Abstract
Instruments intended to assess health-related quality of life (HRQOL) and health status are widely used in research and clinical practice, but with little conceptual guidance there is some uncertainty about what it is that these instruments are actually tapping. Causal models have the potential to provide the required conceptual guidance, not only placing commonly discussed health concepts on a firm scientific foundation, but also allowing medical and psychosocial interventions to be developed in a more focused and efficient manner. Several models, some postulating causal relations, have been proposed over many years, and some have been supported by data. The development and validation of these models has, however, been conducted in a piecemeal fashion. The imperative to develop tailored, cost-effective interventions requires a synthesized approach to developing and testing causal models.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.
No writing assistance was utilized in the production of this manuscript.
• Too little attention is paid to conceptual issues in health-related quality of life (HRQOL) research; for example, definitions, distinguishing elements from determinants.
• The distinction between HRQOL and health status is unclear.
• Models, particularly causal models, are useful in identifying causal pathways between important concepts in health, and also the parties most readily able to intervene on the various pathways to improve health.
• Causal models have greater explanatory power than non-causal models.
• Several models of HRQOL and health status have been proposed.
• The models have many commonalities, but also some important differences.
• Some of these models have been supported by data, most notably Wilson and Cleary’s model.
• Empirical tests of the models have been piecemeal, conducted on different populations and using different measures.