Comparing the use of aggressive end-of life care among frail and non-frail patients with cancer using a claims-based frailty index.

INTRODUCTION: Despite mounting consensus that end-of-life (EOL) care for patients with cancer should focus on improving quality of life, many patients continue to receive aggressive, disease-oriented treatment until death. Within this group, patients with increased frailty may be at higher risk of adverse treatment-related outcomes. We therefore examined the relationship between degree of frailty and receipt of aggressive EOL care among Medicare-insured patients with cancer in Ohio. MATERIALS AND METHODS: From the Ohio Cancer Incidence Surveillance System (OCISS) linked with Medicare claims, we identified patients diagnosed with breast, colorectal, lung, or prostate cancer who died between 2012 and 2016. Frailty was operationalized using a validated claims-based frailty index. Six quality indicators reflecting receipt of aggressive EOL care were identified from claims: (1) any cancer-directed treatment, (2) >1 emergency department (ED) visit, (3) >1 hospital admission, (4) any intensive care unit (ICU) admission in the last 30 days of life, (5) entry to hospice in the last three days of life, and (6) in-hospital mortality. Multivariable logistic regression analysis was performed to control for demographic factors, Medicare and Medicaid dual enrollment, and cancer type and stage in the relationship between frailty and aggressive EOL care. RESULTS: Overall, 31,465 patients met selection criteria. Patients with moderate/severe frailty were less likely than non-/pre-frail patients to receive any aggressive EOL care (adjusted odds ratio [aOR] 0.92 [95% confidence interval 0.86-0.99]). This group was also less likely to undergo cancer-directed treatment in their last 30 days or to enter hospice in their last three days. Increasing frailty was associated with lower odds of admission to the ICU in the last 30 days of life (mild frailty: aOR 0.88 [0.83-0.94]; moderate/severe frailty: aOR 0.85 [0.78-0.92]) or of dying in-hospital (mild frailty: 0.85 [0.79-0.91]; moderate/severe frailty: aOR 0.74 [0.67-0.82]), but higher odds of having >1 ED visit in the last 30 days of life (mild frailty: aOR 1.43 [1.32-1.53]; moderate/severe frailty: aOR 1.61 [1.47-1.77]). DISCUSSION: These findings suggest the need for more explicit discussion of emergency care seeking for patients with cancer at the end of life.

Aggressive end-of-life care across gradients of cognitive impairment in nursing home patients with metastatic cancer.

BACKGROUND: Studies examining end-of-life (EOL) care in older cancer patients are scarce, and prior studies have not accounted for gradients of cognitive impairment (COG-I). We examine EOL care patterns across COG-I gradients, hypothesizing that greater COG-I severity is associated with lower odds of receiving aggressive EOL care. METHODS: Using data from the linked Surveillance Epidemiology and End Results (SEER) -Medicare -Minimum Data Set (MDS) 3.0, we identified patients with nursing facility stays (NFS) and who died with metastatic cancer from 2013 to 2017. Markers of aggressive EOL care were: cancer-directed treatment, intensive care unit admission, >1 emergency department visit, or >1 hospitalization in the last 30 days of life, hospice enrollment in the last 3 days of life, and in-hospital death. In addition to descriptive analysis, we conducted multivariable logistic regression analysis to evaluate the independent association between COG-I severity and receipt of aggressive EOL care. RESULTS: Of the 40,833 patients in our study population, 49.2% were cognitively intact; 24.4% had mild COG-I; 19.7% had moderate COG-I; and 6.7% had severe COG-I. The percent of patients who received aggressive EOL care was 62.6% and 74.2% among those who were cognitively intact and those with severe COG-I, respectively. Compared with cognitively intact patients, those with severe COG-I had 86% higher odds of receiving any type of aggressive EOL care (adjusted odds ratio (aOR): 1.86 (95% confidence interval: 1.70-2.04)), which were primarily associated with higher odds of in-hospital death. The odds of in-hospital death associated with severe COG-I were higher among those with short- than with long-term stays (aOR:2.58 (2.35-2.84) and aOR:1.40 (1.17-1.67), respectively). CONCLUSIONS: Contrary to our hypothesis, aggressive EOL care in older metastatic cancer patients with NFS was highest among those suffering severe COG-I. These findings can inform the development of interventions to help reduce aggressive EOL care in this patient population.

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.

Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries.

Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.