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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.


Spaghetti plots of the number of businesses in the specified category, from 1990 to 2010, with each line representing one of the 23 counties in the NY–NJ–PA CBSA. Red line indicates overall trend
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Fig1: Spaghetti plots of the number of businesses in the specified category, from 1990 to 2010, with each line representing one of the 23 counties in the NY–NJ–PA CBSA. Red line indicates overall trend

Mentions: Using an ecological time-series design, NETS data can be used to examine temporal change in the density and distribution of different types of commercial resources. Figure 1 summarizes the variation in the rate of change for a number of businesses in three illustrative categories (offices or clinics of health practitioners, vigorous physical activity facilities, and large supermarkets) across the 23 counties in our CBSA; each line represents the best-fit linear trend for a single county across the 21-year period. For both clinical offices and commercial venues for vigorous physical activity, the county-specific slopes ranged from fairly stable to more than doubling across the two decades. For large supermarkets, in contrast, the average slope was fairly flat and both increasing and decreasing slopes were observed for particular counties. Because NETS data provide geocodes for each establishment, similar analyses can be conducted at multiple geographic scales that can be as small as the street level. Examining relationships with demographic change can reveal emerging or vanishing disparities in the availability of resources by population sociodemographic (income, race, and ethnicity) and geographic characteristics (urban versus rural areas) [7, 11, 33]. Heterogeneity in the degree to which local business environment changes track with other trends, such as changing neighborhood socioeconomic indicators over time, may help to disentangle competing hypotheses to explain persisting disparities in resource access and health behaviors [34–36].Fig. 1


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)

Spaghetti plots of the number of businesses in the specified category, from 1990 to 2010, with each line representing one of the 23 counties in the NY–NJ–PA CBSA. Red line indicates overall trend
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Spaghetti plots of the number of businesses in the specified category, from 1990 to 2010, with each line representing one of the 23 counties in the NY–NJ–PA CBSA. Red line indicates overall trend
Mentions: Using an ecological time-series design, NETS data can be used to examine temporal change in the density and distribution of different types of commercial resources. Figure 1 summarizes the variation in the rate of change for a number of businesses in three illustrative categories (offices or clinics of health practitioners, vigorous physical activity facilities, and large supermarkets) across the 23 counties in our CBSA; each line represents the best-fit linear trend for a single county across the 21-year period. For both clinical offices and commercial venues for vigorous physical activity, the county-specific slopes ranged from fairly stable to more than doubling across the two decades. For large supermarkets, in contrast, the average slope was fairly flat and both increasing and decreasing slopes were observed for particular counties. Because NETS data provide geocodes for each establishment, similar analyses can be conducted at multiple geographic scales that can be as small as the street level. Examining relationships with demographic change can reveal emerging or vanishing disparities in the availability of resources by population sociodemographic (income, race, and ethnicity) and geographic characteristics (urban versus rural areas) [7, 11, 33]. Heterogeneity in the degree to which local business environment changes track with other trends, such as changing neighborhood socioeconomic indicators over time, may help to disentangle competing hypotheses to explain persisting disparities in resource access and health behaviors [34–36].Fig. 1

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.