Depression and the risk of hospitalization in type 2 diabetes patients: A nested case-control study accounting for non-persistence to antidiabetic treatment
Bibliographic record
Abstract
INTRODUCTION: Depression is one of the most common comorbidities of type 2 diabetes. The relationship between these two diseases seems to be bidirectional. Both conditions separately lead to significant morbidity and mortality, including hospitalization. Moreover, depression is associated with non-persistence with antidiabetic drugs. OBJECTIVES: To measure the effect of depression on morbidity and particularly on all-cause, diabetes-related, cardiovascular-related and major cardiovascular events-related hospitalization, adjusting for non-persistence to antidiabetic drugs and other confounders. METHODS: We performed a nested case-control study within a cohort of type 2 diabetic individuals initiating antidiabetic drugs. Using the health administrative data of the province of Quebec, Canada, we identified all-cause, diabetes-related, cardiovascular-related and major cardiovascular hospitalizations during a maximum follow-up of eight years after the initiation of antidiabetic drug treatment. A density sampling method matched all cases with up to 10 controls by age, sex, and the Elixhauser comorbidity index. The effect of depression on hospitalization was estimated using conditional logistic regressions adjusting for non-persistence to antidiabetic drug treatment and other variables. RESULTS: We identified 41,550 all-cause hospitalized cases, of which 34,437 were related to cardiovascular (CV) diseases, 29,584 to diabetes, and 13,867 to major CV events. Depression was diagnosed in 2.51% of all-cause hospitalizations and 1.16% of matched controls. 69.11% of cases and 72.59% of controls were on metformin monotherapy. The majority (71.62% vs 75.02%, respectively) stayed on metformin monotherapy without adding or switching drugs during follow-up. Non-persistence was at similar rates (about 30%) in both groups. In the multivariable analyses, depression was associated with an increased risk for all-cause hospitalizations, with odds ratios (ORs) ranging from 2.21 (95% CI: 2.07-2.37) to 1.32 (95% CI: 1.22-1.44) according to the model adjustment (from the univariate to the fully adhjusted). CONCLUSION: Depression increased the risk of all-cause hospitalizations among patients treated for diabetes, even after accounting for non-persistence and other potentially confounding factors. These results stress the impact of depression on diabetic patients' use of health care resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".