Kaplan-Meier curve is shown for postoperative time to the first year with more than 14 institution days.
Results reflect the hazard ratios (HRs) of low time at home (multivariable Cox proportional hazards regression time to >14 institution days). Q indicates quintile.
eTable 1. Data Sources
eTable 2. Strategy for Cohort Creation
eTable 3. Definition of Covariates
eFigure 1. Flow Chart of Cohort Creation
eFigure 2. Distribution of Numbers of Days Alive at Home per Patient Following Cancer Surgery, for Each Post-Operative Year
eFigure 3. Probability of High Time at Home for Older Adults Following Cancer Surgery, for Each Post-Operative Year 1 (A), Year 2 (B), Year 3 (C), Year 4 (D), and Year 5 (E) (Kaplan-Meier Curve of Time to >14 Institution-Days Over the Year)
eFigure 4. Probability of High Time at Home for Older Adults Following Cancer Surgery, for Post-Operative Year 1 (A), Year 2 (B), Year 3 (C), Year 4 (D), and Year 5 (E), by Cancer Type (Kaplan-Meier Curve of Time to >14 Institution-Days Over the Year)
eFigure 5. Sensitivity Analyses—Probability of High Time at Home for Older Adults Following Cancer Surgery, From the Day of Post-Operative Discharge (A), Using High Time at Home <21 Institution-Days (B), and Using High Time at Home <28 Institution-Days Year 3 (C) (Kaplan-Meier Curve of Time to Low Time at Home)
Customize your JAMA Network experience by selecting one or more topics from the list below.
Identify all potential conflicts of interest that might be relevant to your comment.
Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.
Err on the side of full disclosure.
If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.
Not all submitted comments are published. Please see our commenting policy for details.
Chesney TR, Haas B, Coburn NG, et al. Patient-Centered Time-at-Home Outcomes in Older Adults After Surgical Cancer Treatment. JAMA Surg. 2020;155(11):e203754. doi:10.1001/jamasurg.2020.3754
How much time do older adults spend at home after receiving surgical treatment for cancer?
In this population-based cohort study of 82 037 patients 70 years or older, most patients spent more than 98% of their time at home during the 5 years after resection for cancer. The probability of having high time at home (defined as ≤14 institution days over 1 year) was 70.3% at postoperative year 1 and 53.2% at postoperative year 5.
This study suggests that time at home data could be used for preoperative counseling regarding surgical cancer treatment to support decision-making, enhance preparedness for the surgical procedure, and set expectations for long-term outcomes.
Functional outcomes are central to cancer care decision-making by older adults.
To assess the long-term functional outcomes of older adults after a resection for cancer using time at home as the measure.
Design, Setting, and Participants
This population-based cohort study was conducted in Ontario, Canada, using the administrative databases stored at ICES (formerly the Institute for Clinical Evaluative Sciences). The analysis included adults 70 years or older with a new diagnosis of cancer between January 1, 2007, and December 31, 2017, who underwent a resection 90 days to 180 days after the diagnosis. Patients were followed up until and censored at the date of death, date of last contact, or December 31, 2018.
Main Outcomes and Measures
The main outcome was time at home, dichotomized as high time at home (defined as ≤14 institution days annually) and low time at home (defined as >14 institution days) during the 5 years after surgical cancer treatment. Time-to-event analyses with Kaplan-Meier methods and multivariable Cox proportional hazards regression models were used.
A total of 82 037 patients were included, with a median (interquartile range) follow-up of 46 (23-80) months. Of these patients, 52 119 were women (63.5%) and the mean (SD) age was 77.5 (5.7) years. The median (interquartile range) number of days at home per days alive per patient was high, at 0.98 (0.94-0.99) in postoperative year 1, 0.99 (0.97-1.00) in year 2, 0.99 (0.96-1.00) in year 3, 0.99 (0.96-1.00) in year 4, and 0.99 (0.96-1.00) in year 5. The probability of high time at home was 70.3% (95% CI, 70.0%-70.6%) at postoperative year 1 and 53.2% (95% CI, 52.8%-53.5%) at postoperative year 5. Advancing age (≥85 years: hazard ratio [HR], 2.11; 95% CI, 2.04-2.18); preoperative frailty (HR, 1.74; 95% CI, 1.68-1.80); high material deprivation (5th quintile: HR, 1.25; 95% CI, 1.20-1.29); rural residency (HR, 1.14; 95% CI, 1.10-1.18); high-intensity surgical procedure (HR, 2.04; 95% CI, 1.84-2.25); and gastrointestinal (HR, 1.23; 95% CI, 1.18-1.27), gynecologic (HR, 1.31; 95% CI, 1.18-1.45), and oropharyngeal (HR, 1.05; 95% CI, 0.95-1.16) cancers were associated with low time at home. Inpatient acute care was responsible for 76.0% and long-term care was responsible for 2.0% of institution days in postoperative year 1. Inpatient days decreased to 31.0% by year 3, but days in long-term care increased over time.
