Influence of the San Francisco, CA, Sugar-Sweetened Beverage Health Warning on Consumer Reactions: Implications for Equity from a Randomized Experiment

Machine Learning Prediction Algorithm for In-Hospital Mortality following Body Contouring

BACKGROUND

Body contouring is a common procedure, but it is worth attention because of concern for a variety of complications, and even the potential for death. As a result, the purpose of this study was to determine the key predictors following body contouring and create models for the risk of mortality using diverse machine learning (ML) models.

METHODS

The National Inpatient Sample database from 2015 to 2017 was queried to identify patients undergoing body contouring. Candidate predictors, such as demographics, comorbidities, personal history, postoperative complications, and operative features, were included. The outcome was in-hospital mortality. Models were compared by area under the curve, accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis.

RESULTS

Overall, 8214 patients undergoing body contouring were identified, among whom 141 (1.72%) died in the hospital. Variable importance plot demonstrated that sepsis was the variable with greatest importance across all ML algorithms, followed by Elixhauser Comorbidity Index, cardiac arrest, and so forth. The naive Bayes model had a higher predictive performance (area under the curve, 0.898; 95% CI, 0.884 to 0.911) among these eight ML models. Similarly, in the decision curve analysis, the naive Bayes model also demonstrated a higher net benefit (ie, the correct classification of in-hospital deaths considering a tradeoff between false-negatives and false-positives) compared with the other seven models across a range of threshold probability values.

CONCLUSION

The ML models, as indicated by this study, can be used to predict in-hospital death for patients at risk who undergo body contouring.

Metabolic diseases and healthy aging: identifying environmental and behavioral risk factors and promoting public health

Aging is a progressive and irreversible pathophysiological process that manifests as the decline in tissue and cellular functions, along with a significant increase in the risk of various aging-related diseases, including metabolic diseases. While advances in modern medicine have significantly promoted human health and extended human lifespan, metabolic diseases such as obesity and type 2 diabetes among the older adults pose a major challenge to global public health as societies age. Therefore, understanding the complex interaction between risk factors and metabolic diseases is crucial for promoting well-being and healthy aging. This review article explores the environmental and behavioral risk factors associated with metabolic diseases and their impact on healthy aging. The environment, including an obesogenic environment and exposure to environmental toxins, is strongly correlated with the rising prevalence of obesity and its comorbidities. Behavioral factors, such as diet, physical activity, smoking, alcohol consumption, and sleep patterns, significantly influence the risk of metabolic diseases throughout aging. Public health interventions targeting modifiable risk factors can effectively promote healthier lifestyles and prevent metabolic diseases. Collaboration between government agencies, healthcare providers and community organizations is essential for implementing these interventions and creating supportive environments that foster healthy aging.

Effects of 4 Interpretive Front-of-Package Labeling Systems on Hypothetical Beverage and Snack Selections: A Randomized Clinical Trial

Importance

Policymakers and researchers have proposed a variety of interpretative front-of-package food labeling systems, but it remains unclear which is most effective at encouraging people to choose healthier foods and beverages, including among people with less education.

Objective

To test the effects of 4 interpretative front-of-package food labeling systems on the healthfulness of beverage and snack selections, overall and by education level.

Design, Setting, and Participants

This randomized clinical trial of a national sample of US adults 18 years and older was conducted online from November 16 to December 3, 2022.

Intervention

Participants were randomized to view products with 1 of 5 food labeling systems, including control (calorie labels only) or 1 of 4 interpretative labeling systems: green ("choose often") labels added to healthy foods; single traffic light labels added to healthy, moderately healthy, and unhealthy foods; physical activity calorie equivalent labels added to all products; and nutrient warning labels added to products high in calories, sugar, saturated fat, or sodium. All conditions had calorie labels on all products.

Main Outcomes and Measures

Participants selected 1 of 16 beverages and 1 of 16 snacks that they wanted to hypothetically purchase. The primary outcomes were calories selected from beverages and from snacks. Secondary outcomes included label reactions and perceptions.

Results

A total of 7945 participants completed the experiment and were included in analyses (4078 [51%] female, 3779 [48%] male, and 88 [1%] nonbinary or another gender; mean [SD] age, 47.5 [17.9 years]). Compared with the control arm, exposure to the green (average differential effect [ADE], -34.2; 95% CI, -42.2 to -26.1), traffic light (ADE, -31.5; 95% CI, -39.5 to -23.4), physical activity (ADE, -39.0; 95% CI, -47.0 to -31.1), or nutrient warning labels (ADE, -28.2; 95% CI, -36.2 to -20.2) led participants to select fewer calories from beverages (all P < .001). Similarly, compared with the control label, exposure to the green (ADE, -12.7; 95% CI, -17.3 to -8.2), traffic light (ADE, -13.7; 95% CI, -18.2 to -9.1), physical activity (ADE, -18.5; 95% CI, -23.1 to -13.9), or nutrient warning labels (ADE, -14.2; 95% CI, -18.8 to -9.6) led participants to select fewer calories from snacks (all P < .001). These effects did not differ by education level. The green labels were rated as less stigmatizing than the other interpretative systems but otherwise generally received the least favorable label reactions and perceptions (eg, elicited less attention, were perceived as less trustworthy), while the nutrient warnings and physical activity labels received the most favorable ratings.

