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Explaining the Disability Paradox: A Cross-Sectional Analysis of the Swiss General Population.

Authors:
  • University of Lucerne, Switzerland, Swiss Paraplegic Research, Nottwil, Switzerland and ICF Research Branch at SPF, Nottwil, Switzerland

Abstract and Figures

Background Disability can be broken down into difficulties in different components of functioning such as impairments and limitations in activities and participation (A&P). Previous studies have produced the seemingly surprising result that persons with severe impairments tend to report high quality of life (QoL) including perceived health regardless of their condition; the so-called “disability paradox”. We aim to study the role of contextual factors (i.e. the personal and environmental situation) in explaining the disability paradox. Methods The Swiss Health Survey provides information on the perceived health of 18,760 participants from the general population. We construct a conditional independence graph applying random forests and stability selection in order to represent the structure of impairment, A&P limitation, contextual factors, and perceived health. Results We find that impairment and A&P limitations are not directly related but only via a cluster of contextual factors. Similarly, impairment and perceived health are not directly related. On the other hand, perceived health is directly connected with A&P limitations. We hypothesize that contextual factors have a moderating and/or mediating effect on the relationship of impairment, A&P limitations, and perceived health. Conclusion The disability paradox seems to dissolve when contextual factors are put into consideration. Contextual factors may be responsible for some persons with impairments developing A&P limitations and others not. In turn, persons with impairments may only then perceive bad health when they experience A&P limitation. Political interventions at the level of the environment may reduce the number of persons who perceive bad health.
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Fellinghauer et al. BMC Public Health 2012, 12:655
http://www.biomedcentral.com/1471-2458/12/655
RESEARCH ARTICLE Open Access
Explaining the disability paradox: a
cross-sectional analysis of the Swiss general
population
Bernd Fellinghauer1,2† , Jan D Reinhardt1,3*†,GeroldStucki
1,3 and Jerome Bickenbach1,3
Abstract
Background: Disability can be broken down into difficulties in different components of functioning such as
impairments and limitations in activities and participation (A&P). Previous studies have produced the seemingly
surprising result that persons with severe impairments tend to report high quality of life (QoL) including perceived
health regardless of their condition; the so-called “disability paradox”. We aim to study the role of contextual factors
(i.e. the personal and environmental situation) in explaining the disability paradox.
Methods: The Swiss Health Survey provides information on the perceived health of 18,760 participants from the
general population. We construct a conditional independence graph applying random forests and stability selection
in order to represent the structure of impairment, A&P limitation, contextual factors, and perceived health.
Results: We find that impairment and A&P limitations are not directly related but only via a cluster of contextual
factors. Similarly, impairment and perceived health are not directly related. On the other hand, perceived health is
directly connected with A&P limitations. We hypothesize that contextual factors have a moderating and/or mediating
effect on the relationship of impairment, A&P limitations, and perceived health.
Conclusion: The disability paradox seems to dissolve when contextual factors are put into consideration. Contextual
factors may be responsible for some persons with impairments developing A&P limitations and others not. In turn,
persons with impairments may only then perceive bad health when they experience A&P limitation. Political
interventions at the level of the environment may reduce the number of persons who perceive bad health.
Keywords: Disability, Human functioning, Perceived health, Impairment, ICF, Graphical models, Activity limitation,
Participation restriction
Background
According to the World Health Organization’s (WHO)
recent World Report on Disability, over a billion peo-
ple live with disabilities and accordingly represent over
15% of the world’s population [1]. This number is steadily
increasing as the world’s health is compromised by
increasing numbers of non-fatal injuries due to road
*Correspondence: jan.reinhardt@paranet.ch
Equal contributors
1Swiss Paraplegic Research (SPF), Guido A. Z¨
ach Str. 4, 6207 Nottwil,
Switzerland
3Department of Health Sciences and Health Policy, University of Lucerne and
SPF, Frohburgstr. 3, 4466 Lucerne, Switzerland
Full list of author information is available at the end of the article
traffic accidents [2,3] and disasters [4] as well as non-
communicable disease [5]. These may lead to chronic
health conditions and disabilities with which people may
live for many years.
