Health Care and Mortality among Persons with Severe Mental Illness
Bibliographic record
Abstract
OBJECTIVE: Reports show disparities in the health care of persons with severe mental illness (SMI), including in countries with universal health insurance. However, the moderating effect on disparities of specific mental health legislation is yet to be studied. The study aimed to investigate equality of health care for people with SMI in a country with a national health insurance and a comprehensive rehabilitation law for persons with mental disabilities. METHOD: A case-control epidemiological study compared health services (laboratory tests, visits to specialists, and medications) provided to users with and without a history of schizophrenia and bipolar disorder ( N = 52,131) and with regard to a subgroup of users with diabetes ( n = 16,280). In addition, we examined the mortality rates of the study population. RESULTS: While service users with schizophrenia were somewhat less likely to meet the same indexes of care as controls, those with bipolar disorder did not differ from their counterparts. Yet, mortality risk among service users with schizophrenia and bipolar disorder was 2.4 and 1.7 times higher, respectively. Rates of services to persons with SMI and comorbid diabetes did not differ from their counterparts. CONCLUSIONS: In Israel, a country with a national health insurance and a rehabilitation law for persons with mental disabilities, service users with bipolar disorder receive equitable levels of general health care. For users with schizophrenia, the disparities exist in some of the health care measures but to a smaller extent than in other countries with universal health insurance. In contrast, mortality rates are elevated in persons with SMI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".