Advertisement

Probable Delirium and Associated Patient Characteristics in Long-Term Care and Complex Continuing Care: A Population-Based Observational Study

      Abstract

      Objectives

      To estimate the prevalence of probable delirium in long-term care (LTC) and complex continuing care (CCC) settings and to describe the resident characteristics associated with probable delirium.

      Design

      Population-based cross-sectional study using routinely collected administrative health data.

      Setting and Participants

      All LTC and CCC residents in Ontario, Canada, assessed with the Resident Assessment Instrument–Minimum Dataset (RAI-MDS) assessment between July 1, 2016, and December 31, 2016 (LTC n=86,454, CCC n=10,217).

      Methods

      Probable delirium was identified via the delirium Clinical Assessment Protocol on the RAI-MDS assessment, which is triggered when individuals display at least 1 of 6 delirium symptoms that are of recent onset and different from their usual functioning. RAI-MDS assessments were linked to demographic and health services utilization databases to ascertain resident demographics and health status. Multivariable logistic regression was used to identify characteristics associated with probable delirium, with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) reported.

      Results

      Delirium was probable in 3.6% of LTC residents and 16.5% of CCC patients. LTC patients displayed fewer delirium symptoms than CCC patients. The most common delirium symptom in LTC was periods of lethargy (44.6% of delirium cases); in CCC, it was mental function varying over the course of the day (63.5% of delirium cases). The odds of probable delirium varied across individual demographics and health characteristics, with increased health instability having the strongest association with the outcome in both care settings (LTC: OR 30.4, 95% CI 26.2-35.3; CCC: OR 21.0, 95% CI 16.7-26.5 for high vs low instability).

      Conclusions and Implications

      There were differences in the presentation and burden of delirium symptoms between LTC and CCC, potentially reflecting differences in delirium severity or symptom identification. Several risk factors for probable delirium in LTC and CCC were identified that may be amenable to interventions to prevent this highly distressing condition.

      Keywords

      Delirium is a neurocognitive complication that manifests as disturbances of attention and cognition, perceptual and delusional abnormalities, and other behavioral disturbances ranging from extreme agitation to hypoactivity.
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      Risk factors are multifactorial, including predisposing (eg, older age and dementia) and precipitating factors (eg, infection and medications).
      • Inouye S.K.
      • Charpentier P.A.
      Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability.
      • Pompei P.
      • Foreman M.
      • Rudberg M.A.
      • et al.
      Delirium in hospitalized older persons: Outcomes and predictors.
      • Lawlor P.G.
      • Gagnon B.
      • Mancini I.L.
      • et al.
      Occurrence, causes, and outcome of delirium in patients with advanced cancer: A prospective study.
      • Inouye S.K.
      Prevention of delirium in hospitalized older patients: Risk factors and targeted intervention strategies.
      • Bohlken J.
      • Kostev K.
      Prevalence and risk factors for delirium diagnosis in patients followed in general practices in Germany.
      Delirium can have negative consequences on patient care as it hinders communication and clinical assessment.
      • Lawlor P.G.
      • Bush S.H.
      Delirium in patients with cancer: Assessment, impact, mechanisms and management.
      The symptoms also cause distress for the patient, family, and health care providers.
      • Breitbart W.
      • Gibson C.
      • Tremblay A.
      The delirium experience: Delirium recall and delirium-related distress in hospitalized patients with cancer, their spouses/caregivers, and their nurses.
      ,
      • Morita T.
      • Hirai K.
      • Sakaguchi Y.
      • et al.
      Family-perceived distress from delirium-related symptoms of terminally ill cancer patients.
      Delirium is associated with negative outcomes, including new or worsening cognitive impairment, hospitalization, and death.
      • Babine R.L.
      • Hyrkäs K.E.
      • Bachand D.A.
      • et al.
      Falls in a tertiary care hospital—association with delirium: A replication study.
      • Lakatos B.E.
      • Capasso V.
      • Mitchell M.T.
      • et al.
      Falls in the general hospital: Association with delirium, advanced age, and specific surgical procedures.
      • Bickel H.
      • Gradinger R.
      • Kochs E.
      • Förstl H.
      High risk of cognitive and functional decline after postoperative delirium.
      • MacLullich A.M.J.
      • Beaglehole A.
      • Hall R.J.
      • Meagher D.J.
      Delirium and long-term cognitive impairment.
      • Pitkala K.H.
      • Laurila J.V.
      • Strandberg T.E.
      • Tilvis R.S.
      Prognostic significance of delirium in frail older people.
      • Morandi A.
      • Di Santo S.G.
      • Zambon A.
      • et al.
      Delirium, dementia, and in-hospital mortality: The results from the Italian Delirium Day 2016, a national multicenter study.
      • McCusker J.
      • Cole M.
      • Abrahamowicz M.
      • et al.
      Delirium predicts 12-month mortality.
      Individuals in continuing care settings, including long-term care (LTC, ie, nursing homes that provide 24-hour nursing and personal care) and complex continuing care (CCC, ie, nonacute facilities that provide specialized complex medical care and regular physician and nursing care for individuals with long-term illnesses or disabilities), are particularly vulnerable to delirium. Yet, we have limited understanding of delirium occurrence and risk in these settings. Much of the research on delirium in older adults focuses on patients in acute care, often in surgical or intensive care settings, who likely have different baseline health status and prognosis than individuals in continuing care, who in contrast have been relatively understudied. Evidence of delirium risk factors from acutely ill hospitalized patients may not generalize to the continuing care setting.
      This study aims to fill this knowledge gap by estimating the prevalence of probable delirium in LTC and CCC and describing factors associated with probable delirium. The findings provide important information on the burden of delirium in continuing care and identify risk factors that may inform to targeted delirium prevention efforts.

      Methods

      This was a cross-sectional study of delirium in LTC and CCC residents in Ontario, Canada, using routinely collected administrative health data. These care settings are publicly funded through the Ontario Health Insurance Program (OHIP). Resident data are collected via the Resident Assessment Instrument–Minimum Dataset (RAI-MDS) by trained staff members who use direct observation, resident records, and communication with care staff, physicians, and family to obtain an accurate and comprehensive understanding of residents’ health and function.
      interRAI
      Welcome—interRAI.
      Assessments are completed on admission, annually, and quarterly, as well as at any significant change in health status. Admission, annual, and change in health status assessments are full assessments, whereas quarterly assessments evaluate a subset of items from the full assessment.

      Study Population

      The study population included all individuals who received a RAI-MDS assessment in an LTC or CCC facility in Ontario between July 1, 2016, and December 31, 2016. We excluded individuals who were <18 or >105 years of age, comatose at assessment, or not eligible for OHIP in the 2 years prior to assessment. If residents had more than 1 RAI-MDS assessments in the 6-month period, we took a random selection of 1 assessment per resident.