Following the WHO’s current bio-psycho-social model
of disability described in the International Classification
of Functioning, Disability and Health (ICF; cf. [6]), disabil-
ity is not an attribute of individuals, but rather a set of
difficulties individuals may experience in interaction with
their social and physical environments [6,7]. Disability can
be broken down into difficulties with functioning in dif-
ferent ICF components: impairments in body functions
and structures, limitations in activities, and restrictions
in participation. All of these components and the rela-
tions between them are further assumed to be affected
© 2012 Fellinghauer et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
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by contextual,i.e.environmental and personal,factors [6].
The United Nations’ Convention on the Rights of Per-
sons with Disabilities (article 29, b; cf. [8]) thus calls
upon state parties to “promote actively an environment
[social and attitudinal] in which persons with disabili-
ties can effectively and fully participate in the conduct of
public affairs”. Against this backdrop, it has also become
a political imperative to research environmental factors’
impact on disability in order to come up with potential
intervention targets [6].
Inordertobeabletoaddressdisabilitywithappro-
priate interventions, we need to take this paradigm shift
(i.e. introducing the contextual situation in a pivotal posi-
tion) seriously and try to better understand the rela-
tionships between the components of disability. In this
respect, studies have produced the seemingly surpris-
ing result, the so-called “disability paradox” introduced
by Albrecht and Devlieger [9], that persons with severe
impairments may nevertheless report high levels of qual-
ity of life (QoL) [1,10]. “In practice, the anomaly is that
patients’ perceptions of personal health, well-being and
life satisfaction are often discordant with their objective
health status and disability” (see [9], p. 978). For exam-
ple, the 2007-2008 Australian National Health Survey
reports that 40 percent of people with a severe or pro-
found impairment rate their health as good, very good, or
excellent [11].
While Albrecht and Devlieger [9] explain this paradox
with a balance theory framework (see [12]) considering an
equilibrium of “body, mind, and spirit” (see [9], p. 978),
we focus on contextual factors. We claim that the disabil-
ity paradox may be resolved if one considers that “people
with the same impairment can experience very different
degrees and types of restrictions [in activity and partici-
pation], depending on the context”, i.e. environmental and
personal factors (cf. [1], p. 22). To evaluate this theoretical
conjecture, we need to assess data on all of the ICF models’
components independently and then explore interactions
betweenthem[6].Inordertodoso,datafromhealth
surveys can be linked to the ICF components [13,14]. As
the ICF does not provide separate lists for activities and
participation we treat limitations in activities and partic-
ipation (A&P limitations) as one component. In the ICF
framework different components of functioning and con-
textual factors furthermore influence each other mutually.
Consequently, many outcomes need to be considered at
the same time.
In order to be able to study these multiple outcomes at
one time, we reformulate the research problem in terms
of conditional independence and then analyze it through
a conditional independence graph [15]. We say that two
events (or two variables) are independent if knowledge
about one of the two does not influence the proba-
bility to observe the other one. Conversely, conditional
independence implies that the probabilities of two events
(or two variables) are independent if knowledge about
additional events (or variables) is available. For instance,
many wheelchair users may experience A&P limitations
becausetheyliveininaccessibleneighborhoods.Thecon-
nection between wheelchair and A&P limitation may
disappear if we condition on neighborhood accessibility.
The objective of our study is to evaluate the role of
contextual factors in the relationship of impairment, lim-
itations in A&P, and perceived health (as one component
of QoL). Therefore, our specific aims are to examine,
against the background of contextual factors, the associ-
ations 1) between impairment, pain, and A&P limitation;
2) between impairment, pain, and perceived health; and
3) between A&P limitation and perceived health. In order
to be able to address these aims simultaneously we apply
conditional independence graphs.
