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Regular version of the site

"Mortality Analysis for Population Health Research using R"

*recommended age
Event ended

The International Laboratory for Population and Health at the Higher School of Economics (Russia) organize the school "Introduction in Mortality Analysis for Population Health Research using R". The school will held for 1 week on 26-30 September, 2022

School description

The school will present essential demographic methods for health and mortality research.  Participants will develop their skills in applying these methods in R.  First, we will introduce the standard life table construction, including additional life table quantities such as measures of inequality. Second, we will discuss the measures of healthy life expectancy. We will also present some decomposition methods that will allow us to investigate differences between mortality measures by age, sex, and cause of death. Finally, we will introduce the mortality forecast based on Lee-Carter model.


09:00-10:00: Orientation Meeting (only on Monday)
10:00-12:00: Lecture+ Practical
12:00-13:30: Lunch break
13:30-15:00: Lecture + Practical
15:00-15:30: Coffee break
15:30-18:00: Work on Individual Projects to be presented at the end of the school

The school will run over a 5-day period, with computer lab sessions scheduled every day. Students should prepare to work full time (40 hours per week) for the duration of the school.
Working language: Russian

Schedule (provisional)

Day 1

  • Life table construction
  • Reading data from the HMD
  • Building the life table according to HMD protocol
  • Implementing Kannisto function for older ages
  • Calculating confidence intervals for life expectancy

Day 2

  • Inequality measures
  • Measures of age-at-death variation (i.e. standard deviation, life disparity, variance, IQR)

Day 3

  • Prevalence-based measure of healthy life expectancy (Sullivan)
  • Multiple decrement life tables
  • Cause-deleted life tables

Day 4

  • Methods of decomposition
  • Kitagawa CDR
  • Arriaga/Andreev/Pressat (discrete)
  • Step-wise replacement
  • Adding causes of death

Day 5

  • Mortality forecasting
  • Lee-Carter method and package demography
  • Project presentation

School prerequisites

Participants should be familiar with basic life table construction. Ideally, participants should also have a basic understanding of decomposition methods. It is recommended that students read Preston et al. (2001) Chapters 2-4 prior to the commencement of the school (or an equivalent textbook covering life tables and decomposition). A basic command of R, including data handling, for-loops, and writing basic functions, is a prerequisite. If you have never used R in your research work, please make sure you have sufficient knowledge before the school starts, e.g. by attending a free online course such as https://www.coursera.org/course/rprog. Alternatively or additionally you can also use the tutorial website at https://www.w3schools.com/r/  or any other R-tutorial which goes into sufficient detail. 


Students are expected to submit a mini-project upon completion of the school. For the project students can either analyze data related to their own research or they can submit a project based on datasets that are provided by the school instructor. The projects and preliminary results will be presented on the last day of class.

Suggested reading

  • Preston S., Heuveline P. and Guillot M. 2001. “Demography: Measuring and Modeling Population Processes”. Wiley-Blackwell.

Further readings with demographic examples and applications will also be provided.

Financial support

There is no tuition fee for this school. Students are expected to pay their own transportation and living costs. However, a limited number of scholarships are available on a competitive basis.

Recruitment of students

  • Master students (second year) or higher
  • A maximum of 20 students will be admitted.

How to apply
To participate in this school, please fill out the Google form

For all questions, please contact Vera Sokolova: tel. +7 495-772-95-90 (ext. 15103), e-mail: vsokolova@hse.ru.