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 particular problem for individuals living in rural areas or those who have a lower socioeconomic status (Arcury et al., 2005, U.S. 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 barriers to transportation in an effort to improve care coordination and access to primary care and reduce health disparities (Henning-Smith et al., 2017Bayne et al., 2019), including creative partnerships between ridesharing services, health care organizations and insurance companies.

Analyzing the Data

The purpose of this analysis is to determine if there is a relationship between access to transportation and health status.

This data is also available in a text-based table format.

Tutorial: Using the Data

Data Sources

U.S. Census, 2017 American Community Survey (data released 2019)

County Health Ranking (data released 2020)

Defining the Columns

A blank entry indicates unreported data. A value of zero is a defined value 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 2019 FIPS Codes for Counties and County Equivalent Entities.

Poor or Fair Health: Self-reported health status is a general measure of health-related quality of life (HRQoL) in a population. Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2017.

Poor Physical Health Days: The average number of days a county's adult respondents report that their physical health was poor in the past 30 days. Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2017.

Poor Mental Health Days: The average number of days a county's adult respondents report that their mental health was poor in the past 30 days. Age-adjusted data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey from 2017.

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 2016.

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 2018 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 2018 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 2018 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 2018 American Community Survey.

Long Commute: The percentage of the county's working population (age 16 and over) who reported having a commute to work of 60 minutes or longer. Data is from the 2018 American Community Survey.

This project is/was supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number UB1RH24206, Information Services to Rural Hospital Flexibility Program Grantees, $1,009,121 (0% financed with nongovernmental sources). This information or content and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.