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Published in final edited form as: J Appl Gerontol. 2018 Oct 26;39(9):935–943. doi: 10.1177/0733464818806834

Driving Status and Transportation Disadvantage Among Medicare Beneficiaries

Miriam Ryvicker 1, Evan Bollens-Lund 2, Katherine A Ornstein 2
PMCID: PMC6486463  NIHMSID: NIHMS1017736  PMID: 30362863

Abstract

Transportation disadvantage may have important implications for the health, well-being, and quality of life of older adults. This study used the 2015 National Health Aging Trends Study, a nationally representative study of Medicare beneficiaries aged 65 and over (N = 7,498), to generate national estimates of transportation modalities and transportation disadvantage among community-dwelling older adults in the United States. An estimated 10.8 million community-dwelling older adults in the United States rarely or never drive. Among nondrivers, 25% were classified as transportation disadvantaged, representing 2.3 million individuals. Individuals with more chronic medical conditions and those reliant on assistive devices were more likely to report having a transportation disadvantage (p < .05). Being married resulted in a 50% decreased odds of having a transportation disadvantage (p < .01). Some individuals may be at higher risk for transportation-related barriers to engaging in valued activities and accessing care, calling for tailored interventions such as ride-share services combined with care coordination strategies.

Keywords: transportation, access to care, service utilization, care coordination

Introduction

Inadequate access to transportation is recognized as a significant barrier to older adults’ social participation, utilization of services in the community, well-being, and quality of life (Chihuri et al., 2016; Dickerson, Molnar, Bedard, Eby, Classen, & Polgar, 2017; Mezuk & Rebok, 2008; Wallace, Hughes-Cromwick, Mull, & Khasnabis, 2005). The concept of transportation disadvantage has been used by state and municipal governments in the United States to identify vulnerable populations who may experience transportation barriers in getting to work, medical appointments, groceries, social activities, and other vital activities (The Florida Legislature, 2016; Lane, Bert, & Heller, 2014). Although definitions vary (Wallace et al., 2005), transportation disadvantage occurs when mobility needs are not being met, due to disability, low income, or social and environmental factors.

Several factors are associated with increased transportation disadvantage. Low-income individuals are at greater risk of missing medical appointments due to transportation problems (Hughes-Cromwich & Wallace, 2006). Individuals with low income and without personal vehicles may be at a particular disadvantage if they live in areas where the supply of public transit or paratransit services is inadequate in meeting the demand (U.S. Department of Transportation & Bureau of Transportation Statistics, 2003b). Residents of rural areas—especially older adults—are at particular risk due to a lack of public transportation (Long et al., 2013; Narva & Sequist, 2010; Probst, Laditka, Wang, & Johnson, 2007; Rosenbloom, 2003). Racial minorities are also more likely to have a transportation disadvantage (Hughes-Cromwich & Wallace, 2006; King, Chen, Dagher, Holt, & Thomas, 2015; Peipins et al., 2011; Probst et al., 2007). Health status affects a person’s ability to obtain transportation. Research from the 2001 National Household Travel Survey indicated that 9% of Americans ages 14 and over have a medical condition that limits their travel (U.S. Department of Transportation & Bureau of Transportation Statistics, 2003b). Almost 2 million Americans with disabilities never leave their homes (U.S. Department of Transportation & Bureau of Transportation Statistics, 2003a).

