Eada M.P. Novilla-Surette, BScN, MHIS1,2, Salimah Z. Shariff, BMath, PhD1,3, Britney Le, MSc3, Richard G. Booth, BScN, PhD1,31Faculty of Health Sciences, Western University, London, ON
2Faculty of Information & Media Studies, Western University, London, ON
3ICES Western, London, ON
Suicide in older adults is a significant overlooked problem worldwide. This is especially true in Canada where a national suicide prevention strategy has not been established.
Using linked health-care administrative databases, this population-level study (2011 to 2015) described the incidence of older adult suicide (aged 65+), and identified clinical and socio-demographic factors associated with suicide deaths.
The findings suggest that suicide remains a persistent cause of death in older adults, with an average annual suicide rate of about 100 per million people over the five-year study period. Factors positively associated with suicide vs. non-suicide death included being male, living in rural areas, having a mental illness, having a new dementia diagnosis, and having increased emergency department visits in the year prior to death; whereas, increased age, living in long-term care, having one or more chronic health condition, and increased interactions with primary health care were negatively associated with a suicide death.
Factors associated with suicide death among older adults highlighted in this study may provide better insights for the development and/or improvement of suicide prevention programs and policies.
Key words: older adult suicide, senior suicide, suicide, factors of suicide, mental health, population health
Suicide is a global phenomenon that afflicts all age groups. Currently, it is the 15th leading cause of death globally resulting in it being labelled as a major public health issue worldwide.(1) In Canada, suicide is the 9th leading cause of death among all age groups which has remained largely unchanged over the last 15 years.(2–5) Despite older adults having the second highest rates of suicide in Canada, resulting in the 12th leading cause of death in this age group,(1–4,6–8) suicide prevention has been overlooked in this cross-section of the population.
To date, the epidemiology of older adult suicide in the Canadian context is lacking. Despite some available information, there remains conflicting understanding of the factors associated with suicide in older adults. For instance, while some reports indicate that diagnosis of dementia, depression, and cancer are associated with older adult suicide,(9–15) others contradict these associations.(16–20) It has been theorized that older adult suicidality sometimes goes unnoticed clinically, as health-care professionals possess a tendency only to categorize an individual as suicidal when they are diagnosed with depression or other mental health issues.(21) This preposition towards privileging depression and other diagnosed mental health issues as a singular causal mechanism to suicidal ideation may result in health-care professionals missing other individual and contextual factors predictive of suicide. In addition, the stigma towards suicide continues to persist,(22) which could also limit the capacity of further evaluating the complex factors of suicide.
With the current limited knowledge regarding older adult suicide in Canada, this study aimed to better understand the prevalence and predictors of suicide in older adults in Ontario, Canada. The objectives of this study were to 1) describe the five-year trend of suicide deaths among older adults in Ontario, Canada (2011 to 2015); 2) develop profiles of older adult suicide versus non-suicide deaths; and 3) identify factors associated with suicide deaths in older adults.
A population-level, retrospective study was conducted using linked administrative health-care databases available at ICES (provincial health care administrative data steward) in order to identify all older adults (aged 65+) who died by either suicide or other non-suicide means between January 2011 and December 2015 in the province of Ontario, Canada. Ontario is the most populous province in Canada, comprising of about 14.7 million people (comprising approximately 40% of Canada), wherein most residents are covered by a single payer health-care insurance system (OHIP [Ontario Health Insurance Plan]).(23,24)
The study population was comprised of older adults (aged 65+), who died between 1 January 2011 and 31 December 2015 in the province of Ontario, Canada. All older adults, aged 65 years and over, were included at the start of the analysis phase to establish the rate and trend of mortality at the population level. Older adults who had a missing or invalid OHIP number (thereby not eligible for health services in Ontario), with invalid demographic information such as age and sex (data cleaning), and not residing in Ontario were excluded from the study.
In Ontario, older adults are eligible for government-funded medication use when they turn 65, through the Ontario Drug Benefit program.(23) To enable a two-year look-back period to establish health status for the second phase of the analysis (identifying factors or predictors associated with older adult suicide deaths), individuals less than 67 years old were further excluded, hereon referred to as the analysis cohort.
