Association between asthma control and asthma cost: Results from a longitudinal study in a primary care setting
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Asthma control can be assessed with the Asthma Control Test (ACT) and a score of 20 or higher indicates good asthma control. Patients pay for their consultation and treatment in the fee-for-service primary healthcare system in Singapore. We hypothesized that achieving asthma control would result in lower asthma costs through reduced acute exacerbations, fewer physician consultations and lower lost productivity. The study compared the healthcare costs of patients who achieved asthma control and those with suboptimal asthma control based on ACT scores. Factors influencing asthma control and healthcare expenditure over time were also examined. METHODS: A total of 736 patients were enrolled into an asthma care programme in two polyclinics during 2008 and 2013. Direct costs of asthma management were derived from the frequency of polyclinic consultations, medication costs and hospitalization. Indirect costs were estimated from lost workdays due to exacerbations. The generalized estimating equation (GEE) approach was used to longitudinally model the factors associated with total healthcare expenditure. RESULTS: Patients with asthma control spent S$48 (US$36) more per doctor visit on asthma drugs (P < 0.01) but incurred S$65 (US$48) less per doctor visit in total costs (P < 0.01) than those with suboptimal asthma control. The savings from achieving asthma control for obese patients were greater than for normal-weight patients (S$42 or the equivalent of US$31; P < 0.05). CONCLUSION: Optimal asthma control was associated with reduced healthcare costs. An effective treatment regimen should also consider other modifiable factors such as weight control to achieve asthma control and eventually reduce asthma costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".