Sapat Alka, Lofaro Ryan J, Trautman Benjamin
School of Public Administration, Florida Atlantic University, USA.
Int J Disaster Risk Reduct. 2022 Jul;77:103066. doi: 10.1016/j.ijdrr.2022.103066. Epub 2022 May 26.
In the absence of a coherent federal response to COVID-19 in the United States, state governments played a significant role with varying policy responses, including in data collection and reporting. However, while accurate data collection and disaggregation is critically important since it is the basis for mitigation policy measures and to combat health disparities, it has received little scholarly attention. To address this gap, this study employs agency theory to focus on state-level determinants of data transparency practices by examining factors affecting variations in state data collection, reporting, and disaggregation of both overall metrics and race/ethnicity data. Using ordered logistic regression analyses, we find that legislatures, rather than governors, are important institutional actors and that a conservative ideology signal and socio-economic factors help predict data reporting and transparency practices. These results suggest that there is a critical need for standardized data collection protocols, the collection of comprehensive race and ethnicity data, and analyses examining data transparency and reductions in information asymmetries as a pandemic response tool-both in the United States and globally.
在美国缺乏针对新冠疫情的协调一致的联邦应对措施的情况下,州政府在采取各种政策应对措施方面发挥了重要作用,包括在数据收集和报告方面。然而,尽管准确的数据收集和分类至关重要,因为它是缓解政策措施的基础以及对抗健康差异的基础,但这方面几乎没有受到学术关注。为了填补这一空白,本研究运用代理理论,通过考察影响州级总体指标和种族/族裔数据收集、报告及分类差异的因素,聚焦于数据透明度实践的州级决定因素。通过有序逻辑回归分析,我们发现立法机构而非州长是重要的制度行为体,保守意识形态信号和社会经济因素有助于预测数据报告和透明度实践。这些结果表明,无论是在美国还是全球,都迫切需要标准化的数据收集协议、全面的种族和族裔数据收集,以及将数据透明度和减少信息不对称作为大流行应对工具的分析研究。