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Environmental Health Economic Analysis Annotated Bibliography

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Details

Research article
Authors

Nguyen QC, Khanna S, Dwivedi P, Huang D, Huang Y, Tasdizen T, Brunisholz KD, Li F, Gorman W, Nguyen TT, Jiang C

Journal

Preventive Medicine Reports

Summary
This research article uses Google Street View images and computer vision to understand the correlation between constructed indicators of urban development and county-level chronic disease. The authors used regression models to estimate these associations. This study suggests that indicators of built environments may be connected with lower chronic disease and decreased premature mortality but there is a modest increase in excessive drinking. This study points out the need for more equity in health resources across the country.
Population

Not available

Health Outcomes

  • Obesity, diabetes, premature mortality

Health Outcome List:

  • Not available

Environmental Agents

List of Environmental Agents:

  • Not available

Source of Environmental Agents:

  • Built environment

Economic Evaluation / Methods and Source

Type:

  • Cost-utility analysis (CUA)

Cost Measures:

  • Not available

Potential Cost Measures:

  • Not available

Benefits Measures:

  • Increasing neighborhood built environments can increase public health

Potential Benefits Measures:

  • Not available

Location:

  • United States

Models Used:

  • Regression models
  • PostgreSQL

Models References:

  • Reference cited in publication - PostgreSQL (https://postgis.net/)

Methods Used:

  • The authors — 1) used national road network data to create a database representative of all street intersections in the US, which included latitude and longitude coordinates; 2) used Google Street View Application Programming Interface (API) to obtain images of the intersections; 3) ran all 16,171,605 images through image data processing to detect pre-defined items such as highways, rural areas and grasslands; 4) used ArcGIS Desktop software to create choropleth maps; and 5) pulling health data from the US Census and county wide censuses, the authors created linear regression models to accociate chronic diseases, premature mortality, and other health outcomes with the built environment.

Sources Used:

  • Google Street View (GSV) API from December 15, 2017- May 14, 2018 images; 2017 Census Topologically Integrated Geographic Encoding and Referencing data set; US Census; 2018 County health rankings; National Vital Statistics system (2014-2016); 2014 Behavioral Risk Fsctor Surveillance System (BRFSS); Disease Control and Prevention's (CDC) 500 Cities project; 2016 U.S. Census TIGER/Line Shapefiles; 2010-2014 American Community Survey; Additional sources cited in the publication.

Economic Citation / Fundings

Citation:

  • Nguyen QC, Khanna S, Dwivedi P, Huang D, Huang Y, Tasdizen T, Brunisholz KD, Li F, Gorman W, Nguyen TT, Jiang C. Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes. Preventive Medicine Reports. 2019. 14.
  • Pubmed
  • DOI

NIEHS Funding:

  • K01ES025433

Other Funding:

  • K99MD012615, P2CHD041041, UL1TR002538, R01LM012849