Association of Patient-Reported Outcomes With Subsequent Nonfatal Self-injury After a New Cancer Diagnosis

Julie Hallet; Rinku Sutradhar; Elie Isenberg-Grzeda; Christopher W Noel; Alyson L Mahar; Simone N Vigod; James Bolton; Julie Deleemans; Wing C Chan; Natalie G Coburn; Antoine Eskander

doi:10.1001/jamaoncol.2022.0203

. 2022 Mar 31;8(5):e220203. doi: 10.1001/jamaoncol.2022.0203

Association of Patient-Reported Outcomes With Subsequent Nonfatal Self-injury After a New Cancer Diagnosis

Julie Hallet ^1,^2,^3,^4,^5,^✉, Rinku Sutradhar ^3,⁵, Elie Isenberg-Grzeda ^6,⁷, Christopher W Noel ^3,^5,⁸, Alyson L Mahar ^3,^5,⁹, Simone N Vigod ^6,¹⁰, James Bolton ¹¹, Julie Deleemans ¹², Wing C Chan ⁵, Natalie G Coburn ^1,^2,^3,^4,⁵, Antoine Eskander ^2,^3,^4,^5,⁸, for the Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group

¹Department of Surgery, University of Toronto, Toronto, Ontario, Canada

²Division of Surgical Oncology, Odette Cancer Centre–Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

³Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada

⁴Clinical Evaluative Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada

⁵ICES, Toronto, Ontario, Canada

⁶Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

⁷Psychosocial Oncology, Odette Cancer Centre–Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

⁸Department of Otolaryngology–Head & Neck Surgery, University of Toronto, Toronto, Canada

⁹Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada

¹⁰Department of Psychiatry, Women’s College Hospital and Research Institute, Toronto, Ontario, Canada

¹¹Department of Psychiatry, University of Manitoba, Winnipeg, Manitoba, Canada

¹²Division of Psychosocial Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Group Information: The members of the Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group appear in Supplement 2.

Accepted for Publication: January 14, 2022.

Published Online: March 31, 2022. doi:10.1001/jamaoncol.2022.0203

^✉

Corresponding Author: Julie Hallet, MD, MSc, Sunnybrook Health Sciences Centre, 2075, Bayview Ave, T2-102, Toronto, Ontario, Canada, M4N 3M5 (julie.hallet@sunnybrook.ca).

Author Contributions: Drs Hallet and Eskander 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: Hallet, Sutradhar, Isenberg-Grzeda, Noel, Mahar, Vigod, Bolton, Deleemans, Eskander.

Acquisition, analysis, or interpretation of data: Hallet, Sutradhar, Isenberg-Grzeda, Mahar, Vigod, Chan, Coburn, Eskander.

Drafting of the manuscript: Hallet, Sutradhar, Noel, Bolton, Eskander.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Hallet, Sutradhar, Noel, Chan, Coburn.

Obtained funding: Hallet, Sutradhar, Eskander.

Administrative, technical, or material support: Hallet, Isenberg-Grzeda, Eskander.

Supervision: Hallet, Sutradhar, Coburn, Eskander.

Other—Patient partner perspective: Deleemans.

Conflict of Interest Disclosures: Dr Hallet reported receiving personal fees (speaking honoraria) from Ipsen Biopharmaceuticals Canada and AAA outside the submitted work. Dr Isenberg-Grzeda reported receiving personal fees from Celgene USA outside the submitted work. Dr Vigod reported receiving royalties for authorship from UpToDate Inc outside the submitted work. Dr Coburn reported receiving salary support as the Clinical Lead in Patient Reported Outcomes and Symptom Management from Ontario Health Cancer Care Ontario during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was funded by the Hanna Research Award from the division of Surgical Oncology at the Odette Cancer Centre–Sunnybrook Health Sciences Centre and by a Sunnybrook Health Sciences Centre Alternate Funding Plan Innovation grant. This study was also 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 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: The Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group collaborators are listed in Supplement 2.

Disclaimer: The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by the Canadian Institute of Health Information. However, the analyses, conclusions, opinions, and statements expressed herein are those of the authors, and not necessarily those of the Canadian Institute of Health Information. Parts of this material are based on data and information provided by Cancer Care Ontario (CCO). The opinions, results, views, and conclusions reported in this article are those of the authors and do not necessarily reflect those of CCO. No endorsement by CCO is intended or should be inferred.

Meeting Presentation: Part of this work was presented at the American Society of Clinical Oncology Annual Meeting; June 4-8, 2021; virtual.

^✉

Corresponding author.

PMCID: PMC8972147 PMID: 35357419

This case-control study examines the associations between patient-reported outcome measures and subsequent nonfatal self-injury in patients with cancer.

Key Points

Question

Are patient-reported outcomes (Edmonton Symptom Assessment System) associated with subsequent nonfatal self-injury (NFSI) in patients diagnosed with cancer?

Findings

In this population-based matched case-control study of 406 patients with a new cancer diagnosis who experienced an NFSI and 1624 non-NFSI control patients, moderate to severe anxiety, depression, and shortness of breath were associated with increase in the odds of NFSI in the following 180 days. Each 10-point increment in the total-symptom score was also associated with increased odds of subsequent NFSI.

Meaning

Routine screening with patient-reported outcomes may be used to identify patients at higher risk for NFSI to enable targeted supportive interventions.

Abstract

Importance

Nonfatal self-injury (NFSI) is a patient-centered manifestation of severe distress occurring in 3 out of 1000 patients after cancer diagnosis. How to identify patients at risk for NFSI remains unknown.

Objective

To examine the associations between patient-reported outcome measures and subsequent NFSI in patients with cancer.

Design, Setting, and Participants

This population-based matched case-control study included adults with a new cancer diagnosis reporting an Edmonton Symptom Assessment System (ESAS) score within 36 months of diagnosis in Ontario, Canada, 2007 to 2019. Data analysis was performed January 2007 to December 2019.

Main Outcomes and Measures

Cases included patients with NFSI, and controls were patients without NFSI. Cases and controls were matched 1:4. Multivariable conditional logistic regression assessed the association between moderate to severe ESAS symptom scores and total ESAS (t-ESAS, range 0-90) score with NFSI in the subsequent 180 days.