The following health-care administrative databases held at ICES were used to gather cohort data characteristics: Registered Persons Database, Office of the Registrar General-Deaths (ORGD) Vital Statistics Database, Ontario Population Estimates and Projections (POP) (Ontario Ministry of Health and Long-Term Care: IntelliHEALTH ONTARIO), Ontario Drug Benefit Claims (ODB), CIHI-Discharge Abstract Database (DAD), CIHI-National Ambulatory Care Reporting System (NACRS), Ontario Health Insurance Plan (OHIP), CIHI-Ontario Mental Health Reporting System (OMHRS), ICES Physician Database (IPDB), Cancer Care Ontario-Ontario Cancer Registry (OCR), and ICES-Derived Cohorts (ASTHMA, CHF, COPD, DEMENTIA, HIV, HYPER, OCCC, ODD, ORAD, OMID). Datasets were linked using unique encoded identifiers and analyzed at ICES. Variables were defined using the International Statistical Classification of Diseases Ninth and Tenth revision (ICD-9 and ICD-10) diagnostic codes, and OHIP fee/diagnostic. Definitions of all variables can be found in Appendices A and B.
The primary outcome of interest was a binary classification of death as “death by suicide vs. other suicide deaths”, identified on ORGD records as having a cause of death (COD) ICD-9 code between E950 and E959; having an underlying COD ICD-10 code between X60 and X84; or having a manner of death code recorded as “suicide”. Our secondary outcome included three categories, with an addition of “probable suicide” as a separate death classification, identified on ORGD records having a COD ICD-9 code between E980 and E987, or E989; or having an underlying COD ICD-10 code between Y10 and Y32, Y34, or Y87. Refer to Appendix B for detailed description of the codes and references used to generate the COD codes
Several socio-demographic and health-related characteristics were collected to describe the analysis cohort and assess factors associated with suicide deaths. The generated profile included pre-existing chronic conditions; new health-care issues (e.g., a recent diagnosis of dementia or cancer); and health-care services utilization (e.g., hospital admissions, emergency room visits, and primary health-care visits). The following look-back periods were selected to capture health and socio-demographic characteristics of older adult deaths (aged 67+): (a) five years for most pre-existing chronic conditions; (b) two years for new diagnoses; and (c) one year for health-care utilization. The following variables were selected to further estimate their association with the ‘death by suicide’ outcome: age, sex, marital status, income, rurality, living in long-term care (LTC) facilities, comorbidities, new health-care issues, and health-care services utilization.
A time trend analysis was utilized to examine changes in rates of older adult (aged 65+) mortality over a five-year time frame. Descriptive statistics were used to describe the characteristics of the analysis cohort. Frequencies and percentages were used to describe categorical characteristics, while means and standard deviations or medians and interquartile ranges (IQR) were used for continuous characteristics.
To compare characteristics across groups for both the primary and secondary outcomes, chi-square was used for categorical data and t-test was used for continuous data to obtain p values. For the primary outcome, a logistic regression, which predicts the odds of an event given an independent variable,(25) was utilized to estimate the odds ratio ‘death by suicide’ given the selected covariates. Results are presented as adjusted odds ratios (AORs) with 95% confidence intervals (CI). Furthermore, a sensitivity analysis was conducted to assess the consistency of the odds ratio estimates after combining the ‘death by suicide’ and the ‘death by probable suicide’ groups and re-running the regression. The two regression results were then compared to observe any differences. All statistical analyses were performed using SAS Version 9.4 (SAS Institute), utilizing a threshold of alpha at 0.05 (α = 0.05).
ICES is an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health-care and demographic data, without consent, for health system evaluation and improvement. The use of data in this project was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board.
Over the five-year study time frame (2011–2015), 368,458 older adult deaths were recorded in the province of Ontario, of which 998 (0.27% of all older adult deaths) were coded as a death by suicide. The population rate of suicide deaths was stable over the years, with a slight upward trend ranging from 91 to 100 per million older adult population (Table 1).
TABLE 1 Number of deaths (rate per million older adult population) by suicide and non-suicide causes from 2011 to 2015
After excluding older adults < 67 years of age, the cohort used for further analysis included 354,967 older adult (aged 67+) mortalities (with 869 suicide deaths) in Ontario, Canada (Figure 1; Table 2) over the five-year study time frame (2011–2015).
