Susan E. Bronskill

Background: People living with dementia frequently present to emergency departments (EDs) and are at increased risk of hospitalization, institutionalization, and other adverse outcomes. Geriatric Emergency Management (GEM) programs are designed to improve emergency care for older adults, but their effectiveness among people living with dementia remains uncertain. We evaluated the association between attendance at an ED with an active GEM program and days alive and at home among older adults living with dementia. Methods: We conducted a population‐based retrospective cohort of Ontario residents aged 65 years or older with identified dementia who had an index ED visit between April 1, 2021, and March 31, 2024. The exposure was attendance at an ED with an active GEM program. Results: The cohort included 145,874 individuals, including 110,288 who presented to EDs with GEM programs and 35,586 who presented to EDs without GEM programs. The median age of the population was 81 years and 58.5% of participants were female. After propensity score overlap weighting and adjustment for facility characteristics, individuals who visited GEM EDs spent 7.5 (95% CI, 3.1–12.0) more days alive and at home during the following year than those who visited non‐GEM EDs. Approximately half of this difference was attributable to fewer days spent in long‐term care homes. Visits to GEM EDs were also associated with fewer subsequent ED visits, although index ED length of stay and hospital admission rates were higher. Conclusions and Relevance: Among older adults living with dementia, care in EDs with GEM programs was associated with more days alive at home over the following year. These findings support continued investment in GEM programs to promote aging at home and to delay institutionalization among people living with dementia.

Background: Virtual care services increased during the COVID-19 pandemic, enabling access to physician consultations. Most studies on virtual care in Parkinson’s disease (PD) have focused on tertiary centers, limiting generalizability. Objective: To describe trends in virtual care use over time in people living with PD in Ontario, Canada, by rural and urban region of residence using health administrative data. Methods: We conducted a population-based repeated cross-sectional study between March 2020 and August 2023, among community-dwelling people living with PD aged ≥66 years in Ontario, Canada. We used Poisson regression models with generalized estimating equations to estimate rate ratios (RR) with 95% confidence intervals (CIs) comparing virtual visit rates in urban versus rural areas across physician specialties (family physicians, neurologists and psychiatrists/geriatricians). Results: Among 24,736 people living with PD in March 2020, 58.6% were male (mean age 77.6 [SD = 7.0] years), and most resided in urban areas (89.9%). Compared to urban residents, rural residents had significantly lower rates of virtual visits to neurologists (adjusted RR = 0.93; 95% CI: 0.90–0.96), family physicians (aRR = 0.73; 95% CI: 0.70–0.77) and geriatricians/psychiatrists (aRR = 0.67; 95% CI: 0.65–0.77). Over 3.5 years, virtual visits declined compared to the first 6 months for neurologists (aRR = 0.23; 95% CI: 0.22–0.25), family physicians (aRR = 0.39; 95% CI: 0.38–0.41) and geriatricians/psychiatrists (aRR = 0.45; 95% CI: 0.40–0.51). Conclusion: People living with PD in urban centers accessed virtual care more frequently than those living rurally. Virtual care utilization decreased over time. The sustainability of virtual care and its impact on health equity among people living with PD requires further study.

Background: Hip fractures are a significant public health concern, associated with substantial morbidity and mortality. Mortality is the most serious consequence of hip fractures, with a 1‐year rate ranging from 14% to 36%. Hospital harms are also more prevalent among older adults with hip fractures, further increasing their risk of mortality. This study aimed to assess the incremental risk of all‐cause mortality up to one‐year post‐discharge among hip fracture patients who experienced hospital harm. Methods: A population‐based retrospective cohort study using linked health administrative data in Ontario, Canada, was conducted. The cohort included individuals aged 50 years and older hospitalized for hip fractures between April 2008 and March 2022 and discharged alive. All‐cause mortality within one‐year post‐discharge was the primary outcome, and hospital harm was the main exposure. We used Cox proportional hazards models to assess the association between hospital harm and mortality, adjusting for covariates (e.g., age, sex, comorbidities, frailty, and surgery status). Results: A total of 131,472 patients admitted for acute hip fractures and discharged alive were included in the study, with a mean age of 80.67 ± 10.56 years. Of these patients, 17.5% experienced hospital harm during their admission for an acute hip fracture. The overall mortality rate was 18.4% within 1‐year post‐discharge. After adjusting for covariates, patients who experienced overall harm had a 42% higher risk of all‐cause mortality during the 1‐year period post‐discharge (adjusted hazard ratio, aHR = 1.42, 95% confidence interval, CI: 1.38–1.46) compared to those without harm. Conclusion: Experiencing hospital harm significantly increased the risk of 1‐year mortality after discharge among hip fracture patients. These findings underscore the importance of improving patient safety and minimizing preventable harm in hospital settings. Mitigation strategies, such as enhanced monitoring and safety protocols, could reduce hospital harm and improve long‐term survival. Future research should focus on evaluating such interventions.

