| Title |
Derivation and validation of a multivariable model to predict when primary care physicians prescribe antidepressants for indications other than depression
|
|---|---|
| Published in |
Clinical Epidemiology, April 2018
|
| DOI | 10.2147/clep.s153000 |
| Pubmed ID | |
| Authors |
Jenna Wong, Michal Abrahamowicz, David L Buckeridge, Robyn Tamblyn |
| Abstract |
Physicians 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. Prediction 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. Among 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 (c) statistic 0.815; 95% CI, 0.787-0.847) and good calibration (ratio of observed to expected events 0.986; 95% CI, 0.842-1.136). In 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 26% |
| Student > Ph. D. Student | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Lecturer | 4 | 9% |
| Student > Postgraduate | 3 | 6% |
| Other | 5 | 11% |
| Unknown | 14 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 21% |
| Nursing and Health Professions | 6 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 7 | 15% |
| Unknown | 18 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 April 2018.
All research outputs
#14,388,641
of 23,043,346 outputs
Outputs from Clinical Epidemiology
#432
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Outputs of similar age
#187,554
of 330,195 outputs
Outputs of similar age from Clinical Epidemiology
#23
of 29 outputs
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