FIGURE 1 Flow chart of cohort selection after meeting the inclusion and exclusion criteria
TABLE 2 Characteristics of older adult who died by suicide and other non-suicide causesa
Detailed characteristics comparing older adults who died by suicide and other non-suicide causes are outlined in Table 2. In univariate analyses, older adults who died by suicide tended to be relatively younger (67–74 vs. 85+) (n = 390, 44.9% vs. n = 138, 15.9%); less likely female (n = 215, 24.7%); less likely to live in LTCs (n = 12, 1.4%); and less likely to live in rural areas (n = 160, 18.4%). In the adjusted analyses (Table 3), increasing age (aOR 0.94, 95% CI: 0.93–0.95) and living in LTC (aOR 0.07, 95% CI: 0.04–0.13) were associated with lower odds of suicide. Male (vs. female) sex (aOR 2.91, 95% CI: 2.47–3.44) and residing in a rural region (aOR 1.32, 95% CI: 1.11–1.58) were associated with higher odds of suicide deaths.
TABLE 3 Adjust odds ratios of characteristics associated with suicide deaths among older adultsa
From the list of medical illnesses (Table 2), the majority of the older adults who died by suicide had a mental health diagnosis, particularly non-psychotic disorders (n = 498, 57.3%), which was higher compared to the older adults in the non-suicide group (57.3% v. 32.6%, p < .001). In the adjusted analysis, mental illness diagnosis showed significantly higher odds of older adult suicide (Table 3). The odds of suicide were 2.75 times higher for psychotic disorder diagnosis (aOR 2.75, 95% CI: 2.08–3.62), and 3.34 times higher for non-psychotic disorder diagnosis (aOR 3.34, 95% CI: 2.91–3.87).
In the adjusted analysis, a new diagnosis of dementia was associated with increased odds of suicide (aOR 1.72, 95% CI: 1.06–2.79), while a new diagnosis of cancer was associated with a substantially lower odds of suicide (aOR 0.32; 95% CI: 0.24–0.44) (Table 3).
In the adjusted analysis, the odds of emergency department use in the year prior to death was associated with moderate increase in the odds of suicide deaths (aOR 1.05, 95% CI 1.02–1.08), whereas visits to a primary care practitioner were associated with a lower odds (aOR 0.98, 95% CI: 0.97–0.99) (Table 3).
A small number of older adults were recorded as dying by probable suicide means (N = 29) (Table 4). In comparison to older adults who died by suicide causes, those who died by probable suicide causes were younger (median 72, vs. 76, p < .001); more often male 37.9% vs. 24.7%, p < .001); more likely to have visited a primary health-care practitioner in the previous year (median 9 vs. 8 visits, p < .001); and more likely widowed (44.8% vs. 26.4%; p < .001). In sensitivity analysis, wherein the outcome included death by suicide or death by probable suicide, results of the primary adjusted analyses remained unchanged (Appendix C).
TABLE 4 Characteristics of older adults who died by suicide and probable suicide causesa
This study demonstrates that suicide remains to be a persistent cause of death among older adults (aged 65+) in Ontario, averaging roughly 200 suicide deaths per year from 2011 to 2015. Among all older adult deaths recorded over the five-year study period, 0.27% was resultant of suicide. Several factors were analyzed in this study to further understand the factors associated with suicide in older adults. Being male, living in rural areas, having a mental illness, a new dementia diagnosis, and having increased emergency department visits were positively associated with suicide deaths; whereas, increased age, living in long-term care, having chronic health conditions, and increased interactions with primary health care were negatively associated with suicide deaths.
