This is a broad area of research undertaken within the School of Computing Sciences at UEA over a period of 20 years. Two different areas of research directly contribute to actuarial applications: research into data-mining and statistical analysis.

In mid-1990s, Prof Rayward-Smith and Dr de la Iglesia developed new approaches to solving optimisation problems by developing metaheuristics and, by the mid-to-late 90s, the expertise was being applied within the newly emerging discipline of data mining. This led to one of the first descriptions of how data mining can be used in industrial applications. Applications included new methods for companies to develop and maintain complex software systems and the use of metaheuristics to mine insurance data.

Aviva used our research into meta-heuristic techniques, data mining and machine learning for both pricing and marketing in general (e.g. car and household) insurance. This is of direct benefit to general insurance customers, because adoption of these techniques allows a more competitively priced, product to be offered to customers, whilst still maintaining Aviva's profitability.

Prof Kulinskaya joined UEA in March 2010 as the Aviva Chair in Insurance Statistics, bringing with her expertise in health applications of multivariate statistical methods. Since 2014, Kulinskaya and co-authors have developed a methodology for modelling the impacts of chronic medical conditions, medical advances and health interventions on longevity and mortality risks at both individual and population level. The methodology is based on advanced methods of design and statistical analysis of the observational data from big health administrative databases, such as The Health Improvement Network (THIN) primary care database and the National Joint register (NJR). It involves the following steps:

  • design a longitudinal case-cohort study with the appropriate cohorts of cases and their controls selected from the big administrative dataset,
  • build a sophisticated survival model enabling evaluation of survival benefits or harms of particular chronic health conditions, treatments and public health interventions,
  • and integrate these individual survival effects to evaluate the population effects.

In [1], we demonstrated that the current internationally recommended thresholds for statin therapy for primary prevention of cardiovascular disease in routine practice may be too low and may lead to overtreatment of younger people and those at low risk of heart disease.

In [2], we quantified the hazards of death after myocardial infarction and found them to be less than reported by previous studies. We also found that standard treatments of aspirin or ACE inhibitors may be of little benefit or even cause harm.

In [3] we compared intensive control of systolic blood pressure (SBP) at 120 mmHg (which is being implemented in the US) to standard control at 140 mmHg and quantified life expectancy implications of the two target SBP levels. We concluded that intensive treatment of SBP may be harmful in the general population in the UK.

Our novel methodology of integrating individual effects to population level longevity changes is presented in [4, 5, 7]. This integration requires a combination of parametric assumptions about the underlying survival distribution, such as the Gompertz or Weibull distribution, with a survival model incorporating a number of modifiers. The latter can use a Cox’s regression when the proportional hazards assumption is satisfied, but may require more complicated modelling of shape and scale parameters. This “double-Cox” model was developed in [6].

Our research was supported by the grant from Institute and Faculty of Actuaries and  by ESRC grant on Smart Data Analytics for Business and Local Government.

A key impact from our research is to enable people with certain health conditions who had previously struggled to get insurance, to get insurance in the future. Full details of our research can be found at

Aviva uses the results of our research in their underwriting, and our results help to quantify the longevity assumptions necessary for numerous longevity and population projections.

We have also developed a free “My Longevity” app  aimed at both Insurance professionals and the general public which models the impact of lifestyle and health choices on life expectancy.


  1. Survival Benefits of Statins for Primary Prevention: A Cohort Study Gitsels L.A., Kulinskaya E. & Steel N. (2016) PLoS ONE 11(11): e0166847. DOI: 10.1371/journal.pone.0166847
  2. Survival prospects after acute myocardial infarction in the United Kingdom: a matched cohort study 1987-2011 Lisanne Gitsels, Elena Kulinskaya & Nicholas Steel  (2017) BMJ Open 7(1) DOI: 10.1136/bmjopen-2016-013570
  3. Optimal systolic blood pressure targets in routine clinical care Gitsels LA, Kulinskaya E., Bakbergenuly, I. & Steel N. (2018) Journal of Hypertension, 2019, 37:837–843 DOI: 10.1097/HJH.0000000000001947
  4. How Medical Advances and Health Interventions Will Shape Future LongevitySessional paper and presentation, IFoA, Edinburgh, June 25 2018Gitsels LA, Kulinskaya E. & Wright N. (2019) British Actuarial Journal, DOI: 10.1017/S1357321719000059
  5. Calculation of changes in individual and period life expectancy based on proportional hazards model of an intervention. Kulinskaya E., Gitsels, LA., Bakbergenuly, I. and Wright N.R Insurance Mathematics and Economics 2020, 93, 27-35
  6. Risk-adjusted CUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset Begun, A., Kulinskaya, E. & MacGregor, A.J. (2019) BMC Medical Research Methodology 19, 217.
  7. Kulinskaya, E. , Gitsels, L.A., Bakbergenuly, I.  and Wright, N.R. Dynamic hazards modelling for predictive longevity risk assessment. Insurance Mathematics and Economics 2020 

Research Team

Prof. Elena Kulinskaya, Ilyas Bakbergenuly,  Lisanne Gitsels, Padma Chutoo, Nurunnahar Akter, Njabulo Nkube


  • Mr. Nigel Wright, Actuary, Aviva
  • Prof.  Nicholas Steel, NMS
  • Dr. Dmitry Pshezhetskiy, NMS