2024-04-29

080_ Exploring Employment Status, Absenteeism, and Disability Pension in Patients Across the Affective-to-Psychotic Spectrum:A Comprehensive Analysis of Contributing Factors

Research Question and Aims

Mental illness is the second most common cause of sick days at work and the primary reason for early retirement, with 17.7% of workdays lost in 2021 due to mental health issues. In Germany, the average duration of sick leave due to mental illness was 35 days (Bundestherapeutenkammer 2018). Employment decline is common after diagnoses such as schizophrenia, bipolar disorder or depression (Christensen et al. 2021). Patients are prone to lose their jobs: the average job tenure of people with serious mental illness is only eight months compared with nine years in the general population (Teixeira et al. 2018). But at the same time, employment ranks very high among the goals desired by patients, and is associated with better outcomes (Marwaha, Durrani, and Singh 2013; Emsley et al. 2011; Dunn, Wewiorski, and Rogers 2008).
This project will investigate factors influencing the status of employment, the number of absence days due to sickness, and receiving a disability pension using data from the PsyCourse Study. We will investigate both salutogenic and risk factors. Additionally, we will investigate factors influencing fluctuation in the employment status.
First, we perform three regression analyses with the phenotypic data and ancestry principle components factors. In a second step, we add the polygenic risk scores for educational attainment and resilience separately. This allows us to see the extent to which the variance changes by polygenic information. And additionally, we will calculate a ‘fluctuation score’ that depicts the transition between employment status (employed – unemployed) between study visits based on the analyses of the multistate model (see Proposal: Stability of employment and relationship status over time in patients from the affective-to-psychotic spectrum). Herewith, we can calculate a fourth regression analysis to investigate fluctuation in the employment relationship.

Analytic Plan

Hypothesis:
We hypothesize that the factors diagnostic group, onset of disease, professional education as well as genetic variables such as the polygenetic risk score of educational attainment and resilience influence
1. the status of employment.
2. the number of absence days due to sickness.
3. whether a disability pension is received.
4. the fluctuation of employment over time.

Participants:
Longitudinal Data from all PsyCourse participants (younger than 65 years old) who completed a minimum of 2 study visits will be used.

Analytic methods:
The analyses will be performed cross-diagnostically in the following diagnostic subgroups: psychotic disorder and affective disorder as well as in the healthy control group (as control group).
The ‘fluctuation score’ is constructed from the linear predictors of multistate models (with two transient states: currently paid employed). Each model has two linear predictors (one for losing a job, one for finding a job), which have to be combined using appropriate weights.

Dependent variables:
currently employment, number of absence days due to sickness, disability pension, ‘fluctuation score of employment’

Independent variables:
PRS Educational attainment, PRS Resilience, professional education, duration of illness, diagnostic group (psychotic or affective), control status

Covariates:
sex, age, ancestry principal components
H1 – 4 : regression analyses

First, we will calculate a multiple linear regression baseline model without PRS. Second, we add the PRS for resilience and separately the PRS for educational attainment. By adding the PRSs to the models, we aim to reduce the residuals.

Resources needed

v1_id
v1_stat
v1_center
v1_interv_date
v1_sex x
v1_age
v1_yob
v1_school
v1_prof_dgr
v1_ed_status
v1_curr_paid_empl
v1_disabl_pens
v1_spec_emp
v1_wrk_abs_pst_5_yrs
v1_cur_work_restr
v1_dur_illness
v1_scid_dsm_dx
v1_scid_dsm_dx_cat
v2_interv_date
v2_chg_empl_stat
v2_curr_paid_empl
v2_disabl_pens
v2_spec_emp
v2_wrk_abs_pst_6_mths
v2_cur_work_restr
v3_interv_date
v3_chg_empl_stat
v3_curr_paid_empl
v3_disabl_pens
v3_spec_emp
v3_wrk_abs_pst_6_mths
v3_cur_work_restr
v4_interv_date
v4_chg_empl_stat
v4_curr_paid_empl
v4_disabl_pens
v4_spec_emp
v4_wrk_abs_pst_6_mths
v4_cur_work_restr
gsa_id