Conclusions and Relevance
This study found that older adults predominantly experienced high time at home after resection for cancer, reflecting the overall favorable functional outcomes in this population. The oldest adults and those with preoperative frailty and material deprivation appeared to be the most vulnerable to low time at home, and efforts to optimize and manage expectations about surgical outcomes can be targeted for this population; this information is important for patient counseling regarding surgical cancer treatment and for preparation for postoperative recovery.
An estimated 70% of incident cancers will be diagnosed in patients aged 65 years or older by 2030.1,2 Because of advances in patient selection, perioperative care, and adjuvant therapies, older adults with cancer are experiencing prolonged survival of up to 47% at 5 years after diagnosis.3,4
Despite these advances, it has become evident that many older adults place a higher importance on outcomes such as long-term functional independence and quality of life than on short-term postoperative outcomes and even mortality.5-9 However, the literature focuses mostly on short-term complications and cancer survival.10-14 Thus, a knowledge gap exists regarding the anticipated recovery and the risk of functional dependency after surgical cancer treatment in older adults. These factors are critical to decision-making and preparedness for surgical cancer procedures, as outlined in geriatric surgical oncology guidelines.15,16
Time at home is an easily measured patient-centered outcome that comprehensively captures what is most important to older adults: being alive, returning home, and limiting institutional stays.17-19 Time at home can be used as a surrogate of functional outcomes for patients,18,20-26 and it has been used to assess treatment for atrial fibrillation, stroke, heart failure, and subarachnoidal hemorrhage.22-26 Similarly, after surgical treatment for cancer, time at home could serve as an indication of functional outcome, and it is more sensitive to patient and disease characteristics than to institutional-level characteristics such as academic status, surgical volume, or number of beds.17,21 However, it has rarely been examined after a surgical procedure.17,19,27
In this cohort study, we examined time at home for older adults in the 5 years after resection for cancer.
We conducted a population-based retrospective cohort study of adults 70 years or older with a new diagnosis of solid malignant neoplasm who underwent a resection. The study was approved by the Sunnybrook Health Sciences Centre Research Ethics Board, which waived informed consent because of the population-based nature of this study. We followed the Reporting of Studies Conducted Using Observational Routinely Collected Data (RECORD) statement.28
Two caregivers and family members of older adults who underwent major cancer surgical treatment were involved in developing the research question, defining outcome measures, and interpreting the results.
Prospectively maintained administrative databases stored at ICES (formerly the Institute for Clinical Evaluative Sciences) in Ontario, Canada, were linked using a unique identification key number for each patient. The Ontario population has universally accessible and publicly funded health care through the Ontario Health Insurance Plan.29
The Ontario Cancer Registry includes all patients with a cancer diagnosis in Ontario.30 The Registered Persons Database contains vital status and demographic data.31 Information regarding the receipt of health services is captured in multiple administrative data sets described in eTable 1 in the Supplement.
We identified all individuals 70 years or older who received a new diagnosis of oropharyngeal, breast, esophageal, gastrointestinal, colorectal, hepatobiliary, pancreatic, genitourinary, gynecologic, and bronchopulmonary cancer or melanoma between January 1, 2007, and December 31, 2017, using International Classification of Diseases for Oncology, Third Edition codes in the Ontario Cancer Registry (eTable 2 in the Supplement). We included patients who underwent resection 90 days to 180 days after cancer diagnosis (eTable 2 in the Supplement).
