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Mortality improvement by socio-economic circumstances in England (1982 to 2006)

Published online by Cambridge University Press:  17 December 2012

Abstract

Assessing longevity risk is crucial to the financial management of annuities and longevity-related financial instruments. Actuaries have been using socio-economic circumstances (SEC) of individuals estimated through postcodes, pension size and occupation to price annuities for prospective customers. Differences in mortality rates of people in different SEC have been discussed extensively but less is known about how their mortality rates have changed over time.

A lack of regular, consistent and credible mortality data for people in different SEC has hampered the study of historical mortality trends. This in turn has made forecasting a greater challenge. To address some of these data issues, we have obtained mortality and population data between 1981 and 2007 for England, divided into SEC quintiles (measured by the relative deprivation of the area of residence according to the Index of Multiple Deprivation (IMD) 2007). Using the data, we have analysed the mortality trends by SEC. These findings can provide insight into mortality improvement for people in different SEC. This can contribute to commercial decisions for annuity businesses, reinsurance and longevity swaps.

Type
Sessional meetings: papers and abstracts of discussions
Copyright
Copyright © Institute and Faculty of Actuaries 2012 

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References

Bajekal, M., Scholes, S., Love, H., Hawkins, N., O'Flaherty, M., Raine, R., Capewell, S. (2012). Analysing recent socioeconomic trends in coronary heart disease mortality in England, 2000–2007: A population modelling study. PLoS Medicine, 9(6), e1001237.CrossRefGoogle ScholarPubMed
Bartley, M. (2004). Health inequality: An introduction to theories, concepts and methods. 4th reprint. Polity Press in association with Blackwell Publishing, Cambridge, UK & Malden, MA. (Reprinted by Polity Press, Cambridge, UK & Malden, MA. 2008).Google Scholar
Boyle, P., Norman, P. (2009). Chapter 19: Migration and Health. In: A companion to health and medical geography/ ed. by T. Brown, S. McLafferty, C. Moon. Wiley-Blackwell, Oxford, UK.Google Scholar
Buck, D., Frosini, F. (2012). Clustering of unhealthy behaviours over time. Implications for policy and practice. The Kings Fund. (http://www.kingsfund.org.uk/publications/unhealthy_behaviours.html)Google Scholar
Continuous Mortality Investigation. (2002). Working Paper 1. An interim basis for adjusting the “92” Series mortality projections for cohort effects. Institute of Actuaries and Faculty of Actuaries.Google Scholar
Continuous Mortality Investigation. (2007). Revise Working Paper 20. Stochastic projection methodologies: Further progress and P-spline model features, example results and implications. Institute of Actuaries and Faculty of Actuaries.Google Scholar
Continuous Mortality Investigation. (2009a). Working Paper 38. A prototype mortality projections model: Part one – An outline of the proposed approach. Institute of Actuaries and Faculty of Actuaries.Google Scholar
Continuous Mortality Investigation. (2009b). Working Paper 39. A prototype mortality projections model: Part two – Detailed analysis. Institute of Actuaries and Faculty of Actuaries.Google Scholar
Continuous Mortality Investigation Self-administered pension schemes mortality committee (2011a). Working Paper 53. An initial investigation into rates of mortality improvement for pensioners of self-administered pension schemes. Institute and Faculty of Actuaries.Google Scholar
Continuous Mortality Investigation (2011b). Working Paper 55. The CMI Mortality Projections Model, CMI_2011. Institute and Faculty of Actuaries.Google Scholar
Currie, I.D., Durban, M., Eilers, P.H.C. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4, 279298.CrossRefGoogle Scholar
Cuthbertson, S.A., Goyder, E.C., Poole, J. (2009). Inequalities in breast cancer stage at diagnosis in the Trent region, and implications for the NHS Breast Cancer Screening Programme. Journal of Public Health, 31(3), 398405.CrossRefGoogle Scholar
Eilers, P.H.C., Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89121.Google Scholar
Gregory, I. (2009). Comparisons between geographies of mortality and deprivation from the 1900s and 2001: spatial analysis of census and mortality statistics. British Medical Journal, 339, b3454.Google Scholar
Hastings, A., Gramley, G., Watkins, D. (2012). Serving deprived community in a recession. Joseph Rowntree Foundation. (http://www.jrf.org.uk/sites/files/jrf/communities-recession-services-full.pdf)Google Scholar
House of Commons Committee of Public Accounts. (2010). Tackling inequalities in life expectancy in areas with the worst health and deprivation. Third report of Session 2010–11. House of Commons London: The Stationary Office Limited. (http://www.publications.parliament.uk/pa/cm201011/cmselect/cmpubacc/470/470.pdf)Google Scholar
Jagger, C., Christensen, K., Murphy, M. (2009). Cohort differences in mortality and morbidity. British Actuarial Journal, 15(Supplement), 6571.Google Scholar
Kivimäki, M., Lawlor, D.A., Davey Smith, G., Kouvonen, A., Virtanen, M., Elovainio, M., Vahtera, J. (2007). Socioeconomic position, co-occurrence of behaviour-related risk factors, and coronary heart disease: the Finnish Public Sector Study. American Journal of Public Health, 97, 874879. doi:10.2105/AJPH.2005.078691.Google Scholar