Ability to drive is a primary focus of transportation research in the United States, with the main focus on safety issues related to decline in functional, visual, and cognitive status as people age, risk factors for driving cessation, and the transition to nondriving status (Bird et al., 2017; Dickerson, Meuel, Ridenour, & Cooper, 2014; Ross, Freed, Edwards, Phillips, & Ball, 2017; Vivoda, Heeringa, Schulz, Grengs, & Connell, 2017). As the predominant form of daily transportation in the United States, driving fulfills a variety of needs for older adults, such as facilitating social engagement and a need for independence and self-identity, as well as practical needs such as shopping and medical appointments (Chihuri et al., 2016; Sanford et al., 2018). There is also growing interest in expanding access to alternative transportation for older adults such as paratransit services, specialized transportation and shuttle services, and on-demand ride-share services such as Uber (Chaiyachati et al., 2018; Dickerson, Molnar, Bedard, Eby, Berg-Weger, et al., 2017; MacLeod et al., 2015; Vivoda, Harmon, Babulal, & Zikmund-Fisher, 2018). Volunteer driver programs also fill a gap for individuals living in areas where the supply of paratransit or on-demand ride services is lacking, as well as for individuals with physical or cognitive limitations (Dickerson, Molnar, Bedard, Eby, Berg-Weger, et al., 2017).

Although a less common focus of research, the use of public transportation among older adults is increasing (Lynott & Figueiredo, 2011). Moreover, public transit may be especially important for the mobility of those who never drove throughout their adult lives. While only 2% of older adults reported never driving in 2008, this group had a higher proportion of women, racial/ethnic minorities, immigrants, and individuals with less education and wealth than older adults with a history of driving (M. Choi & Mezuk, 2013).

Older adults may experience changes in transportation use in a variety of ways, depending on their physical and cognitive health, social support, economic resources, and the geographic contexts in which they live. Prior research has identified health factors that increase the risk for driving restriction or cessation, including cognitive impairment, vision loss, diabetes, and heart failure (Croston, Meuser, Berg-Weger, Grant, & Carr, 2009; Dugan & Lee, 2013; Keay et al., 2009; Kowalski et al., 2012; Seiler et al., 2012; Sims et al., 2011; van Landingham et al., 2013). Driving cessation represents a significant life transition that challenges social participation and preservation of self-identity (Sanford et al., 2018). In addition, driving cessation is linked to an increased risk of depression and nursing home admission (Chihuri et al., 2016; Fonda, Wallace, & Herzog, 2001; Freeman, Gange, Munoz, & West, 2006; Ragland, Satariano, & MacLeod, 2005; Windsor, Anstey, Butterworth, Luszcz, & Andrews, 2007). Given the impact of driving cessation on health and well-being, it is important to consider how social support and other factors could moderate the experience of mobility changes (Silverstein & Turk, 2016). A recent study found that the use of public transportation moderated the impact of driving cessation on well-being in a sample of older adults with vision loss and their social partners, although the impact was more evident among the partners than the ex-drivers (Schryer, Boerner, Horowitz, Reinhardt, & Mock, 2017). A qualitative study on driving cessation among older adults with dementia found that caregivers made efforts to engage the individuals for whom they provide care in meaningful social roles and activities to mitigate the negative emotional impact of driving cessation (Sanford et al., 2018).

Whether brought about by driving cessation, inadequate access to alternative transportation, or a lack of social support, transportation barriers represent a significant risk to older adults’ health and well-being. Given the rapid growth of the aging population—and the functional, visual, and cognitive impairments experienced by many individuals as they age—a comprehensive program of research is needed to understand and address the transportation barriers among older adults at risk for unmet needs for services (Dickerson et al., 2014). Establishing current national estimates of the prevalence of transportation disadvantage among older adults is a key first step to addressing this issue. We use data from the National Health and Aging Trends Study (NHATS; Johns Hopkins School of Public Health & Westat, 2015) to achieve the following aims: (a) generate national estimates of the modes of transportation used by older community-dwelling adults in the United States,; (b) generate national estimates of nondriving older adults with a transportation disadvantage, and (c) identify factors associated with having a transportation disadvantage.