Although suicide is prevalent in older adults, the findings reported in this work are likely an underestimate, due to a range of misclassification and systemic biases related to the reporting of suicide.(26–28) The lack of transparency in reporting older adult suicides may perhaps be due to the lingering stigma and culture surrounding suicide, or the medical/legal complexity of registering suicide cases in general.(8,21) Older adults also tend to be excluded from contemporary suicide prevention programs and policy in Canada, as these programs tend to focus on youth and young adults.(2,3,22,29)
Previous studies have reported the main characteristics of older adults who died by suicide as being younger (aged 65–74), male, and married,(9,11,30) which were consistent with the findings in this study, with the exception of marital associations.(9,11,31–33) Research studies from New Zealand,(30) Denmark,(11) and The United States of America(9) have consistently shown more suicide deaths in older adults who were younger (less than 80 years old), while emphasizing that older adults (aged 80–85+) presented with more physical health issues than those under the age of 80 years.(9,30) Furthermore, these older adults (aged 80+) visited their general practitioners more for physical issues rather than for mental health issues.(9,11,30) The findings in this research study showed the cohort to be relatively younger, although age was not found to be predictive of suicide. A 2019 U.S. descriptive study(9) of 16,924 older adults (aged 65+) reported higher odds of suicide risk for those who were older (aged 75–84; and 85+), which contrasted this research study’s finding in terms of age. This could perhaps be due to the systematic underreporting of suicide deaths in older adults,(26–28) as mentioned previously, or lack of understanding regarding the underlying risk factors associated with suicide deaths during health-care visits. More research is needed to better ascertain the true impacts of various characteristics of older adults and the association with suicide death.
Male gender has also been commonly described in the literature as a predictor of older adult suicide.(1,3,9,11,30) Although not specifically directed to older adult suicide, the influence of men’s health information-seeking behaviours(34) and traditional or stereotypical views of masculinity(35) may perhaps explain this association. While marital status did not produce a significant relationship in this study, other studies reported that various factors, such as gender or income, could influence the association of marital status with older adult suicide.(36,37)
Several studies have also uncovered associations between various physical/mental health conditions with older adult suicide, particularly dementia, depression, and cancer.(9–18,20) Findings from this research study further reinforced that a diagnosis of mental illness appears to be a health condition that is highly associated with older adult suicide. This expected finding suggests that mental illness is an immense factor in older adult suicide that must be effectively managed. Previous research has reported that a new diagnosis of dementia, between six months and three years after initial diagnosis, was associated with older adult suicide.(10,15) In this study, a new diagnosis of dementia showed to be highly associated with older adult suicide. A possible explanation for the increased risk is that older adults who are newly diagnosed with dementia still have the cognitive ability to understand the hardships (i.e., functional/cognitive decline) ahead, and are able to initiate suicide death if they deem themselves potentially incapable in the future.(20,38)
Past research exploring the association of living in a LTC facility and suicide in older adults remains inconclusive.(9,15,39) Two recent 2019 research studies(9,39) completed in the United States claimed that living within or transitioning to LTC facilities is a predictive factor of suicide. Interestingly, the findings of this population-level study demonstrated that admission to LTC showed a reduction in odds of suicide for older adults, which was congruous with the findings of another earlier American research study(15) that reported a lowered suicide risk for nursing home admissions. While more specific research will be needed to clarify these findings, it has been suggested that the protective mechanism of LTC facilities on older adult suicide may be due to the “structured, supervised nature” of LTC facilities and the higher prevalence of patients with advanced cognitive/physical limitations.(15)
Other research from the United States, Canada, and New Zealand observed that older adults who died by suicide commonly visited their family doctor within seven to 30 days before death.(12,14,17,30) While this study examined health-care visits within one year prior to suicide death, further work should be completed to examine if there are any other predictors of suicide related to the window of time between PHC visit and suicide death. Enhanced screening during patient-provider interactions to assess underlying risk factors of suicide, particularly in relation to mental illness and new diagnosis of dementia, should be considered in light of these findings.
Living in a rural environment was another significant factor determined in this study, such that it showed a positive association to suicide. It has been reported that older adults residing in rural and small population areas have the lowest access to health-care services.(40) Moreover, rural residents commonly lack access to family physicians, nurse practitioners, specialty physicians, and other health-care services.(34,40) The lack of access to health-care services in rural areas force rural residents to travel to urban areas to seek care, which may result in more emergency department usage.(34) Future suicide prevention program and health policy for older adults should consider aspects related to health-care access equity.