Methods
Study Population
This study is a secondary analysis of cross-sectional data
on functioning and disability from the Swiss Health Sur-
vey (SHS) in 2007 [16] (Bundesamt f ¨
ur Statistik, Schweizer
Gesundheitsbefragung 2007). Data were obtained from
the Federal Statistics Office of Switzerland. The original
study was based on a stratified random sample of the Swiss
general population aged 15 or older. A telephone survey
was completed by a total of 18,760 persons, corresponding
to a participation rate of 66 percent. The study was con-
ducted by M.I.S.-Trend SA, Lausanne and G ¨
umligen on
behalf of the Swiss Federal Statistics Office. The data pro-
tection is ensured under the federal statistics law and data
protection law. All data was treated highly confidential
and anonymised [17].
Measures
The SHS included various information on functioning,
especially on pain, impairment, and A&P limitation.
Since the respective items had different scale levels, we
dichotomized them so that 1 was indicative of having
some kind of problem. We then constructed sum indices
for pain, impairment, and A&P limitation. Perceived
health was measured with the following question and
answer options: How would you rate your health in gen-
eral? Very good, good, fair, poor, very poor. As personal
factors we included the following demographics, health
behaviors, and socio-economic status (SES). Demograph-
ics comprised gender, age, marital status. Health behaviors
included alcohol consumption, current smoking, and reg-
ular leisure time physical activity (understood as lifestyle).
Years of formal education, equivalent household income,
paid employment, and migration background were used
as indicators of individual level socio-economic status.
For environmental factors, we created two sum indices
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for social network utilization and perceived social sup-
port, respectively. The built sum indices are illustrated
in Table 1. On the macro- or cantonal (county) level, we
obtained information on the cantonal Gross Domestic
Products (GDP), Gini-Coefficients (which are a measure
of income distribution ranging from complete equal-
ity expressed as 0, i.e. every person receiving the same
amount of money, to complete inequality expressed as 1,
i.e. a single person receiving all money), and crime rates
for 2006.
Across all variables used in the index construction or
modeling, less than 0.85 percent of replies were missing.
Imputation of missing values (i.e. estimating them from all
observed data) did not affect the results reported here. We
thus report on the unimputed data.
Statistical Analysis
Most statistical analyses can be represented in terms of a
graph. Nodes represent variables and edges reflect associ-
ations among variables. Conditional independence graphs
(CIG), in particular, describe the association among any
two nodes in the graph conditioned on the remainder of
p2 variables. For example, Tim operates a wheelchair.
However, he also has a lot of support from his wife Nancy.
Thus, he does not experience limitations in A&P. This
statement can be represented in terms of a CIG as shown
in Figure 1. The three variables A&P limitation, supports,
and impairment appear as nodes. The two nodes A&P
limitation and impairment are only connected via a path
of edges through supports. This structure reflects condi-
tional independence of A&P limitation and impairment
conditional on having appropriate supports.
We reproduce an analysis by Fellinghauer et al.[15]
which focused on the question if all p=20 variables are
part of a single connected component. Here, however, we
are mainly focusing on the constructs contextual factors,
limitations in A&P, impairment, and perceived health and
how they are connected. The graphical model we use is
called Graphical Random Forests (GRaFo; cf. [15]) which
can be used to model both discrete variables (e.g. gen-
der) and continuous variables (e.g. age). The method is
based on classification (for discrete outcomes) and regres-
sion (for continuous outcomes) trees (CART; cf. [18]). We
regress each of the pvariables on all remaining p1
variables (i.e. each variable is treated as outcome at some
point). In order to avoid model overfitting (i.e. to avoid
estimating parameters such that they are difficult to gen-
eralize to any other than the observed sample) we use
random feature selection [19], i.e. predictors included in
each model are randomly selected. This makes it neces-
sary to draw sub-samples of the data (bootstrap; cf. [20])
in order to give every candidate predictor the same chance
to be included in each of the models and to enhance reli-
ability of the parameters. As a result we get a number
Table 1 Sum scores
Construct Variable specification
Impairment Problems with . . .
...vision
...hearing
...speaking
...body mass index (i.e. over 30 or under 16)
...urinary incontinence
...defecation
...feeling weak, tired, or a lack of energy
...sleeping
...tachycardia
Range of sum index: 0-9
Pain Pain in...
