A collaborative research project between RGA and the University of Leicester in the UK revealed that non-traditional risk factors allow for a more informed biometric risk assessment.
Future research reports will provide a detailed analysis of new and established biometric risk factors and how they interact to affect mortality and morbidity outcomes. Ask us about an early look at the results.
Through a cutting-edge research collaboration with the University of Leicester in the UK, RGA is leading the effort to understand how biometric and lifestyle risk factors influence a person’s risk of developing chronic disease and dying.
Using the deep phenotypic data from the UK Biobank, this partnership seeks to generate new insights that can improve public health as well as evolve underwriting and pricing practices, and, more broadly, how insurers view biometric risk.
Important note: UK Biobank data was only provided to the academic research team at the University of Leicester led by Professor Tom Yates. RGA does not have access to any of the UK Biobank data.
The full report from this groundbreaking study is expected to be released in early 2025. RGA Senior Health Data Scientist Kishan Bakrania and University of Leicester Professor Tom Yates recently provided a preview of the results, delving into the motivations, methodologies, findings, and implications of this transformative research.
This Q&A article provides a summary of a more in-depth interview with study co-leaders. View the full recorded interview above.
Tom Yates: The UK Biobank is a large cohort of individuals from the UK – half a million adults who were recruited between 2006 and 2010 and who have been studied for a wide range of risk factors and exposures, including lifestyle-related factors. These have been followed up on using our national hospital records and death records so we can see how those risk factors equate to future outcomes.
Kishan Bakrania: The UK Biobank has previously been shown to be an internationally important data set that is helping researchers better understand the causes of cancer, how it develops, treatment, and prevention. It’s going to be a very important data set, not just for cancer but for other chronic diseases and illnesses as well. The Biobank is made up of a large and diverse set of ancestry groups from all corners of the world, so it can definitely serve as a global reference for worldwide populations.
Bakrania: Our primary research interest was largely around expanding our knowledge of traditional and non-traditional risk factors of morbidity and mortality outcomes. For example, BMI can be considered a traditional risk factor, as it is universally accepted and used by insurers and reinsurers when underwriting life and health insurance. On the other hand, something like resting heart rate, which is known to be a strong predictor of health outcomes, can be considered a non-traditional risk factor because it is not typically used in underwriting.
The challenge is understanding the combined mutual impact of such factors on morbidity and mortality outcomes. These sorts of research insights are critical because they will allow us to refine our underwriting philosophies in the near future.
Yates: We know that lifestyle factors like physical activity, nutrition, and sleep are fundamental determinants of health. These are factors that public health researchers are interested in, both in the academic community and the insurance community. The key question is how we can use that data to inform prognostic models of mortality.
Yates: Using the Biobank data, we worked with RGA to look at two research questions.
The first was to define two cohorts – a standard-lives cohort and a chronic-disease cohort. We then separated each into adults 60 years old or younger and adults over 60. We wanted to understand in these two cohorts how lifestyle factors were associated with mortality.
For the second research question, we sought to understand whether we could actually use these relatively easy-to-measure lifestyle factors in place of more established clinical risk factors in standard risk prediction models. The findings were really quite insightful.
Bakrania: In general, our findings were that these lifestyle factors were important risk markers and that they tended to be particularly important in the chronic-disease cohort and the younger-age cohort.
So, for example, if we look at the value of cholesterol in the blood, which requires a blood sample, we found that if we replace cholesterol with resting heart rate or self-reported walking pace, we could actually improve the prognostic discrimination of those models in both younger and older men and in older women. That really does have some potential to change the way things are done.
Bakrania: The work will greatly enhance our biometric research knowledge and will provide us with a deeper understanding of how new rating factors differentiate morbidity and mortality risk. It is likely that these findings will help our industry pursue innovations in underwriting, pricing, and insurance-linked wellness programs.
Adding to this, the insights gained from integrating lifestyle factors into risk assessment models have several tangible benefits for the insurance industry. First, they enable a more personalized approach to underwriting, which can enhance customer satisfaction and loyalty. The ability to offer tailored insurance products that reflect an individual's lifestyle and associated risks can differentiate insurers in a competitive market.
Moreover, these advanced risk models can lead to more accurate pricing strategies. By understanding the nuanced effects of various lifestyle factors on health outcomes, insurers can adjust premiums more fairly, potentially reducing the number of claims and improving profitability.
Bakrania: The proactive approach of using lifestyle data for risk assessment encourages policyholders to adopt healthier habits. It makes them more conscious of how their lifestyle choices directly impact their insurance premiums and their health. This not only benefits individuals but contributes broadly to public health improvement.
Yates: We're trying to extend the work that we've already done with RGA to focus on data collected from activity trackers. The extension into wearable technology through smartwatches and mobile phones is poised to further revolutionize the insurance industry by introducing real-time data tracking and analysis, which can dynamically adjust policy terms and conditions based on the live data received from policyholders' devices. This opens opportunities for even more personalized insurance plans and introduces a new dimension of interaction between the insurer and the insured.
Future research reports will provide a detailed analysis of new and established biometric risk factors and how they interact to affect mortality and morbidity outcomes. Ask us about an early look at the results.
