Skip to main content

Advertisement

We’d like to understand how you use our websites in order to improve them. Register your interest.

Severe pain at the end of life: a population-level observational study

Abstract

Background

Pain is a prevalent symptom at the end of life and negatively impacts quality of life. Despite this, little population level data exist that describe pain frequency and associated factors at the end of life. The purpose of this study was to explore the prevalence of clinically significant pain at the end of life and identify predictors of increased pain.

Methods

Retrospective population-level cohort study of all decedents in Ontario, Canada, from April 1, 2011 to March 31, 2015 who received a home care assessment in the last 30 days of life (n = 20,349). Severe daily pain in the last 30 days of life using linked Ontario health administrative databases. Severe pain is defined using a validated pain scale combining pain frequency and intensity: daily pain of severe intensity.

Results

Severe daily pain was reported in 17.2% of 20,349 decedents. Increased risk of severe daily pain was observed in decedents who were female, younger and functionally impaired. Those who were cognitively impaired had a lower risk of reporting pain. Disease trajectory impacted pain; those who died of a terminal illness (i.e. cancer) were more likely to experience pain than those with frailty (odds ratio 1.66).

Conclusion

Pain is a common fear of those contemplating end of life, but severe pain is reported in less than 1 in 5 of our population in the last month of life. Certain subpopulations may be more likely to report severe pain at the end of life and may benefit from earlier palliative care referral and intervention.

Peer Review reports

Background

Uncontrolled pain is consistently listed by patients as a primary source of fear for end-of-life care [1,2,3]. Palliative care aims to provide relief of pain and other physical symptoms in addition to supportive care for patients and their families at the end of life [4, 5]. Pain is often considered one of the more treatable symptoms in palliative care [6] and a request for assistance with pain management is a common reason for referral to palliative care physician specialists and palliative care teams. Uncontrolled pain is a common reason for palliative patients to present to acute care. Nearly one in ten emergency department visits from oncology patients in the last months of life cited pain as reason for visit [7]. Additionally, nearly 20% of patients who die in hospital experience some degree of pain [8]. Identification of those patients at risk for increased pain near the end of life is important for prompt initiation of a palliative approach and consideration of specialist palliative care referral [6, 9] as there is evidence that pain may be mitigated by palliative care intervention and home visits [10].

The bulk of the current data on the prevalence of pain is limited to specific populations. A systematic review examining studies between 1965 and 2006 demonstrated the pooled prevalence of pain in patients with advanced cancer was 64% [11]. Additionally, increased pain has been reported in advanced cancer patients with mental health illnesses, including depression and anxiety [12,13,14]. Estimates of the prevalence of pain in various late stage non-malignant populations [i.e., congestive heart failure (CHF), end-stage renal disease, chronic obstructive pulmonary disease (COPD)] range from 47 to 93% [15,16,17]. Studies of pain in persons with dementia have consistently demonstrated lower rates of reported pain [18, 19]. These studies, however, do not provide a sense of the prevalence of pain across the general population at end of life nor between disease trajectories (frailty, terminal illness, organ failure, sudden death). This is important as current evidence demonstrates disparities between disease trajectory and access to palliative care services [20]. An American retrospective observational study (N = 4703) demonstrated clinically significant pain in 47% of the population in the last month of life (as reported using non-validated 2 question measurement: participant “often troubled by moderate to severe pain”) [21]. The authors found pain was associated with proximity to death, arthritis and certain demographic factors such as sex, age, race and income. To our knowledge, no studies to date have captured in detail how pain varies across end-of-life trajectories, a wide variety of comorbid chronic diseases, home-based palliative care services, living arrangement (e.g., presence of a family caregiver) and other important patient characteristics such as impairment in function and cognition.

Our goal was to explore pain at the end of life across a wide variety of patient characteristics at a population level. To address the deficit in knowledge, we used multiple health linked databases providing access to detailed covariates in order to observe the frequency and severity of pain in the last month of life. We aimed to identify predictive or protective factors for pain at the end of life as well as potential risk factors that could be targeted for screening and prompt initiation of pain management strategies and palliative care referral.

