Health service use before and after dementia diagnosis: a retrospective matched case–control study
Bibliographic record
Abstract
OBJECTIVES: This study investigated patterns in health service usage among older adults with dementia and matched controls over a 10-year span from 5 years before until 5 years after diagnosis. DESIGN: Population-based retrospective matched case-control study. SETTING: Administrative health data of individuals in Saskatchewan, Canada from 1 April 2008 to 31 March 2019. PARTICIPANTS: The study included 2024 adults aged 65 years and older living in the community at the time of dementia diagnosis from 1 April 2013 to 31 March 2014, matched 1:1 to individuals without a dementia diagnosis on age group, sex, rural versus urban residence, geographical region and comorbidity. OUTCOME MEASURES: For each 5-year period before and after diagnosis, we examined usage of health services each year including family physician (FP) visits, specialist visits, hospital admissions, all-type prescription drug dispensations and short-term care admissions. We used negative binomial regression to estimate the effect of dementia on yearly average health service utilisation adjusting for sex, age group, rural versus urban residence, geographical region, 1 year prior health service use and comorbidity. RESULTS: Adjusted findings demonstrated that 5 years before diagnosis, usage of all health services except hospitalisation was lower among persons with dementia than persons without dementia (all p<0.001). After this point, differences in higher health service usage among persons with dementia compared to without dementia were greatest in the year before and year after diagnosis. In the year before diagnosis, specialist visits were 59.7% higher (p<0.001) and hospitalisations 90.5% higher (p<0.001). In the year after diagnosis, FP visits were 70.0% higher (p<0.001) and all-type drug prescriptions 29.1% higher (p<0.001). CONCLUSIONS: Findings suggest the year before and year after diagnosis offer multiple opportunities to implement quality supports. FPs are integral to dementia care and require effective resources to properly serve this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".