      Data Sources

      This study used administrative health data from ICES, an independent, nonprofit research institute that is authorized to collect and use health care data for the purposes of health system analysis, evaluation, and decision support. The primary data source for this study was the Continuing Care Reporting System, which contains RAI-MDS assessment data. Hospitalization discharge records via the Canadian Institute for Health Information Discharge Abstract Database, physician billing claims, and prescription drugs via the Ontario Drug Benefit database were used to identify comorbidity. Demographics were obtained via the Registered Persons Database. These datasets were linked using unique encoded identifiers and analyzed at ICES.

      Outcome

      The outcome was probable delirium (present or absent) identified via RAI-MDS assessment. The RAI-MDS contains a series of Clinical Assessment Protocols (CAPs) that identify potentially important clinical issues and are triggered by responses to specific assessment items. The delirium CAP is based on 6 symptoms of delirium in the RAI-MDS: easily distracted, periods of altered perception or awareness of surroundings, episodes of disorganized speech, periods of restlessness, periods of lethargy, or mental function varies over the course of the day. The delirium CAP is triggered, indicating the presence of probable delirium, if at least 1 of these 6 behaviors is scored as being present over the 7 days before the assessment and different from the resident's usual functioning. The delirium CAP is not triggered if these behaviors are absent or present but not of recent onset, or if the resident was comatose at assessment.

      Resident Characteristics

      Age at assessment and sex were captured via the Registered Persons Database. Multimorbidity was measured as a count of the following conditions: acute myocardial infarction, arrhythmia, asthma, cancer, congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, dementia, diabetes, hypertension, inflammatory bowel disease, mood and anxiety disorders, other mental health illnesses, osteoarthritis, osteoporosis, renal disease, rheumatoid arthritis, and stroke. These conditions were identified via algorithms that use hospitalization data and physician and prescription drug claims to identify their presence. The dementia algorithm was modified to also include dementia or Alzheimer's disease identified on the RAI-MDS assessment.
      • Gruneir A.
      • Bronskill S.E.
      • Maxwell C.J.
      • et al.
      The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: A retrospective cohort study.
      • Jaakkimainen R.L.
      • Bronskill S.E.
      • Tierney M.C.
      • 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.
      • Koné Pefoyo A.J.
      • Bronskill S.E.
      • Gruneir A.
      • et al.
      The increasing burden and complexity of multimorbidity.
      • Lane N.E.
      • Maxwell C.J.
      • Gruneir A.
      • et al.
      Absence of a socioeconomic gradient in older adults’ survival with multiple chronic conditions.
      • Mondor L.
      • Cohen D.
      • Khan A.I.
      • Wodchis W.P.
      Income inequalities in multimorbidity prevalence in Ontario, Canada: A decomposition analysis of linked survey and health administrative data.
      • Mondor L.
      • Maxwell C.J.
      • Hogan D.B.
      • et al.
      Multimorbidity and healthcare utilization among home care clients with dementia in Ontario, Canada: A retrospective analysis of a population-based cohort.
      • Mondor L.
      • Maxwell C.J.
      • Bronskill S.E.
      • et al.
      The relative impact of chronic conditions and multimorbidity on health-related quality of life in Ontario long-stay home care clients.
      • Muggah E.
      • Graves E.
      • Bennett C.
      • Manuel D.G.
      The impact of multiple chronic diseases on ambulatory care use; a population based study in Ontario, Canada.
      • Petrosyan Y.
      • Bai Y.Q.
      • Koné Pefoyo A.J.
      • et al.
      The relationship between diabetes care quality and diabetes-related hospitalizations and the modifying role of comorbidity.
      • Thavorn K.
      • Maxwell C.J.
      • Gruneir A.
      • et al.
      Effect of socio-demographic factors on the association between multimorbidity and healthcare costs: A population-based, retrospective cohort study.
      The RAI-MDS assessment was used to capture residents’ vision, hearing, communication, locomotion, independence in activities of daily living (ADL), and health instability, measured via the Changes in Health, End-Stage Disease and Symptoms and Signs (CHESS) score, which ranges from 0 (no instability) to 5 (highest level of instability).
      Canadian Institute for Health Information
      Continuing Care Reporting System RAI-MDS 2.0 Output Specifications, 2010–2011.
      Detailed descriptions of variable definitions are provided in Supplementary Table 1.

      Analysis

      Analyses were stratified by care setting (LTC or CCC). We described the proportion of resident assessments with probable delirium and which item(s) and the number of items that triggered the delirium CAP. For analyses identifying factors associated with probable delirium, we only included the subgroup of individuals whose RAI-MDS assessments were full assessments and excluded those whose assessments were quarterly assessments as they did not capture all resident characteristics. We compared characteristics between those with and without probable delirium using chi-square tests and multivariable logistic regression, reporting adjusted odds ratios (ORs) and 95% confidence intervals (CIs). In a sensitivity analysis, we repeated the logistic regression in the full study population, analyzing as independent variables the subset of resident characteristics that were collected in both full and quarterly assessments.

      Results

      The study population included 86,454 LTC residents and 10,217 CCC patients (Figure 1). Delirium was probable in 3126 (3.6%) LTC residents and 1682 (16.5%) CCC patients. Among LTC residents with probable delirium, the most common delirium symptom documented in the RAI-MDS was periods of lethargy (n=1395, 44.6%) followed by periods of restlessness (n=1204, 38.5%) (Table 1). Just more than half (n=1581, 50.6%) of LTC residents with probable delirium had only 1 of the 6 delirium symptoms and 117 (3.7%) had all 6 delirium symptoms documented. In CCC patients with probable delirium, the most common delirium symptom documented in the RAI-MDS was mental functioning that varied over the course of the day (n=1068, 63.5%), followed by periods of lethargy (n=929, 55.2%). Four hundred sixty (27.3%) had only 1 delirium symptom and 142 (8.4%) had all 6 delirium symptoms documented. In LTC, 1341 (42.9%) probable delirium cases were identified on quarterly assessments, whereas 825 (26.4%) were identified on assessments completed because of a change in health status. In CCC, 1374 (81.7%) probable delirium cases were identified on admission assessments, whereas 79 (4.7%) were identified on assessments completed because of a change in health status.
      Table 1Delirium Symptoms in Long-Term Care and Complex Continuing Care Residents
      Delirium in LTC (n=3126)Delirium in CCC (n=1682)
      Symptom of delirium in RAI-MDS
       Easily distracted924 (29.6)633 (37.6)
       Periods of altered perception or awareness of surroundings916 (29.3)569 (33.8)
       Episodes of disorganized speech860 (27.5)587 (34.9)
       Periods of restlessness1204 (38.5)901 (53.6)
       Periods of lethargy1395 (44.6)929 (55.2)
       Mental function varies over the course of the day1127 (36.1)1068 (63.5)
      Number of symptoms of delirium in RAI-MDS
       11581 (50.6)460 (27.3)
       2653 (20.9)403 (24.0)
       3406 (13.0)298 (17.7)
       4226 (7.2)220 (13.1)
       5143 (4.6)159 (9.5)
       6117 (3.7)142 (8.4)
      Assessment type
       Admission443 (14.2)1374 (81.7)
       Quarterly1341 (42.9)204 (12.1)
       Annual517 (16.5)25 (1.5)
       Change in health status825 (26.4)79 (4.7)
      The prevalence of probable delirium in LTC residents differed across resident demographics and health characteristics (Table 2). The prevalence of probable delirium was higher in the oldest (age 95+) and youngest (age 18-44 years) age groups (P < .001) and in residents with a higher number of comorbid conditions (P = .01); dementia (P < .001); cancer (P < .001); anxiety (P = .02); greater levels of impairment in hearing (P < .001), vision (P < .001), and communication (P < .001); wheelchair dependence (P = .004); decreased independence in ADL (P < .001); and increased CHESS scores (P < .001). The unadjusted prevalence of probable delirium did not differ by resident sex (P = .75) or the presence of a seizure disorder (P = .17) or traumatic brain injury (P = .95).
      Table 2Unadjusted and Adjusted Associations Between Resident Characteristics and Delirium in Long-Term Care
      Long-Term Care
      Total

