multiple health disparities among minority adults with mobility limitations: an application of the...

Multiple health disparities among minority adults with mobilitylimitations: An application of the ICF framework and codes

GWYN C. JONES & LISA B. SINCLAIR

Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities, Atlanta,

Georgia, USA

AbstractPurpose. To examine the interface between mobility limitations and minority status and its effect on multiple health andhealth-related domains among adults, using the framework of the International Classification of Functioning, Disability andHealth (ICF).Methods. We combined 8 years of data from the 1997 – 2004 US National Health Interview Survey to investigate healthdisparities among minorities with mobility limitations as defined by the ICF. A total of 79,739 adults surveyed met thesecriteria.Results. Adults with both mobility limitations and minority status experienced the greatest disparities (p5 0.001) inworsening health (adjusted odds ratio [AOR]¼ 8.5), depressive symptoms (AOR¼ 17.2), diabetes (AOR¼ 5.5),hypertension (AOR¼ 3.4), stroke (AOR¼ 7.2), visual impairment (AOR¼ 4.6), difficulty with activities of daily living(AOR¼ 42.7) and instrumental activities of daily living (AOR¼ 27.7), use of special equipment (AOR¼ 28.1), obesity(AOR¼ 3.3), physical inactivity (AOR¼ 2.7), and low workforce participation (AOR¼ 0.35).Conclusions. For most outcome measures, findings supported our hypothesis that persons with both mobilitylimitations and minority status experience greater health disparities than do adults with minority status or mobilitylimitations alone.

Keywords: Disability, ICF, mobility limitations, minorities

Introduction

Purpose

This paper uses the International Classification of

Functioning, Disability and Health (ICF) as a

framework to investigate the interface between

disability and minority status and its effects

on several outcome measures in health and

health-related domains [1]. The disability that is

the focus of this study is mobility limitation,

defined as difficulty walking and moving around

ICF codes (d450-d469) and changing or

maintaining body position (ICF codes d410-

d429) [1 – 3].

People with mobility limitations

Although mobility limitations are commonly asso-

ciated with advancing age, they can and do affect

adults of any age [3]. Promoting health, well-being,

and quality of life among people with mobility

limitations and other disabilities has become an

important public health goal [4 – 7]. Studies have

identified several national health concerns affecting

people with disabilities, including increased func-

tional difficulties and environmental barriers [8,9],

overall poor health [10], and depressive symptoms

[11 – 14]. In addition, research has shown that

people with mobility limitations can develop the

same chronic conditions and health risks that affect

adults without disabilities [8,12,15 – 17]. Other

Correspondence: Gwyn C. Jones, PhD, MSW, Med, Health Scientist, Centers for Disease Control and Prevention, National Center on Birth Defects

and Developmental Disabilities, 1600 Clifton Road, NE, MS-E88 Atlanta, GA 30333, USA. Tel: þ1 404 498 4493. Fax: þ1 404 498 3050.

E-mail: [email protected]

Disability and Rehabilitation, 2008; 30(12 – 13): 901 – 915

ISSN 0963-8288 print/ISSN 1464-5165 online ª 2008 Informa UK Ltd.

DOI: 10.1080/09638280701800392

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studies have addressed adverse health behaviours

among adults with disabilities, such as tobacco [16 –

18] and alcohol use [16,19], obesity [16,19 – 24],

and physical inactivity [16,25 – 27].

Ethnic and racial minorities

Over the past two decades, disparities in adverse

health behaviours and chronic conditions among US

ethnic and racial minorities have been well docu-

mented [5,28 – 30]. Disparities in overall health

status and quality of life have been noted among

these groups [31 – 34]. Researchers have also voiced

concerns about depression among racial and ethnic

minorities and have urged practitioners and policy

makers to incorporate cultural aspects of race and

ethnicity into diagnosis and treatment of health

problems [35 – 38]. Sufficient data indicate that

tobacco and alcohol use [16,39 – 42] and physical

inactivity and obesity [43,45] are prevalent health

risk behaviours among various ethnic and racial

minority groups. Chronic conditions greatly affecting

minorities include hypertension, cardiovascular dis-

ease, cancer, diabetes, and visual impairment [5,28 –

30,46 – 50]. To address these and other health

concerns, calls to action have risen across the USA

to eliminate ethnic and racial disparities and to

establish minority health agenda items [5,51 – 53].

Ethnic and racial minorities with mobility limitations

In 1997, Turk et al. asserted that no literature existed

about the effects of race, class, and gender on the

health status of persons with disabilities [54]. A

search identified available literature in this emerging

area [55]. In a series of articles on spinal cord injury,

Krause noted issues related to ethnicity and race with

overall well-being, depression, and health outcomes

[56 – 61]. Health behaviour studies show that smok-

ing and drinking [62 – 64], physical inactivity

[65,66], and obesity [23,24,67,68] are prominent

risk behaviours among various racial and ethnic

minorities with disabilities. Although depression,

respiratory problems, cardiovascular problems, and

diabetes ranked in the top half of reported secondary

conditions among Native Americans with physical

disabilities [69], chronic secondary conditions

among all minorities with mobility limitations are

not well documented. This study will contribute

much-needed nationally representative data on over-

all reported health status, depressive symptoms,

commonly occurring chronic conditions, functional

activities and participation, and health risk beha-

viours for minorities with mobility limitations. While

ethnic and racial minorities have always been

represented in Healthy People, the HP2010 plan

represents the first deliberate establishment of US

health objectives for people with disabilities. As with

ethnic and racial minority groups, the data indicate

that people with disabilities experience persistent

health disparities [17]. In this study, we examine

health disparities for persons with both of these

human attributes.

