Tobacco Retail Sales in Massachusetts and New Hampshire Before and After Massachusetts' Flavored Tobacco Restriction.

In June 2020, Massachusetts became the first state to implement a comprehensive flavored tobacco restriction. One concern was that Massachusetts residents would travel to New Hampshire to purchase restricted products. This article assesses tobacco sales in both states post-law implementation. Retail scanner data were obtained from the Nielsen Company and Information Resources, Inc (IRI), from 1 year pre-law implementation to 2 years post-law implementation. Data post-law implementation were compared with data from 1 year pre-law implementation (baseline). In Massachusetts, 2 years post-law implementation, flavored and menthol tobacco sales decreased by more than 90%. Total sales decreased by around 20%. In New Hampshire, menthol tobacco sales increased (25.1% in IRI and 18.2% in Nielsen), but total sales changed minimally (<5% increase in IRI, <5% decrease in Nielsen). When data from both states were combined, total sales decreased by around 10%. The net decrease in total tobacco sales across Massachusetts and New Hampshire indicates Massachusetts' flavored tobacco restriction resulted in a reduction in tobacco sales despite potential cross-border purchases.

Evaluation of a Statewide Flavored Tobacco Restriction on Use, Access, and Cessation Among Black and White Tobacco Users in Massachusetts.

PURPOSE: To evaluate the impact of a statewide flavored tobacco restriction among Massachusetts residents who use menthol or flavored tobacco and to assess differences in impact between Black and White users, as the tobacco industry has targeted menthol to Black users. DESIGN: An online survey was distributed through a panel provider and household mailings. SETTING: Eleven Massachusetts communities with higher-than-state-average Black, Indigenous or People of Color populations. SUBJECTS: Black (n = 63) and White (n = 231) non-Hispanic residents who used menthol or other flavored tobacco products in the past year. MEASURES: Impact of the law on use, access, and quitting behaviors. ANALYSIS: Outcomes were assessed between Blacks and Whites using Pearson chi-square tests. RESULTS: Over half (53% of White, 57% of Black) of respondents believed the law made it more difficult to access menthol products; two-thirds (67% of White,64% of Black) accessed menthol products in another state. Black users were significantly more likely to report purchasing menthol products off the street (P ≤ .05). One-third (28% of White, 32% of Black) believed the law made it easier to quit, and one-third (27% of White, 34% of Black) completely quit in the past year. CONCLUSIONS: Flavored tobacco restrictions may positively and equitably impact cessation. Cross-border access and off-the-street purchasing suggest the need for greater cessation support and underscore the importance of a national policy.

Impact of Massachusetts' Statewide Sales Restriction on Flavored and Menthol Tobacco Products on Tobacco Sales in Massachusetts and Surrounding States, June 2020.

In June 2020, Massachusetts implemented a first in the nation statewide law that restricts sales of menthol and other flavored tobacco. Since implementation, sales data indicate high retailer compliance. Drastic decreases were seen in sales of all flavored tobacco. Most neighboring states did not see increases in overall tobacco sales, although New Hampshire saw an initial increase in menthol sales, which was not sustained. We found that menthol restrictions are effective and that federal-level legislation is important, as some cross-border sales highlight. (Am J Public Health. 2022;112(8):1147-1150. https://doi.org/10.2105/AJPH.2022.306879).

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models.

BACKGROUND: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood. METHODS: We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed. RESULTS: Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73-0.78]) and validation (0.64 [interquartile range 0.60-0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59-0.90]) than distantly related cohorts (0.59 [interquartile range 0.43-0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation. CONCLUSIONS: There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making.

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature.

BACKGROUND: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. METHODS: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. RESULTS: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; P<0.001). CONCLUSIONS: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances.

Background More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known. Methods and Results We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c-statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was -1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out-of-hospital cardiac arrest score (9 validations; median c-statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c-statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c-statistic, 0.76). Conclusions Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

Educating the Scientific Workforce on Sex and Gender Considerations in Research: A National Scan of the Literature and Building Interdisciplinary Research Careers in Women's Health Programs.

