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      Reasons for Discontinuation or Change of Selective Serotonin Reuptake Inhibitors in Online Drug Reviews

      research-article
      , PhD 1 , , , PharmD, MS 2 , , MSc 2 , , PharmD, PhD 2 , , MD, MSCE 2 , , PhD 3
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          What reasons for changes in the use of selective serotonin reuptake inhibitors (SSRIs) are reported on a popular health website?

          Findings

          This qualitative study of 667 online drug reviews found that the most common reason for discontinuing SSRI use or switching to another SSRI was adverse events experienced, and the most common reason for dose change was titration. Adverse events categorized under psychiatric disorders (mostly apathy, anxiety, insomnia, and loss of libido), investigation results (mostly weight gain), and reproductive system and breast disorders (mostly sexual dysfunction) appeared disproportionately more often in online drug reviews than in US and UK regulatory adverse event reporting data.

          Meaning

          These results suggest that reasons for changes in SSRI use can be identified in online drug reviews and that adverse events mentioned may reflect those more salient to patients for discontinuing their medication.

          Abstract

          Importance

          Selective serotonin reuptake inhibitors (SSRIs) are a commonly prescribed medication class to treat a variety of mental disorders. However, adherence to SSRIs is low, and uncovering the reasons for discontinuation among SSRI users is an important first step to improving medication persistence.

          Objective

          To identify the reasons SSRIs are discontinued or changed, as reported by patients and caregivers in online drug reviews.

          Design, Setting, and Participants

          This qualitative study used natural language processing and machine learning to extract mentions of changes in SSRI intake from 667 drug reviews posted on the online health forum WebMD from September 1, 2007, to August 31, 2021. The type of medication change, including discontinuation, switch to another medication, or dose change and the reason for the change were manually annotated. In each instance in which an adverse event was reported, the event was categorized using Medical Dictionary for Regulatory Activities primary system organ class (SOC) codes, and its relative frequency was compared with that in spontaneous reporting systems maintained by the US Food and Drug Administration and the UK Medicines and Healthcare Products Regulatory Agency.

          Main Outcomes and Measures

          Reasons for SSRI medication change as assessed using SOC codes.

          Results

          In total, 667 reviews posted by 659 patients or caregivers (516 [78%] of patients were female; 410 [62%] 25-54 years of age) were identified that indicated a medication change: 335 posts indicated SSRI discontinuation, 188 posts indicated dose change, and 179 posts indicated switched medications. Most authors 625 (95%) were patients. The most common reason for medication discontinuation or switching was adverse events experienced, and the most common reason for dose change was titration. Both uptitration and downtitration were initiated by either a health care professional or patient. The most common adverse events were classified by SOC codes as psychiatric disorders, including insomnia, loss of libido, and anxiety. Compared with those in regulatory data, psychiatric adverse events, adverse events recorded by investigations (mostly weight gain) and adverse events associated with the reproductive system (mostly erectile dysfunction) were reported disproportionately more often.

          Conclusions and Relevance

          This qualitative study of online drug reviews found that useful information was provided directly by patients or their caregivers regarding their medication behavior, specifically, information regarding SSRI treatment changes that may inform interventions to improve adherence. These findings suggest that these reported adverse events may be associated with SSRI persistence and that people may feel more inclined to report such events on social media than to clinicians or regulatory agencies.

          Abstract

          This qualitative study describes the reasons for selective serotonin reuptake inhibitor changes as reported in comments and reviews on the online health forum WebMD by patients and caregivers.

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          Most cited references40

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          Three approaches to qualitative content analysis.

          Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
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            Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

            (2022)
            Summary Background The mental disorders included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 were depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, eating disorders, idiopathic developmental intellectual disability, and a residual category of other mental disorders. We aimed to measure the global, regional, and national prevalence, disability-adjusted life-years (DALYS), years lived with disability (YLDs), and years of life lost (YLLs) for mental disorders from 1990 to 2019. Methods In this study, we assessed prevalence and burden estimates from GBD 2019 for 12 mental disorders, males and females, 23 age groups, 204 countries and territories, between 1990 and 2019. DALYs were estimated as the sum of YLDs and YLLs to premature mortality. We systematically reviewed PsycINFO, Embase, PubMed, and the Global Health Data Exchange to obtain data on prevalence, incidence, remission, duration, severity, and excess mortality for each mental disorder. These data informed a Bayesian meta-regression analysis to estimate prevalence by disorder, age, sex, year, and location. Prevalence was multiplied by corresponding disability weights to estimate YLDs. Cause-specific deaths were compiled from mortality surveillance databases. The Cause of Death Ensemble modelling strategy was used to estimate death rate by age, sex, year, and location. The death rates were multiplied by the years of life expected to be remaining at death based on a normative life expectancy to estimate YLLs. Deaths and YLLs could be calculated only for anorexia nervosa and bulimia nervosa, since these were the only mental disorders identified as underlying causes of death in GBD 2019. Findings Between 1990 and 2019, the global number of DALYs due to mental disorders increased from 80·8 million (95% uncertainty interval [UI] 59·5–105·9) to 125·3 million (93·0–163·2), and the proportion of global DALYs attributed to mental disorders increased from 3·1% (95% UI 2·4–3·9) to 4·9% (3·9–6·1). Age-standardised DALY rates remained largely consistent between 1990 (1581·2 DALYs [1170·9–2061·4] per 100 000 people) and 2019 (1566·2 DALYs [1160·1–2042·8] per 100 000 people). YLDs contributed to most of the mental disorder burden, with 125·3 million YLDs (95% UI 93·0–163·2; 14·6% [12·2–16·8] of global YLDs) in 2019 attributable to mental disorders. Eating disorders accounted for 17 361·5 YLLs (95% UI 15 518·5–21 459·8). Globally, the age-standardised DALY rate for mental disorders was 1426·5 (95% UI 1056·4–1869·5) per 100 000 population among males and 1703·3 (1261·5–2237·8) per 100 000 population among females. Age-standardised DALY rates were highest in Australasia, Tropical Latin America, and high-income North America. Interpretation GBD 2019 showed that mental disorders remained among the top ten leading causes of burden worldwide, with no evidence of global reduction in the burden since 1990. The estimated YLLs for mental disorders were extremely low and do not reflect premature mortality in individuals with mental disorders. Research to establish causal pathways between mental disorders and other fatal health outcomes is recommended so that this may be addressed within the GBD study. To reduce the burden of mental disorders, coordinated delivery of effective prevention and treatment programmes by governments and the global health community is imperative. Funding Bill & Melinda Gates Foundation, Australian National Health and Medical Research Council, Queensland Department of Health, Australia.
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              A new taxonomy for describing and defining adherence to medications.