Methods

We conducted a population-based retrospective observational study using linked health administrative databases held at ICES. Our population included all decedents in Ontario, Canada from April 1, 2011 to March 31, 2015 (most recent, complete data available at time of analysis) who received a Resident Assessment Instrument–Home Care (RAI-HC) [22] assessment in the last 30 days of life. The RAI-HC database contains RAI-HC assessments which are conducted for all Ontarians seeking to receive long-stay home care (i.e., anticipated greater than 60 days). These assessments are conducted by trained assessors with input from the clinic team, the patient’s chart, the patient, and caregivers. Demographics, symptomatology, and detailed covariates were collected from each assessment. These covariates include: cognitive functioning, caregiver and living arrangements, activities of daily living (ADLs) on a 0–6 point performance scale (describing the discrete stages of loss in personal hygiene, toileting, locomotion and eating), instrumental activities of daily living (IADLs) (ordinary housework, meal preparation and phone use) [23]. Ethics approval was obtained from the Sunnybrook Health Sciences Centre Research Ethics Board in Toronto, Canada and from the Ottawa Health Science Network Research Ethics Board in Ottawa, Canada.

Data sources

Encrypted health card numbers were used as unique identifiers and linked across several administrative databases held at ICES (Additional file 1). All data were de-identified and anonymized. Deaths and demographics including age and sex were captured from the Registered Persons Database (RPDB). Postal codes of residence were used to derive neighborhood income and rurality at the time of death through the Postal Code Conversion Files which are derived from the Statistics Canada 2011 census. The presence of chronic conditions at death was captured using previously developed—and in some cases validated— chronic disease databases held at ICES [24]. A total of 17 chronic diseases were examined and the number of diseases identified was totaled for each individual [25,26,27,28,29,30,31]. End-of-life trajectories (i.e., frailty, terminal illness, sudden death, organ failure, other) were captured using cause of death information from the Ontario Registrar General Database (ORGD) – deaths. The International Classification of Diseases (ICD-10) codes used to group deaths into these four categories, including validation in the Canadian population, are described elsewhere [20, 32,33,34].

Designated palliative homecare (e.g., from nurses, nurse practitioners, and personal support workers) and physician home visits were captured between 30 days to 6 months prior to death. Palliative home care was captured when a patient was given an end-of-life designation by home care services, which allows them to access additional and often specialized palliative care services. Physician home visits were identified using physician billing claims for services delivered at home, captured in the Ontario Health Insurance Plan (OHIP) database (Additional file 2). The subset of home visits delivered by palliative care physician specialists were identified using a validated definition of greater than 10% of all billings in the previous 2 years classified as palliative care [35]. Palliative home visits and services delivered by non-physician specialties (e.g. nurse practitioners, spiritual care, personal support workers, social workers, etc.) that occurred outside of designated publicly-funded palliative home care (i.e. out-pf-pocket expenses or private insurance) is not captured in available health administrative databases and were therefore not included in our analyses.

Pain at end of life

Reported pain was captured using the RAI-HC database. Data was captured from those who received a RAI-HC assessment in the last month of life, the period associated with the highest pain scores [21]. A validated pain scale that combines pain intensity and frequency from the RAI-HC was applied to generate a four-point pain scale from no pain to severe pain occurring daily [36]. In this scale, severe daily pain was equivalent to an average of 5/10 on a visual analog scale. As pain beyond 4/10 has been shown to be associated with decreased functional status and quality of life [37, 38], we elected to compare decedents with severe daily pain to those without severe daily pain.

Analysis

A logistic regression model was run for the primary outcome of severe daily pain in the last 30 days of life. Decedents with severe daily pain were compared to those without severe daily pain. Covariates of interest included demographics, comorbidities, functional status, and physician home visits in the 6 months to 1 month prior to death. Additionally, we examined the effect of a palliative care specialist being involved in at least one of the visits. The multivariable model examined the independent effect of potential predictors of pain that are available in health administrative databases: age, sex, neighborhood income quintile, rurality, functional status (i.e. ADLs and IADLs), Cognitive Performance Scale (CPS) [39] score, number of comorbidities, and end-of-life trajectories. All analyses were conducted using SAS 9.3 (SAS Institute Inc., Cary, NC).

