To estimate the prevalence and correlates of disruptive pain in a sample of seriously ill home care patients in the Canadian province of Ontario.
Methods:
The design was a cross-sectional analysis of secondary data from 2757 patients aged 65+.
Results:
Overall, 69.0% (n = 1902) had any level of pain and 41.6% (n = 1146) indicated that their pain disrupted their usual activities. In the univariate analysis of demographics, the risk of disruptive pain decreased significantly with increasing age (P < .0001) and was significantly less common among men (P = .0015). Multivariate analysis showed that unsteady gait (relative risk [RR] = 1.37; 95% confidence interval [CI], 1.18-1.59), arthritis (RR = 1.35; 95% CI, 1.23-1.49), symptoms of depression (RR = 1.24; 95% CI, 1.13-1.37), and declines in social activity that the patient rated as distressing (RR = 1.19; 95% CI, 1.08-1.31) were independently associated with disruptive pain.
Conclusion:
Disruptive pain is highly prevalent in this group, and the key factors associated with this outcome represent physical as well as psychosocial domain areas.
Azevedo Sao Leao FerreiraKKimuraMTeixeiraMJ. The WHO analgesic ladder for cancer pain control, twenty years of use. how much pain relief does one get from using it?Support Care Cancer. 2006;14(11):1086–1093.
2.
WilkieDJEzenwaMO. Pain and symptom management in palliative care and at end of life. Nurs Outlook. 2012;60(6):357–364.
3.
LillieAKReadSMallenCCroftPMcBethJ. Musculoskeletal pain in older adults at the end-of-life: a systematic search and critical review of the literature with priorities for future research. BMC Palliat Care. 2013;12(27):1–8.
4.
AbrahamJL. Management of pain and spinal cord compression in patients with advanced cancer. Ann Intern Med. 1999;131(1):37–46.
5.
FinePGDavisMMuirCSchwindDHaileabB. Bridging the gap: pain medicine and palliative care. Pain Med. 2013;14(9):1277–1279.
6.
StrangP. Emotional and social aspects of cancer pain. Acta Oncol. 1992;31(3):323–326.
7.
BostromBSandhMLundbergDFridlundB. Cancer-related pain in palliative care: patient’s perceptions of pain management. J Adv Nurs. 2004;45(4):410–419.
8.
MehtaAChanLS. Understanding of the concept of “total pain”: a prerequisite for pain control. J Hosp Palliat Nurs. 2008;10(1):26–32.
9.
FischerDJVillinesDKimYOEpsteinJBWilkieDJ. Anxiety, depression, and pain: differences by primary cancer. Support Care Cancer. 2010;18(7):801–810.
10.
StoreyP. The vision of hospice and total pain relief. Am J Hosp Palliat Care. 1996;13(1):40–49.
11.
Committee on Approching Death. Dying in America: Improving Quality and Honouring Individual Preferences Near the End of Life. Washington, DC: Institute of Medicine of the National Academies; 2014.
Van BaarsenB. Suffering, loneliness, and the euthanasia choice: an explorative study. J Soc Work End Life Palliat. 2008;4(3):189–213.
14.
TseMLeungRHoS. Pain and psychological well-being of older persons living in nursing homes: an exploratory study in planning patient-centred intervention. J Adv Nurs .2011;68(2):312–321.
Ontario Ministry of Health and Long-Term Care. Policy for the Operation of Short-Stay Beds under the Long-Term Care Homes Act. Toronto, Ontario: Ministry of Health and Long-Term Care; 2010.
17.
LandiFTuaEOnderG. Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care. 2000;38(12):1184–1190.
18.
MorrisJNFriesBESteelK. Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J Am Geriatr Soc. 1997;45(8):1017–1024.
19.
PossJWJutanNMHirdesJP. A review of evidence on the reliability and validity of Minimum Data Set data. Healthc Manage Forum. 2008;21(1):33–39.
20.
interRAI. Use of InterRAI Instruments—Worldwide. 2015.
21.
HirdesJPFrijtersDHTeareGF. The MDS-CHESS scale: a new measure to predict mortality in institutionalized older people. J Am Geriatr Soc. 2003;51(1):96–100.
22.
FernandesSGuthrieDM. A comparison between end-of-life home care clients with cancer and heart failure in Ontario. Home Health Care Serv Q. 2015;34(1):14–29.
FoxELandrum-McNiffKZhongZ. Evaluation of prognostic criteria for determining hospice eligibility in patients with advanced lung, heart or liver disease. JAMA. 1999;282(17):1638–1645.
25.
FriesBESimonSEMorrisJNFlodstromCBooksteinFL. Pain in US nursing homes: validating a pain scale for the Minimum Data Set. Gerontologist. 2001;41(2):173–179.
26.
MorVAngelelliJJonesRRoyJMooreTMorrisJN. Inter-rater reliability of nursing home quality indicators in the US. BMC Health Serv Res. 2003;3(1):20.
27.
BurrowsABMorrisJNSimonSEHirdesJPPhillipsCD. Development of an MDS-based depression rating scale for use in nursing homes. Age Ageing. 2000;29(2):165–172.
28.