      (n=35,666)
      Delirium

      (n=1785)
      No Delirium

      (n=33,881)
      Unadjusted P Value
      P value for statistical test comparing resident characteristics between those with and without delirium.
      Adjusted OR
      Adjusted for all variables in Table 2.


      (95% CI)
      Age, y<.001
       18-4421212 (5.7)200 (94.3)2.52 (1.30-4.88)
       45-5450313 (2.6)490 (97.4)0.91 (0.50-1.67)
       55-64164760 (3.6)1587 (96.4)1.16 (0.84-1.60)
       65-743896154 (4.0)3742 (96.0)1.00
       75-849645453 (4.7)9192 (95.3)0.99 (0.81-1.22)
       85-9416,201865 (5.3)15,336 (94.7)0.95 (0.78-1.16)
       95-1043562228 (6.4)3334 (93.6)0.94 (0.74-1.20)
      Sex.751.00 0.91 (0.81-1.02)
       Male11,370563 (5.0)10,807 (95.0)
       Female24,2961222 (5.0)23,074 (95.0)
      Number of comorbid conditions.01
       0 or 1134054 (4.0)1286 (96.0)1.00
       2 or 38443374 (4.4)8069 (95.6)0.99 (0.72-1.36)
       4-618,774971 (5.2)17,803 (94.8)1.03 (0.76-1.36)
       7-96519355 (5.4)6164 (94.6)0.93 (0.67-1.29)
       10+59031 (5.2)559 (94.8)0.82 (0.50-1.35)
      Dementia<.001
       Yes28,5691556 (5.4)27,013 (94.6)1.64 (1.40-1.92)
       No7097229 (3.2)6868 (96.8)1.00
      Cancer<.001
       Yes3514218 (6.2)3296 (93.8)0.99 (0.84-1.16)
       No32,1521567 (4.9)30,585 (95.1)1.00
      Anxiety disorder.02
       Yes4149238 (5.7)3911 (94.3)1.13 (0.96-1.32)
       No31,5171547 (4.9)29,970 (95.1)1.00
      Depression.05
       Yes10,952586 (5.4)10,366 (94.6)1.03 (0.92-1.15)
       No24,7141199 (4.9)23,515 (95.1)1.00
      Seizure disorder.17
       Yes178587 (4.4)1698 (5.0)0.93 (0.73-1.18)
       No33,8811910 (95.6)31,971 (95.0)1.00
      Traumatic brain injury.95
       Yes41421 (5.1)393 (94.9)0.98 (0.59-1.61)
       No35,2521764 (5.0)33,488 (5.1)1.00
      Hearing<.001
       No impairment11,749957 (4.4)20,792 (95.6)1.00
       Minimal difficulty8545454 (5.3)8091 (94.7)0.99 (0.87-1.12)
       Hears in special situations only4746313 (6.6)4433 (93.4)1.01 (0.86-1.17)
       Highly impaired62661 (9.7)565 (90.3)1.56 (1.14-2.15)
      Vision<.001
       Adequate20,127827 (4.1)19,300 (95.9)1.00
       Impaired9242492 (5.3)8750 (94.7)1.18 (1.04-1.34)
       Moderately impaired2725182 (6.7)2543 (93.3)1.29 (1.07-1.56)
       Highly impaired2816213 (7.6)2603 (92.4)1.19 (0.99-1.43)
       Severely impaired75671 (9.4)685 (90.6)1.57 (1.16-2.11)
      Communication<.001
       Exclusively verbal27,1031255 (4.6)25,848 (95.4)1.00
       Verbal and nonverbal6785405 (6.0)6380 (94.0)1.02 (0.90-1.16)
       Nonverbal only123980 (6.5)1159 (93.5)0.93 (0.70-1.23)
       No communication53945 (8.3)494 (91.7)0.84 (0.57-1.22)
      Locomotion.004
       Wheelchair dependent20,8801104 (5.3)19,776 (94.7)0.75 (0.66-0.85)
       Not wheelchair dependent14,786681 (4.6)14,105 (95.4)1.00
      ADL Self-Performance Hierarchy<.001
       Independent107627 (2.5)1049 (97.5)1.00
       Supervision162327 (1.7)1596 (98.3)0.58 (0.33-0.99)
       Limited363395 (2.6)3538 (97.4)0.84 (0.54-1.31)
       Extensive9692308 (3.2)9384 (96.8)0.86 (0.58-1.30)
       Maximal8442454 (5.4)7988 (94.6)1.13 (0.75-1.71)
       Dependent7518506 (6.7)7012 (93.3)1.16 (0.76-1.76)
       Total dependence3682368 (10.0)3314 (90.0)1.20 (0.78-1.85)
      CHESS score<.001
       0-1 (low instability)26,695549 (2.0)26,146 (98.0)1.00
       2-37476616 (8.2)6860 (91.8)4.17 (3.69-4.71)
       4-5 (high instability)1495620 (41.5)875 (58.5)30.40 (26.19-35.28)
      P value for statistical test comparing resident characteristics between those with and without delirium.
      Adjusted for all variables in Table 2.
      Results from a multivariable logistic regression suggested that the odds of probable delirium in LTC residents was increased in the youngest age group (age 18-44 years) in contrast to those aged 65-74 years (OR 2.52, 95% CI 1.30-4.88); those with dementia (OR 1.64, 95% CI 1.40-1.92); highly impaired hearing compared to those with no hearing impairment (OR 1.56, 95% CI 1.14-2.15); impaired (OR 1.18, 95% CI 1.04-1.34), moderately impaired (OR 1.29, 95% CI 1.07-1.56), and severely impaired vision (OR 1.57, 95% CI 1.16-2.11) compared to those with adequate vision; and moderate (OR 4.17, 95% CI 3.69-4.71) to high (OR 30.40, 95% CI 26.19-35.28) disease instability compared to those with low disease instability based on the CHESS score (Table 2). The odds of probable delirium in LTC residents was lower in individuals who were wheelchair dependent (OR 0.75, 95% CI 0.66-0.85). The odds of probable delirium was also lower in those who required supervision with ADL in contrast to those who were independent with ADL (OR 0.58, 95% CI 0.33-0.99), although there was no evidence of increasing prevalence of probable delirium with subsequent increasing dependency in ADL. These relationships largely did not differ substantially when analyzed in the full LTC study population that included those with quarterly and full RAI-MDS assessments. The exception was an increased odds of probable delirium in individuals with the highest level of dependence in ADL, a decreased odds of probable delirium in individuals with 10+ comorbid conditions, and no significant association between age and probable delirium (see Supplementary Table 2).
      The prevalence of probable delirium in CCC patients also differed across individual demographics and health characteristics (Table 3). The prevalence of probable delirium was higher in older patients (P < .001) and those with dementia (P < .001); cancer (P < .001); anxiety (P = .003); greater levels of impairment in hearing (P < .001), vision (P < .001), and communication (P < .001); decreased independence in ADL (P < .001); and increased CHESS scores (P < .001). The unadjusted prevalence of probable delirium did not differ by resident sex (P = .06), comorbid disease burden (P = .05), the presence of depression (P = .89), seizure disorder (P = .18), traumatic brain injury (P = .55), or wheelchair dependence (P = .24).
      Table 3Unadjusted and Adjusted Associations Between Patient Characteristics and Delirium in Complex Continuing Care
      Complex Continuing Care
      Total

