SARS-CoV-2 Testing for Asymptomatic Patients with Cancer Prior and during Treatment: A Single Centre Experience

Patients with cancer are more vulnerable to severe COVID-19. As a result, routine SARS-CoV-2 testing of asymptomatic patients with cancer is recommended prior to treatment. However, there is limited evidence of its clinical usefulness. The objective of this study is to evaluate the value of routine testing of asymptomatic patients with cancer. Asymptomatic patients with cancer attending Odette Cancer Centre (Toronto, ON, Canada) were tested for SARS-CoV-2 prior to and during treatment cycles. Results were compared to positivity rates of SARS-CoV-2 locally and provincially. All 890 asymptomatic patients tested negative. Positivity rates in the province were 1.5%, in hospital were 1.0%, and among OCC's symptomatic cancer patients were 0% over the study period. Given our findings and the low SARS-CoV-2 community positivity rates, we recommend a dynamic testing model of asymptomatic patients that triggers testing during increasing community positivity rates of SARS-CoV-2.

Laboratory Workup of Lymphoma in Adults

OBJECTIVES

The diagnostic workup of lymphoma continues to evolve rapidly as experience and discovery lead to the addition of new clinicopathologic entities and techniques to differentiate them. The optimal clinically effective, efficient, and cost-effective approach to diagnosis that is safe for patients can be elusive, in both community-based and academic practice. Studies suggest that there is variation in practice in both settings.

THE AIM OF THIS REVIEW IS TO

develop an evidence-based guideline for the preanalytic phase of testing, focusing on specimen requirements for the diagnostic evaluation of lymphoma.

METHODS

The American Society for Clinical Pathology, the College of American Pathologists, and the American Society of Hematology convened a panel of experts in the laboratory workup of lymphoma to develop evidence-based recommendations. The panel conducted a systematic review of the literature to address key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, recommendations were derived based on the available evidence, the strength of that evidence, and key judgments as defined in the GRADE Evidence to Decision framework.

RESULTS

Thirteen guideline statements were established to optimize specimen selection, ancillary diagnostic testing, and appropriate follow-up for safe and accurate diagnosis of indolent and aggressive lymphoma.

CONCLUSIONS

Primary diagnosis and classification of lymphoma can be achieved with a variety of specimens. Application of the recommendations can guide decisions about specimen suitability, diagnostic capabilities, and correct utilization of ancillary testing. Disease prevalence in patient populations, availability of ancillary testing, and diagnostic goals should be incorporated into algorithms tailored to each practice environment.

Predictors of intensive care unit admission in patients with hematologic malignancy

Limited data exist on predictors of intensive care unit (ICU) admission in patients with hematologic malignancy. The objective of this study was to identify predictors of ICU admission in hospitalized patients with hematologic malignancies. A retrospective cohort study was conducted on 820 consecutive admissions of patients with a malignant hematology diagnosis at our institution between March 2009 and December 2015. Backward stepwise selection procedure was conducted for multivariable logistic regression analyses. 820 patients were included, of whom 179 (22%) were admitted to the ICU. Types of hematologic cancers included 71% (N = 578) lymphoid cancer, 18% (N = 151) myeloid cancer, and 10% (N = 80) plasma cell neoplasms. 14% (N = 111) of patients had acute leukemia. Six predictors of admission to ICU were found in multivariable analysis, including disease-related (acute leukemia, curative intent chemotherapy), laboratory-related (platelet count < 50 × 109/L, albumin below normal, LDH above normal at time of admission), and physician-related factors (having advanced directives discussion) (p < 0.0001). A significant proportion of patients with hematologic malignancies admitted to hospital are admitted to ICU. Utilizing the identified predictors of ICU admission may help guide timely informed goals of care discussions with patients before clinical deterioration occurs.

Reimbursement recommendations for cancer drugs supported by phase II evidence in Canada

Background

Phase ii data are increasingly being used as primary evidence for public reimbursement for oncologic drugs. We compared the frequency of reimbursement recommendations for phase ii and phase iii submissions and assessed for variables associated with a positive or conditional recommendation.

Methods

We identified submissions made to the pan-Canadian Oncology Drug Review's Expert Review Committee (perc), of the Canadian Agency for Drugs and Technologies in Health, July 2011 to July 2019, that were supported only by phase ii data. We identified variables within the perc's deliberative framework, including clinical and economic factors, associated with the final reimbursement recommendation. We conducted a multivariable analysis with logistic regression for these variables: feasibility of phase iii study, hematologic indication, and unmet need.