Conclusions and Relevance

In this randomized clinical trial of front-of-package food labeling systems, all 4 interpretative labeling systems reduced calories selected from beverages and from snacks compared with calorie labels, with no differences by education level.

Trial Registration

ClinicalTrials.gov Identifier: NCT05432271.

Developing health and environmental warning messages about red meat: An online experiment

INTRODUCTION

The United States has among the highest per capita red meat consumption in the world. Reducing red meat consumption is crucial for minimizing the environmental impact of diets and improving health outcomes. Warning messages are effective for reducing purchases of products like sugary beverages but have not been developed for red meat. This study developed health and environmental warning messages about red meat and explored participants' reactions to these messages.

METHODS

A national convenience sample of US red meat consumers (n = 1,199; mean age 45 years) completed an online survey in 2020 for this exploratory study. Participants were randomized to view a series of either health or environmental warning messages (between-subjects factor) about the risks associated with eating red meat. Messages were presented in random order (within-subjects factor; 8 health messages or 10 environmental messages). Participants rated each warning message on a validated 3-item scale measuring perceived message effectiveness (PME), ranging from 1 (low) to 5 (high). Participants then rated their intentions to reduce their red meat consumption in the next 7 days.

RESULTS

Health warning messages elicited higher PME ratings than environmental messages (mean 2.66 vs. 2.26, p<0.001). Health warning messages also led to stronger intentions to reduce red meat consumption compared to environmental messages (mean 2.45 vs. 2.19, p<0.001). Within category (health and environmental), most pairwise comparisons of harms were not statistically significant.

CONCLUSIONS

Health warning messages were perceived to be more effective than environmental warning messages. Future studies should measure the impact of these messages on behavioral outcomes.

The impact of pictorial health warnings on purchases of sugary drinks for children: A randomized controlled trial

BACKGROUND

Pictorial warnings on tobacco products are promising for motivating behavior change, but few studies have examined pictorial warnings for sugary drinks, especially in naturalistic environments. This study aimed to examine the impact of pictorial warnings on parents' purchases of sugary drinks for their children in a naturalistic store laboratory.

METHODS AND FINDINGS

Parents of children ages 2 to 12 (n = 325, 25% identifying as Black, 20% Hispanic) completed a shopping task in a naturalistic store laboratory in North Carolina. Participants were randomly assigned to a pictorial warnings arm (sugary drinks displayed pictorial health warnings about type 2 diabetes and heart damage) or a control arm (sugary drinks displayed a barcode label). Parents selected 1 beverage and 1 snack for their child, as well as 1 household good; one of these items was selected for them to purchase and take home. The primary outcome was whether parents purchased a sugary drink for their child. Secondary outcomes included reactions to the trial labels, attitudes toward sugary drinks, and intentions to serve their child sugary drinks. Pictorial warnings led to a 17-percentage point reduction in purchases of sugary drinks (95% CI for reduction: 7% to 27%), with 45% of parents in the control arm buying a sugary drink for their child compared to 28% in the pictorial warning arm (p = 0.002). The impact of pictorial warnings on purchases did not differ by any of the 13 participant characteristics examined (e.g., race/ethnicity, income, education, and age of child). Pictorial warnings also led to lower calories (kcal), purchased from sugary drinks (82 kcal in the control arm versus 52 kcal in the pictorial warnings arm, p = 0.003). Moreover, pictorial warnings led to lower intentions to serve sugary drinks to their child, feeling more in control of healthy eating decisions, greater thinking about the harms of sugary drinks, stronger negative emotional reactions, greater anticipated social interactions, lower perceived healthfulness of sugary drinks for their child, and greater injunctive norms to limit sugary drinks for their child (all p < 0.05). There was no evidence of difference between trial arms on noticing of the labels, appeal of sugary drinks, perceived amount of added sugar in sugary drinks, risk perceptions, or perceived tastiness of sugary drinks (all p > 0.05).

CONCLUSIONS

Pictorial warnings reduced parents' purchases of sugary drinks for their children in this naturalistic trial. Warnings on sugary drinks are a promising policy approach to reduce sugary drink purchasing in the US.

TRIAL REGISTRATION

The trial design, measures, power calculation, and analytic plan were registered before data collection at www.clinicaltrials.gov NCT04223687.