Design and Method

Sample

Data are drawn from the 2015 wave of the NHATS (Kasper & Freedman, 2014), a population-based survey of late-life disability trends and trajectories. NHATS drew a random sample of individuals ages 65 years and older living in the contiguous United States from the Medicare enrollment file on September 30, 2010, with oversampling of those over age 90 and non-Hispanic Blacks. The enrollment file represents 96% of all older adults in the United States. In-person interviews were completed between May and November 2011 and yielded a sample of 8,245 persons, a 71% response rate. Individuals are followed annually and in 2015 the cohort was replenished. Study participants were asked detailed questions about how they performed daily activities in the month before the interview as well as their medical comorbidities, socioeconomic status, and home environment. Among older adults who received assistance with daily activities, information about who provides help, their relationship with the respondent, and what specific assistance they provide was obtained. Our analytic sample included 7,498 community-dwelling participants who reported on their driving status.

Measures

Driving frequency was determined based on report (self or via proxy) of how often participants drove themselves places in the last month (every day, most days, some days, rarely or never). Nondrivers were those who stated they never drove in the last month. Drivers reported whether there were driving situations they avoided (nighttime, bad weather, alone, or highways). Participants also reported on their use of nondriving modes of transportation to get to places in the last month (walked, got ride, taxi, public transportation, van services) and how they got to their regular doctor in the last year. We defined transportation disadvantage as whether the person was unable to participate in social activities due to a transportation problem over the last month; these activities included attending religious services, clubs, classes or other groups, visiting friends or family, or going out for enjoyment (e.g., dinner or a movie).

Older adults’ demographic characteristics included age, gender, race, education, marital status, income, and living arrangements. Clinical data were based on self-report and included whether a doctor had ever told a subject that they had specific health conditions. We created a count of 13 self-reported chronic conditions to reflect multimorbidity: heart attack, heart disease (including angina, congestive heart failure), high blood pressure, arthritis, osteoporosis, diabetes, lung disease, stroke, dementia/Alzheimer’s disease (AD), cancer, depression, anxiety, and broken or fractured hip. Dementia status was based on criteria established by NHATS (Kasper, Freedman, & Spillman, 2013), which incorporated self-report of dementia, the AD-8 screening tool (Galvin et al., 2005), and a cognitive interview that assessed memory, orientation, and executive function. Study participants are asked whether they receive help with basic (eating, getting out of bed, showering, toileting, dressing) and instrumental (laundry, shopping, meal preparation, medication management, getting around outside, bills, and banking) activities of daily living in the month before the interview.

Analysis

We used NHATS sample weights to generate national estimates of driving status, alternate modes of transportation used for general activities, and the presence of transportation disadvantage (Freedman & Spillman, 2016). We examined transportation disadvantage among nondrivers, as the questions about transportation problems were asked of nondrivers only. We compared the demographic, clinical, and functional characteristics of nondrivers who did and did not report a transportation disadvantage. We also compared the modes of transportation used to get to doctor’s appointments by transportation disadvantage. To identify predictors of transportation disadvantage, we estimated a multivariable logistic regression model. The demographic, clinical, and functional measures included in the model reflect an adaptation of the behavioral model of health service use (Andersen, 1995; Andersen et al., 2002) to frame the potential array of factors that might inform how older adults use transportation. We examined potential determinants of transportation disadvantage—namely, predisposing factors (e.g., age, sex, race), need factors (e.g., number of medical conditions, ADL function, and cognition), and enabling factors (e.g., income, education level, use of assistive devices). To determine which variables would be included in the final model, bivariate associations between transportation disadvantage and factors were assessed. Variables that showed a statistically significant effect on the outcome at the 0.10 level and were not highly correlated with other variables (correlation >0.5) in the bivariate analysis were included in the final model. In our regression model, we excluded 157 individuals with missing values. To control for possible biases associated with the use of proxy report, we performed regressions in sensitivity analyses that controlled for proxy status and that excluded all proxy respondents. We also performed analyses that controlled for the importance of engaging in the social activities asked. All analyses were conducted using Stata version 15.

Our analysis used the public use files of the NHATS, which was conducted by Johns Hopkins University. The Johns Hopkins University Institutional Review Board approved the NHATS protocol, and all participants provided informed consent (Johns Hopkins School of Public Health & Westat, 2015).