The International Classification of Functioning,

Disability and Health

Health and risk assessments from the epidemiologic

studies described previously employed useful but

non-standardized measures. Over the past decade,

however, discussions have ranged from acknowl-

edging [70,71] and summarizing [4,72 – 74], to

operationally examining, the International Classifica-

tion of Functioning, Disability and Health (ICF) [75 –

81]. The ICF was developed by the World Health

Organization (WHO) as a companion tool to the

International Classification of Diseases (ICD). Its

purpose is to promote understanding of functioning,

disability, and health by describing these concepts in

terms of health domains and health-related domains

associated with body functions and structures and

activities and participation. It offers a framework for

describing the interactive effects of functioning,

personal activities, social participation, and environ-

mental influences [1]. Among its important features

is the potential to standardize communication within

the classification scheme across disciplines, assess-

ment tools, and continents [82]. The ICF has been

used to create health assessment tools [82 – 84] and

to identify components in other existing tools that are

ICF-related [9,78,83 – 90]. The approach to define

mobility limitations undertaken by Hendershot

(2003) seems directly relevant to this epidemiologic

study [85].

The ICF precursor, the International Classifica-

tion of Impairments, Disabilities, and Handicaps

(ICIDH), was used to derive measures of disability

in the Survey of Income Programme Participation

(SIPP) [83] and as a framework for the 1994 – 1995

US National Health Interview Survey (NHIS)

Disability Supplement [84]. The ICF has been used

more recently to retrospectively map mobility func-

tions in the 1997 NHIS [85]. The current study uses

nationally representative data and the ICF to define

mobility limitations and examine links between

mobility limitations, commonly occurring chronic

conditions, functional activities and participation,

and health behaviours among racial and ethnic

minority and non-minority adults.

Methods

In this retrospective, cross-sectional study, we

examined the interface between mobility limitations

902 G. C. Jones & L. B. Sinclair

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and minority status to identify subpopulations of

adults aged 18 and older who may be experiencing

significant health disparities in several health and

health-related domains. We addressed the following

questions: (i) What is the prevalence of minority

status among adults with mobility limitations? (ii)

How do adults with mobility limitations who are also

members of racial and ethnic minorities differ

demographically from other subgroups?, and (iii) In

what health and health-related domains are adults

with mobility limitations and minority status most

likely to experience significant health disparities? We

defined health disparities as statistically significant

differences between comparison groups for outcome

measures such as health status, chronic conditions,

and health behaviours. We hypothesized a stronger

association between multiple health disparities and

the combination of mobility limitations and minority

status than between disparities and mobility limita-

tions or minority status alone.

Data source

The National Health Interview Survey (NHIS) is a

nationally representative, face-to-face interview sur-

vey of the non-institutionalized civilian population

living in the USA [91]. It is conducted annually by

the US Census Bureau and the National Centre for

Health Statistics (NCHS), Centers for Disease

Control and Prevention, to gather health-related

information about households and individuals.

For decades, the NHIS has been used widely by

health services researchers to address issues of

chronic disease, health status, health risks, and

disability. Since 1997, one adult has been selected

from each surveyed household to participate in the

Sample Adult Core module of the survey.

We focused our investigation on data collected

from the 30,000 or more sample adults selected in

each year of the survey from 1997 to 2004. We made

this choice because the Sample Adult Core contains

specific information about commonly occurring

chronic conditions and health behaviours that is

not found in the main survey of children and

adults or in other survey components for adults.

Additionally, because all but a tiny fraction of

participants in the Sample Adult Core were self-

responding, we were able to virtually eliminate proxy

bias. We chose data from 1997 – 2004 because the

survey design was consistent over that period. The

survey files contain no personal identifiers and are

available to any interested investigator from the

NCHS website.

Beginning with the 1997 adult core questionnaire

[92] as our baseline, we examined survey question-

naires for each survey year. We selected items of

interest that were asked of all sample adults for each

of the 8 years of the survey. Since each item in the

NHIS questionnaires is associated with variables in

the data set for each individual year, we developed a

variable crosswalk (grid) for all variables of interest.

This procedure ensured that each selected variable

measured the same construct across our 8 survey

years. We imported our sample adult data into SPSS

14.0 for Windows [93], creating one data file for

each year from 1997 to 2004. We kept only target

variables in each file, recoding names where neces-

sary and verifying that all variable values measured

the same construct in each individual file, to ensure

parity across files. After ensuring that all eight sample

adult files were identical in content, we merged the

files, using recommended NCHS procedures [91].

We combined the survey years to avoid small cell

sizes that commonly plague disability research. We

repeated these procedures for importing and clean-

ing all other survey data files that contained demo-

graphic and functional status measures not found in

the sample adult files. We divided each final annual

weight in these files by 8, the number of survey years

in our data set, to obtain accurate national estimates

for outcome measures contained in these files [91].

