lable at ScienceDirect

Social Science & Medicine 69 (2009) 964–970

Contents lists avai

Social Science & Medicine

journal homepage: www.elsevier .com/locate/socscimed

Urban built environments and trajectories of mobility disability: Findingsfrom a national sample of community-dwelling American adults (1986–2001)q

Philippa Clarke a,*, Jennifer A Ailshire b, Paula Lantz c

a Institute for Social Research, University of Michigan, 426 Thompson Street, 48106 Ann Arbor, MI, USAb Population Studies Center, University of Michigan, 426 Thompson Street, 48106 Ann Arbor, MI, USAc School of Public Health, and Institute for Social Research, University of Michigan, 426 Thompson Street, 48106 Ann Arbor, MI, USA

a r t i c l e i n f o

Article history:Available online 28 July 2009

Keywords:USADisabilityBuilt environmentAgingTrajectoriesMobility

q This paper was supported by Grant Number K01EHfor Disease Control and Prevention (CDC) and by thegrant R01AG018418.

* Corresponding author. Tel.: þ1 734 647 9611.E-mail address: pjclarke@umich.edu (P. Clarke).

0277-9536/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.socscimed.2009.06.041

a b s t r a c t

As people age, they become more dependent on their local communities, especially when they are nolonger able to drive. Uneven or discontinuous sidewalks, heavy traffic, and inaccessible public trans-portation, are just some of the built environment characteristics that can create barriers for outdoormobility in later adulthood. A small body of literature has been investigating the role of the builtenvironment on disability, but has been limited to cross-sectional analyses. The purpose of this paper isto further advance this area of research by examining the role of the built environment on long-termtrajectories of mobility disability in a national sample of American adults (age 45þ) followed overa 15-year period. Using multilevel logistic growth curve models with nationally representative data fromthe Americans’ Changing Lives Study (1986–2001), we find that trajectories of mobility disability aresteeper in older age groups. Women and those with lower education had a higher odds of mobilitydisability over time. The presence of just one chronic health condition doubled the odds of mobilitydisability at each of the four study waves. Among older adults (age 75þ), living in neighborhoodscharacterized by more motorized travel was associated with an odds ratio for mobility disability that was1.5 times higher in any given year than for older adults living in environments that were more pedestrianfriendly. These results suggest that the built environment can exacerbate mobility difficulties for olderadults. When considering ways to minimize disability as the population ages, simple changes in the builtenvironment may be easier to implement than efforts to change risk factors at the individual level.

� 2009 Elsevier Ltd. All rights reserved.

Older adults are particularly dependent on their local commu-nities, especially when they are unable to drive. As a result ofdeclining health and functional status, financial strain and socialisolation, they are vulnerable to conditions in the urban builtenvironment (Glass & Balfour, 2003; Klinenberg, 2003). Uneven ordiscontinuous sidewalks, heavy traffic, and inaccessible publictransportation, are just some of the built environment character-istics that can create barriers for outdoor mobility, which can havespillover effects on a person’s ability to function independently inthe community (e.g. access shops, banks, and health services). Yet,the effect of the built environment on independence in older adultsis under-studied. The purpose of this research was to examine the

000286-01 from the CentersNational Institute on Aging

All rights reserved.

effect of the built environment on trajectories of mobility disabilityin mid- to late-adulthood.

Disability is generally defined as a substantial limitation in dailylife activities (Americans with Disabilities Act, 1990), and iscommonly measured in terms of difficulty performing activities ofdaily living (ADL) (e.g. eating, bathing, dressing, walking) or morecomplex instrumental activities of daily living (IADL) (e.g. shop-ping, managing finances). Mobility disability is one specific type ofdisability that refers to difficulty walking, either inside or outsidethe home. Although the prevalence of disability among the olderpopulation has been declining since the 1990s (Crimmins, 2004;Freedman, Martin, & Schoeni, 2002; Freedman et al., 2004; Manton& Gu, 2001), disability continues to be a major health and socialissue. The health care costs for disabled older adults are over twiceas high as those for non-disabled older adults (Fried, Ferrucci,Darer, Williamson, & Anderson, 2004; Trupin, Rice, & Max, 1995).Quality of life is typically diminished among older adults whoexperience restrictions in independence (Clarke, Lawrence, & Black,2000; Clarke, Marshall, Black, & Colantonio, 2002). Caregiving is

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970 965

often provided by unpaid family members, with associated health,emotional, and economic costs (Ettner, 1996).

Mobility disability and the built environment

The built environment is generally defined as all buildings,spaces and products that are created or modified by people. Itincludes schools, workplaces, parks/recreation areas, greenways,business areas and transportation systems. The role of accessible,safe, well-designed built environments for optimal health isincreasingly recognized (Jackson & Kochtitzky, 2001; Koplan &Flemming, 2000). With respect to older adults, poorly designedcommunities can make it difficult for people with mobilityimpairments or other disabling conditions to move about in theirenvironment (CDC Department of Health, Accessibility and theEnvironment). The lack of curb cuts (depressed curbs that act asramps in sidewalks), barrier-free sidewalks, pedestrian amenities(benches, shade trees), and four-way cross signals, are some of theenvironmental barriers that can prevent independence in olderadults (Beard et al., 2009; Bowling & Stafford, 2007). Despite theintuitive appeal of the importance of these surrounding contextsfor mobility, research on the effects of the built environment ondisability has been scarce.