FisherKASeowHBrazilKFreemanSSmithTFGuthrieDM. Prevalence and risk factors of depressive symptoms in a Canadian palliative home care population: a cross-sectional study. BioMed Central. 2014;13(10):1–13.
29.
HartmaierSLSloanePDGuessHAKochGGMitchellCMPhillipsCD. Validation of the Minimum Data Set Cognitive Performance Scale: agreement with the Mini-Mental State Examination. J Gerontol A Biol Sci Med Sci. 1995;50(2):M128–M133.
30.
PaquayLLepeleireJDSchoenmakersBYlieffMFontaineOBuntinxF. Comparison of the diagnostic accuracy of the Cognitive Performance Scale (Minimum Data Set) and the Mini-Mental State Exam for the detection of cognitive impairment in nursing home residents. Int J Geriatr Psychiatry. 2007;22(4):286–293.
31.
MofinaAMGuthrieDM. A comparison of home care quality indicator rates in two Canadian provinces. BioMed Central. 2014;14(37):1–11.
32.
MorrisJNFriesBEMorrisSA. Scaling ADLs within the MDS. J Gerontol A Biol Sci Med Sci. 1999;54(11):M546–M553.
33.
StoleePPossJCookRJByrneKHirdesJP. Risk factors for hip fracture in older home care clients. J Gerontol A Biol Sci Med Sci. 2009;64(3):403–410.
34.
SpiegalmanDHertzmarkE. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162(2):199–200.
35.
GlickmanMESowmyaRRSchultzMR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67(8):850–857.
36.
HosmerDWLemeshowS. Applied Logistic Regression. 2nd ed. New York, NY: John Wiley & Sons; 2000.
37.
CookNR. Statistical evaluation of prognostic versus diagnostic models: beyond the roc curve. Clin Chem. 2008;54(1):17–23.
38.
GadermannAMGuhnMZumboBD. Estimating ordinal reliability for Likert-type and ordinal item response data: a conceptual, empirical, and practical guide. Pract Assess Res Eval. 2012;17(3):1–13.
39.
FloraDBCurranPJ. An empirical evaluation of alternative methods of estimation for confirming factor analysis with ordinal data. Psychol Meth. 2004;9(4):466–491.
40.
The SAS System for Windows[computer program]. Cary, NC: The SAS Institute Inc; 2001.
41.
HirdesJPFriesBEMorrisJN. Home care quality indicators (HCQIs) based on the MDS-HC. Gerontologist. 2004;44(5):665–679.
42.
ScuddsRJOstbyeT. Pain and pain-related interference with function in older Canadians: the Canadian Study of Health and Aging. Disabil Rehabil. 2001;23(15):654–664.
43.
PeatGThomasEHandyJCroftP. Social networks and pain interference with daily activties in middle and old age. Pain. 2004;112(3):397–405.
44.
ReynoldsKSHansonLCDeVellisRFHendersonMSteinhauserKE. Disparities in pain management between cognitively intact and cognitively impaired nursing home residents. J Pain Symptom Manage. 2008;35(4):388–396.
45.
SmithAKCenzerISKnightSJ. The epidemiology of pain during the last two years of life. Ann Intern Med. 2010;153(9):563–569.
46.
OsborneTLJensenMPEhdeDMhanleyMAKraftG. Psychosocial factors associated with pain intensity, pain-related interference, and psychological functioning in persons with multiple sclerosis and pain. Pain. 2007;127(1-2):52–62.
47.
ArolaHMNichollsEMallenCThomasE. Self-reported pain interference and symptoms of anxiety and depression in community-dwelling older adults: can a temporal relationship be determined?. Eur J Pain. 2010;14(9):966–971.
48.
SyrjalaKLChapkoME. Evidence for a biopsychosocial model of cancer treatment-related pain. Pain. 1995;61(1):69–79.
49.
PutzkeJDRichardsJSHickenBLDeVivoMJ. Interference due to pain following spinal cord injury: important predictors and impact on quality of life. Pain. 2002;100:231–242.
50.
HigginsonIJEvansCJ. What is the evidence that palliative care teams improve outcomes for cancer patients and their families?Cancer J. 2010;16(5):423–435.
51.
JordhoyMSFayersPSaltnesTAhlner-ElmqvistMJannertMKaasaS. A palliative-care intervention and death at home: a cluster randomised trial. Lancet. 2000;356(9233):888–893.
52.
GreerJAJacksonVAMeierDETemelJS. Early integration of palliative care services with standard oncology care for patients with advanced cancer. CA Cancer J Clin. 2013;63(5):350–363.
53.
TemelJSGreerJAMuzikanskyA. Early palliative care for patients with metastic non-small-cell lung cancer. N Engl J Med.2010;363(8):733–742.
54.
FisherKASeowHDeclercqAFreemanSCohenJGuthrieDM. Patient characteristics associated with prognostic awareness: a study on a Canadian palliative care population using the interRAI palliative care instrument. J Pain Symptom Manage. 2015; 49(4):716–725.
55.
interRAI. InterRAI Palliative Care (PC) Assessment Form and User’s Manual, 9.1. Ann Arbor, MI: InterRAI; 2014.
56.
PaetkauSSwitzerGKasperkiJ. Advancing high quality, high value palliative care in Ontario: declaration of partnership and commitment to action. 2013. Report No.: 5.