Patients were excluded if their date of death preceded their date of diagnosis, they had another cancer diagnosis within 5 years before the index cancer diagnosis, they had 2 or more cancer diagnoses recorded on the same index diagnosis date, or they had fewer than 180 days of follow-up. We also excluded individuals who were already in long-term care facilities (nursing homes) on the index date given that they did not have an opportunity to spend time at home.
The outcome of interest was time at home, dichotomized as low vs high time at home. Days alive and days away from home (institution days) were identified using previously reported strategies, looking at the total number of institution days (individual or consecutive) over a prespecified period.17,19 Any day spent in inpatient acute care, emergency department, inpatient mental health care, inpatient rehabilitation, or a long-term care facility was counted as an institution day.32 We described the number of days alive and time at home per patient per year as a continuous variable over 5 years after resection by subtracting the number of institution days from the total number of days alive each year. Consistent with previous reports, the data were strongly skewed.17,19,24 Therefore, informed by data distribution, patient advisers, and the existing literature, we created a dichotomous outcome by determining a clinically meaningful number of institution days at more than 14 institution days per patient per year.17,33 A reduction in time at home of 14 days or more in 1 year was previously defined as clinically significant and associated with functional decline.33 High time at home was defined as 14 or fewer institution days, and low time at home was defined as more than 14 institution days.
We used time-to-event analysis with the event being low time at home and the time of event being the date of the 15th institution day. Patients were followed up until and censored at the date of death, date of last contact, or December 31, 2018. This timeline allowed an opportunity for follow-up of a minimum of 12 months for each patient.
The type of institution was examined as a function of the institution in which patients spent institution days. The number of days spent in each institution type was divided by the total number of institution days per patient in each year to compute the proportion of institution days for each institution type.
Age and sex were obtained from the Registered Persons Database. Rural residency was determined using the rurality index of Ontario based on the postal code of the patients’ primary residence.34 Socioeconomic status was captured using the Material Deprivation Index, a composite index of the inability of individuals or households to afford consumption goods and activities that are typical in a society at a given point in time, categorized into quintiles.35,36 Frailty was captured with the Johns Hopkins Adjusted Clinical Groups system frailty marker.37-40 The frailty marker identifies 12 clusters of frailty-defining diagnoses across cardinal domains in geriatric assessment and has been externally validated against the Vulnerable Elders Survey score.38,41,42 The comorbidity burden was measured using the Adjusted Clinical Groups system score, with a cutoff of 10, indicating a high burden.39,40 The surgical procedure intensity was divided into high or low intensity using a standardized classification.43 Covariates are detailed in eTable 3 in the Supplement.
Data were missing for rural residency in 632 patients (0.8%) and for material deprivation in 514 patients (0.6%). We used a complete-case analysis approach for the multivariable analysis.
Kaplan-Meier curves and estimates were computed from the date of the surgical procedure to the end of follow-up. Multivariable Cox proportional hazards regression models were constructed to identify factors associated with time at home. Relevant covariates were identified a priori as potential factors associated with time at home. Variables were selected on the basis of clinical relevance and existing literature: age (categorical: 75-79 years, 80-84 years, or ≥85 years), sex, rural residency, material deprivation quintile, preoperative frailty, cancer type, surgical procedure intensity, and diagnosis year (categorical: 2007-2011 or 2012-2017). Results were reported as hazard ratios (HRs) with 95% CIs. Time at home immediately after and later on after the surgical procedure may represent discrete clinical patterns; therefore, we constructed additional Kaplan-Meier curves and Cox proportional hazards regression models for each year after postoperative discharge. Patients who were alive at the beginning of the year were entered in each analysis. The beginning of each postoperative year was defined as time 0.
Statistical significance was set at 2-sided P ≤ .05. All analyses were conducted with SAS Enterprise Guide, version 7.1 (SAS Institute Inc.). Data analyses were performed from September 2019 to December 2019.
We conducted 2 sensitivity analyses. First, because short-term time at home was most affected by postoperative length of stay, we computed time at home from the date of discharge after resection.17,19 Second, we computed high time at home with alternative event definitions, with cutoffs for institution days of 21 and 28 days.