A study of older adults living in the Houston area found thatcurb cuts, continuous sidewalks, and bus shelters were non-exis-tent in the vast majority (75%) of respondent neighborhoods(Markham & Gilderbloom, 1998). As a result, fewer than 10% ofrespondents used public transportation even though close to half ofthese adults lived within two blocks of a bus stop. Qualitative workfrom a study of older adults in North Carolina (Debnam, Harris,Morris, Parikh, & Shirey, 2002) found that poor-quality and incon-sistent sidewalks contributed to respondents’ inability to walkindependently outside their homes. Participants mentioned thatsome sidewalks were too high to negotiate, and others lacked curbcuts. One person commented ‘‘you can go a stretch and there isa sidewalk but then all of a sudden you are either walking in grass,or mud, or the road’’ (Debnam et al., 2002, p. 21).

A parallel body of work in the transportation literature has beendocumenting the relationship between the built environment andtravel choices, emphasizing the importance of the ‘‘3Ds’’: density,diversity and design (Cervero & Kockelman, 1997). Compactneighborhoods, measured either through housing or populationdensity, are associated with more walking trips and less motorizedtravel (Cervero, 2002; Cervero & Duncan, 2003; Cervero & Kockel-man, 1997). Land use diversity (where retail, office, and commercialbuildings co-exist with residential housing) promotes more in-neighborhood walking and cycling trips (Cervero, 1996; Cervero &Duncan, 2003; Frank & Pivo, 1994). Pedestrian oriented designs thatencourage walking include the continuity and quality of sidewalks,the quality and accessibility of public transit stops, frequency offour-way cross signals, and the availability of pedestrian amenities(i.e. seats, shade trees, civic squares, and street lights) (Cervero &Duncan, 2003; Kochera, 2003).

Freedman, Grafova, Schoeni, and Rogowski (2008) used nationaldata from the 2002 Health and Retirement Study to examine the linkbetween neighborhood characteristics (captured by indicators fromthe 2000 Census) and stages of the disablement process amongadults age 55 and older. The authors found that neighborhoodsocioeconomic advantage was associated with a reduced risk oflower body limitations. They also found marked gender differences.Among men, neighborhood socioeconomic disadvantage was asso-ciated with greater risk of ADL disability, while living in a censustract area with greater street connectivity was associated witha lower risk of IADL disability (again for men, but not for women).However, the effect sizes for the disability outcomes were very small

in magnitude, likely due to the fact that various different types ofdisability were combined within one index (IADL or ADL). CertainIADL items (e.g. outdoor mobility disability) may be more responsiveto the built environment than others (e.g. dressing or bathing).

Using data from the Duke Established Populations for Epide-miologic Studies of the Elderly (EPESE) project, Clarke and George(2005) investigated the relationship between the built environmentand IADL disability with a sample of older adults living in centralNorth Carolina. The built environment was captured through U.S.Census indicators of land use diversity (the degree to which retail,office, and commercial buildings co-exist with residential housing).Consistent with the transportation literature (Cervero, 1996;Cervero & Duncan, 2003; Frank & Pivo, 1994), decreased land usediversity was found to precipitate car dependent neighborhoods,which was inversely associated with independence in IADL.

In a subsequent paper, Clarke, Ailshire, Bader, Morenoff, andHouse (2008) used data from the Chicago Community Adult HealthStudy (CCAHS) to examine the effect of the built environment onmobility disability. Using objective data collected by external ratersthrough the method of Systematic Social Observation (Raudenbush& Sampson, 1999), the relationship between block-level charac-teristics and mobility disability (difficulty walking 2–3 blocks) wasexamined among adults age 45 and over. Among Chicago residentswith lower extremity impairments, the odds of reporting severemobility disability were over four times greater for those living inneighborhoods with streets in poor condition (e.g. cracks, brokencurbs, potholes) compared to those living in neighborhoods withstreets in good condition.

Yet, like the EPESE data, the CCAHS data are cross sectional andlimited to a geographically defined subpopulation, preventing anunderstanding of the dynamic changes in disability among Amer-ican adults living across diverse built environments as they age.Research on disability trajectories in later life has tended toexamine the effect of individual factors (e.g. sociodemographic andhealth status) on trajectory patterns to the neglect of environ-mental characteristics (Kelley-Moore & Ferraro, 2004; Li, 2005;Taylor & Lynch, 2004). Yet, surrounding social and physical envi-ronments are likely to be consequential for independence and tovary over time. Individuals can experience a variety of conditions asthey move in and out of different neighborhoods over the lifecourse (Sampson, Morenoff, & Gannon-Rowley, 2002). Even overa defined period of time, neighborhood characteristics may changesubstantially following infrastructure and community develop-ment, and as adults age in place, their risk for mobility disabilitymay change as their surrounding contexts are modified. Thepurpose of this paper is to further advance this area of research byexamining the role of the built environment on long-term trajec-tories of mobility disability in a national sample of American adultsfollowed over a 15-year period.

