Projecting the Number of Elderly with Cognitive Impairment in China Using a Multi‐State Dynamic Population Model

Abstract

China is aging rapidly, and the number of Chinese elderly with dementia is expected to rise. This paper projects, up to year 2060, the number of Chinese elderly within four distinct cognitive states. A multi‐state population model was developed using system dynamics and parametrized with age–gender‐specific transition rates (between intact, mild, moderate and severe cognitive impairment and death) estimated from two waves (2012 and 2014) of a community‐based cohort of elderly in China aged ≥65 years (N = 1824). Probabilistic sensitivity analysis and the bootstrap method was used to obtain the 95% confidence interval of the transition rates. The number of elderly with any degree of cognitive impairment increases; with severe cognitive impairment increasing the most, at 698%. Among elderly with cognitive impairment, the proportion of very old elderly (age ≥ 80) is expected to rise from 53% to 78% by 2060. This will affect the demand for social and health services China. Copyright © 2017 System Dynamics Society

A System Dynamics Simulation Applied to Healthcare: A Systematic Review

“…Some studies implement the entire core part of the stock-flow diagram [ 84 , 89 , 91 , 93 , 94 , 95 ], while other studies implemented only the diagnosed part (blue box), in which there is an additional flow from the population to diagnosed patients [ 82 , 96 , 97 , 98 ]. Furthermore, several of the selected studies only implemented the undiagnosed-to-diagnosed part of the stock-flow diagram (orange box) without expanding on different stages of chronic diseases [ 79 , 80 , 81 , 83 , 85 , 87 , 92 , 99 ].…”

Trends in functional disability and cognitive impairment among the older adult in China up to 2060: estimates from a dynamic multi-state population model

“…In order to project the number of older adults in China with cognitive impairment and functional disability, we developed and validated a dynamic multi-state population model [ 27 , 28 ] which simulates the population of China and tracks the transition of older Chinese adults 65 years and older from 2010 to 2060, to and from six health states—(1) active older adult without cognitive impairment (where active is older adults with no ADL limitation), (2) active older adult with cognitive impairment, (3) older adult with 1 to 2 ADL limitations, (4) older adult with cognitive impairment and 1 to 2 ADL limitations, (5) older adult with 3 or more ADL limitations, and (6) older adult with cognitive impairment and 3 or more ADL. Within each health state, the population was further divided into two-dimensional vector: age (from age 65–100 and older) and gender (male and female).…”

“…At the end of each year, the surviving population in each age cohort flows to the subsequent cohort, with the exception of the final age cohort—age 100 and older. The health states with cognitive impairment are further divided into three categories—mild, moderate and severe—cognitive impairment with age-specific, gender-specific and health state specific transition rates accounting for the movements across cognitive impairment categories; the cognitive impairment transition rates are reported in earlier publication in the reference as cited [ 28 ]. Transition across health states is determined by 1-year age-specific, gender-specific and health state specific transition rates.…”

Evaluating the impact of upstream and downstream interventions on chronic kidney disease and dialysis care: a simulation analysis

“…Subscripts allow for detailed disaggregation of each health state by age—single age cohorts from age 0 to age 100 and over—and shift each age cohort at the end of the year to ensure appropriate ageing of the population to avoid cohort blending (Eberlein and Thompson, 2013). A detailed illustration of the application of subscripts to health states and the equations that allow for shifting age cohorts at the end of the year can be found in the literature as cited (Ansah et al ., 2017). The flow variable “ageing healthy” shifts the healthy population at the end of the year from one age cohort to another, while the flow variable “ageing” shifts the population with diabetes at the end of each year from one age cohort to another.…”