A parametric approach to modeling changes in general health and cognition: developing a stochastic model of aging

Add to your list(s) Download to your calendar using vCal

• Arnold Mitnitski, PhD, Department of Medicine, Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
• Wednesday 18 June 2008, 13:00-14:00
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge.

If you have a question about this talk, please contact Nikolaos Demiris.

I will discuss a novel approach which is being developed by our group at Dalhousie University, Halifax, to summarizing health transitions as a stochastic process of changes in health states. Instead of analyzing health transitions separately in each direction (improvement, worsening and death) we suggest a general parametric model (e.g., based on a Poisson density function) which allows estimating the probabilities of changes (including adverse outcomes) as a function of the current state.

The basic model has four population specific parameters, two of which represent health transitions of survivors; the other two represent the probability of death. I will show how covariates can be incorporated in the model. The approach is illustrated in representative Canadian populations for: (i) transitions in general health status (deficits accumulation count/the frailty index), (ii) transitions in cognitive states (represented by the errors in the Modified Mini Mental State Examination Score), and (iii) transitions between cognitive impairments of different degrees. The performances of the models are always very high, R2 ranging from 0.89 to 0.99, and robust across settings.

I will also discuss the general stochastic mechanism of organism-environment interactions, coupled with a decrease of stress-resistance, to explain the observed patterns of how health state changes with age. This allows an understanding the origin of health deficit (and similarly, cognitive deficits) accumulation, can be used to classify different populations. This approach has the potential for a wide range of applications in analyzing age-related health trajectories.

This talk is part of the MRC Biostatistics Unit Seminars series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Immunology
• Cambridge talks
• Cardiovascular Epidemiology Unit Special Seminars
• Chris Davis' list
• Department of Public Health and Primary Care
• Graduate-Seminars
• Interested Talks
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge
• Life Sciences
• Life Sciences
• ME Seminar
• MRC Biostatistics Unit Seminars
• Primary Care
• PublicHealth@Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• primary care
• rp587

Note that ex-directory lists are not shown.