      (n=8017)
      Delirium

      (n=1478)
      No Delirium

      (n=6539)
      Unadjusted P Value
      P value for statistical test comparing patient characteristics between those with and without delirium.
      Adjusted OR
      Adjusted for all variables in Table 3.


      (95% CI)
      Age, y<.001
       18-4419024 (12.6)166 (87.4)1.09 (0.65-1.86)
       45-5430141 (13.6)260 (86.4)0.97 (0.64-1.47)
       55-64834134 (16.1)700 (83.9)0.98 (0.75-1.28)
       65-741495250 (16.7)1245 (83.3)1.00
       75-842448464 (19.0)1984 (81.0)1.05 (0.86-1.28)
       85-942387477 (20.0)1910 (80.0)1.00 (0.81-1.22)
       95-10436288 (24.3)274 (75.7)1.19 (0.85-1.66)
      Sex.06
       Male3521681 (19.3)2840 (80.7)1.00
       Female4496797 (17.7)3699 (82.3)0.92 (0.81-1.06)
      Number of comorbid conditions.05
       0 or 142867 (15.7)361 (84.3)1.00
       2 or 31806307 (17.0)1499 (83.0)1.11 (0.79-1.55)
       4-63979758 (19.0)3221 (81.0)1.12 (0.80-1.55)
       7-91631304 (18.6)1327 (81.4)0.97 (0.68-1.37)
       10+17342 (24.3)131 (75.7)1.23 (0.73-2.07)
      Dementia<.001
       Yes2400543 (22.6)1857 (77.4)1.59 (1.37-1.84)
       No5617935 (16.6)4682 (83.4)1.00
      Cancer<.001
       Yes2504704 (28.1)1800 (71.9)1.10 (0.95-1.28)
       No5513774 (14.0)4739 (86.0)1.00
      Anxiety disorder.003
       Yes809180 (22.2)629 (77.8)1.18 (0.94-1.47)
       No72081298 (18.0)5910 (82.0)1.00
      Depression.89
       Yes1453266 (18.3)1187 (81.7)1.17 (0.98-1.41)
       No65641212 (18.5)5352 (81.5)1.00
      Seizure disorder.18
       Yes40685 (20.9)321 (79.1)1.17 (0.87-1.58)
       No76111393 (18.3)6218 (81.7)1.00
      Traumatic brain injury.55
       Yes15225 (16.6)126 (83.4)1.32 (0.81-2.16)
       No78661453 (18.5)6413 (81.5)1.00
      Hearing<.001
       No impairment5831961 (16.5)4870 (83.5)1.00
       Minimal difficulty1419299 (21.1)1120 (78.9)1.07 (0.90-1.27)
       Hears in special situations only558144 (25.8)414 (74.2)1.11 (0.87-1.43)
       Highly impaired20974 (35.4)135 (64.6)1.56 (1.08-2.24)
      Vision<.001
       Adequate5506840 (15.3)4666 (84.7)1.00
       Impaired1850400 (21.6)1450 (78.4)1.09 (0.93-1.27)
       Moderately impaired424164 (38.7)260 (61.3)2.91 (2.25-3.75)
       Highly impaired14654 (37.0)92 (63.0)2.02 (1.34-3.04)
       Severely impaired9120 (22.0)71 (78.0)1.26 (0.71-2.26)
      Communication<.001
       Exclusively verbal6244974 (15.6)5270 (84.4)1.00
       Verbal and nonverbal1351421 (31.2)930 (68.8)1.90 (1.62-2.23)
       Nonverbal only20154 (26.9)147 (73.1)1.66 (1.13-2.46)
       No communication22129 (13.1)192 (86.9)1.05 (0.67-1.64)
      Locomotion.24
       Wheelchair dependent5182936 (18.1)4246 (81.9)1.13 (0.98-1.31)
       Not wheelchair dependent2835542 (19.1)2293 (80.9)1.00
      ADL Self-Performance Hierarchy<.001
       Independent2598 (3.1)251 (96.9)1.00
       Supervision27120 (7.4)251 (92.6)1.87 (0.78-4.47)
       Limited1383130 (9.4)1253 (90.6)1.87 (0.88-3.97)
       Extensive1452138 (9.5)1314 (90.5)1.58 (0.74-3.36)
       Maximal1295311 (24.0)984 (76.0)3.10 (1.46-6.57)
       Dependent2462587 (23.8)1875 (76.2)2.27 (1.08-4.78)
       Total dependence895284 (31.7)611 (68.3)2.53 (1.19-5.39)
      CHESS score<.001
       0-1 (low instability)3130120 (3.8)3010 (96.2)1.00
       2-33094484 (15.6)2610 (84.4)4.37 (3.53-5.41)
       4-5 (high instability)1793874 (48.7)919 (51.3)21.05 (16.73-26.48)
      P value for statistical test comparing patient characteristics between those with and without delirium.
      Adjusted for all variables in Table 3.
      Results from a multivariable logistic regression suggested that the odds of probable delirium in CCC patients remained increased in those with dementia (OR 1.59, 95% CI 1.37-1.84), highly impaired hearing compared with no hearing impairment (OR 1.56, 95% CI 1.08-2.24), moderately (OR 2.91, 95% CI 2.25-3.75) and highly (OR 2.02, 95% CI 1.34-3.04) impaired vision compared to adequate vision, verbal and nonverbal communication (OR 1.90, 95% CI 1.62-2.23) or nonverbal communication (OR 1.66, 95% CI 1.13-2.46) compared with exclusively verbal communication, requiring extensive support (OR 3.10, 95% CI 1.46-6.57) or are dependent (OR 2.27, 95% CI 1.08-4.78) or totally dependent (OR 2.53, 95% CI 1.19-5.39) on their activities of daily living compared with independent, and moderate (OR 4.37, 95% 3.53-5.41) to high (OR 21.05, 95% CI 16.73-26.48) disease instability compared with low disease instability on the CHESS (Table 3). These relationships did not differ substantially when evaluated in the full CCC study population that included those with quarterly and full RAI-MDS assessments (see Supplementary Table 3).

