Inequality in mortality between Black and White Americans by age, place, and cause and in comparison to Europe, 1990 to 2018.

Although there is a large gap between Black and White American life expectancies, the gap fell 48.9% between 1990 and 2018, mainly due to mortality declines among Black Americans. We examine age-specific mortality trends and racial gaps in life expectancy in high- and low-income US areas and with reference to six European countries. Inequalities in life expectancy are starker in the United States than in Europe. In 1990, White Americans and Europeans in high-income areas had similar overall life expectancy, while life expectancy for White Americans in low-income areas was lower. However, since then, even high-income White Americans have lost ground relative to Europeans. Meanwhile, the gap in life expectancy between Black Americans and Europeans decreased by 8.3%. Black American life expectancy increased more than White American life expectancy in all US areas, but improvements in lower-income areas had the greatest impact on the racial life expectancy gap. The causes that contributed the most to Black Americans' mortality reductions included cancer, homicide, HIV, and causes originating in the fetal or infant period. Life expectancy for both Black and White Americans plateaued or slightly declined after 2012, but this stalling was most evident among Black Americans even prior to the COVID-19 pandemic. If improvements had continued at the 1990 to 2012 rate, the racial gap in life expectancy would have closed by 2036. European life expectancy also stalled after 2014. Still, the comparison with Europe suggests that mortality rates of both Black and White Americans could fall much further across all ages and in both high-income and low-income areas.

Quality-Adjusted Life-Years Lost Due to COVID-19 Mortality: Methods and Application for The Netherlands.

OBJECTIVES: The COVID-19 pandemic has increased mortality worldwide considerably in 2020. Nevertheless, it is unknown how the increase in mortality translates into a loss in quality-adjusted life-years (QALYs), which is a function of age and the health condition of the deceased patient at time of death. We estimate the QALYs lost in The Netherlands as a result of deaths because of COVID-19 in 2020. METHODS: As a starting point, we use estimates of underlying diseases and the number of COVID-19 deaths in nursing homes as a proxy for underlying health status. In a next step, these are combined with estimates of excess mortality rates and quality of life for different groups to calculate QALYs lost. We compare the results with an alternative scenario, in which COVID-19 deaths occurred randomly across the population regardless of underlying conditions. For this alternative scenario, we use population mortality and average quality of life by age and sex. RESULTS: Accounting for underlying health status, we estimate that QALYs lost because of COVID-19 mortality are on average 3.9 per death for men and 3.5 for women. This is approximately 3.5 QALYs less than when not taking selective mortality into account. Given 16 308 excess deaths, this translates into 61 032 QALYs lost because of COVID-19. CONCLUSIONS: We conclude that QALYs lost because of COVID-19 mortality are still substantial, even if mortality is strongly concentrated in people with poor health.

Distributional consequences of including survivor costs in economic evaluations.

Medical interventions that increase life expectancy of patients result in additional consumption of non-medical goods and services in 'added life years'. This paper focuses on the distributional consequences across socio-economic groups of including these costs in cost effectiveness analysis. In that context, it also highlights the role of remaining quality of life and household economies of scale. Data from a Dutch household spending survey was used to estimate non-medical consumption and household size by age and educational attainment. Estimates of non-medical consumption and household size were combined with life tables to estimate what the impact of including non-medical survivor costs would be on the incremental cost effectiveness ratio (ICER) of preventing a death at a certain age. Results show that including non-medical survivor costs increases estimated ICERs most strongly when interventions are targeted at the higher educated. Adjusting for household size (lower educated people less often live additional life years in multi-person households) and quality of life (lower educated people on average spend added life years in poorer health) mitigates this difference. Ignoring costs of non-medical consumption in economic evaluations implicitly favors interventions targeted at the higher educated and thus potentially amplifies socio-economic inequalities in health.

Can your house keep you out of a nursing home?

We examine the impact of the accessibility of an older individual's house on her use of nursing home care. We link administrative data on the accessibility of all houses in the Netherlands to data on long-term care use of all older persons from 2011 to 2014. We find that older people living in more accessible houses are less likely to use nursing home care. The effects increase with age and are largest for individuals aged 90 or older. The effects are stronger for people with physical limitations than for persons with cognitive problems. We also provide suggestive evidence that older people living in more accessible houses substitute nursing home care by home care.

Long-term care use after a stroke or femoral fracture and the role of family caregivers.

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.

The effect of trends in health and longevity on health services use by older adults.

BACKGROUND: The 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. METHODS: Using 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. RESULTS: Hospital 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. CONCLUSIONS: By 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.

