Running head: GIVING UP THE KEYS
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Giving up the Keys:
How Driving Cessation Affects Engagement in Later Life*
Angela L. Curl, Ph.D.1
James D. Stowe, M.S.2,3
Teresa M. Cooney, Ph.D. 2
Christine M. Proulx, Ph.D.2
University of Missouri
1
709 Clark Hall, School of Social Work, University of Missouri, Columbia, MO 65211,
curla@missouri.edu, 573.882.6206 (corresponding author)
2
Department of Human Development and Family Studies, 314 Gentry Hall, Columbia, MO,
65211, 573.882.4035, cooneyt@missouri.edu; proulxc@missouri.edu
3
207 McHaney Hall, Frank L. Mitchell Jr., MD Trauma Center, University of Missouri Hospital,
Columbia, MO 65212, stowejd@health.missouri.edu, 573.884.6381
*
An earlier version of this paper was presented at the annual meeting of the Gerontological
Society of America, San Diego, November, 2012.
This is a pre-copyedited, author-produced PDF of an article accepted for publication in The
Gerontologist following peer review. The version of record is available online at:
http://gerontologist.oxfordjournals.org/content/early/2013/05/06/geront.gnt037
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Abstract
Purpose of the study. Many older adults consider driving vital to maintaining their preferred
lifestyle and engagement with society, yet it is normative for individuals to eventually stop
driving. This study examined the impact of driving cessation on older adults’ productive and
social engagement, and whether mental and physical health mediated this relationship.
Design and Methods. Multilevel modeling was used to analyze longitudinal data (N = 4,788
adults age 65+) from the Health and Retirement Study (1998 – 2010).
Results. Productive engagement (paid work, formal and informal volunteering) was negatively
affected when older adults stopped driving, but social engagement was not immediately
compromised by the transition to non-driver status. The role of health and mental health as
mediators in explaining this relationship was negligible.
Implications. The results suggest that interventions aimed at maintaining non-drivers’
participation in productive roles should focus on factors other than enhancement of health and
well-being to spur greater engagement (e.g., availability of and barriers to use of public
transportation). Also important in the intervention process is planning for mobility transitions.
Future research should test for geographic (e.g., urban vs. rural) differences in the impact of
driving cessation on productive and social engagement.
Keywords: driving cessation, engagement, employment, volunteering, health status
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Giving Up the Keys: How Driving Cessation Affects Engagement in Later Life
Driving is a critical component of autonomy, freedom of choice, and engagement for
older adults, just as it is for younger people. As individuals age, it is normative to eventually stop
driving. Foley and associates (2002) estimate that men can expect to live seven years, and
women ten years, beyond their ability to drive safely. Yet, driving cessation is known to
negatively impact older adults’ wellbeing and it is reasonable to expect that it impacts other
dimensions of life as well. The current study examines the impact of driving cessation on older
adults’ productive and social engagement over time because these activities are critical to
sustained well-being in later life (Matz-Costa, Besen, James, & Pitt-Catsouphes, 2012).
Despite its normative nature, few plan to stop driving (Adler, 2010), perhaps because the
transition can be unexpected, as in response to a crisis medical condition (e.g., stroke; Staplin,
Lococo, Martell, & Stutts, 2012). Some older adults stop driving due to medical impairments
(King et al., 2011) or feelings of poor health (Dellinger, Sehgal, Sleet, & Barrett-Connor, 2001).
Others stop driving due to finances or personal choice (Adler & Rottunda, 2006). For many,
driving cessation is a process that occurs over several months or years (Dickerson et al., 2007),
even when conditions that impact safety exist (e.g., dementia; Croston, Meuser, Berg-Weger,
Grant, & Carr, 2009). The data analyzed in this study capture driving transitions occurring over
time and their time-varying impact on productive and social engagement.
Longitudinal results demonstrate that driving cessation predicts poor outcomes for older
adults. For example, former drivers can expect poorer health trajectories (Edwards, Lunsman,
Perkins, Rebok, & Roth, 2009), increased rates of depression (Fonda, Wallace, & Herzog, 2001),
institutionalization (Freeman, Gange, Munoz, & West, 2006) and mortality (Edwards, Perkins,
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Ross, & Reynolds, 2009), and decreased out-of-home activities (Marottoli et al., 2000) compared
to older drivers. To rule out declining health, rather than driving cessation, as the primary
contributor to these outcomes, these studies controlled for health status. Thus, the evidence
suggests that driving cessation itself leads to challenges in later life rather than the alternative
explanation that those experiencing challenges are simply more likely to cease driving.