      Discussion

      This study examined the prevalence of and factors associated with probable delirium in Ontario continuing care facilities. Overall, 3.6% of LTC residents and 16.5% of CCC patients had probable delirium at their RAI-MDS assessment. In both LTC and CCC, increased health instability had the strongest association with probable delirium. Other predictors included the presence of dementia, impaired hearing, and impaired vision. Considering the high prevalence of dementia in LTC and high health instability in CCC, these characteristics likely had the greatest overall contribution to the prevalence of probable delirium in these 2 care settings. Although the odds of probable delirium was highest among the youngest LTC residents, this likely reflects the high levels of physical and intellectual disability in younger residents.
      • Fries B.E.
      • Wodchis W.P.
      • Blaum C.
      • et al.
      A national study showed that diagnoses varied by age group in nursing home residents under age 65.
      ,
      • Persson D.I.
      • Ostwald S.K.
      Younger residents in nursing homes.
      The lower odds of probable delirium in LTC residents who were wheelchair dependent may be due to increased contact with LTC staff, resulting in closer monitoring and intervention for any subtle changes in cognition. Further research is needed to explore the potential pathways that may explain the observed relationships between demographic and health characteristics and probable delirium.
      The most common symptom of probable delirium in LTC was related to the hypoactive subtype, namely, periods of lethargy. The high proportion of probable delirium cases in LTC with this hypoactive delirium symptom is in line with the evidence that the hypoactive delirium subtype is more common than the hyperactive subtype among older adults.
      • Bellelli G.
      • Morandi A.
      • Di Santo S.G.
      • et al.
      “Delirium Day”: A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool.
      • Robinson T.N.
      Motor subtypes of postoperative delirium in older adults.
      • Evensen S.
      • Saltvedt I.
      • Lydersen S.
      • et al.
      Delirium motor subtypes and prognosis in hospitalized geriatric patients—A prospective observational study.
      In contrast, the symptom profile in CCC care did not suggest that 1 delirium subtype was more predominant. The most common symptom was mental function varying over the course of the day, followed by periods of lethargy and periods of restlessness. This difference in symptom presentation between CCC and LTC may reflect important differences in delirium subtypes within these 2 care settings, which should be further investigated in future research.
      This study identified differences in delirium symptom burden between LTC and CCC. More than half of LTC probable delirium cases displayed 1 delirium symptom whereas almost half of CCC probable delirium cases displayed 3 or more symptoms. These findings suggest that probable delirium in CCC patients may be more severe than in LTC residents, at least in regard to symptom burden. However, there are other factors that may be contributing to these results. First, there are documented differences in delirium detection and diagnosis according to its motoric subtype, with hyperactive delirium easier to identify than hypoactive delirium given the extreme behavioral symptoms.
      • Bui L.N.
      • Pham V.P.
      • Shirkey B.A.
      • Swan J.T.
      Effect of delirium motoric subtypes on administrative documentation of delirium in the surgical intensive care unit.
      If LTC residents are more likely to experience the hypoactive subtype, it is possible that their symptoms are less likely to be documented or not as easily identified by RAI-MDS assessors. Second, delirium symptoms in LTC residents may be incorrectly attributed to other comorbid conditions, particularly dementia or depression, which were both common in our study population.
      • Fong T.G.
      • Davis D.
      • Growdon M.E.
      • et al.
      The interface between delirium and dementia in elderly adults.
      • Fong T.G.
      • Tulebaev S.R.
      • Inouye S.K.
      Delirium in elderly adults: Diagnosis, prevention and treatment.
      • Mittal V.
      • Muralee S.
      • Williamson D.
      • et al.
      Review: Delirium in the elderly: A comprehensive review.
      This could contribute to a lower apparent symptom burden in LTC. There are also differences in the staffing and training as well as potential differences in the degree to which individuals are monitored and screened for delirium in these settings, which may contribute to differences in symptom identification and documentation across LTC and CCC.
      Contrary to expectations, in both LTC and CCC, a high proportion of probable delirium cases were identified on routine admission, annual, or quarterly RAI-MDS assessments, rather than on assessments triggered by a change in health status. These findings suggest that most of the probable delirium cases were identified incidentally through routine assessment. Although the potential for delirium going undetected in LTC and CCC is concerning considering the importance of early identification and treatment of delirium, we must recognize that there may be other factors contributing to these results. First, although assessments may be triggered by a change in health status, facilities may choose to perform a quarterly or annual assessment if one is due. Second, the delirium CAP has not been validated. There is potential for false positives, considering that only 1 symptom needs to be present for the CAP to be triggered. Finally, a high proportion of probable delirium cases in CCC were identified on the RAI assessment conducted on admission to CCC, which may reflect a higher risk of new delirium during the admission period or patients being admitted to CCC with pre-existing delirium.
      This study identified potential modifiable risk factors for probable delirium in continuing care, which may guide delirium prevention efforts. Sensory impairments were associated with an increased odds of probable delirium, a finding that is supported by previous research in older adults.
      • Elie M.
      • Cole M.G.
      • Primeau F.J.
      • Bellavance F.
      Delirium risk factors in elderly hospitalized patients.
      Sensory impairments are ideal targets for interventions to prevent delirium, with simple efforts that can reduce the impact of visual and hearing impairments (eg, magnification aids, large print texts, hearing aids).
      • Inouye S.K.
      • Bogardus S.T.
      • Charpentier P.A.
      • et al.
      A multicomponent intervention to prevent delirium in hospitalized older patients.
      Although the presence of dementia was associated with an increased odds of probable delirium, because dementia is highly prevalent in LTC the presence of dementia may not on its own be a good indicator of delirium risk but rather should be incorporated into a multifactorial approach to delirium risk identification that includes additional risk factors. Increased health instability was associated with an increased odds of probable delirium in both care settings. Higher scores on the CHESS scale have been shown to predict mortality in a range of care settings.
      • Hirdes J.P.
      • Frijters D.H.
      • Teare G.F.
      The MDS-CHESS scale: A new measure to predict mortality in institutionalized older people.
      • Hirdes J.P.
      • Poss J.W.
      • Mitchell L.
      • et al.
      Use of the interRAI CHESS scale to predict mortality among persons with neurological conditions in three care settings.
      • Mor V.
      • Intrator O.
      • Unruh M.A.
      • Cai S.
      Temporal and geographic variation in the validity and internal consistency of the Nursing Home Resident Assessment Minimum Data Set 2.0.
      • Lee J.S.W.
      • Chau P.P.H.
      • Hui E.
      • et al.
      Survival prediction in nursing home residents using the Minimum Data Set subscales: ADL Self-Performance Hierarchy, Cognitive Performance and the Changes in Health, End-stage disease and Symptoms and Signs scales.
      Delirium is particularly common among individuals at the end of life, which may explain the observation of a higher odds of probable delirium with greater health instability.