Results

We identified 139 submissions with a perc final recommendation. In 27 instances (19%), the submission had only phase ii evidence, and a positive recommendation was issued for 63% of them (the positive recommendation rate was 82% for submissions with phase iii evidence). Clinical benefit (p < 0.001), unmet need (p = 0.047), and patient alignment (p = 0.015) were associated with a positive recommendation. If a future phase iii study was deemed feasible for submissions with only phase ii evidence, then in univariable (p = 0.040) and multivariable analysis (p = 0.024), the perc was less likely to recommend reimbursement (odds ratio: 0.132).

Conclusions

Although more than half the oncologic submissions with phase ii data were recommended for public reimbursement, compared with submissions having phase iii data, they were less likely to be recommended. A positive or conditional recommendation was more likely if clinical benefit and alignment with patient values was demonstrated. The perc was less likely to recommend reimbursement for submissions with phase ii evidence if a phase iii trial was deemed possible.

Real-World Costs of Azacitidine Treatment in Patients With Higher-Risk Myelodysplastic Syndromes/Low Blast-Count Acute Myeloid Leukemia

PURPOSE

Azacitidine (AZA) is a standard of care for higher-risk myelodysplastic syndrome (MDS)/low blast-count acute myeloid leukemia (AML). Despite this, there is a paucity of data on the real-world health care resource utilization costs of AZA in this population.

METHODS

We linked the Ontario AZA MDS registry-higher-risk MDS/low blast-count AML-to population-based health system administrative databases. Patients were observed for 24 months after first AZA and censored at the earliest of 90 days after last AZA, date of death, time of AML induction/stem-cell transplantation, or March 31, 2016. Costs (2015 Canadian dollars) were expressed as standardized mean and median 28-day costs. Univariable quantile regression was used to explore the association of baseline patient and disease characteristics and median cost. Multivariable quantile regression was used to explore predictors of median costs.

RESULTS

Among 877 patients in the registry, mean standardized 28-day cost per patient was $17,638 (median, $15,272; interquartile range [IQR], $11,869-$19,580) and $13,450 (median, $11,043; IQR, $7,981-$14,882) excluding the cost of AZA. Major nondrug drivers of cost were cancer clinic visits and inpatient care (mean standardized 28-day cost, $4,631; median, $1,558; IQR, $238-$4,961). Transfusion dependence at AZA initiation (P = .001) and greater comorbid disease burden (P = .009) were independently associated with increased cost.

CONCLUSION

Our cohort of patients with uniformly higher-risk MDS/low blast-count AML treated with AZA demonstrates substantial costs of care above and beyond the cost of AZA alone. These results provide insight into the costs of AZA in the real world with implications for resource allocation.

Are Surrogate Endpoints Unbiased Metrics in Clinical Benefit Scores of the ASCO Value Framework?

BACKGROUND

Clinical benefit scores (CBS) are key elements of the ASCO Value Framework (ASCO-VF) and are weighted based on a hierarchy of efficacy endpoints: hazard ratio for death (HR OS), median overall survival (mOS), HR for disease progression (HR PFS), median progression-free survival (mPFS), and response rate (RR). When HR OS is unavailable, the other endpoints serve as "surrogates" to calculate CBS. CBS are computed from PFS or RR in 39.6% of randomized controlled trials. This study examined whether surrogate-derived CBS offer unbiased scoring compared with HR OS-derived CBS.

METHODS

Using the ASCO-VF, CBS for advanced disease settings were computed for randomized controlled trials of oncology drug approvals by the FDA, European Medicines Agency, and Health Canada in January 2006 through December 2017. Mean differences of surrogate-derived CBS minus HR OS-derived CBS assessed the tendency of surrogate-derived CBS to overestimate or underestimate clinical benefit. Spearman's correlation evaluated the association between surrogate- and HR OS-derived CBS. Mean absolute error assessed the average difference between surrogate-derived CBS relative to HR OS-derived CBS.

