Utilizing Consumer Health Posts to Identify Underlying Factors Associated with Patients' Attitudes towards Antidepressants

BACKGROUND: Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in a significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. Patients’ drug review posts in online healthcare communities may provide a significant insight into patients’ attitudes towards antidepressants and address the challenges of self-report methods such as patients’ recruitment. OBJECTIVE: The primary objective of this study is to use consumer health posts to identify factors affecting the patient’s attitude towards the drugs, which in turn, is a strong determinant of treatment non-adherence. METHODS: We randomly selected 892 patients’ drug reviews from a healthcare forum “askapatient.com”. We used the Framework Method to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. In the next step, four annotators coded the drug reviews at the sentence level using the analytical framework. To test the hypotheses, we first managed missing values using different imputation methods. Then, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude. RESULTS: Analysis of the sample showed that the drug reviews were posted from February 2001 to February 2016. The majority of the patients (76%; n= 680) were female. Approximately two-thirds of the patients (n=540) were less than 40 years of age. Testing the association between variables and attitude showed that “experience of adverse drug reactions (ADRs)” ( x=¿ 31.11, P= 2.91e-06), “perceived distress from ADRs” ( x=¿ 231.6, P= 2.2e-16), “drug effectiveness” ( x=¿ 548.52, P= 2.2e-16), “complaint about the lack of knowledge” ( x=¿ 59.36, P= 3.96e-12), “experience of withdrawal” ( x=¿ 55.63, P= 2.4e-11), and “duration of usage” (F-value= 43.66, P = 6.76e-11) were significantly associated with patients’ attitudes towards antidepressants. While variables “age” (F-value= 0.72, P = 0.4) and “gender” ( x=¿ 2.7, P= 0.21) were not associated with the patients’ attitudes. Moreover, modeling the relationship between variables and attitudes showed that “drug effectiveness” and “perceived distress from adverse drug reactions” were the two most significant factors affecting patients’ attitude toward antidepressants. CONCLUSIONS: Patients’ self-report experiences of medications in online healthcare communities can provide a unique insight for identifying underlying factors associated with patients’ perceptions and attitudes towards antidepressants. However, it cannot be used as a replacement for self-report methods due to the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.

[1]  Q. Gu,et al.  Antidepressant Use Among Persons Aged 12 and Over:United States,2011-2014. , 2017, NCHS data brief.

[2]  Deidre J Smith,et al.  Medication attitudes and beliefs in patients with psychotic and affective disorders on maintenance treatment , 2012, Human psychopharmacology.

[3]  C. De las Cuevas,et al.  Risk factors for non-adherence to antidepressant treatment in patients with mood disorders , 2013, European Journal of Clinical Pharmacology.

[4]  D. Klein,et al.  Increased waking salivary cortisol and depression risk in preschoolers: the role of maternal history of melancholic depression and early child temperament. , 2009, Journal of child psychology and psychiatry, and allied disciplines.

[5]  Graciela Gonzalez-Hernandez,et al.  Utilizing social media data for pharmacovigilance: A review , 2015, J. Biomed. Informatics.

[6]  N. Gale,et al.  Using the framework method for the analysis of qualitative data in multi-disciplinary health research , 2013, BMC Medical Research Methodology.

[7]  James E. Bartlett,et al.  Organizational research: Determining appropriate sample size in survey research , 2001 .

[8]  R A Haslam,et al.  Anxiety and depression in the workplace: effects on the individual and organisation (a focus group investigation). , 2005, Journal of affective disorders.

[9]  Tetsuo Shimizu,et al.  Risk factors for drug nonadherence in antidepressant-treated patients and implications of pharmacist adherence instructions for adherence improvement , 2012, Patient preference and adherence.

[10]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[11]  J. Read,et al.  Understanding the non-pharmacological correlates of self-reported efficacy of antidepressants , 2015, Acta psychiatrica Scandinavica.

[12]  S. A. Jacob,et al.  Attitudes and beliefs of patients with chronic depression toward antidepressants and depression , 2015, Neuropsychiatric disease and treatment.

[13]  Claudio Violato,et al.  Knowledge of and attitudes towards depression and adherence to treatment: the Antidepressant Adherence Scale (AAS). , 2010, Journal of affective disorders.

[14]  Koen Demyttenaere,et al.  Six-month compliance with antidepressant medication in the treatment of major depressive disorder , 2008, International clinical psychopharmacology.

[15]  Taishiro Kishimoto,et al.  Non‐adherence to medication in patients with psychotic disorders: epidemiology, contributing factors and management strategies , 2013, World psychiatry : official journal of the World Psychiatric Association.

[16]  J. Weinman,et al.  The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication , 1999 .

[17]  K. Demyttenaere,et al.  Development of an antidepressant compliance questionnaire , 2004, Acta psychiatrica Scandinavica.

[18]  M. Gili,et al.  Therapeutic adherence in primary care depressed patients: a longitudinal study. , 2014, Actas espanolas de psiquiatria.

[19]  Ron Artstein,et al.  Survey Article: Inter-Coder Agreement for Computational Linguistics , 2008, CL.

[20]  Francisco Acosta,et al.  Beliefs about depression and its treatments: associated variables and the influence of beliefs on adherence to treatment. , 2013 .

[21]  Stefan Priebe,et al.  Are attitudes towards medication adherence associated with medication adherence behaviours among patients with psychosis? A systematic review and meta analysis , 2013, Social Psychiatry and Psychiatric Epidemiology.

[22]  B. Williams Patient satisfaction: a valid concept? , 1994, Social science & medicine.

[23]  Klaas van der Meer,et al.  Health beliefs and perceived need for mental health care of anxiety and depression--the patients' perspective explored. , 2008, Clinical psychology review.

[24]  Lyle H. Ungar,et al.  Identifying potential adverse effects using the web: A new approach to medical hypothesis generation , 2011, J. Biomed. Informatics.

[25]  Kathleen M Griffiths,et al.  Systematic Review on Internet Support Groups (ISGs) and Depression (1): Do ISGs Reduce Depressive Symptoms? , 2009, Journal of medical Internet research.

[26]  T. Hogan,et al.  A self-report scale predictive of drug compliance in schizophrenics: reliability and discriminative validity , 1983, Psychological Medicine.

[27]  Yen S. Low,et al.  Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art , 2014, Drug Safety.