Beyond the established impact of driving cessation on a variety of health outcomes, less
is known about how cessation influences engagement in later life. Anecdotally, older adults
expect their lives to change for the worse once they stop driving. Yet, limited evidence exists
regarding how engagement—specifically trajectories of work and social involvement—is
affected by driving cessation. Buys and Carpenter (2002) found mixed outcomes in their
qualitative data from a metropolitan sample. Whereas some older adults reported maintenance of
work and volunteer roles after driving cessation, others considered driving necessary to continue
working. One European study found that retirement from a job often precipitates driving
cessation for older adults (Raitanen, Tormakangas, Mollenkopf, & Marcellini, 2003), but how
driving cessation impacts work was not reported. Similar to paid employment, volunteering may
be affected by driving status. Given that adults are more likely to stop than start volunteering in
older age (Butrica, Johnson, & Zedlewski, 2009), driving cessation also may limit volunteering
in late life. Informal volunteering, such as giving help to relatives or friends, also may be
reduced by driving cessation, especially if social networks are spread over greater distance.
Social engagement also appears highly contingent on driving. In one qualitative study,
older women who had stopped driving reported less involvement in social activities, largely due
to transportation difficulties (Bryanton, Weeks, & Lees, 2010). In terms of friendship network
size, a 50% reduction was observed after persons ceased driving (controlling for self-rated
GIVING UP THE KEYS
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health) in a longitudinal investigation conducted with an urban sample (Mezuk & Rebok, 2008).
Both poor mental and physical health are tied to low social engagement and perceived isolation
(Cornwell & Waite, 2009), thus their role in the link between driving and social engagement
cannot be ignored.
Evidence suggests that income may modify the association between driving cessation and
engagement. Poverty research indicates that not owning or driving a car limits access to
employment (Grengs, 2010), and for many, driving is associated with a lifestyle that is difficult
to achieve through other means. Among wealthier suburbanites, driving appears especially
important for maintaining social activity (Farber & Páez, 2009). Indeed, Ragland, Satariano, and
MacLeod (2004) found that low income was a predictor of avoiding or limiting driving in a
sample of older adults. Having greater income may expand transportation options once driving
ceases, thereby buffering effects on engagement for the non-driver. Therefore, income is an
important moderator to test in the association between driving cessation and engagement.
Given the known impact of health on driving cessation, and the demonstrated
consequences of cessation for physical and mental health, it is informative to study driving
cessation with longitudinal data that allow for consideration of both pre- and post-driving health
assessments. This approach can reveal whether poor physical and mental health account for the
association between cessation and engagement, or if other factors explain this association.
Determining whether health and mental health mediate the relationship between driving
cessation and engagement may inform the development of interventions for older former drivers
as this information may help dictate the nature, scope, and timing of cessation-related
interventions. Finally, a longitudinal design is valuable for addressing how the impact of driving
cessation shifts over time. Fonda et al. (2001) suggest that cessation may be most disruptive
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immediately following the driving transition, with outcomes possibly attenuating over time as
non-drivers adapt. Using multiple data points, we can address this question of potential
adaptation to driving cessation or intensified negative consequences over time.
Why Cessation Impacts Engagement
A parsimonious explanation for why change in an individual’s mobility impacts
engagement arises from theories that explain person-environment transactions. Fuller’s Task
Capability Interface (TCI; 2005) model connects outcomes to the difficulty of a task and the
level of an individual’s ability. Changes in the person (e.g., driving ability) or environment (e.g.,
route to work) determine an outcome depending upon the individual’s ability to adapt to the
change. Although a vehicle collision is the typical outcome of interest when using Fuller’s
model, it can be applied to an individual’s general mobility. For example, if volunteering
becomes difficult when driving ceases, the model suggests that volunteering will be reduced. If
the individual can adapt to driving cessation (e.g., through finding alternative transportation),
then a loss or decline in volunteerism would be circumvented. In this study, we hypothesize that
driving cessation results in increased mobility challenges and therefore negatively impacts
engagement outcomes. Thus, Fuller’s TCI model (2005) can be used to understand why driving
cessation may impact engagement in later life from a mobility perspective.