Marmot Review. (2010). Fair Society, Healthy Lives. Strategic Review of Health Inequalities in England post-2010. (http://www.instituteofhealthequity.org/projects/fair-society-healthy-lives-the-marmot-review/fair-society-healthy-lives-full-report)Google Scholar
McLoone, P. (2001). Targeting deprived areas within small areas in Scotland. Population Study, 323, 374375.Google Scholar
Murphy, M. (2009). The ‘Golden Generations’ in historical context. British Actuarial Journal, 15(Supplement), 151184.Google Scholar
Noble, M., McLennan, D., Wilkinson, K., Whitworth, A., Barnes, H. Social Disadvantage Research Centre, University of Oxford. Dibben C, University of St Andrews. (2008). The English indices of deprivation 2007. Department for Communities and Local Government.Google Scholar
Norman, P. (2010). Identifying change over time in small area socio-economic deprivation. Applied Spatial Analysis and Policy, 3, 107138.Google Scholar
Norman, P., Boyle, P., Rees, P. (2005). Selective migration, health and deprivation: a longitudinal analysis. Social Science and Medicine, 60, 27552771.Google Scholar
Norman, P., Simpson, L., Sabater, A. (2008). Estimating with confidence and hindsight: New UK small area population estimates for 1991. Population, Space and Place, 14(5), 449472.Google Scholar
Power, E., Miles, A., von Wagner, C., Robb, K., Wardle, J. (2009). Uptake of colorectal cancer screening: system, provider and individual factors and strategies to improve participation. Future Oncology, 5(9), 1737117388.CrossRefGoogle ScholarPubMed
Raine, R., Wong, W., Ambler, G., Hardoon, S., Petersen, I., Morris, R., Bartley, M., Blane, D. (2009). Sociodemographic variations in the contribution of secondary drug prevention to stroke survival at middle and older ages: Cohort study. British Medical Journal, 338, b1279. doi:10.1136/bmj.b1279.Google Scholar
Raine, R., Wong, W., Scholes, S., Ashton, C., Obichere, A., Ambler, G. (2010). Social variations in access to hospital care for patients with colorectal, breast, and lung cancer between 1999 and 2006: Retrospective analysis of hospital episode statistics. British Medical Journal, 340, b5479.Google Scholar
Rees, P., Norman, P., Brown, D. (2004). A framework for progressively improving small area population estimates. Journal of the Royal Statistical Society, 167(1), 536.Google Scholar
Richards, S. (2008). Postcode ratings for mortality. Presented to the Momentum Convention 2008. (http://www.richardsconsulting.co.uk/Momentum_2008_print_version.pdf)Google Scholar
Robjohns, N. Update on the latest work of the CMI. Mortality and longevity one-day seminars, March 2010. Presented to the Actuarial Profession, 17 March 2010 (London) and 25 March 2010 (Leeds). Available at: http://www.actuaries.org.uk/research-and-resources/pages/continuous-mortality-investigation-presentationsGoogle Scholar
Scholes, S., Bajekal, M., Love, H., Hawkins, N., Raine, R., O'Flaherty, M., Capewell, S. (2012). Persistent socioeconomic inequalities in cardiovascular risk factors in England over 1994–2008: A time-trend analysis of repeated cross-sectional data. BMC Public Health, 12, 129.CrossRefGoogle ScholarPubMed
Steptoe, A., Hamer, M., Butcher, L., Lin, J., Brydon, L., Kivimäki, M., Blackburn, E., Erusalimsky, J.D. (2011). Educational attainment but not measures of current socioeconomic circumstances are associated with leukocyte telomere length in healthy older men and women. Brain Behavior and Immunity, 25(7), 12921298.Google Scholar
Telford, P.G., Browne, B.A., Collinge, E.J., Fulcher, P., Johnson, B.E., Little, W., Lu, J.L.C., Nurse, J.M., Smith, D.W., Zhang, F. (2011). Developments in the management of annuity business. British Actuarial Journal, 16(3), 471551.Google Scholar
Wanless, D., Pattison, J., McPherson, K., Haberman, S., Blakemore, C., Wong, W., Lu, J. (2012). Life expectancy: Past and future variations by socio-economic group in England & Wales. Longevity Science Advisory Panel. (http://www.longevitypanel.co.uk/docs/life-expectancy-by-socio-economic-group.pdf)Google Scholar
Wardle, J., Rapoport, A., Miles, T., Afuape, T., Duman, M. (2001). Mass education for obesity prevention: The penetration of the BBC's ‘Fighting Fat, Fighting Fit’ campaign. Health Education Research, 16(3), 343. (http://www.ncbi.nlm.nih.gov/pubmed?term=wang%20mcpherson%20marsh%20gortmaker)Google Scholar
Weller, D., Coleman, D., Robertson, R., Butler, P., Melia, J., Campbell, C., Parker, R., Patnick, J., Moss, S. (2007). The UK colorectal cancer screening pilot: Results of the second round of screening in England. 2007. British Journal of Cancer, 97, 16011605.Google Scholar
Whynes, D.K., Mangham, C.M., Balfour, T.W., Scholefield, J.H. (2010). Analysis of deaths occurring within the Nottingham trial of faecal occult blood screening for colorectal cancer. Gut, 59(8), 10881093.Google Scholar
Willets, R.C. (2004). The cohort effect: Insights and explanations. British Actuarial Journal, 10(4), 833877.Google Scholar
Willets, R.C., Gallop, A.P., Leandro, P.A., Lu, J.L.C., MacDonald, A.S., Miller, K.A., Richards, S.J., Robjohns, N., Ryan, J.P., Waters, H.R. (2004). Longevity in the 21st century (with discussion). British Actuarial Journal, 10(4), 685832, 878-898.Google Scholar