Validity of a Prescription Claims Database to Estimate Medication Adherence in Older Persons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Prescription claims data have been used to estimate refill medication adherence through calculations of cumulative medication acquisition (CMA) and cumulative medication gap (CMG) values. Few studies have assessed the validity of these calculated rates. OBJECTIVES: We sought to assess the validity of CMA and CMG calculated from the Manitoba prescription claims database (DPIN) against pill count medication adherence, targeting overall medications and angiotensin converting enzyme inhibitors (ACEIs). METHODS: Using a survey of a convenience sample of subjects recruited through community pharmacies, subjects who were eligible for study (ie, 65 years or older, noninstitutionalized, taking 2 or more "discrete" prescribed medications, including an ACEI, and willing to provide informed consent) were studied. Pill counts were conducted on all prescribed medicines during 3 home interviews over the course of 4 months. Ten months of DPIN data also were collected on each subject. RESULTS: The concordance between CMA and pill count for overall medications was 411/522 (79%) and for ACEIs was 89/101 (88%) with no systematic differences (McNemar's P = 0.68 and P = 0.097, respectively). CMG and pill count showed even better concordance of 438/514 (85%) for overall medications and 96/101 (95%) for ACEIs, although systematic differences were noted for overall medications (McNemar's P = 0.0012) but not for ACEIs (McNemar's P = 0.500). Spearman's rank correlations were weak for all comparisons. CONCLUSIONS: The high concordance between prescription claims database and pill counts suggested that the rate with which patients refill their medications usually is consistent with the rate they consume them. DPIN is not accurate for nondiscrete dosage forms or medications prescribed for "as-required" use.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it