Finally, we back coded (matched) NHIS survey

questions used in selecting variables for the analysis

to the ICF framework [77,85].

Data analysis

We analysed data from our combined survey files to

examine the effects of mobility limitation and

minority status on measures of (i) health status,

(ii) commonly-occurring chronic conditions, (iii)

functional activities and participation, and (iv)

health behaviours among adults aged 18 years and

older. For the workforce participation measure, we

included only adults who were 18 – 64 years old.

We evaluated associations between dichotomous

outcomes and covariates of interest, using frequency

tables (cross-tabulations) for individual covariates

and logistic regression for collections of covariates

of interest. For polychotomous outcome measures,

we fit generalized logit models, using the same

independent predictors that we used in our logistic

modeling.

Because of the complex stratified cluster sampling

design used in conducting the NHIS, we used

SUDAAN 9.0 computer software [94,95] to estimate

the standard errors, to take into account both the

sampling weights and the multistage clustering de-

sign of the survey. In all cross-tabulations and logistic

models, we tested for effects of mobility limitation,

minority status, and the combination of these two

predictors. In the logistic regression procedures, the

effects of interest were evaluated after adjusting

for age and sex. We had a sample of more than

Ethnic and racial minorities with mobility limitations 903

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258,000 self-responding adults, aged 18 years and

older. This group served as the denominator for

many of our analyses.

Independent measures

Mobility limitations. We derived our definition of

mobility limitations from the ICF and from current

literature on disability and health [1,14,85]. In the

ICF, specific codes are assigned to each health and

health-related domain [1]; questions in the NHIS

on functioning, activities, and participation are

based on the ICF codes [85]. We chose NHIS

questions related to two domains in the Mobility

section of the ICF: changing and maintaining body

position (ICF codes d410-d429), and walking and

moving (ICF codes d450-d469) [1,85,92]. We

defined mobility limitations as difficulty walking

¼ mile, difficulty climbing 10 steps without

resting, difficulty bending or stooping, or difficulty

with sustained sitting or standing [14]. Because we

were interested in adults with mobility limitations

as a group, we did not apply ICF qualifiers that are

associated with the d-codes.

Ethnic and racial minority status. All respondents who

said that they were members of a US minority racial

or ethnic group were classified as having minority

status. Respondents who reported that they were

Hispanic, African American, Asian, Pacific Islander,

Native American, or of multiple racial and ethnic

origin, were included in this category. Non-Hispanic

whites served as the comparison group for minority

respondents.

Dependent measures

For each of our four comparison groups – adults with

both mobility limitations and minority status, adults

with mobility limitations alone, adults with minority

status alone, and adults with neither mobility

limitations nor minority status – we examined 5

demographic measures, 2 health status measures, 10

commonly occurring chronic conditions, 4 measures

of functional activities and participation, and 6

measures of health behaviours.

Demographics. We selected age groups (18 – 24

years, 25 – 44 years, 45 – 64 years, and 65 years and

older) commonly used in data reports published by

NCHS [96] to describe our sample. Both age group

and sex were used in all logistic regression models.

We also looked at the covariates education (high

school or less vs more than high school), annual

income (�$20,000 vs 5$20,000), and marital status

(married, not married) to identify demographic

disparities in our sample.

Health status. Physical health and mental health play

crucial roles in the daily and long-term functioning,

community participation, and well-being of people

with disabilities [11]. We were particularly interested

in identifying groups that were at higher risk of

worsening health because intervention in these

domains can enhance and maintain quality of life

for people with disabilities. We compared two

summary measures of health status: physical health

(health better, worse, or about the same as 12

months ago) and symptoms of depression (mild,

moderate, severe, or no symptoms). For compar-

isons of health status, respondents who reported that

their health remained the same over the past 12

months served as our reference group.

For comparisons on depressive symptoms (ICF

codes b152-b159), no similar variable was available

for our mental health measure. We constructed a

measure for depressive symptoms (mild, moderate,

severe, no symptoms) based on responses to survey

variables used in the Kessler K6 Scale [97 – 100]. For

almost a decade, the K6 Scale has been a part of the

World Health Organization’s series of screening

surveys. Over time, it has demonstrated sensitivity

and specificity in detecting the prevalence of mood

and anxiety disorders [101]. The K6 Scale includes

questions on feelings of sadness, hopelessness,

nervousness, restlessness, worthlessness, and the

sense that everything is an effort, that significantly

interfere with the respondent’s daily activities [92].

The NHIS incorporates these questions in its sample

adult questionnaires. Respondents are asked to rate

the extent to which each feeling interfered with their

activities: none of the time, a little of the time, some

of the time, most of the time, and all of the time.

Thus each respondent could rank level of difficulty

caused by each of these feelings from 1 to 5, with 1

indicating the most difficulty and 5 indicating the

least difficulty. We reverse-coded the values for these

six variables and summed across variable scores

(unweighted) for each respondent to obtain the

respondent’s K6 Scale score. We then recoded K6

Scale scores in the following way: Respondents with

a score of 6 were rated as having no symptoms of

depression (ICF qualifier¼ 0). They served as our

reference group for study comparisons on level of

depressive symptoms; scores of 7 – 12 indicated mild

symptoms that were problematic a little of the time

(ICF qualifier¼ 1); scores of 13 – 18 indicated

moderate symptoms that were problematic some of

the time (ICF qualifier¼ 2); and scores of 19 or

higher indicated severe depressive symptoms that

were problematic most of the time or all of the time

(ICF qualifier¼ 3).

