Does Adherence to Medications for Type 2 Diabetes Differ Between Individuals With Vs Without Schizophrenia?
Bibliographic record
Abstract
Individuals with schizophrenia are at increased risk for poor health outcomes and mortality. This may be due to inadequate self-management of co-occurring conditions, such as type 2 diabetes. We compared adherence to oral hypoglycemic medications for diabetes patients with vs without comorbid schizophrenia. Using Veterans Affairs (VA) health system administrative data, we identified all patients with both schizophrenia and type 2 diabetes and with at least one oral hypoglycemic prescription fill in fiscal year 2002 (N = 11 454) and a comparison group of patients with diabetes who were not diagnosed with schizophrenia (N = 10 560). Nonadherence was operationalized as having a medication possession ratio indicating receipt of less than 80% of needed hypoglycemic medications. Poor adherence was less prevalent among diabetes patients with (43%) than without schizophrenia (52%, P < .001). In multivariable analyses, having schizophrenia was associated with a 25% lower likelihood of poor adherence compared with not having schizophrenia (adjusted odds ratio: 0.75, 95% confidence interval: 0.70-0.80). Poorer adherence was associated with black race, homelessness, depression, substance use disorder, and medical comorbidity. Having more outpatient visits, a higher proportion of prescriptions delivered by mail, lower prescription copayments, and more complex medication regimens were each associated with increased adherence. Among veterans with diabetes receiving ongoing VA care, overall hypoglycemic medication adherence was low, but individuals with comorbid schizophrenia were more likely to be adherent to these medications. Future studies should investigate whether factors such as comanagement of a chronic psychiatric illness or regular contact with mental health providers bestow benefits for diabetes self-management in persons with schizophrenia.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".