Departments of Medicine and Pediatrics, University of California Los Angeles, Los Angeles, California (Dr Arons); Department of Public Health Policy and Management, College of Global Public Health, New York University, New York, New York (Dr Pomeranz); and Philip R. Lee Institute for Health Policy Studies, Department of Family and Community Medicine, University of California San Francisco, San Francisco, California (Dr Hamad).
J Public Health Manag Pract. 2021 Jan/Feb;27(1):E9-E18. doi: 10.1097/PHH.0000000000001039.
There is wide variation in the number and types of obesity policies enacted across states, and prior studies suggest that partisan factors may not fully explain this variation. In this exploratory analysis, we examined the association of a broad array of state-level factors with the number and types of obesity policies across states.
We analyzed 32 predictor variables across 7 categories of state-level characteristics. We abstracted data from 1652 state obesity policies introduced during 2009-2014. We used multilevel regression models and principal component analysis to examine the association between state-level characteristics and policy outcomes.
Our outcome measures included whether bills involved topics that were public health-oriented or business interest-oriented, whether bills were enacted into law, and the number of introduced bills and enacted laws per state.
Numerous state-level characteristics were associated with obesity-related bill introduction and law enactment, and different state characteristics were associated with public health-oriented versus business interest-oriented policies. For example, state-level demographics, economic factors, policy environment, public programs, and the prevalence of obesity's downstream consequences were associated with the number of public health laws whereas obesity prevalence and policy environment were associated with the number of business interest laws.
Our results support the hypothesis that a variety of factors contribute to a complex state obesity policymaking environment, highlighting the need for future research to disentangle these key predictors.
各州颁布的肥胖政策数量和类型存在广泛差异，先前的研究表明党派因素可能无法完全解释这种差异。在这项探索性分析中，我们研究了一系列广泛的州级因素与各州肥胖政策数量和类型的关联。
我们分析了 7 类州级特征的 32 个预测变量。我们从 2009 年至 2014 年期间引入的 1652 项州肥胖政策中提取数据。我们使用多层次回归模型和主成分分析来研究州级特征与政策结果之间的关联。
我们的观察指标包括法案是否涉及公共卫生或商业利益导向的主题，法案是否颁布成为法律，以及每个州引入的法案和颁布的法律数量。
许多州级特征与肥胖相关的法案提出和法律颁布有关，不同的州级特征与公共卫生导向和商业利益导向的政策有关。例如，州级人口统计学、经济因素、政策环境、公共计划以及肥胖下游后果的流行程度与公共卫生法律的数量有关，而肥胖流行程度和政策环境与商业利益法律的数量有关。
我们的结果支持这样一种假设，即多种因素促成了复杂的州肥胖政策制定环境，这突出表明需要未来的研究来理清这些关键预测因素。