Methods

Data

Data came from the Americans’ Changing Lives (ACL) survey(House, Kessler, & Herzog, 1990; House et al., 1994), a stratified,multistage area probability sample of non-institutionalized adultsage 25 and over, living in the coterminous United States, and fol-lowed over a 15-year period. African Americans and adults age 60and over were over sampled. The first wave of the survey wasconducted in 1986 with 3617 adults (68% sample response rate forindividuals or 70% for households). Surviving respondents were re-interviewed in 1989 (N¼ 2867), in 1994 (N¼ 2562), and again in2001/2002 (N¼ 1787). All data and analyses were weighted to takeaccount of different rates of selection as well as differential non-

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970966

response over the 15 years of the survey. After weighting, the fullACL sample is representative of the age, gender, and race distri-bution of the population age 25 years and older living in the UnitedStates in 1986. In order to focus on the age group most at risk forhealth-related disability (National Center for Health Statistics,2006), we restricted our analyses to adults age 45 and over at anypoint in the survey (i.e. respondents could be as young as 30 atbaseline but we only used their data once they reached the age of45 at wave 4). We also excluded respondents living in rural areas ateach follow-up (<500 persons per square kilometer (StatisticsCanada, 2001)) in order to focus on characteristics in the urbanbuilt environment. The final sample size for our analyses was 1821adults.

Built environment measuresNeighborhood data were obtained from the U.S. Census for each

wave of the survey using the census tract as a proxy for neigh-borhood. Census tracts have on average about 4000 people and aredesigned to capture homogenous areas that roughly map toneighborhoods. Each respondent’s address at each wave was geo-coded to the 1980, 1990, and 2000 census tract, and linked to theU.S. Decennial Census for each year. For Wave 2 (1989) and Wave4 (2001) of the ACL survey, we used the 1990 and 2000 Censuscharacteristics, respectively. For wave 1 (1986), we used a weightedaverage of the 1980 and 1990 census characteristics for each tract,and for wave 3 we used a weighted average of the 1990 and 2000census data to capture neighborhood characteristics in 1994.Although census tract boundaries can change over time, onlya small proportion of cases in our data (<3%) were non-moversbetween 1989 and 2001, rendering tract boundary changes a minorissue in our analyses.

We focus on four indicators available in the U.S. Census over thestudy period (Census 1980–2000) that act as proxies for builtenvironment characteristics. Population density is a ratio of thenumber of persons per square kilometer in each tract. Sincecompact neighborhoods are associated with fewer vehicle trips andmore pedestrian travel (Frank & Pivo, 1994), they may have moreaccessible built environments for persons traveling on foot. Simi-larly, census tracts with a higher proportion of workers whocommute to work by public transit or by walking (versus private car)are more likely to be characterized by continuous sidewalks, four-way stop signals, and pedestrian amenities (Cervero & Duncan,2003). The age structure of a tract may also be an indicator ofaccessibility if tracts with a higher proportion of older adults havemore pedestrian amenities (benches, lighting, shade trees) tosupport independent pedestrian travel. We, therefore, includeda measure of the proportion of persons in each tract who are age 65and older. Finally, following others in the literature (Beard et al.,2009; Bowling & Stafford, 2007; Freedman et al., 2008) we includedan indicator of the social and economic resources in each tractunder the assumption that disadvantaged tracts may have fewerresources to maintain barrier-free sidewalks or pedestrian ameni-ties (Crawford et al., 2008). Neighborhood socioeconomic disadvan-tage is an average of five census indicators: percent female headedfamilies, percent households on public assistance income, percentfamilies in poverty, adult unemployment rate, and percent black.Factor loadings ranged from 0.75 to 0.94, with coefficientalpha¼ 0.91–0.92 across the four waves.

Individual measuresOutdoor mobility disability was captured by the respondent’s

self-reported level of difficulty walking several blocks. At each waverespondents were asked, ‘‘Do you currently have any difficultywalking several blocks because of your health?’’, assessed on a fivecategory scale (no difficulty, a little difficulty, some difficulty, a lot of

difficulty, or cannot do) (assistive technology was not mentioned inthe question wording). We created a dummy indicator for mobilitydisability by contrasting those who report that they experience a lotof difficulty or are unable to walk several blocks with those whoreport only some/little difficulty, or no difficulty.

Individual controlsThrough social selection processes over the life course, indi-

viduals at greater risk for disability and physical impairments (e.g.women, minority, lower educated and older adults, and those withmultiple health problems) may be more likely to live in neighbor-hoods characterized by less accessible built environments. Anal-yses, therefore, controlled for key sociodemograhpic and healthfactors that aim to minimize selection bias in the results. Socio-demographic factors capture underlying behaviors and resourcesthat are associated with disability over the adult life course,including age, gender, marital status, race/ethnicity, and education.Age was measured in years (age 45þ) and classified into four agegroups for analyses (age 45–54, age 55–64, age 65–75, and age 75and over). Female is a dummy variable coded 1 for female and 0 formale. Racial/ethnic minority is a dummy variable coded 1 for non-white (Black, Hispanic, Asian and Native American) and 0 for white.Education, which tends to be completed by early adulthood, wasmodeled using two dummy variables contrasting less than a highschool education, and high school diploma, with college degree orhigher. These are time-invariant indicators that do not changemarkedly over the life course.

