Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis

End-of-life emergency department use and healthcare expenditures among older adults: A nationally representative study

BACKGROUND

Emergency department (ED) visits at end-of-life may cause financial strain and serve as a marker of inadequate access to community services and health care. We sought to examine end-of-life ED use, total healthcare spending, and out-of-pocket spending in a nationally representative sample.

METHODS

Using Medicare Current Beneficiary Survey data, we conducted a pooled cross-sectional analysis of Medicare beneficiaries aged 65+ years with a date of death between July 1, 2015 and December 31, 2021. Our primary outcomes were ED visits, total healthcare spending, and out-of-pocket spending in the 7, 30, 90, and 180 days preceding death. We estimated a series of zero-inflated negative binomial models identifying patient characteristics associated with the primary outcomes.

RESULTS

Among 3812 older adult decedents, 610 (16%), 1207 (31.7%), 1582 (41.5%), and 1787 (46.9%) Medicare beneficiaries had ED visits in the final 7, 30, 90, and 180 days, respectively, of life. For Medicare beneficiaries with at least one ED visit in the final 30 days of life, the median total and out-of-pocket costs were, respectively, $12,500 and $308, compared, respectively, with $278 and $94 for those without any ED visits (p < 0.001 for both comparisons). Having a diagnosis of dementia (odds ratio [OR] 0.71; 95% confidence interval [CI] 0.51-0.99; p = 0.04) and being on hospice status during the year of death (OR 0.56; 95% CI 0.48-0.66; p = <0.001) were associated with a decreased likelihood of having an ED visit. Having dementia was associated with a decreased likelihood of having any healthcare spending (OR 0.50; 95% CI 0.36-0.71; p = 0.001) and any out-of-pocket spending (OR 0.51; 95% CI 0.36-0.72; p = <0.001).

CONCLUSIONS

One in three older adults visit the ED in the last month of life, and approximately one in two utilize ED services in the last half-year of life, with evidence of associated considerable total and out-of-pocket spending.

Improving Cognitive Function with Nutritional Supplements in Aging: A Comprehensive Narrative Review of Clinical Studies Investigating the Effects of Vitamins, Minerals, Antioxidants, and Other Dietary Supplements

Cognitive impairment and dementia are burgeoning public health concerns, especially given the increasing longevity of the global population. These conditions not only affect the quality of life of individuals and their families, but also pose significant economic burdens on healthcare systems. In this context, our comprehensive narrative review critically examines the role of nutritional supplements in mitigating cognitive decline. Amidst growing interest in non-pharmacological interventions for cognitive enhancement, this review delves into the efficacy of vitamins, minerals, antioxidants, and other dietary supplements. Through a systematic evaluation of randomized controlled trials, observational studies, and meta-analysis, this review focuses on outcomes such as memory enhancement, attention improvement, executive function support, and neuroprotection. The findings suggest a complex interplay between nutritional supplementation and cognitive health, with some supplements showing promising results and others displaying limited or context-dependent effectiveness. The review highlights the importance of dosage, bioavailability, and individual differences in response to supplementation. Additionally, it addresses safety concerns and potential interactions with conventional treatments. By providing a clear overview of current scientific knowledge, this review aims to guide healthcare professionals and researchers in making informed decisions about the use of nutritional supplements for cognitive health.

Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study

Background

Cardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.

Methods

Data were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests-a machine learning approach-to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.

Results

Our sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.

Conclusion

Machine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs.

Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003-2019

Many studies have examined factors associated with individuals of high or low healthcare spending in a given year. However, few have studied how healthcare spending changes over multiple years and which factors are associated with the changes. In this study, we examined the dynamic patterns of healthcare spending over a three-year period, among a nationally representative cohort of Medicare beneficiaries in the U.S. and identified factors associated with these patterns. We extracted data for 30,729 participants from the national Medicare Current Beneficiary Survey (MCBS), for the period 2003-2019. Using multistate Markov (MSM) models, we estimated the probabilities of year-to-year transitions in healthcare spending categorized as three states (low (L), medium (M) and high (H)), or to the terminal state, death. The participants, 13,554 (44.1%), 13,715 (44.6%) and 3,460 (11.3%) were in the low, medium and high spending states at baseline, respectively. The majority of participants remained in the same spending category from one year to the next (L-to-L: 76.8%; M-to-M: 71.7%; H-to-H: 56.6 %). Transitions from the low to high spending state were significantly associated with older age (75-84, ≥85 years), residing in a long-term care facility, greater assistance with activities of daily living, enrollment in fee-for-service Medicare, not receiving a flu shot, and presence of specific medical conditions, including cancer, dementia, and heart disease. Using data from a large population-based longitudinal survey, we have demonstrated that MSM modelling is a flexible framework and useful tool for examining changes in healthcare spending over time.

Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning

Background Existing studies on cardiovascular diseases (CVDs) often focus on individual-level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county-level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

Incidence of Aggressive End-of-Life Care Among Older Adults With Metastatic Cancer Living in Nursing Homes and Community Settings

IMPORTANCE

Nearly 10% of the 1.5 million persons residing in nursing homes (NHs) have received or will receive a diagnosis of cancer. Although aggressive end-of-life (EOL) care is common among community-dwelling patients with cancer, little is known about such patterns of care among NH residents with cancer.

OBJECTIVE

To compare markers of aggressive EOL care between older adults with metastatic cancer who are NH residents and their community-dwelling counterparts.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used the Surveillance, Epidemiology, and End Results database linked with the Medicare database and the Minimum Data Set (including NH clinical assessment data) for deaths occurring from January 1, 2013, to December 31, 2017, among 146 329 older patients with metastatic breast, colorectal, lung, pancreas, or prostate cancer, with a lookback period in claims data through July 1, 2012. Statistical analysis was conducted between March 2021 and September 2022.

EXPOSURES

Nursing home status.

MAIN OUTCOMES AND MEASURES

Markers of aggressive EOL care were cancer-directed treatment, intensive care unit admission, more than 1 emergency department visit or more than 1 hospitalization in the last 30 days of life, hospice enrollment in the last 3 days of life, and in-hospital death.

RESULTS

The study population included 146 329 patients 66 years of age or older (mean [SD] age, 78.2 [7.3] years; 51.9% men). Aggressive EOL care was more common among NH residents than community-dwelling residents (63.6% vs 58.3%). Nursing home status was associated with 4% higher odds of receiving aggressive EOL care (adjusted odds ratio [aOR], 1.04 [95% CI, 1.02-1.07]), 6% higher odds of more than 1 hospital admission in the last 30 days of life (aOR, 1.06 [95% CI, 1.02-1.10]), and 61% higher odds of dying in the hospital (aOR, 1.61 [95% CI, 1.57-1.65]). Conversely, NH status was associated with lower odds of receiving cancer-directed treatment (aOR, 0.57 [95% CI, 0.55-0.58]), intensive care unit admission (aOR, 0.82 [95% CI, 0.79-0.84]), or enrollment in hospice in the last 3 days of life (aOR, 0.89 [95% CI, 0.86-0.92]).

CONCLUSIONS AND RELEVANCE

Despite increased emphasis to reduce aggressive EOL care in the past several decades, such care remains common among older persons with metastatic cancer and is slightly more prevalent among NH residents than their community-dwelling counterparts. Multilevel interventions to decrease aggressive EOL care should target the main factors associated with its prevalence, including hospital admissions in the last 30 days of life and in-hospital death.

The Financial Impact of Palliative Care and Aggressive Cancer Care on End-of-Life Health Care Costs

BACKGROUND

Medicare cancer expenditures in the last month of life have increased. Aggressive cancer care at the end-of-life (ACEOL) is considered poor quality care. We used Geisinger Health Plan (GHP) last month's costs for cancer patients who died in 2018 and 2019 to determine the costs of and influence of Palliative Care (PC) on ACEOL.

METHOD

Patients with GHP ages 18-99 who died in 2018 and 2019 were included. Demographic, clinical characteristics, and Charlson Comorbid Index were compared across care groups defined as no ACEOL indicator, 1 or more than 1 indicator. Differences between groups were compared with Kruskal-Wallis tests and one-way ANOVA for 3 groups. Median two-sample tests and independent t-tests compared groups of 2. A P-value </= .05 indicated statistical significance.

RESULTS

Of 608 eligible patients; 261 had no indicator, 133 had 1 and 214 > 1. There were incremental cost increases with each additional ACEOL indicator (p = < .0001). Palliative Care <90 days before death was associated with increased costs while consultations >90 days before death lowered cost (P < .0001) due to reduced chemotherapy in the last month. Completed ADs reduced cost by $4000.

DISCUSSION

ACEOL indicators multiply costs during the last month of life. Palliative care instituted >90 days before death reduces chemotherapy in the last month of life and AD reduces health care costs.

CONCLUSION

Cancer health care costs increase with indicators of ACEOL. Palliative care consultations >90 days before death; ADs reduce cancer health care costs.