Racial Disparity in Referral for Catheter Ablation for Atrial Fibrillation at a Single Integrated Health System

Background Guidelines recommend catheter ablation of atrial fibrillation (AFCA) as an option for rhythm control. Studies have shown that Black patients are less likely to undergo AFCA compared with White patients. We investigated whether differences in referral patterns play a role in this observed disparity. Methods and Results Using an integrated repository from the electronic medical record at Northwestern Medicine, we conducted a retrospective cohort study of outpatients with newly diagnosed atrial fibrillation. Baseline characteristics by race and ethnicity were compared. Logistic regression models adjusted for socioeconomic and health factors were constructed to determine the association between race and ethnicity and binary dependent variables including referrals and visits to general cardiology and cardiac electrophysiology (EP) and AFCA. Of 5445 patients analyzed, 4652 were non‐Hispanic White (NHW) and 793 were non‐Hispanic Black (NHB). In adjusted models, NHB patients initially diagnosed with atrial fibrillation in internal medicine and primary care had a significantly greater odds of referral to general cardiology; among all patients in the cohort, there was no significant difference in the odds of referral to EP between NHB and NHW patients; and there were no differences in the odds of completing a visit in general cardiology or EP. Among patients completing an EP visit, NHB patients were less likely to undergo AFCA (odds ratio, 0.63 [95% CI, 0.40–0.98], P=0.040). Conclusions Similar referral rates to general cardiology and EP were observed between NHB and NHW patients. Despite this, NHB patients were less likely to undergo AFCA

Dissemination of novel biostatistics methods: Impact of programming code availability and other characteristics on article citations

<div><p>Background</p><p>As statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader.</p><p>Methods</p><p>Statistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles.</p><p>Results</p><p>Of 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal’s website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal’s website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32).</p><p>Conclusion</p><p>These analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.</p></div

Comparisons between studies with and without code available.

<p>Comparisons between studies with and without code available.</p

Final multivariable model predicting citation count, using a generalized linear mixed model with random journal effects included.

<p>Final multivariable model predicting citation count, using a generalized linear mixed model with random journal effects included.</p

Bivariate associations between citation counts and article, author, and journal characteristics.

<p>Bivariate associations between citation counts and article, author, and journal characteristics.</p

Association of Dysregulated Central Pain Processing and Response to Disease–Modifying Antirheumatic Drug Therapy in Rheumatoid Arthritis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163608/2/art41440_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163608/1/art41440.pd