Skip to main content
CMAJ : Canadian Medical Association Journal logoLink to CMAJ : Canadian Medical Association Journal
. 2018 Nov 26;190(47):E1376–E1383. doi: 10.1503/cmaj.180551

Comparative risk of harm associated with trazodone or atypical antipsychotic use in older adults with dementia: a retrospective cohort study

Jennifer A Watt 1, Tara Gomes 1, Susan E Bronskill 1, Anjie Huang 1, Peter C Austin 1, Joanne M Ho 1, Sharon E Straus 1,
PMCID: PMC6246047  PMID: 30478215

Abstract

BACKGROUND:

Trazodone is increasingly prescribed for behavioural and psychological symptoms of dementia, but little is known about its risk of harm. Our objective was to describe the comparative risk of falls and fractures among older adults with dementia dispensed trazodone or atypical antipsychotics.

METHODS:

The study cohort included adults with dementia (excluding patients with chronic psychotic illnesses) living in long-term care and aged 66 years and older. Data were obtained from routinely collected, linked health administrative databases in Ontario, Canada. We compared new users of trazodone with new users of atypical antipsychotics (quetiapine, olanzapine or risperidone) between Dec. 1, 2009, and Dec. 31, 2015. The primary outcome was a composite of fall or major osteoporotic fracture within 90 days of first prescription. Secondary outcomes were falls, major osteoporotic fractures, hip fractures and all-cause mortality.

RESULTS:

We included 6588 older adults dispensed trazodone and 2875 dispensed an atypical antipsychotic, of whom 95.2% received a low dose of these medications. Compared with use of atypical antipsychotics, use of trazodone was associated with similar rates of falls or major osteoporotic fractures (weighted hazard ratio [HR] 0.89, 95% confidence interval [CI] 0.73 to 1.07), major osteoporotic fracture (weighted HR 1.03, 95% CI 0.73 to 1.47), falls (weighted HR 0.91, 95% CI 0.75 to 1.11) and hip fractures (weighted HR 0.92, 95% CI 0.59 to 1.43). Use of trazodone was associated with a lower rate of mortality (weighted HR 0.75, 95% CI 0.66 to 0.85).

INTERPRETATION:

Trazodone is not a uniformly safer alternative to atypical antipsychotics, given the similar risk of falls and fractures among older adults with dementia.


The prevalence of dementia in Canada is 7.1%, but the rate approaches 25% among Canadians aged 85 years and older.1 Importantly, 61.9% of residents in continuing care facilities have dementia, and 48% have demonstrated aggressive behaviours.2 The behavioural and psychological symptoms of dementia (e.g., aggression) can lead to caregiver burden and difficulty in providing safe and timely care for patients with dementia.36 Despite the limited evidence of treatment efficacy, both antipsychotics and trazodone (an antidepressant medication) are commonly used to alleviate the behavioural and psychological symptoms of dementia.7 In 2013, 34% of older adults with dementia living in a long-term care facility in Ontario, Canada, were dispensed an atypical antipsychotic, and 21.3% were dispensed trazodone.3,8 Similarly high rates of antidepressant and atypical antipsychotic use were reported among patients with dementia in the United States and Europe.5,9,10

Antipsychotics are associated with substantial harm among older adults with dementia, including myocardial infarction, aspiration pneumonia and death.1113 There is growing concern about the use of antipsychotics for indications other than the treatment of chronic psychotic illnesses.1421 Clinical practice guidelines and quality-improvement initiatives have encouraged clinicians to decrease antipsychotic use in older adults with dementia.15,1722 Comparatively, little is known about the risk of harm from trazodone in older adults with dementia — despite its increasing use.3,8,9 In Ontario, the prevalence of trazodone prescriptions has risen sharply, from 7.7% in 2002 to 21.3% in 2013.3 Randomized controlled trials (RCTs) of trazodone in patients with dementia described adverse effects, including parkinsonism, drowsiness, dizziness and hypotension, which could contribute to an increased risk of falls or fractures; only one study reported a patient was lost to follow-up in its placebo arm for having a fracture.2327 Understanding how potential harms associated with trazodone compare with the harms associated with antipsychotics is important because we know there are risks associated with antipsychotic use in older adults.28 Clinicians might use trazodone to treat the behavioural and psychological symptoms of dementia instead of antipsychotics to avoid these risks.

We examined the comparative risk of the composite outcome of falls and major osteoporotic fractures, falls, major osteoporotic fractures, hip fractures and all-cause mortality among older adults with dementia dispensed trazodone or atypical antipsychotics.