Quantifying income inequality in years of life lost to COVID-19: a prediction model approach using Dutch administrative data.

BACKGROUND: Low socioeconomic status and underlying health increase the risk of fatal outcomes from COVID-19, resulting in more years of life lost (YLL) among the poor. However, using standard life expectancy overestimates YLL to COVID-19. We aimed to quantify YLL associated with COVID-19 deaths by sex and income quartile, while accounting for the impact of individual-level pre-existing health on remaining life expectancy for all Dutch adults aged 50+. METHODS: Extensive administrative data were used to model probability of dying within the year for the entire 50+ population in 2019, considering age, sex, disposable income and health care use (n = 6 885 958). The model is used to predict mortality probabilities for those who died of COVID-19 (had they not died) in 2020. Combining these probabilities in life tables, we estimated YLL by sex and income quartile. The estimates are compared with YLL based on standard life expectancy and income-stratified life expectancy. RESULTS: Using standard life expectancy results in 167 315 YLL (8.4 YLL per death) which is comparable to estimates using income-stratified life tables (167 916 YLL with 8.2 YLL per death). Considering pre-existing health and income, YLL decreased to 100 743, with 40% of years lost in the poorest income quartile (5.0 YLL per death). Despite individuals in the poorest quartile dying at younger ages, there were minimal differences in average YLL per COVID-19 death compared with the richest quartile. CONCLUSIONS: Accounting for prior health significantly affects estimates of YLL due to COVID-19. However, inequality in YLL at the population level is primarily driven by higher COVID-19 deaths among the poor. To reduce income inequality in the health burden of future pandemics, policies should focus on limiting structural differences in underlying health and exposure of lower income groups.

Variation in Excess Mortality Across Nursing Homes in the Netherlands During the COVID-19 Pandemic.

OBJECTIVES: Nursing home residents constituted a vulnerable population during the COVID-19 pandemic, and half of all cause-attributed COVID-19 deaths occurred within nursing homes. Yet, given the low life expectancy of nursing home residents, it is unclear to what extent COVID-19 mortality increased overall mortality within this population. Moreover, there might have been differences between nursing homes in their ability to protect residents against excess mortality. This article estimates the number of excess deaths among Dutch nursing home residents during the pandemic, the variation in excess deaths across nursing homes, and its relationship with nursing home characteristics. DESIGN: Retrospective, use of administrative register data. SETTING AND PARTICIPANTS: All residents (N = 194,432) of Dutch nursing homes (n = 1463) in 2016-2021. METHODS: We estimated the difference between actual and predicted mortality, pooled at the nursing home level, which provided an estimate of nursing home-specific excess mortality corrected for resident case-mix differences. We show the variation in excess mortality across nursing homes and relate this to nursing home characteristics. RESULTS: In 2020 and 2021, the mortality probability among nursing home residents was 4.0 and 1.6 per 100 residents higher than expected. There was considerable variation in excess deaths across nursing homes, even after correcting for differences in resident case mix and regional factors. This variation was substantially larger than prepandemic mortality and was in 2020 related to prepandemic spending on external personnel and satisfaction with the building, and in 2021 to prepandemic staff absenteeism. CONCLUSIONS AND IMPLICATIONS: The variation in excess mortality across nursing homes was considerable during the COVID-19 pandemic, and larger compared with prepandemic years. The association of excess mortality with the quality of the building and spending on external personnel indicates the importance of considering differences across nursing home providers when designing policies and guidelines related to pandemic preparedness.

Do different shocks in health matter for wealth?

We study the effect of hospital admissions for specific conditions on wealth for the Dutch population aged 70-79. We consider 14 disease groups that affect mortality and disability to different extents. We apply a "difference-in-timing" design comparing individuals admitted in one year to similar individuals admitted later. Because the protection against income risks and medical spending is high, we can identify changes in saving behaviors driven by preferences for consumption, saving, and bequests, rather than by changes in budget constraints. Although the health shocks differ in mortality and disability, we find hardly any impact on wealth across conditions.

Measuring Nursing Home Performance Using Administrative Data.

To improve the quality of nursing home care, reliable estimates of outcomes are essential. Obtaining such estimates requires optimal use of limited data, especially for small homes. We analyze the variation in mortality and hospital admissions across nursing homes in the Netherlands during the years 2010-2013. We use administrative data on all nursing home clients. We apply mixed-effects survival models, empirical Bayes estimation, and machine-learning techniques to optimally use the available longitudinal data. We find large differences in both outcomes across nursing homes, yet the estimates are surrounded by substantial uncertainty. We find no correlation between performance on mortality and avoidable hospital admissions, suggesting that these are related to different aspects of quality. Hence, caution is needed when evaluating the performance of individual nursing homes, especially when the number of outcome indicators is limited.

