Evidence map›Paper›PMID 36001362›Full record

ArticleJMIR public health and surveillance2022

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison.

Ross Gore, Christopher J Lynch, Craig A Jordan, Andrew Collins, R Michael Robinson, Gabrielle Fuller, Pearson Ames, Prateek Keerthi, Yash Kandukuri

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ross GoreVirginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States.ORCID 0000-0003-4065-6146
Christopher J LynchVirginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States.ORCID 0000-0002-4830-7488
Craig A JordanVirginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States.ORCID 0000-0003-3892-7701
Andrew CollinsEngineering Management & Systems Engineering, Old Dominion University, Norfolk, VA, United States.ORCID 0000-0002-8012-2272
R Michael RobinsonVirginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States.ORCID 0000-0001-5295-930X
Gabrielle FullerDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States.ORCID 0000-0002-2186-8162
Pearson AmesDepartment of Economics, Chapman University, Orange, CA, United States.ORCID 0000-0002-6575-1679
Prateek KeerthiHampton Roads Biomedical Research Consortium, Norfolk, VA, United States.ORCID 0000-0001-9901-5461
Yash KandukuriVirginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States.ORCID 0000-0001-9053-736X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

PedestriansAccidents, TrafficBicyclingCensus TractCitiesHumansbicycle pathsbicyclingdata analysisdiabetesdigital neighborhoodsfactor analysishigh blood pressurepedestrian pathsphysical healthwalking

Identifiers

PMID36001362
PMCPMC9453587

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.