Dataset Information

What do patients learn about psychotropic medications on the web? A natural language processing study.

ABSTRACT:

Background

Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment.

Methods

We applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug classes (antidepressants, antipsychotics, and mood stabilizers), as a means of seeing if articles referencing these medications were generally positive or generally negative in tone. We compared between medications of the same class, and across medication classes.

Results

Across 12,733 web search results, significant within-class differences in positive (antidepressants: F(24,2682)?=?2.97, p < 0.001; antipsychotics: F(16,4029) = 3.25, p < 0.001; mood stabilizers: F(8,2371)?=?6.88, p < 0.001) and negative sentiment (antidepressants: F(24,6282)?=?11.17, p < 0.001; antipsychotics: F(16, 4029) ?=?12.13, p < 0.001; mood stabilizers: F(8, 2371)?=?13.28, p < 0.001) were identified. Among these were significantly greater negative sentiment for the antidepressants sertraline, duloxetine, venlafaxine, and paroxetine, and for the antipsychotics, quetiapine and risperidone. Conversely, lithium preparations and valproate exhibited less negative sentiment than other mood stabilizing medications.

Limitations

While these results provide a novel means of comparing medications, the present analyses cannot be linked to individual patient consumption of this information, or to its influence on their future clinical interactions.

Conclusions

Overall, a subset of psychotropic medications were associated with significantly more negative sentiment. Characterizing these differences may allow clinicians to anticipate patient willingness to initiate or continue medications.

SUBMITTER: Hart KL

PROVIDER: S-EPMC6921244 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

What do patients learn about psychotropic medications on the web? A natural language processing study.

Hart Kamber L KL Perlis Roy H RH McCoy Thomas H TH

Journal of affective disorders 20190910

<h4>Background</h4>Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment.<h4>Methods</h4>We applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug ...[more]

PMID: 31539672

Similar Datasets

Project description:ImportancePeople with schizophrenia are commonly treated with psychotropic medications in addition to antipsychotics, but there is little evidence about the comparative effectiveness of these adjunctive treatment strategies.ObjectiveTo study the comparative real-world effectiveness of adjunctive psychotropic treatments for patients with schizophrenia.Design, setting, and participantsThis comparative effectiveness study used US national Medicaid data from January 1, 2001, to December 31, 2010, to examine the outcomes of initiating treatment with an antidepressant, a benzodiazepine, a mood stabilizer, or another antipsychotic among adult outpatients (aged 18-64 years) diagnosed with schizophrenia who were stably treated with a single antipsychotic. Data analysis was performed from January 1, 2017, to June 30, 2018. Multinomial logistic regression models were used to estimate propensity scores to balance covariates across the 4 medication groups. Weighted Cox proportional hazards regression models were used to compare treatment outcomes during 365 days on an intention-to-treat basis.Main outcomes and measuresRisk of hospitalization for a mental disorder (primary), emergency department (ED) visits for a mental disorder, and all-cause mortality.ResultsThe study cohort included 81 921 adult outpatients diagnosed with schizophrenia (mean [SD] age, 40.7 [12.4] years; 37 515 women [45.8%]) who were stably treated with a single antipsychotic and then initiated use of an antidepressant (n = 31 117), a benzodiazepine (n = 11 941), a mood stabilizer (n = 12 849), or another antipsychotic (n = 26 014) (reference treatment). Compared with initiating use of another antipsychotic, initiating use of an antidepressant was associated with a lower risk (hazard ratio [HR], 0.84; 95% CI, 0.80-0.88) of psychiatric hospitalization, whereas initiating use of a benzodiazepine was associated with a higher risk (HR, 1.08; 95% CI, 1.02-1.15); the risk associated with initiating use of a mood stabilizer (HR, 0.98; 95% CI, 0.94-1.03) was not significantly different from initiating use of another antipsychotic. A similar pattern of associations was observed in psychiatric ED visits for initiating use of an antidepressant (HR, 0.92; 95% CI, 0.88-0.96), a benzodiazepine (HR, 1.12; 95% CI, 1.07-1.19), and a mood stabilizer (HR, 0.99; 95% CI, 0.94-1.04). Initiating use of a mood stabilizer was associated with an increased risk of mortality (HR, 1.31; 95% CI, 1.04-1.66).Conclusions and relevanceIn the treatment of schizophrenia, initiating adjunctive treatment with an antidepressant was associated with reduced risk of psychiatric hospitalization and ED visits compared with initiating use of alternative psychotropic medications. Associations of benzodiazepines and mood stabilizers with poorer outcomes warrant clinical caution and further investigation.

Dataset Information

What do patients learn about psychotropic medications on the web? A natural language processing study.

Background

Methods

Results

Limitations

Conclusions

Publications

What do patients learn about psychotropic medications on the web? A natural language processing study.

Similar Datasets

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