Has COVID-19 increased inequality in mortality by income in the Netherlands?

BACKGROUND: In the Netherlands in 2020, COVID-19 deaths were more concentrated among individuals with a lower income. At the same time, COVID-19 was a new cause that also displaced some deaths from other causes, potentially reducing income-related inequality in non-COVID deaths. Our aim is to estimate the impact of the COVID-19 pandemic on the income-related inequality in total mortality and decompose this into the inequality in COVID-attributed deaths and changes in the inequality in non-COVID causes. METHODS: We estimate excess deaths (observed minus trend-predicted deaths) by sex, age and income group for the Netherlands in 2020. Using a measure of income-related inequality (the concentration index), we decompose the inequality in total excess mortality into COVID-19 versus non-COVID causes. RESULTS: Cause-attributed COVID-19 mortality exceeded total excess mortality by 12% for the 65-79 age group and by about 35% for 80+ in the Netherlands in 2020, implying a decrease in the number of non-COVID deaths compared with what was predicted. The income-related inequality in all-cause mortality was higher than predicted. This increase in inequality resulted from the combination of COVID-19 mortality, which was more unequally distributed than predicted total mortality, and the inequality in non-COVID causes, which was less unequal than predicted. CONCLUSION: The COVID-19 pandemic has led to an increase in income-related inequality in all-cause mortality. Non-COVID mortality was less unequally distributed than expected due to displacement of other causes by COVID-19 and the potentially unequal broader societal impact of the pandemic.

The Value of Health in a Cost-Effectiveness Analysis: Theory Versus Practice.

A cost-effectiveness analysis has become an important method to inform allocation decisions and reimbursement of new technologies in healthcare. A cost-effectiveness analysis requires a threshold to which the cost effectiveness of a new intervention can be compared. In principle, the threshold ought to reflect opportunity costs of reimbursing a new technology. In this paper, we contrast the practical use of this threshold within a CEA with its theoretical underpinnings. We argue that several assumptions behind the theoretical models underlying this threshold are violated in practice. This implies that a simple application of the decision rules of CEA using a single estimate of the threshold does not necessarily improve population health or societal welfare. Conceptual differences regarding the interpretation of the threshold, widely varying estimates of its value, and an inconsistent use within and outside the healthcare sector are important challenges in informing policy makers on optimal reimbursement decision and setting appropriate healthcare budgets.

Informal Care Costs According to Age and Proximity to Death to Support Cost-Effectiveness Analyses.

BACKGROUND: Costs of informal care are ignored in many cost-effectiveness analyses (CEAs) conducted from a societal perspective; however, these costs are relevant for lifesaving interventions targeted at the older population. In this study, we estimated informal care costs by age and proximity to death across European regions and showed how these estimates can be included in CEAs. METHODS: We estimated informal care costs by age and proximity to death using generalised linear mixed-effects models. For this, we selected deceased singles from the Survey of Health, Ageing and Retirement, which we grouped by four European regions. We combined the estimates of informal care costs with life tables to illustrate the impact of including informal care costs on the incremental cost-effectiveness ratio (ICER) of a hypothetical intervention that prevents a death at different ages. RESULTS: Informal care use, and hence informal care costs, increase when approaching death and with increasing age. The impact of including informal care costs on the ICER varies between €200 and €17,700 per quality-adjusted life-year gained. The impact increases with age and is stronger for women and in southern European countries. CONCLUSION: Our estimates of informal care costs facilitate including informal care costs in CEAs of life-extending healthcare interventions. Including these costs may influence decisions as it leads to reranking of life-extending interventions compared with interventions improving quality of life.

Health trajectories across the work exit transition in the 1990s, 2000s, and 2010s: the role of working conditions and policy.

