Drug utilization patterns of antidepressants in Federal Neuro-Psychiatric Hospital Lagos, Nigeria
Bibliographic record
Abstract
Backgroud: The paucity of information on prescribing patterns and use of antidepressants in accordance with practice guidelines necessitated this study in Nigeria.Objective: To assess the prescribing patterns of antidepressants, average cost of prescriptions and the index of rational drug prescribing (IRDP) in a Nigerian tertiary care hospital.Methods: A retrospective study which involved the assessment of 683 prescriptions and case records of patients who received antidepressants from 1st January 2013 to 31st December 2014 was conducted. Information on diagnosis, patients’ demographics, prescribing patterns and cost of medications was obtained therefrom. Compliance to the World Health Organization (WHO) prescribing indicators and Nigerian Standard Treatment Guidelines (STG) was assessed. The IRDP for antdepressants was determined using a validated mathematical model. The statistical analysis was performed using SPSS version 20.Results: Tricyclic antidepressants (TCAs) were the most commonly prescribed drug group (61.3%), followed by selective serotonin re-uptake inhibitors (SSRIs) with a total of 38.7%. On the average, three drugs were prescribed per prescription, while 60.3% and 38.3% of the drugs were prescribed from National Essential Medicine List (NEML) and STG respectively. The IRDP was 3.96 over 5 points. The average cost of drugs per prescription was 4.2 USD. The cost of drugs in the prescriptions written according to STG was lower compared to that in prescriptions not compliant with the STG (p < .001).Conclusions: TCAs are the most commonly prescribed antidepressants due to their affordability. The generic prescribing, medicines prescribed from the NEML and in compliance with the STG were less than the WHO standard. The rational drug use is suboptimal. Better prescribing habits, affordability and use of newer antidepressants should be encouraged by the hospital management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".