...head
...chest
...stomach
...back
...hands
...joints
Range of sum index: 0-6
Activity & Problems with independently...
participation ...walking
limitation ...eating
...gettingup frombed orchair
...dressing
...using the toilet
...takingashowerorbath
...preparing meals
...using a telephone
...doing the laundry
...caring for finances/accounting
...using public transport
...doing major household tasks
...doing shopping
Range of sum index: 0-13
Social support Having...
...no feelings of loneliness
...no feeling of missing someone to turn to
...at least one supportive family member
...someone to turn to
Range of sum index: 0-4
Social network utilization At least weekly...
...visits from family
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Table 1 Sum scores Continued
...phone calls with family
...visits from friends
...phone calls with friends
...participation in clubs/associations/parties
Range of sum index: 0-5
Construction rules of sum indices for functioning (pain, impairment, activity and
participation limitation) and social integration (social support and social network
utilization) from 37 dichotomous (yes=1/no=0) variables. Reproduced from [15].
of trees, i.e. a Random Forest [21]. Eventually, the GRaFo
framework uses an algorithm to determine the most sta-
ble associations (stability selection; cf. [22]) and selects
the most relevant edges/associations of variables to be
included in the final graph. The general idea of GRaFo
istospecifyanupperboundontheexpectednumber
of false positives (i.e. false edges) E(V)that we are will-
ing to accept in the worst case. According to this bound,
edges that are sufficiently stable will be selected. Further-
more, GRaFo is conservative (with respect to achieving
this bound) as a recent simulation study found that the
observed false positive error is largely below the specified
bound on false positives for p100 [15].
In this study, we applied GRaFo for p=20 and an upper
error bound of E(V)5 false positives. We report for
each edge from which bound on E(V)it is included in the
graph (up to the maximum bound of size of 5). In gen-
eral, smaller bounds on E(V)in the figure indicate more
stable associations.
Figure 1 Conditional independence graph. The figure shows an
example of a conditional independence graph. Due to the support of
Nancy, Tim - who opperates in a wheelchar - is not limited in his
participatation in common activities (e.g. going to a pub).
Results
A description of the study population is displayed in
Table 2. We find that over 70% of the participants have
at least one impairment and over 15% of the participants
report one or more limitations in activity and partici-
pation. Specific impairments range from 42% reporting
lack of energy and drive to 1.5% reporting problems with
speaking; specific A&P limitations range from 12% report-
ing difficulties in accomplishing major household tasks to
0.5% reporting eating problems. Pain is most prevalent
in the back (44%) and least prevalent in the chest (9%).
Table 2 Individual-level variables
ICF Component Total
Functioning
Perceived health
median (90%Q; 10%Q) 2.0 (3.0; 1.0)
Pain mean (SD) 1.6 (1.4)
At least one pain problem, n(%) 14,166 (75.8)
Impairment, mean (SD) 1.4 (1.3)
At least one impairment, n(%) 12,458 (70.6)
A&P limitation, mean (SD) 0.4 (1.4)
At least on A&P limitation, n(%) 2,941 (15.8)
Personal Factors
Age mean (SD) 49.6 (18.5)
Male n(%) 8,424 (44.9)
Lifestyles
Alcohol in grams per day,
mean (SD) 9.2 (15.4)
Current smokers, n(%) 5,091 (27.2)
Low physical activity, n(%) 4,775 (25.5)
Socio-economic
status
Paid employment, n(%) 11,497 (61.3)
Years of formal education,
mean (SD) 13.0 (3.5)
Income, mean (SD) 4,154.1 (3,058.6)
Foreign origin of at
least one parent, n(%) 5,128 (28.7)
Environment
Social support, mean (SD) 3.3 (0.8)
Social networks, mean (SD) 3.2 (1.2)
Married, n(%) 9,539 (50.9)
The table gives a description of all variables on the individual level in terms of
mean and standard deviation (SD) or number of cases (n) and percent of sample
(%). For perceived health we report the median and the 10% and
90% quantiles (Q).
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Figure 2 Macro-level variables. The figure shows the macro-level indicators across cantons.