The Present Study
The primary goal of this study is to examine whether driving cessation leads to
prospective changes in productive and social engagement for adults, ages 65 and older. A
secondary aim is to determine whether mental and physical health mediate the association
between driving cessation and engagement. Using longitudinal data from a nationally
representative sample, we test the following hypotheses:
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1. Driving cessation will reduce the likelihood of productive engagement, specifically paid
employment, formal volunteering, and informal volunteering.
2. Driving cessation will reduce social engagement.
3. Given the established link between driving cessation and health, mental and physical
health will mediate the association between driving cessation and engagement changes.
4. Higher income will moderate the negative impact of driving cessation on engagement.
Controlling for factors associated with driving cessation and engagement is necessary in an
analysis of change over time. Predictors of engagement in later life include age, income,
education, gender, marital status (Matz-Costa et al., 2012), and minority status (Tang, Copeland,
& Wexler, 2012). Many of these same background factors have been linked to driving cessation:
age (Foley et al., 2002), gender (Chipman, Payne & McDonough, 1998), marital status (Chipman
et al.) and income (Ragland et al., 2004).
Design and Methods
Data
We employed longitudinal data from seven waves (each collected about two years apart,
from 1998-2010) of the Health and Retirement Study (HRS). This is a nationally representative
study of non-institutionalized adults age 50 and over, together with their spouses/partners
regardless of age, with oversamples of Blacks, Hispanics, and Florida residents (Heeringa &
Connor, 1995). The RAND Corporation prepared data files that facilitate longitudinal data
analyses; we used RAND’S version L file (St. Clair et al., 2011), merged with the raw variables
on driving cessation from HRS.
For inclusion in this study, respondents had to meet these criteria:
1. Interviewed in 1998 (excluded 9,287 respondents);
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2. Age 65 or older (excluded 10,599), as the driving cessation variables were only asked
of older adults;
3. Primary HRS respondent (excluded 3,504), to meet the statistical assumption of
independent observations;
4. Reported being able to drive at baseline (excluded 5,098);
5. Person-level analysis weight greater than zero at baseline (excluded 27; St. Clair et
al., 2011); and,
6. Non-missing and non-proxy responses (persons with missing data or proxy responses
were excluded for that wave only; excluded 283 at baseline).
These criteria resulted in a final unweighted sample of 4,788 individuals.
Measures
Dependent variables. Four outcome variables were assessed. Paid employment was
created from RAND’s labor force status variable, which included seven groups: working fulltime, working part-time, unemployed, partly retired, retired, disabled, or not in the labor force.
Respondents were coded as employed (= 1) if they were working full-time, part-time, or were
partly retired, otherwise paid employment was coded 0. For formal volunteering, respondents
were asked “Have you spent any time in the past 12 months doing volunteer work for religious,
educational, health-related or other charitable organizations?” (1 = yes, 0 = no). For informal
volunteering, respondents were asked “Altogether, about how many hours did you spend in the
past 12 months helping friends, neighbors, or relatives who did not live with you and did not pay
you for the help?” Any hours greater than zero were coded 1, otherwise informal volunteering
was coded 0. For social engagement, respondents were asked “How often do you get together
with any of your neighbors (or people in or near your facility) just for a chat or for a social
GIVING UP THE KEYS
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visit?” and a follow-up question about the unit of time (i.e., per day, per week, etc.) for their
response. Based on the number of contacts reported, we standardized these reports by calculating
a variable indicating days of social contact per week, with a range of 0 - 7.
Independent variables. To determine driving status, respondents were asked “Are you
able to drive?” We recoded responses as 1 = no longer able to drive and 0 = still able to drive. In
addition, a variable was created to indicate how many waves had passed since the first driving
cessation that occurred during the study (coded 0, 1, 2, etc., to be consistent with the
measurement of time). Time was indexed with the wave of data collection (0, 1, 2, etc.).
Demographic controls were coded as: gender (1 = female, 0 = male), race (1 = non-White, 0 =
White), years of education (0 - 17+) at baseline, age at baseline, marital status (1 =
married/partnered; 0 = single), and annual household income (computed by RAND based on all
sources of income for the last calendar year for the respondent and spouse/partner). An
interaction term was also created between “not able to drive” and “household income” to test
hypothesis 4.