Results

General Transportation Use

The majority of participants drove themselves to places two or more times per week, whereas 25% rarely or never drove (Table 1). This nondriving group represents an estimated 10.8 million community-dwelling older adults in the United States. One third of all individuals who drove in the previous month reported that they avoided driving alone, at night, on highways and/or in bad weather, representing an additional 10.9 million older adults. Alternative modes of transportation used by driving status are shown in Table 2. Among participants who never drove within the past month (heretofore referred to as “nondrivers”), the most common modes of transportation were getting a ride from a family member or friend (86%, or 7.6 million older adults) and walking (49%, or 4.3 million), followed by 17% using public transit (1.5 million), 10% taxis and 13% van/shuttle for seniors (participants could check multiple response options). Drivers also made use of alternate forms of transportation. More than half of drivers (52%, 17.6 million individuals) reported walking, one third got rides from family or friends, representing 11 million older adults, and 6% used public transportation, representing 2.1 million individuals.

Table 1.

Driving Frequency and Avoidance Among Community-Dwelling Older Medicare Beneficiaries, 2015 (N = 7,498).

N Weighted % National estimate
Driving Frequency
 Every day (7 days a week) 2,452 39.44 16,879,925
 Most days (5-6 days a week) 1,580 23.74 10,159,028
 Some days (2-4 days a week) 922 11.67 4,994,034
 Rarely (<1 day a week) 296 3.72 1,590,661
 Never 2,248 21.42 9,169,304
Avoids driving alone/at night/in weathera 2,027 32.31 10,866,924
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a

Among those who reported driving in the past month.

Table 2.

Modes of Transportation Other Than Driving Used by Older Adults.

Drivers (N = 5,250)
 Nondrivers (N = 2,248)
N Weighted % National estimate N Weighted % National estimate
Walked 2,572 52.47 17,632,258 950 48.99 4,335,546
Got ride from family/friends 1,855 33.00 11,084,317 1,896 85.77 7,583,178
Van or shuttle provided by place of residence 34 0.55 184,160 141 6.90 611,697
Van or shuttle for seniors 68 0.87 293,586 297 12.62 1,116,936
Public transportation 297 6.12 2,058,575 317 17.04 1,509,455
Taxi 173 3.53 1,186,731 206 10.30 912,201
Other 338 7.66 2,574,840 57 3.68 325,983
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Note. Multiple modes of transportation may be reported per participant.

Transportation Disadvantage

Approximately one quarter of the nondrivers reported a transportation disadvantage, representing an estimated 2.3 million community-dwelling older adults nationally. Table 3 reports the characteristics of the nondriving sample for those with and without transportation disadvantage. Relative to those without any reported disadvantage, disadvantaged individuals were slightly older (mean age 80.0 vs. 79.3), more likely to be unmarried (78% vs. 62%), White (67% vs. 61%), and more educated (69% vs. 63% >High school). The disadvantaged group had a greater proportion receiving help with at least one instrumental activities of daily living (IADL; 66% vs. 59%), using assistive devices for ADLs (90% vs. 80%), and a lower proportion with probable dementia (22% vs. 30%). Transportation disadvantage was not associated with living in a metropolitan area or geographic region.

Table 3.

Sample Characteristics Among Nondriving Older Adults, by Transportation Disadvantage.