Methods

This manuscript is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and RECORD (Reporting of Studies Conducted Using Observational Routinely-collected Data) statements for the reporting of observational studies.29,30

Setting and data sources

We created our cohort using the linked health administrative databases at ICES in Ontario, Canada. Ontario has a largely publicly funded health care system, in which individuals aged 65 years or older have guaranteed housing in long-term care facilities when necessary, and universal coverage for physician services and most prescription medications. Patient-level information was linked using an encoded version of each patient’s health insurance program number.31 We linked data from the following databases: Ontario Drug Benefit, ICES Physician Database, Ontario Mental Health Reporting System, National Ambulatory Care Reporting System, Ontario Health Insurance Plan Database, Registered Persons Database, Discharge Abstract Database and Continuing Care Reporting System.32 A description of these databases is found in Appendix 1a, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1. These databases are accurate and reliable.31,3337

Study design

We implemented a retrospective cohort study design. Our index date was the date of first prescription of an exposure or comparator drug. We identified patients who were dispensed a study drug and had a full interRAI (International Resident Assessment Instrument) assessment within 30 days before cohort entry. The Resident Assessment Instrument–Minimum Data Set is a validated assessment that contains information about aspects of patients’ health, including independence in activities of daily living and severity of cognitive impairment.38,39 We required assessments within 30 days to ensure a close temporal relation between drug exposure and data on patients’ state of health. This cohort included adults aged 66 years or older with dementia who were living in long-term care facilities and newly dispensed oral trazodone or atypical antipsychotics (quetiapine, olanzapine or risperidone) between Dec. 1, 2009, and Dec. 31, 2015. We identified patients with dementia using the validated algorithm of Jaakkimainen and colleagues, and diagnostic codes from the interRAI assessment.33,40 The algorithm of Jaakkimainen and colleagues has a sensitivity of 79.3% and specificity of 99.1% for identifying dementia. Our observation window was 90 days, which was chosen to balance the need for sufficient time for accrual of events with the need to lessen the chance of residual confounding. The maximum follow-up date was Mar. 31, 2016.

We excluded participants from our cohort if they did not have a complete interRAI assessment within 30 days before cohort entry, received any antipsychotics or trazodone within the year before cohort entry, did not have a history of dementia, were dispensed 2 or more of our exposure drugs on the date of cohort entry, had a diagnosis of a chronic psychotic illness within 2 years of cohort entry, received palliative care services within 180 days of cohort entry, received the study drugs above a prespecified maximum total daily dose at cohort entry, or were younger than 66 or older than 105 years.4042

Patients in our exposure group received oral trazodone (maximum total daily dose: 300 mg) and those in our comparator group received oral quetiapine (maximum total daily dose: 300 mg), risperidone (maximum total daily dose: 3 mg) or olanzapine (maximum total daily dose: 10 mg). Equivalency ratios were calculated as the mean of equally efficacious doses of drugs across RCTs (Appendix 1b, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1.).43,44

We derived stabilized inverse probability of treatment weights from a propensity score model in which exposure status was regressed on a set of measured baseline covariates. Covariates were selected for inclusion in the propensity score model based on the existing literature and clinical judgment (see Appendix 2, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1, for the variables included in the propensity score model).45 As a sensitivity analysis, inverse probability of treatment weights were also derived from a high-dimensional propensity score model.46 The following variables were forced into the high-dimensional propensity score: age, sex, Cognitive Performance Scale score, dependency in activities of daily living, year of cohort entry, presence of delusions, Aggressive Behaviours Scale score, Depression Rating Scale score, weight, wheelchair ambulation, number of geriatrician visits, and history of major osteoporotic fracture or falls leading to hospital presentation.4749

We defined our outcomes with input from 12 care partners of older adults with dementia. Outcomes were ranked in descending order of importance from among commonly reported safety outcomes (e.g., mortality and stroke) in studies of pharmacologic treatments for the behavioural and psychologic symptoms of dementia.11,50,51 We defined our primary outcome as a composite of a fall resulting in an emergency department visit or major osteoporotic fracture. A major osteoporotic fracture was defined as a fracture of the hip, pelvis, humerus or forearm.52 These outcomes have been identified in administrative databases with a high positive predictive value and level of agreement during medical chart re-abstraction.31,5255 Our secondary outcomes were all-cause mortality and the components of our primary outcome: falls, major osteoporotic fracture and hip fracture. We included a tracer outcome (cataract surgery) to assess the sensitivity of our findings to unmeasured confounding.5658 All International Classification of Diseases, 10th Revision (ICD-10) codes and algorithms used to define patients’ baseline characteristics and study outcomes are found in Appendix 3, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1.31,33,4042,52,5968

Statistical analysis

We compared baseline characteristics of patients in our exposure and comparator groups with standardized differences.69 Groups were considered similar if the standardized difference was less than 0.1.70,71 We reported the proportion of patients missing data for individual baseline characteristics. Stabilized inverse probability of treatment weights were derived from the estimated propensity score. Treatment weights were inspected for outlying values (> 50).72 Unweighted and weighted cause-specific hazard ratios (HRs) were derived from cause-specific hazards models that accounted for the competing risk of death.73,74 We chose the cause-specific hazard model because we wanted to understand the association between our exposure and the rate of outcome in participants who were event-free and thus at risk of the event. This addresses a primarily etiologic question.75 Robust standard errors were used to account for within-subject homogeneity in outcomes induced by weighting.76 We verified that the HR did not vary over time. We based our primary analyses on an intention-to-treat principle whereby patients in the cohort were followed until the first of the following: outcome of interest, death or 90 days after index date. In secondary analyses, we censored patients in the cohort if they were dispensed a drug from the other exposure group during the 90-day follow-up period. Weighted incidence rates are reported as the number of events per 100 person-years. Risk differences were calculated as the weight-adjusted difference in absolute risk among patients dispensed trazodone minus the absolute risk in patients dispensed atypical antipsychotics at 90 days. Where numbers permitted, we planned to conduct subgroup analyses of outcomes based on age, sex and dementia severity. We also planned to describe the effect of drug dose on outcomes using dose as a time-varying covariate in an unweighted Cox proportional hazards model incorporating all of the characteristics described in Appendix 2.