The findings of this study have implications and future directions for research and policy. For instance, future exploration regarding the factors surrounding living in LTC and aging-in-place should be conducted, particularly those living in rural or remote areas. Current and future digital health technology should also be examined in order to influence action and support for older adult suicide prevention programs/policies. It is evident that the increased adoption of digital health technologies (i.e., electronic health record, remote patient monitoring, telemedicine, etc.) across Canada has allowed health-care providers to efficiently access patient health information, which aids in the decision-making process and quality of care.(41–44) With the continued usage and innovation of digital health technology to help span the care continuum, the assessment and evaluation of patient needs should be further integrated into healthy system planning.(45)
Further, the legalization of medical assistance in dying (MAiD) in many jurisdictions, societal awareness of MAiD, and the impact of MAiD interventions upon older adult suicide should also all be considered in future work. From a policy perspective, ongoing training of health-care providers to improve suicide screening assessments on older adults must also be explored in further depth. Health-care providers can take a proactive role toward advocating for the needs of older adults, by developing care models and supportive mechanisms that can better identify at-risk individuals. Moreover, the needs of older adults who are systematically oppressed due to historical prejudice and discrimination, such as those who are homeless or part of the 2SLGBTQ+ community, should not be overlooked as well.(46–48)
The findings from this population-based research study provided insights related to the complexity of suicide in older adults in Ontario, Canada. The interlinked population-level data provided a comprehensive overview of the prevalence and the factors directly associated with older adult suicide, which can be used to inform decision-making processes surrounding suicide prevention programs and policies, both provincially and nationally.
While the study possessed strengths, there are several limitations that should be considered when interpreting the findings of this study. First, the accuracy of suicide deaths listed in this study may not fully express the true number of older adult suicides in Ontario, Canada. Even with defining suicide deaths in older adults using ORGD requirements, suicide deaths could still potentially have been misclassified or underreported.(1,8,21) Further research to examine the issue of misclassification or underreporting of suicide deaths and the specific characteristic profile of older adults who died by suicide or experienced suicide attempts should be sought. Second, as with studies utilizing secondary data,(49–51) the variables selected for inclusion in the study were limited to those captured by health-care administrative data, and in some cases, were not as specific as would have been preferred. Although efforts to control for confounding were undertaken, due to the administrative nature of the source data, residual confounding is likely. For example, factors previously identified as being associated with suicide deaths (i.e., chronic pain, new diagnosis of specific mental illnesses, and other social determinants of health) could not be included because they were either unavailable or poorly defined in the administrative data sources. Third, the exclusion criteria of this study meant that the health inequities of other older adult subcohorts (i.e., newcomers, individuals experiencing homelessness, 2SLBTQ+) could not be assessed in further depth. With the reported rise of emergency shelter usage among homeless older adults in Canada,(47) economic barriers of newcomers accessing health-care services during a three-month wait period prior to provincial coverage,(52) and lack of mental health services stemming from traumatic experiences faced by 2SLGBTQ+ Canadian older adults,(48) it is essential that these factors—along with other unmet needs—be further explored in future research. Finally, while MAiD was legalized in Canada in 2016, this medical intervention was purposefully excluded in this study through the selection of the 2011–2015 period in an effort to reduce the potential of residual confounding. While excluding MAiD could be conceived as a study limitation, the results of this study could be used to inform future MAiD-specific research related to older adults in the province of Ontario.
With an average of 200 deaths of older adults (aged 65+) in Ontario per year for five years (2011–2015), it is important to be aware that suicide exists in the older adult population. Although not an exhaustive list, the factors highlighted in this population-based study provide a better understanding of the complexity of suicide in older adults, and can be used to provide insights for the improvement of programs and policies related to this demographic.
This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care. The study was completed at the ICES Western site, where core funding is provided by the Academic Medical Organization of Southwestern Ontario, the Schulich School of Medicine and Dentistry, Western University, and the Lawson Health Research Institute. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information, IntelliHEALTH Ontario, and Cancer Care Ontario. Parts of this report are based on Ontario Registrar General information on deaths, the original source of which is ServiceOntario (Ministry of Government Services). We thank IQVIA Solutions Canada Inc. for use of their Drug Information Database. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.
We have read and understood the Canadian Geriatrics Journal’s policy on conflicts of interest disclosure and declare no conflicts of interest.
