Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set.
Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set.
复制标题
DOI：
10.1186/s13104-015-1482-4
复制
发表时间：
2015-09-29
影响因子：
1.8
通讯作者：
Lovasi, Gina S
中科院分区：
文献类型：
--
作者：
Kaufman, Tanya K;Sheehan, Daniel M;Lovasi, Gina S
背景技术背景：食品零售商、酒类零售店、体育活动设施和医疗设施的密度与饮食、体育活动和医疗条件的管理有关。然而，大多数研究都依赖于横截面研究。在本文中，我们评估了一个数据源提出的方法问题，该数据源越来越多地用于描述当地商业环境的变化：国家建立时间序列（NETS）数据集。NETS等纵向数据提供了机会，可以评估获得资源的差别如何影响人口健康，考虑整个生命过程中多种环境影响之间的相互关系，并更好地了解它们的相互作用和累积的健康影响。纵向数据还引入了新的数据管理、可扩展性和业务分类挑战。通过对纽约市（NY，USA）周边23个县21年的数据进行地理编码准确性和分类研究，我们发现与健康相关的商业环境随着时间的推移发生了很大变化。我们注意到，重新地理编码数据可能会提高空间精度，特别是在早期。本文的目的是使NETS数据的未来公共卫生应用更有效，因为数据的大小和复杂性可能难以在其2年的数据许可期内充分利用。此外，NETS和其他“大数据”的标准化方法将促进各项研究结果的准确性和可比性。
BACKGROUND: The densities of food retailers, alcohol outlets, physical activity facilities, and medical facilities have been associated with diet, physical activity, and management of medical conditions. Most of the research, however, has relied on cross-sectional studies. In this paper, we assess methodological issues raised by a data source that is increasingly used to characterize change in the local business environment: the National Establishment Time Series (NETS) dataset.DISCUSSION: Longitudinal data, such as NETS, offer opportunities to assess how differential access to resources impacts population health, to consider correlations among multiple environmental influences across the life course, and to gain a better understanding of their interactions and cumulative health effects. Longitudinal data also introduce new data management, geoprocessing, and business categorization challenges. Examining geocoding accuracy and categorization over 21 years of data in 23 counties surrounding New York City (NY, USA), we find that health-related business environments change considerably over time. We note that re-geocoding data may improve spatial precision, particularly in early years. Our intent with this paper is to make future public health applications of NETS data more efficient, since the size and complexity of the data can be difficult to exploit fully within its 2-year data-licensing period. Further, standardized approaches to NETS and other "big data" will facilitate the veracity and comparability of results across studies.