All Disadvantaged No disadvantage p value
n 2,248 581 1,667
Estimate 9,177,518 2,346,978 6,830,540
Age at interview or death, mean 79.44 79.98 79.26 0.21
Age category, %
 Age <75 33.22 32.20 33.58 0.68
 Age 75-84 35.79 32.59 36.89 0.15
 Age 85 + 30.99 35.21 29.54 0.04*
Female, % 70.98 77.44 68.76 0.00**
Race, %
 White Non-Hispanic 62.63 67.15 61.05 0.07
 Black Non-Hispanic 14.61 12.67 15.28 0.08
 Other (American Indian/Asian/Native Hawaii) 7.11 8.25 6.72 0.50
 Hispanic 15.65 11.93 16.95 0.03*
Income category, %
 <$15,000 38.23 42.46 36.78 0.06
 $15,000-$29,999 30.81 31.97 30.40 0.53
 $30,000-$59,999 19.63 16.34 20.76 0.03*
 >$60,000 11.33 9.24 12.05 0.21
Medicaid, % 32.59 33.34 32.33 0.74
Education level, %
< High School 35.34 31.33 36.76 0.02*
 High School/GED 30.78 27.77 31.84 0.12
 Some college 20.57 26.35 18.52 0.00**
 ⩾ Bachelors 13.31 14.54 12.88 0.42
Marital status, %
 Married or living with partner 34.08 21.98 38.24 0.00**
 Separated, divorced, or widowed 59.57 73.21 54.89 0.00**
 Never married 6.16 4.81 6.62 0.26
Residential care, excluding nursing home, % 15.40 16.26 15.10 0.57
Lives in metropolitan area, % 85.95 85.52 86.09 0.80
Geographic region, %
 Northeast 24.48 22.55 25.14 0.25
 Midwest 17.09 17.04 17.11 0.98
 South 35.57 37.50 34.91 0.28
 West 22.86 22.92 22.84 0.98
Help with 1 + ADL, % 42.45 43.42 42.12 0.59
Help with 1 + IADL, % 61.04 66.13 59.30 0.02*
Uses any assistive device for ADLs, % 82.87 89.85 80.47 0.00**
Count of self-reported medical conditions, mean 3.86 4.22 3.74 0.00**
Count of self-reported medical conditions, category, %
 0-1 self-reported conditions 12.87 8.79 14.27 0.02*
 2-4 self-reported conditions 49.98 48.16 50.61 0.52
 5+ self-reported conditions 37.15 43.05 35.12 0.01*
Selected chronic conditions (self-reported), %
 Diabetes 34.93 38.70 33.64 0.12
 Ever had heart attack 18.62 20.82 17.87 0.17
 Lung disease 21.62 23.44 21.00 0.23
 High blood pressure 73.92 77.08 72.84 0.09
Probable dementia, % 28.26 22.14 30.35 0.01**
Number in social network (max. 5), mean 2.07 2.16 2.04 0.14
Had someone sit in with them on doctor’s visits, % 62.11 60.84 62.55 0.46
Responded to NHATS via proxy, % 16.01 10.07 18.06 0.00**
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Note. GED = General Educational Development; ADL = activities of daily living; IADL = Instrumental activities of daily living; NHATS = National Health and Aging Trends Study.

*

p < .05.

**

p < .01.

Modes of Transportation to the Doctor by Transportation Disadvantage

Modes of transportation to the doctor differed by transportation disadvantage (Table 4). A lower proportion of those with disadvantage relied on family or friends for a ride (60% vs. 66%). Of the 2.3 million older adults estimated to have a transportation disadvantage, 1.4 million (60%) relied on family or friends for a ride to doctor’s appointments, while roughly 253,000 relied on a van or shuttle service for seniors.

Table 4.

Mode of Transportation to the Doctor Among Nondrivers, by Transportation Disadvantage (N = 2,248).

No transportation disadvantage
 Transportation disadvantage
% Population estimate % Population estimate
No regular doctors visit reported 5.69 385,649 5.57 130,447
Got ride 66.26 4,492,203 60.38 1,413,787
Van/Shuttle from home or for seniors 9.92 672,659 10.78 252,529
Public transit 5.78 391,777 4.21 98,494
Taxi 1.85 125,454 4.20 98,236
Walked 4.31 292,278 1.95 45,640
Home visit 3.13 211,977 6.62 154,939
Other 1.60 108,330 3.94 92,365
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Predictors of Transportation Disadvantage