      Study Limitations

      This study had several limitations. Because it was cross-sectional, we cannot establish temporality between many patient characteristics and probable delirium. The delirium CAP has not been validated against diagnostic tools for delirium. We know that delirium is often undiagnosed and not adequately documented within administrative data.
      • Bui L.N.
      • Pham V.P.
      • Shirkey B.A.
      • Swan J.T.
      Effect of delirium motoric subtypes on administrative documentation of delirium in the surgical intensive care unit.
      ,
      • Katznelson R.
      • Djaiani G.
      • Tait G.
      • et al.
      Hospital administrative database underestimates delirium rate after cardiac surgery.
      ,
      • Kim D.H.
      • Lee J.
      • Kim C.A.
      • et al.
      Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.
      Conversely, the symptoms that trigger the delirium CAP are not pathognomonic for delirium and may be due to other health conditions rather than delirium. Thus, it is likely that there was misclassification in our outcome measurement in both directions—with some incorrectly identified as being delirium-free due to undocumented or unrecognized symptoms, and others incorrectly identified as having probable delirium because of other comorbid conditions that presented similarly to delirium. We were only able to evaluate relationships between individual characteristics and probable delirium in the subset of individuals who received full RAI-MDS assessments and excluding those who received quarterly assessments. However, in sensitivity analyses evaluating the subset of characteristics that were collected in both full and quarterly assessments, we generally observed minimal differences with the results of the primary analyses. Finally, although this study evaluated delirium in LTC residents and CCC patients in 2016, we do not expect that the results would differ substantially had we analyzed a more recent cohort.

      Conclusions and Implications

      Individuals in continuing care are at high risk of delirium, given their poor health and functional and cognitive status. This study identified differences in the presentation and burden of delirium symptoms between individuals in LTC and CCC. CCC patients had a higher symptom burden than those in LTC, potentially reflecting more severe delirium or differences in symptom identification between the 2 care settings. Several risk factors for probable delirium in LTC and CCC were identified, including sensory impairments that may be amenable to interventions to prevent this highly distressing condition.

      Supplementary Data

      Supplementary Table 1Additional Information on Study Variable Definitions
      VariableCategory Definitions
      VisionAdequate: Sees fine details, including regular print in newspapers/books

      Impaired: Sees large print, but cannot see regular print in newspapers/books

      Moderately impaired: Limited vision, is not able to see newspaper headlines but can identify objects in environment

      Highly impaired: Ability to identify objects in environment is in question, but eyes appear to follow objects in environment

      Severely impaired: No vision, sees only light colours or shapes, or eyes do not appear to follow objects (especially people walking by)
      HearingAdequate: Hears normal conversational speech, including when using the telephone, watching television, and engaged in group activities

      Minimal difficulty: Hears speech at conversational levels but has difficulty hearing when not in quiet listening conditions or not in one-on-one situations

      Hears in special situations only: Although hearing deficient, can compensate when speaker adjusts tonal quality and speaks distinctly, or can hear only if speaker's face is visible

      Highly impaired: Absence of useful hearing. Hears only some sounds and frequently fails to respond, even when the speaker adjusts tonal quality, speaks distinctly, or is positioned face-to-face. No comprehension of conversational speech even when speaker makes maximum adjustments.
      ADL independenceBased on individuals’ performance with locomotion, eating, toileting, and personal hygiene.

      Coded on a 7-point scale, from 0 (independent), 1 (requires supervision), 2 (requires limited assistance with at least 1 of the 4 ADL), 3 (requires at least extensive assistance in personal hygiene or toileting), 4 (requires extensive assistance with eating or locomotion), 5 (total dependence in eating and locomotion), to 6 (total dependence).
      CHESS scoreBased on 9 assessment items: decline in cognition, decline in ADL, dehydration, edema, shortness of breath, vomiting, end-stage disease, weight loss of 5% or more in the last 30 days or 10% or more in the last 180 days, and leaving 25% or more of food uneaten at most meals
      ADL, activities of daily living; CHESS, Changes in Health, End-Stage Disease and Signs and Symptoms.
      Supplementary Table 2Sensitivity Analysis Results: Unadjusted and Adjusted Associations Between Resident Characteristics and Delirium in Long-Term Care, Analyzing the Full LTC Study Population and Only Those Variables Collected on Both Full and Quarterly RAI-MDS Assessments
      Long-Term Care
      Overall

      (n=86,454)
      Delirium

      (n=3126)
      No Delirium

      (n=83,328)
      Unadjusted P Value
      P value for statistical test comparing resident characteristics between those with and without delirium.
      Adjusted OR
      Adjusted for all variables in Supplementary Table 2.


