The Nutrition Environment Measurements Survey: An Assessment of the Vending Machine Food and Drink Environment at Georgia State University

Purpose: Vending machines are a component of the food environment that influences dietary choices. Previous vending machine studies have focused on schools and work sites. The purpose of this study was to utilize the Nutrition Environment Measurements Survey-Vending (NEMS-V) online tool to evaluate and rank the nutritional value of the vending environment of a large urban university. Methods: A sample size of 40 vending machines at Georgia State University (GSU) was chosen. A list of products in each machine was recorded and given either a red, yellow or green ranking based on their nutrient content. Finally, the NEMS-V online tool was used to generate a report card for each individual machine and for the entire university. Results: No vending machines were given either the Gold (greater than 50% items ranked green or yellow) or Silver (greater than 40% items ranked green or yellow) ranking. Five machines were given the Bronze level ranking, which meant the machines contained at least 30% yellow or green items. The remaining 35 machines contained less than 30% green or yellow items and were therefore not able to be awarded a ranking. Out of the 40 total machines sampled, less than 30% of them could be ranked and therefore the university could not be given an overall award. Conclusions: The poor nutritional quality of the vending environment at Georgia State University indicates a need for change. Improving the number of vending items from red to yellow or green will offer more variety and more nutritious choices for students

Growth of the digital footprint of the society of critical care medicine annual congress: 2014-2020

Objectives: Since 2014, the Society of Critical Care Medicine has encouraged “live-tweeting” through the use of specific hashtags at each annual Critical Care Congress. We describe how the digital footprint of the Society of Critical Care Medicine Congress on Twitter has evolved at a time when social media use at conferences is becoming increasingly popular. Design: We used Symplur Signals (Symplur LLC, Pasadena, CA) to track all tweets containing the Society of Critical Care Medicine Congress hashtag for each annual meeting between 2014 and 2020. We collected data on the number of tweets, tweet characteristics, and impressions (i.e., potential views) for each year and data on the characteristics of the top 100 most actively tweeting users of that Congress

Improving multidisciplinary involvement at the critical care congress through social media

Learning Objectives: The Society of Critical Care Medicine promotes a collaborative multidisciplinary practice, with a team consisting of professionals in different fields. Participation in social media activities, such as live-tweeting a meeting provides a unique opportunity for members of all professions to participate, and may allow for inclusion of a more diverse group in discussions. We assessed participation using social media metrics by physicians and non-physicians in conversations at the annual Critical Care Congress (CCC). Methods: Symplur Analytics were used to compare the characteristics of those who tweeted using the 2015 and 2016 CCC hashtag (#CCC44 and #CCC45, respectively). Characteristics of the top 50 participants during the conference were compared. Allied health professionals (AHP) were defined as non-physician healthcare providers. Results: There was an increase in tweets and participants from 2015 (4,374 tweets, 625 participants) to 2016 (14,358 tweets, 1,693 participants). In 2015, 27 of the top 50 tweeters were physicians, 7 were AHP (2 nurses, 2 advanced practitioners, and 1 pharmacist), 10 were organizations, and 6 were other non-healthcare individuals. In 2016, 22 of the top 50 tweeters were physicians, 17 were AHP (2 nurses, 5 advanced practitioners, 5 pharmacists, 2 dietitians, and 3 other providers), 8 were organizations, and 3 were other non-healthcare individuals. There were significantly more AHP participating in social media during 2016 CCC compared to 2015 (34% vs 14% of the top 50 accounts; p=0.047). The number of followers of AHP accounts were significantly fewer than the number of followers of the physician accounts (800 ± 626 followers vs 1,608 ± 2,282 followers; p=0.02), but there was no significant difference in number of tweets (7,403 ± 8,993 vs 16,764 ± 36,230 tweets; p=0.1) or duration of time on Twitter (4.8 ± 2.3 vs 5.1 ± 2.1 yrs; p=0.6). Conclusions: Social media is another way to engage all heath care professionals in academic conversations that occur during CCC. More effort should be made to increase inclusion in this important venue for multidisciplinary conversation

Impact of pharmacists to improve patient care in the critically ill: A large multicenter analysis using meaningful metrics with the medication regimen complexity-ICU (MRC-ICU)

Objectives: Despite the established role of the critical care pharmacist on the ICU multiprofessional team, critical care pharmacist workloads are likely not optimized in the ICU. Medication regimen complexity (as measured by the Medication Regimen Complexity-ICU [MRC-ICU] scoring tool) has been proposed as a potential metric to optimize critical care pharmacist workload but has lacked robust external validation. The purpose of this study was to test the hypothesis that MRC-ICU is related to both patient outcomes and pharmacist interventions in a diverse ICU population. Design: This was a multicenter, observational cohort study. Setting: Twenty-eight ICUs in the United States. Patients: Adult ICU patients. Interventions: Critical care pharmacist interventions (quantity and type) on the medication regimens of critically ill patients over a 4-week period were prospectively captured. MRC-ICU and patient outcomes (i.e., mortality and length of stay [LOS]) were recorded retrospectively. Measurements and main results: A total of 3,908 patients at 28 centers were included. Following analysis of variance, MRC-ICU was significantly associated with mortality (odds ratio, 1.09; 95% CI, 1.08-1.11; p \u3c 0.01), ICU LOS (β coefficient, 0.41; 95% CI, 00.37-0.45; p \u3c 0.01), total pharmacist interventions (β coefficient, 0.07; 95% CI, 0.04-0.09; p \u3c 0.01), and a composite intensity score of pharmacist interventions (β coefficient, 0.19; 95% CI, 0.11-0.28; p \u3c 0.01). In multivariable regression analysis, increased patient: pharmacist ratio (indicating more patients per clinician) was significantly associated with increased ICU LOS (β coefficient, 0.02; 0.00-0.04; p = 0.02) and reduced quantity (β coefficient, -0.03; 95% CI, -0.04 to -0.02; p \u3c 0.01) and intensity of interventions (β coefficient, -0.05; 95% CI, -0.09 to -0.01). Conclusions: Increased medication regimen complexity, defined by the MRC-ICU, is associated with increased mortality, LOS, intervention quantity, and intervention intensity. Further, these results suggest that increased pharmacist workload is associated with decreased care provided and worsened patient outcomes, which warrants further exploration into staffing models and patient outcomes