Results

Of 408 858 patients reporting 1 or more ESAS assessments, 425 patients experienced NFSI and reported an ESAS score in the preceding 180 days. Of those, 406 cases were matched to 1624 control patients without an NFSI. Cases reported a higher proportion of moderate to severe symptoms and higher t-ESAS score than controls prior to the event. After adjustment, moderate to severe anxiety (odds ratio [OR], 1.61; 95% CI, 1.14-2.27), depression (OR, 1.66; 95% CI, 1.20-2.31), and shortness of breath (OR, 1.65; 95% CI, 1.18-2.31) and each 10-point increase in t-ESAS score (OR, 1.51; 95% CI, 1.40-1.63) were independently associated with higher odds of subsequent NFSI.

Conclusions and Relevance

In this case-control study, reporting moderate to severe anxiety, depression, and shortness of breath and an increasing t-ESAS score after cancer diagnosis were associated with higher odds of NFSI in the following 180 days. These data support the prospective use of routine ESAS screening as a means of identifying patients at higher risk for NFSI to improve supportive care.

Introduction

A diagnosis of cancer carries substantial emotional and psychological weight that can place patients at increased risk of psychological distress across the cancer trajectory.¹ Intentional self-injury is a severe manifestation of distress that can represent coping mechanisms to relieve stress, mental health disorders, or hopelessness. Previous work has focused on depression and the risk of suicide among patients with cancer^2,3,4,5,6; however, in patients with cancer, it is challenging to identify risk of suicide or implement effective prevention strategies.⁷ Furthermore, the repercussions of psychological distress are broader than suicide. The burden of psychological distress is increased when considering severe sequalae, such as nonfatal self-injury (NFSI).^8,9,10 We previously showed that NFSI occurred in 3 out of 1000 patients after a cancer diagnosis, which was much more frequent than suicide in that population.¹¹ Nonfatal self-injury represents a patient-centered, objectively measured manifestation of severe distress. In addition to being more common than the narrower outcome of suicide, NFSI presents an opportunity for identification of at-risk patients and interventions for risk reduction.

Recognition of distress in oncology practices is a priority for cancer agencies.^11,12 However, linking patients to appropriate psychosocial interventions based on the presence or risk of distress remains a challenge.^{2,13,14,15,16} This problem may come from a limited ability to identify patients at highest risk of harm. As recommended in various guidelines, patients are routinely screened for distress via patient-reported outcome measures (PROMs), but their effectiveness in clinical practice is limited.^11,12,17,18 Limited use of psychosocial interventions and undermanagement of distress may be related to a limited understanding of the repercussions of psychological distress, lack of buy-in from clinicians, inconsistent screening, and difficulties identifying the most vulnerable patients with distress.¹⁹

Currently, there are limited data regarding how to identify patients at high risk for NFSI. The use of routine symptom screening in cancer care provides a unique opportunity to examine whether reporting severe symptoms is associated with NFSI. Therefore, we sought to examine the associations between routinely collected PROMs and the risk of subsequent NFSI following a cancer diagnosis.

Methods

Study Design

We linked administrative health care data sets to conduct a case-control study using population-based data. The study was approved by the Research Ethics Board at Sunnybrook Health Sciences Centre and followed the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement.²⁰ The use of the data in this project is authorized under section 45 of Ontario’s Personal Health Information Act, and as such, patient informed consent was waived. A cancer survivor (J.D.) was part of the research team and was involved in developing the research question, planning, conducting, and interpreting the analysis.

Data Sources

The Ontario Cancer Registry is a provincial database comprising all patients with a cancer diagnosis (excluding nonmelanoma skin cancers) since 1964.^21,22 The Registered Persons Database contains vital status and demographic data. Information regarding health services provision is included in the Canadian Institute of Health Information Discharge Abstract Database, the National Ambulatory Care Reporting System, the cancer Activity Level Reporting, the Ontario Health Insurance Plan (OHIP) Claims Database, and the Ontario Mental Health Reporting System.²³ Data sets are detailed in eTable 1 in Supplement 1. These data sets were linked using unique encoded identifiers at ICES.

Study Cohort

This study was conducted among a cohort of persons with valid OHIP insurance from 2007 to 2019. Ontario’s 14.6 million residents benefit from universally accessible and publicly funded health care through OHIP.²⁴

Adults (≥18 years old) with a cancer diagnosis between January 1, 2007, and March 31, 2019, were identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) O.3 codes (eTable 2 in Supplement 1). If more than 1 cancer diagnosis existed in the study window, the earliest date was selected. Patients were excluded if they were older than 105 years, had more than 1 cancer diagnosis on the same day, the date of last contact was missing, or if they died on or prior to their cancer diagnosis date. Finally, we retained patients who completed an Edmonton Symptom Assessment System (ESAS) assessment within 36 months of the index cancer diagnosis.

Identification of Cases and Controls

The outcome of interest was an NFSI event after cancer diagnosis, defined as an emergency department visit with a self-injury of intentional (ICD-10-CA codes X61-X84) or undetermined intent (ICD-10-CA codes Y10-Y19 and Y28) code in any diagnostic field.^25,26,27 When the event resulted in death during the same admission (ie, a fatal self-injury event or a death by suicide), this was treated as a completed suicide, and not NFSI. We focused this analysis on NFSI because it represents a distinct type of event from suicide that is more common, has opportunity for risk identification, and has different needs for intervention and potential prevention strategies. Nonfatal self-injury was identified in the 36 months following cancer diagnosis, or until death, date of last contact, or end of study date (March 31, 2020), whichever came first. This offered an opportunity for minimum 12 months of observation for all patients. The 36-month window was selected based on prior work that observed that the majority of NFSI after a cancer diagnosis occur within that time period, as well as to ensure the availability of ESAS data.¹¹

Patients were considered cases if they experienced the outcome of NFSI event within 36 months following their cancer diagnosis and had an ESAS score recorded in the 180 days prior to the NFSI event (Figure 1). Of the 12% of patients who had more than 1 NFSI event following cancer diagnosis, only the first was retained as the index outcome event.

