Matrix Approaches to Health Demography
Start: 5 November 2018
End: 16 November 2018
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
Instructor: Hal Caswell
Health demography is particularly concerned with measures of longevity, healthy longevity, and life lost due to poor health; with the dynamics of individual transitions among health and disease states; with the projection of the future health composition of populations; and with analyses accounting for causes of death. This course will introduce matrix methods for the analysis of health demography. Matrix methods describe the dynamics of individuals, cohorts, and populations. They generalize and extend classical life table methods.
In this course, matrix methods will be used to analyze models based on prevalence and incidence data. The methods will go beyond the fixation on mean results and will incorporate variance and stochasticity. The class will introduce methods based on Markov chains, Markov chains with rewards, multistate matrix models, and matrix calculus. The new analyses will be compared, where possible, to traditional approaches.
Although the applications will focus on human populations, all of these topics have direct applications in animal and plant demography. Biodemographers and population biologists interested in new methods for demographic analysis are encouraged to apply.
The course will be a mixture of lectures, discussions, and computer exercises.
You should be familiar with basic demography (human, plant, or animal), including life tables, mortality and fertility schedules, population projections, and their applications. It will be important to be familiar with the basic operations of matrix algebra (matrices, vectors, multiplication, inverses, eigenvalues and eigenvectors). You should be fluent in Matlab or R (it is possible, but not guaranteed, that some support will be available for users of Stata).
Students will be evaluated on the basis of computer exercises and class participation.
Specific readings and resources will be provided before the beginning of the course. The following are some useful sources:
A comprehensive treatment of classical methods for health demography:
Siegel, J.S. 2012. The demography and epidemiology of human health and aging. Springer-Verlag.
Basic sources for matrix population models:
Caswell, H. 2001. Matrix population models. Second edition. Sinauer Associates.
Keyfitz, N. and H. Caswell. 2005. Applied mathematical demography. 3rd edition. Springer-Verlag.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs. If you are accepted, MPIDR can provide advice on convenient places to stay in Rostock.
Recruitment of students
- Applicants should either be enrolled in a PhD program (those well on their way to completion will be favored) or have received their PhD.
- A maximum of 15 students will be admitted.
- The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
- Applications should be sent by email to the MPIDR (address below). Please begin your email message with a statement saying that you apply for course IDEM 134 – Matrix Approaches to Health Demography. You also need to attach the following items integrated in *a single pdf file*: (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a short description of your knowledge of life tables and matrix algebra and of your fluency in Matlab or R.
- Send your email to Heiner Maier (email@example.com).
- Application deadline is 15 August 2018.
- Applicants will be informed of their acceptance by 15 September 2018.
- Applications submitted after the deadline will be considered only if space is available.
Update: A major component of this course will involve the matrix analysis of multistate models that combine age and health status. The statistical estimation of these models from data is a rich and interesting topic in itself, but will not be covered in IDEM 134. Please be advised that another course devoted to these procedures will take place from 22-25 October 2018 at the MPIDR. For more information, see IDEM 104: Multistate Models: Life course analysis from event histories and panel data; http://tinyurl.com/multistate2018.