Occupational class, gender, mental distress and use of psychotherapy

Abstract Introduction Previous studies have shown the effects of occupational class and gender on mental distress and use of psychotherapy. However, less is known about the mental distress-based use of psychotherapy in different occupational classes. Objectives The aim of our study is to show how the prevalence of mental distress and the use of long-term psychotherapy correlates in different occupational classes by gender. Methods Data were drawn from the Rise of Mental Vulnerability Study (psychotherapy) and FinHealth 2017 Study (mental distress). Adjusting for age, we calculated GHQ caseness, psychotherapy use rate, and the ratio between GHQ caseness and psychotherapy use rate in three occupational classes (upper non-manual employees, lower non-manual employees, and manual workers) separately for men and women. Age-adjustment was performed through regression analysis by using model generated predicted values of GHQ caseness and psychotherapy use rate at the average sample age of 40. Results In the group of upper non-manual men there were 10 persons with severe mental distress for every single person having used psychotherapy. For lower non-manual men and manual male workers the numbers were 14 and 31, respectively. In the group of upper non-manual women there were six persons with severe mental distress for every person having used psychotherapy. For lower non-manual women and manual female workers these numbers were nine and 18, respectively. Conclusions We found differences in the mental distress-based use of state-subsidized long-term psychotherapy between occupational classes. For upper non-manual workers, the use of therapy best meets their mental distress. The opposite is true for manual workers. We also found differences between men and women, but these findings should be confirmed with larger datasets.


Introduction:
The concept of health capital views health as a form of capital that produces healthy time to individual. This stock of capitalhealth that is -can decrease or increase. The potential of psychotherapy as individual's investment to (mental) health capital has been rarely studied in population level.

Objectives:
The aim of our study is to shed light on the returns on individual-level investments in health capital. We consider the use of psychotherapy as an investment in health capital. This investment offers potential returns for individual as a higher level of subsequent income. However, these returns are potentially heterogenous: we aim to show to whom the use of psychotherapy is a sound investment in health capital. Methods: We model the effects of mental health, and subsequent treatments such as the use of psychotropics and psychotherapy on income using two-way fixed effects regression.

Results:
Preliminary results show that different parts of working-age population seem to have different potential returns related to the use of psychotherapy. These heterogenous effects are related to previously reported socioeconomic status related disparities: the level of human capital i.e. income and education play a role in the profitability of the individual level investment made in the health capital by the use of psychotherapy.

Conclusions:
The use of psychotherapy has heterogenous effects on the income of individuals. The potential of this investment to produce health capital varies with education, the level of income prior to the use of psychotherapy.

Objectives:
Previous studies indicate socioeconomic inequalities in psychotherapy utilization. The aim of this study was to assess the associations of individual annual incomes with the utilization of long-term rehabilitative psychotherapy during nine-year follow-up in men and women. As secondary analyses we assessed the association of main activity with the utilization of psychotherapy.

Methods:
For this study, we selected those from a random sample of the working-age population (18-55 years) with information about income at each time point during the follow-up from 2011 to 2019 (N = 736 613). Psychotherapy usa during the follow-up period served as dependent variable and sosiodemographic variables, annual incomes and main activity (employed, unemployed, studying, other) were used as independent variables. To examine change in the psychotherapy use rates over time, we used sex-stratified generalized estimating equations logistic regression models with predicted marginal probabilities.

Results:
Psychotherapy use rate was constantly higher among women than in men (in 2011 0.8% and 0.2%) and increased from 2011 to 2019 among both genders and income quartiles (among women 174% -231% and among men 213% -248% increase between quartiles). Among men, psychotherapy use rate was highest among lowest income quartile throughout the study interval. Among women such difference was not observed. Among women, studentś psychotherapy use increased significantly when compared to other groups from 2011 to 2019 (299% increase vs 89% -210% increase among other groups). A similar pattern was seen among studying men versus other groups.

Conclusions:
Between 2011 and 2019 the probability of having psychotherapy increased among both genders. Unexpectedly, pro-rich psychotherapy use rate was not observed. The highest probability to use psychotherapy in lowest income quartile might be linked with differences in health care systems for students and for other.

Introduction:
Previous studies have shown the effects of occupational class and gender on mental distress and use of psychotherapy. However, less is known about the mental distress-based use of psychotherapy in different occupational classes.