Results

In Ontario, between April 1, 2011 to March 31, 2015, there were 370,524 deaths. We captured data from 20,349 decedents who received a RAI-HC assessment in the last month of life (5.5% of total decedent population). The average age of our cohort was 81.4 years. The majority were female (51.6%) and lived in an urban setting. 42.8% had 5 or more chronic conditions. Less than 1 in 5 people (17.2%) reported severe daily pain using the validated pain scale (Table 1), with 30.3% of decedents reporting no pain. The majority (73.8%) felt they had adequate pain control at baseline or with medications, however 42.4% described pain that disrupted usual activities.

Table 1 Reported pain in decedents with a RAI-HCa assessment in the last 30 days of life

Factors associated with severe daily pain

Demographics

The proportion of severe daily pain was higher in those who died at a younger age (Fig. 1a).

Fig. 1
figure1

a. Pain scale percentages stratified by age in years. b. Resident Assessment Instrument-Home Care pain scale percentages stratified by sex

Among female decedents, 18.4% reported severe daily pain compared to 15.9% of male decedents (Fig. 1b; Table 2). Younger decedents had a higher risk severe daily pain; 34.0% of 0–49-year-olds compared to only 13.3% of those aged 90+. Rurality and income were not found to significantly impact risk of severe daily pain. Those with 5+ chronic conditions reported more severe daily pain (17.8%) than those with 0–2 or 3–4 (17.5 and 16.3% respectively).

Table 2 Cohort characteristics by pain severity in the last 30 days of life

Reported severe daily pain varied with living arrangements (Table 3): decedents who lived in a private community home with or without homecare reported higher severe daily pain (17.5, 18.2%) than those who lived in an assisted living or residential care facility (15.9, 14.5%). Those who lived with relatives were more likely to report severe daily pain (with spouse:18.4%, with spouse and others:19.0%, with child:18.7%) compared to those who lived alone (17.1%) or with non-relatives (15.3%). Decedents with reported caregiver stress had increased pain compared to those with no caregiver stress (18.3% vs. 16.4%).

Table 3 Cohort characteristics by pain severity in the last 30 days of life

Functional status

In examining ADLs (Table 3), reported severe daily pain was highest in those who were dependent (19.5%) and lowest in those who were totally dependent (15.8%). Similarly, pain severity generally trended up with increasing impairment in IADLs to a maximum of great difficulty in 2 out of 3 IADLs as collected on the RAI-HC (20.1%). Those decedents with great difficulty carrying out all three IADLs reported lower than average severe daily pain (14.7%).

Clinical factors

Reported severe daily pain decreased with worsening cognitive impairment, with 20.3% of cognitively intact persons reporting severe daily pain compared to 12.8% with very severe cognitive impairment. Pain scores varied with end-of-life trajectory. Those with frailty (e.g., dementia), organ failure (e.g., COPD or CHF) and sudden death had a lower proportion reporting severe daily pain than those with terminal illness (e.g., cancer) (Table 3). The following chronic conditions were associated with increased risk of severe daily pain (Table 2): rheumatoid arthritis (23.9%), mood and anxiety disorders (19.5%), renal failure (19.4%), cancer (19.3%), osteoarthritis (19.1%) and other mental health illness (18.7). Many cardiac conditions (acute myocardial infarction, congestive heart failure, hypertension) as well as chronic neurological conditions [history of stroke (16.0%) and dementia (10.6%)] were associated with lower than average reports of severe daily pain.

Physical symptoms as reported on the RAI-HC associated with higher severe daily pain include dyspnea (19.2%), anorexia (22.2%), emesis (29.5%), constipation (31.4%) and edema (20.2%) (Table 4). Increasing severity of pressure ulcers were also associated with higher rates of pain. Additionally, psychological symptoms such as loneliness and sad mood were associated with increased reports of severe daily pain.

Table 4 Symptomology self-reported in RAI-HCa by pain severity in the last 30 days of life

System factors

A minority of decedents received designated palliative home care or a physician home visit between 30 days to 6 months prior to death, at 22.9 and 13.2% respectively. Decedents who received designated palliative home care had higher severe daily pain in the last 30 days of life than those without (21.8% vs 15.9%). A trend was also demonstrated toward increased pain in those who received a physician home visit. Pain trended upward with time since self-reported admission to hospital with 14.8% of those in hospital versus 19.9% in those who had not reported a hospitalization in the previous 180 days.

