Pathway analysis of relationships among community development, active travel behavior, body mass index, and self-rated health

Li, Xiaobing; Hu, Qinglin; Liu, Jun; Nambisan, Shashi; Khattak, Asad J.; Lidbe, Abhay; Lee, Hee Yun

doi:10.1080/15568318.2021.1872123

Cited by 10 publications

(7 citation statements)

References 42 publications

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“…Marginal effects represent the estimated changes in predictions for the dependent variable when there is a change in an independent variable (one unit value change for continuous variables or a change of categories for categorical variables) while all other variables are held constant ( Williams, 2012 , Liu et al, 2015 , Liu and Khattak, 2017 , Liu and Khattak, 2018 ). Marginal effects are often calculated after model estimation to show how predictors associate with the dependent variable in the model ( Fu et al, 2022 , Li et al, 2022 , Liu et al, 2016 , Liu et al, 2021 , Zhang et al, 2022 ). Different methods of calculating the marginal effects of machine learning models were provided by literature, such as the marginal effect at the mean ( Silva Filho et al, 2021 , Sun et al, 2020 ), the marginal effect at the representative value ( Silva Filho et al, 2021 ), and partial dependence ( Molnar, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Nie

et al. 2023

Travel Behaviour and Society

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Section: Methodsmentioning

confidence: 99%

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Nie

et al. 2023

Travel Behaviour and Society

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“…Individuals' self-reported health was assessed using a five-point scale from "poor" to "excellent." This scale is a robust predictor of mortality and morbidity (Subramanian et al, 2010) and has been previously applied in transport studies (e.g., Li et al, 2021). We then merged the responses into three new categories for further analysis: "poor and fair," "good," and "very good and excellent."…”

Section: Sample and Data Measurementmentioning

confidence: 99%

Dockless bike-sharing’s impact on mode substitution and influential factors: Evidence from Beijing, China

Chen¹,

Lierop²,

Ettema³

2022

JTLU

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As a newly emerged bike-sharing system, dockless bike-sharing has the potential to positively influence urban mobility by encouraging active cycling and drawing users from car, public transit and walking. However, scant empirical research explores the extent to which dockless bike-sharing replaces other travel modes for different travel purposes. There is a lack of knowledge about how dockless bike-sharing users’ personal characteristics and neighborhood environment features influence their mode substitution behaviors. Using survey data collected from residents in Beijing and geodata of land use and public transit, we conduct four multinomial logistic models to explore potential mode-substitution behaviors influenced by dockless bike-sharing for four travel purposes: work or education commuting, sports and leisure, grocery shopping, and recreational activities such as shopping, eating and drinking. The results indicate that, for the majority of respondents, dockless bike-sharing systems potentially substitute for walking or public transit. In addition, our analysis of travel attitudes points out that dockless bike-sharing not only attracts bicycle lovers but also users with a preference or positive attitude toward other travel modes. The positive association between the length of bicycle paths and the likelihood of potentially replacing public transit or motorized vehicles by dockless bike-sharing also reveals that the cycling infrastructure of residential neighborhood could be an important facilitator for users of public transit and motorized vehicles to switch to dockless bike-sharing systems.

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“…Geographic healthcare access (GHA) (defined as the degree to which healthcare services are spatially available to geographically defined groups) to primary care and hospital-based care is crucial for communities to care for their health 11–13 and is a predictor of healthcare utilization 14–16 . A significant proportion of Latinos/as/x who migrated and settled in southern US states at the beginning of the 21st century live in rural areas where GHA to primary healthcare services and hospitals often is inadequate 17,18 .…”

Section: Latinos/as/x In Alabamamentioning

confidence: 99%

Geographic Access to Primary Healthcare Services among Latinos/as/x in Western Alabama

Morales-Alemán

Ferreti

et al. 2022

Southern Medical Journal

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Objectives: Alabama's Latino/a/x population grew 278% from 2000 to 2018. Tuscaloosa County, located in the largely rural region of western Alabama, also experienced a significant influx of Latino/as/x during this time frame. Geographic healthcare access (GHA) to primary care and hospitals is crucial for immigrant Latino/as/x to care for their health, but few studies have characterized it. The goals of this article were to describe the availability (defined as number of provider locations) and accessibility (defined as travel impedance between potential patients and provider locations) of primary healthcare services and to discuss potential strategies to address these healthcare access challenges.Methods: We drew data from the US Census Bureau, American Community Survey 5-year estimates, Blue Cross Blue Shield national doctor and hospital finder database, the Alabama Department of Public Health, and Tuscaloosa Transit Authority. We used geographic data, geographic information systems, and spatial analyses to characterize the availability and accessibility of primary care services and hospitals for Latinos/as/x in Tuscaloosa County using ESRI, ArcGIS 10.6.1. We showed the distribution of Latinos/as/x by census tract with choropleth mapping and mapped primary healthcare providers alongside public transit routes and hospital driving times to support our findings.Results: This work demonstrated that Latinos/as/x in Tuscaloosa County were concentrated in more rural areas surrounding the county's city center, presenting significant barriers to GHA. These areas had fewer primary care providers and limited public transit. Many Latinos/as/x in this county had to travel ≥45 minutes to a hospital.Conclusions: Outreach and technology-based approaches, including home visit programs, mobile health units, and telemedicine, may be particularly important in bridging the GHA gaps for this and other largely rural populations the southeastern United States. Some of this potential was unlocked during the coronavirus disease 2019 crisis. These gains should be leveraged toward sustainable healthcare access initiatives for rural Latino/a/x populations.

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Pathway analysis of relationships among community development, active travel behavior, body mass index, and self-rated health

Cited by 10 publications

References 42 publications

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Dockless bike-sharing’s impact on mode substitution and influential factors: Evidence from Beijing, China

Geographic Access to Primary Healthcare Services among Latinos/as/x in Western Alabama

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