Median perceived health equals “good”. Figure 2 shows the
distribution of the macro-variables across the Swiss can-
tons which is rather similar with the exception of Basel
city (BS), Geneva (GE), and Zug (ZG) which have com-
parably high per capita GDPs and/or crime rates. The
Gini-coefficient ranges from around 0.25 in the county Uri
to more than 0.5 in the neighbor county Schwyz.
Figure 3 shows the resulting graph from our application
of GRaFo to the data on functional health from the SHS
with casewise deletion of missing values for a bound on
the expected number of false positives E(V)5.
Impairment (including pain) in the top-left corner and
limitations in activity and participation in the top-right
corner are not directly connected, but only via the block
of contextual factors in the middle (e.g. social support
or being married). Impairment and perceived health are
also not directly connected. Instead, every path between
them contains both contextual factors and limitations
E(V)<=4
E(V)<=4
E(V)<=4
E(V)<=1
E(V)<=5
E(V)<=1
E(V)<=2
E(V)<=1
E(V)<=4
E(V)<=5
E(V)<=4
E(V)<=2
E(V)<=1
E(V)<=2
E(V)<=1
E(V)<=2
paid work
male
social support (index)
married
A&P limitation (index)
general health perception
impairment (index)
pain (index)
age
education
income
alcohol
Gini coefficient
gross domestic product (GDP)crime rate
Figure 3 Conditional independence graph. The figure shows a conditional independence graph of the p=20 variables (nodes) remaining after
construction of indices based on the 2007 Swiss Health Survey estimated with GRaFo. Edges were selected with respect to an upper bound of 5 on
the expected number of false positives E(V). For example, E(V)2 is to be interpreted as an edge which is present in a graph in which we expect
up to 2 false edges. We cannot set this bound to 0 as this would be equivalent to a graph in which can be certain that all shown edges are correct
(consequently, the algorithm would suggest an empty graph). Five nodes (social network utilization, migration background, smoker, work
restriction, and leisure time physical activity) were isolated (no edges) and thus neglected. Reproduced from [15].
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in activities and participation. Limitations in activities
and participation and perceived health are directly con-
nected. There are two pathways leading from impairment
to A&P limitation. One leads from social support via
being married, income, education, and gender to paid
work (socio-economic pathway). The other one leads from
social support and being married to age and paid work
(chronological pathway).
In the following, we will focus on the moderating and
mediating role of contextual factors. A moderator may
affect the strength and/or direction between a predictor
and an outcome variable. A mediator on the other hand is
affected by the predictor and accounts for the behavior of
the outcome variable (see also [23]).
In terms of conditional independence, the upper path
may be interpreted, with all other conditions held con-
stant, as follows: impairment is independent of being
married given perceived support, support is independent
of age given marital status, paid work is independent of
marital status given age (and at least one of the vari-
ables on the second pathway), and A&P limitations are
independent of age given paid work. It follows that A&P
limitations are independent of impairment given sup-
ports, marital status, age, and paid work which show
a chain connection. This connection type alludes to a
mediating relationship within the conditioning compo-
nent, potentially starting from social roles related to age:
older age makes it more likely to be married which makes
social support more probable in turn; age moreover influ-
ences the likelihood of being in paid employment. We
can hypothesize that married persons in employable age
may be more often in paid employment and have less
activity limitations in spite of impairments due to better
supports.
The lower path may be interpreted, with all other condi-
tions held constant, as follows: impairment is independent
of marital status given support, support is independent
of income given marital status, being married is indepen-
dent of education given income and age, while income is
independent of gender given educational level, age and
marital status. Finally, educational level is independent of
having employment and thus A&P limitation given gender
and age of the person in question. Starting from sex and
age roles, we may hypothesize that gender and age mod-
erate the likelihood of having paid work while education
and age moderate the impact of gender, having employ-
ment, and income on marriage and good supports which
make it, in turn, less likely that impairments become A&P
limitations.
The variables migration background, social network uti-
lization, smoker, work restriction, and leisure time phys-
ical activity were neglected in the graph as they are not
connected to any other node. Also, the three canton-level
environmental factors form a separate component and are
not related to the functional health component or any of
the five neglected nodes.