Depressive symptomology was measured at each wave using the 8-item Centers for
Epidemiologic Studies Depression Scale (Radloff, 1977). For each symptom, respondents
reported if they had felt it “much of the time” during the past week (1 = yes, 0 = no); responses
were summed to form a scale with a 0-8 range, indicating number of depressive symptoms
(Cronbach’s alpha = 0.71 at baseline). Cognitive ability was based on word recall, and ability to
count backwards, identify an object by its description, give the date, and name the U.S. President
and Vice President. Scores were summed to form the total cognitive ability score, ranging from
0-35, with higher numbers indicating greater cognitive functioning. For chronic conditions,
respondents reported if they had ever been diagnosed by a doctor with any of seven major
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conditions (high blood pressure, diabetes, cancer, chronic lung disease, heart problems, stroke, or
arthritis). Reported conditions were summed, with a range of 0 - 7. Respondents also were asked
if they had any limitations with three instrumental activities of daily living (IADLs): using the
telephone, managing money, and taking medications. “Yes” responses were summed, with a
range of 0 - 3, with higher numbers indicating more IADL limitations. Self-rated health was
based on the question “Would you say your health is excellent, very good, good, fair, or poor?”
(0 – 4 range; higher = better health).
For the multivariate analyses, education, age, cognitive function and income were
transformed to aid in model convergence and intercept interpretation. Education and total
cognitive ability were grand mean centered. Age was centered by subtracting the minimum age
(65, due to sample selection criteria) from all values. Because data were skewed, household
income was transformed by adding 100 to all values and then taking the natural log
transformation; this logged variable was then centered on the sample mean of 10.32.
Data Analysis
Data were analyzed using multilevel modeling with Hierarchical Linear Modeling
(HLM) 6.08 software (Raudenbush, Bryk, & Congdon, 2009), with full maximum likelihood
estimation and HRS-provided person-level weights (based on Current Population Survey
demographics). Multilevel modeling is appropriate given the hierarchical nature of longitudinal
data (repeated observations within persons). This method requires respondents to have complete
data within a wave, but data at every wave is not necessary to be included in the analyses. This
approach also permits modeling of both fixed/average effects of predictors on the outcome and
random effects (which give information about the variability around the fixed effect). For the
analyses, the distribution of social engagement was treated as continuous, while paid
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employment, formal volunteering, and informal volunteering were all estimated as Bernoulli
(dichotomous, 0 or 1) outcomes.
This study utilized nested models, starting with a baseline model consisting of the
intercept and time slope, which were entered as fixed and random effects. Subsequent models
contained all the predictors of the previous model, plus additional predictors. The second model
addressed hypotheses 1 and 2 by adding the driving cessation variables (not able to drive and
waves since driving cessation) as fixed and random effects, plus the demographic control
variables (gender, race, education, age, marital status, and household income), which were
entered as fixed effects. The third model (Model 3) added the physical and mental health
variables as fixed effects to test the mediating effects of health factors on driving cessation’s
impact on engagement (hypothesis 3). Model 4 added the interaction term between driving
cessation and income to test hypothesis 4. A significant interaction term would indicate that the
effects of driving cessation on a given outcome vary by income level. All the predictors were
entered in the models as time-varying, with the exception of gender, race, education, and age.
Results
Table 1 presents weighted and unweighted descriptive statistics for the study variables
at baseline. On average, respondents indicated few depressive symptoms, chronic conditions, and
IADL limitations (M = 1.47, M = 1.70, and M = .05, respectively). Most respondents (59%) were
engaged in informal volunteering, while a substantial minority was engaged in formal
volunteering (35%) or paid employment (21%). For social engagement, respondents reported
social contact with friends or neighbors about two days per week at baseline (M = 2.07).
Multivariate models estimating the impact of driving cessation on productive and social
engagement are presented in Tables 2 and 3. Table 2 shows results from multivariate models
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estimating two of the outcomes of interest, formal volunteering and informal volunteering
(outcomes “a” and “b,” respectively), and Table 3 presents those for paid employment and
social engagement (outcomes “c” and “d,” respectively). The likelihood of older adults engaging
in each of the four outcomes declined over time as indicated by the significant coefficients (all
with odds < 1.00) for the time variables in Model 1. The significant coefficients for driving
cessation (i.e., not able to drive) in Model 2 support hypothesis 1 by indicating that driving
cessation further reduces productive engagement in older age, beyond the effects of time. The
likelihood of formally and informally volunteering drops 68% and 70%, respectively, after
individuals report no longer driving. The likelihood of employment decreases approximately
79% for individuals who report no longer driving, controlling for demographic variables.
