Validity of a Prescription Claims Database to Estimate Medication Adherence in Older Persons
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.
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 | 0.042 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".