New research published in Economics and Human Biology demonstrates that elevated blood tests and nurse-collected physical measures predict progression to disability two to four years ahead for individuals without a disability history. In a new study, published in Economics and…
New research published in Economics and Human Biology demonstrates that elevated blood tests and nurse-collected physical measures predict progression to disability two to four years ahead for individuals without a disability history.
In a new study, published in Economics and Human Biology, Apostolos Davillas and Steve Pudney (University of Sheffield) demonstrate that elevated blood tests and nurse-collected physical measures predict progression to disability at two to four years.
The wider social impacts of ill-health – on quality of life, personal and social functioning, and social costs of disease – depend critically on the duration and severity of disability. Disability is associated with loss of employment and serious consequences for families. It typically implies long-lasting impairments that may prevent independent living and generate substantial public costs. This is particularly so in the UK, where disability prevalence is above the European Union average and has risen rapidly from 11.9 million in 2013/14 to 13.3 million in 2015/16. A crucial question for researchers and policymakers is whether the demand for care services will be curbed by gains in disability-free life expectancy, alongside the projected continuing gains in longevity. An answer to this question requires a better understanding of the processes leading to disability, allowing the development of strategies and screening programmes to address disability more efficiently.
The study examines the predictive power of a wide range of biomarkers for future disability risks, over and above the potential role of conventional measures of self-assessed health (SAH). Applying nationally representative, longitudinal U.K. data (Understanding Society), the study followed a cohort of individuals without disability history at baseline to estimate predictive models for disability two to four years ahead. To explore the robustness of the results, the study considers alternative disability measures, ranging from the number of functional difficulties reported to the receipt of disability benefits. We found a quantitatively and statistically significant predictive role for a large set of nurse-collected and blood-based biomarkers.
In subsequent analyses, we develop latent variable models to identify any distinct dimension of health that predicts future disability and is captured by the biomarkers but missed by the conventional SAH measures. We found that, although SAH has performed well, it was far from perfect as a leading indicator for disability risks. SAH tended to underrepresent grip strength, lung and liver function, and stress-related steroid hormone levels, while overemphasising adiposity, hypertension, and cholesterol levels. These results highlight the presence of distortions in SAH, in the sense of dimensions of health that are given too much or too little weight by SAH in predicting disability.
The publication of the study is timely, given the UK government’s recent prevention green paper. Our study provides novel insights on targeting certain population groups at risk of disability progression and tackling the relevant future public costs. To support this, exclusive reliance on self-reported subjective measures should be avoided. Our project demonstrates scope – for physical health, at least – for wider use of objective measures of health (including blood-based biomarkers) by social scientists, health economists, and policymakers.
Citation
Davillas, A., & Pudney, S., 2019. Biomarkers as precursors of disability. Economics & Human Biology. DOI. RePEc.
An error has occurred, please try again later.
This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.
Keeping this cookie enabled helps us to improve our website.
Please enable Strictly Necessary Cookies first so that we can save your preferences!