Spin within systematic review abstracts on antiplatelet therapies after acute coronary syndrome: a cross-sectional study.

OBJECTIVES: Spin is a reporting practice in which study results are misrepresented by overestimating efficacy or underestimating harm. Prevalence of spin varies between clinical specialties, and estimates are based almost entirely on clinical trials. Little is known about spin in systematic reviews. DESIGN: We performed a cross-sectional analysis searching MEDLINE and Embase for systematic reviews and meta-analyses pertaining to antiplatelet therapies following acute coronary syndrome on 2 June 2020. Data were extracted evaluating the presence of spin and study characteristics, including methodological quality as rated by A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2). All data extraction was conducted in a masked, duplicate manner from 2 June 2020 to 26 June 2020. PARTICIPANTS AND SETTING: Not applicable. PRIMARY AND SECONDARY OUTCOME MEASURES: We assessed abstracts of systematic reviews on antiplatelet therapy following acute coronary syndrome and evaluated the prevalence of the nine most severe types of spin. We additionally explored associations between spin and certain study characteristics, including quality. RESULTS: Our searches returned 15 263 articles, and 185 systematic reviews met inclusion criteria. Of these 185 reviews, 31.9% (59/185) contained some form of spin in the abstract. Seven forms of spin (1, 2, 3, 4, 5, 7 and 9) among the nine most severe were identified. No instances of types 6 or 8 were found. There were no statistically significant relationships between spin and the evaluated study characteristics or AMSTAR-2 appraisals. CONCLUSIONS: Spin was present in abstracts for systematic reviews and meta-analyses; subsequent studies are needed to identify correlations between spin and specific study characteristics. There were no statistically significant associations between spin and study characteristics or AMSTAR-2 ratings; however, implementing changes will ensure that spin is reduced in the field of cardiology as well as other fields of medicine.

Evaluation of Spin in the Abstracts of Systematic Reviews and Meta-Analyses Focused on Tinnitus.

HYPOTHESIS: The objective was to investigate the prevalence of spin in abstracts of systematic reviews and meta-analyses covering the treatment of tinnitus. We hypothesized that spin would be present in these articles and a significant relationship would exist between spin usage and extracted study characteristics. BACKGROUND: Spin, the misrepresentation of study findings, can alter a clinician's interpretation of a study's results, potentially affecting patient care. Previous work demonstrates that spin is present in abstracts of randomized clinical trials. METHODS: Using a cross-sectional analysis, we conducted a systematic search using MEDLINE and Embase databases on June 2, 2020, for systematic reviews focused on tinnitus treatment. Investigators performed screening and data extraction in a masked, duplicate fashion. RESULTS: Forty systematic reviews met inclusion criteria, and spin was identified in four of them. Spin in abstracts most frequently occurred when conclusions claimed the beneficial effect of the experimental treatment despite high risk of bias in primary studies (n = 3). The other form of spin found was the conclusion claims safety based on nonstatistically significant results with a wide confidence interval (n = 1). There was no significant association between spin and any of our extracted study characteristics. CONCLUSION: Spin was observed in 10% of abstracts of systematic reviews and meta-analyses covering the treatment of tinnitus. Although this percentage may be small, we recommend that medical journals provide a more detailed framework for abstract structure and require the inclusion of risk of bias assessment results in abstracts to prevent the incorporation of spin.

Spin the Abstracts of Systematic Reviews and Meta-Analyses Regarding the Treatment of Ménière's Disease.

OBJECTIVES: To identify, quantify, and characterize the presence of spin-specific strategies leading to misrepresentation of study results-in the abstracts of systematic reviews and meta-analyses of Ménière's disease treatment. METHODS: Using a cross-sectional design, we searched MEDLINE and Embase on May 28, 2020, for systematic reviews and meta-analyses focused on Ménière's disease treatment. Returned searches were screened, and data were extracted in a masked, duplicate fashion. RESULTS: Our sample included 36 systematic reviews and meta-analyses. Of the 36 included studies, 22 (61.1%) abstracts contained spin while 14 (38.9%) did not. The most common spin types were selective reporting of benefit (10/36, 27.8%) or harm (8/36, 22.2%). Other types of spin occurred when findings were extrapolated to the global improvement of the disease (5/36, 13.9%), beneficial effects were reported with high risk of bias in primary studies (3/36, 8.3%), and when beneficial effects were extrapolated to an entire class of interventions (1/36, 2.8%). No instances of other spin types occurred. Abstracts containing spin were substantively associated with studies of critically low methodological quality compared with studies with low and moderate quality. No studies had a methodological rating of high quality. No associations were observed between spin and intervention types, journal recommendation of adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, or funding. We found a negative correlation (r = -.31) between abstract word limit and presence of spin. CONCLUSIONS: Our study highlights that spin in the abstracts of systematic reviews of Ménière's disease is common, and it further enhances the discussion surrounding spin in abstracts of scientific research. Spin in an abstract does not discredit a study's findings; however, its occurrence should be eliminated.

Public interest in Cannabis during election season: a Google Trends analysis.