As a sensitivity analysis, we derived stabilized inverse probability of treatment weights from the estimated high-dimensional propensity score. Re-weighted cause-specific HRs were derived for our primary and secondary outcomes. Lastly, we repeated our weighted regression analyses using a subdistribution hazard model that accounted for the competing risk of death.77 With the subdistribution hazard model, we are estimating the association of exposure with the cumulative incidence function.78 Formally, the subdistribution HR compares the instantaneous rate of the outcome in the risk set of participants who are event-free or who have experienced a competing event, while the cause-specific hazard model focuses on comparing the rates of the outcome in participants who are currently event-free. All analyses were conducted using SAS, version 9.4.

Ethics approval

This study was approved by the University of Toronto and Sunnybrook Health Sciences Centre research ethics boards.

Results

Our cohort consisted of 9463 patients: 6588 were newly dispensed trazodone and 2875 were newly dispensed atypical antipsychotics (Figure 1). There were no outlying stabilized inverse probability of treatment weights. Among patients dispensed atypical antipsychotics, 275 (9.6%) were dispensed olanzapine, 1511 (52.6%) were dispensed quetiapine and 1089 (37.9%) were dispensed risperidone. Almost all patients were dispensed a low dose of the exposure or comparator drug: 95.2% of patients were dispensed a dose that was less than or equivalent to 2.5 mg of olanzapine per day, 4.1% were dispensed a moderate dose and 0.7% were dispensed a high dose. After applying inverse probability of treatment weights, exposure and comparator groups were similar at baseline (Table 1). The mean age of patients on the date of cohort entry was 85.3 (standard deviation 7.2) years, and 68.7% were female.

Figure 1:

Figure 1:

Flow diagram of study cohort creation. Note: interRAI = International Resident Assessment Instrument.

Table 1:

Selected baseline characteristics* of older adults dispensed trazodone or atypical antipsychotics

Characteristic No. (%) of participants Crude standardized difference Weighted standardized difference
Trazodone
n = 6588
Atypical antipsychotics
n = 2875
Demographic characteristics
Age, yr, mean ± SD 85.48 ± 7.22 85.00 ± 7.07 0.07 0.02
Female sex 4594 (69.7) 1908 (66.4) 0.07 0.01
Weight, kg, mean ± SD 65.86 ± 29.10 65.17 ± 29.99 0.02 < 0.01
Income quintile
 Missing 66 (1.0) 36 (1.3) 0.02 0.01
 1 (lowest) 1549 (23.5) 663 (23.1) 0.01 0.01
 2 1274 (19.3) 532 (18.5) 0.02 < 0.01
 3 1335 (20.3) 600 (20.9) 0.01 0.01
 4 1254 (19.0) 508 (17.7) 0.04 0.01
 5 (highest) 1110 (16.8) 536 (18.6) 0.05 < 0.01
Dependency in ADLs, mean ± SD 5.20 ± 1.33 5.35 ± 1.16 0.12 0.03
Health care use
Prescriptions, mean ± SD 19.45 ± 9.97 17.63 ± 9.68 0.18 0.01
Emergency department visits, mean ± SD 1.52 ± 2.85 1.39 ± 1.95 0.05 0.01
Inpatient hospital admissions, mean ± SD 0.66 ± 1.00 0.58 ± 0.95 0.09 0.02
Johns Hopkins ACG (version 10) ADGs
 0–5 1601 (24.3) 812 (28.2) 0.09 < 0.01
 6–10 2173 (33.0) 965 (33.6) 0.01 < 0.01
 ≥ 11 2814 (42.7) 1098 (38.2) 0.09 < 0.01
Medications
Anticoagulants 1498 (22.7) 515 (17.9) 0.12 0.01
Hypoglycemic agents 1416 (21.5) 570 (19.8) 0.04 0.03
Antihypertensives 4606 (69.9) 1895 (65.9) 0.09 0.02
Antilipemics 3015 (45.8) 1252 (43.5) 0.04 0.01
Glucocorticoids 607 (9.2) 205 (7.1) 0.08 0.02
Hormone therapies 150 (2.3) 32 (1.1) 0.09 0.02
Osteoporosis treatments 1551 (23.5) 663 (23.1) 0.01 0.01
Antiparkinsonian agents 501 (7.6) 220 (7.7) 0.00 0.01
Pain medications 2892 (43.9) 1106 (38.5) 0.11 0.02
Psychoactive agents 4842 (73.5) 2273 (79.1) 0.13 0.01
Medical history
Alcohol use 329 (5.0) 128 (4.5) 0.03 0.02
Cardiac arrhythmias 1838 (27.9) 683 (23.8) 0.09 0.01
Cerebrovascular disease 1526 (23.2) 607 (21.1) 0.05 0.01
Delusions or hallucinations 251 (3.8) 266 (9.3) 0.22 0.01
Depression 2072 (31.5) 871 (30.3) 0.03 0.01
Diabetes mellitus 2285 (34.7) 947 (32.9) 0.04 < 0.01
Falls 1619 (24.6) 679 (23.6) 0.02 0.01
Major osteoporotic fracture 975 (14.8) 373 (13.0) 0.05 0.02
Ischemic heart disease 1774 (26.9) 678 (23.6) 0.08 0.01
Hypertension 5564 (84.5) 2296 (79.9) 0.12 0.01
Parkinsonism 481 (7.3) 212 (7.4) 0.00 0.01
Wheelchair ambulation 3676 (55.8) 1364 (47.4) 0.17 0.01