Objectives: The health needs of people living with young-onset dementia are largely undocumented. The aim of this study was to describe health system use for community-dwelling people living with new young-onset dementia at a population level. Methods: We conducted a matched retrospective cohort study using routinely collected population-based health administrative data in Ontario, Canada, from January 2006 to March 2021. We matched 92% of people living with young-onset dementia to a comparison group (17,215 of 18,760) without dementia. Rates of health service use over time were estimated using Poisson regression. Long-term care placement and mortality were estimated using Cox regression. Results: People living with young-onset dementia had higher previous health system use compared to their matched counterparts. This included higher rates of general hospital admissions (rate ratio (RR) = 2.04 95% CI: 1.94, 2.15), emergency department visits (RR = 1.38 95% CI:1.30, 1.46), mental health admissions (RR = 4.03 95% CI:3.33, 4.88) and were more likely to experience hospital discharge delays (RR = 4.72 95% CI: 4.21, 5.29). Dementia specialist visits were 3.80 times higher (95% CI 3.40 to 4.25). The hazard of mortality for people with young-onset dementia was over three times higher compared to those without young-onset dementia (hazard ratio (HR) = 3.43 95% CI: 3.23, 3.65). Conclusions: We observed higher rates of secondary and tertiary health system utilization among people living with new young-onset dementia versus matched comparators. This complex pattern of health system use highlights the need for health system planning to address continuous care for people living with young-onset dementia.

Background: Multimorbidity, ≥ 2 chronic conditions as per the World Health Organization, can complicate aging in place and lead to earlier transitions to nursing homes. To inform supportive service intervention planning in naturally occurring retirement communities (NORCs), which are areas where large proportions of older adults reside, we examined clustering patterns of specific chronic conditions among NORC residents. Methods: We conducted a cross‐sectional analysis of a population‐based cohort of community‐dwelling NORC residents aged ≥ 66 years as of January 1, 2022, in Ontario, Canada. A provincial NORC registry of high‐rise buildings was linked with individual‐level health administrative records by postal code to identify NORC residents. Multimorbidity (≥ 2 of 17 measured chronic conditions), sociodemographic, clinical, and health service use (HSU) characteristics in NORC residents were assessed. Baseline descriptive statistics of the NORC cohort were compared by multimorbidity status, overall, and by sex, using standardized differences (STD). Latent class analysis (LCA) identified groups (known as classes) with similar patterns of co‐occurring chronic conditions. Results: Among 200,565 older NORC residents (63% female, 89% in urban settings), 84% lived with multimorbidity. Multimorbid residents were older (mean 78 vs. 74 years; STD 0.58) and more had low‐income status (19% vs. 9%; STD 0.27) than non‐multimorbid counterparts. LCA resulted in a 6‐class solution. Class 1 (13% of the cohort with multimorbidity) was characterized by conditions including osteoarthritis, osteoporosis, cancer, and mood disorders, class 2 (36%) by hypertension, osteoarthritis, osteoporosis, cancer, and mood disorders, class 3 (23%) by diabetes, class 4 (10%) by cardiovascular conditions, class 5 (8%) by respiratory conditions, and class 6 (10%) contained above average prevalences for 16 conditions and had increased HSU, including unscheduled emergency visits (48%). Conclusion: Findings suggest a substantial burden of multimorbidity in NORC residents and reinforce the importance of designing programs in NORCs to help older adults with multimorbidity age in place.

IntroductionPrescribing for people living with dementia can be challenging. Emerging research methods present an opportunity to learn about complex patterns of medication use and leverage this understanding to optimize care. ObjectiveWe describe network analysis, an unsupervised machine learning method, to understand population-level prescribing in older adults living with dementia. MethodsWe included community-dwelling adults aged 67 and older, newly ascertained as having dementia between April 1, 2014, and March 31, 2016 in Ontario, Canada. Using medication dispensation data, we created network graphs at ascertainment and five years later. Each node represented a medication subclass; subclasses concurrently dispensed to the same individual were considered linked by an edge. Attributes of networks were used to characterize prescribing across individuals: nodes, edges (including network density), and node centrality metrics. ResultsWe identified 99,064 individuals with incident dementia, of which 15,222 were alive and not living in a nursing home after five years. Network graphs visually demonstrated trends at the subclass level, such as a high prevalence of cardiovascular medications, and showed changes between times, such as an increase in dispensation of central nervous system active medications, particularly cholinesterase inhibitors (15.5\% at index compared to 26.4\% at five years). Co-dispensing (edge width) remained mostly consistent over time. Metrics derived from the networks highlighted differences, such as increased density (proportion of co-dispensed medication subclasses of all possible pairs) at five years compared to at ascertainment. Node centrality established frequently prescribed medication subclasses (statins, proton pump inhibitors, and beta blockers) as important within networks in this population. ConclusionsThis study offered an introductory review of the fundamental aspects of network analysis and demonstrated the complexity of prescribing patterns in people with incident dementia at the population-level. This showed that networks analysis can be used in future studies to compare population-level prescribing patterns across patient subgroups, prescribers, settings of care and regions to identify important differences.

