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Business Data Categorization and Refinement for Application in Longitudinal Neighborhood Health Research: a Methodology

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Abstract

Retail environments, such as healthcare locations, food stores, and recreation facilities, may be relevant to many health behaviors and outcomes. However, minimal guidance on how to collect, process, aggregate, and link these data results in inconsistent or incomplete measurement that can introduce misclassification bias and limit replication of existing research. We describe the following steps to leverage business data for longitudinal neighborhood health research: re-geolocating establishment addresses, preliminary classification using standard industrial codes, systematic checks to refine classifications, incorporation and integration of complementary data sources, documentation of a flexible hierarchical classification system and variable naming conventions, and linking to neighborhoods and participant residences. We show results of this classification from a dataset of locations (over 77 million establishment locations) across the contiguous U.S. from 1990 to 2014. By incorporating complementary data sources, through manual spot checks in Google StreetView and word and name searches, we enhanced a basic classification using only standard industrial codes. Ultimately, providing these enhanced longitudinal data and supplying detailed methods for researchers to replicate our work promotes consistency, replicability, and new opportunities in neighborhood health research.

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Data Availability

The detailed methods, corresponding code or syntax, and reports summarizing aggregated data developed during the current study are available from the corresponding author on reasonable request, subject to limits specified in data use or licensing agreements.

Abbreviations

D & B:

Dun and Bradsteet

GIS:

Geographic Information System

JCC:

Jewish Community Centers

NAICS:

North American Industry Classification System

NETS:

National Establishment Time Series

R & I:

Restaurants & Institutions

SIC:

Standard Industrial Classification

YMCA :

Young Men’s Christian Associations

ZCTA:

Zip Code Tabulation Area

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Acknowledgments

We are especially grateful to Dornsife School of Public Health, Drexel University (Janene Brown, Dustin Fry, Sharon Dei-Tumi), LeBow School of Business, Drexel University (Erik Dolson), and Columbia University (Brennan Rhodes Bratton) for their outstanding research assistance in auditing the ambiguous SIC codes. This work was enhanced by expert input by the Columbia University Built Environment Health Group (Tanya K. Kaufman, Nicolas Berger) for their work in categorizing the healthcare- and food-relevant locations in the New York-New Jersey-Pennsylvania metropolitan area. We are also grateful to the University of Alabama-Birmingham (Suzanne E. Judd) and Drexel University (Amy Auchincloss) for their input on the food categories; the New York Academy of Medicine (David S. Siscovick) and Rutgers Cancer Institute of New Jersey (Jennifer Tsui) for their input on the healthcare categories; University of Pittsburgh (Christina Mair) for her input on social and depression-related categories; and the investigators and staff of the “Communities Designed to Support Cardiovascular Health” team [National Institute of Aging (1R01AG049970, 3R01AG049970-04S1)] for their valuable contributions. This work was supported by the National Institute on Aging (1R01AG049970, 3R01AG049970-04S1); National Heart, Lung, and Blood Institute (grant R01HL131610); the Pennsylvania Department of Health (SAP #4100072543); the Urban Health Collaborative at Drexel University; and the generous gift from Dana and David Dornsife to the Drexel University Dornsife School of Public Health, whose funding made this study possible.

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, PA, Philadelphia, USA

    Jana A. Hirsch & Gina S. Lovasi

  2. Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA

    Jana A. Hirsch, Kari A. Moore, Yuzhe Zhao, Felicia J. Bayer & Gina S. Lovasi

  3. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, NY, USA

    Jesse Cahill, James Quinn & Andrew Rundle

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  1. Jana A. Hirsch
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Contributions

AR and GS conceived this project and obtained funding to execute it. JC, JQ, JH, KM, YZ, and FB helped classify, process, and analyze the dataset for this manuscript including statistical and geospatial elements. JH, KM, FB, and GS interpreted the data and were major contributors in writing the manuscript. All the authors read, edited, and approved the final manuscript.

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Correspondence to Gina S. Lovasi.

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Hirsch, J.A., Moore, K.A., Cahill, J. et al. Business Data Categorization and Refinement for Application in Longitudinal Neighborhood Health Research: a Methodology. J Urban Health 98, 271–284 (2021). https://doi.org/10.1007/s11524-020-00482-2

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