Moreover, the variable indicating length of time since reporting driving cessation was significant
for both formal and informal volunteering, indicating that those who stop driving experience a
faster decline in volunteering over time than those who do not. Once physical and mental health
were included (Model 3) this relationship was no longer significant for informal volunteering.
Contrary to hypothesis 2, reported driving cessation did not predict frequency of social
engagement (Model 2d of Table 3). However, length of time since driving cessation was
statistically significant in this model, demonstrating that declines in social engagement over time
were more pronounced for those who ceased driving than for those who did not.
Although several aspects of well-being also were associated with productive and social
engagement, support for hypothesis 3 was not found. Health factors did not mediate the
association between driving cessation and the outcome variables as evidenced by the fact that the
coefficients for driving cessation remained significant (and strong) after inclusion of the health
and mental health variables in Models 3a, 3b, and 3c. Not surprisingly, self-rated health and
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cognitive ability contributed to the likelihood of employment and both kinds of volunteering, and
were related to higher levels of social engagement. Alternatively, greater depressive
symptomatology reduced the likelihood of employment and both types of volunteering, and was
related to less frequent social engagement. More chronic conditions reduced the odds of
employment and informal volunteering, but were not associated with formal volunteering or
social engagement. IADL limitations predicted reduced odds of formal and informal
volunteering, and lower levels of social engagement, but were unrelated to employment. Thus,
health status is clearly linked to productive and social engagement in later life, but its role as a
mediator in explaining the effects of driving cessation on productive and social engagement for
older adults is negligible.
Hypothesis 4 is tested with Model 4 in Tables 3 and 4. The interaction term for driving
cessation and income at the bottom of each table indicates limited support for hypothesis 4. Only
with regard to employment (Model 4c) is the interaction significant. The significant positive
coefficient indicates that for persons who are unable to drive, higher income contributes to the
likelihood of continued paid employment.
Graphs shown in Figure 1 illustrate the expected productive and social engagement of
older adults over time, based on final multivariate models for each outcome. These graphs
compare the trajectories of engagement for those who can still drive and those unable to drive as
of 2002. Because driving cessation has both a short-term and long-term impact on productive
and social engagement, we set the occurrence of driving cessation early enough in the time series
to capture both effects.
As illustrated, driving cessation reported at wave 3 (2002) has an immediate marked
impact on the likelihood of employment, and both formal and informal volunteering. For both
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types of volunteering, differences remain between those who ceased driving and those with
continued driving ability at the end of the study period, even though both groups reported
declines in engagement over time. For employment, in contrast, the difference between the two
groups by the last wave appears negligible, because the effect of driving cessation is immediate
and most people eventually stop working. The graph for social engagement shows a slightly
different pattern of results than the other outcomes. Although reports of driving status do not
distinguish social engagement at wave 3, declines in social involvement over the course of the
study are more pronounced for those who have lost the ability to drive compared to those who
still are able.
Discussion
Many older adults consider driving vital to maintaining their preferred lifestyle and
engagement with society, yet limited empirical evidence exists regarding the effects of driving
cessation on productive and social engagement. To advance such inquiry, we used nationallyrepresentative longitudinal data to examine the impact of driving cessation on older adults’
employment, formal and informal volunteering, and social engagement over time. Additionally,
we tested the mediating role of health factors and the moderating role of income in the
connection between driving cessation and changes in engagement.
Our results indicate that employment, formal volunteering, and informal volunteering
are all negatively affected when older adults stop driving. Older adults’ employment was
affected most by this transition, perhaps due to the challenges non-drivers face finding accessible
and convenient jobs. These results support Fuller’s model in that adaptation is difficult when the
most common form of transportation is no longer an option for older adults. Driving cessation’s
impact on employment generalizes beyond metropolitan areas—as studied by Buys and
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Carpenter (2002). Whether such effects on employment vary by geographic locale, however,
remains unknown. Future research should use geocodes to test whether driving cessation impacts
employment most severely in non-metro areas where public transportation and employment
options are likely limited.
