Does Adherence to Medications for Type 2 Diabetes Differ Between Individuals With Vs Without Schizophrenia?
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it