Adherence to Lipid-lowering Therapy and the Use of Preventive Health Services: An Investigation of the Healthy User Effect
Bibliographic record
Abstract
Patients who adhere to preventive therapies may be more likely to engage in a broad spectrum of behaviors consistent with a healthy lifestyle. Because many of these behaviors cannot be measured easily, observational studies of outcomes associated with the long-term use of preventive therapies are subject to the so-called "healthy user bias." To better understand this effect, the authors examined the association between adherence to statin therapy and the use of preventive health services in a Pennsylvania cohort of 20,783 new users of statins between 1996 and 2004. After adjustment for age, gender, and various comorbid conditions, patients who filled two or more prescriptions for a statin during a 1-year ascertainment period were more likely than patients who filled only one prescription to receive prostate-specific antigen tests (hazard ratio (HR)=1.57, 95% confidence interval (CI): 1.17, 2.19), fecal occult blood tests (HR=1.31, 95% CI: 1.12, 1.53), screening mammograms (HR=1.22, 95% CI: 1.09, 1.38), influenza vaccinations (HR=1.21, 95% CI: 1.12, 1.31), and pneumococcal vaccinations (HR=1.46, 95% CI: 1.17, 1.83) during follow-up. These results suggest that patients who adhere to chronic therapies are more likely to seek out preventive health services, such as screening tests and vaccinations. Further work is needed to identify study design and analysis methods that can be used to minimize the healthy user bias in studies of preventive therapies.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".