PURPOSE: Understanding the end-of-life psychosocial needs of cancer patients at home is a knowledge gap. This study describes the trajectory of psychosocial symptoms in the last 6 months of life among cancer decedents who were receiving home care.
METHODS: Observational population-based cohort study of cancer decedents who were receiving home care services between 2007 and 2014. Decedents had to have at least one home care assessment in the last 6 months of life for inclusion. Outcomes were the presence of psychosocial symptoms (i.e., anxiety, loneliness, depression, social decline, caregiver distress, and cognitive decline) at each week before death.
RESULTS: Our cohort included 27,295 unique cancer decedents (30,368 assessments), of which 58% died in hospital. Fifty-six percent were older than 74, and 47% were female. The prevalence of all symptoms increased approaching death, except loneliness. Social decline (48%-78%) was the most prevalent psychosocial symptom, though loneliness was reported in less than 10% of the cohort. Caregiver distress rose over time from 15%-27%. A third of the cohort reported issues with cognitive impairment. Multivariate regression showed that physical symptoms such as uncontrolled pain, impairment in independent activities of daily living, and a high level of health instability all significantly worsened the odds of having a psychosocial symptom in the last 3 months of life.
CONCLUSION: In this large home care cancer cohort, trajectories of psychosocial symptoms worsened close to death. Physical symptoms, such as uncontrolled pain, were associated with having worse psychosocial symptoms at end of life.
Background: Early referral of cancer patients for palliative care significantly improves the quality of life. It is not clear which patients can benefit from an early referral, and when the referral should occur. A Delphi Panel study proposed 11 major criteria for an outpatient palliative care referral.
Objective: To operationalize major Delphi criteria in a cohort of lung cancer patients, using a prospective approach, by linking health administrative data.
Design: Population-based observational cohort study.
Setting/Subjects: The study population comprised 38,851 cases of lung cancer in the Ontario Cancer Registry, diagnosed from January 1, 2012, to December 31, 2016.
Measurements: We operationalized 6 of the 11 major criteria (4 diagnosis or prognosis based and 2 symptom based). Patients were considered eligible (index event) for palliative care if they qualified for any criterion. Among eligible patients, we identified those who received palliative care.
Results: Twenty-eight thousand one hundred sixty-four patients were eligible for palliative care by qualifying for either the diagnosis- or prognosis-based criteria (n = 21,036, 76.5%), or for symptom-based criteria (n = 7128, 23.5%). A total of 23,199 (82.4%) patients received palliative care. The median time from palliative care eligibility to the receipt of first palliative care or death or maximum study follow-up was 56 days (range = 17–348).
Conclusions: We operationalized six major criteria that identified the majority of lung cancer patients who were eligible for palliative care. Most eligible patients received the palliative care before death. Future research is warranted to test these criteria in other cancer populations.
Background: In 2007, Cancer Care Ontario began standardised symptom assessment as part of routine care using the Edmonton Symptom Assessment System (ESAS).
Aim: The purpose of this study was to evaluate the impact of ESAS on receipt of palliative care when compared with a matched group of unexposed patients.
Design: A retrospective-matched cohort study examined the impact of ESAS screening on initiation of palliative care services provided by physicians or homecare nurses. The study included adult patients diagnosed with cancer between 2007 and 2015. Exposure was defined as completing =1 ESAS during the study period. Using 4 hard and 14 propensity score-matched variables, patients with cancer exposed to ESAS were matched 1:1 to those who were not. Matched patients were followed from first ESAS until initiation of palliative care, death or end of study.
Results: The final cohort consisted of 204 688 matched patients with no prior palliative care consult. The pairs were well matched. The cumulative incidence of receiving palliative care within the first 5 years was higher among those exposed to ESAS compared with those who were not (27.9% (95% CI: 27.5% to 28.2%) versus 27.9% (95% CI: 27.5% to 28.2%)), when death is considered as a competing event. In the adjusted cause-specific Cox proportional hazards model, ESAS assessment was associated with a 6% increase in palliative care services (HR: 1.06, 95% CI: 1.04 to 1.08).
Conclusion: We have demonstrated that patients exposed to ESAS were more likely to receive palliative care services compared with patients who were not exposed. This observation provides real-world data of the impact of routine assessment with a patient-reported outcome.