Commonly occurring chronic conditions. Because the

presence of chronic health conditions may intensify


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the effects of a disability and may cause further

health declines in adults with mobility limitations,

we examined data on 10 commonly occurring

chronic conditions. Six of these conditions (dia-

betes, hypertension, stroke, heart problems, breath-

ing problems, and cancer) were diagnosed by a

physician. Respondents were included in the heart

problem category (ICF codes b410-b429) if they

reported physician-diagnosed myocardial infarction,

angina, coronary heart disease, or other heart

problems. Respondents were included in the

breathing problems category (ICF codes b440-

b449) if they reported having physician-diagnosed

emphysema, asthma, or chronic bronchitis. We did

not have enough information to attribute disability

causality to a specific condition. Respondents were

identified as having joint symptoms (ICF codes

b280-b289) and low back pain (ICF code b28013)

by self-report. Our hearing impairment (ICF code

b230) and visual impairment (ICF codes b210-

b229) measures were also self-reported as difficulty

hearing and trouble seeing, even with glasses or

contact lenses.

Functional activities, social participation, and environ-

ment. We included four measures of functional

status: difficulty with activities of daily living

(ADLs) such as eating, dressing, and bathing

(ICF codes d510-d599); difficulty with instrumen-

tal activities of daily living (IADLs) such as

shopping for groceries (ICF codes d610-d629),

doing housework (ICF codes d630-d669), socializ-

ing with friends and family (ICF codes d710-779),

and participation in community events (ICF codes

d910-d999); use of special equipment such as

canes and wheelchairs to negotiate the environment

(ICF codes e110-e129); and participation in the

workforce (for adults age 18 – 64 years, ICF codes

d840-d859).

Health behaviours. We examined six measures of

health behaviours for each group of adults, including

current smoking (every day or some days per week)

and current drinking (at least once per week),

overweight but not obesity, obesity, morbid obesity,

and physical inactivity. We included all alcohol use

because people with mobility limitations often take

multiple prescription medications on a regular basis,

and many of these medications indicate on their

labels that they should not be taken by persons who

are drinking any alcohol. Research has shown that

commonly prescribed drugs, such as nonsteroidal

anti-inflammatory drugs (NSAIDs) and selective

serotonin reuptake inhibitors (SSRIs) can have

adverse health outcomes for people who use alcohol

while taking them [102,103]. We used three mea-

sures to indicate levels of weight control problems

(ICF code b530): overweight, but not obesity (BMI

�25 but 530), obesity (BMI� 30, and a subgroup

of obesity – morbid obesity (BMI� 40) [67,91].

Respondents were categorized as being physically

inactive if they did not exercise at least once a week,

or if they never exercised at all. Our intention was to

identify adults who were the most physically inactive,

rather than those who met any national exercise

criteria.

Results

Population

Table I describes our study population by sample size,

percentages, and weighted population estimates.

Our unweighted total sample size for the 1997 –

2004 study period was 258,279 adults aged 18 years

and older. Totals differed for some analytical

procedures, depending on response rates for specific

survey items. Respondents who said that they had

any difficulty performing activities related to chan-

ging and maintaining body position or to walking

and moving around without the help of another

person or without using special equipment were

categorized as having a mobility limitation. A total of

79,739 adults reported having a mobility limitation.

Persons with no difficulty performing these activities,

and who used no special equipment were included in

the ‘no mobility limitation’ category. A total of

87,562 adults met the criteria for minority status.

Table I. Sample sizes and population estimates.

N Weighted % 95% CI Population estimate

Mobility limitations 79,739 28.9 28.6, 29.3 59,013,495

No mobility limitations 178,540 71.1 70.7, 71.4 144,959,746

Minority status 87,562 26.4 25.8, 27.0 53,798,323

Non-minority status 170,627 73.6 73.0, 74.2 150,176,919

Mobility limitations and minority status 22,633 21.2 20.5, 21.9 12,524,312

No mobility limitations, non-minority status 113,521 71.5 70.5, 72.2 103,687,735

CI, confidence interval. Data source: Author calculations based on National Health Interview Survey (1997 – 2004). Hyattsville, MD:

Centers for Disease Control and Prevention, National Center for Health Statistics. Accessed 5 January 2006 from: http://www.cdc.gov/nchs/

nhis.htm


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A total of 22,633 respondents reported both mobility

limitations and minority status.

Demographics

Our findings on demographic characteristics are

summarized in Table II.

The distribution of adults with mobility limitations

was skewed toward the 65-and-older age group

(33.9%). Females predominated among minorities

(52.3%), adults with mobility limitations (59.7%),

and adults with mobility limitations who were

members of a minority group (62.5%). Adults with

mobility limitations who were also members of

minority groups were the most likely to have low

educational attainment (64.8%), to have annual

incomes below $20,000 (51.2%), and to be unmar-

ried (65.9%).

Findings for health status, depressive symptoms,

commonly occurring chronic conditions, functional

activities and participation, and health behaviours are

summarized in Table III. Findings reported in Table

III for main and interactive effects were significant at

p5 0.001 for chi square tests and adjusted odds

ratios (AOR).

