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Using Big Health and Actuarial data for Modelling Longevity and Morbidity risks (KULINSKAYAU16IFA) - 1 of 3

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  • Full or part time
    Prof Kulinskaya
  • Application Deadline
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  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

It is well known that longevity is increasing considerably both in developed and developing countries, UK inclusive. To be able to establish the drivers of this change, and to predict how they may change over time and how this would affect life expectancy, researchers need to harvest Big Health Data [1], i.e. to access large health databases, and to use sophisticated statistical methods for modelling the mortality experience of participating populations using individual level health data. We intend to use the subset from The Health Improvement Network (THIN) primary care database (http://www.thin-uk.net/) comprising 3.4 million patients born before 1960 for this research project [2]. Modelling longevity and morbidity is of essential importance both to public health and to the actuarial research. Multilevel multiple imputation [3] will be used to account for missing values. To translate the results obtained from Big Health Data to the reference population of relevance to the actuarial community Big Actuarial Data such as the Continuing Mortality Investigation data are of the utmost importance.

The main objectives of the PhD studentship funded by the Institute and Faculty of Actuaries is the development of novel statistical and actuarial methods for modelling mortality, modelling trends in morbidity, assessing basis risk [4] (i.e. achieving translation from general population to a population of interest) and evaluating longevity improvement based on Big Health and Actuarial Data.

The aims of this PhD studentship is to identify and quantify the key factors affecting mortality and longevity [4], such as lifestyle choices, medical conditions and/or interventions, modelling of temporal changes in the factors affecting morbidity and mortality; evaluation of plausible scenarios in mortality trends due to medical advances or lifestyle changes; and development of software tools to forecast longevity risk.

This project is one of three.

PhD start dates: Home/EU candidates will start on 1 October 2016; international candidates will start on 1 January 2017

Funding Notes

This PhD studentship is funded by The Institute and Faculty of Actuaries for three and a half years. Funding is available to UK/EU applicants and comprises of payment of tuition fees and an initial annual stipend of £14,057. Overseas applicants may apply but they will be required to fund the difference between home/EU and overseas tuition fees (in 2016/17 the difference is £9,679 but fees are subject to an annual increase).

References

i) Schneeweiss, S. Learning from Big Health Care Data. The New England Journal of Medicine 2014; 370:2161-2163.
ii) Blak BT, Thompson M, Dattani H, et al. Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates. Informatics in primary care 2011;19(4):251-5.
iii) Marston L, Carpenter JR, Walters KR, et al. Issues in multiple imputation of missing data for large general practice clinical databases. Pharmacoepidem Drug Safe 2010;19(6):618-26. doi:10.1002/pds
iv) Longevity Science Panel (2014). What is Aging? Can we delay it? http://www.longevitypanel.co.uk/viewpoint/what-is-ageing-can-we-delay-it/
v) Longevity Basis Risk, A Methodology for Assessing Basis Risk. Research Report by Cass Business School and Hymans Robertson LLP (2014)

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