Note: ACG = Adjusted Clinical Groups System, ADGs = Aggregated Diagnosis Groups, ADLs = activities of daily living, SD = standard deviation.

*

See Appendix 2 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1) for the complete baseline characteristics of our study cohort.

Unless stated otherwise.

In our primary analysis, patients newly dispensed trazodone experienced a similar rate of falls and major osteoporotic fractures after inverse probability of treatment weighting (cause-specific HR 0.89, 95% confidence interval [CI] 0.73 to 1.07; Table 2) compared with those initiating atypical antipsychotics. We also found similar rates of our secondary outcomes of falls (cause-specific HR 0.91, 95% CI 0.75 to 1.11), major osteoporotic fractures (cause-specific HR 1.03, 95% CI 0.73 to 1.47), and hip fractures (cause-specific HR 0.92, 95% CI 0.59 to 1.43). However, patients dispensed trazodone had a lower rate of all-cause mortality (HR 0.75, 95% CI 0.66 to 0.85). In our tracer analysis, there was no difference in the rate of cataract surgery between new users of trazodone or atypical antipsychotics (HR 0.67, 95% CI 0.33 to 1.34).

Table 2:

Primary and secondary analyses of the comparative risk of primary and secondary outcomes for new users of trazodone versus atypical antipsychotics within 90 days

Outcome Crude HR (95% CI) Weighted
Cause-specific HR (95% CI) Events/100 person-years Risk difference, % (95% CI)
Trazodone Atypical antipsychotic
Primary intention-to-treat analyses
Fall or major osteoporotic fracture 0.71 (0.60 to 0.85) 0.89 (0.73 to 1.07) 23 25 −0.5 (−1.5 to 0.5)
All-cause mortality 0.78 (0.70 to 0.86) 0.75 (0.66 to 0.85) 60 77 −4.3 (−6.0 to −2.6)
Fall 0.72 (0.60 to 0.86) 0.91 (0.75 to 1.11) 22 24 −0.3 (−1.4 to 0.7)
Major osteoporotic fracture 0.81 (0.59 to 1.12) 1.03 (0.73 to 1.47) 8 7 0.1 (−0.5 to 0.7)
Hip fracture 0.72 (0.48 to 1.08) 0.92 (0.59 to 1.43) 5 5 −0.06 (−0.5 to 0.4)
Cataract surgery 0.86 (0.44 to 1.67) 0.67 (0.33 to 1.34) 2 3 −0.1 (−0.4 to 0.1)
Secondary analyses (censoring on switching exposure group)
Fall or major osteoporotic fracture 0.67 (0.56 to 0.81) 0.84 (0.69 to 1.02) 21 24 −0.8 (−1.8 to 0.2)
All-cause mortality 0.78 (0.71 to 0.85) 0.80 (0.73 to 0.89) 87 105 −4.4 (−6.3 to −2.5)
Fall 0.68 (0.57 to 0.82) 0.86 (0.71 to 1.05) 20 23 −0.6 (−1.6 to 0.4)
Major osteoporotic fracture 0.74 (0.53 to 1.03) 0.94 (0.65 to 1.34) 7 7 −0.07 (−0.6 to 0.5)
Hip fracture 0.66 (0.43 to 0.99) 0.84 (0.53 to 1.32) 4 5 −0.2 (−0.6 to 0.3)

Note: CI = confidence interval, HR = hazard ratio.

The results of our secondary analyses were consistent with those of our primary analyses (Table 2). The baseline characteristics of our cohort and our conclusions were unchanged using the high-dimensional propensity score to derive our inverse probability of treatment weights (Appendix 4, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1). The subdistribution hazard analysis showed that the cumulative incidence function for falls or major osteoporotic fracture, falls, major osteoporotic fractures, and hip fractures were similar between the 2 drug groups (Appendix 5, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.180551/-/DC1). We did not do our planned analysis of time-varying dose because almost all patients in our cohort were dispensed a low-dose equivalent. We also did not conduct subgroup analyses because our sample was too small to derive meaningful effect estimates.