CONTEXT: Understanding the magnitude and risk-factors for symptom burden of cancer patients at the end-of-life is critical to guiding effective patient- and system-level interventions.
OBJECTIVES: We aimed to estimate the prevalence of severe patient-reported symptoms among cancer outpatients during the six months before death and to identify patient groups at a higher risk for reporting severe symptoms.
METHODS: This was a retrospective cohort study of cancer decedents at regional cancer centers from 2010-2016. Patient-reported Edmonton Symptom Assessment System (ESAS) scores from the last six months-of-life were linked to administrative databases. The proportion of patients reporting severe symptom scores (>7) for anxiety, depression, drowsiness, lack of appetite, nausea, pain, shortness of breath, tiredness, and overall wellbeing during the six months before death was described. Multivariable modified Poisson regression analyses were used to identify risk-factors for reporting severe symptom scores.
RESULTS: 22,650 of 39,084 cancer decedents had =1 symptom record in the last six months of life, resulting in 92,757 ESAS assessments. Severe scores were highest for tiredness (56%), lack of appetite (46%), and impaired wellbeing (45%). The proportion of patients reporting severe symptom scores was stable before progressively increasing at 3 months prior to death. Elderly, women, patients with high comorbidity, immigrants, living in urban areas or with high material deprivation were at increased risk of reporting severe scores.
CONCLUSIONS: Despite an integrated symptom screening program, rates of severe patient-reported symptom scores prior to death were high for cancer outpatients. Patient subgroups at increased risk of severe symptom burden may benefit from targeted interventions. Ongoing review of routinely collected symptom data may be used to assess the supportive care needs and guide targeted interventions at the health-system level.
Background: Few measures exist to assess the quality of care received by home care clients, especially at the end of life.
Objective: This project examined the rates across a set of quality indicators (QIs) for seriously ill home care clients.
Design: This was a cross-sectional descriptive analysis of secondary data collected using a standardized assessment tool, the Resident Assessment Instrument for Home Care (RAI-HC).
Setting/Subjects: The sample included RAI-HC data for 66,787 unique clients collected between January 2006 and March 2018 in six provinces. Individuals were defined as being seriously ill if they experienced a high level of health instability, had a prognosis of less than six months, and/or had palliative care as a goal of care.
Measurements: We compared individuals with cancer (n = 21,119) with those without cancer (n = 47,668) on demographic characteristics, health-related outcomes, and on 11 QIs.
Results: Regardless of diagnosis, home care clients experienced high rates (i.e., poor performance) on several QIs, namely the prevalence of falls (cancer = 42.4%; noncancer = 55%), daily pain (cancer = 48.3%; noncancer = 43.2%), and hospital admissions (cancer = 48%; noncancer = 46.6%). The QI rates were significantly lower (i.e., better performance) for the cancer group for three out of the 11 QIs: falls (absolute standardized difference [SD] = 0.25), caregiver distress (SD = 0.28), and delirium (SD = 0.23).
Conclusions: On several potential QIs, seriously ill home care clients experience high rates, pointing to potential areas for quality improvement across Canada.
BACKGROUND: Several studies have demonstrated the benefits of early initiation of end-of-life care, particularly homecare nursing services. However, there is little research on variations in the timing of when end-of-life homecare nursing is initiated and no established benchmarks.
METHODS: This is a retrospective cohort study of patients with a cancer-confirmed cause of death between 2004 and 2009, from three Canadian provinces (British Columbia, Nova Scotia, and Ontario). We linked multiple administrative health databases within each province to examine homecare use in the last 6 months of life. Our primary outcome was mean time (in days) to first end-of-life homecare nursing visit, starting from 6 months before death, by region. We developed an empiric benchmark for this outcome using a funnel plot, controlling for region size.
RESULTS: Of the 28 regions, large variations in the outcome were observed, with the longest mean time (97 days) being two-fold longer than the shortest (55 days). On average, British Columbia and Nova Scotia had the first and second shortest mean times, respectively. The province of Ontario consistently had longer mean times. The empiric benchmark mean based on best-performing regions was 57 mean days.
CONCLUSIONS: Significant variation exists for the time to initiation of end-of-life homecare nursing across regions. Understanding regional variation and developing an empiric benchmark for homecare nursing can support health system planners to set achievable targets for earlier initiation of end-of-life care.
