UK-China Health And Social Challenges Ageing Project: present and future burden of dementia, and policy responses

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There are 10.4M cases of dementia in China, and this number will rise. China's rapid ageing process is occurring at an earlier stage of economic development than UK and other countries, posing great demands on health and care services. Policy development in response to this situation would benefit from authoritative forecasts, to 2050, of the epidemiology and economics of China's older population in respect of disease burden, related costs of health and social care, and prospects for future prevention of cardiovascular disease, dementia and disability. The 65+ population of UK and China is 12.7 M and 173 M respectively. Thoughtful comparison of health and social care policy across the contrasting countries will be scientifically illuminating and valuable to policy makers. We propose to build a simulation model for the Chinese population, based on that recently developed for UK (BMJ 2017, Lancet Public Health 2017), for direct comparison of headline statistics and indicators to 2050. The model synthesises the major observed epidemiological and demographic trends, in order to forecast disease burden for dementia, disability and cardiovascular disease, and life expectancy (total life expectancy (LE), disabled LE, and disability-free LE) for the next three decades. A key determinant of the future burden of dementia is its incidence trend. Our longitudinal analysis of the Chinese Longitudinal Healthy Longevity Study (CLHLS) shows age-adjusted dementia incidence declined 2.8% per year in the period 1998-2014, after accounting for non-random dropout and competing risks. Such rapid changes in disease occurrence, together with China's ageing demographic, mean that current forecasts are likely to be inaccurate. Scenario-modelling functions are built in to allow the user to simulate the impacts of health interventions and social trends in risk factors, such as smoking and obesity, on dementia and other age-related outcomes (Diabetologia 2019). The robustness of modelled estimates is tested with sensitivity analysis.Our approach is to use repeat measures of cognitive and physical function in CLHLS and the China Health and Retirement Longitudinal Study Health (CHARLS) to identify DSM4-compliant dementia cases. We plan a validation study using a clinical neuropsychologist interview as point of reference. For the socioeconomic function of the model, health and social care costs, including indirect costs associated with caregiving, will draw on WHO-SAGE-China as well as CLHLS and CHARLS data. The project will use the modelled forecasts to examine the fit between projected dependency burden and policy responses, particularly Long-Term Care (LTC). This objective involves mixed methods, carried out by experienced policy analysts in both countries. The first step will be qualitative analysis of the policy development process for the current LTC pilot projects in China. Second, quantitative evaluation of the extent to which the stated policy intentions, and pilot projects, are likely to meet projected needs, will be conducted jointly by policy analysts and modelling teams along with assessment of relative economic costs. We plan a 'trans-positional' approach (Sen 1993), to consider both the internal view (CN researcher of CN system) and external view (UK researcher of CN system) thereby making explicit the cultural (or positional) dependence of observation. Reconciling divergent 'positional' observations leads to useful understanding, not otherwise achieved. Our interdisciplinary team will be led by epidemiologists from UCL and Sun Yat-Sen University (SYSU) together with economists (UCL/Institute for Fiscal Studies, SYSU, Guangzhou Medical University (GZMU)), statisticians and mathematician (SYSU), neuropsychologists (GZMU), clinicians (GZMU, UCL), policy analysts (Nuffield Trust, SYSU) and others. The study will make significant scientific and policy-relevant contributions in China, UK and internationally, beyond the three-year project.