Tevfik Bedirhan Üstün

The International Classification of Functioning, Disability and Health: a new tool for understanding disability and health

Reliable and timely information about the health of populations is part of the World Health Organizations mandate in the development of international public health policy. To capture data concerning functioning and disability, or non-fatal health outcomes, WHO has recently published the revised International Classification of Functioning, Disability and Health (ICF). In this article, the authors briefly outline the revision process and discuss the rationale for the ICF and the needs that it serves in rehabilitation. The ICF is shown to be an essential tool for identifying and measuring efficacy and effectiveness of rehabilitation services, both through functional profiling and intervention targeting. Existing applications of the ICF in rehabilitation are then surveyed. The ICF, in short, offers an international, scientific tool for understanding human functioning and disability for clinical, research, policy development and a range of other public health uses.

Harmonizing WHO’s International Classification of Diseases (ICD) and International Classification of Functioning, Disability and Health (ICF): importance and methods to link disease and functioning

BackgroundTo understand the full burden of a health condition, we need the information on the disease and the information on how that disease impacts the functioning of an individual. The ongoing revision of the International Classification of Diseases (ICD) provides an opportunity to integrate functioning information through the International Classification of Functioning, Disability and Health (ICF).DiscussionPart of the ICD revision process includes adding information from the ICF by way of “functioning properties” to capture the impact of the disease on functioning. The ICD content model was developed to provide the structure of information required for each ICD-11 disease entity and one component of this content model is functioning properties. The activities and participation domains from ICF are to be included as the value set for functioning properties in the ICD revision process.SummaryThe joint use of ICD and ICF could create an integrated health information system that would benefit the implementation of a standard language-based electronic health record to better capture and understand disease and functioning in healthcare.

Ontology development for the masses: creating ICD-11 in WebProtégé

The World Health Organization is currently developing the 11th revision of the International Classification of Diseases (ICD-11). ICD is the standard diagnostic classification used in health care all over the world. In contrast to previous ICD revisions that did not have a formal representation and were mainly available as printed books, ICD-11 uses OWL for the formal representation of its content. In this paper, we report on our work to support the collaborative development of ICD-11 in WebProtege--a web-based ontology browser and editor. WebProtege integrates collaboration features directly into the editing process. We report on the results of the evaluation that we performed during a two-week meeting with the ICD editors in Geneva. We performed the evaluation in the context of the editors learning to use WebProtege to start the ICD-11 development. Participants in the evaluation were optimistic that collaborative development will work in this context, but have raised a number of critical issues.

WHO Collaborative Study: An epidemiological survey of psychological problems in general health care in 15 centers worldwide

This paper describes the epidemiological considerations in the design of a large longitudinal multi-center collaborative study which investigates the form, frequency, course and outcome of psychological problems commonly seen in primary health care settings in fifteen different sites around the world. The study used a two-stage sampling design: in the first stage the GHQ-12 was administered to 25,916 persons aged 18 to 65 consulting health care services. In the second stage 5,438 were selected for detailed examination using standardized instruments (Composite International Diagnostic Instrument-CIDI and WHO Social Disability Schedule). The sample was followed up at three months and one year to provide information on course and outcome. The project has produced a large database which examines multiple traits (symptoms and diagnosis, disability, management, outcome) using multiple methods (self-report, physician, and interviewer data), thus allowing the exploration of the nature of psychological disorders a...

Harmonizing routinely collected health information for strengthening quality management in health systems: requirements and practice

Objective Our aim was to specify the requirements of an architecture to serve as the foundation for standardized reporting of health information and to provide an exemplary application of this architecture. Methods The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) served as the conceptual framework. Methods to establish content comparability were the ICF Linking Rules. The Rasch measurement model, as a special case of additive conjoint measurement, which satisfies the required criteria for fundamental measurement, allowed for the development of a common metric foundation for measurement unit conversion. Secondary analysis of data from the North Yorkshire Survey was used to illustrate these methods. Patients completed three instruments and the items were linked to the ICF. The Rasch measurement model was applied, first to each scale, and then to items across scales which were linked to a common domain. Results Based on the linking of items to the ICF, the majority of items were grouped into two domains, Mobility and Self-care. Analysis of the individual scales and of items linked to a common domain across scales satisfied the requirements of the Rasch measurement model. The measurement unit conversion between items from the three instruments linked to the Mobility and Self-care domains, respectively, was demonstrated. Conclusions The realization of an ICF-based architecture for information on patients’ functioning enables harmonization of health information while allowing clinicians and researchers to continue using their existing instruments. This architecture will facilitate access to comprehensive and consistently reported health information to serve as the foundation for informed decision-making.

