Obesity prevention across the US: A review of state‐level policies from 2009 to 2019

Cleveland, Lauren P.; Grummon, Anna H.; Konieczynski, Elsa M; Mancini, Sally; Rao, Anjali; Simon, Denise; Block, Jason P.

doi:10.1002/osp4.621

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…There are numerous reviews of obesity policies available. [60][61][62] In 2018, Tseng et al conducted a systematic review of studies using a "natural experiment" design to test the effectiveness of an array of obesity policies, concluding that there was "…no evidence that policies intending to promote physical activity and healthy eating had beneficial effects on weight/BMI and most studies had a high risk of bias." 62 Where does that leave population health research?…”

Section: Policy Considerationsmentioning

confidence: 99%

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Obesity as a Main Threat to Future Improvements in Population Health: Policy Opportunities and Challenges

Mehta¹

2023

Milbank Quarterly

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Obesity has emerged as a main threat to future improvements in population health, and there is little evidence that the epidemic is retreating. The traditional model of “calories in, calories out,” which has guided public health policy for decades, is increasingly viewed as far too simple a framing to explain the evolution of the epidemic or guide public policy. Advances in the science of obesity, coming from many fields, highlight the structural nature of the risk, which has provided an evidence base to justify and guide policies toward addressing the social and environmental drivers of obesity. Societies and researchers need to play the long game in that widespread reductions in obesity in the short run are unlikely. Nonetheless, there are opportunities. Policies specifically targeting the food environment such as taxing high‐calorie beverages and foods, restricting the marketing of junk foods to children, enhancing food labeling, and improving the dietary environment at schools may yield long‐run benefits.

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Section: Policy Considerationsmentioning

confidence: 99%

“…Evaluation of obesity policies remains challenging because they are not often amenable to randomized trials and require long follow‐ups to best judge their efficacy. There are numerous reviews of obesity policies available 60–62 . In 2018, Tseng et al.…”

Section: Policy Considerationsmentioning

confidence: 99%

Obesity as a Main Threat to Future Improvements in Population Health: Policy Opportunities and Challenges

Mehta¹

2023

Milbank Quarterly

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“…Obesity is not just a matter of aesthetics or body image; it is a multifaceted problem with severe health implications. Individuals affected by obesity are at a higher risk of developing chronic conditions such as type 2 diabetes, cardiovascular diseases, hypertension, and certain cancers [1][2][3][4]. Moreover, obesity has been linked to reduced quality of life, decreased life expectancy, and increased healthcare costs.…”

Section: Introductionmentioning

confidence: 99%

“…These guidelines typically emphasize a comprehensive approach that includes dietary modifications, increased physical activity, behavior change strategies, and, in some cases, medical interventions. Nevertheless, combating obesity remains a challenge due to its multifaceted nature and the need for personalized interventions [2].…”

Section: Introductionmentioning

confidence: 99%

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight

Huang,

Huang

2024

PLoS ONE

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Objective and aims Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. Methods Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017–2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis. Results There were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034). Conclusion In conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

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Obesity prevention across the US: A review of state‐level policies from 2009 to 2019

Cleveland

Grummon

Konieczynski

et al. 2022

Obesity Science & Practice

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Objective Uniquely, state legislators may enact obesity prevention policies tailored to each state's needs and take diverse policy approaches to address obesity prevalence. The objective of this study was to identify and describe state‐level obesity‐related policies between 2009 and 2019. Methods Using a database of legislation covering 2009–2019, researchers categorized obesity‐related legislation by status (proposed/enacted), topic, and environment impacted. Researchers determined the number of policies proposed; enacted, by political party control; obesity prevalence, by states over time. Results 3256 obesity‐related policies were proposed among 50 states and Washington DC between 2009 and 2019. Collectively, 18% (593) of policies were enacted; California (96), New and Jersey (57) enacted the most. Across environment and topics, the most enacted policies categorized in school environment (226) and school nutrition (150) topic area. Most policies were proposed (496) and enacted (77) in 2011. On average, Democrat‐controlled states had higher enactment rates than Republican‐controlled states, as did states with lower (vs. higher) obesity prevalence. Conclusions States have actively pursued obesity‐related legislation across multiple topics and environments from 2009 to 2019, with mixed enactment rates. Evaluating the impact of these policies, alone and in combination, will be important to determine whether these state‐level efforts reduce obesity.

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Obesity prevention across the US: A review of state‐level policies from 2009 to 2019

Cited by 7 publications

References 30 publications

Obesity as a Main Threat to Future Improvements in Population Health: Policy Opportunities and Challenges

Obesity as a Main Threat to Future Improvements in Population Health: Policy Opportunities and Challenges

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight

Obesity prevention across the US: A review of state‐level policies from 2009 to 2019

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