Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison
BackgroundAdding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-08-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2022/8/e37379 |
| _version_ | 1827858093022642176 |
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| author | Ross Gore Christopher J Lynch Craig A Jordan Andrew Collins R Michael Robinson Gabrielle Fuller Pearson Ames Prateek Keerthi Yash Kandukuri |
| author_facet | Ross Gore Christopher J Lynch Craig A Jordan Andrew Collins R Michael Robinson Gabrielle Fuller Pearson Ames Prateek Keerthi Yash Kandukuri |
| author_sort | Ross Gore |
| collection | DOAJ |
| description |
BackgroundAdding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions.
ObjectiveOur work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?”
MethodsWe conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes.
ResultsWe show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%).
ConclusionsWe propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics. |
| first_indexed | 2024-03-12T12:49:54Z |
| format | Article |
| id | doaj.art-bf35fd8a354a4931a88acb872933235d |
| institution | Directory Open Access Journal |
| issn | 2369-2960 |
| language | English |
| last_indexed | 2024-03-12T12:49:54Z |
| publishDate | 2022-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Public Health and Surveillance |
| spelling | doaj.art-bf35fd8a354a4931a88acb872933235d2023-08-28T22:56:46ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602022-08-0188e3737910.2196/37379Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model ComparisonRoss Gorehttps://orcid.org/0000-0003-4065-6146Christopher J Lynchhttps://orcid.org/0000-0002-4830-7488Craig A Jordanhttps://orcid.org/0000-0003-3892-7701Andrew Collinshttps://orcid.org/0000-0002-8012-2272R Michael Robinsonhttps://orcid.org/0000-0001-5295-930XGabrielle Fullerhttps://orcid.org/0000-0002-2186-8162Pearson Ameshttps://orcid.org/0000-0002-6575-1679Prateek Keerthihttps://orcid.org/0000-0001-9901-5461Yash Kandukurihttps://orcid.org/0000-0001-9053-736X BackgroundAdding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. ObjectiveOur work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” MethodsWe conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. ResultsWe show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). ConclusionsWe propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics.https://publichealth.jmir.org/2022/8/e37379 |
| spellingShingle | Ross Gore Christopher J Lynch Craig A Jordan Andrew Collins R Michael Robinson Gabrielle Fuller Pearson Ames Prateek Keerthi Yash Kandukuri Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison JMIR Public Health and Surveillance |
| title | Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison |
| title_full | Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison |
| title_fullStr | Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison |
| title_full_unstemmed | Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison |
| title_short | Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison |
| title_sort | estimating the health effects of adding bicycle and pedestrian paths at the census tract level multiple model comparison |
| url | https://publichealth.jmir.org/2022/8/e37379 |
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