Figure 1. — ^aIf more than 1 Edmonton Symptom Assessment System (ESAS) record was available within the same 6-month time window, the one closest to the outcome was selected. For controls, an index event date that corresponded to the matched case’s nonfatal self-injury date was assigned.

Patients who did not experience an NFSI after cancer diagnosis were the potential pool of controls. Each case was hard-matched to 4 controls without replacement. Controls were assigned an index date (dummy event date) corresponding to the matched case’s NFSI event date. Controls had to be alive and NFSI-free at the index date. Cases and controls were hard-matched on lag time between cancer diagnosis and index date, ESAS assessment in the 180 days before the index date, and patient and cancer characteristics. Patient and cancer characteristics for hard-matching were age at cancer diagnosis (±5 years), sex, prior self-injury (yes/no), and cancer type (bone and soft tissue, breast, bronchopulmonary, central nervous system, endocrine, gastrointestinal, genitourinary, gynecologic, hematopoietic and lymphoma, head and neck, skin [melanoma], and other). Prior self-injury was included as a matching covariate based on prior data showing a strong association with NFSI after cancer diagnosis.¹¹

We ensured similar look-back periods between cases and controls for collecting information on the primary exposure and other covariates. The cases who could not be matched were excluded from the case-control analysis.

Exposure

The exposures of interest were PROMs captured by the ESAS assessment for both cases and controls within the 180 days prior to their index date. If more than 1 ESAS assessment was recorded within 180 days prior to the index date, the assessment closest to the index date was used.

The ESAS is a validated and reliable PROM that assesses 9 common cancer-associated symptoms (eFigure in Supplement 1).^28,29,30 Since 2007, Ontario routinely collects ESAS data at all outpatient cancer visits. Patients rate each symptom from 0 (absence of symptom) to 10 (worst possible symptom). The ESAS scores were treated as (1) a binary variable of moderate to severe vs mild for each individual symptom, and (2) continuous as total summed ESAS (t-ESAS) score calculated by adding all 9 individual symptom scores together (range, 0-90). For all symptoms except depression, moderate to severe scores were defined as 4 or higher, as this threshold is correlated with a clinically significant symptom burden, functional impairment, and poor quality of life in patients with cancer.^30,31 For depression, moderate to severe symptoms were defined as 2 or higher, which has been identified as the optimal cutoff for screening of clinical depression in patients with cancer.³²

Covariates

Age and sex were obtained from the Registered Persons Database. Rural living was determined using the rurality index of Ontario based on the patients’ primary residence.³³ Socioeconomic status was captured using the community-level Material Deprivation, a composite measure of the inability for individuals or households to afford consumption goods and activities typical in a society, categorized into quintiles.^34,35 Comorbidity burden was measured using the Elixhauser algorithm using a 2-year look-back window. Total comorbidity score was dichotomized with a cutoff of 4 or higher for high comorbidity burden.^36,37 Cancer site and cancer stage at time of diagnosis (American Joint Committee on Cancer staging manual, 7th edition) were abstracted from the Ontario Cancer Registry.³⁸ Psychiatric illness in the 5 years prior to cancer diagnosis was defined as 4 mutually exclusive groups representing levels of severity of mental health care needs¹³: (1) inpatient severe psychiatric illness: any hospitalization for a mood or psychotic disorder; (2) outpatient severe psychiatric illness: 2 or more psychiatrist or emergency department visits for a mood or psychotic disorder; (3) other mental illness: fewer than 2 outpatient visits with a physician or emergency department visit with any other mental illness–related diagnosis codes; and (4) none: no history of mental health services use.¹³ Prior self-injury was captured in the 5 years prior to cancer diagnosis. Cancer treatment was captured between the date of cancer diagnosis and the index date and categorized as none, surgery, chemotherapy, chemoradiation/radiation therapy, or surgery combined with neoadjuvant or adjuvant therapy (chemotherapy or chemoradiation/radiation therapy). Surgery was captured as any resection of target organs using the Canadian Classification for Health Interventions. Covariates are detailed in eTable 3 in Supplement 1. All covariates were measured at time of cancer diagnosis.

Statistical Analysis

Baseline characteristics were described for the entire cohort. Categorical variables were reported as absolute numbers and percentages, and continuous variables were reported as means with SDs or medians with IQRs. Between-group comparisons in the entire cohort prior to matching used standardized mean differences, with a difference greater than 10% considered significant.³⁹ We compared the characteristics of patients who did and did not record an ESAS assessment in the entire cohort to appreciate the potential bias introduced by ESAS reporting (eTable 4 in Supplement 1).⁴⁰ We then compared the characteristics of patients with and without NFSI (eTable 5 in Supplement 1). The nonmatched characteristics of cases and controls were compared using standardized differences.

The association between ESAS scores and NFSI in the following 180 days was assessed using univariable and multivariable conditional logistic regression models accounting for the matched design. Two types of models were created. The first type examined the ESAS exposure as a binary variable (moderate to severe vs mild) for each of the 9 symptoms; all individual symptoms were included in the same model, first in an unadjusted analysis including only the 9 symptoms in a multivariable model, and second in an adjusted analysis in a model that also included possible confounders. The second type of model examined the exposure of t-ESAS; only t-ESAS score as a continuous variable (by 10-point increments) was in the univariable analysis for an unadjusted analysis and further adjusted for possible confounders in multivariable analysis. Nonmatched covariates to adjust for in the models were selected a priori based on clinical relevance and the existing literature, while preserving a parsimonious model: history of psychiatric illness and therapy received prior to NFSI.¹⁰ Directed acyclic graphs were developed to separate potential mediators and confounders and select covariates for the model.⁴¹ We subsequently conducted a sensitivity analysis that adjusted for additional covariates in the model that showed differences between cases and controls after matching: comorbidity burden, material deprivation, and cancer stage. Collinearity was assessed, defined as variance inflation factor of 2.5 or greater. Results from the regression models were reported as odds ratios (ORs) with 95% CIs.