Interpretation

In this population-based study of older adults with dementia, we found that patients dispensed trazodone had a rate of falls, major osteoporotic fractures and hip fractures comparable to those of patients dispensed atypical antipsychotics; however, their rate of all-cause mortality within 90 days was significantly lower, as discussed below. In Ontario, falls and fractures are not uncommon among residents of long-term care facilities: 2.8% had a fall resulting in an emergency department visit or hospital admission within 90 days of entering into a study cohort and 3.3% had a fracture of the hip, pelvis, wrist or humerus over a 1-year period.79,80 Among residents of Ontario long-term care facilities who were not prescribed an antipsychotic, 2.7% of residents died within 30 days and 15.1% of residents died within 180 days of cohort entry.81

In the broader literature concerning trazodone, its association with a patient’s risk of fall or fracture is unclear.80,8284 In a cross-sectional analysis of older men living in the community, trazodone use was not associated with an increased risk of fracture; however, these men had less burden of illness than our study cohort.84 A study of older adults in long-term care facilities found an increased risk of fall and fracture among patients dispensed trazodone.80 The association between trazodone use and death among older adults with dementia was a novel finding. Could the greater risk of death associated with atypical antipsychotic use be related to an altered cardiometabolic profile? Although both trazodone and atypical antipsychotics have been associated with an increased risk of falls and fractures, antipsychotic use has also been associated with an increased risk of myocardial infarction and stroke in patients with dementia.12,50,80

Limitations

Despite our use of a clinically derived propensity score using detailed clinical data, the nonrandomized nature of our study means that it is possible for unmeasured confounding to influence our findings. However, our results did not change in our sensitivity analysis, in which we implemented a high-dimensional propensity score model, and the results of our primary and secondary analyses were consistent. Because of software limitations, we were unable to estimate the cumulative incidence functions in the weighted samples. To limit confounding from frailty and medication noncompliance, we chose a moderately to severely frail population of patients with dementia living in long-term care facilities. This might limit the generalizability of our findings to less frail older adults outside of a long-term care setting, but frailty has prognostic importance in older adults.85,86 Our outcome of falls was limited to patients presenting to hospital.52,87 Therefore, the number of events we observed is likely an underestimate of the number of falls associated with trazodone and atypical antipsychotics; however, there should not be any differential misclassification between our exposure and comparator groups (assuming that patients in each group experienced falls of similar severity).

Conclusion

As clinicians move to decrease antipsychotic use, we should not consider trazodone as a uniformly safer alternative to atypical antipsychotics, because trazodone use was associated with a comparable risk of falls and major osteoporotic fractures to atypical antipsychotics — drugs associated with these adverse outcomes in our patient population.11,28 As appropriate prescribing campaigns target antipsychotic use in older adults with dementia, we will need to consider how these campaigns could lead to collateral changes in clinical practice.14,17

Acknowledgements

The authors thank Dr. Zahra Goodarzi for her help in conceptualizing our stakeholder engagement survey. The authors also thank the following people for completing our survey: Dr. Camilla Wong, Ms. Mary-Anne Lee, Ms. Joanna Stanley, Ms. Denise Watt, Ms. Hazel Sebastian, Dr. Marie Patton, Ms. Loralee Fox, Ms. Junyan Shi, Dr. Jayna Holroyd-Leduc and Dr. David Hogan. The authors thank Dr. Andrea Tricco and Dr. Areti-Angeliki Veroniki for their ongoing contributions to the completion of Dr. Jennifer Watt’s doctoral thesis, of which this manuscript is a component. The authors thank Brogan Inc., Ottawa, for use of its Drug Product and Therapeutic Class Database.

Footnotes

Competing interests: Tara Gomes has received grant funding from the Ontario Ministry of Health and Long-Term Care. All other authors have no conflicts of interest to declare.

This article has been peer reviewed.

Contributors: Jennifer Watt, Tara Gomes, Susan Bronskill, Peter Austin, Joanne Ho and Sharon Straus designed the study. Anjie Huang and Jennifer Watt completed all data analysis. Jennifer Watt drafted the manuscript, which all of the authors revised. All of the authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.

Funding: This study was funded by the breaKThrough (knowledge translation) Program of St. Michael’s Hospital. Jennifer Watt is supported by a Canadian Institutes of Health Research (CIHR) doctoral research award and the University of Toronto Department of Medicine Eliot Phillipson Clinician–Scientist Training program. Sharon Straus is funded by a Tier 1 Canada Research Chair in Knowledge Translation. Peter Austin is funded by a Mid-Career Investigator Award from the Heart and Stroke Foundation. This study was also supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Data sets provided by ICES were linked using unique encoded identifiers and analyzed at ICES.

The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and statements expressed herein are those of the authors, and not necessarily those of CIHI.

Data sharing: The data set from this study is held securely in coded form at ICES. Although data-sharing agreements prohibit ICES from making the data set publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www.ices.on.ca/DAS.

Disclaimer: This study was supported by ICES, which is funded by an annual grant from the Ontario MOHLTC. The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.