A total of 82 037 patients were included (eFigure 1 in the Supplement) with a median (interquartile range) follow-up of 46 (23-80) months. Of these patients, 52 119 were women (63.5%) and 29 918 were men (36.4%) with a mean (SD) age of 77.5 (5.7) years (Table 1). Perioperative mortality rate was 4.7% (n = 3831) at 90 days, and 5742 patients (7.0%) were discharged to long-term care after resection. Overall, 34 044 patients (41.5%) died during the follow-up period.
The distribution of number of days alive at home per patient for each postoperative year is presented in eFigure 2 in the Supplement. The median (interquartile range) number of days at home per days alive per patient was high and ranged from 0.98 (0.94-0.99) in postoperative year 1, 0.99 (0.97-1.00) in year 2, 0.99 (0.96-1.00) in year 3, 0.99 (0.96-1.00) in year 4, to 0.99 (0.96-1.00) in year 5. Across all 5 years after the surgical procedure, 40 919 (49.9%) to 51 623 patients (71.1%) had fewer than 7 institution days, and 55 181 (67.3%) to 55 362 patients (76.9%) had fewer than 14 institution days (Figure 1B).
The probability of spending high time at home was 70.3% (95% CI, 70.0%-70.6%) at postoperative year 1, 61.0% (95% CI, 60.7%-61.4%) at year 3, and 53.2% (95% CI, 52.8%-53.5%) at year 5 (Figure 1A and C). The preoperative characteristics associated with time at home are presented in Table 2. Female sex (HR, 0.89; 95% CI, 0.87-0.91) and more recent year of diagnosis (HR, 0.83; 95% CI, 0.81-0.85) were associated with superior probability of high time at home. Advancing age category (≥85 years: HR, 2.11; 95% CI, 2.04-2.18) and worsening levels of material deprivation (5th quintile: HR, 1.25; 95% CI, 1.20-1.29) were associated with inferior probability of high time at home. Patients with preoperative frailty (HR, 1.74; 95% CI, 1.68-1.80), rural vs urban residency (HR, 1.14; 95% CI, 1.10-1.18), and high- vs low-intensity surgical procedure (HR, 2.04; 95% CI, 1.84-2.25) also presented inferior probability of high time at home. Breast (HR, 0.82; 95% CI, 0.74-0.91), melanoma (HR, 0.88; 95% CI, 0.78-0.99), and genitourinary (HR, 0.71; 95% CI, 0.68-0.75) cancers were associated with superior probability of high time at home. Compared with bronchopulmonary cancers, gastrointestinal (HR, 1.23; 95% CI, 1.18-1.27), gynecologic (HR, 1.31; 95% CI, 1.18-1.45), and oropharyngeal (HR, 1.05; 95% CI, 0.95-1.16) cancers were associated with inferior probability of high time at home.
When examining time at home in each postoperative year conditional to survival in the previous year (eFigures 3 and 4 in the Supplement), the loss of time at home was highest in postoperative year 1, particularly in the first 60 days. In postoperative years 2 to 5, the probability of high time at home decreased, but by less than 10% overall (97.3% to 88.2% for year 2; 96.7% to 88.2% for year 3; 95.9% to 87.6% for year 4; 95.2% to 87.1% for year 5). Sensitivity analyses using the cutoff for high time at home of 21 and 28 days are presented in eFigure 5 in the Supplement.
Factors associated with time at home in each postoperative year are presented in Figure 2. Although similar factors were associated with high time at home in each postoperative year, the magnitude of the associations varied for each year. The probability of high time at home over 1 year became progressively lower from year 1 to year 5 for advancing age categories and preoperative frailty. The outcome of high-intensity surgical procedure decreased after postoperative year 1 and stabilized from year 2 to year 5. Overall, the HR of low time at home after high-intensity surgical procedure associated with female sex, material deprivation, and rural residence were unchanged over the years. Although gastrointestinal and gynecologic cancers had a lower probability of high time at home in postoperative year 1 compared with bronchopulmonary cancer, the association was not statistically significant in subsequent years.
