The “red herring” after 20 years: ageing and health care expenditures

Breyer, Friedrich; Lorenz, Normann

doi:10.1007/s10198-020-01203-x

Cited by 30 publications

(19 citation statements)

References 33 publications

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“…With regard to the expenditure side of SHI in particular, the effect of an increase in life expectancy on the age-related health expenditures seems to be unclear and has been part of a controversial debate in the health economic literature over the last 20 years [47]. Zweifel et al [48][49][50] call the observed correlation between rising age and rising health care expenditure a "red herring" because the majority of this correlation is due to high health care spending at the end of life.…”

Section: Comparatively Robust Resultsmentioning

confidence: 99%

“…However, it is questionable whether this conclusion can really be drawn. As Breyer and Lorenz [47] argue, there are many reasons to believe that the aging of society will lead to rising health care expenditures in the future. In our study, this discussion is reflected in the range of our results for the population scenarios G2L2W0 and base year structure.…”

Section: Comparatively Robust Resultsmentioning

confidence: 99%

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Indicators for Measuring Intergenerational Fairness of Social Security Systems—The Case of the German Social Health Insurance

Fetzer

Moog

2021

Sustainability

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The issue of fiscal sustainability is often labelled as a synonym for intergenerational fairness; however, pay-as-you-go schemes such as the German Social Health Insurance (SHI) involve a “natural” amount of intergenerational redistribution from younger net payers to older net beneficiaries. We calculate intertemporal balance sheets of SHI and compare two generational accounting approaches (GAC and GAIB) with an alternative measure of intergenerational fairness, SM, which we derive from Settergren and Mikula (2005). Our results indicate that the SM concept leads to similar implications concerning the amount of intergenerational redistribution as classical measures of fiscal sustainability. For the SM approach, the balance sheet of SHI shows a rate of unfunded benefits of 25 percent. Closing this gap requires an increase of the contribution rate by 30 to 40 percent. This total effect can be separated into an effect due to the current population structure (10 p.p.), the increase in life expectancy (10 p.p.), and medical technical progress (about 10 to 20 p.p.).

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Section: Comparatively Robust Resultsmentioning

confidence: 99%

Section: Comparatively Robust Resultsmentioning

confidence: 99%

Indicators for Measuring Intergenerational Fairness of Social Security Systems—The Case of the German Social Health Insurance

Fetzer

Moog

2021

Sustainability

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“…However, there may be other reasons or scenarios when withholding and "rationing" intensive care treatment may be deemed appropriate, especially for old people (29). Health economic considerations, which weigh the considerable costs of an intensive care stay with an associated high mortality on the one hand and the high morbidity among the survivors on the other, are understandable and necessary (30,31). However, it is well established, especially in the health economics' literature, that it is not the chronological age, but the remaining life expectancy, that predicts cost and morbidity and that these two are not necessarily the same thing (32).…”

Section: Discussionmentioning

confidence: 99%

ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock

Bruno

Wernly²,

Mamandipoor³

et al. 2021

Front. Med.

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Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01).Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.

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“…Further calculations are executed using Microsoft Excel. 7 In Germany, this methodology is also used for allocating insureds to risk groups as part of the morbidity-based risk-adjustment scheme in the Statutory Health Insurance [23]. 8 Insureds with single diagnoses F34.1 or F38.1 (short depressive episodes) or isolated outpatient diagnosis in the previous year are not excluded from incidence calculation in order to identify new cases with a documented beginning depressive episode in the preobservation year.…”

Section: Datasetmentioning

confidence: 99%

“…In contrast, Fries (1980) [3] assumes that an increasing life expectancy leads to a compression of morbidity. Given these somehow contradictory hypotheses, the influence of proximity to death and treatment spending as a function of remaining life expectancy are controversially discussed among health economists [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Healing, surviving, or dying? – projecting the German future disease burden using a Markov illness-death model

2021

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Background In view of the upcoming demographic transition, there is still no clear evidence on how increasing life expectancy will affect future disease burden, especially regarding specific diseases. In our study, we project the future development of Germany’s ten most common non-infectious diseases (arthrosis, coronary heart disease, pulmonary, bronchial and tracheal cancer, chronic obstructive pulmonary disease, cerebrovascular diseases, dementia, depression, diabetes, dorsal pain and heart failure) in a Markov illness-death model with recovery until 2060. Methods The disease-specific input data stem from a consistent data set of a major sickness fund covering about four million people, the demographic components from official population statistics. Using six different scenarios concerning an expansion and a compression of morbidity as well as increasing recovery and effective prevention, we can show the possible future range of disease burden and, by disentangling the effects, reveal the significant differences between the various diseases in interaction with the demographic components. Results Our results indicate that, although strongly age-related diseases like dementia or heart failure show the highest relative increase rates, diseases of the musculoskeletal system, such as dorsal pain and arthrosis, still will be responsible for the majority of the German population’s future disease burden in 2060, with about 25–27 and 13–15 million patients, respectively. Most importantly, for almost all considered diseases a significant increase in burden of disease can be expected even in case of a compression of morbidity. Conclusion A massive case-load is emerging on the German health care system, which can only be alleviated by more effective prevention. Immediate action by policy makers and health care managers is needed, as otherwise the prevalence of widespread diseases will become unsustainable from a capacity point-of-view.

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The “red herring” after 20 years: ageing and health care expenditures

Cited by 30 publications

References 33 publications

Indicators for Measuring Intergenerational Fairness of Social Security Systems—The Case of the German Social Health Insurance

Indicators for Measuring Intergenerational Fairness of Social Security Systems—The Case of the German Social Health Insurance

ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock

Healing, surviving, or dying? – projecting the German future disease burden using a Markov illness-death model

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