Department of Health Policy, Stanford University, Stanford, CA, USA.
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Med Decis Making. 2024 Feb;44(2):175-188. doi: 10.1177/0272989X231218024. Epub 2023 Dec 30.
The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey.
We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up.
Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds.
We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape.
Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making.
Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs.
The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.
在非代表性、大规模、低成本的调查数据中，存在选择偏差的可能性会限制其在人群健康测量和公共卫生决策中的应用。我们开发了一种方法，用于调整来自大规模美国 COVID-19 趋势和影响调查的县级 COVID-19 疫苗接种覆盖率预测中的偏差。
我们开发了一个多步骤回归框架，以调整预测的县级疫苗接种覆盖率平台中的选择偏差。我们的方法包括向美国社区调查进行后分层，调整观察到的协变量的差异，以及对无偏参考指标进行二次归一化。作为一个案例研究，我们前瞻性地应用该框架预测 5 至 11 岁儿童的县级长期疫苗接种覆盖率。我们将我们的方法与儿童 5 至 11 岁的 3 个月覆盖率的临时观察指标进行了比较，并使用长期覆盖率估计值来监测疫苗接种扩大速度的公平性。
我们的预测表明，全国疫苗接种覆盖率的上限较低（46%），发现了显著的地域异质性（美国各县之间的范围从 11%到 91%不等），并突出了在 COVID-19 疫苗为 5 至 11 岁儿童紧急使用授权后的 3 个月内扩大规模的步伐方面存在广泛的差异。
我们依赖于疫苗犹豫与观察到的覆盖率之间的历史关系，这可能无法捕捉 COVID-19 政策和流行病学格局的快速变化。
我们的分析展示了一种利用多种信息来源的不同优势的方法，以便在时间和地理尺度上生成必要的估计，以便进行主动决策。
设计结合了不同及时性、空间分辨率和代表性优势的综合健康测量系统，可以使数据收集的效益相对于成本最大化。
COVID-19 大流行促使进行了大规模的调查数据收集工作，这些工作优先考虑及时性和样本量，而不是人口代表性。这些大规模、低成本、非代表性的数据中存在选择偏差的可能性，导致人们对其在人群健康测量中的应用提出了质疑。我们开发了一个多步骤回归框架，用于调整迄今为止在美国进行的最大公共卫生调查——美国 COVID-19 趋势和影响调查中的县级疫苗接种覆盖率预测中的偏差。我们的研究展示了利用多种数据源的不同优势生成主动公共卫生决策所需的时间和地理尺度估计值的价值。
