Adherence to Oral Antihyperglycemic Agents Among Older Adults With Mental Disorders and Its Effect on Health Care Costs, Quebec, Canada, 2005-2008.
Bibliographic record
Abstract
INTRODUCTION: Nonadherence to oral antihyperglycemic agents (OHAs) leads to an increase in use of health care resources and overall expenditures due to type 2 diabetes and its complications. People with type 2 diabetes are almost twice as likely to have anxiety and depression as the general population. Our aim was to examine health care costs associated with adherence to OHAs and the effect of depression and anxiety disorders on these in older adults with type 2 diabetes. METHODS: We used data from a representative sample (N = 2,811) of community-dwelling adults in Quebec aged 65 years or older who participated in the Étude sur la Santé des Aînés survey. The final sample consisted of 301 participants who were diagnosed with type 2 diabetes and who were taking OHAs. Total health care costs were calculated as the sum of the costs of hospitalizations and outpatient clinic services. Adherence to OHAs was measured using the medication possession ratio. Depression and anxiety disorders were assessed using criteria from the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition. We also analyzed data by the Charlson Comorbidity Index, age, sex, education, and marital status, using generalized linear models. RESULTS: Nonadherence among people without depression or anxiety was associated with higher total health care costs ($4,477; 95% confidence interval [CI], $3,754-$5,201; P < .001), as was nonadherence among people with depression or anxiety ($11,124; 95% CI, $9,685-$12,562; P < .001). CONCLUSION: Improving adherence to OHAs among people with type 2 diabetes, particularly those with underlying mental disorders such as depression or anxiety, can decrease health care costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".