RESULTS

CBS derived from mOS, HR PFS, mPFS, and RR overestimated HR OS-derived CBS in 58%, 68%, 77%, and 55% of pairs and overall by an average of 5.62 (n=90), 6.86 (n=110), 29.81 (n=101), and 3.58 (n=108), respectively. Correlation coefficients were 0.80 (95% CI, 0.70-0.86), 0.38 (0.20-0.53), 0.20 (0.00-0.38), and 0.01 (-0.18 to 0.19) for mOS-, HR PFS-, mPFS-, and RR-derived CBS, respectively, and mean absolute errors were 11.32, 12.34, 40.40, and 18.63, respectively.

CONCLUSIONS

Based on the ASCO-VF algorithm, HR PFS-, mPFS-, and RR-derived CBS are suboptimal surrogates, because they were shown to be biased and poorly correlated to HR OS-derived CBS. Despite lower weighting than OS in the ASCO-VF algorithm, PFS still overestimated CBS. Simple rescaling of surrogate endpoints may not improve their validity within the ASCO-VF given their poor correlations with HR OS-derived CBS.

Reimbursement recommendations for cancer drugs supported by phase II evidence in Canada

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Background: Historically, pharmaceutical companies submitted phase III evidence for consideration of public reimbursement; however, phase II data is being more commonly used as primary evidence. Whether submissions with phase II data lead to similar rates of positive reimbursement recommendations as phase III data has not been comprehensively investigated. We compared frequency of reimbursement recommendations between phase II and phase III submissions for oncologic drugs and assessed for factors associated with a positive or conditional recommendation. Methods: We identified all submissions with phase II data from the CADTH pCODR’s expert review committee (pERC) recommendations from July 2011 to July 2019. We identified fourteen binary variables relating to clinical benefit, patient-based values, economic impact, and adoption feasibility. We used Fisher’s exact test to characterize associations between all variables and the final recommendation. We conducted multivariable analysis with logistic regression for three variables: feasibility of phase III study, hematologic indication, and unmet need. Results: We identified 139 submissions with a pERC final recommendation. Twenty-seven (19%) submissions were supported by phase II evidence, with 63% having a positive recommendation in comparison to 82% among submissions with phase III evidence. Clinical benefit (p < 0.001), gap in current treatment standards (p = 0.047), and patient alignment (p = 0.015) were associated with a positive recommendation, whereas the future feasibility of conducting a phase III study was associated with a negative recommendation (p = 0.040). No significant association was found between the recommendation and factors related to cost effectiveness or adoption feasibility. In multivariable analysis, only feasibility of a phase III study was significantly associated with a negative recommendation (p = 0.024, OR = 0.132). Conclusions: Oncologic submissions with phase II data were less likely to be recommended for public reimbursement than phase III studies. Positive or conditional recommendation was more likely if they demonstrated clinical benefit and aligned with patient values. pERC was less likely to recommend a submission with phase II if a phase III trial was either possible or already initiated.

Examining the relationship between the clinical benefit of oncology drug indications and the time from pan-Canadian Oncology Drug Review (pCODR) recommendation to public reimbursement

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Background: We examined if publicly reimbursed oncology drug indications with evidence of high clinical benefit, as measured by the American Society of Clinical Oncology Value Framework v2 (ASCO-VF), and European Society for Medical Oncology Magnitude of Clinical Benefit Scale v1.1 (ESMO-MCBS), received reimbursement status faster than those with lower clinical benefit from the time of pCODR recommendation. Methods: Oncology drug indications that received pCODR recommendations between Jan 2012 and July 2018 were identified. Indications that did not receive provincial reimbursement, without notice of compliance, or received a negative pCODR recommendation were excluded. The relationship between clinical benefit, as measured by ASCO-VF and ESMO-MCBS, and the time to reimbursement was evaluated using Spearman correlation coefficient, univariable, and multivariable linear regression analyses. Results: Overall, 84 indications met inclusion criteria yielding 80 ASCO-VF and 66 ESMO-MCBS scores. The mean ASCO-VF and ESMO-MCBS scores were 38.8 (SD = 23.8) and 3.0 (SD = 1.1) respectively. Higher ASCO-VF and ESMO-MCBS scores had low correlation with shorter time to provincial funding, (rho = -0.15, 95%CI -0.24, -0.06) and (rho = -0.25, 95%CI -0.34, -0.16) respectively. Univariable analyses showed that manufacturer reported incremental cost effectiveness ratio (ICER) values, year of pCODR recommendation, province and cancer type were associated with time to public reimbursement (all p < 0.0001). After adjusting for potential confounders in the respective multivariable analysis, ASCO-VF (p = 0.29) and ESMO-MCBS (p = 0.15) scores were not significantly associated with time to public reimbursement. Year of pCODR recommendation remained associated with time to public reimbursement (p < 0.001). Earlier years (2012-2014) had a shorter time to reimbursement (mean = 10.4 months) than later years (2015-2018) (mean = 14.5 months). Other factors that were associated with time to reimbursement in multivariable analysis were province (p < 0.001) and cancer type (p < 0.001). Conclusions: Currently, oncology drug indication with evidence of high clinical benefit do not appear to be funded faster than those with low clinical benefit. This suggests the need to prioritize cancer drug indications based on clinical benefit in order to allow for timely public reimbursement of cancer drugs with higher clinical benefit to patients.

