Trajectories of Care of People Living with a Major Neurocognitive Disorder: A State Sequence Analysis
Bibliographic record
Abstract
Context. The type and level of healthcare services required to address the needs of older adults living with major neurocognitive disorders (MNCD) fluctuate over disease progression while being complexified by multimorbidity and inequality factors. Thus, their trajectories of care (TC - the pattern of healthcare use over time) may vary significantly. Objective. We aimed to 1) Propose a typology of TC of people living with MNCD; 2) Describe and compare their characteristics by TC type; 3) Evaluate the association between TC membership, socioeconomic factors, and self-perceived health. Study Design and Analysis. Retrospective cohort study. The TCs were developed using a multidimensional state sequence analysis approach based on the ‘6W’ model of Care Trajectories, conceptualizing TCs according to 6 dimensions. Dataset. We used data from the Care Trajectories -Enriched Data (TorSaDE) cohort, a linkage between five waves of the Canadian Community Health Survey (CCHS) (2007-2016), and health administrative data from the Quebec provincial health-insurance board (1996-2016). Population Studied. Community-dwelling individuals who: 1) participated in at least one cycle of the CCHS (the date of the last CCHS completion is the index date); 2) were 65 years or older at the time of the index date; 3) had a diagnosis of MNCD at the index date. Outcome Measures. TCs were defined as sequences of healthcare use in the two years preceding the index date, using the following information: 1) Type of care units consulted (1. Hospitalization, 2. Emergency department, 3. Outpatient clinic, 4. Primary care clinic); 2) Type of healthcare care professionals consulted (1. Geriatrician/psychiatrist/neurologist, 2. Other specialists, 3. Family physician. We identified the MNCD before the index date using a validated algorithm. Results. The study cohort included 690 individuals living with MNCD, grouped into three distinct type of TC: 1) Low healthcare use (n=377; 54.6%); 2) High primary care use (n=159; 23.0%); 3) High overall healthcare use (n=154; 22.3%). TC type 3 membership was associated with younger age, being a male, living in urban areas, and a poorer perceived health status. Conclusions. Further understanding of how subgroups of patients use healthcare services over time could help highlight fragility areas in the allocation of geriatric care resources and implement best practices, especially in the context of resource shortage.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".