Health is not just the absence of disease

We thank Stuckler and Reeves for their commentary1 on our re-evaluation3 of claims made by Banks et al.4 and others that the English are healthier than the US Americans. Living in England, Stuckler and Reeves may be forgiven for concluding their commentary by saying ‘So should you live in the US or England? Judging on the health data alone, we find the weight of evidence still (slightly) favours—England’. The point of our article was not to suggest that people move countries, but rather to propose a better methodology for tackling the difficult public health challenge of comparing health across populations. They1 begin by claiming that our aim was to operationalize the well-known World Health Organization (WHO) 1948 definition of health2. Our aim was actually the very different one of arguing that it is a mistake to adopt the Banks et al.4 understanding of health merely as the absence of disease. We rather claim that health needs to be measured as a vector of functioning in a parsimonious set of domains that matches the intuitive notion of health such that one can compare the health of people with (for example) diabetes and those with depression, an approach proposed by Salomon et al.5 We read with pleasure when Stuckler and Reeves1 point out the convergence of inferences obtained by examining recent Global Burden Disease (GBD) 2010 efforts6 and our own. We agree on this. The difference between us is that whereas the GBD says that the health differences between the USA and the UK are trivial, we say they are really, really trivial! To see this, consider their Table 1,1 in which healthy life expectancy (HALE) at age 50 is reported to be about 25 years for both the UK and the USA, with a 0.4-year advantage for the UK. If healthy survival after age 50 has a Poisson distribution, this difference amounts to 8% of a standard deviation. Life expectancy (LE), by contrast, at age 50 is about 31–32 years for the UK and the USA, with a 0.8-year advantage for the UK. If survival after age 50 has a Poisson distribution, this difference amounts to 14% of a standard deviation. But these differences are indeed very, very small. Imagine a sample of 1000 persons aged 50 years and older from the UK and a matching number from the USA. Taking all possible pairs, selecting one person from the UK and one from the USA, if we predicted a longer life expectancy for the person from the UK we would be right 52% of the time, instead of the 50% of the time that might be expected if the life expectancy distributions were identical in the USA and UK or by chance. Our Rasch-based health metric,3 on the other hand, suggests a smaller difference (about 2% of a standard deviation) and implies a correct guess about health in 51% of pairs of USA/UK elders instead of the 50% expected if the distributions were identical. A related feature of our approach,3 which Stuckler and Reeves1 call ‘a major limitation’, is our reliance on self-report data, and the fact that culture is an important driver of how respondents answer questions about their health. In fact, our results argue for diminishing differences between the USA and the UK after these cross-national differences in self-reporting of health are adjusted. In other words, our results are based on the Rasch model, in which the health score is estimated after correcting for differential item functioning (DIF) and therefore accounting for reporting biases and hence population invariant. We think that culture and, more broadly, all social and environmental differences between the USA and the UK, influence self-reported health. The Rasch and DIF scoring procedure—with different thresholds for different sex, age and national groups—is an attempt to address this. Although we do not report it, had we done our Rasch scoring of health without correction for DIF by national group, we might have had larger USA/UK difference in health. This means that it is possible that all of the (neglible) health differences between the USA and the UK may be due to measurement error caused by cultural differences in the self-reporting of health. Regarding the ‘curious reporting conventions’, at least one is the convention of this journal, namely that of reporting 90% rather than 95% confidence intervals for the main result, following Sterne and Davey Smith.7 As for the suggestion that the goodness-of-fit tests we used would not permit our conclusion, this would be quite correct if we had based our conclusion on these tests. In fact, we only used these tests as further supporting evidence for our main conclusions, which were derived from the regression coefficients resulting from the linear additive model as reported in Table 4 of our paper.3 Another concern of theirs1 is that although we object to Banks et al.4 looking only at a few health conditions, we, it would seem, reduce all health conditions to a single unidimensional scale. But this is to misconstrue the fundamental difference between ‘counting diseases’ as a measure of severity (like counting apples and oranges and then deciding which of these two groups are sweeter overall) and constructing a metric of health based on the functioning domains that are constitutive of the essence of health. Finally, we are told that we have constructed a ‘straw man’1 by citing examples of where the Banks et al.4 conclusions about UK health advantage have been relied on. It suffices to invite readers to peruse the Institute of Medicine Report U.S. Health in International Perspective: Shorter Lives, Poorer Health (2013)8 which cites Banks et al., and similar studies, extensively. Finally, although we are reluctant to recommend that US Americans immigrate to the UK to improve their health, we definitely would recommend, when comparing the health of populations, to supplement comparing prevalence of diseases with a more nuanced and rich analysis based on a fuller conception of health, since after all, health is more than the absence of disease… Conflict of interest: None.