Formal and informal volunteering also declined when driving stopped, yet these
changes were less dramatic than those related to employment. Unlike most paid work,
volunteering can be performed with another person, making it possible to continue this activity
even after driving has ceased, especially if married to a spouse who also volunteers (Rotolo &
Wilson, 2006). Such a strategy whereby one volunteers with a spouse or friend would be
considered successful adaptation in Fuller’s model.
The notion that newly transitioned non-drivers may adapt to cessation by depending on
rides from significant others should be further studied with dyadic research designs. Like most
driving research, a limitation of the current study is that it focused on individuals rather than
couple dyads. Thus, our analyses controlled for the respondents’ marital status, but did not
consider how driving status of the spouse/partner, or transitions in the partner’s driving status,
affected the respondents’ engagement. Though Fonda and colleagues (2001) found that having a
spouse that drives did not buffer the impact of driving cessation on heightened depression for
new non-drivers, we expect that cessation’s negative impact on engagement would be eased by
having a driving spouse with whom productive and social activities, other than paid work, can be
shared.
Informal helping may be affected less by driving cessation than paid work because
older adults can help others without going far from home. For example, grandparents may offer
drop-in child care for non-resident grandchildren (Luo, LaPierre, Hughes, & Waite, 2012).
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Nevertheless, even informal volunteering declined when driving ceased. Thus, being a driver
facilitates helping others.
Unlike paid work, formal and informal volunteering, social engagement was not
immediately compromised when adults transitioned to non-driver status. Though prior evidence
suggested that driving cessation inhibited out-of-home activities (Marottoli et al., 2000), our use
of a more precise measure of social engagement (distinct from other out-of-home activities)
clarified outcome differences based on type of out-of-home activity. Still, the HRS data analyzed
herein restricted social involvement to contacts with neighbors and persons living nearby. Surely
neighborly connections are less dependent on driving than are social relationships outside one’s
proximate environment, which is as issue for future research to consider. While one possibility is
that reduced mobility may lead to less interaction with more distant network members for
persons who stop driving, an alternative scenario is that family and friends compensate for this
mobility restriction by becoming more attentive and involved (perhaps by telephone) with a
newly transitioned non-driver. Furthermore, getting rides from others may create opportunities
for increased interpersonal interaction. Thus, driving cessation need not invariably reduce social
interactions and may even enhance social engagement for some older adults (Freund & Vine,
2010).
The longitudinal perspective applied in this study allowed assessment of both immediate
changes in engagement associated with driving cessation and protracted effects. The results
revealed that, over time, formal volunteering and social engagement both declined more sharply
for former drivers than for current drivers, controlling for health and other factors. This drop in
engagement could occur if the alternatives to driving that new non-drivers initially use, such as
receiving rides from others, become less desirable or available over time. Indeed, some non-
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drivers in Bryanton et al.’s (2010) qualitative study reported feeling like a nuisance when they
repeatedly asked for rides from others. Such concerns may become more common over time if
the willingness of family and friends to transport former drivers wanes.
Our extended longitudinal approach also revealed that changes in social engagement for
non-drivers occurred despite no immediate effect of driving cessation on social contact. The
marked reduction in social engagement with nearby network members that non-drivers
experienced over time, relative to drivers, may be a consequence of a related relocation. Studies
indicate that driving cessation often leads to changes in living arrangements (Adler, 2010;
Myers, Janssen-Grieve, Crizzle, & Brown, 2012), which may put older non-drivers at a
disadvantage for socializing even in their near environments, resulting in isolation and
dissatisfaction with their social contacts (Dupuis-Blanchard, Neufeld, & Strang, 2009).
Also examined was whether the effects of driving cessation on engagement were
moderated by household income. A significant interaction occurred for paid employment only,
with greater income buffering the negative impact of driving cessation on likelihood of work.
Perhaps higher paid workers have greater workplace flexibility (Tang & MacDermid
Wadsworth, 2010), making driving status less relevant for them. Or, higher income workers may
have greater incentive and ability to continue working, and thus use their resources to find
alternative ways to stay productively engaged despite no longer driving.
This analysis assessed whether physical and mental health changes previously associated
with driving cessation mediated the effects of cessation on engagement. That is, do reductions in
employment, volunteering, informal assistance and social engagement following driving
cessation primarily result because of associated increases in health problems and depression?