Background: The Ontario Neurodegenerative Disease Research Initiative (ONDRI) was a prospective cohort study that enrolled persons with varied neurodegenerative pathologies including Alzheimer's disease and/or mild cognitive impairment (ADMCI), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and cerebrovascular disease± cognitive impairment (CVD) who underwent clinical and functional assessments in Ontario, Canada. We sought to examine long‐term differences in health system use, transition to nursing home, and mortality across neurodegenerative pathologies via linkage with provincial health administrative data. Methods: Of 520 participants enrolled in the ONDRI study, 478 provided consent and were linked to health administrative databases. This included 122 persons with ADMCI, 38 with ALS, 47 with FTD, 116 with PD, and 155 with CVD. Participants were followed from the year prior to their enrollment date (2014‐2017) to March 31 ^st 2024, death, or loss of health insurance eligibility, whichever occurred first. Rates of health service use, transition to nursing home, and mortality were calculated by neurodegenerative pathology and expressed per 100 person‐years. Results: Persons with PD were most likely to be male, while those with ADMCI showed the lowest proportion males (75.9% vs. 54.1%). Those with ADMCI were older compared to persons with ALS (mean 70.2 vs. 61.7 years). Follow‐up time ranged from an average of 2.8 years in persons with ALS to 8.0 years in those with CVD. Persons with ALS had the highest mortality rate, while those CVD had the lowest (35.3 vs. 2.4 per 100 person‐years). Persons with all neurodegenerative diseases showed a high use of health services, varying by service type. Persons with ALS showed the highest rate of home care visits, including visits for nursing, personal support, and therapies, followed by persons with FTD. Persons with PD had the highest rate of fall‐related emergency department visits and hospitalizations. Persons with FTD had the highest rate of transition to nursing home. Conclusions: Our findings demonstrate that persons with neurodegenerative pathologies require different health services, with varying intensity, to meet their needs. Linkage of health administrative data to clinical cohorts supports examination of health service milestones and allows for long‐term follow‐up at relatively low cost.

Background: Older adults living with dementia are a heterogenous population which can make studying optimal medication management challenging. Hierarchical clustering, an unsupervised machine learning method, can be used to summarize complex patterns of concurrently prescribed drug therapies across individuals. We aimed to determine if prescribing clusters (derived by grouping individuals dispensed similar medications) were associated with fall‐related hospitalizations in older adults living with dementia. Method: We identified a cohort of 99,046 older adults (aged ≥67 years) recently diagnosed with dementia in Ontario, Canada between 2014 and 2016 using health administrative data. Individuals were assigned to one of six distinct prescribing clusters: high cardiovascular (angiotensin‐converting enzyme‐specific) (22.6% of the population), central nervous system active (21.3%), hypothyroidism (22.9%), respiratory (3.9%), and angiotensin receptor blocker‐specific cardiovascular (6.1%), and a group with lower dispensation of medications in general (23.1%)). The outcome was fall‐related hospitalizations (emergency department or acute care) over one year following the dementia diagnosis. Cause‐specific survival models estimated the hazard of fall‐related hospitalizations by prescribing cluster, accounting for demographic characteristics, chronic conditions, and history of Beers criteria medications. Result: Five percent of the cohort experienced a fall‐related hospitalization within the first year of physician‐diagnosed dementia, with highest prevalence in the CNS‐active cluster (5.3%) and the lowest in general low medication use cluster (4.1%). The CNS‐active cluster had a significantly higher relative hazard of fall‐related hospitalization after multivariable adjustment (HR, 1.12, 95% CI: [1.03‐1.22]), compared to those with generally limited medication use. Conclusion: The hazard of fall‐related hospitalizations differed across prescribing clusters in persons recently diagnosed with dementia, but these differences did not persist after adjustment for key covariates. These methods can be used in future pharmacoepidemiology studies to summarize large amounts of medication data that can then be incorporated into outcomes or causal research.