Healthcare resource utilization in myeloproliferative neoplasms: a population-based study from Ontario, Canada

Health resource utilization (HRU) and associated factors of high cost are not well understood in myeloproliferative neoplasms (MPNs). In this population-based, retrospective matched-cohort study, we used administrative health databases of Ontario, Canada to measure treatment costs and HRU for patients with MPN from 2004 to 2016 and compared them to matched controls. In 7130 patients with MPN [essential thrombocythemia (ET) = 3481; polycythemia vera (PV) = 2618; myelofibrosis (MF) = 1031], the mean annualized treatment costs were $16,646 for ET (controls, $7070); $16,360 for PV (controls, $7293); and $25,863 for MF (controls, $7386). Out of the total costs, the largest expenditure was on acute hospital care (ET: 57%, PV: 57%, MF: 66%). Older age (≥65), male gender, patients not seen by a specialist, and greater comorbidity burden were independent predictors of higher costs (p < 0.05). In addition, history of venous thrombosis in patients with ET and PV was associated with significantly higher treatment costs (p < 0.05).

Quantifying the Survival Benefits of Oncology Drugs With a Focus on Immunotherapy Using Restricted Mean Survival Time

Background: Restricted mean survival time (RMST) overcomes limitations of current measures of survival benefits because it directly captures information of the entire area under Kaplan-Meier survival curves. Using RMST difference (absolute survival benefit) and RMST ratio (relative survival benefit), we quantified the magnitude of survival benefits of recent oncology drugs and compared immunotherapies with nonimmunotherapies. Methods: Kaplan-Meier curves were extracted from phase II/III randomized controlled trials used by the FDA for oncology drug approvals from January 2011 through November 2017 with overall survival (OS) or progression-free survival (PFS) as primary endpoints. RMST differences, ratios, and their 95% confidence intervals were meta-analyzed to estimate absolute and relative survival benefits of contemporary oncology drugs and to compare immunotherapies with nonimmunotherapies. Meta-regression was conducted to adjust for potential confounders. Results: Ninety-four trials with a total of 51,639 patients were included. Overall absolute survival benefits (RMST differences) were 1.55 months for OS (95% CI, 1.32–1.77) and 2.99 months for PFS (95% CI, 2.65–3.33). Overall relative survival benefits (RMST ratios) were 1.11 for OS (95% CI, 1.09–1.13) and 1.42 for PFS (95% CI, 1.36–1.48). Immunotherapy absolute PFS benefit was less than that of nonimmunotherapy (1.56 vs 3.23 months), whereas immunotherapy absolute OS benefit was larger than that of nonimmunotherapy by 0.59 months (2.02 vs 1.43 months). Adjusted OS RMST difference was 0.91 months greater for immunotherapy than for nonimmunotherapy after adjusting for confounders. Conclusions: Absolute survival benefits of recent oncology drugs are modest. Survival benefits of immunotherapies are not dramatically superior to those of nonimmunotherapies. Routine reporting and use of RMST may help patients, physicians, and payers make more informed and responsible decisions regarding the care of patients with cancer.

Health system costs for cancer medications and radiation treatment in Ontario for the 4 most common cancers: a retrospective cohort study

BACKGROUND

Previous costing and resource estimates for cancer have not been complete owing to lack of comprehensive data on cancer-related medication and radiation treatment. Our objective was to calculate the mean overall costs per patient of cancer-related medications and radiation, as well as by disease subtype and stage, in the first year after diagnosis for the 4 most prevalent cancers in Ontario.