Elements of this study were supported through funding provided by the Lawson Health Research Institute Internal Research Fund (Booth); and through an Early Researcher Award (2017–2022) (Booth) from the Ontario Ministry of Research, Innovation and Science.
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TABLE A1 ICES databases used in the study and their descriptions
TABLE A2 Databases and codes used to define medical conditions
TABLE A3 Databases and codes used to define mortality
TABLE B1 Chronic liver disease (BC_CLD) variable—definitions of codes used(9)
TABLE B2 Chronic kidney disease (BC_CKD) variable—definitions of codes used(9–13)
Table B3 Chronic dialysis user (BC_CDU) variable—definitions of codes used(13,14)
TABLE B4 Mental illness–psychotic disorders (BC_PSY) variable—definitions of codes used(20,21)
TABLE B5 Mental illness–non-psychotic disorders (BC_nPSY) variable—definitions of codes used(20,21)
TABLE B6 Mental illness–substance abuse disorders (BC_SUB) variable—definitions of codes used(20,21)
TABLE B7 Mental illness–social problems and others, not including dementia (BC_OTH) Variable—definitions of codes used(20,21)
TABLE B8 ‘Death by suicide’ (OUT_SUIC) outcome—definitions of codes used(22–26)
TABLE B9 ‘Death by probable suicide’ (OUT_NONSUIC_PROB) outcome—definitions of codes used(22–26)
1 Schultz S, Rothwell D, Chen Z. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chron Dis Injur Canada. 2013;33(3):160–66. http://search.proquest.com/docview/1442472455/
2 Austin P, Daly P, Tu J. A multicenter study of the coding accuracy of hospital discharge administrative data for patients admitted to cardiac care units in Ontario. Am Heart J. 2002;144(2):290–96. https://doi.org/10.1067/mhj.2002.123839
3 Gershon A, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying Patients with physician-diagnosed asthma in health administrative databases. Can Resp J. 2009;16(6):183–88. https://doi.org/10.1155/2009/963098
4 Gershon A, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying individuals with physician diagnosed COPD in health administrative databases. COPD. 2009;6(5):388–94. https://doi.org/10.1080/15412550903140865
5 Hux J, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care. 2002;25(3):512–16. https://doi.org/10.2337/diacare.25.3.512
6 Lipscombe L, Hwee J, Shah L, Booth G, Tu K. Identifying diabetes cases from administrative data: a population-based validation study. BMC Health Serv Res. 2018;18(1):316. https://doi.org/10.1186/s12913-018-3148-0
7 Tu K, Campbell N, Chen Z, Cauch-Dudek K, McAlister F. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1(1):e18–e26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801913/pdf/OpenMed-01-e18.pdf
8 Tu K, Chen Z, Lipscombe L. Prevalence and incidence of hypertension from 1995 to 2005: a population-based study. CMAJ. 2008;178(11):1429–35. https://doi.org/10.1503/cmaj.071283
9 Weisman A, Tomlinson GA, Lipscombe LL, Perkins BA, Hawker GA. Association between allopurinol and cardiovascular outcomes and all-cause mortality in diabetes: A retrospective, population-based cohort study. Diabetes Obesity Metabol. 2019;21(6)1322–29. https://doi.org/10.1111/dom.13656
10 Fleet JL, Dixon SN, Shariff SZ, et al. Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes. BMC Nephrol. 2013;14(1).