Logistic regression models for odds of severe daily pain

Adjusting for multiple covariates as listed in our methods, females had greater odds of having severe daily pain [OR = 1.25; 95% Confidence Interval (CI): 1.16 to 1.35] (Table 5). The odds ratio of severe daily pain was 0.31 in the decedents aged 90+ compared to 0–49 (95% CI: 0.23 to 0.42). Those with severe or very severe cognitive impairment had an OR of 0.68 and 0.52, respectively, compared to those who were cognitively intact. When examining disease trajectory, compared to frailty, those with terminal illness were more likely to report severe daily pain (OR 1.66, (95% CI: 1.46 to 1.88). Decedents with designated palliative home care had greater odds of increased pain compared to those without [OR 1.13 (95% CI: 1.03 to 1.24)]. Conversely, the trend seen with physician home visits was no longer statistically significant for specialist or non-specialist home visits when all covariates were accounted for [OR 1.12 (95% CI: 0.99 to 1.26) and 1.14 (95% CI: 0.91 to 1.44)].

Table 5 Multivariate logistic regression for factors associated with severe daily pain among the last 30 days of life

Discussion

We examined the proportion of severe daily pain reported in the last 30 days of life using population-based administrative databases. We observed that less than 1 in 5 decedents (17.2%) report severe daily pain. This level of pain is considered inadequately treated and would likely be associated with lower quality of life and functional impairment [37, 38]. We identified multiple demographic, clinical and system factors associated with increased end-of-life pain, many of which have not been previously described. Notably, disease trajectory impacted reported severe daily pain at the end of life. Those with terminal illness (i.e. cancer) and other had higher odds of reporting pain than those with frailty, sudden death or organ failure (cardiac or pulmonary). Interestingly, renal failure is categorized into the other disease trajectory and was associated with increased reported pain. Although this is a condition that is not typically considered inherently painful, it is possible that pain in this population may be undertreated, possibly due to fear of using analgesic medications that may worsen renal function or are renally cleared. Additionally, increased pain reported by females and younger decedents could be hypothesized to be related to the specific illness or trajectory related to these populations; however, this trend is persistent when disease trajectory was accounted for. The increased reported pain in those receiving palliative services may have been related to referral bias where those with increased pain are more likely to receive a palliative care referral. However, only a small minority received a palliative home care designation or physician home visit despite being close to death. This is consistent with other jurisdictions signaling large room for improvement in access to palliative care services [35, 40].

Our study addresses a gap in the previous literature by examining end-of-life pain in a large sample, using a validated pain scale and conducting analyses adjusting for multiple potential confounders. The proportion of pain reported in this study is lower than previously reported by other population research [21]. This may be attributed to our study examining those with daily severe pain compared to previous research including intensity (moderate-severe) but not considering frequency when determining clinical significance. Previous studies [11,12,13, 21] have demonstrated an association between pain and select comorbidities: arthritis, cancers and mental health conditions, which was again shown in our population. We demonstrated lower reported pain in persons with neurological impairment (dementia and post-stroke). Decreased reported pain in those with reduced cognitive functioning was maintained with confounders such as age, frailty and gender accounted for. This is consistent with previous studies demonstrating that pain may be underreported in those with cognitive impairment [18, 19]. It is difficult to infer if perceived pain levels are in fact lower or if those with cognitive impairment are unable to vocalize pain.

Strengths and limitations

We examined a wide array of health care services at the end of life for a large, population-based decedent cohort. This is possible in Ontario, comprising of approximately 40% of the Canadian population, where well-developed health administrative databases are linked at an individual level for a range of publicly-funded health services. Previous studies have focused on specific populations or had limited access to other health care services utilized by decedents. We recognize the data used for this study is relatively old, although there were no significant policy or practice changes since 2015 that would reasonably be expected to influence the relevance of our findings to current practice. While used widely as a clinical assessment tool in many settings, we also acknowledge that the validation for the RAI-HC pain scale was completed in elderly patients in nursing homes, potentially limiting the generalizability of this scale. Additionally, one of our primary limitations is that our data is collected from those who have received a RAI-HC assessment in the last month of life. This may limit the generalizability to those in long-term care home (nursing home), community, or hospital settings who have not been assessed for publicly funded home services (about 40% of decedent population) [41]. This approach also does not capture palliative home care received through private (out-of-pocket) expenses or nurse practitioner palliative home visits. Nevertheless, the RAI-HC provided us with a rare large population-based cohort that contained detailed information about patient-centered variables and outcomes (symptoms, living arrangements, caregiver information), beyond what has previously been presented in literature.

