This is the first study to apply a CIG to data on functioning 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 proportion of people with impairments reports good health and quality of life through conditioning on contextual factors, i.e. socio-economic determinants. We found that perceived health and A&P limitations are independent of impairment conditional on some of the considered contextual factors. An array of environmental and personal factors seems responsible for the translation of impairment 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 functioning 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 relationship 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 scenario (Figure 4c), the strength of the association between impairments and A&P limitations is influenced by contextual 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 current data situation. For instance, environmental factors such as health services, assistive technology, rehabilitation program expenditure, and availability were not assessed in this study. We may thus only say, that some contextual factors pose an important contribution to solve the disability paradox at least with regard to the perceived health component of QoL.
Surprisingly, we do not find any association of functioning, perceived health, personal factors, or micro-level (i.e. participant level) environmental factors with the macro-level indicators accounting for the clustering of the subjects in cantons. However, a time lag between macro-indicators and individual disability is possible (e.g. exposure 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 considered 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 societal 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 candidate variables such as social network utilization did not appear in the graph. One reason may be our relatively conservative 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 original survey team. Furthermore, our research is limited by missing values in the data. We cannot exclude the possibility 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, evidence 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 information 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 independence information but may be difficult to check in practice. 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 comparing 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 factors interact in the disablement process. This presupposes that better measures of both personal [30] and environmental 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 integration 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 policies on the lived experience of persons with disabilities [34] possible.