On the other hand, time-varying variables capture the dynamicsof changing social and physical risk factors for disability overadulthood. We focus on marital status, financial resources andphysical health over time. Marital status was captured by threedummy variables contrasting divorced/separated, widowed, andnever married, with married respondents. Income was categorizedaccording to two dummy variables contrasting those witha combined household income of less than $10,000 per year, or$10,000–$30,000 per year, to those with an income of $30,000 orhigher ($15,000 and $40,000 are used as the cutpoints in the fourthwave of data in order to adjust for inflation by 2001). Due to itemnon-response on the income questions, we used imputed incomevalues provided in the ACL data (House et al., 1994).

We included two measures of physical health that are related tothe onset of disability (Stuck et al., 1999). At each wave, we createdan index of the number of medically diagnosed chronic healthconditions self-reported by the respondent in the past 12 months(i.e. heart disease, diabetes, cancer, arthritis, hypertension, stroke,emphysema, fractures). We also accounted for body mass index(BMI¼weight in kilograms/(height in meters)2) using two dummyvariables contrasting underweight (BMI< 18.5) and obese(BMI� 30) adults with adults with a BMI of 18.5–30 (NIH, 2000) (nodifference was found between the overweight and normal weightcategories in the analyses with respect to trajectories of disability,so these were collapsed together to form the reference group).

Statistical analyses

We used growth curve models (Singer & Willet, 2003) toexamine adult trajectories of mobility disability over the 15-yearstudy period (1986–2001). In order to facilitate parameter inter-pretation, we center time at the first year of data collection (1986).We analyzed a two-level logit model, with multiple observationsnested within persons over time. Although there are potentiallythree levels, the clustering at the neighborhood level in oursubsample is quite sparse (3.3 respondents per tract in wave 1),particularly over the follow-up waves (1.5 respondents per tract bywave 4). With less than three observations per tract, simulations

Table 1Weighted percents and means for study sample (N¼ 1821). Americans’ ChangingLives Study (age 45þ) (1986).

Variable Weighted percent ormean (SD)

Individual characteristicsAge cohorts

45–54 36.6355–64 24.4465–74 23.9475þ 14.99

Female 54.56

RaceMinority 14.01White 85.99

EducationLess than high school 38.90High school or higher 61.10

Marital statusMarried 67.93Separated/divorced 13.46Widowed 11.14Never married 7.47

Annual household income<$10,000 19.11$10,000–$30,000 37.64$30,000 or higher 43.25

Number of chronic health conditionsNone 38.97One 28.65Two 15.84Three or more 16.54

Body massUnderweight 1.76Normal weight/overweight 83.39Obese 14.85

Severe mobility disability (%) 7.41

Neighborhood characteristicsPopulation density (persons per km2) 2537.75 (3699.42)Percent non-automobile commuter (range 0–91.4%) 9.12 (10.91)Percent age 65þ (range 1.6–70.8%) 13.82 (6.41)Neighborhood disadvantage index (range 2.08–60.52) 11.07 (8.40)

SD¼ standard deviation.

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970 967

suggest that estimates of neighborhood variance will be over-inflated and that estimators will be less efficient (Clarke &Wheaton, 2007). Moreover, the data are not strictly hierarchicalsince individuals are not nested within the same neighborhoodsover time. Rather, neighborhoods are time-varying (entering themodel at level-1) as respondents move in and out of neighborhoodsand also as neighborhood characteristics change over time. Sucha data structure calls for a cross-classified model (Raudenbush &Bryk, 2002), but the current software capabilities for non-linearcross nested models using weighted data are limited. We, therefore,use only a two-level model in our analyses.

The structure of this model can be expressed by equations attwo levels. At level-1 (within-person model), mobility disability attime t is nested within individuals (i). For the case of a binaryoutcome with a binomial error distribution, the logit link functionis used to regress the log odds of the response probability, orproportion with disability, on a linear predictor set of level-1independent variables:

ProbðdisabilitytiÞ ¼ ln

Pij

1� Pij

!¼ p0i þ p1iðyear� 1986Þti

(1)

where p0i is the expected log odds of mobility disability for person iat the first wave of the survey (since year is centered at 1986), andp1i captures the annual rate of change in the log odds of disabilityover the 15-year study period. For the binomial error distribution,the level-1 error variance is a function of the population proportionðs2 ¼ ðpij=ð1� pijÞÞÞ, and is not estimated separately (by usinga scale factor of 1).

The level-1 parameters are then modeled as a function of indi-vidual characteristics (at level two). The level two (between-person) submodel assumes that the log odds of disability at eachtime point varies according to individual characteristics, and weexplicitly model these differences as follows:

p0i ¼ b00 þ b01ðeducÞiþe0i (2.1)

p1i ¼ b10 þ b11ðeducÞi (2.2)

Here, for example, the intercept and slopes from Eq. (1) aremodeled as a function of education, where b01 represents thedifference in the initial log odds of disability (in 1986) for someonewith less than a high school education compared to someone witha high school degree or higher, and b11 captures the difference inthe rate of change in the log odds of disability over time for thosewith and without high school education. The residual error ðe0iÞcaptures between-person variation in the intercept, which weattempt to explain through the incorporation of additional person-level variables. With only four data points we focus only on thefixed effects of secular time, constraining random variance in yearto zero (although we test interactions between time and the otherpredictor variables to see whether trajectory slopes vary by personcharacteristics). Substituting Eqs. (2.1) and (2.2) into Eq. (1) gives usthe full composite model.