Background: This study was a national scan of education resources on integrating sex and gender considerations into research. The purpose was to assess capacity for educating researchers and to identify gaps, with implications for implementation of guidelines or mandates to consider sex and gender differences in research. Information sources were U.S. training programs in women's health and sex/gender difference research, Building Interdisciplinary Research Careers in Women's Health (BIRCWH), and published peer-reviewed biomedical literature. Materials and Methods: This descriptive study used multiple methods: a national survey and a comprehensive literature review. BIRCWH leaders responded to a survey regarding education on sex/gender difference research for BIRCWH scholars (response rate 100%, 20 of 20). A comprehensive literature review was conducted for 1993-2018. Results: Nearly half (45%) of BIRCWH institutions offered education on integrating sex or gender differences in clinical translational research; of those, roughly half (54%) offered in-person training and one-third (31%) offered content within existing for-credit courses. Respondents preferred online training (84%) to in-person offerings or reference materials (47% and 42% respectively). Published indexed literature on sex or gender differences has quadrupled since 1993, although growth in these publications remained flat in the most recent six years. Conclusions: Published resources to educate researchers on integrating sex and gender differences into medical research have increased, and BIRCWH programs connect scholars to national resources. Educational gaps remain due to limited access to curricula on applied research approaches, design, and methods for sex/gender difference research. BIRCWH programs desire curricula that are easily accessible online and asynchronously; sanctioned and supported by national thought leaders; linked to required training such as rigor and reproducibility; foster collaboration; and offer practical applications. Evidence-based, high-quality educational curricula and a dissemination plan are needed to enhance the adoption and integration of sex and gender into scientific endeavors.

RE-AIM in the Real World: Use of the RE-AIM Framework for Program Planning and Evaluation in Clinical and Community Settings.

Background: The RE-AIM framework has been widely used in health research but it is unclear the extent to which this framework is also used for planning and evaluating health-related programs in clinical and community settings. Our objective was to evaluate how RE-AIM is used in the "real-world" and identify opportunities for improving use outside of research contexts. Methods: We used purposive and snowball sampling to identify clinical and community health programs that used RE-AIM for planning and/or evaluation. Recruitment methods included surveys with email follow-up to funders, implementers, and RE-AIM working group members. We identified 17 programs and conducted structured in-depth interviews with key informants (n = 18). Across RE-AIM dimensions, respondents described motivations, uses, and measures; rated understandability and usefulness; discussed benefits and challenges, strategies to overcome challenges, and resources used. We used descriptive statistics for quantitative ratings, and content analysis for qualitative data. Results: Program content areas included chronic disease management and prevention, healthy aging, mental health, or multiple, often behavioral health-related topics. During planning, most programs considered reach (n = 9), adoption (n = 11), and implementation (n = 12) while effectiveness (n = 7) and maintenance (n = 6) were considered less frequently. In contrast, most programs evaluated all RE-AIM dimensions, ranging from 13 programs assessing maintenance to 15 programs assessing implementation and effectiveness. On five-point scales, all RE-AIM dimensions were rated as easy to understand (Overall M = 4.7 ± 0.5), but obtaining data was rated as somewhat challenging (Overall M = 3.4 ± 0.9). Implementation was the most frequently used dimension to inform program design (M = 4.7 ± 0.6) relative to the other dimensions (3.0-3.9). All dimensions were considered similarly important for decision-making (average M = 4.1 ± 1.4), with the exception of maintenance (M = 3.4 ± 1.7). Qualitative corresponded to the quantitative findings in that RE-AIM was reported to be a practical, easy to understand, and well-established implementation science framework. Challenges included understanding differences among RE-AIM dimensions and data acquisition. Valuable resources included the RE-AIM website and collaborating with an expert. Discussion: RE-AIM is an efficient framework for planning and evaluation of clinical and community-based projects. It provides structure to systematically evaluate health program impact. Programs found planning for and assessing maintenance difficult, providing opportunities for further refinement.