Transportation and Health Status

Purpose

This analysis aims to determine if there is a relationship between access to transportation and health status.

A lack of access to transportation is an example of structural inequity preventing health access (Syed et al., 2013, Rozenfeld et al., 2020). Lack of transportation is a problem for individuals living in rural areas or with lower socioeconomic status (Arcury et al., 2005, US Dept Transportation, 2019). According to estimates, approximately 25% of lower-income patients cancel or miss appointments due to barriers to transportation (Syed et al., 2013). There is a growing focus on addressing transportation barriers to improve care coordination and access to primary care and reduce health disparities (Henning-Smith et al., 2017, Bayne et al., 2019), including creative partnerships between ridesharing services, health care organizations, and insurance companies.

This scenario includes benchmarks for each appropriate variable. State-level benchmarks are accessible by filtering by state. Scenarios with multiple outcome variables require filtering by the specific outcome to return the corresponding benchmarks.

This data is also available as an Excel Spreadsheet.

Transportation Health Status 2022.xlsx (374.24 KB)

Tutorial Video
Data Sources
Defining The Columns

In this tutorial video, we look at Transportation and Health Status. The video guides you through how to use Tableau data analysis to determine if there is a relationship between access to transportation and health status.

Transportation and Health Status Tutorial Video

U.S. Census, 2017 American Community Survey (data released 2021)

County Health Ranking (data released 2022)

A blank entry indicates unreported data. A value of zero is defined and does not represent unreported data.

State: The abbreviated name of the state where the county is located.

County: The name of the county where the information was collected. County names are listed as provided on the United States Census Bureau's list of 2020 FIPS Codes for Counties and County Equivalent Entities.

Poor or Fair Health: Self-reported health status is a general measure of a population's health-related quality of life (HRQoL). Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2018.

Poor Physical Health Days: The average number of days a county's adult respondents report poor physical health in the past 30 days. Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2018.

Poor Mental Health Days: The average number of days a county's adult respondents report their mental health was poor in the past 30 days. Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2018.

Physical Inactivity: The percentage of adults age 20 and over reporting no leisure-time physical activity. Data is from the Centers for Disease Control and Prevention (CDC) Diabetes Interactive Atlas from 2017.

No Vehicle Available: The percentage of the county's working population (age 16 and over) who reported having no vehicle available to commute to work. Data is from the 2019 American Community Survey.

Drove Alone to Work: The percentage of the county's working population (age 16 and over) who reported driving alone to commute to work. Data is from the 2019 American Community Survey.

Carpool to Work: The percentage of the county's working population (age 16 and over) who reported carpooling on their commute to work. Data is from the 2019 American Community Survey.

Worked at Home: The percentage of the county's working population (age 16 and over) who reported working at home. Data is from the 2019 American Community Survey.

Long Commute: The percentage of the county's working population (age 16 and over) who reported commuting to work for 60 minutes or longer. Data is from the 2019 American Community Survey.