All models were estimated in HLM Version 6.06 using restrictedpenalized quasi-likelihood (Raudenbush & Bryk, 2002). Analysesbegan by estimating an unconditional growth model. We thenexamined how trajectories of disability vary by age and other soci-odemographic characteristics. Finally, we included the built envi-ronment variables to test their effects after adjusting for individualrisk factors. Because we hypothesized that older adult mobility maybe more susceptible to built environment characteristics, we testedinteractions between the built environment variables and age. Sincemortality accounts for a substantial loss of subjects in this study

(33% by wave 4), we also include a dummy indicator for death overthe course of the study to address the possibility that those whosurvived throughout the study are systematically different (withrespect to their risk of mobility difficulties) from those who did not(over and above the other covariates in the model). A two-tailedalpha was used to assess statistical significance. We report effects aslogit coefficients as well as odds ratios (OR) with associated 95%confidence intervals (CI).

Results

Table 1 describes the characteristics of the sample at baseline(1986), weighted to account for the sampling design. The majority ofrespondents were married, with a high school education or higher, ofnormal weight, and free of any health problems in the first wave of thesurvey. The weighted proportion reporting severe mobility disabilityinwave 1 was 7.4%, and this rate increased over the course of the studyto 9.3% at wave 2, 13.8% at wave 3, and 18.7% by wave 4. In 1986,respondents lived in census tract neighborhoods characterized byvariability in age structure, commuting practices, and neighborhoodsocioeconomic resources. Our interest is the extent to which theseneighborhood characteristics are associated with the risk of disabilityover time.

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970968

Trajectories of mobility disability

Results from the growth curve models are presented in Table 2.We found no evidence of non-linearity in the trajectory, usingeither polynomial terms or spline age segments. Mobility disabilityincreases linearly over time, but the rate of increase varies by age(Model A).

Among the youngest age group (age 45–54 at baseline), there isno significant change in the odds of mobility disability over the15-year study period. But for the older age groups, the odds ofdisability increase over time. Among those age 75 and over, the logodds of mobility disability increases by 0.121 each year. Convertingthis coefficient into a probability ð1=ð1þ ebx ÞÞ indicates that thepredicted probability of experiencing mobility disability increasesby 47% each year in this age group. Fig. 1 plots the predicted logsodds of mobility disability over time for each age group.

Fig. 1. Trajectories of mobility disability by age: Americans’ Changing Lives Study(1986–2001).

Adjusting for individual characteristics

Model B in Table 2 adds the individual sociodemographic andhealth characteristics. After adjusting for age, trajectories ofmobility disability do not vary by marital status or race/ethnicity,

Table 2Multilevel logistic regression coefficients for trajectories of mobility disability: American

Growth model Odds ratio þSocio-d

Model A Model B

Individual fixed effectsIntercepta ðp0iÞ() �3.050*** 0.048 �6.050**Age 55–64c 0.485 1.624 �0.255Age 65–74c 0.918** 2.505 �0.177Age 75þc 1.567*** 4.791 0.052Femaled 0.377*Racial/ethnic minoritye �0.025<HS educationf 1.365**HS educationf 0.969**Separated/divorcedg 0.304Widowedg �0.103Never marriedg �0.650Income <$10Kh 0.419Income $10–30Kh 0.0391 Health conditioni 0.707**2 Health conditionsi 1.550**3 or more conditionsi 2.394**Underweightj 1.217**Obesej 0.485**Dead by 2001 1.130**

Neighborhood fixed effectsb

Population density% Age 65þNB socioeconomic disadvantage% Non-automobile commuters% Non-auto� age 55–64% Non-auto� age65to74% Non-auto� age75þ

Rate of changeYear 0.033 1.034 0.060*Year*age 55–64 0.060* 1.062 0.062*Year*age 65–74 0.051* 1.052 0.072*Year*age 75þ 0.121*** 1.128 0.163**

a Log odds of mobility disability in 1986 at age 45 HS¼ high school NB¼ neighborhoob Neighborhood variables are standardized around their mean *p< 0.05 **; p< 0.01 **c Reference group is age 45–54.d Reference group is male.e Reference group is white.f Reference group is college degree or higher.g Reference group is married.h Reference group is $30,000þ.i Reference group is no health conditions.j Reference group is normal weight or overweight.

but women have a consistently higher odds of disability over time(OR¼ 1.46, 95% CI¼ 1.04–2.04). Individuals from a lower SEP (lessthan a college degree) report significantly higher log odds ofdisability over time (OR¼ 3.9, 95% CI¼ 2.2–6.9 for less than high

s’ Changing Lives Study (1986–2001), age 45þ (N¼ 1821).

emographic controls Odds ratio þBuilt environment Odds ratio

Model C

* 0.002 �6.166*** 0.0020.755 �0.184 0.8320.838 �0.049 0.9521.054 0.205 1.2281.458 0.389* 1.4750.974 0.112 1.118

* 3.915 1.387*** 4.003* 2.635 0.994*** 2.701

1.355 0.302 1.3520.902 �0.115 0.8920.522 �0.629 0.5331.520 0.465 1.5911.040 0.034 1.0342.028 0.724** 2.062

* 4.713 1.572*** 4.817* 10.592 2.433*** 11.387* 3.378 1.200*** 3.319* 1.624 0.485*** 1.624* 3.097 1.121*** 3.068

�0.135 0.874�0.084 0.920�0.072 0.931

0.169 1.184�0.106 0.899�0.114 0.892�0.380*** 0.684

1.062 0.063** 1.0651.063 0.060 1.0621.074 0.067* 1.070

* 1.177 0.174*** 1.190

d.*; p< 0.001 (two-tailed).

