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Encouraging and helping elderly to postpone a nursing home admission appears to be a win-win that keeps long-term care spending in check and is in line with the target population's preferences, but there is little evidence about its effects. We study the causal impact of nursing home admission eligibility using Dutch administrative data and exploiting variation between randomly assigned assessors in their tendency to grant eligibility for a nursing home admission. We find a drop in medical care use when eligibility is granted, especially in hospital admissions, while total healthcare spending is unaffected. This suggests that postponing an admission may not always be a win-win after all.

Background There has been a shift from institutional care towards home care, and from formal to informal care to contain long-term care (LTC) costs in many countries. However, substitution to home care or informal care might be harder to achieve for some conditions than for others. Therefore, insight is needed in differences in LTC use, and the role of potential informal care givers, across specific conditions. We analyze differences in LTC use of previously independent older patients after a fracture of femur and stroke, and in particular examine to what extent having a partner and children affects LTC use for these conditions. Methods Using administrative data on Dutch previously independent older people (55+) with a fracture of femur or stroke in 2013, we investigate their LTC use in the year after the condition takes place. We use administrative treatment data to select individuals who were treated by a medical specialist for a stroke or femoral fracture in 2013. Subsequent LTC use is measured as using no formal care, home care, institutional care or being deceased at 13 consecutive four-weekly periods after initial treatment. We relate long-term care use to having a partner, having children, other personal characteristics and the living environment. Results The probability to use no formal care 1 year after the initial treatment is equally high for both conditions, but patients with a fracture are more likely to use home care, while patients with a stroke are more likely to use institutional care or have died. Having a spouse has a negative effect on home care and institutional care use, but the timing of the effect, especially for institutional care, differs strongly between the two conditions. Having children also has a negative effect on formal care use, and this effect is consistently larger for patients with a fracture than patients with a stroke. Conclusion As the condition and the effect of potential informal care givers matter for subsequent long-term care use, policy makers should take the expected prevalence of specific conditions within the older people population into account when designing long-term care policies.

Aging-in-place policies substitute home care for nursing home admissions (NHA). They appear to be a win-win by keeping public spending in check and being in line with personal preferences, but have hitherto not been evaluated. We study the impact of NHA eligibility using Dutch administrative data and exploiting variation between randomly assigned assessors in their tendency to grant admission. The impact on mortality is zero, but with considerable effect heterogeneity. Moreover, aging-in-place policies come at the cost of increased curative care, especially hospital admissions, and do not reduce total healthcare spending, suggesting they may not be a win-win after all.

We investigate the dynamic relationship between several dimensions of health and health care expenditures for older individuals. Health data from the Longitudinal Aging Survey Amsterdam is combined with data on hospital and long term care use. We estimate a latent variable based jointly on observed health indicators and expenditures. Annual transition probabilities between states of the latent variable are estimated using a Markov model. States associated with good current health and low annual health care expenditures are not associated with lower cumulative health care expenditures over remaining lifetime. We conclude that, although the direct health care cost saving effect is limited, the considerable gain in healthy lifeyears can make investing in the improvement of health of the older population worthwhile. © 2012 Elsevier B.V.

Although the consequences of population aging for growth in health expenditures have been widely investigated, research on this topic is rather fragmented. Therefore, these consequences are not fully understood. This paper reviews the consequences of population aging for health expenditure growth in Western countries by combining insights from epidemiological and health economics research. Based on a conceptual model of health care use, we first review evidence on the relationship between age and health expenditures to provide insight into the direct effect of aging on health expenditure growth. Second, we discuss the interaction between aging and the main societal drivers of health expenditures. Aging most likely influences growth in health expenditures indirectly, through its influence on these societal factors. The literature shows that the direct effect of aging depends strongly on underlying health and disability. Commonly used approximations of health, like age or mortality, insufficiently capture complex dynamics in health. Population aging moderately increases expenditures on acute care and strongly increases expenditures on long-term care. The evidence further shows that the most important driver of health expenditure growth, medical technology, interacts strongly with age and health, i.e., population aging reinforces the influence of medical technology on health expenditure growth and vice versa. We therefore conclude that population aging will remain in the centre of policy debate. Further research should focus on the changes in health that explain the effect of longevity gains on health expenditures, and on the interactions between aging and other societal factors driving expenditure growth. Keywords: Population aging, Morbidity, Technology, Health expenditures, Acute care, Long-term care