The type of institutional services used changed over time after the surgical procedure (Figure 3). Although inpatient acute care days were responsible for 76.0% of institution days in postoperative year 1, this percentage decreased to 38.0% in year 2 and 31.0% in year 3. An opposite trajectory was observed for long-term care, with 2.0% in year 1 progressively increasing to 6.0% in year 5. During the study period, 5865 patients (7.1%) were admitted to long-term care facilities. For patients with low time at home, long-term care was responsible for most institution days starting in postoperative year 2, with an even split between inpatient acute care days (46.0%) and long-term care days (44.0%) days in year 5. For those with high time at home, the proportion of acute care inpatient days decreased after year 1, and emergency department days were responsible for 21.0% of institution days in year 5. For those patients, long-term care days represented less than 1% of institution days for the postoperative year.
This population-based cohort study reported data on time at home for older adults after first-time resection for cancer. Most older adults in the study experienced more than 98% of their days at home annually during the postoperative 5 years, highlighting the favorable functional outcomes of older adults selected for a surgical procedure. Most patients who survived after surgical treatment were likely to have high time at home (≤14 institution days) until postoperative year 4. Starting in postoperative year 5, 1 of 2 patients who survived until that period were likely to experience low time at home (>14 institution days).
Studies that assessed the quality of life or independence in older adults after the surgical procedure are few and limited to a follow-up of 6 months.44,45 Although measures and risk factors of morbidity, mortality, and discharge disposition to facilities other than the home have been reported, such short-term outcomes do not always matter most to older adults who value functional outcomes.10-14 In the postoperative period, specifically, loss of time at home is critical because it is associated with poor functional outcomes, including self-rated health, mobility impairment, depression, limited social activity, and difficulty with self-care.33 Three previous studies examined short-term time at home at 30 to 180 days after the surgical procedure.17,19,27 Advancing age was found to be associated with reduced time at home, but these analyses did not specifically focus on older adults.17,19,27 The current study is a unique addition to the literature, filling the knowledge gap in long-term functional outcomes in geriatric surgical oncologic care.16,46-48
In this study, most of the older adults selected for resection did well postoperatively, with a high proportion experiencing high time at home, which suggests little functional dependence over time. This information is important to communicate to physicians and patients for whom care may be de-escalated because of perceived higher risk. Lost time at home occurred mostly in the first postoperative year; after year 1, few patients who survived experienced substantial changes in the probability of high time at home.
Information about time at home can be used at 3 time points in clinical practice to enable patient-centered care. First, in patients deemed eligible for surgical treatment, expectations regarding time at home and associated risk of functional decline can be addressed during preoperative counseling and decision-making to ensure that the surgical cancer intervention is aligned with personal goals and values. Second, after the decision to undergo a surgical procedure is made, information about time at home could be communicated to the immediate and long-term postoperative support team to optimize time at home. Third, when managing prolonged and complicated postoperative admissions, this information can help with estimating the balance between management of complications and likelihood of survival, quality of life, and long-term dependence. Advancing age, preoperative frailty, and material deprivation are the patient characteristics that are associated with low time at home beyond the immediate postoperative period. Efforts to improve preoperative assessment, counseling, and support could be targeted for those who are more vulnerable.
Most of the institution days occurred in acute care inpatient settings, a finding that is consistent with the known high use of acute care services by older adults with cancer.49 For patients with low time at home, long-term care admission substantially contributed to institution days, which may represent appropriate support for functional decline. However, for the 7% of previously independent patients who transitioned to a long-term care facility after the surgical procedure, time at home quickly decreased to 0. Better community or home care support to limit those admissions are recommended for further investigation. For patients with high time at home, emergency department visits accounted for 1 of 5 institution days beyond postoperative year 1. Earlier identification of needs and transitions in care over time may help to improve time at home after the surgical cancer procedure. Strategies used in medical geriatrics care could be implemented in surgical cancer care, such as early involvement of geriatrics teams in the immediate and long-term postoperative period, establishing acute care geriatrics units for surgical patients, and telemonitoring programs for higher-risk patients.50-54 Validated individual prognostication tools that incorporate patient and disease characteristics to predict postoperative time at home could assist with personalized counseling, decision-making, preparation for the procedure, and postoperative monitoring.
This study has several strengths. The population-based design of this study allowed for a real-world assessment of older adults who underwent resection for cancer and who had data that were available across the entire continuum of care. We used high-quality data to create the cohort, define the exposure, and measure outcomes. Furthermore, input from patient and family advisers ensured the clinical relevance and usefulness of the results.