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970 969

school education) but after adjusting for education, income has noeffect on mobility trajectories.

As expected, health problems are strongly associated withmobility disability. Even having just one chronic health problemresults in a higher odds of disability over time (adjusted OR¼ 2.03,95% CI¼ 1.26–3.71). The risk is magnified for those with multiplecomorbidities. Being underweight or obese is also associated withincreased mobility disability, all other things being equal. Theaddition of these individual health and sociodemographic charac-teristics accounts for the age differences in disability at baseline(intercept coefficients for age group, Model B), but age differencesremain in the rate of change in disability over time.

Adjusting for built environment characteristics

Model C in Table 2 adds the four built environment characteris-tics that were hypothesized to influence the odds of disability overtime neighborhood characteristics are grand mean centered andstandardized around their mean so that a unit change in the measurerepresents a one standard deviation change). We found that pop-ulation density, neighborhood age structure, and neighborhoodsocioeconomic disadvantage had no effect on trajectories of mobilitydisability. However, the commuting practices of workers in eachneighborhood were found to be associated with mobility disability,but only among older respondents (age 75þ). The effects were in theexpected direction for those age 55–74, but not statistically signifi-cant. After adjusting for individual sociodemographic and healthcharacteristics that may simultaneously increase the risk of mobilitydisability as well as select people into less accessible built environ-ments, older adults living in neighborhoods with a low proportion ofworkers who commute to work by walking or by public transit (onestandard deviation below the mean) report significantly higher oddsof mobility disability over time. The predicted log odds of disabilityfor older adults living in low non-automobile commuting areas is0.416 (0.205� 0.169þ 0.380) compared to a predicted log odds of�0.006 (0.205þ 0.169� 0.380) in high non-automobile commutingareas. Exponentiating these predicted log odds indicates that, overtime, the odds ratio for mobility disability is 1.5 times higher forolder adults living in areas with a low proportion of non-automobilecommuters.

We found no interactions between the proportion of publictransit commuters in a neighborhood and other risk factors formobility disability (the number of health conditions, education,underweight, or obese). Nor did we find that the trajectory slopesvaried by the proportion of public transit commuters.

Discussion

The purpose of this paper was to examine the relationshipbetween the built environment and trajectories of mobility disabilityover adulthood. Using four waves of nationally representative dataover a 15-year period we found that trajectories of mobility disabilityare steeper for older Americans. We also found that women andthose with lower education had a higher odds of mobility disabilityover time. As expected, health problems were strongly associatedwith mobility disability, and even the addition of just one chronichealth condition doubled the odds of mobility disability across time.Individual health and sociodemographic risk factors accounted forthe age differences in trajectories of mobility disability at baseline,but did not explain the differences in slopes by age.

We found no variation in the effect of health problems onmobility disability across different built environments. Yet, we didfind that living in a census tract characterized by a low proportionof workers who commute to work by public transit or by walkingwas associated with an increased odds of mobility disability among

older adults (age 75þ). For older Americans living in neighbor-hoods characterized by more motorized travel, the odds ratio formobility disability was 1.5 times higher in any given year than forthose living in neighborhoods that were more pedestrian friendly.These results are consistent with earlier studies that found thatgreater street connectivity (Freedman et al., 2008) and higher landuse diversity (Clarke & George, 2005) were associated with reducedIADL disability in older adults. All three of these census-basedmeasures (street connectivity, land use diversity, proportion non-automobile commuters) act as proxy indicators for the underlyingdesign and layout of urban communities with respect to walkabilityand pedestrian access (Cervero, 1996; Cervero & Duncan, 2003;Frank & Pivo, 1994). Such characteristics are likely to influencemobility independence in older adults, which has spillover effectson the ability to remain independent in the community (whenmeasured through disability in instrumental activities).

However, our work extends the boundaries of the existing (andsmall) body of literature in this area by using nationally represen-tative longitudinal data with prospective measures of disability andthe built environment collected over a 15-year period for individ-uals at different stages of adulthood. As a result, we move beyondsimple correlational analyses, by controlling for disability statusand neighborhood characteristics in the prior wave of the survey.We find that living in a more pedestrian friendly neighborhood isassociated with a reduced odds of mobility disability in later life,even after controlling for health problems, disability, and neigh-borhood characteristics at the previous wave. The use of time-varying covariates also allows us to better control for ongoingsociodemographic and health risk factors that may select olderadults into less accessible built environments over time. Whilequestions about causality remain (and are a key issue in theneighborhood literature more generally (Oakes, 2004)), we havenonetheless made a step in the right direction by progressingbeyond simple cross-sectional associations.