We looked at missing data for key variables. Data were missing for rural residency in 0.1% and material deprivation in 0.7% of the cohort. Because the proportion of complete cases was high (99.93%) and there was no systematic reason or pattern for data missingness, we performed a complete case analysis whereby patients with missing data were excluded from matching and outcome analysis. This approach results in only minimal loss of precision and bias that would be unlikely to alter the results of the analysis.⁴²

All analyses were 2-sided, and statistical significance was set at P < .05. Data analysis was performed January 2007 to December 2019. Analyses were conducted using SAS Enterprise Guide 7.1 (SAS Institute).

Results

We identified a cohort of 806 910 patients, of which 408 858 (50.7%) reported 1 or more ESAS scores within 36 months of their cancer diagnosis (Figure 2; eTable 4 in Supplement 1); the case-control study was designed from this cohort. Of all patients, 753 patients had an NFSI (eTable 5 in Supplement 1), including 425 patients who had an ESAS score within 180 days preceding the NFSI event. Finally, 406 cases (19 without hard matches) with NFSI and preceding ESAS score were matched to 1624 controls. The characteristics of cases and controls are summarized in the Table.

Table. Characteristics of Matched Cases and Controls.

Characteristic	No. (%)		Standardized difference, %^a
Characteristic	NFSI cases (n = 406)	No NFSI controls (n = 1624)	Standardized difference, %^a
Age at diagnosis, y^b
18-39	41 (10.1)	155 (9.5)	2
40-49	63 (15.5)	248 (15.3)	1
50-59	105 (25.9)	427 (26.3)	1
60-69	109 (26.8)	435 (26.8)	0
≥70	88 (21.7)	359 (22.1)	1
Sex^b
Female	224 (55.2)	896 (55.2)	0
Male	182 (44.8)	728 (44.8)	0
High comorbidity burden (Elixhauser ≥4)	50 (12.3)	98 (6.0)	22
Rural residence	39 (9.6)	139 (8.6)	4
Material deprivation quintile
1st (Least deprived)	70 (17.2)	394 (24.3)	17
2nd	76 (18.7)	359 (22.1)	8
3rd	69 (17.0)	317 (19.5)	7
4th	78 (19.2)	268 (16.5)	7
5th (Most deprived)	107 (26.4)	273 (16.8)	23
Missing	6 (1.5)	13 (0.8)	6
Prior self-injury^b^,^c	25 (6.2)	100 (6.2)	0
Severe psychiatric illness history^c
Inpatient	37 (9.1)	48 (3.0)	26
Outpatient	28 (6.9)	44 (2.7)	20
Other	194 (47.8)	544 (33.5)	29
None	147 (36.2)	988 (60.8)	51
Primary cancer site^b
Breast	79 (19.5)	316 (19.5)	0
Bronchopulmonary	78 (19.2)	312 (19.2)	0
Central nervous system	8 (2.0)	32 (2.0)	0
Gastrointestinal	72 (17.7)	288 (17.7)	0
Genitourinary	45 (11.1)	180 (11.1)	0
Gynecologic	34 (8.4)	136 (8.4)	0
Hematopoietic and lymphoma	49 (12.1)	196 (12.1)	0
Head and neck and endocrine	18 (4.4)	72 (4.4)	0
Skin (melanoma)	8 (2.0)	32 (2.0)	0
Other	15 (3.7)	60 (3.7)	0
Cancer stage
I	65 (16.0)	145 (8.9)	22
II	67 (16.5)	252 (15.5)	3
III	93 (22.9)	347 (21.4)	4
IV	75 (18.5)	456 (28.1)	23
Missing or unknown	106 (26.1)	424 (26.1)	0
Treatment prior to index event (NFSI or dummy event date)
None	57 (14.0)	74 (4.6)	33
Surgery only	54 (13.3)	60 (3.7)	35
Chemotherapy only	47 (11.6)	317 (19.5)	22
Chemoradiation or radiation therapy	92 (22.7)	505 (31.1)	19
Surgery combined with chemotherapy and/or radiation therapy	156 (38.4)	668 (41.1)	6

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Abbreviation: NFSI, nonfatal self-injury.

^{^a}

Standardized differences greater than 10% were considered significant.

^{^b}

Cases and controls were matched for age (±5 years, sex, prior self-injury, and primary cancer site).

^{^c}

Within 5 years prior to cancer diagnosis.

The percentages of cases and controls reporting moderate to severe symptoms are presented in Figure 3. A higher proportion of cases with NFSI reported moderate to severe symptoms than controls. The largest absolute differences were observed for anxiety, depression, drowsiness, tiredness, and lack of well-being. Cases also reported a higher median (IQR) t-ESAS score of 4 (1-6) compared with 1 (0-4) for controls (P < .001).

The univariable and multivariable associations between ESAS scores and NFSI are depicted in Figure 4 and eTable 6 in Supplement 1. On univariable analysis, reporting moderate to severe symptoms was associated with NFSI for each individual symptom. On multivariable analysis, when accounting for all other symptoms in a model adjusted for severe psychiatric illness history and cancer treatment, reporting moderate to severe anxiety (OR, 1.61; 95% CI, 1.14-2.27), depression (OR, 1.66; 95% CI, 1.20-2.31), and shortness of breath (OR, 1.65; 95% CI, 1.18-2.31) was independently associated with increased odds of NFSI. In a separate analysis, each 10-point increment in t-ESAS score (from 0 to 90) was independently associated with increased odds of NFSI (OR, 1.51; 95% CI, 1.40-1.63). In the sensitivity analysis further adjusting for comorbidity burden, material deprivation, and cancer stage, the results remained consistent (eTable 7 in Supplement 1).

Discussion

In this population-based case-control study, we observed an independent association between patient-reported symptom burden, as assessed by ESAS scores, and the subsequent risk of NFSI. Patients reporting moderate to severe anxiety, depression, and shortness of breath had a 61%, 66%, and 65% increase in the odds of NFSI in the following 180 days compared with controls. The t-ESAS score was also associated with the occurrence of NFSI; for each increase of 10 points in the t-ESAS score, there was a 51% increase in the odds of NFSI in the following 180 days. These findings are important to enhance the use of screening ESAS scores to better support patients. Scores from ESAS assessments can be used to identify patients at higher risk of NFSI, indicating higher level of distress, and help direct tailored assessment and intervention.