METHODS

We conducted a retrospective cohort study using provincial health administrative databases to identify population health system resources and costs for all patients diagnosed with breast, colorectal, lung or prostate cancer between Jan. 1, 2010, and Dec. 31, 2015 in Ontario. The primary outcome measure was the overall average cost per patient in the 365 days after diagnosis for cancer-related medications and radiation treatment, calculated with the use of 2 novel costing algorithms. We determined the cost by disease, disease subtype and stage as secondary outcomes.

RESULTS

There were 168 316 Ontarians diagnosed with cancer during the study period, 50 141 with breast cancer, 38 108 with colorectal cancer, 34 809 with lung cancer and 45 258 with prostate cancer. The mean per-patient cost for cancer-related medications was $8167 (95% confidence interval [CI] $8023-$8311), $6568 (95% CI $6446-$6691), $2900 (95% CI $2816-$2984) and $1211 (95% CI $1175-$1247) for breast, colorectal, lung and prostate cancer, respectively. The corresponding mean radiation treatment costs were $18 529 (95% CI $18 415-$18 643), $15 177 (95% CI $14 899-$15 456), $10 818 (95% CI $10 669-$10 966) and $16 887 (95% CI $16 648-$17 125). In general, stage III and IV cancers were the most expensive stages for both medications and radiation across all 4 disease sites.

INTERPRETATION

Our work updates previous costing estimates to help understand costs and resources critical to health care system planning in a single-payer system. More refined costing estimates are useful as inputs to allow for more robust health economic modelling and health care system planning.

The generation of two specific cancer costing algorithms using Ontario administrative databases

Cancer treatment and management have become increasingly economically burdensome. Consequently, to help with planning health service delivery, it is vital to understand the associated costs. Administrative databases can be used to help understand and generate real-world system-level costs. Using databases to generate costs can take one of two approaches: top-down or bottom-up. Top-down approaches disaggregate the total health care spending from a global health care budget by sector and provider. A bottom-up approach begins with individual-level health care use and its costs, which are then aggregated.

Prevalence of Inappropriateness of Parenteral Vitamin B12 Administration in Ontario, Canada

This population-based cohort study evaluated how often intramuscular vitamin B₁₂ was prescribed to Canadian elderly patients when not medically necessary.

The impact of pricing strategy on the costs of oral anti-cancer drugs

Background

The soaring costs of anti‐cancer drugs pose a threat to the sustainability of cancer care. The pricing strategy chosen by manufacturers can impact the costs of oral anti‐cancer drugs during dose modifications, but this issue remains under‐recognized in the literature. In general, with the flat pricing strategy, there is a single fixed price for each tablet regardless of dosage strength, whereas with linear pricing, the price of each tablet increases with its dose. We hypothesize that flat pricing will have increased drug costs compared to linear pricing during dose reductions since the cost remains fixed despite decreased dose requirements. This practice may have significant financial implications considering the high costs, extensive utilization, and frequent occurence of dose reductions with anti‐cancer drugs. 

Methods

Oral anti‐cancer drugs reviewed by the pan‐Canadian Oncology Drug Review program between 2011 and 2018 were identified. The cost per mg and cost per 28‐day cycle were calculated for dose levels −2 to +2. The percent change in cost per mg and cost per cycle during dose modifications from the standard dose were calculated. We conducted Mann‐Whitney U and Fisher‐exact tests to compare the association between drug costs during dose reductions and pricing strategy.

Results

In this study, 30 oral anti‐cancer drugs for use in 41 indications were analyzed; 44% of drugs used linear pricing and 56% used flat pricing. Dose reductions increased the mean cost per mg for drugs with linear pricing by 14.7% (range: 0%‐50%) at dose level −1 and 17.2% (range: 0%‐50%) at dose level −2 and flat pricing by 60.8% (range: 19%‐100%) at dose level −1 and 99.1% (range: 0%‐300%) at dose level −2. The cost per mg was significantly increased in drugs using flat pricing compared to linear pricing when dose reduction to either level ‐1 (P = 0.010) or level ‐2 (P = 0.006) occurred. The mean cost per cycle was decreased for drugs using linear pricing by 20.9% (range: −40% to 0%) at dose level −1 and 48.7% (range: −60% to −25%) at dose level −2 and flat pricing by 0.8% (range −6% to 0%) at dose level −1 and 11.0% (range: −50% to 100%) at dose level −2. The cost per cycle was significantly decreased in drugs with linear pricing compared to flat pricing when the standard dose is reduced to either dose level ‐1 (P = 0.005) or dose level ‐2 (P = 0.026).