References

  • 1.Dementia in Canada, including Alzheimer’s disease: highlights from the Canadian Chronic Disease Surveillance System. Ottawa: Public Health Agency of Canada; 2017. [Google Scholar]
  • 2.CCRS continuing care reporting system: profile of residents in continuing care facilities 2016–2017. Ottawa: Canadian Institute for Health Information; 2017. [Google Scholar]
  • 3.Iaboni A, Bronskill SE, Reynolds KB, et al. Changing pattern of sedative use in older adults: a population-based cohort study. Drugs Aging 2016;33: 523–33. [DOI] [PubMed] [Google Scholar]
  • 4.Martinez C, Jones RW, Rietbrock S. Trends in the prevalence of antipsychotic drug use among patients with Alzheimer’s disease and other dementias including those treated with antidementia drugs in the community in the UK: a cohort study. BMJ Open 2013;3: pii:e002080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Breining A, Bonnet-Zamponi D, Zerah L, et al. Exposure to psychotropics in the French older population living with dementia: a nationwide population-based study. Int J Geriatr Psychiatry 2017;32:750–60. [DOI] [PubMed] [Google Scholar]
  • 6.Brodaty H, Draper B, Low L-F. Nursing home staff attitudes towards residents with dementia: strain and satisfaction with work. J Adv Nurs 2003; 44:583–90. [DOI] [PubMed] [Google Scholar]
  • 7.Kales HC, Gitlin LN, Lyketsos CG. Assessment and management of behavioral and psychological symptoms of dementia. BMJ 2015;350:h369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Vasudev A, Shariff SZ, Liu K, et al. Trends in psychotropic dispensing among older adults with dementia living in long-term care facilities: 2004–2013. Am J Geriatr Psychiatry 2015;23:1259–69. [DOI] [PubMed] [Google Scholar]
  • 9.Macías Saint-Gerons D, Huerta Alvarez C, Garcia Poza P, et al. Trazodone utilization among the elderly in Spain. A population-based study. Rev Psiquiatr Salud Ment 2016;11:208–15. [DOI] [PubMed] [Google Scholar]
  • 10.Driessen J, Baik SH, Zhang Y. Trends in off-label use of second-generation antipsychotics in the Medicare population from 2006 to 2012. Psychiatr Serv 2016;67:898–903. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gill SS, Bronskill SE, Normand S-LT, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med 2007;146:775–86. [DOI] [PubMed] [Google Scholar]
  • 12.Huang KL, Fang CJ, Hsu CC, et al. Myocardial infarction risk and antipsychotics use revisited: a meta-analysis of 10 observational studies. J Psychopharmacol 2017;31:1544–55. [DOI] [PubMed] [Google Scholar]
  • 13.Herzig SJ, LaSalvia MT, Naidus E, et al. Antipsychotics and the risk of aspiration pneumonia in individuals hospitalized for nonpsychiatric conditions: a cohort study. J Am Geriatr Soc 2017;65:2580–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Looking for balance: antipsychotic medication use in Ontario long-term care homes. Toronto: Health Quality Ontario; 2015. [Google Scholar]
  • 15.Antipsychotic use in the elderly: final consolidated report. Toronto: Ontario Drug Policy Research Network; 2015. [Google Scholar]
  • 16.Prince M, Bryce R, Albanese E, et al. The global prevalence of dementia: a systematic review and meta-analysis. Alzheimers Dement 2013;9:63–75.e2 [DOI] [PubMed] [Google Scholar]
  • 17.Treating disruptive behaviour in people with dementia. Markham (ON): Canadian Geriatrics Society (Choosing Wisely Canada); 2014. [Google Scholar]
  • 18.Five things physicians and patients should question. Philadelphia: American Psychiatric Association (Choosing Wisely); 2015. [Google Scholar]
  • 19.Ten things clinicians and patients should question. New York: American Geriatrics Society (Choosing Wisely); 2015. [Google Scholar]
  • 20.American Geriatrics Society Beers Criteria Update Expert Panel. American Geriatrics Society 2015 updated Beers criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2015;63:2227–46. [DOI] [PubMed] [Google Scholar]
  • 21.Reus VI, Fochtmann LJ, Eyler AE, et al. The American Psychiatric Association practice guideline on the use of antipsychotics to treat agitation or psychosis in patients with dementia. Am J Psychiatry 2016;173:543–6. [DOI] [PubMed] [Google Scholar]
  • 22.Dementia: Assessment, management and support for people living with dementia and their carers (draft for consultation). London (UK): National Institute for Health and Care Excellence; 2018. [PubMed] [Google Scholar]
  • 23.Teri L, Logsdon RG, Peskind E, et al. Treatment of agitation in AD: a randomized, placebo-controlled clinical trial. Neurology 2000;55:1271–8. [DOI] [PubMed] [Google Scholar]
  • 24.Camargos EF, Louzada LL, Quintas JL, et al. Trazodone improves sleep parameters in Alzheimer disease patients: a randomized, double-blind, and placebo-controlled study. Am J Geriatr Psychiatry 2014;22:1565–74. [DOI] [PubMed] [Google Scholar]
  • 25.Sultzer DL, Gray KF, Gunay I, et al. A double-blind comparison of trazodone and haloperidol for treatment of agitation in patients with dementia. Am J Geriatr Psychiatry 1997;5:60–9. [PubMed] [Google Scholar]
  • 26.Lawlor BA, Radcliffe J, Molchan SE, et al. A pilot placebo-controlled study of trazodone and buspirone in Alzheimer’s disease. Int J Geriatr Psychiatry 1994; 9:55–9. [Google Scholar]
