How many fit all? Latent class analysis of administrative data on healthcare utilization by persons with dementia in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Persons with dementia have complex and heterogeneous needs in the year following diagnosis, which leads to extensive use of healthcare services. A focus on addressing their differential needs would better enable effective interventions and care planning to prevent unnecessary use of services. OBJECTIVES: The aim of the present study was first to identify differential healthcare use groups using latent class analysis and secondly, to complete a descriptive analysis to highlight the sociodemographic factors, comorbidities and medication use associated with membership in the identified healthcare user groups.METHODS: This retrospective cohort study used provincial administrative data to identify an incident cohort of older adults with dementia. Persons were included if aged 65 and older, community-dwelling and diagnosed with dementia based on one of three criteria (prescription profile consistent with dementia, one hospitalization with dementia code or 3 physician visits with a dementia code) between April 1 2015 and March 31 2016. A latent class analysis was conducted to identify subgroups of differential healthcare users based on family physician, cognition specialist, other specialist, emergency department visits, hospital, and alternate level of care (ALC) use, as well as long-term care (LTC) admissions and mortality. A descriptive analysis was conducted to better understand the sociodemographic, comorbidities, psychotropic medication use and polypharmacy that characterized each group of healthcare users.RESULTS: The study cohort was of 15, 584 persons newly diagnosed with dementia. Four groups of healthcare users were identified: Low Users (36.4% of the persons), Ambulatory-Centric Users (27.5%), High Acute Hospital Users (23.6%) and LTC-Destined Users (12.5%). The Low Users were likely a heterogeneous group of persons with met and unmet needs, Ambulatory-Centric Users were notably disproportionately male and the youngest group, High Acute Hospital Users had the highest comorbidities, and the LTC-Destined Users were the eldest and had the highest use of ALC. CONCLUSION: The identification of defined subgroups of healthcare users with dementia among a heterogeneous cohort of persons with dementia provides context for further research and interventions targeted to the differential needs of 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".