Objectives:
The aim of our study is to show how the prevalence of mental distress and the use of long-term psychotherapy correlates in different occupational classes by gender. Methods: Data were drawn from the Rise of Mental Vulnerability Study (psychotherapy) and FinHealth 2017 Study (mental distress). Adjusting for age, we calculated GHQ caseness, psychotherapy use rate, and the ratio between GHQ caseness and psychotherapy use rate in three occupational classes (upper non-manual employees, lower non-manual employees, and manual workers) separately for men and women. Age-adjustment was performed through regression analysis by using model generated predicted values of GHQ caseness and psychotherapy use rate at the average sample age of 40.

Results:
In the group of upper non-manual men there were 10 persons with severe mental distress for every single person having used psychotherapy. For lower non-manual men and manual male 15th European Public Health Conference 2022 workers the numbers were 14 and 31, respectively. In the group of upper non-manual women there were six persons with severe mental distress for every person having used psychotherapy. For lower non-manual women and manual female workers these numbers were nine and 18, respectively.

Conclusions:
We found differences in the mental distress-based use of statesubsidized long-term psychotherapy between occupational classes. For upper non-manual workers, the use of therapy best meets their mental distress. The opposite is true for manual workers. We also found differences between men and women, but these findings should be confirmed with larger datasets.

Introduction:
Studies on mental health inequalities are usually based on limited sets of mental health indicators.

Objectives:
Using a large number of mental health indicators, we explored whether it is possible to identify similar hierarchical rankings regardless of mental health indicators (incl. psychotherapy) among employees representing different socio-demographic statuses, and which groups of employees have the highest mismatch between mental health symptom and treatment.

Methods:
Employees representing different occupational classes and employees from four different areas of Finland were studied and compared. We used national register data to define psychotropic medication (purchases), sickness absence for mood disorders, and the use of psychotherapy between 2017 and 2019 and national survey data from the FinHealth 2017 Study to define the level of psychological symptoms (BDI, GHQ). We assessed the risk of each outcome by population group separately for men and women, and estimated the mismatch between symptoms (BDI/GHQ caseness) and treatment (psychotropic drugs/therapy).

Results:
In all the studied groups, the prevalence of mental health indicators was mostly considerably higher among women than men. The risk of register-based mental health indicators was typically higher among lower non-manual employees. In the case of some mental health indicators, we observed significant interactions between occupation class and region. Some stark mismatches were detected between symptoms and treatment in some populations, whereas at the other end of the spectrum, the correspondence between symptoms and the mobilization of care was rather high.

Conclusions:
Although gender is strongly linked to mental health indicators, occupational class and region influence mental health profiles in the population. There are considerable inequalities between populations in the level of professional care associated with mental health problems.
Health services and public health researchers provide timely and critical evidence to answer real-world policy questions and work extensively with policymakers at the macro, meso and micro levels of government. One goal shared by researchers and policymakers is to foster evidence-informed policy and program development to ensure that policy initiatives provide the greatest benefit possible to individuals and society. Among other sources of data, meso-level datasets are usually comprising contextual data aggregated at various geographical areas such as cities, counties, and regions. Although meso-level data are sometimes used as proxies for individual level data, they can also be used to explore complex questions at the population level. This workshop aims to provide a unique, interprofessional, European conversation about how to translate meso-level research evidence into meaningful insights or recommendations. It brings together a group of high-level people from academia, think tanks, and companies who are involved in generating, transferring, or using meso-level evidence to inform public health and health care policy in Germany and France. In the first presentation, Schüttig et al. use district-level data from Germany to suggest that increased spending and improved continuity of care may be effective ways to reduce the rate of potentially avoidable hospitalizations among patients with type 2 diabetes. Then, Mercier et al. analyze department (district)-level data from France to quantify the impact of the population-based prevalence of diabetes and psychiatric conditions, of air pollution, of socioeconomic variables, and of meteorological factors on the spread of COVID-19 during the first lockdown. Rodts et al, in a collaboration between a think tank and a small company, use a broad set of district-level variables to classify French 'departements' into homogeneous clusters in terms in needs and explore the discrepancies between total health care spending and needs at the population level. Finally, Mâlatre-Lansac et al. build on these studies to discuss how data can be used to inform public health and health care policy making in Europe. In addition, they suggest future directions to improve meso-level data-driven policy at the local, national, and European levels. Beyond methodological points, the discussion will address ethical issues in the use of meso-level data, and how to improve the availability of data, and the ability of local, regional, and national policymakers to use research evidence efficiently. It is designed as a regular workshop with 4 presentations (10 minutes each), ample audience interaction through Q&A after each presentation and one freehand poll in the introduction of each presentation. Key messages: Meso-level data can be efficiently leveraged to inform health care policy.