Our study was limited bya reliance on proxy measures of the builtenvironment using indicators from the U.S. Census. We were alsoconstrained by a reliance on administrative definitions of neigh-borhoods (census tracts), which may not be the most appropriatelevel of aggregation. More direct measures of built environmentcharacteristics within more respondent-centered neighborhoodsare likely to have stronger effects (Clarke et al., 2008). Of the fiveneighborhood built environment indicators we examined, only thecommuting practices in the census tract were significantly related tomobility disability. This is probably because the proportion of non-automobile commuters is likely to capture the structure of the builtphysical environment in the neighborhood in terms of sidewalkcontinuity, pedestrian amenities, four-way stop signals, and otherfactors associated with non-motorized travel, which can havea positive spillover effect on mobility independence among olderadults. However, a reliance on proxy indicators precludes us fromunderstanding exactly what this measure is capturing (e.g. avail-ability of public transportation, pollution levels, street connectivity,etc.). Further research in this area could greatly enhance ourunderstanding of environmental barriers and facilitators formobility independence in older adults.

This study contributes to the emerging body of literature on therole of the built environment in the disablement process byexamining effects on long-term trajectories of disability overa 15-year period. We identified heterogeneity in disability trajec-tories by built environment characteristics. From a longitudinalperspective, as older adults move in and out of different neigh-borhoods (or as neighborhood characteristics change over this15-year period), their risk for outdoor mobility disability changes(they change trajectories). Thus, mobility disability could ostensiblybe reduced in older adults if their communities were adapted to

P. Clarke et al. / Social Science & Medicine 69 (2009) 964–970970

better accommodate more pedestrian travel. When consideringways to minimize disability as the population ages, changes to thebuilt environment (e.g. continuous, barrier-free sidewalks, curbcuts, benches, shade trees, street lights) may be easier to imple-ment than efforts to change risk factors at the individual level.

Acknowledgements

Additional funds were made available to the first author fromthe University of Michigan Center for Social Inequality Mind andBody (NIH/NICHHD grant R24HD04786, Life Course Core) tosupport the analyses. The content of this paper is solely theresponsibility of the authors and do not necessarily represent theofficial views of the sponsors. An earlier version of this paper waspresented at the 60th Annual Scientific Meeting of the Geronto-logical Society of America, San Francisco, CA, November 18, 2007.

References

Americans with Disabilities Act. (1990). Public law 101-336, 42 USC [sections]12101. www.dol.gov/esa/regs/statutes/ofccp/ada.htm. Accessed June 12.06.07.

Beard, J. R., Blaney, S., Cerda, M., Frye, V., Lovasi, G. S., Ompad, D., et al. (2009).Neighborhood characteristics and disability in older adults. The Journals ofGerontology Series B: Psychological Sciences and Social Sciences. doi:10.1093/geronb/gbn018. Advance Access published on January 29, 2009.

Bowling, A., & Stafford, M. (2007). How do objective and subjective assessments ofneighbourhood influence social and physical functioning in older age? Findingsfrom a British survey of ageing. Social Science & Medicine, 64(12), 2533–2549.

Centers for Disease Control and Prevention, Department of Environmental Health.Accessibility and the environment. http://www.cdc.gov/healthyplaces/healthtopics/accessibility.htm. Accessed 20.03.07.

Cervero, R. (1996). Mixed land-uses and commuting: evidence from the AmericanHousing Survey. Transportation Research – Part A, 30, 361–377.

Cervero, R. (2002). Built environments and mode choice: toward a normativeframework. Transportation Research – Part D, 7, 265–284.

Cervero, R., & Duncan, M. (2003). Walking, bicycling, and urban landscapes: evidencefrom the San Francisco Bay area. American Journal of Public Health, 93, 1478–1483.

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: density, diversity,and design. Transportation Research – Part D, 2, 199–219.

Clarke, P., Ailshire, J., Bader, M., Morenoff, J., & House, J. (2008). Mobility disability andthe urban built environment. American Journal of Epidemiology, 168, 506–513.

Clarke, P., & George, L. K. (2005). The role of the built environment in thedisablement process. American Journal of Public Health, 95(11), 1933–1939.

Clarke, P., Lawrence, J. M., & Black, S. E. (2000). Changes in quality of life over thefirst year after stroke: findings from the Sunnybrook Stroke Study. Journal ofStroke and Cerebrovascular Diseases, 9(3), 121–127.

Clarke, P., Marshall, V. W., Black, S. E., & Colantonio, A. (2002). Well-being followingstroke in Canadian seniors: findings from the Canadian Study of Health andAging. Stroke, 33, 1016–1021.

Clarke, P., & Wheaton, B. (2007). Addressing data sparseness in contextual pop-ulation research: using cluster analysis to create synthetic neighbourhoods.Sociological Methods and Research, 35, 311–351.

Crawford, D., Timperio, A., Giles-Corti, B., Ball, K., Hume, C., Roberts, R., et al. (2008).Do features of public open spaces vary according to neighbourhood socio-economic status? Health and Place, 14, j889–893.

Crimmins, E. M. (2004). Trends in the health of the elderly. Annual Review of PublicHealth, 25, 79–98.

Debnam, K., Harris, J., Morris, I., Parikh, S., & Shirey, L. (2002). Durham countysocially isolated older adults: An action-oriented community diagnosis. Depart-ment of Health Behavior and Health Education, School of Public Health,University of North Carolina at Chapel Hill.

