University of Washington School of Public Health, Seattle, WA, USA.
BMC Med Res Methodol. 2020 Dec 8;20(1):298. doi: 10.1186/s12874-020-01174-w.
In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open."
We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain.
We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding.
This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.
近几个月来，多项工作试图对 COVID-19 社会疏离政策反应进行特征描述。这些工作使用了各种编码框架，但许多工作依赖的编码方法可能无法充分描述各州“重新开放”时社会疏离政策的梯度变化。
我们开发了一种 COVID-19 社会疏离强度框架，该框架足够具体和敏感，可以捕捉这种梯度变化。我们基于对来自 12 个美国州样本的政策进行回顾，开发了一个社会疏离强度框架，该框架由 16 个领域和每个领域的 0-5 强度等级组成。
我们发现，我们样本中平均每日强度最高的州是宾夕法尼亚州、华盛顿州、科罗拉多州、加利福尼亚州和新泽西州，佐治亚州、佛罗里达州、马萨诸塞州和得克萨斯州的平均每日强度最低。虽然有些领域（如餐馆和电影院）的政策强度分布呈双峰模式，与二元（是/否）编码兼容，但其他领域（如儿童保育和宗教聚会）的分布则更加多样化，如果没有更细致的编码，这些分布可能会被忽略。
这种详细的强度框架揭示了社会疏离政策反应之间的细微差别和复杂性。开发用于构建政策分类法和编码流程的标准化方法可能有助于更严格的政策分析并改进疾病建模工作。
