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

Kaufman TK, Sheehan DM, Rundle A, Neckerman KM, Bader MD, Jack D, Lovasi GS - BMC Res Notes (2015)

Bottom Line: 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.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.Further, standardized approaches to NETS and other "big data" will facilitate the veracity and comparability of results across studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, 8th Floor, New York, NY, 10032, USA. tkk2109@columbia.edu.

ABSTRACT

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.

No MeSH data available.


Total number of business establishments in NY–NJ–PA CBSA 23 counties, for the years 1990–2010, and the effect of geoprocessing decisions
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Fig5: Total number of business establishments in NY–NJ–PA CBSA 23 counties, for the years 1990–2010, and the effect of geoprocessing decisions

Mentions: NETS documentation suggested that because D&B only began geocoding their establishment data in 2000, geocodes for prior years tend to be less accurate [47]. Walls and Associates seek to assign establishments in the same building the same geocoded coordinates and to ensure consistency since D&B has changed geocoding vendors [47]. As delivered, 31 % of businesses in NETS had not been geocoded with precision better than the zip code level in 1990, 16 % in 2000, and 2 % in 2010 (Fig. 5). After re-geocoding, 5 % of businesses in 1990 still did not have geocodes with precision better than the zip code level, 2 % in 2000, and 1 % in 2010. An additional table shows this in more detail (see Additional file 2). Following re-geocoding, we restricted attention to business locations with a final prioritized address coordinate geocoded to the point level or street segment. Our resulting analytical sample consisted of 2,962,119 business locations (2,701,356 businesses) in our 23 county region with geocode accuracy of street level or higher.Fig. 5


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

Kaufman TK, Sheehan DM, Rundle A, Neckerman KM, Bader MD, Jack D, Lovasi GS - BMC Res Notes (2015)

Total number of business establishments in NY–NJ–PA CBSA 23 counties, for the years 1990–2010, and the effect of geoprocessing decisions
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4588464&req=5

Fig5: Total number of business establishments in NY–NJ–PA CBSA 23 counties, for the years 1990–2010, and the effect of geoprocessing decisions
Mentions: NETS documentation suggested that because D&B only began geocoding their establishment data in 2000, geocodes for prior years tend to be less accurate [47]. Walls and Associates seek to assign establishments in the same building the same geocoded coordinates and to ensure consistency since D&B has changed geocoding vendors [47]. As delivered, 31 % of businesses in NETS had not been geocoded with precision better than the zip code level in 1990, 16 % in 2000, and 2 % in 2010 (Fig. 5). After re-geocoding, 5 % of businesses in 1990 still did not have geocodes with precision better than the zip code level, 2 % in 2000, and 1 % in 2010. An additional table shows this in more detail (see Additional file 2). Following re-geocoding, we restricted attention to business locations with a final prioritized address coordinate geocoded to the point level or street segment. Our resulting analytical sample consisted of 2,962,119 business locations (2,701,356 businesses) in our 23 county region with geocode accuracy of street level or higher.Fig. 5

Bottom Line: 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.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.Further, standardized approaches to NETS and other "big data" will facilitate the veracity and comparability of results across studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, 8th Floor, New York, NY, 10032, USA. tkk2109@columbia.edu.

ABSTRACT

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.

No MeSH data available.