Proximity to death and health care expenditure increase revisited: A 15-year panel analysis of elderly persons

Determinants of public health expenditure in the EU

BACKGROUND

Public health expenditure is one of the fastest-growing spending items in EU member states. As the population ages and wealth increases, governments allocate more resources to their health systems. In view of this, the aim of this study is to identify the key determinants of public health expenditure in the EU member states.

METHODS

This study is based on macro-level EU panel data covering the period from 2000 to 2018. The association between explanatory variables and public health expenditure is analyzed by applying both static and dynamic econometric modeling.

RESULTS

Although GDP and out-of-pocket health expenditure are identified as the key drivers of public health expenditure, there are other variables, such as health system characteristics, with a statistically significant association with expenditure. Other variables, such as election year and the level of public debt, result to exert only a modest influence on the level of public health expenditure. Results also indicate that the aging of the population, political ideologies of governments and citizens' expectations, appear to be statistically insignificant.

CONCLUSION

Since increases in public health expenditure in EU member states are mainly triggered by GDP increases, it is expected that differences in PHE per capita across member states will persist and, consequently, making it more difficult to attain the health equity sustainable development goal. Thus, measures to reduce EU economic inequalities, will ultimately result in reducing disparities in public health expenditures across member states.

End-of life medical spending and care pathways in the last 12 months of life: A comprehensive analysis of the national claims database in France

BACKGROUND

To inform policy makers on efficient provision of end-of-life care, we estimated the 12-month medical expenditures of French decedents in 2015.

METHODS

We estimated total medical expenditures by service type and diagnosis category, and analyzed care pathways for breast cancer, dementia, chronic obstructive lung disease.

RESULTS

501,121 individuals died in 2015, 59% of whom were in a hospital at the time of death. The aggregated spending totaled 9% of total health expenditures, a mean of €28,085 per capita, 44% of which was spent during the last 3 months of life. Hospital admissions represented over 70% of total expenditures; 21.3% of the population used hospital palliative care services in their last year of life. Analyses performed on breast cancer, dementia and lung disease found that differences in care pathways markedly influenced spending and were not simply explained by patients characteristics.

CONCLUSION

Diagnoses and care trajectories, including repeated hospital stays, are the main drivers of the last year of life expenditures. Our data suggests that early identification of patients requiring palliative care and community-based end-of-life service delivery is feasible and could better support patients, families and caregivers with constant or reduced costs.

Variation in End-of-Life Trajectories in Persons Aged 70 Years and Older, Sweden, 2018‒2020

Objectives. To analyze variation in end-of-life trajectories with regard to elder care and medical care and how they relate to age, gender, and causes of death. Methods. We analyzed all deaths of persons at age 70 years and older between the years 2018 and 2020 in Sweden, using a linkage of population registers. We applied latent class analysis to identify distinct types of end-of-life trajectories. Results. We identified 6 different types of end-of-life trajectories. The types differed substantially in the amount of utilized elder care and medical care before death. Deaths characterized by high levels of elder care and medical care utilization become more common with age. The trajectory types show distinct cause-of-death profiles. Conclusions. Most deaths today do not comply with what is often referred to as a "good" death (e.g., retaining control or requiring low levels of elder care). The results suggest that longer lifespans partly result from a prolonged dying process. Public Health Implications. The current modes of dying call for a discussion about how we want to die in an era of increasing lifespans and aging societies. (Am J Public Health. 2023;113(7):786-794. https://doi.org/10.2105/AJPH.2023.307281).

10-year follow-up study on medical expenses and medical care use according to biological age: National Health Insurance Service Health Screening Cohort (NHIS-HealS 2002~2019)

OBJECTIVES

The world is witnessing a sharp increase in its elderly population, accelerated by longer life expectancy and lower birth rates, which in turn imposes enormous medical burden on society. Although numerous studies have predicted medical expenses based on region, gender, and chronological age (CA), any attempt has rarely been made to utilize biological age (BA)-an indicator of health and aging-to ascertain and predict factors related to medical expenses and medical care use. Thus, this study employs BA to predict factors that affect medical expenses and medical care use.

MATERIALS AND METHODS

Referring to the health screening cohort database of the National Health Insurance Service (NHIS), this study targeted 276,723 adults who underwent health check-ups in 2009-2010 and kept track of the data on their medical expenses and medical care use up to 2019. The average follow-up period is 9.12 years. Twelve clinical indicators were used to measure BA, while the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses were used as the variables for medical expenses and medical care use. For statistical analysis, this study employed Pearson correlation analysis and multiple regression analysis.

RESULTS

Regression analysis of the differences between corrected biological age (cBA) and CA exhibited statistically significant increases (p<0.05) in all the variables of the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses.

CONCLUSIONS

This study quantified decreases in the variables for medical expenses and medical care use based on improved BA, thereby motivating people to become more health-conscious. In particular, this study is significant in that it is the first of its kind to predict medical expenses and medical care use through BA.