Conclusion

We observed multiple demographic, clinical and system factors associated with increased pain at the end of life. Clinicians should recognize severe daily pain is common but perhaps not proportional to the fear of suffering in pain that many experience when contemplating end of life [2]. Regardless this is still a significant number of people who report severe pain, and prompt screening and management of pain should be considered, particularly for those with increased risk factors. Improvements in access and quality of care likely would reduce the prevalence of severe pain at the end of life, given previous studies showing large gaps in palliative care provision [41].

Availability of data and materials

The data that support the findings of this study are available from ICES, but restrictions apply to the availability of these data according to ICES policies and provincial and federal privacy laws to protect individual patient data, and so are not publicly available. As the data custodian, all requests for data should go through ICES. Please contact the corresponding author (PT) should you have questions about accessing study data.

Abbreviations

CHF:

Congestive heart failure

COPD:

Chronic obstructive pulmonary disease

RAI-HC:

Resident assessment instrument – home care

ADLs:

Activities of daily living

IADLs:

Instrumental activities of daily living

RPDB:

Registered Persons Database

ORGD:

Ontario Registrar General Database

ICD-10:

International classification of diseases

OHIP:

Ontario health insurance plan

CPS:

Cognitive performance scale

OR:

Odds ratio

CI:

Confidence interval

References

  1. 1.

    Steinhauser KE, Christakis NA, Clipp EC, McNeilly M, McIntyre L, Tulsky JA. Factors considered important at the end of life by patients, family, physicians, and other care providers. JAMA. 2000;284(19):2476–82.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    BMA End-of-Life Care and Physician-Assisted Dying Steering Group: End-of-life care and physician-assisted dying. 3 Reflections and recommendations, https://www.bma.org.uk/endoflifecare. Accessed 17 November 2018.

  3. 3.

    Boström B, Sandh M, Lundberg D, Fridlund B. Cancer-related pain in palliative care: patients’ perceptions of pain management. J Adv Nurs. 2004;45(4):410–9.

    PubMed  Article  Google Scholar 

  4. 4.

    Kaasa S, Loge JH. Quality of life in palliative care: principles and practice. Palliat Med. 2003;17(1):11–20.

    PubMed  Article  Google Scholar 

  5. 5.

    Von Gunten CF. Interventions to manage symptoms at the end of life. J Palliat Med. 2005;8(Suppl 1):s88–94.

    Article  Google Scholar 

  6. 6.

    Yang GM, Ewing G, Booth S. What is the role of specialist palliative care in an acute hospital setting? A qualitative study exploring views of patients and carers. Palliat Med. 2012;26(8):1011–7.

    PubMed  Article  Google Scholar 

  7. 7.

    Barbera L, Taylor C, Dudgeon D. Why do patients with cancer visit the emergency department near the end of life? Can Med Assoc J. 2010;182(6):563–8.

    Article  Google Scholar 

  8. 8.

    Gonzales MJ, Pantilat SZ. Pain at the end of life. Hosp Med Clin. 2012;1(1):e109–23.

    Article  Google Scholar 

  9. 9.

    Johnson CE, Girgis A, Paul CL, Currow DC. Cancer specialists’ palliative care referral practices and perceptions: results of a national survey. Palliat Med. 2008;22(1):51–7.

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Higginson IJ, Finlay IG, Goodwin DM, Hood K, Edwards AG, Cook A, et al. Is there evidence that palliative care teams alter end-of-life experiences of patients and their caregivers? J Pain Symptom Manag. 2003;25(2):150–68.

    Article  Google Scholar 

  11. 11.

    Van den Beuken-van Everdingen MHJ, De Rijke JM, Kessels AG, Schouten HC, van Kleef M, Patin J. Prevalence of pain in patients with cancer: a systematic review of the past 40 years. Ann Oncol. 2007;18(9):1437–49.

    Article  Google Scholar 

  12. 12.

    Spiegel D, Sands S, Koopman C. Pain and depression in patients with cancer. Cancer. 1994;74(9):2570–8.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Delgado-Guay M, Parsons HA, Li Z, Palmer JL, Bruera E. Symptom distress in advanced cancer patients with anxiety and depression in the palliative care setting. Support Care Cancer. 2009;17(5):573–9.