This study also has several limitations. The data used were not collected specifically for the purposes of the research question and were analyzed retrospectively. We lacked data regarding preoperative performance status, cognitive function, and burden of cancer such that the association of these variables with time at home could not be assessed. We examined long-term outcomes during the 5 years after the surgical treatment, which limited inferences of the association between resection and time at home later on after surgery. However, it was not the intention of this study to infer a direct causal relationship. We focused on a pragmatic description of the natural history of time at home after the surgical procedure. Because of the skewed nature of time at home data, dichotomizing the outcome was a sensible approach. However, we acknowledge that the cutoff point may have altered the results by changing the number of patients who experienced the event of interest. Sensitivity analyses varying time 0 and the institution day cutoff did not alter the observed trends or the conclusions. In addition, there may be multiple transitions in the quantity of time at home, residential location, and care settings for each patient over time, which are complex associations that could not be fully ascertained with health care administration data sets. Consistent with previous reports, the definition of time at home was based on time away from health care institutions.17,19 Although this definition cannot assess whether a patient is in their own home or staying with family or friends, it does measure the concept of being away from institutions and being within a home setting.
This study found that, over the 5 years after undergoing resection for cancer, older adults spent more than 98% of their time at home. The loss of time at home was highest during postoperative year 1 and stabilized thereafter. A small proportion of older adults had a high number of institution days after surgical cancer treatment, mostly owing to long-term care admissions. Older adults were most vulnerable to institution days as they advanced in age and had preoperative frailty; these patients should be targeted by efforts to optimize and manage expectations about outcomes. We believe the results of this study establish a baseline for using time at home as a patient-centered end point, for examining the value of surgical cancer therapies for older adults, and for developing interventions to minimize institution days.
Accepted for Publication: June 8, 2020.
Corresponding Author: Julie Hallet, MD, MSc, Division of General Surgery, Sunnybrook Health Sciences Centre–Odette Cancer Centre, 2075 Bayview Ave, T2-063, Toronto, ON M4N 3M5, Canada (email@example.com).
Published Online: October 7, 2020. doi:10.1001/jamasurg.2020.3754
Author Contributions: Dr Hallet and Mr Zhao had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Chesney, Coburn, Zuk, Hallet.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Chesney, Coburn, Hallet.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Haas, Coburn, Mahar, Zhao, Hsu, Hallet.
Obtained funding: Coburn, Zuk, Hallet.
Administrative, technical, or material support: Chesney, Zuk, Hallet.
Supervision: Coburn, Wright, Hallet.
Conflict of Interest Disclosures: Dr Coburn reported receiving salary support from Cancer Care Ontario (CCO) as the lead for patient-reported outcomes. Dr Wright reported receiving speaking honoraria from Novartis Oncology, a research grant from Roche, and salary support from CCO as the skin site lead and the quality and knowledge transfer lead for surgical oncology. Dr Hallet reported receiving personal fees from Ipsen Biopharmaceuticals Canada and Novartis Oncology outside the submitted work. No other disclosures were reported.
Funding/Support: This study was funded by operating grants 419955 from the Canadian Institutes of Health Research and P.HSR.156 from the Ontario Institute for Cancer Research. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Group information: Members of the RESTORE-Cancer (Recovery After Surgical Therapy for Older Adults Research–Cancer) Group, as well as collaborators to this work, are as follows: Laura Davis, MSc, Sunnybrook Research Institute, Toronto, Ontario, Canada; Ines Menjak, MD, MSc, Odette Cancer Centre–Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Dov Gandell, MD, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Douglas Manuel, MD, MSc, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Frances C Wright, MD, MEd, Odette Cancer Centre–Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Lesley Gotlib-Conn, PhD, Sunnybrook Research Institute, Toronto, Ontario, Canada; Grace Paladino, patient and family adviser; and Pietro Galluzzo, patient and family adviser.
Disclaimer: The opinions, views, results, and conclusions expressed herein are those of the authors and are independent from the funding sources. Parts of this material were based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI) and CCO. No endorsement by ICES, Ontario MOHLTC, CIHI, and CCO was intended or should be inferred.