An analysis of diabetes mortality risk

This research sought to update understanding following improvements to treatment and deepen the understanding of the mortality risk associated with Type 1 or Type 2 diabetes, including relative risk in the presence of co-morbidities. Specifically, we developed a model to provide mortality predictions at a granular level for lives with and without diabetes. The model is tailored for use by the insurance industry to provide an updated source from which to appreciate the risk posed when underwriting people with diabetes. By providing an updated and deeper understanding of mortality risk, the research’s aim is to improve access to insurance for those individuals living with diabetes. The model combines industry standard underwriting risk factors, such as age, gender, deprivation index, body mass index (BMI), smoker status, blood pressure (BP) and cholesterol level, with various co-morbidities related to diabetes. A comprehensive analysis of mortality risk factors, between 2010 and 2019, for people with and without diabetes is undertaken on over 1.2 million records based on Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics (HES) and Office for National Statistics (ONS) death registrations data. Cox proportional hazards models are used to estimate the probability of death, stratified by gender across three distinct populations: Type 1 diabetes, Type 2 diabetes and a general population sample. The model outputs produced are permutations of the following: gender; population split by general sample, Type 1 and Type 2 diabetes; and a time-dependent exponential model and a time-invariant homogeneous model. A Shiny model application allows interaction with the model outputs (https://0jv7e6-scott-reid.shinyapps.io/diabmdl/) and spreadsheets provide additional explanation. Useful insights were obtained through industry discussions on the variation of existing market practice against that implied by the results. Key rating factors were generally aligned with market practice, such as age, BMI, BP, cholesterol and years since a diabetes diagnosis. However, for a few significant mortality risks impacting co-morbidities, the results did not adhere to prior expectations. Exploratory work suggested that the order and sequencing of key co-morbidities for diabetes must be included in future model development.