Childhood vaccination data are made available at a school level in some U.S. states. These data can be geocoded and may be considered as having a high spatial resolution. However, a school only represents the destination location for the set of students that actually reside and interact within some larger areal region, creating a spatial mismatch. Public school districts are often used to represent these regions, but fail to account for private schools and school of choice programs. We offer a new approach to estimate childhood vaccination coverage rates at a community level by integrating school level data with population commuting information. The resulting mobility-adjusted vaccine coverage estimates resolve the spatial mismatch problem and are more aligned with the geographic scale at which public health policies are implemented. We illustrate the utility of our approach using a case study on diphtheria, tetanus, and pertussis (DTP) vaccination coverage for kindergarten students in California. The modeled community-level DTP coverage estimates yield a statewide coverage of 92.37%, which is highly similar to the 92.44% coverage rate calculated from the original school-level data.

中文翻译：

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一种使用学校数据和人口流动信息估算社区疫苗接种覆盖率的方法

美国一些州的学校提供​​儿童疫苗接种数据。这些数据可以被地理编码并且可以被认为具有高空间分辨率。但是，学校仅代表实际居住在某个较大区域内并与之互动的一组学生的目的地位置，从而造成空间不匹配。公立学区经常被用来代表这些地区，但没有考虑到私立学校和学校的选择项目。我们通过将学校层面的数据与人口通勤信息相结合，提供了一种在社区层面估算儿童疫苗接种覆盖率的新方法。由此产生的流动性调整疫苗覆盖率估计解决了空间不匹配问题，并且更符合实施公共卫生政策的地理范围。我们使用加州幼儿园学生白喉、破伤风和百日咳 (DTP) 疫苗接种覆盖率的案例研究来说明我们方法的实用性。模拟的社区级 DTP 覆盖率估计产生了 92.37% 的全州覆盖率，这与根据原始学校级数据计算的 92.44% 的覆盖率非常相似。