Mental health and its socioeconomic inequality in Sweden: the role of demographic changes over time
Bibliographic record
Abstract
Abstract: Our aim is to study trends in mental ill-health and socioeconomic-related mental health inequalities over time in Sweden. We also make a first attempt at disentangling why we see such a development, by decomposing any changes in terms of changes in selected demographic and socioeconomic characteristics among the population. A secondary aim is to consider how different indicators for mental ill-health, as well as different measures of inequality, affect the conclusions we draw. Register data from the Swedish Interdisciplinary Panel and the Swedish Living Conditions Survey (administered by Statistics Sweden) are used to study trends in mental ill-health and mental health inequalities over the years 1994-2011. The study population comprises of working age individuals aged 31-64 living in Sweden. Four indicators of mental ill-health are used in the main analysis: self-reported anxiety, psychiatric inpatient diagnosis, psychiatric outpatient diagnosis and death by suicide. The results show that psychiatric diagnoses (in- and outpatient) increased substantially amongst 31 - 64 year olds between 1994 and 2011. Self-reported anxiety remained stable and suicides decreased. These results show that the different indicators of mental ill-health are not reflective of each other and how we measure mental ill-health largely affect the conclusions we draw. The mental ill-health indicators which suggest there is an increase in mental ill-health (in- and outpatient diagnosis) partly depend on attitudes, help-seeking behaviour and diagnostic practice. Thus, we cannot say that mental ill-health actually has increased. However, all mental ill-health indicators are becoming increasingly concentrated among women and among those not participating in the labour force, and psychiatric diagnoses are increasingly concentrated among those lowest educated. Income-related mental health inequalities in Sweden are substantial, and have increased significantly between 1994 and 2011, both regarding absolute and relative inequalities. More than 30 percent of self-reported anxiety and suicides, and half or all psychiatric in- and outpatient diagnoses, are found among the poorest fifth of the population. The decomposition results show that distributional changes in the population explain the increase in suicide inequality and partly explain the increase in psychiatric inpatient diagnosis inequality. However, overall, only small changes in the level of mental ill-health and mental health inequalities are explained by changes in the population characteristics we study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".