A Model to Compare International Hospital Bed Numbers, including a Case Study on the Role of Indigenous People on Acute 'Occupied' Bed Demand in Australian States

Comparing international or regional hospital bed numbers is not an easy matter, and a pragmatic method has been proposed that plots the number of beds per 1000 deaths versus the log of deaths per 1000 population. This method relies on the fact that 55% of a person's lifetime hospital bed utilization occurs in the last year of life-irrespective of the age at death. This is called the nearness to death effect. The slope and intercept of the logarithmic relationship between the two are highly correlated. This study demonstrates how lines of equivalent bed provision can be constructed based on the value of the intercept. Sweden looks to be the most bed-efficient country due to long-term investment in integrated care. The potential limitations of the method are illustrated using data from English Clinical Commissioning Groups. The main limitation is that maternity, paediatric, and mental health care do not conform to the nearness to death effect, and hence, the method mainly applies to adult acute care, especially medical and critical care bed numbers. It is also suggested that sensible comparison can only be made by comparing levels of occupied beds rather than available beds. Occupied beds measure the expressed bed demand (although often constrained by access to care issues), while available beds measure supply. The issue of bed supply is made complex by the role of hospital size on the average occupancy margin. Smaller hospitals are forced to operate at a lower average occupancy; hence, countries with many smaller hospitals such as Germany and the USA appear to have very high numbers of available beds. The so-called 85% occupancy rule is an "urban myth" and has no fundamental basis whatsoever. The very high number of "hospital" beds in Japan is simply an artefact arising from "nursing home" beds being counted as a "hospital" bed in this country. Finally, the new method is applied to the expressed demand for occupied acute beds in Australian states. Using data specific to acute care, i.e., excluding mental health and maternity, a long-standing deficit of beds was identified in Tasmania, while an unusually high level of occupied beds in the Northern Territory (NT) was revealed. The high level of demand for beds in the NT appears due to an exceptionally large population of indigenous people in this state, who are recognized to have elevated health care needs relative to non-indigenous Australians. In this respect, indigenous Australians use 3.5 times more occupied bed days per 1000 deaths (1509 versus 429 beds per 1000 deaths) and 6 times more occupied bed days per 1000 population (90 versus 15 beds per 1000 population) than their non-indigenous counterparts. The figure of 1509 beds per 1000 deaths (or 4.13 occupied beds per 1000 deaths) for indigenous Australians is indicative of a high level of "acute" nursing care in the last months of life, probably because nursing home care is not readily available due to remoteness. A lack of acute beds in the NT then results in an extremely high average bed occupancy rate with contingent efficiency and delayed access implications.

System Complexity in Influenza Infection and Vaccination: Effects upon Excess Winter Mortality

Unexpected outcomes are usually associated with interventions in complex systems. Excess winter mortality (EWM) is a measure of the net effect of all competing forces operating each winter, including influenza(s) and non-influenza pathogens. In this study over 2400 data points from 97 countries are used to look at the net effect of influenza vaccination rates in the elderly aged 65+ against excess winter mortality (EWM) each year from the winter of 1980/81 through to 2019/20. The observed international net effect of influenza vaccination ranges from a 7.8% reduction in EWM estimated at 100% elderly vaccination for the winter of 1989/90 down to a 9.3% increase in EWM for the winter of 2018/19. The average was only a 0.3% reduction in EWM for a 100% vaccinated elderly population. Such outcomes do not contradict the known protective effect of influenza vaccination against influenza mortality per se—they merely indicate that multiple complex interactions lie behind the observed net effect against all-causes (including all pathogen causes) of winter mortality. This range from net benefit to net disbenefit is proposed to arise from system complexity which includes environmental conditions (weather, solar cycles), the antigenic distance between constantly emerging circulating influenza clades and the influenza vaccine makeup, vaccination timing, pathogen interference, and human immune diversity (including individual history of host-virus, host-antigen interactions and immunosenescence) all interacting to give the observed outcomes each year. We propose that a narrow focus on influenza vaccine effectiveness misses the far wider complexity of winter mortality. Influenza vaccines may need to be formulated in different ways, and perhaps administered over a shorter timeframe to avoid the unanticipated adverse net outcomes seen in around 40% of years.

Effects of medical insurance system on the hospitalization cost of acute myocardial infarction patients

BACKGROUND

Acute myocardial infarction is still a burden on Chinese patients. Whether different medical insurance system have any influence on the hospitalization cost and therapeutic effect of acute myocardial infarction patient needs further investigation.

METHOD

In this study, 600 patients were stratified by health insurance status to investigate the cost effectiveness.