11 Haroon NN, Austin PC, Shah BR, Wu J, Gill SS, Booth GL. Risk of dementia in seniors with newly diagnosed diabetes: a population-based study. Diabetes Care. 2015;38(10):1868–75. doi:10.2337/dc15-0491
12 Rosella L, Kornas K, Huang A, Bornbaum C, Henry D, Wodchis WP. Accumulation of chronic conditions at the time of death increased in Ontario from 1994 to 2013. Health Affairs. 2018;37(3):464–72. doi:10.1377/hlthaff.2017.1150
13 Wald R, Quinn RR, Adhikari NK, et al. Risk of chronic dialysis and death following acute kidney injury. Am J Med. 2012;125(6):585–93. doi:10.1016/j.amjmed.2012.01.016
14 Quinn R, Laupacis C, Austin E, et al. Using administrative datasets to study outcomes in dialysis patients: a validation study. Medical Care. 2010;48(8):745–50. https://doi.org/10.1097/MLR.0b013e3181e419fd
15 Widdifield J, Bombardier C, Bernatsky S, et al. An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance. BMC Musculoskel Disord. 2014;15(1):216. https://doi.org/10.1186/1471-2474-15-216
16 Benchimol E, Guttmann A, Mack D, et al. Validation of international algorithms to identify adults with inflammatory bowel disease in health administrative data from Ontario, Canada. J Clin Epidemiol. 2014;67(8):887–96. https://doi.org/10.1016/j.jclinepi.2014.02.019
17 Antoniou T, Zagorski B, Loutfy M, Strike C, Glazier R. Validation of case-finding algorithms derived from administrative data for identifying adults living with human immunodeficiency virus infection. PLoS ONE. 2011;6(6):e21748. https://doi.org/10.1371/journal.pone.0021748
18 Hall S, Schulze K, Groome P, Mackillop W, Holowaty E. Using cancer registry data for survival studies: the example of the Ontario Cancer Registry. J Clin Epidemiol. 2006;59(1):67–76. https://doi.org/10.1016/j.jclinepi.2005.05.001
19 Jaakkimainen RL, Bronskill SE, Tierney M, et al. Identification of physician-diagnosed Alzheimer’s disease and related dementias in population-based administrative data: a validation study using family physicians’ electronic medical records. J Alzheimer Dis. 2016;54(1):337–49. https://pubmed.ncbi.nlm.nih.gov/27567819/. Accessed 24 May 2020.
20 Steele LS, Glazier RH, Lin E, Evans M. Using administrative data to measure ambulatory mental health service provision in primary care. Medical Care. 2004;42(10):960–965. https://doi.org/10.1097/00005650-200410000-00004
21 Simcoe Muskoka District Health Unit. Simcoe Muskoka Health Stats (n.d.). OMHRS (Ontario Mental Health Reporting System). http://www.simcoemuskokahealthstats.org/resources/data-sources/omhrs
22 Statistics Canada. Leading causes of death, total population, by age group. Table 13-10-0394-01. Ottawa: Statistics Canada; 2020. https://doi.org/10.25318/1310039401-eng
23 Saunders NR, Lebenbaum M, Stukel TA, et al. Suicide and self-harm trends in recent immigrant youth in Ontario, 1996–2012: a population-based longitudinal cohort study. BMJ Open. 2017;7(9):e014863. https://doi.org/10.1136/bmjopen-2016-014863
24 World Health Organization. ICD-10 version:2019. Chapter 20—External Causes of Morbidity & Mortality: Internal self-harm (X60–X84. Geneva: WHO; 2020. https://icd.who.int/browse10/2019/en#/X60-X84
25 Canadian Institute for Health Information [CIHI]. Canadian coding standards for version 2018 ICD-10-CA and CCI. Ottawa: CIHI; 2018. https://secure.cihi.ca/free_products/CodingStandards_v2018_EN.pdf
26 ICD9Data.com. Supplementary Classification of External Causes of Injury and Poisoning E000–E999 >. ICD; 2013. Retrieved from http://www.icd9data.com/2013/Volume1/E000-E999/E950-E959/default.htm
27 Fralick M, Thiruchelvam D, Tien HC, Redelmeier DA. Risk of suicide after a concussion. CMAJ. 2016;188(7):497–504. https://doi.org/10.1503/Cmaj.150790
28 Zaheer J, Jacob B, de Oliveira C, Rudoler D, Juda A, Kurdyak P. Service utilization and suicide among people with schizophrenia spectrum disorders. Schizophrenia Res. 2018;202:347–53. doi:10.1016/j.schres.2018.06.025
29 Grigoriadis S, Wilton AS, Kurdyak PA, et al. Perinatal suicide in Ontario, Canada: a 15-year population-based study. CMAJ. 2017;189(34):E1085–E1092. doi:10.1503/cmaj.170088 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5573543/?tool=pmcentrez&report=abstract
Canadian Geriatrics Journal, Vol. 25, No. 2, June 2022