It has been demonstrated repeatedly that time to death is a much better predictor of health care expenditures than age. This is known as the 'red herring' hypothesis. In this article, we investigate whether this is also the case regarding disease-specific hospital expenditures. Longitudinal data samples from the Dutch hospital register (n=11 253 455) were used to estimate 94 disease-specific two-part models. Based on these models, Monte Carlo simulations were used to assess the predictive value of proximity to death and age on disease-specific expenditures. Results revealed that there was a clear effect of proximity of death on health care expenditures. This effect was present for most diseases and was strongest for most cancers. However, even for some less fatal diseases, proximity to death was found to be an important predictor of expenditures. Controlling for proximity to death, age was found to be a significant predictor of expenditures for most diseases. However, its impact is modest when compared to proximity to death. Considering the large variation in the degree to which proximity to death and age matter for each specific disease, we may speak not only of age as a 'red herring' but also of a 'carpaccio of red herrings'.

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BackgroundLong-term care is often associated with high health care expenditures. In the Netherlands, an ageing population will likely increase the demand for long-term care within the near future. The development of risk profiles will not only be useful for projecting future demand, but also for providing clues that may prevent or delay long-term care utilization. Here, we report our identification of predictors of long-term care utilization in a cohort of hospital patients aged 65+ following their discharge from hospital discharge and who, prior to hospital admission, were living at home.MethodsThe data were obtained from three national databases in the Netherlands: the national hospital discharge register, the long-term care expenses register and the population register. Multinomial logistic regression was applied to determine which variables were the best predictors of long-term care utilization. The model included demographic characteristics and several medical diagnoses. The outcome variables were discharge to home with no formal care (reference category), discharge to home with home care, admission to a nursing home and admission to a home for the elderly.ResultsThe study cohort consisted of 262,439 hospitalized patients. A higher age, longer stay in the hospital and absence of a spouse were found to be associated with a higher risk of all three types of long-term care. Individuals with a child had a lower risk of requiring residential care. Cerebrovascular diseases [relative risk ratio (RRR) = 11.5] were the strongest disease predictor of nursing home admission, and fractures of the ankle or lower leg (RRR = 6.1) were strong determinants of admission to a home for the elderly. Lung cancer (RRR = 4.9) was the strongest determinant of discharge to the home with home care.ConclusionsThese results emphasize the impact of age, absence/presence of a spouse and disease on long-term care utilization. In an era of demographic and epidemiological changes, not only will hospital use change, but also the need for long-term care following hospital discharge. The results of this study can be used by policy-makers for planning health care utilization services and anticipating future health care needs.