Predictive Models in Health and Morbidity Trends Related to Insurance
Over recent decades, medical interventions and advances have become important drivers of health and morbidity trends.
In this project we will develop, evaluate and assess statistical predictive models for morbidity risk and underlying trends,
relating to particular major conditions such as heart disease, diabetes and cancer, using data from major USA- and
UK-based sources. The principal aim is to address the timely need to develop robust predictive models for rapidly
changing morbidity risks and relevant impact on health-related insurance. A Bayesian approach will be employed, to
allow for uncertainty quantification, under which model diagnostics, assessment and selection will be considered for a
range of models including machine learning-based approaches. The application of such methodology in the context of
this project is novel and at the forefront of current practice.
· Prof. George Streftaris, PI
HWU, School of MACS
· Prof. Angus Macdonald, CoI, HWU
· Dr. Torsten Kleinow, CoI, HWU
· Ian Duncan, CoI, UCSB
· Dr. Erengul Dodd, CoI, UoS
· Alex Jose (PhD, HWU)
· Arık A., Dodd E., Streftaris G. (2020) Cancer morbidity trends and regional differences in England – A Bayesian analysis.
PLoS ONE 15(5): e0232844. https://doi.org/10.1371/journal.pone.0232844
· Protection, Health and Care Conference, IFoA, Webinar, August 2020.
Cancer morbidity risk modelling – regional variation over time.
· Arık A., Dodd E., Cairns, A.J., Streftaris G. (under preparation, 2020)
Socioeconomic disparities in cancer incidence and mortality in England and the
impact of age-at-diagnosis on cancer mortality.