Discussion
This is the first study to apply a CIG to data on func-
tioning and disability structured according to the new
WHO disability model [6]. In that we tried to explain
the so-called disability paradox that a relevant propor-
tion of people with impairments reports good health and
quality of life through conditioning on contextual fac-
tors, i.e. socio-economic determinants. We found that
perceived health and A&P limitations are independent of
impairment conditional on some of the considered con-
textual factors. An array of environmental and personal
factors seems responsible for the translation of impair-
ment or pain into A&P limitations (or vice versa). Health
perception was still dependent on A&P limitations when
conditioned on all contextual factors and other function-
ing domains in the model. For example, Tim, who uses
a wheelchair, may have no activity limitations, because
he has a lot of support from Nancy and access to many
assistive technologies. Bob, on the other hand, is fairly
isolated, does not find work because of his age, and may
thus be limited in activities and participation in many
areas.
The findings thus support a central role of contextual
factors in the moderation and mediation of the rela-
tionship between impairment and limitations in activity
and participation. Figure 4 shows different possibilities to
translate the found conditional independence relation into
directed graphs: In the case of an explanation (Figure 4a),
contextual factors determine both impairment and A&P
limitations. The conditioning on contextual factors thus
explains any correlation between impairment and A&P
limitation. Mediation (Figure 4b), in turn, means that
impairments influence contextual factors, e.g. decrease
the socio-economic status of a person (drift), which
then influences A&P limitations. In the moderation sce-
nario (Figure 4c), the strength of the association between
impairments and A&P limitations is influenced by con-
textual factors, e.g. in some contexts there might be no
association of impairments and A&P limitations at all,
while in others a strong relation might be found. Apart
from social support, the other connecting variables may
be viewed as personal factors in terms of ICF. However,
they oftentimes imply an impact of external expectations
as well [24], most evident in the case of gender and
paid employment (also see the socio-economic pathway
above). We thus tend to rather speak of contextual factors
than differentiating environmental and personal factors as
in the ICF, particularly against the background of the cur-
rent data situation. For instance, environmental factors
such as health services, assistive technology, rehabilitation
program expenditure, and availability were not assessed
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a
c
b
Figure 4 Potential structures of the disability paradox depicted as directed graphs: Explanation (a) vs. Mediation (b) vs. Moderation (c)
The figure shows potential structures of the disability paradox depicted as directed graphs. More specifically, we formulate hypotheses on
the relationship of limitations in activities and participation, contextual factors, impairment, and perceived health. Three different structuresare
suggested: explanation (a) vs. mediation (b) vs. moderation (c). A moderator may affect the strength and/or direction between a predictor and an
outcome variable. A mediator on the other hand is affected by the predictor and accounts for the behavior of the outcome variable [23]. As a special
case of moderation we can regard explanation.
in this study. We may thus only say, that some contextual
factors pose an important contribution to solve the dis-
ability paradox at least with regard to the perceived health
component of QoL.
Surprisingly, we do not find any association of func-
tioning, perceived health, personal factors, or micro-level
(i.e. participant level) environmental factors with the
macro-level indicators accounting for the clustering of the
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subjects in cantons. However, a time lag between macro-
indicators and individual disability is possible (e.g. expo-
sure to macro-level inequality at baseline may moderate
disability at follow up) and has been shown elsewhere
[25]. Also, transfers between cantons and social policy
measures to redistribute income within the cantons and
expenditure on rehabilitation services [26] were not con-
sidered in this study. Perhaps, social inequality on the
macro-level may not play a major role in a country such
as Switzerland, since it has an extremely high level of soci-
etal welfare as compared to many other countries. If we
remove the three macro-level variables GDP, Gini, and
crime rate from the model, the connectivity of the micro-
level component does not change. In general, several can-
didate variables such as social network utilization did not
appear in the graph. One reason may be our relatively con-
servative upper bound on the error that results in a graph
which contains only very stable edges. On the other hand,
this implies that we may miss some associations that were
not as stable.