While the importance of distress monitoring in cancer care is clear, many clinicians have limited knowledge regarding formal and informal methods of intervention.⁴³ Thus, it is important to have a systematic way of identifying and addressing patients with severe distress. Nonfatal self-injury is an important product of distress.^11,44 Although our group recently outlined factors associated with NFSI after cancer diagnosis,¹¹ to our knowledge, there is currently no literature about how to identify patients at-risk in clinical practice.

Symptom screening programs using PROMs are implemented in cancer centers across various jurisdictions and may present an opportunity to identify patients at higher risk for distress and associated NFSI.^11,12,45 Unfortunately, symptom screening rarely leads to interventions in clinical practice.^18,46 Our study identified an opportunity to use existing PROMs screening programs to identify patients at risk for NFSI. Beyond anxiety and depression, the shortness of breath metric is interesting. Prior work in noncancer populations showed that shortness of breath is a troubling symptom and can be associated with increased risk of NFSI, even after adjusting for history of mental illness.^47,48 Therefore, attention should be paid to reporting of shortness of breath as a physical symptom, but also as an overlapping symptom potentially manifesting distress.

Effective interventions exist to prevent NFSI in patients with cancer, including psychotherapy, pharmacotherapy, integrated collaborative care, muscle relaxation, therapeutic walking, and alternative cancer treatment.⁷ Support can be implemented via protocols and pathways for integrated psychosocial assessment and interventions triggered when identifying high-risk patients. This includes formal support and referral pathways and potentially new models for assessment and triage, such as telemedicine or phone assessment.^49,50,51 While such pathways for management of symptoms and distress exist, they may not be used frequently.¹⁸ Therefore, we suggest that moderate to severe anxiety, depression, or shortness of breath or a high t-ESAS score should be flagged by screening programs and targeted for psychosocial evaluation, support, and longitudinal follow-up. For example, automated systems could be put in place for interventions in high-risk patients. Health care professionals should also be educated about the risks associated with reporting of moderate to severe symptoms.⁴⁹

Limitations

This study has limitations. This is a retrospective study, and the administrative data used were not collected specifically to answer this research question. Although we used an approach that has been demonstrated as reliable and specific, the detection of NFSI is based on measurable encounters with the health care system and therefore may underestimate these events.²⁵ Reporting bias with the ESAS may also exist. The case-matched analysis was limited to patients who recorded ESAS scores during outpatient cancer clinic visits, and the risk estimates reported may not apply the same to those patients less likely to fill out ESAS screening.⁴⁰ The case-control design with robust matching is a key strength of the current study. However, we acknowledge that we used a matching strategy designed to produce conservative effect estimates of the association between ESAS and NFSI. It is possible that the inclusion of covariates such as prior self-injury resulted in overmatching. This would not alter the direction of the findings but would lead to underestimating the effect estimates. Thus, the conclusions regarding the association between ESAS scores and subsequent NFSI remain. Finally, we used high-quality longitudinal data and had access to a large population-based cohort of patients to build a case-control study within a health system where access to care is not compounded by insurance status and loss to follow-up is minimal, ensuring optimal capture of outcomes.

Conclusions

In this case-control study, reporting moderate to severe anxiety, depression, and shortness of breath and increasing t-ESAS score after cancer diagnosis were independently associated with higher odds of NFSI in the following 180 days. These data support the prospective use of routine ESAS screening as a means of identifying patients at higher risk for NFSI to improve supportive care. Patients with at-risk ESAS scores should receive tailored psychosocial assessment, management, and longitudinal follow-up.

Supplement 1.

eTable 1. Data sources

eTable 2. Strategy for cohort creation

eTable 3. Covariates definitions

eTable 4. Characteristics of patients who did and did not record ESAS scores in the 36 months following cancer diagnosis, for the entire cohort

eTable 5. Characteristics of patients recording ESAS scores with and without non-fatal self-injury following cancer diagnosis, for the entire cohort

eTable 6. Unadjusted and adjusted* odds ratio of non-fatal self-injury within 180 days of symptoms scoring for patients with moderate-to-severe symptoms compared to those without moderate-to-severe symptoms

eTable 7. Sensitivity analysis - Adjusted* odds ratio of non-fatal self-injury within 180 days of symptoms scoring for patients with moderate-to-severe symptoms compared to those without moderate-to-severe symptoms

eFigure. Cancer Care Ontario Edmonton Symptom Assessment System form

Click here for additional data file.^{(618.3KB, pdf)}

Supplement 2.

Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group Members

Click here for additional data file.^{(115.1KB, pdf)}

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2.Carlson LE, Waller A, Groff SL, Giese-Davis J, Bultz BD. What goes up does not always come down: patterns of distress, physical and psychosocial morbidity in people with cancer over a one year period. Psychooncology. 2013;22(1):168-176. doi: 10.1002/pon.2068 [DOI] [PubMed] [Google Scholar]
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4.Anguiano L, Mayer DK, Piven ML, Rosenstein D. A literature review of suicide in cancer patients. Cancer Nurs. 2012;35(4):E14-E26. doi: 10.1097/NCC.0b013e31822fc76c [DOI] [PubMed] [Google Scholar]
5.Calati R, Fang F, Mostofsky E, et al. Cancer and suicidal ideation and behaviours: protocol for a systematic review and meta-analysis. BMJ Open. 2018;8(8):e020463. doi: 10.1136/bmjopen-2017-020463 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Misono S, Weiss NS, Fann JR, Redman M, Yueh B. Incidence of suicide in persons with cancer. J Clin Oncol. 2008;26(29):4731-4738. doi: 10.1200/JCO.2007.13.8941 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Bolton JM, Walld R, Chateau D, Finlayson G, Sareen J. Risk of suicide and suicide attempts associated with physical disorders: a population-based, balancing score-matched analysis. Psychol Med. 2015;45(3):495-504. doi: 10.1017/S0033291714001639 [DOI] [PubMed] [Google Scholar]
8.Kawashima Y, Yonemoto N, Inagaki M, Inoue K, Kawanishi C, Yamada M. Interventions to prevent suicidal behavior and ideation for patients with cancer: a systematic review. Gen Hosp Psychiatry. 2019;60:98-110. doi: 10.1016/j.genhosppsych.2019.07.003 [DOI] [PubMed] [Google Scholar]
9.Skegg K. Self-harm. Lancet. 2005;366(9495):1471-1483. doi: 10.1016/S0140-6736(05)67600-3 [DOI] [PubMed] [Google Scholar]
10.Geulayov G, Casey D, McDonald KC, et al. Incidence of suicide, hospital-presenting non-fatal self-harm, and community-occurring non-fatal self-harm in adolescents in England (the iceberg model of self-harm): a retrospective study. Lancet Psychiatry. 2018;5(2):167-174. doi: 10.1016/S2215-0366(17)30478-9 [DOI] [PubMed] [Google Scholar]
11.Noel CW, Eskander A, Sutradhar R, et al. ; Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group . Incidence of and factors associated with nonfatal self-injury after a cancer diagnosis in Ontario, Canada. JAMA Netw Open. 2021;4(9):e2126822. doi: 10.1001/jamanetworkopen.2021.26822 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Holland JC, Bultz BD; National comprehensive Cancer Network (NCCN) . The NCCN guideline for distress management: a case for making distress the sixth vital sign. J Natl Compr Canc Netw. 2007;5(1):3-7. doi: 10.6004/jnccn.2007.0003 [DOI] [PubMed] [Google Scholar]
13.Bultz BD, Groff SL, Fitch M, et al. Implementing screening for distress, the 6th vital sign: a Canadian strategy for changing practice. Psychooncology. 2011;20(5):463-469. doi: 10.1002/pon.1932 [DOI] [PubMed] [Google Scholar]
14.Ng HS, Roder D, Koczwara B, Vitry A. Comorbidity, physical and mental health among cancer patients and survivors: an Australian population-based study. Asia Pac J Clin Oncol. 2018;14(2):e181-e192. doi: 10.1111/ajco.12677 [DOI] [PubMed] [Google Scholar]
15.Martinez MR, Pasha A. Prioritizing mental health research in cancer patients and survivors. AMA J Ethics. 2017;19(5):486-492. doi: 10.1001/journalofethics.2017.19.5.msoc2-1705 [DOI] [PubMed] [Google Scholar]
16.Naughton MJ, Weaver KE. Physical and mental health among cancer survivors: considerations for long-term care and quality of life. N C Med J. 2014;75(4):283-286. doi: 10.18043/ncm.75.4.283 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Carlson LE, Angen M, Cullum J, et al. High levels of untreated distress and fatigue in cancer patients. Br J Cancer. 2004;90(12):2297-2304. doi: 10.1038/sj.bjc.6601887 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565. doi: 10.1200/JCO.2015.63.0830 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Pereira JL, Chasen MR, Molloy S, et al. Cancer care professionals’ attitudes toward systematic standardized symptom assessment and the Edmonton Symptom Assessment System after large-scale population-based implementation in Ontario, Canada. J Pain Symptom Manage. 2016;51(4):662-672.e8. doi: 10.1016/j.jpainsymman.2015.11.023 [DOI] [PubMed] [Google Scholar]
20.Basch E, Deal AM, Dueck AC, et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA. 2017;318(2):197-198. doi: 10.1001/jama.2017.7156 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Hallet J, Davis LE, Isenberg-Grzeda E, et al. Gaps in the management of depression symptoms following cancer diagnosis: a population-based analysis of prospective patient-reported outcomes. Oncologist. 2020;25(7):e1098-e1108. doi: 10.1634/theoncologist.2019-0709 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Benchimol EI, Smeeth L, Guttmann A, et al. ; RECORD Working Committee . The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 [DOI] [PMC free article] [PubMed] [Google Scholar]
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25.Juurlink D, Preyra C, Croxford R, et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Institute for Clinical Evaluative Sciences. Accessed February 28, 2022. https://www.ices.on.ca/Publications/Atlases-and-Reports/2006/Canadian-Institute-for-Health-Information
26.Canada Health Act. Published July 26, 2004. Accessed February 29, 2020. https://www.canada.ca/en/health-canada/services/health-care-system/canada-health-care-system-medicare/canada-health-act.html
27.Bethell J, Rhodes AE. Identifying deliberate self-harm in emergency department data. Health Rep. 2009;20(2):35-42. [PubMed] [Google Scholar]
28.Mahar AL, Cramm H, Aiken AB, et al. A retrospective cohort study comparing non-fatal self-harm emergency department visits between Canadian veterans living in Ontario and matched civilians. Int Rev Psychiatry. 2019;31(1):25-33. doi: 10.1080/09540261.2019.1580685 [DOI] [PubMed] [Google Scholar]
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30.Bruera E, Kuehn N, Miller MJ, Selmser P, Macmillan K. The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care. 1991;7(2):6-9. doi: 10.1177/082585979100700202 [DOI] [PubMed] [Google Scholar]
31.Nekolaichuk C, Watanabe S, Beaumont C. The Edmonton Symptom Assessment System: a 15-year retrospective review of validation studies (1991--2006). Palliat Med. 2008;22(2):111-122. doi: 10.1177/0269216307087659 [DOI] [PubMed] [Google Scholar]
32.Richardson LA, Jones GW. A review of the reliability and validity of the Edmonton Symptom Assessment System. Curr Oncol. 2009;16(1):55-64. doi: 10.3747/co.v16i1.261 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Selby D, Cascella A, Gardiner K, et al. A single set of numerical cutpoints to define moderate and severe symptoms for the Edmonton Symptom Assessment System. J Pain Symptom Manage. 2010;39(2):241-249. doi: 10.1016/j.jpainsymman.2009.06.010 [DOI] [PubMed] [Google Scholar]
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35.Kralj B. Measuring “rurality” for purposes of health-care planning: an empirical measure for Ontario. Ont Med Rev. 2000;67(10):33-52. [Google Scholar]
36.Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992;82(5):703-710. doi: 10.2105/AJPH.82.5.703 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. Data sources

eTable 2. Strategy for cohort creation

eTable 3. Covariates definitions

eTable 4. Characteristics of patients who did and did not record ESAS scores in the 36 months following cancer diagnosis, for the entire cohort

eTable 5. Characteristics of patients recording ESAS scores with and without non-fatal self-injury following cancer diagnosis, for the entire cohort

eFigure. Cancer Care Ontario Edmonton Symptom Assessment System form

Click here for additional data file.^{(618.3KB, pdf)}

Supplement 2.

Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group Members

Click here for additional data file.^{(115.1KB, pdf)}

[coi220007r1] 1.Mitchell AJ, Chan M, Bhatti H, et al. Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: a meta-analysis of 94 interview-based studies. Lancet Oncol. 2011;12(2):160-174. doi: 10.1016/S1470-2045(11)70002-X [DOI] [PubMed] [Google Scholar]

[coi220007r2] 2.Carlson LE, Waller A, Groff SL, Giese-Davis J, Bultz BD. What goes up does not always come down: patterns of distress, physical and psychosocial morbidity in people with cancer over a one year period. Psychooncology. 2013;22(1):168-176. doi: 10.1002/pon.2068 [DOI] [PubMed] [Google Scholar]

[coi220007r3] 3.Spoletini I, Gianni W, Caltagirone C, Madaio R, Repetto L, Spalletta G. Suicide and cancer: where do we go from here? Crit Rev Oncol Hematol. 2011;78(3):206-219. doi: 10.1016/j.critrevonc.2010.05.005 [DOI] [PubMed] [Google Scholar]

[coi220007r4] 4.Anguiano L, Mayer DK, Piven ML, Rosenstein D. A literature review of suicide in cancer patients. Cancer Nurs. 2012;35(4):E14-E26. doi: 10.1097/NCC.0b013e31822fc76c [DOI] [PubMed] [Google Scholar]

[coi220007r5] 5.Calati R, Fang F, Mostofsky E, et al. Cancer and suicidal ideation and behaviours: protocol for a systematic review and meta-analysis. BMJ Open. 2018;8(8):e020463. doi: 10.1136/bmjopen-2017-020463 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r6] 6.Misono S, Weiss NS, Fann JR, Redman M, Yueh B. Incidence of suicide in persons with cancer. J Clin Oncol. 2008;26(29):4731-4738. doi: 10.1200/JCO.2007.13.8941 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r7] 7.Bolton JM, Walld R, Chateau D, Finlayson G, Sareen J. Risk of suicide and suicide attempts associated with physical disorders: a population-based, balancing score-matched analysis. Psychol Med. 2015;45(3):495-504. doi: 10.1017/S0033291714001639 [DOI] [PubMed] [Google Scholar]

[coi220007r8] 8.Kawashima Y, Yonemoto N, Inagaki M, Inoue K, Kawanishi C, Yamada M. Interventions to prevent suicidal behavior and ideation for patients with cancer: a systematic review. Gen Hosp Psychiatry. 2019;60:98-110. doi: 10.1016/j.genhosppsych.2019.07.003 [DOI] [PubMed] [Google Scholar]

[coi220007r9] 9.Skegg K. Self-harm. Lancet. 2005;366(9495):1471-1483. doi: 10.1016/S0140-6736(05)67600-3 [DOI] [PubMed] [Google Scholar]

[coi220007r10] 10.Geulayov G, Casey D, McDonald KC, et al. Incidence of suicide, hospital-presenting non-fatal self-harm, and community-occurring non-fatal self-harm in adolescents in England (the iceberg model of self-harm): a retrospective study. Lancet Psychiatry. 2018;5(2):167-174. doi: 10.1016/S2215-0366(17)30478-9 [DOI] [PubMed] [Google Scholar]

[coi220007r11] 11.Noel CW, Eskander A, Sutradhar R, et al. ; Enhanced Supportive Psycho-oncology Canadian Care (ESPOC) Group . Incidence of and factors associated with nonfatal self-injury after a cancer diagnosis in Ontario, Canada. JAMA Netw Open. 2021;4(9):e2126822. doi: 10.1001/jamanetworkopen.2021.26822 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r12] 12.Holland JC, Bultz BD; National comprehensive Cancer Network (NCCN) . The NCCN guideline for distress management: a case for making distress the sixth vital sign. J Natl Compr Canc Netw. 2007;5(1):3-7. doi: 10.6004/jnccn.2007.0003 [DOI] [PubMed] [Google Scholar]

[coi220007r13] 13.Bultz BD, Groff SL, Fitch M, et al. Implementing screening for distress, the 6th vital sign: a Canadian strategy for changing practice. Psychooncology. 2011;20(5):463-469. doi: 10.1002/pon.1932 [DOI] [PubMed] [Google Scholar]

[coi220007r14] 14.Ng HS, Roder D, Koczwara B, Vitry A. Comorbidity, physical and mental health among cancer patients and survivors: an Australian population-based study. Asia Pac J Clin Oncol. 2018;14(2):e181-e192. doi: 10.1111/ajco.12677 [DOI] [PubMed] [Google Scholar]

[coi220007r15] 15.Martinez MR, Pasha A. Prioritizing mental health research in cancer patients and survivors. AMA J Ethics. 2017;19(5):486-492. doi: 10.1001/journalofethics.2017.19.5.msoc2-1705 [DOI] [PubMed] [Google Scholar]

[coi220007r16] 16.Naughton MJ, Weaver KE. Physical and mental health among cancer survivors: considerations for long-term care and quality of life. N C Med J. 2014;75(4):283-286. doi: 10.18043/ncm.75.4.283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r17] 17.Carlson LE, Angen M, Cullum J, et al. High levels of untreated distress and fatigue in cancer patients. Br J Cancer. 2004;90(12):2297-2304. doi: 10.1038/sj.bjc.6601887 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r18] 18.Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565. doi: 10.1200/JCO.2015.63.0830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r19] 19.Pereira JL, Chasen MR, Molloy S, et al. Cancer care professionals’ attitudes toward systematic standardized symptom assessment and the Edmonton Symptom Assessment System after large-scale population-based implementation in Ontario, Canada. J Pain Symptom Manage. 2016;51(4):662-672.e8. doi: 10.1016/j.jpainsymman.2015.11.023 [DOI] [PubMed] [Google Scholar]

