Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set

• Published in: BMC research notes 2015-09, Vol.8 (1), p.507-507, Article 507
• Main Authors: Kaufman, Tanya K, Sheehan, Daniel M, Rundle, Andrew, Neckerman, Kathryn M, Bader, Michael D M, Jack, Darby, Lovasi, Gina S
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Classification; Commerce; Data collection; Databases as Topic; Delivery of Health Care; Geographic information systems; Grocery industry; Health; Health Personnel; Humans; Information management; Limited liability companies; Longitudinal Studies; Measurement; Motor Activity; Neighborhoods; Population; Project Note; Public health; Studies; Supermarkets; Time Factors
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-015-1482-4

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. 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.