An important limitation of our study is the cross-
sectional nature of the data that makes it impossible to
draw conclusions on causality or even model feedback
loops: For example, A&P limitation and impairment may
reinforce each other through environmental and personal
factors. There may also be an issue with our selection of
variables that was restricted by the choices of the origi-
nal survey team. Furthermore, our research is limited by
missing values in the data. We cannot exclude the possi-
bility that missing data occur not independently of levels
of impairment, A&P limitation, or perceived health of the
respondents. Assuming that observations are missing at
random, an imputation did not change the resulting graph
(not shown). In addition, results in perceived health may
have been influenced by response shift [27]. However, evi-
dence of response shift in disabled persons’ reporting of
QoL is very weak [28].
A disadvantage of our method is the lack of a clear
statement regarding the type of relationship among two
nodes A and B. Neither do we know whether A causes B
or vice versa, nor do we have information whether large
values of A facilitate large values of B. The former infor-
mation cannot be easily obtained from our framework as
we only study associations (for causal graphs see e.g. [29]).
The latter limitation arises mainly from computational
limitations and future research may produce a version of
GRaFo which can also provide this kind of information.
Also, GRaFo is based on certain technical assumptions
[15] that are required to estimate conditional indepen-
dence information but may be difficult to check in prac-
tice. However, given the high face validity of the findings
and the achievement of control over false positives in
a simulation study for a comparable mixed setting [15],
the results of the GRaFo procedure seem satisfactory.
Eventually, we only studied perceived health and not other
components of QoL which leads to difficulties in com-
paring findings with the theory suggested by Albrecht
and Devlieger [9]. However, we did not have data on life
satisfaction and well-being.
Future research needs to develop more specific
hypotheses about how environmental and personal fac-
tors interact in the disablement process. This presupposes
that better measures of both personal [30] and environ-
mental factors [31] as well as connecting pathways are
established. Such pathways may assist the development of
suitable interventions which avoid unintended side effects
[32]. This is facilitated by the parallel study of the relation
of multiple variables. In order to come up with suggestions
on the policy level, the impact of macro and meso factors
needs to be better understood and modeled. More fine-
grained indicators than the ones used in our study need
to be operationalized. On the country level, classifications
of disability policies along a compensation and an integra-
tion component exist (see e.g. [33]). On the data collection
level, the geographical linking of individual data to smaller
units than cantons is desirable to better understand the
macro-micro link. Conversely, better macro-level data,
e.g. on the accessability of the surrounding neighborhood
must be made publicly available by the statical offices in
order to make meaningful research on the impact of poli-
cies on the lived experience of persons with disabilities
[34] possible.
Conclusion
Summarily, impairments do not necessarily lead to
decreased perceived health if the translation of impair-
ments into A&P limitations can be avoided. Similarly,
impairments do not necessarily lead to A&P limitations.
Modifiable environmental factors, such as social supports,
moderate or mediate the relationship between body and
activity and participation. Therefore, the number of per-
sons with impairments who feel healthy and show high
levels of performance in activity and participation may be
increased with appropriate contextual interventions.
Competing interests
The authors declare that there are no financial or non-financial competing
interests.
Authors’ contributions
Bernd Fellinghauer performed the statistical analyses and drafted the
manuscript. Jan Reinhardt wrote the first draft, interpreted the results and
drafted the manuscript. Gerold Stucki drafted parts of the manuscript. Jerome
Bickenbach was involved in the interpretation of results, drafting of the
manuscript, and provided general supervision. All authors have read and
approved the final version of the manuscript.
Author details
1Swiss Paraplegic Research (SPF), Guido A. Z¨
ach Str. 4, 6207 Nottwil,
Switzerland. 2Seminar f¨
ur Statistik, ETH Zurich, R¨
amistr. 101, 8092 Zurich,
Switzerland. 3Department of Health Sciences and Health Policy, University of
Lucerne and SPF, Frohburgstr. 3, 4466 Lucerne, Switzerland.
Fellinghauer et al. BMC Public Health 2012, 12:655 Page 9 of 9
http://www.biomedcentral.com/1471-2458/12/655
Received: 16 January 2012 Accepted: 3 July 2012
Published: 15 August 2012
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Cite this article as: Fellinghauer et al.:Explaining the disability paradox: a
cross-sectional analysis of the Swiss general population. BMC Public Health
2012 12:655.
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