PURPOSE: We examined health trajectories of Dutch older workers across their exit from the workforce in the 1990s, 2000s, and 2010s, testing the hypothesis that pre-post-exit health trajectories of workers with favourable and unfavourable working conditions increasingly diverged over time due to policy measures to extend working life. METHODS: The Longitudinal Aging Study Amsterdam includes baseline samples in 1992/1993, 2002/2003 and 2012/2013 with two 3-year follow-up waves each. Selected respondents were aged 55 years and over who exited from a paid job within the first or second 3-year interval, up to and including the statutory retirement age (N = 522). Pre-post-exit trajectories were modelled using Generalized Estimating Equations with outcomes self-rated health and physical limitations and determinants physical demands, psychosocial demands, and psychosocial resources. RESULTS: Average work exit age rose from 60.7 in the 1990s to 62.9 in the 2010s. On average, self-rated health decreased somewhat over successive periods and did not show pre-post-exit change; average physical limitations increased substantially both over successive periods and from pre- to post-exit. No support is found for our hypothesis. However, regardless of work exposures, we found sharp pre-post-exit increases in physical limitations in the 2010s. CONCLUSION: Although these findings provide no support for our hypothesis of diverging health trajectories over time based on work exposure, they show that exiting at a higher age is linked to poorer pre- and post-exit health and to pre-post-exit increases in physical limitations, suggesting greater health care costs in the near future.

Acute hospital use in older adults following the 2015 Dutch reform of long-term care: an interrupted time series analysis.

BACKGROUND: In 2015, the Dutch government implemented a long-term care (LTC) reform primarily designed to promote older adults to age-in-place. Increased proportions of older adults living in the community might have resulted in more and longer acute hospitalisations. The aims of this study were to evaluate whether the Dutch 2015 LTC reform was associated with immediate and longitudinal increases in the monthly rate of acute clinical hospitalisation and monthly average hospital length of stay (LOS) in adults aged 65 years or older. METHODS: In this interrupted time series analysis of national hospital data (2009-18), we evaluated the association of the Dutch 2015 LTC reform with the monthly rate of acute clinical hospitalisation and monthly average LOS for older adults (aged ≥65 years). Patient-level episodic hospital data were provided by Dutch Hospital Data. Records were included that were defined as an acute clinical hospital admission for which a medical specialist decided treatment was necessary within 24 h. The analysis controlled for population growth (Dutch population data was provided by Statistics Netherlands) and seasonality, and calculated adjusted incident rate ratios (IRR). FINDINGS: Before the 2015 LTC reform, the rate of acute monthly hospitalisation was increasing (IRR 1·002 [95% CI 1·001-1·002]). A positive average reform effect was observed (1·116 [1·070-1·165]), accompanied by a negative change in trend (0·997 [0·996-0·998]) that resulted in a decreasing trend over the post-reform period (0·998 [0·998-0·999]). The pre-reform trend of LOS was decreasing (0·998 [0·997-0·998]), and the 2015 reform exhibited a positive change in trend (1·002 [1·002-1·003]) that resulted in a stabilisation of LOS in the post-reform period (0·999 [0·999-1·000]). INTERPRETATION: Our findings suggest that the increase in the rate of acute hospitalisation after the reform implementation was temporary, whereas the increase in LOS post-reform appeared to last longer than expected. These results have the potential to inform policy makers about effects of ageing-in-place LTC strategies on health and curative care. FUNDING: The Netherlands Organization for Health Research and Development, the Yale Claude Pepper Center, and the National Center for Advancing Translational Sciences, National Institutes of Health. TRANSLATION: For the Dutch translation of the abstract see Supplementary Materials section.

The relationship between baseline health and longitudinal costs of hospital use.

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.

Estimating the costs of non-medical consumption in life-years gained for economic evaluations.

Including the costs of non-medical consumption in life years gained in economic evaluations of medical interventions has been controversial. This paper focuses on the estimation of these costs using Dutch data coming from cross-sectional household surveys consisting of 56,569 observations covering the years 1978-2004. We decomposed the costs of consumption into age, period and cohort effects and modelled the non-linear age and cohort patterns of consumption using P-splines. As consumption patterns depend on household composition, we also estimated household size using the same regression modeling strategy. Estimates of non-medical consumption and household size were combined with life tables to estimate the impact of including non-medical survivor costs on an incremental cost-effectiveness ratio (ICER). Results revealed that including non-medical survivor costs substantially increases the ICER, but the effect varies strongly with age. The impact of cohort effects is limited but ignoring household economies of scale results in a significant overestimation of non-medical costs. We conclude that a) ignoring the costs of non-medical consumption results in an underestimation of the costs of life prolonging interventions b) economies of scale within households with respect to consumption should be accounted for when estimating future costs.

Better off at home? Effects of nursing home eligibility on costs, hospitalizations and survival.

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.

A multifaceted feedback strategy alone does not improve the adherence to organizational guideline-based standards: a cluster randomized trial in intensive care.

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. ISRCTN: ISRCTN50542146.