RESULT

Compared with free medical care, patients with other health insurance status have a significantly lower age (P ˂ 0.05-0.001), the youngest of which is new rural cooperative medical system. The hospital expense, nursing fee, length of stay, daily hospitalization cost, daily drug cost, daily nursing cost and percent of nursing cost of different health insurance status were statistically significant. ANCOVA analyses controlling for age showed that the differences of hospital expenses, nursing fee, length of stay and daily hospitalization cost were still statistically significant. Further studies found that health insurance status was the leading factors influencing length of stay (β = - 0.305, P = 0.0000001), nursing costs (β = - 0.319, P = 0.004), daily hospitalization costs (β = 0.296, P = 0.0001) and occurrence of clinical events (β = - 0.186, OR = 0.830, 95% CI 0.694-0.993, P = 0.041).

CONCLUSIONS

The hospitalization cost, length of stay, nursing work and therapeutic effect of acute myocardial infarction patients are affected by different health insurance status and age.

Analysis of Healthcare Expenditures in Bulgaria

The growth of public expenditure worldwide has set the priority on assessment of trends and establishment of factors which generate the most significant public costs. The goal of the current study is to review the tendencies in public healthcare expenditures in Bulgaria and to analyze the influence of the demographic, economic, and healthcare system capacity indicators on expenditures dynamics. A retrospective, top-down, financial analysis of the healthcare system expenditures was performed. Datasets of the National Statistical Institute (NSI), National Health Insurance Fund (NHIF), and National Center of Public Health and Analysis (NCPHA) were retrospectively reviewed from2014–2019 to collect the information in absolute units of healthcare expenditures, healthcare system performance, demographics, and economic indicators. The research showed that increasing GDP led to higher healthcare costs, and it was the main factor affecting the cost growth in Bulgaria. The number of hospitalized patients and citizens in retirement age remained constant, confirming that their impact on healthcare costs was negligible. In conclusion, the population aging, average life expectancy, patient morbidity, and hospitalization rate altogether impacted healthcare costs mainly due to the multimorbidity of older people and the rising need for outpatient hospital services and medications.

Use of medical services by older Australian women with dementia: a longitudinal cohort study

OBJECTIVE

To assess the use of Medicare-subsidised health services by women with and without dementia.

METHODS

Data from women of the 1921-26 birth cohort of the Australian Longitudinal Study on Women's Health were linked to various administrative datasets to ascertain dementia diagnosis. The use of subsidised general practitioner (GP) services (75+ health assessments [HAs], chronic disease management meetings [CDMs], multidisciplinary case conferences [MCCs]) and specialist and allied health services between 2000 and 2013 for these women was analysed using longitudinal GEE models.

RESULTS

A total of 9,683 women were included with 1,444 (15%) women identified as having dementia. Compared to women with no dementia indication, women with dementia had more yearly non-emergency GP attendances (short [<30 minutes] IRR=1.11 [1.07, 1.13]; long [>30 minutes] IRR=1.11 [1.04, 1.19]) and fewer specialist attendances (IRR=0.91 [0.85, 0.97]) and were more likely to have an emergency GP attendance (OR=2.29 [2.05, 2.57]). There were no significant differences in the odds of having either a HA or CDM or using allied health services for women with and without dementia indicators.

CONCLUSIONS

The overall use of services designed to improve the prevention and coordination of the care of older people with chronic conditions was low. Women with dementia were no more likely to access these services. Implications for public health: There is underuse of some primary and allied healthcare services designed for people with complex chronic conditions. These could be better used by women with dementia to improve the management of complex comorbidities (e.g. CDMs), to prevent the onset of disability (e.g. physiotherapy), and enhance needs assessment and service access (e.g. HAs).

Feature Selection for Health Care Costs Prediction Using Weighted Evidential Regression

Although many authors have highlighted the importance of predicting people's health costs to improve healthcare budget management, most of them do not address the frequent need to know the reasons behind this prediction, i.e., knowing the factors that influence this prediction. This knowledge allows avoiding arbitrariness or people's discrimination. However, many times the black box methods (that is, those that do not allow this analysis, e.g., methods based on deep learning techniques) are more accurate than those that allow an interpretation of the results. For this reason, in this work, we intend to develop a method that can achieve similar returns as those obtained with black box methods for the problem of predicting health costs, but at the same time it allows the interpretation of the results. This interpretable regression method is based on the Dempster-Shafer theory using Evidential Regression (EVREG) and a discount function based on the contribution of each dimension. The method "learns" the optimal weights for each feature using a gradient descent technique. The method also uses the nearest k-neighbor algorithm to accelerate calculations. It is possible to select the most relevant features for predicting a patient's health care costs using this approach and the transparency of the Evidential Regression model. We can obtain a reason for a prediction with a k-NN approach. We used the Japanese health records at Tsuyama Chuo Hospital to test our method, which included medical examinations, test results, and billing information from 2013 to 2018. We compared our model to methods based on an Artificial Neural Network, Gradient Boosting, Regression Tree and Weighted k-Nearest Neighbors. Our results showed that our transparent model performed like the Artificial Neural Network and Gradient Boosting with an R2 of 0.44.