Health status

Adults in minority groups were the most likely to

report that their health was better than it was a year

ago (19.4%, AOR¼ 1.2). We had no way to

determine what the prior year level of health status

was for our comparison groups, so it is difficult to

know exactly what ‘better health’ means for mino-

rities. Percentages of having worse health increased

across comparison groups, with adults with mobility

limitations who were members of minority groups

reporting the highest percentage of worse health

(22.2%, AOR¼ 8.5 vs 3.5%, AOR¼ 1.0 for adults

with neither attribute of interest). Adults with neither

mobility limitations nor minority status were the

most likely to report stable health (79.9%,

AOR¼ 1.0).

Depressive symptoms

The highest percentage of respondents with mild

depressive symptoms (46.7%, AOR¼ 3.2) was

among adults with mobility limitations. Adults with

both mobility limitations and minority status were

the most likely to report experiencing moderate

(18.4%, AOR¼ 8.9) or severe depressive symptoms

(9.0%, AOR¼ 17.2).

Commonly occurring chronic conditions

We examined 10 commonly occurring chronic

conditions for each of our comparison groups. Our

analysis of main effects revealed that minorities were

more likely than non-minorities to have diabetes

Table II. Demographic characteristics of adults with mobility limitations by minority status.

Domains

No mobility

limitations,

non-minority status Minority status Mobility limitations

Mobility limitations

and minority status

% 95% CI % 95% CI % 95% CI % 95% CI

Age

18 – 24 years 14.7 14.2, 15.2 17.2 16.8, 17.7 5.1 4.9, 5.4 7.1 6.6, 7.6

25 – 44 years 44.7 44.2, 45.1 47.0 46.4, 47.5 24.6 24.2, 25.1 30.6 29.7, 31.5

45 – 64 years 30.2 29.7, 30.6 25.8 25.3, 26.3 36.4 35.9, 36.9 36.6 35.7, 37.5

�65 years 10.5 10.2, 10.8 10.0 9.6, 10.4 33.9 33.3, 34.5 25.7 24.7, 26.7

Sex

Male 51.2 50.9, 51.6 47.7 47.3, 48.2 40.3 39.9, 40.7 37.5 36.7, 38.2

Female 48.8 48.4, 49.1 52.3 51.8, 52.7 59.7 59.3, 60.1 62.5 61.8, 63.3

Education completed

�High school 39.1 38.4, 39.8 56.6 55.8, 57.4 56.3 55.6, 56.9 64.8 63.8, 65.8

4High school 60.9 60.2, 61.6 43.4 42.6, 44.2 43.7 43.1, 44.4 35.2 34.2, 36.2

Annual income

�$20,000 75.8 75.3, 76.3 59.9 59.1, 60.6 60.2 59.6, 60.8 48.8 47.6, 49.0

5$20,000 24.2 23.7, 24.7 40.1 39.4, 40.9 39.8 39.2, 40.4 51.2 50.0, 52.4

Marital status

Married 52.8 52.1, 53.5 41.0 40.3, 41.6 44.4 43.8, 45.0 34.1 33.2, 35.1

Not married 47.2 46.5, 47.9 59.9 58.4, 59.7 55.6 55.0, 56.2 65.9 64.9, 66.8

CI, confidence interval. Data source: Author calculations based on National Health Interview Survey (1997 – 2004). Hyattsville, MD:

Centers for Disease Control and Prevention, National Center for Health Statistics. Accessed 5 January 2006 from: http://www.cdc.gov/nchs/

nhis.htm


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(7.3% vs 2.9%), hypertension (22.4% vs 16.5%),

stroke (2.0% vs 0.7%), heart problems (7.4% vs

7.0%), breathing problems (11.1% vs 10.5%), low

back pain (23.8% vs 20.0%), joint problems (27.4%

vs 25.5%), and visual impairment (8.4% vs 4.9%).

However, in the logistic regression models, the racial

and ethnic differences for heart problems, breathing

problems, low back pain, joint problems, and hearing

impairment disappeared when we controlled for age,

sex, and mobility limitations status. Some of these

conditions were more problematic for adults with

mobility limitations alone than they were for

individuals with mobility limitations and minority

status. Adults with mobility limitations alone had the

highest percentages for 6 of these conditions: heart

problems (23.9%, AOR¼ 3.0), breathing problems

(21.3%, AOR¼ 2.2), low back pain (49.5%,

AOR¼ 4.7), joint symptoms (66.7%, AOR¼ 5.7),

cancer (12.6%, AOR¼ 1.7), and hearing impairment

(31.1%, AOR¼ 2.8).

For the remaining conditions, the percentages

increased across comparison groups with adults who

had both mobility limitations and minority status

reporting the highest percentages. These conditions

included diabetes (18.5%, AOR¼ 5.5), hypertension

(46.3%, AOR¼ 3.4), stroke (6.9%, AOR¼ 7.2), and

visual impairment (21.4%, AOR¼ 4.6).