              Interest in patient adherence has increased in recent years, with a growing literature that shows the pervasiveness of poor adherence to appropriately prescribed medications. However, four decades of adherence research has not resulted in uniformity in the terminology used to describe deviations from prescribed therapies. The aim of this review was to propose a new taxonomy, in which adherence to medications is conceptualized, based on behavioural and pharmacological science, and which will support quantifiable parameters. A systematic literature review was performed using MEDLINE, EMBASE, CINAHL, the Cochrane Library and PsycINFO from database inception to 1 April 2009. The objective was to identify the different conceptual approaches to adherence research. Definitions were analyzed according to time and methodological perspectives. A taxonomic approach was subsequently derived, evaluated and discussed with international experts. More than 10 different terms describing medication-taking behaviour were identified through the literature review, often with differing meanings. The conceptual foundation for a new, transparent taxonomy relies on three elements, which make a clear distinction between processes that describe actions through established routines ('Adherence to medications', 'Management of adherence') and the discipline that studies those processes ('Adherence-related sciences'). 'Adherence to medications' is the process by which patients take their medication as prescribed, further divided into three quantifiable phases: 'Initiation', 'Implementation' and 'Discontinuation'. In response to the proliferation of ambiguous or unquantifiable terms in the literature on medication adherence, this research has resulted in a new conceptual foundation for a transparent taxonomy. The terms and definitions are focused on promoting consistency and quantification in terminology and methods to aid in the conduct, analysis and interpretation of scientific studies of medication adherence. © 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                17 July 2023
                July 2023
                17 July 2023
                : 6
                : 7
                : e2323746
                Affiliations
                [1 ]Department of Health Sciences, University of York, York, United Kingdom
                [2 ]Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
                [3 ]Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, California
                Author notes
                Article Information
                Accepted for Publication: May 30, 2023.
                Published: July 17, 2023. doi:10.1001/jamanetworkopen.2023.23746
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Golder S et al. JAMA Network Open.
                Corresponding Author: Su Golder, PhD, Department of Health Sciences, University of York, York YO10 5DD, United Kingdom ( su.golder@ 123456york.ac.uk ).
                Author Contributions: Drs Golder and Medaglio had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Golder, Medaglio, Hennessy, Gonzalez Hernandez.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Golder, Gonzalez Hernandez.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Golder.
                Obtained funding: Golder, Gonzalez Hernandez.
                Administrative, technical, or material support: O’Connor.
                Supervision: Golder, Gonzalez Hernandez.
                Conflict of Interest Disclosures: Dr Golder reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study outside the submitted work. Dr O’Connor reported receiving grants from the NIH during the conduct of the study. Dr Hennessy reported receiving grants from the NIH during the conduct of the study and from Pfizer outside the submitted work; and receiving personal fees from the Medullary Thyroid Cancer Consortium (Novo Nordisk, AstraZeneca, GlaxoSmithKline, and Eli Lilly and Company) and Merck & Co outside the submitted work. Dr Gross reported receiving grants from the NIH during the conduct of the study; and receiving personal fees from Pfizer outside the submitted work. Dr Gonzalez Hernandez reported receiving grants from the NIH during the conduct of the study; and receiving personal fees from Roche outside the submitted work.
                Funding/Support: This work was funded by R01 grant LM011176 (Dr Gonzalez Hernandez) from the NIH National Library of Medicine. Dr Medaglio was supported by T32 award GM075766 from the National Institute of General Medical Sciences.
                Role of the Funder/Sponsor: The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Disclaimer: The views expressed in this study are those of the authors and not necessarily those of the sponsor.
                Data Sharing Statement: See Supplement 2.
                Article
                zoi230698
                10.1001/jamanetworkopen.2023.23746
                10352861
                37459097
                ad940022-4279-40ad-b996-d852243ea98b
                Copyright 2023 Golder S et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 21 February 2023
                : 30 May 2023
                Funding
                Funded by: National Library of Medicine
                Funded by: National Institute of General Medical Sciences
                Categories
                Research
                Original Investigation
                Online Only
                Psychiatry

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