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JMIR Ment Health. 2018 Sep 30;5(4):e10726. doi: 10.2196/10726.

Identifying the Underlying Factors Associated With Patients' Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews.

Author information

1
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.
2
Department of Health Informatics and Administration, College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
3
Section of Medical Informatics, Department of Health Science Research, Mayo Clinic, Rochester, MN, United States.
4
Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
5
Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.
6
UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States.
7
Department of Occupational Science & Technology, College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
8
Industrial and Manufacturing Engineering, College of Engineering & Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.

Abstract

BACKGROUND:

Nonadherence to antidepressants is a major obstacle to deriving antidepressants' therapeutic benefits, resulting in significant burdens on the individuals and the health care system. Several studies have shown that nonadherence is weakly associated with personal and clinical variables but strongly associated with patients' beliefs and attitudes toward medications. Patients' drug review posts in online health care communities might provide a significant insight into patients' attitude toward antidepressants and could be used to address the challenges of self-report methods such as patients' recruitment.

OBJECTIVE:

The aim of this study was to use patient-generated data to identify factors affecting the patient's attitude toward 4 antidepressants drugs (sertraline [Zoloft], escitalopram [Lexapro], duloxetine [Cymbalta], and venlafaxine [Effexor XR]), which in turn, is a strong determinant of treatment nonadherence. We hypothesized that clinical variables (drug effectiveness; adverse drug reactions, ADRs; perceived distress from ADRs, ADR-PD; and duration of treatment) and personal variables (age, gender, and patients' knowledge about medications) are associated with patients' attitude toward antidepressants, and experience of ADRs and drug ineffectiveness are strongly associated with negative attitude.

METHODS:

We used both qualitative and quantitative methods to analyze the dataset. Patients' drug reviews were randomly selected from a health care forum called askapatient. The Framework method was used to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. Then, 4 annotators coded the drug reviews at the sentence level using the analytical framework. After managing missing values, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude.

RESULTS:

A total of 892 reviews posted between February 2001 and September 2016 were analyzed. Most of the patients were females (680/892, 76.2%) and aged less than 40 years (540/892, 60.5%). Patient attitude was significantly (P<.001) associated with experience of ADRs, ADR-PD, drug effectiveness, perceived lack of knowledge, experience of withdrawal, and duration of usage, whereas oth age (F4,874=0.72, P=.58) and gender (χ24=2.7, P=.21) were not found to be associated with patient attitudes. Moreover, modeling the relationship between variables and attitudes showed that drug effectiveness and perceived distress from adverse drug reactions were the 2 most significant factors affecting patients' attitude toward antidepressants.

CONCLUSIONS:

Patients' self-report experiences of medications in online health care communities can provide a direct insight into the underlying factors associated with patients' perceptions and attitudes toward antidepressants. However, it cannot be used as a replacement for self-report methods because of the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.

KEYWORDS:

antidepressive agents; attitude; chronic disease; community networks; data mining; depression; framework method; internet; medication adherence; patient-centered care; perception; social media

PMID:
30287417
DOI:
10.2196/10726
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