Functional activities and social participation

We examined three outcome measures for functional

activities for all adults in our sample and one

measure of community participation for adults 18 –

64 years old. The occurrence of difficulty with ADLs

such as eating, bathing, and dressing increased

across comparison groups, with the lowest occur-

rence among non-minority adults without mobility

limitations (0.1%, AOR¼ 1.0), and the highest

occurrence among adults with both mobility limita-

tions and minority status (7.0%, AOR¼ 42.7). The

same pattern occurred for difficulties with IADLs

such as doing housework, preparing meals, shopping

for groceries and other necessities, and socializing

with family and friends. Adults with neither minority

status nor mobility limitations reported the lowest

level of difficulty with these activities (0.5%,

AOR¼ 1.0), compared with 15.0% (AOR¼ 27.7)

of adults with mobility limitations and minority

status. The same pattern held true for use of special

equipment, such as a cane or wheelchair, to negotiate

the environment. Adults with neither attribute of

interest were the least likely to report using special

equipment (0.7%, AOR¼ 1.0), compared with

adults with both mobility limitations and minority

status (18.9%, AOR¼ 28.1). Among adults aged

18 – 64 years old, nearly three-fourths of adults

without mobility limitations and with non-minority

status (74.0%, AOR¼ 1.0) reported that they were

currently working at a job or business. Workforce

participation dropped to 64.4% (AOR¼ 0.75) for

minorities, to 55.8% (AOR¼ 0.49) for adults with

mobility limitations, and to 48.7% (AOR¼ 0.35) for

adults with mobility limitations who also belong to a

racial or ethnic minority group.

Health behaviours

We examined six measures of health risk behaviours

for our sample of survey respondents: current

smoking, current drinking, overweight but not

obesity, obesity, morbid obesity, and physical in-

activity.

Current smoking. Adults were classified as current

smokers if they smoked cigarettes daily or several

days per week. Members of minority groups were the

least likely to be current smokers (19.8%,

AOR¼ 0.73), and adults with mobility limitations

were the most likely to smoke on a weekly basis

(23.9%, AOR¼ 1.5).

Current drinking. Adults who consumed some alcohol

on a weekly basis were included in the current

drinker category. More than 70% of adults with no

mobility limitations and no minority status said that

they consumed some alcohol on a weekly basis.

Adults with mobility limitations were the next most

likely to use alcohol weekly (53.2%, AOR¼ 0.78).

Slightly more than half (50.7%, AOR¼ 0.45) of

minority adults without limitations drank some

alcohol each week. Adults with mobility limitations

and minority status were the least likely group to use

alcohol on a weekly basis (43.0%, AOR¼ 0.39).

Overweight but not obesity. Adults whose body mass

index (BMI) ranged from 25 to 29.9 were classified

as being overweight but not obese. We found that

adults with neither mobility limitations nor minority

status were the most likely individuals to meet these

criteria (34.3%, AOR¼ 1.0). The percentages of

overweight declined across comparison groups, with

adults who had both mobility limitations and

minority status having the lowest percentage of

people who met the overweight criteria (31.2%,

AOR¼ 0.87).

Obesity. Adults whose BMI was 30 or more met the

criteria for obesity. The occurrence of obesity in our

sample increased across comparison groups. Adults

with neither mobility limitations nor minority status

were the least likely group to be obese (15.8%,

AOR¼ 1.0). For adults who were members of racial

and ethnic minority groups, obesity rose to 24.0%

(AOR¼ 1.4). Obesity rose to 30.8% (AOR¼ 2.4)


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among adults with mobility limitations, and to

36.7% (AOR¼ 3.3) among adults with both mobility

limitations and minority status.

Morbid obesity. A subgroup of adults who met the

criteria for obesity was classified as morbidly obese if

their BMI was 40 or higher. We found the same

pattern for morbid obesity as for obesity. Adults

without mobility limitations or minority status were

the least likely to be morbidly obese (1.2%,

AOR¼ 1.0), while morbid obesity increased to

3.4% (AOR¼ 1.6) for minorities, to 5.4%

(AOR¼ 4.8) for adults with mobility limitations,

and to 7.9% (AOR¼ 7.8) for adults with both

mobility limitations and minority status.

Physical inactivity. Adults were categorized as physi-

cally inactive if they did not engage in some form of

moderate or vigorous exercise on a weekly basis, or if

they never exercised at all. Similar to obesity and

morbid obesity for adults in our sample, physical

inactivity rose across comparison groups. Physical

inactivity was reported by 31.3% (AOR¼ 1.0) of

adults without mobility limitations and minority

status, 49.4% (AOR¼ 1.9) of adults with minority

status, 50.1% (AOR¼ 1.6) of adults with mobility

limitations, and 58.5% (AOR¼ 2.7) of adults with

both mobility limitations and minority status.

We found several patterns of disparities in the health

and health-related domains reported here. Compared

with adults with no mobility limitations, people with

mobility limitations were more likely to have worsen-

ing health, symptoms of depression, all chronic

conditions represented here, difficulties with ADLs

and IADLs, and difficulties with workforce participa-

tion. Although the confidence intervals for ADLs

among adults with mobility limitations and minority

status were wider than what is customarily acceptable

for data stability, the findings were still statistically

significant at p50.001 for this outcome measure.

Adults with mobility limitations and minority status

were also more likely than adults without limitations to

be current smokers, to be obese or morbidly obese,

and to be physically inactive. In cases where disparities

existed between minorities and non-minority, non-

limited adults on measures such as worsening health,

moderate and severe depressive symptoms, diabetes,

hypertension, stroke, visual impairment, difficulties

with ADLs and IADLs, workforce participation,

obesity, morbid obesity, and physical inactivity, adults

with both mobility limitations and minority status had

the greatest differences.