INTRODUCTION: Given that 72% of internet users seek out health information using an internet search engine (Google being the most popular); we sought to investigate the public internet search interest in cannabis as a health topic when cannabis legislation appeared on state ballots and during presidential elections. MATERIALS AND METHODS: We searched Google Trends for "cannabis" as a health topic. Google Trends data were extracted during the time period of May 1, 2008 to May 1, 2019 for the United States (US) and select states (18) within the US including: Alaska, Arizona, Arkansas, California, Colorado, Florida, Maine, Massachusetts, Michigan, Missouri, Nevada, North Dakota, Ohio, Oregon, Oklahoma, South Dakota, Utah, and Washington when cannabis was on the ballot. These state elections were referenda, not legislative votes. We then compared the internet search interest for cannabis before and after each election. To evaluate whether any associations with changes in the volume of cannabis internet searches were specific to the cannabis topic, or also occurred with other topics of general interest during an election year, the authors ran additional analyses of previously popular debated policies during Presidential Elections that may act as control topics. These policies included Education, Gun Control, Climate Change, Global Warming, and Abortion. We used the autoregressive integrated moving average (ARIMA) algorithm to forecast expected relative internet search interests for the 2012 and 2016 Presidential Elections. Individual variables were compared using a linear regression analysis for the beta coefficients performed in Stata Version 15.1 (StataCorp). RESULTS: Public internet search interest for "cannabis" increased during the voting month above the previous mean internet search interest for all 18 bills. For the US, observed internet search interest during each Presidential Election was 26.9% [95% CI, 18.4-35.4%] greater than expected in 2012 and 29.8% [95% CI, 20.8-38.8%] greater than expected in 2016. In 2016, significant state-level findings included an increase in relative internet search rates for cannabis in states with higher usage rates of cannabis in the past month (Coeff (95% CI), 3.4 (2.8-4.0)) and past month illicit drug use except cannabis rates (Coeff (95% CI), 17.4 (9.8-25.0)). Relative internet search rates for cannabis from 2008 to 2019 were also associated with increased cannabis usage in the past month (Coeff (95% CI), 3.1 (2.5-3.7)). States with higher access to legal cannabis were associated with higher relative internet search volumes for cannabis (Coeff (95% CI), 0.31 (0.15-0.46)). Of the five additional policies that were searched as topics, only two showed an increase in internet search interest during each Presidential Election. Climate Change increased by 3.5% [95% CI, - 13-20%] in 2012 and 20.1% [95% CI, 0-40%] in 2016 while Global Warming increased by 1.1% [95% CI, - 19-21%] in 2012 and 4.6% [95% CI, - 6-15%] in 2016. CONCLUSION: Based on these results, we expect public interest in cannabis will spike prior to the Presidential election in 2020. Of the five selected control policies, only two showed an increase in internet search interest during both Presidential Elections and neither exceeded the internet search increase of cannabis. These results may indicate the growing awareness of cannabis in the US and mark a possible target for the timely dissemination of evidence-based information regarding cannabis and its usage/side-effects during future elections. Consequently, the results of this study may be important to physicians since they will likely receive an increased volume of questions relating to cannabis and its therapeutic uses during election season from interested patients. We recommend establishing a cannabis repository of evidence-based information, providing physician education, and a dosing guide be created to enable physicians to provide high quality care around the issue of cannabis.

Using Structural Equation Modeling to Examine the Influence of Social, Behavioral, and Nutritional Variables on Health Outcomes Based on NHANES Data: Addressing Complex Design, Nonnormally Distributed Variables, and Missing Information.

BACKGROUND: Structural equation modeling (SEM) is a multivariate analysis method for exploring relations between latent constructs and measured variables. As a theory-guided approach, SEM estimates directional pathways in complex models based on longitudinal or cross-sectional data where randomized control trials would either be unethical or cost prohibitive. However, this method is infrequently used in nutrition research, despite recommendations by epidemiologists for its increased use. OBJECTIVES: The aim of this study was to explore 3 key methodologic areas for consideration by researchers when conducting SEM with complex survey datasets: the use of sampling weights, treatment of missing data, and model estimation techniques. METHODS: With the use of data from NHANES waves 2005-2010, we developed an SEM to estimate the relation between the latent construct of depression and measured variables of food security, tobacco use (serum cotinine), and age. We used a hierarchic approach to compare 5 SEM model iterations through the use of: 1 and 2) complete cases without and with the application of sampling weights; 3) an applied missingness dataset to test the accuracy of multiple imputation (MI); 4) the full NHANES dataset with imputed data and sampling weights; and 5) a final respecified model. Each iteration was conducted with maximum likelihood (ML) and quasimaximum likelihood with the Satorra-Bentler correction (QML) to compare path coefficients, standard errors, and model fit statistics. RESULTS: Path coefficients differed between 15.68% and 19.17% among model iterations. Nearly one-third of the cases had missing values, and MI reliably imputed values, allowing all cases to be represented in the final model iterations. QML provided better model fit statistics in all iterations. CONCLUSIONS: Nutrition epidemiologists should use complex weights, MI, and QML as a best-practices approach to SEM when conducting analyses with complex design survey data.