ArticleClinical epidemiology2018
Derivation and validation of a multivariable model to predict when primary care physicians prescribe antidepressants for indications other than depression.
Article in Clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multilevel modelling of neonatal mortality in Ghana: Does household and community levels matter?Heliyon · 2023Article
- Maternal Depression and Antidepressant Use During Pregnancy and Associations with Depressive Symptoms and Suicidality in Adolescent Children.Clinical epidemiology · 2023Article
- Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes MellitusBioMed research international · 2021Article
- Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.Epidemiology (Cambridge, Mass.) · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePhysicians commonly prescribe antidepressants for indications other than depression that are not evidence-based and need further evaluation. However, lack of routinely documented treatment indications for medications in administrative and medical databases creates a major barrier to evaluating antidepressant use for indications besides depression. Thus, the aim of this study was to derive a model to predict when primary care physicians prescribe antidepressants for indications other than depression and to identify important determinants of this prescribing practice.
methodsPrediction study using antidepressant prescriptions from January 2003-December 2012 in an indication-based electronic prescribing system in Quebec, Canada. Patients were linked to demographic files, medical billings data, and hospital discharge summary data to create over 370 candidate predictors. The final prediction model was derived on a random 75% sample of the data using 3-fold cross-validation integrated within a score-based forward stepwise selection procedure. The performance of the final model was assessed in the remaining 25% of the data.
resultsAmong 73,576 antidepressant prescriptions, 32,405 (44.0%) were written for indications other than depression. Among 40 predictors in the final model, the most important covariates included the molecule name, the patient's education level, the physician's workload, the prescribed dose, and diagnostic codes for plausible indications recorded in the past year. The final model had good discrimination (concordance (
conclusionIn the absence of documented treatment indications, researchers may be able to use health services data to accurately predict when primary care physicians prescribe antidepressants for indications other than depression. Our prediction model represents a valuable tool for enabling researchers to differentiate between antidepressant use for depression versus other indications, thus addressing a major barrier to performing pharmacovigilance research on antidepressants.
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