Our analysis indicates that the answer is “no.” After including physical, cognitive and mental
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health indicators into our models, the influence of driving cessation on employment, formal and
informal volunteering remained statistically significant and relatively unchanged. Thus, it is not
the case that driving cessation deters productive engagement through its negative impact on
either physical health or depression. Yet, preventive efforts aimed at maintaining physical and
cognitive health cannot be overlooked given the significant impact of these factors on driving
cessation (Staplin et al., 2012).
Our results corroborate previous findings revealing the significant negative impact of
driving cessation on older adults’ lives (e.g., Edwards et al., 2009; Freeman et al., 2006), and
extended this research to productive and social engagement. The serious consequences of driving
cessation for older adults are crucial to impart during the training of health and social services
professionals who will routinely encounter issues of mobility when serving older patients and
clients. Education should acknowledge the critical impact of driving cessation on older person’s
lives and emphasize ways in which the transition from driving can be postponed as long as
possible. Moreover, when professionals come in contact with older people to conduct
assessments and develop care plans, it is critical to discuss driving transition planning.
Normalizing the process of driving cessation and making it a routine topic of late-life planning
are key to ensuring that viable pathways exist to maintaining engagement after giving up the
keys.
Policy implications also stem from our findings. The maintenance of accessible,
affordable, and desirable transportation alternatives is needed. Additionally, reimbursement for
health care activities that help people to identify the appropriate time for driving cessation, or
extend the years of safe driving, such as Certified Driver Rehabilitation Specialist (CDRS)
programs, is important to preventing reduced engagement among older persons. Contrary to
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these suggestions, Stav and colleagues (2011) report a recent precipitous decline in the number
of programs oriented toward maintaining driving ability among older adults. Finally, the
flexibility of workplace policies may also require review if employers want to ensure the
continued contributions of valued older workers, some of whom may be unable to drive.
The persistent, direct effect of driving cessation on productive engagement suggests
that interventions aimed at maintaining non-drivers’ participation in productive roles should
focus on factors other than enhancement of health and well-being. Person-centered approaches
that emphasize planning for driving cessation should be used in interventions (Berg-Weger,
Meuser, & Stowe, in press). Our results suggest that if employment and volunteering are
expected and desired in later life, efforts should be made to maintain driving, or some alternative
form of mobility. Early intervention surrounding mobility transitions could optimize adaptation
and enhance opportunities for continued engagement of older non-drivers.
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Table 1
Distribution of Study Variables at Baseline, Unweighted (N=4,788) and Weighted
Unweighted
Weighted
Study Variable
M (SD)
M (SD)
Gender (1=female)
48.3%
50.5%
Race (1=non-White)
12.5%
9.1%
Marital status (1=married/partnered)
54.2%
49.6%
Education (0-17+ years)
12.18 (3.19)
12.29 (3.06)
Age (65-97 years)
73.75 (6.47)
73.74 (6.22)
37,645 (49,678)
37,801 (53,223)
Depressive symptomatology (CES-D; 0-8)
1.47 (1.76)
1.45 (1.76)
Chronic conditions (0-7)
1.70 (1.23)
1.70 (1.23)
Self-rated health (0-4)
2.12 (1.12)
2.14 (1.21)
IADL limitations (0-3)
.05 (.26)