Ettner, S. L. (1996). The opportunity costs of elder care. Journal of Human Resources,31, 189–205.

Frank, L., & Pivo, G. (1994). The impacts of mixed use and density on the utilizationof three modes of travel: the single occupant vehicle, transit, and walking.Transportation Research Record, 1466, 44–52.

Freedman, V. A., Crimmins, E. M., Schoeni, R. F., Spillman, B. C., Aykan, H., Ellen, A.,et al. (2004). Resolving inconsistencies in trends in old-age disability: reportfrom a technical working group. Demography, 41, 417–441.

Freedman, V. A., Grafova, I. B., Schoeni, R. F., & Rogowski, J. (2008). Neighborhoodsand disability in later life. Social Science & Medicine, 66, 2253–2267.

Freedman, V. A., Martin, L. G., & Schoeni, R. F. (2002). Recent trends in disability andfunctioning among older adults in the United States: a systematic review. JAMA,288, 3137–3146.

Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untanglingthe Concepts of disability, frailty, and comorbidity: implications for improvedtargeting and care. Journals of Gerontology, Series A: Biological Sciences andMedical Sciences, 59, M255–263.

Glass, T., & Balfour, J. (2003). Neighborhoods, aging, and functional limitations. InI. Kawachi, & L. Berkman (Eds.), Neighborhoods and health (pp. 303–334).Oxford, UK: Oxford University Press.

House, J. S., Kessler, R. C., & Herzog, A. R. (1990). Age, socioeconomic status, andhealth. Milbank Quarterly, 68(3), 383–411.

House, J. S., Lepkowski, J. M., Kinney, A. M., Mero, R. P., Kessler, R. C., & Herzog, A. R.(1994). The social stratification of aging and health. Journal of Health and SocialBehavior, 35, 213–234.

Jackson, R. J., & Kochtitzky, C. (2001). Creating a healthyenvironment: impactof the builtenvironment on public health. Sprawl Watch Clearinghouse monograph series.http://www.cdc.gov/healthyplaces/articles/Creating%20A%20Healthy%20Environment.pdf Available from.

Kelley-Moore, J. A., & Ferraro, K. F. (2004). The Black/White disability gap: persistentinequality in later life? Journals of Gerontology, Series B: Psychological Sciencesand Social Sciences, 59, S34–43.

Klinenberg, E. (2003). Heat wave: A social autopsy of disaster in Chicago (Illinois).Chicago: University of Chicago Press.

Kochera, A. (2003). Livable communities: research results relevant to makingcommunities livable for residents aging in place. The Gerontologist, 43(Specialissue 1), 445–446.

Koplan, J., & Flemming, D. (2000). Current and future public health challenges.JAMA, 284, 1696–1698.

Li, L. W. (2005). Predictors of ADL disability trajectories among low-income frailelders in the community. Research on Aging, 27, 615–642.

Manton, K. G., & Gu, X. (2001). Changes in the prevalence of chronic disability in theUnited States black and non-black population above age 65 from 1982 to 1999.Proceedings of the National Academy of Science, 98, 6354–6359.

Markham, J. P., & Gilderbloom, J. I. (1998). Housing quality among the elderly:a decade of changes. International Journal of Aging and Human Development,46(1).

National Center for Health Statistics. (2006). Health, United States, 2006. Withchartbook on trends in the health of Americans. Hyattsville, MD: National Centerfor Health Statistics.

National Institutes of Health and National Heart, Lung, and Blood Institute (NIH).(October 2000). The practical guide: Identification, evaluation and treatment ofoverweight and obesity in adults. Report No.: 00–4084. National Institutes ofHealth and National Heart, Lung, and Blood Institute.

Oakes, J. M. (2004). The (mis)estimation of neighborhood effects: causal infer-ence for a practicable social epidemiology. Social Science & Medicine, 58,1929–1952.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications anddata analysis methods. Thousand Oaks: Sage Publications.

Raudenbush, S. W., & Sampson, R. J. (1999). Ecometrics: toward a science ofassessing ecological settings, with application to the systematic social obser-vation of neighborhoods. Sociological Methodology, 29, 1–41.

Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing ‘‘neighbor-hood effects’’: social processes and new directions in research. Annual Review ofSociology, 28, 443–478.

Singer, J. D., & Willet, J. B. (2003). Applied longitudinal data analysis. New York:Oxford University Press.

Statistics Canada. (2001). 2001 Census dictionary. Ottawa: Statistics Canada. Cata-logue No. 92-378-XPE.

Stuck, A. E., Walthert, J. M., Nikolaus, T., Bula, C. J., Hohmann, C., & Beck, J. C. (1999).Risk factors for functional status decline in community-living elderly people:a systematic literature review. Social Science & Medicine, 48(4), 445–469.

Taylor, M. G., & Lynch, S. M. (2004). Trajectories of impairment, social support anddepressive symptoms in later life. Journals of Gerontology: Social Sciences, 59,S238–246.

Trupin, L., Rice, D. P., & Max, W. (1995). Medical expenditures for people withdisabilities in the United States, 1987. Washington, DC: U.S. Dept of Education,National Institute on Disability and Rehabilitation Research.