      (95% CI)
      Age, y<.001
       18-4441113 (3.2)398 (96.8)1.56 (0.86-2.81)
       45-54120027 (2.3)1173 (97.8)0.92 (0.61-1.38)
       55-643967107 (2.7)3860 (97.3)1.00 (0.79-1.27)
       65-749386286 (3.0)9100 (97.0)1.00
       75-8423,286784 (3.4)22,502 (96.6)0.99 (0.86-1.14)
       85-9439,1881514 (3.9)37,674 (96.1)1.00 (0.87-1.14)
       95+9016395 (4.4)8621 (95.6)0.99 (0.84-1.17)
      Sex.89
       Male26,977972 (3.6)26,005 (96.4)1.00
       Female59,4772154 (3.6)57,323 (96.4)0.90 (0.83-0.98)
      Number of comorbid conditions.002
       0 or 13373100 (3.0)3273 (97.0)1.00
       2 or 3136445 (3.3)1319 (96.7)0.99 (0.79-1.24)
       4-620,793685 (3.3)20,108 (96.7)0.98 (0.79-1.23)
       7-945,4941682 (3.7)43,812 (96.3)0.91 (0.72-1.15)
       10+15,430614 (4.0)14,816 (96.0)0.68 (0.46-0.99)
      Dementia<.001
       Yes71,2332741 (3.8)68,492 (96.2)1.52 (1.35-1.71)
       No15,221385 (2.5)14,386 (97.5)1.00
      Depression.06
       Yes28,8391092 (3.8)27,747 (96.2)1.04 (0.96-1.13)
       No57,6152034 (3.5)55,581 (96.5)1.00
      ADL Self-Performance Hierarchy<.001
       Independent283945 (1.6)2794 (98.4)1.00
       Supervision430677 (1.8)4229 (98.2)1.06 (0.73-1.55)
       Limited7933171 (2.2)7762 (97.8)1.15 (0.82-1.61)
       Extensive23,929676 (2.8)23,253 (97.2)1.31 (0.96-1.77)
       Maximal20,898814 (3.9)20,084 (96.1)1.35 (0.99-1.85)
       Dependent18,185829 (4.6)17,356 (95.4)1.35 (0.99-1.83)
       Total dependence8364514 (6.1)7850 (93.9)1.43 (1.04-1.97)
      CHESS score<.001
       0-1 (low instability)67,9611232 (1.8)66,729 (98.2)1.00
       2-316,4541131 (6.9)15,323 (93.1)3.94 (3.62-4.29)
       4-5 (high instability)2039763 (37.4)1276 (62.6)31.07 (27.72-34.82)
      ADL, activities of daily living; CI, confidence interval; OR, odds ratio; SD, standard deviation; CHESS, Changes in Health, End-Stage Disease and Symptoms and Signs.
      P value for statistical test comparing resident characteristics between those with and without delirium.
      Adjusted for all variables in Supplementary Table 2.
      Supplementary Table 3Sensitivity Analysis Results: Unadjusted and Adjusted Associations Between Resident Characteristics and Delirium in Complex Continuing Care, Analyzing the Full CCC Study Population and Only Those Variables Collected on Both Full and Quarterly RAI-MDS Assessments
      Complex Continuing Care
      Overall

      (n=10,217)
      Delirium

      (n=1682)
      No Delirium

      (n=8535)
      Unadjusted P Value
      P value for statistical test comparing patient characteristics between those with and without delirium.
      Adjusted OR
      Adjusted for all variables in Supplementary Table 3.


      (95% CI)
      Age, yr<.001
       18-4428928 (9.7)261 (90.3)1.06 (0.67-1.68)
       45-5444057 (13.0)383 (87.0)1.11 (0.78-1.56)
       55-641123162 (14.4)961 (85.6)1.04 (0.82-1.31)
       65-741900286 (15.1)1614 (84.9)1.00
       75-842965515 (17.4)2450 (82.6)1.08 (0.90-1.29)
       85-943028541 (17.9)2487 (82.1)1.06 (0.89-1.27)
       95+47293 (19.7)379 (80.3)1.21 (0.90-1.62)
      Sex.27
       Male4680791 (16.9)3889 (83.1)1.00
       Female5537891 (16.1)4646 (83.9)0.90 (0.80-1.01)
      Number of comorbid conditions.09
       0 or 156981 (14.2)488 (85.8)1.00
       2 or 32247341 (15.2)1906 (84.8)0.96 (0.71-1.29)
       4-65061866 (17.1)4195 (82.9)1.00 (0.75-1.34)
       7-92100347 (16.5)1753 (83.5)0.86 (0.63-1.17)
       10+24047 (19.6)193 (80.4)0.96 (0.61-1.53)
      Dementia<.001
       Yes3483649 (18.6)2834 (81.4)1.58 (1.38-1.80)
       No67341033 (15.3)5701 (84.7)1.00
      Depression.86
       Yes1996326 (16.3)1670 (83.7)1.22 (1.05-1.42)
       No82211356 (16.5)6865 (83.5)1.00
      ADL Self-Performance Hierarchy<.001
       Independent32111 (3.4)310 (96.6)1.00
       Supervision30522 (7.2)283 (92.8)1.58 (0.73-3.41)
       Limited1635154 (9.4)1481 (90.6)1.84 (0.97-3.49)
       Extensive1924172 (8.9)1752 (91.1)1.58 (0.84-3.00)
       Maximal1507338 (22.4)1169 (77.6)3.36 (1.79-6.32)
       Dependent3104658 (21.2)2446 (78.8)2.31 (1.24-4.33)
       Total dependence1421327 (23.0)1094 (77.0)2.53 (1.34-4.78)
      CHESS score<.001
       0-1 (low instability)4681208 (4.4)4473 (95.6)1.00
       2-33628562 (15.5)3066 (84.5)3.81 (3.22-4.52)
       4-5 (high instability)1908912 (47.8)996 (52.2)18.98 (15.92-22.63)
      ADL, activities of daily living; CHESS: Changes in Health, End-Stage Disease and Symptoms and Signs; CI, confidence interval; OR, odds ratio; SD, standard deviation.
      P value for statistical test comparing patient characteristics between those with and without delirium.
      Adjusted for all variables in Supplementary Table 3.

