias in the first place.

Methods

Monte Carlo simulations were used to examine the degree to which selective non-response leads to biased estimates of associations between risk factors and health outcomes when persons with the highest levels of health problems are under-represented or totally missing from the sample. This was examined under different response rates and different degrees of dependency between non-response and study variables.

Results

Response rate per se had little effect on bias. When extreme values on the health outcome were completely missing, rather than under-represented, results were heavily biased even at a 70% response rate. In most situations, 50–100% of this bias could be prevented by including some persons with extreme scores on the health outcome in the sample, even when these persons were under-represented. When some extreme scores were present, estimates of associations were unbiased in several situations, only mildly biased in other situations, and became biased only when non-response was related to both risk factor and health outcome to substantial degrees.

Conclusions

The potential for preventing bias by including some extreme scorers in the sample is high (50–100% in many scenarios). Estimates may then be relatively unbiased in many situations, also at low response rates. Hence, researchers should prioritize to spend their resources on recruiting and retaining at least some individuals with extreme levels of health problems, rather than to obtain very high response rates from people who typically respond to survey studies. This may contribute to preventing bias due to selective non-response in longitudinal studies of risk factors and health outcomes.