Lasting Income Costs of Mental and Physical Illness.

Importance: Income losses following a hospital diagnosis can dampen career trajectories, slow social mobility, and worsen health states. Few cost-of-illness studies examine individual income loss across diseases, life stages, and socioeconomic characteristics. Objective: To estimate long-term personal income losses following hospital diagnosis for selected mental and physical disorders. Design, Setting, and Participants: This observational population-based matched cohort study used prospectively collected Danish register data. All nonretired Danish residents aged 18 to 65 years from 2000 to 2018 were eligible for inclusion. Exposed individuals were matched to controls by year, age, sex, marital status, household size, immigration status, baseline personal income, education level, labor market attachment, and hospitalization and comorbidity history. Data were analyzed from January 2025 to January 2026. Exposure: Incident hospital diagnosis of depression, alcohol use disorder (AUD), stroke, or breast cancer. Hospital contacts for identification included inpatient and outpatient encounters at general and psychiatric treatment facilities. Main Outcomes and Measures: The outcome of interest was personal disposable income for 10 years after inclusion. The difference between the average exposed and control incomes was measured, and standardized losses were estimated to compare across disease populations. Results: Among 4 925 341 Danish residents included, there were 125 769 individuals with hospital diagnoses of depression, 77 206 with AUD, 82 151 with stroke, and 36 868 with breast cancer who were successfully matched to controls. In the third year after hospital diagnosis, income losses for male individuals were 11.8% (95% CI, 11.6%-12.0%) of control income in the same year for depression, 8.3% (95% CI, 8.1%-8.5%) for AUD, and 3.2% (95% CI, 3.0%-3.4%) for stroke. Losses for female individuals in the same year were 7.3% (95% CI, 7.2%-7.5%) of control income for depression, 5.5% (95% CI, 5.3%-5.7%) for AUD, 1.2% (95% CI, 1.1%-1.4%) for stroke, and 0.7% (95% CI, 0.5%-0.9%) for breast cancer. In the 10th year after exposure, losses totaled 13.7% (95% CI, 13.4%-14.0%), 10.4% (95% CI, 10.1%-10.7%), and 4.3% (95% CI, 4.0%-4.6%) for male individuals and 10.2% (95% CI, 10.0%-10.4%), 6.7% (95% CI, 6.4%-7.0%), 2.4% (95% CI, 2.1%-2.6%), and 0.6% (95% CI, 0.3%-0.9%) for female individuals for depression, AUD, stroke, and breast cancer, respectively. Income losses for those with mental disorders started in the 2 years preceding hospital diagnosis. Differences in loss magnitude between individuals with mental and physical conditions were not explained by differential disease risk. Income losses were greatest among those 30 to 50 years old but grew disproportionately for younger individuals and for those outside of the labor market. Some subgroups, including male individuals 25 to 29 years old with depression and female individuals with AUD receiving welfare benefits, were particularly vulnerable, with losses 5 years after diagnosis of 19.6% (95% CI, 18.9%-20.4%) and 6.1% (95% CI, 5.6%-6.6%), respectively. Conclusions and Relevance: In this cohort study, income loss after disease diagnosis appeared to shape long-term income trajectories. Increased attention on the younger population and those outside of the workforce is warranted.