An epidemiological study of psychotropic medication and obesity-related conditions using administrative data
Bibliographic record
Abstract
Introduction: Weight gain is a well-known side-effect of many psychotropics. There are less data on the over-65s even though they are more likely to receive psychotropics than younger populations. It is possible they may differ in terms of obesity-related conditions secondary to psychotropics, especially as increasing age is independently associated with both diabetes and hypertension.Aim: To compare the incidence of two potentially obesity-related conditions (diabetes and hypertension) in psychiatric patients receiving the following psychotropic drugs with those not receiving them: antipsychotics, antidepressants and mood stabilisers.Method: A nested case-control study of a population-based cohort of all psychiatric patients in contact with either specialist services or primary care using administrative data from Nova Scotia, Canada (population = 1 million).Results: We identified 608 cases of diabetes and 1056 of hypertension, as well as an equal number of controls for each condition. Amitriptyline, SSRIs and olanzapine were associated with an increased risk of presenting with hypertension after 6 months of prescription. Olanzapine was also significantly associated with diabetes after 6 months (OR=2.35 (95%CI=1.11=5.82)). We found no statistically significant differences in the cases and controls in terms of potential confounders with the exception of socio-economic class and schizophrenia in hypertension, but not diabetes.Conclusion: Our results suggest that the association of psychotropics and obesity related conditions applies to the over-65s as well as younger populations. Within drug classes, there are drugs that have a greater association than others, and this may be a factor when choosing a specific agent.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".