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Mindstretcher - ranking chronic diseases

Study
Results
Disease clusters
Disease categories
Patient characteristics
Comment
The man on the stair

The question of which chronic disease most impacts upon quality of life can lighten many a dreary hour. Anecdote piles upon anecdote, but the problem is that the plural of anecdote is not data. What is needed is surveys using the same instrument to measure quality of life, used in large enough samples of patients, with a similar range of disease severity, and with a similar demographic base. Even then there may be problems in interpretation, but it might provide some better insights into disease impact on people. A study with about 15,000 patients from Holland gives us just this [1]. But prospective readers should beware. This is definitely a two-brains paper, but worth conquering.

Study


All research groups known to examine chronic diseases in the Netherlands were contacted to see what data sets were available. Studies had to use a standardised quality of life instrument, have full coverage of quality of life domains, include a range of chronic diseases, be big (at least 200 patients), have medically confirmed diagnoses, be obtained since 1992 and be geographically broad.

Eight data sets broadly fulfilling these categories were obtained, with information on about 15,000 people. They all used SF-36 or SF-24. These were analysed by quality of life dimension (physical functioning, physical role functioning, bodily pain, general health, vitality, social functioning and mental health) according to:

The method used was the ranking of mean scores. Thus if three diseases scored (say) 5, 10, and 15 (with 5 the 'best' score), then they would be ranked 1, 2 and 3. This was done for all quality of life domains, and the ranks for individual domains added together. This summed rank produces low scores for the diseases or disease clusters causing the least distress, and high scores for those causing the most problems.

Results


Disease clusters


The summed rank scores for chronic disease clusters are shown in the Figure. Musculoskeletal conditions, renal disease, cerebrovascular/neurological conditions and gastrointestinal conditions impacted most severely on quality of life.

Figure: Summed rank scores for disease clusters. Higher scores imply poorer quality of life



Disease categories


Some examples are useful here. For instance, in musculoskeletal conditions, ostoeoathritis had more adverse impact than back impairments, which scored higher (worse) than rheumatoid arthritis. For neurological conditions, Parkinson's disease or epilepsy, multiple sclerosis and stroke scored higher than migraine or neuromuscular disease. For psychiatric disorders depression scored worse than anxiety which in turn was worse than alcohol problems.

Patient characteristics


Patients who were older, female, had a low level of education, were not living with a partner, and/or had at least one comorbid condition had the poorest quality of life.

Comment


There will be limits to how far these sort of data can be subdivided and still give us valid conclusions. So where there is the largest agglomeration of information is where the strongest conclusions lie. For this analysis, this is with the comparisons across disease categories.

Moreover, there are also issues within the quality of life measures that an overall ranking will not highlight. This will be in the difference between mental and physical functioning, for instance.

Many professionals will not be overly surprised at the ranking of disease clusters, or the categories within each cluster, or the conclusions regarding patient characteristics. But where there is limited information there will always be room for argument. Though this ranking exercise should not be over-interpreted, it does give us a firmer platform on which to base decision-making, and on which to base research efforts.

The man on the stair


But hang on a minute, advised the man on the stair. Do you really believe that musculoskeletal conditions cause a greater impact on quality of life than, say visual impairment? Does this result have face validity? Could it simply be wrong?

What might be the causes of a wrong answer? Well the combining of data in the meta-analysis may be incorrect, though the authors seem to have done a pretty fine job, and discuss in detail possible problems and why they are unlikely to occur. Then there are the original studies themselves used in the combining process. Issues of validity of the original study was part of the defining process of the analysis, which is why only eight of about 30 studies actually made it into the review. The others were excluded because they were judged inadequate.

Then there is the method used for measuring quality of life, in this case SF-36. This measurement tool has been around for ages, is much used, and for which there are manuals and methods written down in exquisite detail. Validity has been measured in lots of different ways, hasn't it? And all that has been done so that we can be confident of the results obtained by using it.

The only other conclusion left would be that SF-36 is fundamentally flawed. That would have major repercussions, especially on all those discussions on things like cost per QALY that policy makers use for judging whether medicines can be bought by the NHS or other healthcare providers. Fundamental stuff, this, and why this paper [1] is so important about thinking about thinking about healthcare delivery.

References:

  1. MAG Sprangers et al. Which chronic conditions are associated with a better or poorer quality of life? Journal of Clinical Epidemiology 2000 53: 895-907.
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