Avoidable hospital use in community-dwelling persons living with dementia: impact of health service interventions, and primary care continuity
Bibliographic record
Abstract
Persons with dementia have twice the acute hospital use (Emergency Department (ED) visits and hospital admissions) as older persons without dementia. This hospital use dramatically impacts their health and quality of life. A share of this hospital use might be avoidable with appropriate ambulatory care. However, to date, we do not know how to reduce these potentially avoidable hospital use. The aim of this PhD thesis was to investigate how avoidable hospital use of community-dwelling persons with dementia could be reduced, especially by measuring the impact of health service interventions or primary care continuity on potentially avoidable hospital use. Addressing this overarching aim was accomplished in four articles. The first article aimed at measuring the impact of health service interventions on potentially avoidable hospital use in community-dwelling persons with dementia. I conducted a systematic literature review and meta-analysis to synthesize available evidence on the impact of health service interventions on hospital use in dementia compared to usual care. Despite a comprehensive systematic literature review and meta-analysis, including predominantly unpublished data, no health service intervention beyond usual care was found to reduce hospital use in community-dwelling persons with dementia. The three remaining articles aimed at measuring the impact of primary care continuity on potentially avoidable hospital use in community-dwelling persons with dementia. In the second article, I conducted a descriptive study of hospital use of community-dwelling persons living with dementia in Quebec, over the last 15 years using the Quebec provincial administrative database. I estimated that around 40 and 60 per 100 person-year of community-dwelling persons with dementia had at least one hospitalization and one ED visit during the year of diagnosis, respectively. Between 20 and 30% of those hospitalized, depending on the indicator, had a potentially avoidable hospital use, with average length of Alternate Level of Care (ALC) stay of more than 4.5 months. Most indicators remained constant over the 15 years. In the third article, I described in a Method Brief, for a non-expert audience how advanced statistical methods can be used to strengthen causal inference from observational data, especially with propensity scores; the method I am using in the fourth article. In the fourth article, I measured the association between high primary care continuity and potentially avoidable hospital use in community-dwelling persons with dementia in Quebec. I estimated, using an observational retrospective cohort, with inverse probability of treatment weighting using the propensity score, that high continuity with a primary care physician was significantly associated with fewer potentially avoidable hospitalizations (Ambulatory Care Sensitive Conditions (ACSC) hospitalization and 30-day readmission). In addition, high primary care continuity was significantly associated with fewer ED visits and hospitalizations. The relative risk reduction for Ambulatory Care Sensitive Condition hospitalization (general population definition) in those exposed to high primary care continuity was 0.82 (95% confidence Interval (CI) [0.72;0.94]; P=.004) compared to the unexposed. The relative risk reduction for Ambulatory Care Sensitive Condition hospitalization (older population definition) was 0.87 (CI [0.79;0.95]; P=.002). The relative risk reduction for 30-day hospital readmission was 0.81 (CI [0.72;0.92]; P<.001). The relative risk reduction for hospitalization and Emergency Department visits were 0.90 (CI [0.86;0.94]; P<.001), and 0.92 (CI [0.90;0.95]; P<.001), respectively. In this PhD thesis, I generated evidence that could ultimately inform healthcare policies aiming at reducing avoidable hospital use in community-dwelling persons with dementia
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".