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      Patient-Reported Reasons for Switching or Discontinuing Statin Therapy: A Mixed Methods Study Using Social Media

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          Abstract

          Introduction

          Statin discontinuation can have major negative health consequences. Studying the reasons for discontinuation can be challenging as traditional data collection methods have limitations. We propose an alternative approach using social media.

          Methods

          We used natural language processing and machine learning to extract mentions of discontinuation of statin therapy from an online health forum, WebMD ( http://www.webmd.com). We then extracted data according to themes and identified key attributes of the people posting for themselves.

          Results

          We identified 2121 statin reviews that contained information on discontinuing at least one named statin. Sixty percent of people posting declared themselves as female and the most common age category was 55–64 years. Over half the people taking statins did so for < 6 months. By far the most common reason given (90%) was patient experience of adverse events, the most common of which were musculoskeletal and connective tissue disorders. The rank order of adverse events reported in WebMD was largely consistent with those reported to regulatory agencies in the US and UK. Data were available on age, sex, duration of statin use, and, in some instances, adverse event resolution and rechallenge. In some instances, details were presented on resolution of the adverse event and rechallenge.

          Conclusion

          Social media may provide data on the reasons for switching or discontinuation of a medication, as well as unique patient perspectives that may influence continuation of a medication. This information source may provide unique data for novel interventions to reduce medication discontinuation.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s40264-022-01212-0.

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          Most cited references55

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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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            Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170 000 participants in 26 randomised trials

            Summary Background Lowering of LDL cholesterol with standard statin regimens reduces the risk of occlusive vascular events in a wide range of individuals. We aimed to assess the safety and efficacy of more intensive lowering of LDL cholesterol with statin therapy. Methods We undertook meta-analyses of individual participant data from randomised trials involving at least 1000 participants and at least 2 years' treatment duration of more versus less intensive statin regimens (five trials; 39 612 individuals; median follow-up 5·1 years) and of statin versus control (21 trials; 129 526 individuals; median follow-up 4·8 years). For each type of trial, we calculated not only the average risk reduction, but also the average risk reduction per 1·0 mmol/L LDL cholesterol reduction at 1 year after randomisation. Findings In the trials of more versus less intensive statin therapy, the weighted mean further reduction in LDL cholesterol at 1 year was 0·51 mmol/L. Compared with less intensive regimens, more intensive regimens produced a highly significant 15% (95% CI 11–18; p<0·0001) further reduction in major vascular events, consisting of separately significant reductions in coronary death or non-fatal myocardial infarction of 13% (95% CI 7–19; p<0·0001), in coronary revascularisation of 19% (95% CI 15–24; p<0·0001), and in ischaemic stroke of 16% (95% CI 5–26; p=0·005). Per 1·0 mmol/L reduction in LDL cholesterol, these further reductions in risk were similar to the proportional reductions in the trials of statin versus control. When both types of trial were combined, similar proportional reductions in major vascular events per 1·0 mmol/L LDL cholesterol reduction were found in all types of patient studied (rate ratio [RR] 0·78, 95% CI 0·76–0·80; p<0·0001), including those with LDL cholesterol lower than 2 mmol/L on the less intensive or control regimen. Across all 26 trials, all-cause mortality was reduced by 10% per 1·0 mmol/L LDL reduction (RR 0·90, 95% CI 0·87–0·93; p<0·0001), largely reflecting significant reductions in deaths due to coronary heart disease (RR 0·80, 99% CI 0·74–0·87; p<0·0001) and other cardiac causes (RR 0·89, 99% CI 0·81–0·98; p=0·002), with no significant effect on deaths due to stroke (RR 0·96, 95% CI 0·84–1·09; p=0·5) or other vascular causes (RR 0·98, 99% CI 0·81–1·18; p=0·8). No significant effects were observed on deaths due to cancer or other non-vascular causes (RR 0·97, 95% CI 0·92–1·03; p=0·3) or on cancer incidence (RR 1·00, 95% CI 0·96–1·04; p=0·9), even at low LDL cholesterol concentrations. Interpretation Further reductions in LDL cholesterol safely produce definite further reductions in the incidence of heart attack, of revascularisation, and of ischaemic stroke, with each 1·0 mmol/L reduction reducing the annual rate of these major vascular events by just over a fifth. There was no evidence of any threshold within the cholesterol range studied, suggesting that reduction of LDL cholesterol by 2–3 mmol/L would reduce risk by about 40–50%. Funding UK Medical Research Council, British Heart Foundation, European Community Biomed Programme, Australian National Health and Medical Research Council, and National Heart Foundation.
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              Information bias in health research: definition, pitfalls, and adjustment methods

              As with other fields, medical sciences are subject to different sources of bias. While understanding sources of bias is a key element for drawing valid conclusions, bias in health research continues to be a very sensitive issue that can affect the focus and outcome of investigations. Information bias, otherwise known as misclassification, is one of the most common sources of bias that affects the validity of health research. It originates from the approach that is utilized to obtain or confirm study measurements. This paper seeks to raise awareness of information bias in observational and experimental research study designs as well as to enrich discussions concerning bias problems. Specifying the types of bias can be essential to limit its effects and, the use of adjustment methods might serve to improve clinical evaluation and health care practice.
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                Author and article information

                Contributors
                su.golder@york.ac.uk
                Journal
                Drug Saf
                Drug Saf
                Drug Safety
                Springer International Publishing (Cham )
                0114-5916
                1179-1942
                7 August 2022
                7 August 2022
                2022
                : 45
                : 9
                : 971-981
                Affiliations
                [1 ]GRID grid.5685.e, ISNI 0000 0004 1936 9668, Department of Health Sciences, , University of York, ; York, YO10 5DD UK
                [2 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                Author information
                http://orcid.org/0000-0002-8987-5211
                Article
                1212
                10.1007/s40264-022-01212-0
                9402720
                35933649
                e456974f-ea2b-47a2-bcfe-6e13b145df16
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 10 July 2022
                Funding
                Funded by: NLM NIH
                Award ID: NIH NLM 1R01
                Award Recipient :
                Categories
                Original Research Article
                Custom metadata
                © Springer Nature Switzerland AG 2022

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