Purpose: The impact of specialized pediatric palliative care (SPPC) teams on patterns of end-of-life care is unknown. We sought to determine (1) which children with cancer access SPPC and (2) the impact of accessing SPPC on the risk of experiencing high-intensity end-of-life care (intensive care unit admission, mechanical ventilation, or in-hospital death).
Methods: Using a provincial childhood cancer registry, we assembled a retrospective cohort of Ontario children with cancer who died between 2000 and 2012 and received care through pediatric institutions with an SPPC team. Patients were linked to population-based administrative data capturing inpatient, outpatient, and emergency visits. Children were classified as having SPPC, general palliative care, or no palliative care on the basis of SPPC clinical databases, physician billing codes, or inpatient diagnosis codes.
Results: Of the 572 children, 166 (29%) received care from an SPPC team for at least 30 days before death, and 100 (17.5%) received general palliative care. SPPC involvement was significantly less likely for children with hematologic cancers (OR, 0.3; 95% CI, 0.3 to 0.4), living in the lowest income areas (OR, 0.4; 95% CI, 0.2 to 0.8), and living further from the treatment center (OR, 0.5; 95% CI, 0.4 to 0.5). SPPC was associated with a five-fold decrease in odds of intensive care unit admission (OR, 0.2; 95% CI, 0.1 to 0.4), whereas general palliative care had no impact. Similar associations were seen with all secondary indicators.
Conclusion: When available, SPPC, but not general palliative care, is associated with lower intensity care at the end of life for children with cancer. However, access remains uneven. These results provide the strongest evidence to date supporting the creation of SPPC teams.
BACKGROUND: Studies have demonstrated the strong association between increased end-of-life homecare nursing use and reduced acute care utilization. However, little research has described the utilization patterns of end-of-life homecare nursing and how this differs by region and community size.
METHODS: A retrospective population-based cohort study of cancer decedents from Ontario, British Columbia, and Nova Scotia was conducted between 2004 and 2009. Provinces linked administrative databases which provide data about homecare nursing use for the last 6 months of life for each cancer decedent. Among weekly users of homecare nursing in their last six months of life, we describe the proportion of patients receiving end-of-life homecare nursing by province and community size.
RESULTS: Our cohort included 83,746 cancer decedents across 3 provinces. Patients receiving end-of-life nursing among homecare nursing users increased from weeks -26 to -1 before death by: 78% to 93% in British Columbia, 40% to 81% in Ontario, and 52% to 91% in Nova Scotia. In all 3 provinces, the smallest community size had the lowest proportion of patients using end-of-life nursing compared to the second largest community size, which had the highest proportion.
CONCLUSIONS: Differences in end-of-life homecare nursing use are much larger between provinces than between community sizes.
Background: Population-based research to identify underserviced populations and the impact of palliative care (PC) is limited as the validity of such data to identify PC services is largely unknown.
Objective: To determine the validity of using such data to identify the involvement of specialized pediatric PC teams among children with cancer.
Design: Retrospective cohort.
Subjects: Ontario children with cancer who died between 2000 and 2012, received care through a pediatric institution with a specialized PC team and a clinical PC database.
Measurements: All patients in the clinical databases were linked to population-based health services administrative databases. Six algorithms were created to indicate the use of formal pediatric PC teams based on the record type (physician billings vs. inpatient records vs. both) and number of eligible codes required (=1 vs. =2). Each was validated against the pediatric PC clinical databases.
Results: The cohort comprised 572 children; 243 were in the clinical databases. Algorithms using only inpatient records had high specificity (80%–95%) but poor sensitivity (21%–56%). Including physician billings increased sensitivity but lowered specificity. The algorithm with overall best performance required =2 physician billing or inpatient diagnosis codes indicating PC [sensitivity 0.79 (95% CI 0.73–0.84), specificity 0.58 (95% CI 0.53–0.64)].
Conclusions: Health administrative data identifies involvement of specialized pediatric PC teams with good sensitivity but low specificity. Studies using such data alone to compare patients receiving and not receiving specialized pediatric PC are at significant risk of misclassification and potential bias. Population-based PC databases should be established to conduct rigorous population-based PC research..