Table 5 reports the results of a logistic regression examining predictors of transportation disadvantage among nondrivers. Controlling for age, sex, race, income, education levels, functional status and other factors, individuals who were married or lived with a partner had a lower odds of transportation disadvantage, OR = 0.46; 95% confidence interval (CI) = [0.34, 0.63]. Black participants had a lower odds of transportation disadvantage (OR=0.72; 95% CI = [0.52, 0.995]). The odds of transportation disadvantage increased with each additional self-reported medical condition (OR = 1.10; 95% CI = [1.03, 1.17]) and with the use of any assistive devices for ADLs (OR = 1.64; 95% CI = [1.10, 2.46]). Individuals with probable dementia had a lower odds of transportation disadvantage (OR = 0.53; 95% CI = [0.36, 0.76]). Age, sex, education level, income, and receiving help with ADLs and IADLs were not significant predictors of transportation disadvantage in the multivariable analysis.

Table 5.

Predictors of Transportation Disadvantage Among Nondriving Older Adults (N = 2,091).

Odds ratio 95% CI
Age category (ref = 74 and younger)
 75-84 0.812 [0.567, 1.164]
 85 + 0.937 [0.650, 1.352]
Female 1.235 [0.895, 1.706]
Race (ref = White)
 Black non-Hispanic 0.721 [0.522, 0.995]*
 Hispanic 0.748 [0.455, 1.229]
Education high school or greater 1.169 [0.880, 1.551]
Income less than US$15,000 1.065 [0.785, 1.445]
Married or living with partner 0.462 [0.337, 0.633]**
Probable dementia 0.525 [0.364, 0.760]**
Receives help with 1 + ADL 0.907 [0.692, 1.188]
Receives help with 1 + IADL 1.281 [0.902, 1.820]
Uses any assistive device for ADLs 1.644 [1.100, 2.456]*
Count of self-reported medical conditions 1.096 [1.030, 1.165]**
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Note. CI = confidence interval; Ref = reference group; ADL = activities of daily living; IADL = Instrumental activities of daily living.

*

p < .05.

**

p < .01.

Discussion

This study estimated that 2.3 million nondriving, community-dwelling older adults in the United States have a transportation disadvantage. This group relies more heavily on transportation from family and friends to attend medical appointments, potentially placing them at greater risk for transportation barriers in accessing medical care. This risk may be of particular concern for those with weaker social support systems. These findings are consistent with a prior NHATS study which found an association between relying on family and friends for rides and restriction in social activities, especially among those with Medicaid (Lehning, Kim, Smith, & Choi, 2018). Our findings expand upon this work by establishing national estimates of transportation disadvantage, and by examining socioeconomic and geographic correlates of transportation disadvantage. In the multivariable model, independent predictors of transportation disadvantage included being unmarried and the use of an assistive device for ADLs.

It is also noteworthy that an estimated 10.9 million older adults who drive at least occasionally avoid driving alone, at night, on highways, and/or in bad weather. As NHATS did not administer the transportation barrier questions to drivers, it is not possible to estimate how many of these 10.9 million driving-avoidant individuals experience a transportation disadvantage using our current measure. Nevertheless, this group may be at risk for a transportation disadvantage that could affect their ability to engage in social activities and/or access medical care and other services in the community. Prior research suggests that avoiding challenging driving situations is part of a gradual process of self-regulation in which many drivers transition to driving cessation (Dickerson, Molnar, Bedard, Eby, Berg-Weger, et al., 2017; Molnar et al., 2013).

It is worth noting that we found an unexpected inverse association between dementia status and transportation disadvantage. Participants with probable dementia had significantly lower odds of disadvantage in our adjusted model. Because individuals with cognitive impairment often rely on proxy report, in a sensitivity analysis we included proxy status in our model to account for the potential subjectivity of the proxy respondents and limited our analysis to nonproxy survey respondents (data not shown). The persistent protective effect of dementia status against transportation disadvantage is unexpected and warrants further investigation, in light of previous research on the relationship between dementia and driving, including the onset of driving difficulties throughout the AD trajectory and the transition to nondriving status (Brown & Ott, 2004; Roe et al., 2017; Stout et al., 2018; Velayudhan et al., 2018).

