Rural-urban differences in use of health services before and after dementia diagnosis: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Rural-urban differences in health service use among persons with prevalent dementia are known. However, the extent of geographic differences in health service use over a long observation period, and prior to diagnosis, have not been sufficiently examined. The purpose of this study was to examine yearly rural-urban differences in the proportion of patients using health services, and the mean number of services, in the 5-year period before and 5-year period after a first diagnosis of dementia. METHODS: This population-based retrospective cohort study used linked administrative health data from the Canadian province of Saskatchewan to investigate the use of five health services [family physician (FP), specialist physician, hospital admission, all-type prescription drug dispensations, and short-term institutional care admission] each year from April 2008 to March 2019. Persons with dementia included 2,024 adults aged 65 years and older diagnosed from 1 April 2013 to 31 March 2014 (617 rural; 1,407 urban). Matching was performed 1:1 to persons without dementia on age group, sex, rural versus urban residence, geographic region, and comorbidity. Differences between rural and urban persons within the dementia and control cohorts were separately identified using the Z-score test for proportions (p < 0.05) and independent samples t-test for means (p < 0.05). RESULTS: Rural compared to urban persons with dementia had a lower average number of FP visits during 1-year and 2-year preindex and between 2-year and 4-year postindex (p < 0.05), a lower likelihood of at least one specialist visit and a lower average number of specialist visits during each year (p < 0.05), and a lower average number of all-type prescription drug dispensations for most of the 10-year study period (p < 0.05). Rural-urban differences were not observed in admission to hospital or short-term institutional care (p > 0.05 each year). CONCLUSIONS: This study identified important geographic differences in physician services and all-type prescription drugs before and after dementia diagnosis. Health system planners and educators must determine how to use existing resources and technological advances to support care for rural persons living with dementia.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".