[coi220007r20] 20.Basch E, Deal AM, Dueck AC, et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA. 2017;318(2):197-198. doi: 10.1001/jama.2017.7156 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r21] 21.Hallet J, Davis LE, Isenberg-Grzeda E, et al. Gaps in the management of depression symptoms following cancer diagnosis: a population-based analysis of prospective patient-reported outcomes. Oncologist. 2020;25(7):e1098-e1108. doi: 10.1634/theoncologist.2019-0709 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r22] 22.Benchimol EI, Smeeth L, Guttmann A, et al. ; RECORD Working Committee . The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r23] 23.Robles SC, Marrett LD, Clarke EA, Risch HA. An application of capture-recapture methods to the estimation of completeness of cancer registration. J Clin Epidemiol. 1988;41(5):495-501. doi: 10.1016/0895-4356(88)90052-2 [DOI] [PubMed] [Google Scholar]

[coi220007r24] 24.Clarke EA, Marrett LD, Kreiger N. Cancer registration in Ontario: a computer approach. IARC Sci Publ. 1991;(95):246-257. [PubMed] [Google Scholar]

[coi220007r25] 25.Juurlink D, Preyra C, Croxford R, et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Institute for Clinical Evaluative Sciences. Accessed February 28, 2022. https://www.ices.on.ca/Publications/Atlases-and-Reports/2006/Canadian-Institute-for-Health-Information

[coi220007r26] 26.Canada Health Act. Published July 26, 2004. Accessed February 29, 2020. https://www.canada.ca/en/health-canada/services/health-care-system/canada-health-care-system-medicare/canada-health-act.html

[coi220007r27] 27.Bethell J, Rhodes AE. Identifying deliberate self-harm in emergency department data. Health Rep. 2009;20(2):35-42. [PubMed] [Google Scholar]

[coi220007r28] 28.Mahar AL, Cramm H, Aiken AB, et al. A retrospective cohort study comparing non-fatal self-harm emergency department visits between Canadian veterans living in Ontario and matched civilians. Int Rev Psychiatry. 2019;31(1):25-33. doi: 10.1080/09540261.2019.1580685 [DOI] [PubMed] [Google Scholar]

[coi220007r29] 29.Mason SA, Nathens AB, Byrne JP, et al. Association between burn injury and mental illness among burn survivors: a population-based, self-matched, longitudinal cohort study. J Am Coll Surg. 2017;225(4):516-524. doi: 10.1016/j.jamcollsurg.2017.06.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r30] 30.Bruera E, Kuehn N, Miller MJ, Selmser P, Macmillan K. The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care. 1991;7(2):6-9. doi: 10.1177/082585979100700202 [DOI] [PubMed] [Google Scholar]

[coi220007r31] 31.Nekolaichuk C, Watanabe S, Beaumont C. The Edmonton Symptom Assessment System: a 15-year retrospective review of validation studies (1991--2006). Palliat Med. 2008;22(2):111-122. doi: 10.1177/0269216307087659 [DOI] [PubMed] [Google Scholar]

[coi220007r32] 32.Richardson LA, Jones GW. A review of the reliability and validity of the Edmonton Symptom Assessment System. Curr Oncol. 2009;16(1):55-64. doi: 10.3747/co.v16i1.261 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r33] 33.Selby D, Cascella A, Gardiner K, et al. A single set of numerical cutpoints to define moderate and severe symptoms for the Edmonton Symptom Assessment System. J Pain Symptom Manage. 2010;39(2):241-249. doi: 10.1016/j.jpainsymman.2009.06.010 [DOI] [PubMed] [Google Scholar]

[coi220007r34] 34.Rhondali W, Perceau E, Berthiller J, et al. Frequency of depression among oncology outpatients and association with other symptoms. Support Care Cancer. 2012;20(11):2795-2802. doi: 10.1007/s00520-012-1401-3 [DOI] [PubMed] [Google Scholar]

[coi220007r35] 35.Kralj B. Measuring “rurality” for purposes of health-care planning: an empirical measure for Ontario. Ont Med Rev. 2000;67(10):33-52. [Google Scholar]

[coi220007r36] 36.Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992;82(5):703-710. doi: 10.2105/AJPH.82.5.703 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r37] 37.Matheson FI, Dunn JR, Smith KLW, Moineddin R, Glazier RH. Development of the Canadian Marginalization Index: a new tool for the study of inequality. Can J Public Health. 2012;103(8)(suppl 2):S12-S16. doi: 10.1007/BF03403823 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r38] 38.Austin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the Charlson Comorbidity Index and Elixhauser Score work. Med Care. 2015;53(9):e65-e72. doi: 10.1097/MLR.0b013e318297429c [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi220007r39] 39.Gutacker N, Bloor K, Cookson R. Comparing the performance of the Charlson/Deyo and Elixhauser comorbidity measures across five European countries and three conditions. Eur J Public Health. 2015;25(suppl 1):15-20. doi: 10.1093/eurpub/cku221 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Association of Patient-Reported Outcomes With Subsequent Nonfatal Self-injury After a New Cancer Diagnosis

Julie Hallet, MD, MSc

Rinku Sutradhar, PhD

Elie Isenberg-Grzeda, MDCM

Christopher W Noel, MD

Alyson L Mahar, PhD

Simone N Vigod, MD, MSc

James Bolton, MD

Julie Deleemans, PhD

Wing C Chan, MPH

Natalie G Coburn, MD, MPH

Antoine Eskander, MD, ScM

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design

Data Sources

Study Cohort

Identification of Cases and Controls

Figure 1. Timeline for Measure of Exposure and Outcome.

Exposure

Covariates

Statistical Analysis

Results

Figure 2. Flowchart of Study Cohort Creation.

Table. Characteristics of Matched Cases and Controls.

Figure 3. Proportion of Patients Reporting Moderate to Severe Symptoms for Cases and Controls Prior to Outcome.

Figure 4. Unadjusted and Adjusteda Odds Ratio of Nonfatal Self-injury Within 180 Days of Symptoms Scoring for Patients With Moderate to Severe Symptoms Compared With Those Without Moderate to Severe Symptoms.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Figure 4. Unadjusted and Adjusted^a Odds Ratio of Nonfatal Self-injury Within 180 Days of Symptoms Scoring for Patients With Moderate to Severe Symptoms Compared With Those Without Moderate to Severe Symptoms.