Master Settlement Agreement (MSA) Spending and Tobacco Control Efforts

<div><p>We investigate whether the distributions to the states from the Tobacco Master Settlement Agreement (MSA) in 1998 is associated with stronger tobacco control efforts. We use state level data from 50 states and the District of Columbia from four time periods post MSA (1999, 2002, 2004, and 2006) for the analysis. Using fixed effect regression models, we estimate the relationship between MSA disbursements and a new aggregate measure of strength of state tobacco control known as the Strength of Tobacco Control (SoTC) Index. Results show an increase of $1 in the annual per capita MSA disbursement to a state is associated with a decrease of −0.316 in the SoTC mean value, indicating higher MSA payments were associated with weaker tobacco control measures within states. In order to achieve the initial objectives of the MSA payments, policy makers should focus on utilizing MSA payments strictly on tobacco control activities across states.</p></div

Fixed effects regression results of SoTC index and sub-component indexes.

<p>Source: Authors' analysis of the data set used in the study.</p><p>Note: T-statistics are shown in the parentheses.</p><p>*<i>p</i><0.05;</p><p>**<i>p</i><0.01.</p><p>Fixed effects regression results of SoTC index and sub-component indexes.</p

Average SoTC values across the 4 years (99, 02, 04, 06) by state, from highest to lowest.

<p>Source: Authors' analysis of the data set used in the study.</p><p>Average SoTC values across the 4 years (99, 02, 04, 06) by state, from highest to lowest.</p

Youth and Adult Smoking Rates.

<p>Youth and Adult Smoking Rates.</p

Fixed effects regression results of SoTC index and sub-component indexes, excluding states that securitized MSA payments before 2006.

<p>Source: Authors' analysis of the data set used in the study.</p><p>Note: T-statistics are shown in the parentheses.</p><p>*<i>p</i><0.05;</p><p>**<i>p</i><0.01.</p><p>Fixed effects regression results of SoTC index and sub-component indexes, excluding states that securitized MSA payments before 2006.</p

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

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

Comparisons between studies with and without code available.

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

<i>Fli1</i>^<i>+/-</i> T cells have significantly lower levels of Neuraminidase 1 (<i>Neu1</i>) message and NEU activity compared to <i>Fli1</i>^<i>+/+</i> T cells during early disease.

<p>cDNA was amplified from RNA isolated from T cells of MRL/lpr <i>Fli1</i>^<i>+/+</i> and <i>Fli1</i>^<i>+/-</i> 10-12 week-old mice (A) and 17-18 week-old mice (C). <i>Neu1</i> and <i>Neu3</i> message levels were measured by real-time PCR and normalized to <i>β-actin</i> levels. B) NEU activity was measured as described in the methods. Relative levels in the NEU activity assay were calculated to combine all animals across experiments as described in the methods. The ‘n’ represents data from individual animals and p values are provided within the figure.</p