Demographic projections of disability Luc Bonneux, Nicole Van der Gaag, Govert Bijwaard, Joop de Beer Projections, migration and Health Netherlands Interdisciplinary Demographic Institute The Hague

Life tables Alive Death By age Transitions Dwelling time in the state “alive” = life expectancy By age Transitions Free of disability Death With disability Disability free life expectancy Duration of disability Conditioned by risk Risk factor dependent

Prognosis 4 countries (NL, Sweden, south Europe, New member state) became NL, Spain, Germany, Poland Mortality forecasts, adjusted for riskfactor history –Smoking, BMI, education, alcohol Disability forecast, conditional on mortality forecast Scenarios, juggling with risk factors and/or technology (cardiovascular disease)

Katz ADL Disability Independent, YES or NO. (But often in several scores, depending of difficulties: none, little, severe, impossible): D ressing, E ating, A mbulating, T oileting, H ygiene 1. Bathing (sponge bath, tub bath, or shower). Disabled if needs assistance in bathing more than one part of body. 2. Dressing – Disabled if needs assistance to get clothes and get dressed. In Katz, exception is made for tying shoes. 3. Toileting – Able if goes to toilet room, uses toilet, arranges clothes, and returns without any assistance (may use cane or walker for support and may use bedpan/urinal at Night). Incontinence, inability to controls bowel and bladder completely without occasional "accidents" is disabled. 4. Transferring - Moves in and out of bed and chair without assistance (may use can or walker). 5. Feeding - Feeds self without assistance (except for help with cutting meat or buttering bread).

Model Starts at age 55 –No migration, fertility –No risk factor change after age 55 Smokers/drinkers don’t quit anymore, except for existing disease Education does not change BMI –Weight gain at old age not strong determinant of mortality –Weight loss predictive of death Health outcomes at ages of 55 and over of policy changes before age 55

Added assumptions No recovery (is limited for ADL): then incidence can be estimated from prevalence and mortality

Data State at onset: mortality and disability –Individual records of propective / panel data –Age, Gender, BMI, smoking, education, alcohol at onset –Event rates of disability and mortality in follow up. By default: SHARE (two rounds for NL, Germany, Spain, one for Poland). NL: + Rotterdam study (check for consistency SHARE)

Macrodata Age (yr)*gender* education*calendar year *smoking (current, ever, never)*BMI (BMI categories, preferably obesity also in 30+ and 35+)*Alcohol use (SHARE insufficient)

Forecasts Mortality forecasts from EUROSTAT Fitting distributions of disability and risk factor status… Policy scenarios –Education (young age) –BMI (before age 55) –Smoking (before age 55) –? Alcohol use

(Use of prospective administrative data) Effect of health care and health care interventions Cardiovascular disease important cause of death (MI, Stroke, other) and disability (heart failure, stroke, vascular dementia) Link (socio economic status, education) to (CV disease: MI, stroke) to (long term care) to (mortality) Model the effects of CVD prevention (Prospective studies with BMI / smoking?)

Minimal data needs Population distribution * prevalence of risk factor * prevalence of disability (recovery set to zero) Relative risks of mortality * disability * risk factor status –Can be constructed from the literature

Deliverable ADL disability by four countries by risk factor status, age, gender and calendar period Distributions of ADL (1+, 2+, …) (Use of registry data in policy support for disability forecasts?)

Timeline End Mai: –Model up and running for the Netherlands –Preparation of publications –Preparation of data from Spain, Germany, Poland End September –Model up and running for four countries

Copyright ©2009 BMJ Publishing Group Ltd. Neovius, M. et al. BMJ 2009;338:b496 Fig 1 Cumulative mortality according to obesity status (underweight (BMI =30)) and smoking status over 38 years of observation

Copyright ©2009 BMJ Publishing Group Ltd. Neovius, M. et al. BMJ 2009;338:b496 Fig 2 Unadjusted incidence rates for mortality showing combined effects of BMI and smoking (n=45 920). Light smoker=1-10 cigarettes/day; heavy smoker >10 cigarettes/day

Projections of obesity