    Article  Google Scholar 

  14. 14.

    Laird BJA, Scott AC, Colvin LA, McKeon AL, Murray GD, Fearon KC, et al. Pain, depression, and fatigue as a symptom cluster in advanced cancer. J Pain Symptom Manag. 2011;42(1):1–11.

    Article  Google Scholar 

  15. 15.

    Solano JP, Gomes B, Higginson IJ. A comparison of symptom prevalence in far advanced cancer, AIDS, heart disease, chronic obstructive pulmonary disease and renal disease. J Pain Symptom Manag. 2006;31(1):58–69.

    Article  Google Scholar 

  16. 16.

    Murtagh FEM, Addington-Hall J, Higginson IJ. The prevalence of symptoms in end-stage renal disease: a systematic review. Adv Chronic Kidney Dis. 2007;14(1):82–99.

    PubMed  Article  Google Scholar 

  17. 17.

    Zambroski CH, Moser DK, Bhat G, Ziegler C. Impact of symptom prevalence and symptom burden on quality of life in patients with heart failure. Eur J Cardiovasc Nurs. 2005;4(3):198–206.

    PubMed  Article  Google Scholar 

  18. 18.

    Radbruch L, Sabatowski R, Loick G, Jonen-Thielemann I, Kasper M, Gondek B, et al. Cognitive impairment and its influence on pain and symptom assessment in a palliative care unit: development of a minimal documentation system. Palliat Med. 2000;14(4):266–76.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Dubé CE, Mack DS, Hunnicutt JN, Lapane KL. Cognitive impairment and pain among nursing home residents with cancer. J Pain Symptom Manag. 2018;55(6):1509–18.

    Article  Google Scholar 

  20. 20.

    Seow H, O’Leary E, Perez R, Tanuseputro P. Access to palliative care by disease trajectory: a population-based cohort of Ontario decedents. BMJ Open. 2018;8(4):e021147.

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Smith AK, Cenzer IS, Knight SJ, Puntillo KA, Widera E, Williams BA, et al. The epidemiology of pain during the last 2 years of life. Ann Intern Med. 2010;153(9):563–9.

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Home Care (HC) – interRAI, http://www.interrai.org/home-care.html. Accessed 17 November 2018.

  23. 23.

    Describing Outcome Scales (RAI-MDS 2.0). Canadian Institute for Health Information, https://www.cihi.ca/en/outcome_rai-mds_2.0_en.pdf. Accessed 17 November 2018.

  24. 24.

    Muggah E, Graves E, Bennett C, Manuel DG. The impact of multiple chronic diseases on ambulatory care use; a population based study in Ontario, Canada. BMC Health Serv Res. 2012;12:452.

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Thavorn K, Maxwell CJ, Gruneir A, Bronskill SE, Bai Y, Pefoyo AJK, et al. Effect of socio-demographic factors on the association between multimorbidity and healthcare costs: a population-based, retrospective cohort study. BMJ Open. 2017;7(10):e017264.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Mondor L, Maxwell CJ, Hogan DB, Bronskill SE, Gruneir A, Lane NE, et al. Multimorbidity and healthcare utilization among home care clients with dementia in Ontario, Canada: a retrospective analysis of a population-based cohort. PLoS Med. 2017;14(3):e1002249.

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Petrosyan Y, Bai YQ, Pefoyo AJK, Gruneir A, Thavorn K, Maxwell CJ, et al. The relationship between diabetes care quality and diabetes-related hospitalizations and the modifying role of comorbidity. Can J Diabetes. 2017;41(1):17–25.

    PubMed  Article  Google Scholar 

  28. 28.

    Mondor L, Maxwell CJ, Bronskill SE, Gruneir A, Wodchis WP. The relative impact of chronic conditions and multimorbidity on health-related quality of life in Ontario long-stay home care clients. Qual Life Res. 2016;25(10):2619–32.

    PubMed  Article  Google Scholar 

  29. 29.

    Lane NE, Maxwell CJ, Gruneir A, Bronskill SE, Wodchis WP. Absence of a socioeconomic gradient in older adults’ survival with multiple chronic conditions. EBioMedicine. 2015;2(12):2094–100.