  • 27.Lebert F, Stekke W, Hasenbroekx C, et al. Frontotemporal dementia: a randomised, controlled trial with trazodone. Dement Geriatr Cogn Disord 2004; 17:355–9. [DOI] [PubMed] [Google Scholar]
  • 28.Fraser LA, Liu K, Naylor KL, et al. Falls and fractures with atypical antipsychotic medication use: a population-based cohort study. JAMA Intern Med 2015; 175:450–2. [DOI] [PubMed] [Google Scholar]
  • 29.Benchimol EI, Smeeth L, Guttmann A, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med 2015;12:e1001885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.von Elm E, Altman DG, Egger M, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg 2014;12:1495–9. [DOI] [PubMed] [Google Scholar]
  • 31.Juurlink D, Preyra C, Croxford R, et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Toronto: Institute for Clinical Evaluative Sciences; 2006. [Google Scholar]
  • 32.interRAI. Ann Arbor (MI) Available: www.interrai.org (accessed 2018 Oct. 17). [Google Scholar]
  • 33.Wodchis WP, Naglie G, Teare GF. Validating diagnostic information on the minimum data set in Ontario hospital-based long-term care. Med Care 2008; 46: 882–7. [DOI] [PubMed] [Google Scholar]
  • 34.Levy AR, O’Brien BJ, Sellors C, et al. Coding accuracy of administrative drug claims in the Ontario Drug Benefit database. Can J Clin Pharmacol 2003;10: 67–71. [PubMed] [Google Scholar]
  • 35.Gibson D, Richards H, Chapman A. The national ambulatory care reporting system: factors that affect the quality of its emergency data. Int J Inform Qual 2008; 2: 97–114. [Google Scholar]
  • 36.Raina P, Torrance-Rynard V, Wong M, et al. Agreement between self-reported and routinely collected health-care utilization data among Seniors. Health Serv Res 2002;37:751–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Ontario Mental Health Reporting System, data quality documentation. Ottawa: Canadian Institute for Health Information; 2016. [Google Scholar]
  • 38.Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods 2012;3:80–97. [DOI] [PubMed] [Google Scholar]
  • 39.Hirdes JP, Ljunggren G, Morris JN, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res 2008;8:277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Jaakkimainen RL, Bronskill SE, Tierney MC, 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;54:337–49. [DOI] [PubMed] [Google Scholar]
  • 41.Kurdyak P, Lin E, Green D, et al. Validation of a population-based algorithm to detect chronic psychotic illness. Can J Psychiatry 2015;60:362–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Paquay L, De Lepeleire J, Schoenmakers B, et al. 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;22:286–93. [DOI] [PubMed] [Google Scholar]
  • 43.Davis JM. Dose equivalence of the antipsychotic drugs. J Psychiatr Res 1974; 11:65–9. [DOI] [PubMed] [Google Scholar]
  • 44.Leucht S, Samara M, Heres S, et al. Dose equivalents for second-generation antipsychotic drugs: the Classical Mean Dose method. Schizophr Bull 2015; 41: 1397–402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Austin PC, Grootendorst P, Andersen GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 2007;26: 734–53. [DOI] [PubMed] [Google Scholar]
  • 46.Schneeweiss S, Rassen JA, Glynn RJ, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 2009;20:512–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Morris JN, Fries BE, Mehr DR, et al. MDS Cognitive Performance Scale. J Gerontol 1994;49:M174–82. [DOI] [PubMed] [Google Scholar]
  • 48.Perlman CM, Hirdes JP. The aggressive behavior scale: a new scale to measure aggression based on the minimum data set. J Am Geriatr Soc 2008;56:2298–303. [DOI] [PubMed] [Google Scholar]
  • 49.Burrows AB, Morris JN, Simon SE, et al. Development of a minimum data set-based depression rating scale for use in nursing homes. Age Ageing 2000;29: 165–72. [DOI] [PubMed] [Google Scholar]
  • 50.Gill SS, Rochon PA, Herrmann N, et al. Atypical antipsychotic drugs and risk of ischaemic stroke: population based retrospective cohort study. BMJ 2005;330: 445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Brodaty H, Ames D, Snowdon J, et al. A randomized placebo-controlled trial of risperidone for the treatment of aggression, agitation, and psychosis of dementia. J Clin Psychiatry 2003;64:134–43. [DOI] [PubMed] [Google Scholar]
  • 52.Welk B, McArthur E, Fraser LA, et al. The risk of fall and fracture with the initiation of a prostate-selective alpha antagonist: a population-based cohort study. BMJ 2015;351:h5398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Luther SL, French DD, Powell-Cope G, et al. Using administrative data to track fall-related ambulatory care services in the Veterans Administration Healthcare system. Aging Clin Exp Res 2005;17:412–8. [DOI] [PubMed] [Google Scholar]
  • 54.Tamblyn R, Reid T, Mayo N, et al. Using medical services claims to assess injuries in the elderly: sensitivity of diagnostic and procedure codes for injury ascertainment. J Clin Epidemiol 2000;53:183–94. [DOI] [PubMed] [Google Scholar]
  • 55.Jean S, Candas B, Belzile E, et al. Algorithms can be used to identify fragility fracture cases in physician-claims databases. Osteoporos Int 2012;23: 483–501. [DOI] [PubMed] [Google Scholar]
