The effects of patient education programs on medication use among asthma and COPD patients: a propensity score matching with a difference-in-difference regression approach
Bibliographic record
Abstract
BACKGROUND: Adherence to medication is one of the critical determinants of successful management of chronic diseases including asthma and chronic obstructive pulmonary disease (COPD). Given that poor adherence with self-management medication is very common among asthma and COPD patients, interventions that improve the use of chronic disease management medications for this patient group have potential to generate positive health outcomes. In an effort to improve asthma and COPD care, the Lung Association of Saskatchewan has implemented an intervention by providing access to effective and high quality asthma and COPD education for both patients and health care professionals along with increasing access to spirometry. By evaluating the impacts of this intervention, our purpose in this paper is to examine the effectiveness of spirometry use, and asthma and COPD education in primary care setting on medication use among asthma and COPD patients. METHODS: At the time of the intervention, the Lung Association of Saskatchewan has not assigned a control group. Therefore we used a propensity score matching to create a control group using administrative health databases spanning 6 years prior to the intervention. Using Saskatchewan administrative health databases, the impacts of the intervention on use of asthma and COPD medications were estimated for one to four years after the intervention using a difference in difference regression approach. RESULTS: The paper shows that overall medication use for the intervention group is higher than that of the control group. On average, intervention group uses more asthma and COPD drugs. Within the asthma and COPD drugs, this intervention creates a persistent effect over time in the form of higher utilization of chronic management drugs equivalent to $157 and $195 in a given year during four years after the intervention. CONCLUSIONS: The study suggests that effective patient education and increasing access to spirometry increases the utilization of chronic disease management drugs among asthma and COPD patients. This type of interventions with patient education focus has potential to save healthcare dollars by providing better disease management among this patient group.
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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 | 0.051 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".