Distinctive mental health profiles in the working population: A nation-wide study from Finland

Abstract 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.


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, Schu ¨ttig 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, Ma ˆlatre-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. iii280 European Journal of Public Health, Volume 32 Supplement 3, 2022