Health care utilization is expected to rise in the coming decades. Not only will the aggregate need for health care grow by changing demographics, so too will per capita utilization. It has been suggested that trends in health care utilization may be age-specific. In this paper, age-specific trends in health care utilization are presented for different health care sectors in the Netherlands, for the period 1981-2009. For the hospital sector we also explore the link between these trends and the state of medical technology. Using aggregated data from a Dutch health survey and a nationwide hospital register, regression analysis was used to examine age-specific trends in the probability of utilizing health care. To determine the influence of medical technology, the growth in age-specific probabilities of hospital care was regressed on the number of medical patents while adjusting for confounders related to demographics, health status, supply and institutional factors. The findings suggest that for most health care sectors, the trend in the probability of health care utilization is highest for ages 65 and up. Larger advances in medical technology are found to be significantly associated with a higher growth of hospitalization probability, particularly for the higher ages. Age-specific trends will raise questions on the sustainability of intergenerational solidarity in health care, as solidarity will not only be strained by the ageing population, but also might find itself under additional pressure as the gap in health care utilization between elderly and non-elderly grows over time. For hospital care utilization, this process might well be accelerated by advances in medical technology.

Contains fulltext : 155179.pdf (Publisher’s version ) (Open Access) BACKGROUND: Organizational data such as bed occupancy rate and nurse-to-patient ratio are related to clinical outcomes and to the efficient use of intensive care unit (ICU) resources. Standards for these performance indicators are provided in guidelines. We studied the effects of a multifaceted feedback strategy to improve the adherence to these standards. METHODS: In a cluster randomized controlled study design the intervention ICUs received extensive monthly feedback reports, they received outreach visits and initiated a quality improvement team. The control ICUs received limited quarterly feedback reports only. We collected primary data prospectively within the setting of a Dutch national ICU registry over a 14-month study period. The target indicators were bed occupancy rate (aiming at 80 % or below) and nurse-to-patient ratio (aiming at 0.5 or higher). Data were collected per 8-h nursing shift. Logistic regression analysis was performed. For both study end points, the odds ratios (OR) for improvements at follow-up versus at baseline were calculated separately for control and intervention ICUs. RESULTS: We analyzed data on 67,237 nursing shifts. The bed occupancy rate did not improve in the intervention group compared to baseline (adjusted OR 0.88; 95 % confidence interval (CI), 0.62-1.27) or compared to control group (OR 0.67; 95 % CI 0.39-1.15). The nurse-to-patient ratio did not improve (OR 0.72; 95 % CI 0.41-1.26 compared to baseline and OR 0.65; 95 % CI 0.35-1.19 compared to control group). CONCLUSIONS: A multifaceted feedback intervention did not improve the adherence to guideline-based standards on the organizational issues bed occupancy rate and nurse-to-patient ratio in the ICU. The reasons may be a limited confidence in data quality, the lack of practical tools for improvement, and the relatively short follow-up. TRIAL REGISTRATION: ISRCTN: ISRCTN50542146.

In this paper, we investigate the relationship between baseline health and costs of hospital use over a period of eight years. We combine cross-sectional survey data with information from the Dutch national hospital register. Four different indicators of health (self-perceived health, long-term impairments, ADL limitations and comorbidity) are considered. We find that for ages 50 to 70, differences in hospital costs between good health and bad health are substantial and persist during the whole time period. However, for higher ages expected hospital costs for individuals in bad health decline rapidly and become lower than those for people in good health after about six to seven years. The higher mortality rate among people in bad health is the primary cause here. Our results are confirmed for all four health indicators. We conclude that relying on better health to contain healthcare expenditures is too optimistic, and the interaction between health and mortality should be taken into account when projecting healthcare costs. Healthy ageing is important, but more for health gains than for cost savings.

BackgroundThe effect of population aging on future health services use depends on the relationship between longevity gains and health. Whether further gains in life expectancy will be paired by improvements in health is uncertain. We therefore analyze the effect of population ageing on health services use under different health scenarios. We focus on the possibly diverging trends between different dimensions of health and their effect on health services use.MethodsUsing longitudinal data on health and health services use, a latent Markov model has been estimated that includes different dimensions of health. We use this model to perform a simulation study and analyze the health dynamics that drive the effect of population aging. We simulate three health scenarios on the relationship between longevity and health (expansion of morbidity, compression of morbidity, and the dynamic equilibrium scenario). We use the scenarios to predict costs of health services use in the Netherlands between 2010 and 2050.ResultsHospital use is predicted to decline after 2040, whereas long-term care will continue to rise up to 2050. Considerable differences in expenditure growth rates between scenarios with the same life expectancy but different trends in health are found. Compression of morbidity generally leads to the lowest growth. The effect of additional life expectancy gains within the same health scenario is relatively small for hospital care, but considerable for long-term care.ConclusionsBy comparing different health scenarios resulting in the same life expectancy, we show that health improvements do contain costs when they decrease morbidity but not mortality. This suggests that investing in healthy aging can contribute to containing health expenditure growth.