Conclusions

Overall, flat pricing had significantly greater costs compared to linear pricing during dose reductions of anti‐cancer drugs. While there is a general expectation that the cost of drugs should decrease with dose reduction, drugs with flat pricing were generally found to have increased cost per mg and no change in the cost per cycle. The resultant increased spending on drug acquisition (despite purchasing lower doses) lead to financial wastage, which has significant implications on cost‐effectiveness considerations and budgets. Future economic evaluations should take into consideration the hidden costs associated with dose reductions of flat priced drugs.

Do the ASCO Value Framework and the ESMO Magnitude of Clinical Benefit Scale measure Absolute or Relative clinical benefit?

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Background: It is unclear whether the clinical benefit scores from the ASCO or ESMO valuation frameworks were intended to measure absolute or relative survival benefit. To empirically examine the measurement characteristics of these frameworks, we compared their survival efficacy components (ASCO-CBS and ESMO-PMCBGs) with established measures of absolute (median survival difference and restricted mean survival time (RMST) difference) and relative (HRs) survival benefit. Methods: FDA’s Hematology/Oncology Approvals and Safety Notifications pages were reviewed to identify phase III RCTs cited in oncology drug approvals between January 2006 and December 2017. In our primary analysis, ASCO-CBS and ESMO-PMCBGs were calculated for the included trials using the framework defined endpoint (Fr). Sensitivity analyses were conducted by calculating the scores using 1) Fr + tail-of-curve bonus points (ASCO) or long-term plateau adjustments (ESMO) (defined as Fr + TOC), 2) overall survival (OS) data only, and 3) progression-free survival (PFS) data only. For both primary and sensitivity analyses, Spearman correlation coefficients were calculated to examine the relationships between 1) ASCO-CBS/ESMO-PMCBGs and RMST difference, 2) ASCO-CBS/ESMO-PMCBGs and median survival difference, and 3) ASCO-CBS/ESMO-PMCBGs and HRs. Results: 107 RCTs were included. Compared to measures of absolute survival, ESMO-PMCBG showed low-moderate correlations with RMST difference and moderate-high correlations with median survival difference (Rho = 0.44 and 0.64, respectively). ASCO-CBS showed low-moderate correlations with both measures of absolute benefit (Rho = 0.43 and 0.44 for RMST difference and median survival, respectively). Compared to a relative measure of survival (HRs), ESMO-PMCBG revealed a moderate correlation (Rho = 0.47) while ASCO-CBS showed a higher correlation (Rho = 0.76). Conclusions: Both frameworks do not perform solely as absolute measures of survival benefit. We recommend that the frameworks consider incorporating a direct measure of absolute clinical benefit, such as RMST difference, into the survival efficacy components of their algorithms.

Days at Home in the Last 6 Months of Life: A Patient-Determined Quality Indicator for Cancer Care

PURPOSE:

Quality end-of-life care (EoLC) is a key aspect of oncology. Days at home in the last 6 months of life represents a novel, patient-driven quality indicator of EoLC. We measured days at home in a large population of patients with cancer in Ontario, Canada. Trends over time and predictors of more or less time at home were also determined.

METHODS:

We conducted a population-based retrospective study using health administrative data linked by unique, encoded identifiers and analyzed at the ICES. Quantile regression was used to determine significant predictors of more or less time at home.

RESULTS:

Of 72,987 patients who died of cancer in Ontario, Canada and met our inclusion criteria, the median number of days spent at home in the last 6 months of life was 164 (interquartile range [IQR], 144 to 175 days) of a possible 180 days. Patients with hematologic cancers spent significantly fewer days at home (156; IQR, 134 to 170 days). The strongest predictors of more time at home were male sex (+2.87 days relative to female sex; CI, 2.43 to 3.31 days) and receipt of palliative care before the last 6 months of life (+2.38 days; CI, 1.95 to 2.08 days). Additional predictors included income, age, cancer type, comorbidity burden, and health region. The majority of patients (69.7%) did not die at home.

CONCLUSION:

Days at home in the last 6 months of life, obtained from administrative data, can be used as a measure of quality EoLC. Predictors of days at home may prove valuable targets for future policy intervention.