Impact of a chronic disease self-management program on health care utilization in rural communities: a retrospective cohort study using linked administrative data
Bibliographic record
Abstract
BACKGROUND: Internationally, chronic disease self-management programs (CDSMPs) have been widely promoted with the assumption that confident, knowledgeable patients practicing self-management behavior will experience improved health and utilize fewer healthcare resources. However, there is a paucity of published data supporting this claim and the majority of the evidence is based on self-report. METHODS: We used a retrospective cohort study using linked administrative health data. Data from 104 tele-CDSMP participants from 13 rural and remote communities in the province of Ontario, Canada were linked to administrative databases containing emergency department (ED) and physician visits and hospitalizations. Patterns of health care utilization prior to and after participation in the tele-CDSMP were compared. Poisson Generalized Estimating Equations regression was used to examine the impact of the tele-CDSMP on health care utilization after adjusting for covariates. RESULTS: There were no differences in patterns of health care utilization before and after participating in the tele-CDSMP. Among participants ≤ 66 years, however, there was a 34% increase in physician visits in the 12 months following the program (OR = 1.34, 95% CI 1.11-1.61) and a trend for decreased ED visits in those >66 years (OR = 0.59, 95% CI 0.33-1.06). CONCLUSIONS: This is the first study to examine health care use following participation in the CDSMP in a Canadian population and to use administrative data to measure health care utilization. Similar to other studies that used self-report measures to evaluate health care use we found no differences in health care utilization before and after participation in the CDSMP. Future research needs to confirm our findings and examine the impact of the CDSMP on health care utilization in different age groups to help to determine whether these interventions are more effective with select population groups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".