Discussion

To understand how disability affects an individual,

one must look beyond the diagnosis of a particular

mental or physical condition to the influence of the

built environment, social and cultural attitudes and

practises, and national policies affecting disability

and health [4,105 – 107]. While no existing national

data set incorporates all of these elements [4], our

study attempts to map the person-in-environment

relationship to disability and health [107] by applying

the ICF to nationally representative data on disability

and its interface with minority status for several

health and health-related domains [1].

This study has important implications for oper-

ationalizing the ICF; identifying links between

mental and physical health status, commonly occur-

ring chronic conditions, functional activities, parti-

cipation, and one’s environment; and affirming the

impact of race and ethnicity on health in the USA.

These endeavors have been difficult to accomplish to

date because of a lack of national surveillance of

important participation and environmental factors

that enhance or negatively impact health. An

approach to including such questions in the NHIS

has been piloted to obtain data for Healthy People

2010 objective 6 – 10 (access to health and wellness

programmes), 6 – 11 (having needed assistive devices

and technology), and 6 – 12 (environmental barriers

affecting participation in home, school, work, and

community) [5]. The data were collected in 2002

and partly published in DATA2010 [17]. In addition

to the specific chapter on disability in HP2010,

people with disabilities are included as a demo-

graphic group in other chapters, but the data are not

as current for this group, as they are for people

without disabilities [5]. Conversely, our paper

provides data parity for adults with and without

mobility limitations, and our findings highlight

disparities that have continued to exist over time.

In addition to including ICF domains in national

surveys, it will be useful to incorporate personal

health risk behaviours and the behaviours and

attitudes of health professionals in the ICF [4].

In planning this study and analyzing the data, we

attempted to map relationships between independent

and dependent measures by using ICF coding. In

some ways, we were successful, and in others, we

were blocked because no codes existed for some

critical health and health-related domains.

Defining disability

We used d4 codes from the Activities and Participa-

tion – Mobility section of the ICF (d410-d429) to

define mobility limitations in this study. By using

ICF codes to define our disability measures, we felt

that we could more accurately describe NHIS

respondents who were experiencing difficulties in

moving around in their homes and communities.

If specific diagnostic codes for the International


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Classification of Diseases (ICD) [109] had been

included with the chronic disease variables we used

in this analysis, we might have garnered most of our

present sample, but the diagnostic codes may not

have identified specific activities that respondents

said were difficult for them to perform. For this

investigation, we did not examine the severity of

difficulty in performing functional activities related

to moving around and changing body position, as

our purpose was to explore the effects of the interface

between racial and ethnic minority status and

mobility limitations at all levels of severity. However,

we do acknowledge the compelling effect of disability

severity on every aspect of life for people with

mobility limitations [16,85,110,111].

Physical health status

We found no specific codes for overall health status

in the ICF. We used the self-rating measure available

in the data set to measure this important construct.

Depressive symptoms

The ICF categorizes depressive symptoms and other

mood disorders in the Body Functioning section of

the taxonomy (ICF codes b152-b159), and qualifiers

are available to indicate severity of symptoms, but

there is no way to directly back code NHIS data on

depressive symptoms to the ICF without first

computing the K6 Scale, another WHO classification

system to study the effects of depressive symptoms

[102]. Depressive symptoms are more common

among people with mobility limitations than among

other disability groups, and they are more prevalent

among people with disabilities than among people

without disabilities [11,14], but limited cell sizes for

individuals who stated that they were limited all of

the time by their negative feelings prevented us from

applying the full range of available qualifiers for the

b1 codes. Our findings for moderate and severe

depressive symptoms among racial and ethnic

minorities with mobility limitations speak to the

need to incorporate larger numbers of minorities

with disabilities into the NHIS sampling frame to

obtain more stable estimates of minorities with

severe and complete impairments in mental health.

Commonly occurring chronic conditions

This is perhaps the first nationally representative US

epidemiologic study to use the ICF to examine

clusters of commonly occurring chronic conditions

among people with mobility limitations, including

ethnic and racial minorities. The ICF looks at organ

system functioning, rather than specific chronic

conditions [1]. Though the ICF was not designed

to address individual chronic conditions [73], this

study employs ICF codes for clustered groups of

chronic conditions within organ system functioning

(heart problems: b410-b429, breathing problems:

b440-b449, and joint symptoms: b280-b289) that

would otherwise be classified individually in the

ICD. Clearly, there is a need to study the overlap

between the ICD and the ICF for chronic conditions

and to continue efforts to develop guidelines for

mapping ICF domains to existing national surveys

[79 – 81]. In our analysis, we included everyone who

reported having the 10 commonly occurring chronic

conditions studied. Neither the ICF nor the NHIS

data provided enough information to allow us to

determine which conditions may have caused a

mobility limitation and which conditions were

secondary to a primary disability. We were hindered

in our mapping of the ICF framework to adults with

mobility limitations who were also members of racial

and ethnic minorities because the ICF has no codes

for personal factors, such as race and ethnicity,

gender, age, or marital status. Having such codes

would facilitate a more precise picture of our

population and help to identify demographic groups

needing timely intervention. A next reasonable step

is to identify other relevant ICF domains that are

associated with chronic conditions in people with

disabilities, not as a cause-and-effect model, but as a

model for targeted interventions.