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Gruneir A, Bronskill SE, Maxwell CJ, Bai YQ, Kone AJ, Thavorn K, et al. The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study. BMC Health Serv Res. 2016;16:154.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Pefoyo AJK, Bronskill SE, Gruneir A, Calzavara A, Thavorn K, Petrosyan Y, et al. The increasing burden and complexity of multimorbidity. BMC Public Health. 2015;15:415.

    PubMed  Article  Google Scholar 

  32. 32.

    Lunney JR, Lynn J, Foley DJ, Lipson S, Guralnik JM. Patterns of functional decline at the end of life. JAMA. 2003;289(18):2387–92.

    PubMed  Article  Google Scholar 

  33. 33.

    Vital Statistics - Death Database (CVSD). Statistics Canada, http://www23.statcan.gc.ca/imdb/p2SV.pl? Function=getSurvey&SDDS=5047 (2011, accessed 19 March 2020).

  34. 34.

    Fassbender K, Fainsinger RL, Carson M, Finegan BA. Cost trajectories at the end of life: the Canadian experience. J Pain Symptom Manag. 2009;38(1):75–80.

    Article  Google Scholar 

  35. 35.

    Tanuseputro P, Beach S, Chalifoux M, Wodchis WP, Hsu AT, Seow H, et al. Associations between physician home visits for the dying and place of death: a population-based retrospective cohort study. PLoS One. 2018;13(2):e0191322.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Fries BE, Simon SE, Morris JN, Flodstrom C, Bookstein FL. Pain in U.S. nursing homes: validating a pain scale for the minimum data set. Gerontologist. 2001;41(2):173–9.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Wang XS, Cleeland CS, Mendoza TR, Engstrom MC, Liu S, Xu G, et al. The effects of pain severity on health-related quality of life: a study of Chinese cancer patients. Cancer Interdiscip Int J Am Cancer Soc. 1999;86(9):1848–55.

    CAS  Google Scholar 

  38. 38.

    Serlin RC, Mendoza TR, Nakamura Y, Edwards KR, Cleeland CS. When is cancer pain mild, moderate or severe? Grading pain severity by its interference with function. Pain. 1995;61(2):277–84.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Morris JN, Fries BE, Mehr DR, Hawes C, Phillips C, Mor V, et al. MDS cognitive performance scale©. J Gerontol. 1994;49(4):M174–82.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Åbom B, Kragstrup J, Vondeling H, Bakketeig LS, Stovring H. Defining cancer patients as being in the terminal phase: who receives a formal diagnosis, and what are the effects? J Clin Oncol. 2005;23(30):7411–6.

    Article  Google Scholar 

  41. 41.

    Tanuseputro P, Budhwani S, Bai YQ, Wodchis WP. Palliative care delivery across health sectors: a population-level observational study. Palliat Med. 2017;31(3):247–57.

    PubMed  Article  Google Scholar 

Download references

Acknowledgments

Not applicable.

Funding

This research was supported by a research grant from the Bruyère Centre for Individualized Health and from the Ontario Ministry of Health and Long-Term Care (MOHLTC) to the Ontario QUILT (QUality for Individuals who require Long-Term support) Network (grant ID #255). This study was also supported by ICES, which is funded by an annual grant from the Ontario MOHLTC. The views expressed in this paper are the views of the authors and do not necessarily reflect those of the funders. The funders had no influence on the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Author information

Affiliations

Authors

Contributions

All authors had 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. MH, SB, and PT conceived and designed the study. RT acquired the data and conducted statistical analysis. MH, SB, RT, JL, and PT interpreted the data. MH drafted the manuscript. All authors provided revisions for important intellectual content and approved the final version for publication.

Corresponding author

Correspondence to Peter Tanuseputro.

Ethics declarations

Ethics approval and consent to participate

Ethics approval was obtained from the Sunnybrook Health Sciences Centre Research Ethics Board in Toronto, Canada and from the Ottawa Health Science Network Research Ethics Board in Ottawa, Canada.

Consent for publication

Not applicable.

Competing interests

The authors declare they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hagarty, A.M., Bush, S.H., Talarico, R. et al. Severe pain at the end of life: a population-level observational study. BMC Palliat Care 19, 60 (2020). https://doi.org/10.1186/s12904-020-00569-2

Download citation

Keywords

  • Pain
  • End-of-life
  • Palliative care
  • Palliative medicine
  • Palliative homecare