  • 56.Shrank WH, Patrick AR, Brookhart MA. Healthy user and related biases in observational studies of preventive interventions: a primer for physicians. J Gen Intern Med 2011;26:546–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Antoniou T, Macdonald EM, Hollands S, et al. Proton pump inhibitors and the risk of acute kidney injury in older patients: a population-based cohort study. CMAJ Open 2015;3:E166–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Kwong JC, Li P, Redelmeier DA. Influenza morbidity and mortality in elderly patients receiving statins: a cohort study. PLoS One 2009;4:e8087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Tu K, Wang M, Jaakkimainen RL, et al. Assessing the validity of using administrative data to identify patients with epilepsy. Epilepsia 2014; 55: 335–43. [DOI] [PubMed] [Google Scholar]
  • 60.Hall R, Mondor L, Porter J, et al. Accuracy of administrative data for the coding of acute stroke and TIAs. Can J Neurol Sci 2016;43:765–73. [DOI] [PubMed] [Google Scholar]
  • 61.Hux JE, Ivis F, Flintoft V, et al. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002;25:512–6. [DOI] [PubMed] [Google Scholar]
  • 62.Tu K, Mitiku T, Lee DS, et al. Validation of physician billing and hospitalization data to identify patients with ischemic heart disease using data from the Electronic Medical Record Administrative data Linked Database (EMRALD). Can J Cardiol 2010;26:e225–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Tu K, Campbell NR, Chen Z-L, et al. Accuracy of administrative databases in identifying patients with hypertension. Open Med 2007;1:e18–26. [PMC free article] [PubMed] [Google Scholar]
  • 64.Butt DA, Tu K, Young J, et al. A validation study of administrative data algorithms to identify patients with parkinsonism with prevalence and incidence trends. Neuroepidemiology 2014;43:28–37. [DOI] [PubMed] [Google Scholar]
  • 65.Widdifield J, Bernatsky S, Paterson JM, et al. Accuracy of Canadian health administrative databases in identifying patients with rheumatoid arthritis: a validation study using the medical records of rheumatologists. Arthritis Care Res (Hoboken) 2013;65:1582–91. [DOI] [PubMed] [Google Scholar]
  • 66.Cadarette SM, Jaglal SB, Raman-Wilms L, et al. Osteoporosis quality indicators using healthcare utilization data. Osteoporos Int 2011;22:1335–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Schultz SE, Rothwell DM, Chen Z, et al. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can 2013;33:160–6. [PubMed] [Google Scholar]
  • 68.Austin PC, van Walraven C, Wodchis WP, et al. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Med Care 2011;49:932–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med 2015; 34:3661–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput 2009;38:1228–34. [Google Scholar]
  • 72.Lee BK, Lessler J, Stuart EA. Weight trimming and propensity score weighting. PLoS One 2011;6:e18174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Cox DR. Regression models and life-tables. J R Stat Soc [Ser A] 1972;34:187–220. [Google Scholar]
  • 74.Allison PD. Discrete-time methods for the analysis of event histories. Sociol Methodol 1982;13:61–98. [Google Scholar]
  • 75.Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation 2016;133:601–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Austin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med 2016;35:5642–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94:496–509. [Google Scholar]
  • 78.Austin PC, Fine JP. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med 2017;36:4391–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Papaioannou A, Kennedy CC, Ioannidis G, et al. Comparative trends in incident fracture rates for all long-term care and community-dwelling seniors in Ontario, Canada, 2002–2012. Osteoporos Int 2016;27:887–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Macri JC, Iaboni A, Kirkham JG, et al. Association between antidepressants and fall-related injuries among long-term care residents. Am J Geriatr Psychiatry 2017;25:1326–36. [DOI] [PubMed] [Google Scholar]
  • 81.Gill SS, Bronskill SE, Normand S-LT. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med 2007;146:775–86. [DOI] [PubMed] [Google Scholar]
  • 82.Naples JG, Kotlarczyk MP, Perera S, et al. Non-tricyclic and non-selective serotonin reuptake inhibitor antidepressants and recurrent falls in frail older women. Am J Geriatr Psychiatry 2016;24:1221–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Coupland C, Dhiman P, Morriss R, et al. Antidepressant use and risk of adverse outcomes in older people: population-based cohort study. BMJ 2011; 343: d4551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Haney EM, Chan BKS, Diem SJ, et al. Association of low bone mineral density with selective serotonin reuptake inhibitor use by older men. Arch Intern Med 2007;167:1246–251. [DOI] [PubMed] [Google Scholar]
  • 85.Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005;173:489–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146–56. [DOI] [PubMed] [Google Scholar]
  • 87.Butt DA, Mamdani M, Austin PC, et al. The risk of falls on initiation of antihypertensive drugs in the elderly. Osteoporos Int 2013;24:2649–57. [DOI] [PubMed] [Google Scholar]

Articles from CMAJ : Canadian Medical Association Journal are provided here courtesy of Canadian Medical Association

RESOURCES