.05 (.25)
Cognitive ability (0-35)
22.40 (4.89)
22.57 (4.77)
Paid employment (1=working)
20.8%
20.8%
Formal volunteering (1=yes)
34.6%
34.7%
Informal volunteering (1=yes)
59.2%
59.2%
2.07 (2.55)
2.07 (2.54)
Household income (annual)
Social engagement (days/week; 0-7)
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Table 2
Multilevel Model Results (Odds Ratios) Predicting Formal and Informal Volunteering From Driving Cessation and Other Factors
Formal Volunteering
Informal Volunteering
Fixed Effects
Model 1a
Model 2a
Model 3a
Model 4a
Model 1b
Model 2b
Model 3b
Model 4b
Intercept
0.48**
0.44**
0.33**
0.33**
1.59**
3.23**
2.07**
2.07**
Time (waves) since baseline
0.85**
0.89**
0.94**
0.94**
0.75**
0.78**
0.81**
0.81**
Not able to drive (1=yes)
0.32**
0.41**
0.41**
0.30**
0.39**
0.40**
Waves since driving cessation
0.79**
0.83**
0.83**
0.88*
0.93
0.93
Gender (1=female)
1.60**
1.49**
1.49**
0.82**
0.76**
0.76**
Race (1=non-White)
1.41**
1.61**
1.61**
1.03
1.17
1.17*
Education (0-17+ years)1
1.15**
1.11**
1.11**
1.08**
1.04**
1.04**
Age at baseline1
0.97**
0.99**
0.99**
0.94**
0.95**
0.95**
Marital status (1=married/partnered)
1.07
1.03
1.03
0.91
0.89*
0.89*
Household income (annual, logged)1
1.24**
1.19**
1.18**
1.14**
1.07**
1.07*
CES-D (depressive symptoms) (0-8)
0.93**
0.93**
0.95**
0.95**
Chronic conditions (0-7)
0.96
0.96
0.96*
0.96*
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27
Self-rated health (0-4)
1.20**
1.20**
1.28**
1.28**
IADL limitations (0-3)
0.83**
0.83**
0.84**
0.84**
Cognitive ability (0-35)1
1.04**
1.04**
1.03**
1.03**
Not able to drive*Income
1.04
1.07
Random Effects
Intercept
3.18**
3.07**
3.02**
3.02**
1.34**
1.25**
1.17**
1.17**
Time slope
0.04
0.03
0.03
0.03
0.02
0.02
0.02
0.02
Not able to drive (1=yes)
0.15
0.16
0.16
0.10
0.08
0.08
Waves since driving cessation
0.07
0.06
0.06
0.11
0.08
0.08
Note. Convergence criterion = 0.000001; full maximum likelihood estimation. N = 4,788. 1Centered. *p < .05. **p < .01.
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28
Table 3
Multilevel Model Results Predicting Paid Employment and Social Engagement From Driving Cessation and Other Factors
Paid Employment (Odds Ratios)
Social Engagement (Event Rate Ratios)
Fixed Effects
Model 1c
Model 2c
Model 3c
Model 4c
Model 1d
Model 2d
Model 3d
Model 4d
Intercept
0.21**
0.85
0.63**
0.63**
1.60**
1.67**
1.52**
1.52**
Time (waves) since baseline
0.62**
0.63**
0.67**
0.67**
0.97**
0.97**
0.98**
0.98**
Not able to drive (1=yes)
0.21**
0.30**
0.24**
0.95
1.01
0.99
Waves since driving cessation
0.94
1.02
1.03
0.87**
0.89**
0.89**
Gender (1=female)
0.60**
0.55**
0.55**
0.99
0.98
0.98
Race (1=non-White)
1.21
1.36*
1.37*
1.08
1.11*
1.11*
Education (0-17+ years)1
0.99
0.96*
0.96*
1.00
0.99
0.99
Age at baseline1
0.88**
0.89**
0.89**
1.01**
1.01**
1.01**
Marital status (1=married/partnered)
0.59**
0.57**
0.57**
0.78**
0.77**
0.77**
Household income (annual, logged)1
2.29**
2.20**
2.17**
0.98
0.97
0.98
CES-D (depressive symptoms) (0-8)
0.96*
0.96*
0.98**
0.98**
Chronic conditions (0-7)
0.88**
0.88**
1.02
1.02
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Self-rated health (0-4)
1.27**
1.27**
1.04**
1.04**
IADL limitations (0-3)
0.80
0.80
0.94*
0.94*
Cognitive ability (0-35)1
1.03**
1.03**
1.01**
1.01**
Not able to drive*Income
1.63*
0.92
Random Effects
Intercept
3.35**
3.46
3.49
3.49
0.96**
0.92**
0.92**
0.92**
Time slope
0.04
0.04
0.03
0.03
0.06**
0.05**
0.05**
0.05**
Not able to drive (1=yes)
0.30
0.28
0.26
1.05**
1.05**
1.05**
Waves since driving cessation
0.26
0.22
0.21
0.36**
0.36**
0.35**
Note. Convergence criterion = 0.0001; full maximum likelihood estimation. N = 4,788. 1Centered. *p < .05. **p < .01.
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Figure 1
Differences in Trajectories of Drivers vs. Non-Drivers for Productive and Social Engagement
30
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