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Derivation and validation of a multivariable model to predict when primary care physicians prescribe antidepressants for indications other than depression

Overview of attention for article published in Clinical Epidemiology, April 2018
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Mentioned by

twitter
2 tweeters

Citations

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1 Dimensions

Readers on

mendeley
16 Mendeley
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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 13%
Student > Master 2 13%
Student > Doctoral Student 2 13%
Student > Ph. D. Student 2 13%
Professor > Associate Professor 2 13%
Other 3 19%
Unknown 3 19%
Readers by discipline Count As %
Medicine and Dentistry 5 31%
Nursing and Health Professions 2 13%
Engineering 2 13%
Unspecified 2 13%
Agricultural and Biological Sciences 1 6%
Other 1 6%
Unknown 3 19%

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
#7,394,349
of 12,818,993 outputs
Outputs from Clinical Epidemiology
#214
of 398 outputs
Outputs of similar age
#139,071
of 270,423 outputs
Outputs of similar age from Clinical Epidemiology
#15
of 22 outputs
Altmetric has tracked 12,818,993 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 398 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,423 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.