Our results expand upon prior research that estimated that each year millions of people in the United States do not obtain necessary medical care due to a lack of transportation. This group is disproportionately older, female, non-White, and of lower socioeconomic status (SES; Wallace et al., 2005). Our results focused on older adults differed somewhat from these previous findings. Age and sex were not significant independent predictors of transportation disadvantage. Furthermore, low SES was not a predictor of disadvantage in our analysis. In addition, the transportation disadvantaged group had a greater concentration of White participants in our analysis, with Black participants having lower odds of disadvantage in multivariable analysis. This finding diverges from prior research which suggests that older adults who belong to racial and ethnic minority groups in the United States are at heightened risk of mobility restriction and driving cessation (Babulal, Williams, Stout, & Roe, 2018). Marital status was the strongest demographic predictor in our model; being married or living with a partner reduced the odds of transportation disadvantage by a half. This suggests that the social support associated with living with a spouse or partner may be a protective factor, regardless of other demographic or socioeconomic characteristics. Given differences in our findings from prior literature, more research is needed to further develop the measure and identify predictors of transportation disadvantage in a national sample.

Some study limitations should be noted. Our measure of transportation disadvantage is based on source questions that ask about transportation barriers related to social activities only; the NHATS does not ask about barriers related to other types of activities such as shopping. NHATS also does not specifically ask about transportation barriers to accessing medical care; thus we do not have a more direct measure of disadvantage related to medical appointments. A more comprehensive measure of transportation disadvantage would also capture whether medical needs were unmet due to transportation disadvantage, such as missed medical appointments or inability to get to a pharmacy. Moreover, our measure of transportation disadvantage applied to nondrivers only due to skip patterns in the data. Furthermore, as respondents might value some social activities more than others, we conducted additional analyses to determine whether the extent to which individuals rated these activities as important to them affected our results. Importance of individual types of activities was not substantively different by presence of transportation disadvantage. Moreover, controlling for importance in the logistic regression did not substantially change the results (data not shown). Finally, participants who responded to the NHATS during winter months may have been biased toward reporting a transportation barrier due to recent experience with inclement weather, reflecting a potentially spurious factor unrelated to their individual characteristics. Prior research indicates that inclement weather is one of the conditions in which older drivers reduce their driving (Molnar et al., 2014); it is possible that transportation disadvantage in our nondriving sub-sample was also influenced by seasonal factors. Future work should examine seasonal variability in transportation patterns as it relates to transportation disadvantage.

We have documented that a substantial number of older adults in the United States experience a transportation disadvantage, which may result in a lack of engagement in social activities, missed medical care, adverse health outcomes, and diminished quality of life. Our work points to the need to prioritize transportation barriers for nondriving older adults. State governments should continue efforts to assess the scope of transportation disadvantage in their localities (The Florida Legislature, 2016; Lane et al., 2014), while designing local policies that target unmet transportation needs among older adults. Further innovation is needed to better support older adults in utilizing new transportation services to participate in social activities and reduce the detrimental impact of social isolation (H. Choi, Irwin, & Cho, 2015; Shaw et al., 2017; Taylor, Taylor, Nguyen, & Chatters, 2018). This represents both a challenge and an opportunity to develop new approaches to meeting this need for older adults in the community and improving their quality of life while aging in place.

Conclusion

A sizable group of older adults experience transportation disadvantage which may be due to inadequate social support as well as physical and functional impairment. Future work is necessary to document long-term implications of these disadvantages for older adults’ social engagement, health, and wellbeing. Tailored interventions combining ride services with care coordination strategies (Onyekere, Ross, Namba, Ross, & Mann, 2016; Powell, Doty, Casten, Rovner, & Rising, 2016) may be needed to overcome transportation barriers that prevent older adults from effectively accessing health care and other essential services in the community.

Acknowledgments

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr. Ryvicker was supported by the National Institute on Aging (K01AG039463). Dr. Ornstein was supported by the National Institute on Aging (K01AG047923). The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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