Chronic Diseases and the Stock of Health: Quantification Using EQ-5D-5L

Chronic diseases affect individuals’ health status and its impact is reflected by the stock of health, which measures the amount of health of a population in a given period of time. In a new study published in Health Economics, Maria Errea and colleagues assess the health-related quality of life of a population using the EQ-5D-5L instrument to, first, estimate the impact of a set of 30 chronic diseases and, second, rank diseases according to their impact on the stock of health for a country.

In a new study published in Health Economics, Maria Errea and colleagues assess the health-related quality of life of a population using the EQ-5D-5L instrument to, first, estimate the impact of a set of 30 chronic diseases and, second, rank diseases according to their impact on the stock of health for a country.

The impact of chronic diseases is difficult to measure, with no consensus on definitions and classifications of disease. Definitions vary in several respects, including duration, need for medical attention, contagiousness, and associated risks factors. This heterogeneity makes comparisons across diseases very difficult. In our new paper, we propose to measure the stock of health of a country that would be lost due to chronic diseases.

Describing work conducted in collaboration with Eduardo Sanchez-Iriso and Juan M. Cabases from the Public University of Navarra, this paper, recently published in Health Economics, provides an estimate of chronic disease impact in terms of stock of health loss in a population. The main outcome is a ranking of diseases according to their impact on the population.

The purpose is to outline a method for estimating the impact of chronic diseases for a country using data from the EQ-5D-5L. We establish a ranking of chronic diseases based on their estimated impact on the stock of health. Our hope is that this could be used to inform prioritisation in health care. If similar information from similar sources is collected in other countries, we encourage researchers to replicate the exercise in other settings.

In our study, we used data from the Spanish National Health Survey 2011/12. This was, in fact, the first survey that included the EQ-5D-5L questionnaire. We estimate a linear regression model and apply weights (e.g. proportion of people suffering diabetes) to our estimates, in order to ascertain the impact of each disease on the population. The survey is representative of the Spanish population. Therefore, we extrapolate our estimates to the whole country.

We found that chronic diseases in Spain represent around 19% of the stock of health losses of the country, compared with a country free of those diseases. Mental illnesses, embolism, and stroke were the diseases found to have the largest impact.

The Spanish National Health Survey is conducted every 5 years, meaning that estimates could be revised in future. Comparative studies could be conducted to look at the evolution of the burden of disease over time. However, the last wave of the survey (2016/17) excluded the EQ-5D questionnaire. Therefore, comparisons could be made in the incidence rates of the diseases but not in losses in the stock of health.

Clearly, there are barriers to work of this nature. Definitions of chronic disease may change over time or vary between settings, as might the use of different measures of health-related quality of life. We hope that this paper will highlight the value of such research in the context of chronic diseases, often stigmatised in society. Estimates such as those that we have produced can inform policy design and priorities. Ensuring consistency in the collection of information – over time within a setting, and across settings – could serve to benefit the stock of health of a population.

Citation

Sánchez‐Iriso, E., Errea Rodríguez, M., and Cabasés Hita, J. M., 2019. Valuing health using EQ‐5D: The impact of chronic diseases on the stock of health. Health Economics. DOI.

Related research

Devlin, N.J., Parkin, D. and Browne, J., 2010. Patient-reported outcome measures in the NHS: new methods for analysing and reporting EQ-5D data. Health Economics, 19(8), pp.886–905. DOI. RePEc.

Devlin, N.J., Shah, K.K., Feng, Y., Mulhern, B. and Hout, B. van, 2018. Valuing health-related quality of life: An EQ-5D-5L value set for England. Health Economics, 27(1), pp.7–22. DOI. RePEc.

Feng, Y., Devlin, N., Bateman, A., Zamora, B. and Parkin, D., 2019. Distribution of the EQ-5D-5L Profiles and Values in Three Patient Groups. Value in Health, 22(3), pp.355–361. DOI. RePEc.