Objectives: (1) To compare the number of hospital days used by survivors with those by persons in their last, second last, and third last year of life in relation to age; (2) to analyse lifelong hospital utilisation in relation to life expectancy. Design: Cohort study using a 10% sample (stratified by age and sex) of persons insured by one sickness fund. Setting: Germany, 1989Germany, -1995. Subjects: 69 847 survivors (with a minimum of three more years to live), 1385 persons in last, 1368 in second last, and 1333 in third last year of life. Results: The number of days spent in hospital in the last year of life was lowest for the young (24.2 days under age 25) and the old (23.2 days at age 85+) and was greatest at ages 55-64 (40.6 days). The ratio of days to survivors was highest at age 35-44 (31.0) and fell continously thereafter to 4.3 at age 85+. Similar patterns were seen for hospital days in the second and third year before death, except that peaks were at 35-44 years (22.5 and 13.7 days respectively). Calculated lifelong number of hospital days increased with age from 54.8 (death at age 20) to 201.0 (age 90). Numbers of hospital days per year of life, averaged over the entire lifespan, were stable at 2.0-2.2 for deaths between age 50 and 90 (and up to 2.7 at age 20). Conclusions: Lifelong hospital utilisation for persons who die at 50 or later is directly proportional to the number of years lived. These data contradict results from cross sectional studies that suggest an exponential rise in health care costs as longevity increases. They have important implications for projections of future health care expenditure.T he belief that health care costs rise steeply with age is considered "common knowledge" by most clinicians, politicians, health care researchers, and lay people alike. Demographic changes leading to an aging population, a consequent rise in chronic diseases, and technological advances are considered by many to form a triad that will make health care costs unbearable in the future. The belief persists despite a growing body of evidence in support of a more complicated picture. The belief is apparently supported by cross sectional data showing a relation between age and (rising) costs.Fuchs was the first to point to the fact that the relation between age and health care utilisation or costs is biased by the fact that the percentage of people in their last year of life (which costs well above average) is increasing rapidly with age.1 He hypothesised that if mortality in all age groups above 65 would be assumed to be constant, health care costs with age would also be constant.US Medicare data support this assumption. In addition, however, two further things complicate the picture: (1) health care costs for persons in their last year of life reach a maximum at about the age of 70 years and fall with higher age, and (2) health care costs for the group of survivors rise until the age of about 85, reach a maximum and fall with higher age.2 The marginal increase in lifetime costs associated with an addi...

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None of the three sources can be considered ideal. Part of the differences could be explained by methodological and regional effects. More insight could be gained by comparing data at the individual level. According to recent legislation, data from all statutory sickness funds are supposed to be merged. This would simplify such comparisons and most likely would allow for more valid information regarding the incidence and treatment of AMI and many other diseases.

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The statutory sickness fund AOK Lower Saxony developed a specific course program for a target group of up to 55-year old employees with common back pain in the early satge of chronification. Comparative evaluation of effects in study participants and controls was achieved by quality of life and performance data, i.e. days of sick leave (DSL). Medium and long-term change inquality of life (6 and 12 months after the course) was assessed by means of the SF-36 dimensions for cases and controls and compared in bivariate and multivariate test. 92 of 197 baseline participants (or 127 participants who completed the programme) and 483 controls were included in the medium term effect analysis. Significant medium and long-term net efffects could only be shown for pain dimensions. Anonymised DSL data for all 5409 insured persons who were initially selected as potiential participants for the regionally restricted course programme in the second quarter of 1997 were available for the period from 01-01-1996 to 30-06-1999. For controlling any selection bias and potential regional differences, the initial DSL trends of the 197 baseline course participants were compared with two control groups, namely other insured from the programme region and control regions. DSL trends in the three groups prior to the course program were identical. Subsequently DSL due to ICD-9 Codes 710-739 were reduced more substantially in course participants than in controls: 25.5 versus 33 or 32 days respectively. A covariance analysis model that also considered socio-economic factors and pre-intervention DSL levels yeilded a 14-day reduction in DSL (p value 0.023). This is a clear effect on DSL trends taht may be utilised for net savings for AOK. This is also the first proof of a backpain programme outside company settings taht permits net savings.