Functional activities and participation in the

environment

We examined four measures of functional activities

and participation in the environment: difficulties

with ADLs such as eating, bathing, and dressing

(ICF codes d510-d559); difficulties with IADLs

such as shopping for groceries and other necessities

(ICF codes d610-d629), doing housework (ICF

codes d630-d669), socializing with family and

friends (ICF codes d710-d779); participating in

community events (ICF codes d910-d999); and

workforce participation for adults 18 – 64 years old

(ICF codes d840-d859). It is notable that the ICF

has codes for functional activities and participation,

but the ICF did not allow us the opportunity of

coding the severity of difficulty experienced by NHIS

participants. Indeed, Whiteneck (2006) and others

have noted the lack of specificity and difficulty with

consistent coding for activities and participation

domains [4,112].

The 1997 – 2004 NHIS has few questions relating

to disability-specific barriers and environmental

factors. We examined data related to ICF codes

e110-e129 for use of special equipment to negotiate

the environment, but our data set did not

include variables that would permit a more detailed


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assessment of assistive devices and equipment used

by survey respondents. In general, we found the

same pattern for this domain that held true for many

of our other outcome measures.

Health behaviours

Neither the ICD nor the ICF provides a standard

nomenclature for characterizing human behaviour.

With the exception of weight management pro-

blems (ICF code b530), we found no ICF codes

related to important health behaviour practises.

Mitra et al. (2005) found that smoking, obesity,

and physical inactivity were strongly correlated with

depression in people with disabilities [12]. Other

researchers have found a strong correlation be-

tween obesity and/or physical inactivity, mobility

limitations, and race and ethnicity [11,20,21]. The

absence of ICF codes for health behaviours

prevented us from mapping some crucial factors

documented in our study and in the work of other

researchers that strongly impact the health of

minorities with mobility limitations. Our findings

on behaviours underscore the need to make

behavioural health promotion programmes cultu-

rally appropriate and accessible to people with

disabilities, and to develop new strategies and

interventions universally accessible to people with

mobility limitations who also belong to a racial or

ethnic minority group. For example, an accessible

intervention might aim to help people with

mobility limitations, including minorities, select

nutritional foods and address barriers to physical

activity at home and within community health

promotion programmes, services, and facilities

[66,111].

Study limitations

This study has several limitations. First, our data

were self-reported by NHIS participants and are

subject to recall bias. The data are cross-sectional

with no time reference for the conditions studied.

Other than accounting for levels of physical health

status and depressive symptoms, the data do not

account for severity of mobility limitations or

severity of ADLs and IADLs. As a result, the

findings may not be generalizeable to populations

with mild or severe mobility limitations, or to

people with other types of disabilities. The NHIS

study population includes only non-institutionalized

civilian adults and does not identify people living in

nursing homes or other long-term care facilities.

Thus, our findings may not apply to adults with

severe disabilities living in these types of facilities.

We looked at minorities as a group because of small

cell sizes for some minorities with respect to our

low-prevalence outcome measures. Small cell sizes

would especially be true for logistic models with

several indicators in the model, making the findings

more unstable and less applicable to affected

subgroups.

Additionally, researchers do not agree on the

specificity in the ICF coding for activities and

participation [4,112]. ICF users often have

difficulty deciding if a particular factor would fit

best under Activities, which are usually performed

separately by an individual, or under Participation,

which includes activities typically performed with

others [4,112].

Conclusions

Our findings highlight several areas of health

disparities both for adults with mobility limitations

and for adults who have mobility limitations and

belong to a racial or ethnic minority group. These

disparities occur in the domains of mental and

physical health status, commonly occurring chronic

conditions, functional activities and social participa-

tion, and health behaviours. Our results also indicate

an urgent need for standing community-based health

promotion programmes and interventions that, from

the onset of planning, reflect human diversity that

includes various cultures and disabilities. We were

unable to identify any such programmes [55]. We

note that health promotion efforts tend to focus on

racial and ethnic culture or disability. For example,

REACH 2010 is a community-based initiative

targeting ethnic and racial minorities to prevent

prevalent chronic conditions, but having a chronic

condition does not necessarily indicate the presence

of a disability. Living Well with a Disability is a

community-based intervention that was originally

designed to improve the health of adults with

mobility impairments [113].

A community collaborative effort among these

programmes and organizations could substantially

enhance the health and well-being of minorities with

disabilities by bringing together multi-disciplinary

perspectives to health promotion for minorities with

disabilities.

Despite its limitations, this study contributes

uniquely to a better understanding of the links

between the ICF and the NHIS, an existing national

survey; chronic conditions among people with and

without mobility limitations; and the profound

impact of disability and race and ethnicity on health.

The ICF presents innovative opportunities to convey

standardized disability and health information across

disciplines and nationalities, but it can be vastly

improved by adding personal factor demographic

codes and providing more specificity for activity and

participation codes.


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Disclaimer

The findings and conclusions in this paper are those

of the authors and do not necessarily represent the

views of the Centers for Disease Control and

Prevention.

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