      References

        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Publishing, Washington, DC2013
        • Inouye S.K.
        • Charpentier P.A.
        Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability.
        JAMA. 1996; 275: 852-857
        • Pompei P.
        • Foreman M.
        • Rudberg M.A.
        • et al.
        Delirium in hospitalized older persons: Outcomes and predictors.
        J Am Geriatr Soc. 1994; 42: 809-815
        • Lawlor P.G.
        • Gagnon B.
        • Mancini I.L.
        • et al.
        Occurrence, causes, and outcome of delirium in patients with advanced cancer: A prospective study.
        Arch Intern Med. 2000; 160: 786-794
        • Inouye S.K.
        Prevention of delirium in hospitalized older patients: Risk factors and targeted intervention strategies.
        Ann Med. 2000; 32: 257-263
        • Bohlken J.
        • Kostev K.
        Prevalence and risk factors for delirium diagnosis in patients followed in general practices in Germany.
        Int Psychogeriatr. 2018; 30: 511-518
        • Lawlor P.G.
        • Bush S.H.
        Delirium in patients with cancer: Assessment, impact, mechanisms and management.
        Nat Rev Clin Oncol. 2015; 12: 77-92
        • Breitbart W.
        • Gibson C.
        • Tremblay A.
        The delirium experience: Delirium recall and delirium-related distress in hospitalized patients with cancer, their spouses/caregivers, and their nurses.
        Psychosomatics. 2002; 43: 183-194
        • Morita T.
        • Hirai K.
        • Sakaguchi Y.
        • et al.
        Family-perceived distress from delirium-related symptoms of terminally ill cancer patients.
        Psychosomatics. 2004; 45: 107-113
        • Babine R.L.
        • Hyrkäs K.E.
        • Bachand D.A.
        • et al.
        Falls in a tertiary care hospital—association with delirium: A replication study.
        Psychosomatics. 2016; 57: 273-282
        • Lakatos B.E.
        • Capasso V.
        • Mitchell M.T.
        • et al.
        Falls in the general hospital: Association with delirium, advanced age, and specific surgical procedures.
        Psychosomatics. 2009; 50: 218-226
        • Bickel H.
        • Gradinger R.
        • Kochs E.
        • Förstl H.
        High risk of cognitive and functional decline after postoperative delirium.
        Dement Geriatr Cogn Disord. 2008; 26: 26-31
        • MacLullich A.M.J.
        • Beaglehole A.
        • Hall R.J.
        • Meagher D.J.
        Delirium and long-term cognitive impairment.
        Int Rev Psychiatry. 2009; 21: 30-42
        • Pitkala K.H.
        • Laurila J.V.
        • Strandberg T.E.
        • Tilvis R.S.
        Prognostic significance of delirium in frail older people.
        Dement Geriatr Cogn Disord. 2005; 19: 158-163
        • Morandi A.
        • Di Santo S.G.
        • Zambon A.
        • et al.
        Delirium, dementia, and in-hospital mortality: The results from the Italian Delirium Day 2016, a national multicenter study.
        J Gerontol Ser A. 2019; 74: 910-916
        • McCusker J.
        • Cole M.
        • Abrahamowicz M.
        • et al.
        Delirium predicts 12-month mortality.
        Arch Intern Med. 2002; 162: 457
        • interRAI
        Welcome—interRAI.
        (Available at:)
        https://www.interrai.org/
        Date accessed: November 29, 2019
        • Gruneir A.
        • Bronskill S.E.
        • Maxwell C.J.
        • 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
        • Jaakkimainen R.L.
        • Bronskill S.E.
        • Tierney M.C.
        • 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 Alzheimers Dis. 2016; 54: 337-349
        • Koné Pefoyo A.J.
        • Bronskill S.E.
        • Gruneir A.
        • et al.
        The increasing burden and complexity of multimorbidity.
        BMC Public Health. 2015; 15: 415
        • Lane N.E.
        • Maxwell C.J.
        • Gruneir A.
        • et al.
        Absence of a socioeconomic gradient in older adults’ survival with multiple chronic conditions.
        EBioMedicine. 2015; 2: 2094-2100
        • Mondor L.
        • Cohen D.
        • Khan A.I.
        • Wodchis W.P.
        Income inequalities in multimorbidity prevalence in Ontario, Canada: A decomposition analysis of linked survey and health administrative data.
        Int J Equity Health. 2018; 17: 90
        • Mondor L.
        • Maxwell C.J.
        • Hogan D.B.
        • 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: e1002249
        • Mondor L.
        • Maxwell C.J.
        • Bronskill S.E.
        • et al.
        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: 2619-2632
        • Muggah E.
        • Graves E.
        • Bennett C.
        • Manuel D.G.
        The impact of multiple chronic diseases on ambulatory care use; a population based study in Ontario, Canada.
        BMC Health Serv Res. 2012; 12: 452
        • Petrosyan Y.
        • Bai Y.Q.
        • Koné Pefoyo A.J.
        • et al.
        The relationship between diabetes care quality and diabetes-related hospitalizations and the modifying role of comorbidity.
        Can J Diabetes. 2017; 41: 17-25
        • Thavorn K.
        • Maxwell C.J.
        • Gruneir A.
        • 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: e017264
        • Canadian Institute for Health Information
        Continuing Care Reporting System RAI-MDS 2.0 Output Specifications, 2010–2011.
        Canadian Institute for Health Information, Ottawa2009: 120
        • Fries B.E.
        • Wodchis W.P.
        • Blaum C.
        • et al.
        A national study showed that diagnoses varied by age group in nursing home residents under age 65.
        J Clin Epidemiol. 2005; 58: 198-205
        • Persson D.I.
        • Ostwald S.K.
        Younger residents in nursing homes.
        J Gerontol Nurs. 2009; 35: 22-31
        • Bellelli G.
        • Morandi A.
        • Di Santo S.G.
        • et al.
        “Delirium Day”: A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool.
        BMC Med. 2016; 14: 106
        • Robinson T.N.
        Motor subtypes of postoperative delirium in older adults.
        Arch Surg. 2011; 146: 295
        • Evensen S.
        • Saltvedt I.
        • Lydersen S.
        • et al.
        Delirium motor subtypes and prognosis in hospitalized geriatric patients—A prospective observational study.
        J Psychosom Res. 2019; 122: 24-28
        • Bui L.N.
        • Pham V.P.
        • Shirkey B.A.
        • Swan J.T.
        Effect of delirium motoric subtypes on administrative documentation of delirium in the surgical intensive care unit.
        J Clin Monit Comput. 2017; 31: 631-640
        • Fong T.G.
        • Davis D.
        • Growdon M.E.
        • et al.
        The interface between delirium and dementia in elderly adults.
        Lancet Neurol. 2015; 14: 823-832
        • Fong T.G.
        • Tulebaev S.R.
        • Inouye S.K.
        Delirium in elderly adults: Diagnosis, prevention and treatment.
        Nat Rev Neurol. 2009; 5: 210-220
        • Mittal V.
        • Muralee S.
        • Williamson D.
        • et al.
        Review: Delirium in the elderly: A comprehensive review.
        Am J Alzheimers Dis Other Demen. 2011; 26: 97-109
        • Elie M.
        • Cole M.G.
        • Primeau F.J.
        • Bellavance F.
        Delirium risk factors in elderly hospitalized patients.
        J Gen Intern Med. 1998; 13: 204-212
        • Inouye S.K.
        • Bogardus S.T.
        • Charpentier P.A.
        • et al.
        A multicomponent intervention to prevent delirium in hospitalized older patients.
        N Engl J Med. 1999; 340: 669-676
        • Hirdes J.P.
        • Frijters D.H.
        • Teare G.F.
        The MDS-CHESS scale: A new measure to predict mortality in institutionalized older people.
        J Am Geriatr Soc. 2003; 51: 96-100
        • Hirdes J.P.
        • Poss J.W.
        • Mitchell L.
        • et al.
        Use of the interRAI CHESS scale to predict mortality among persons with neurological conditions in three care settings.
        PloS One. 2014; 9: e99066
        • Mor V.
        • Intrator O.
        • Unruh M.A.
        • Cai S.
        Temporal and geographic variation in the validity and internal consistency of the Nursing Home Resident Assessment Minimum Data Set 2.0.
        BMC Health Serv Res. 2011; 11: 78
        • Lee J.S.W.
        • Chau P.P.H.
        • Hui E.
        • et al.
        Survival prediction in nursing home residents using the Minimum Data Set subscales: ADL Self-Performance Hierarchy, Cognitive Performance and the Changes in Health, End-stage disease and Symptoms and Signs scales.
        Eur J Public Health. 2009; 19: 308-312
        • Katznelson R.
        • Djaiani G.
        • Tait G.
        • et al.
        Hospital administrative database underestimates delirium rate after cardiac surgery.
        Can J Anaesth. 2010; 57: 898-902
        • Kim D.H.
        • Lee J.
        • Kim C.A.
        • et al.
        Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.
        Pharmacoepidemiol Drug Saf. 2017; 26: 945-953