Scottish Longitudinal Study
Development & Support Unit

Current Projects

Project Title:

Flexible aging: new ways to measure the diverse experience of population ageing in Scotland

Project Number:



John MacInnes (University of Edinburgh)
Jeroen Spijker (University of Edinburgh)

Start Date:

13 February 2013


The project takes a demographic approach to studying population aging in Scotland and its implications. The objectives are:

  1. Reassessing the concepts of population aging and the size and composition of the ‘old’ population by constructing alternative measurements of aging based on
    1. years of remaining healthy life expectancy
    2. proportion of total life expectancy remaining.
      Results will be compared with conventional (“fixed”) measures (e.g. old-age dependency ratio).
  2. Based in 1) to construct an “ageing taxonomy” (by health status, SES, marital status, …) and to explore the possibility to distinguish between ‘active’ and ‘frail’ elderly.
  3. To review the implications of the results for forecasted future dependency ratios and the demand for health and social care.

What we know:
Measures of population ageing based solely on fixed chronological ages can be misleading (Lutz et al. 2008) as they don’t assume future progress in important factors like remaining life expectancy and disability rates. Age should therefore be considered in terms of years left until death rather than a fixed age boundary (Sanderson and Scherbov 2005): adjusting measures for longevity change show a slower pace of change than those based on the traditional definition of age. Consequently, the expected (financial) burden of an ageing society is likely to be less than thought.


1. An example of an alternative ageing indicator is the Prospective old age dependency ratio, i.e.:

eq 1

Most of the to be constructed ageing indicators are based on the methodology published in Sanderson and Scherbov (2007). However, one devised by the authors is the Real Prospective Old-Age Dependency Ratio. This is calculated by:

eq 2

However, to be able to calculate this for Scotland and its NHS regions SLS data is required.

2. The incorporation of socioeconomic categories (educational level) and subjective health (known predictor of mortality) in the ageing indicators, information that is captured in successive censuses on which the SLS in based.

Therefore, by using SLS data our study contributes to the knowledge base on population health of sub-groups in Scotland such as those by Boyle et al. (2009), Clemens et al. (2009) and Young et al. (2010), studies that also used SLS data.


  1. Boyle, P., Z. Feng, and G. Raab. 2009. "Does widowhood increase mortality risk? Comparing different causes of spousal death to test for selection effects." in Scottish Longitudinal Study (SLS) Research Working Paper Series 4. Edinburgh.

  2. Clemens, T., P. Boyle, and F. Popham. 2009. "Unemployment, mortality and the problem of healthrelated selection: Evidence from the Scottish Longitudinal Study." in Scottish Longitudinal Study (SLS) Research Working Paper Series 2. Edinburgh.

  3. Lutz, W., W. Sanderson, and S. Scherbov. 2008. "Global and Regional Population Ageing: How Certain Are We of its Dimensions?" Journal of Population Ageing 1(1):75-97.

  4. Sanderson, W.C.and S. Scherbov. 2007. "A new perspective on population aging." Demographic Research 16(2):27-58.

  5. Young, H., E. Grundy, D. O’Reilly, and P. Boyle. 2010. "Self-rated health and mortality in the UK: results from the first comparative analysis of the England & Wales, Scotland and Northern Ireland longitudinal studies." Population Trends 139:11-36.

Related Outputs (viewable on CALLS Hub):

Explore the variables held in the SLS data dictionary.

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