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Record W2109257994 · doi:10.18553/jmcp.2006.12.7.537

Assessing Potentially Inappropriate Prescribing in the Elderly Veterans Affairs Population Using the HEDIS 2006 Quality Measure

2006· article· en· W2109257994 on OpenAlexaff
Mary Jo Pugh, Joseph T. Hanlon, John E. Zeber, Arlene S. Bierman, John E. Cornell, Dan R. Berlowitz

Bibliographic record

VenueJournal of Managed Care Pharmacy · 2006
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Toronto
FundersHealth Services Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsMedicineBeers CriteriaVeterans AffairsLogistic regressionMedical Expenditure Panel SurveyPopulationFamily medicineEmergency medicineHealth careGerontologyGeriatricsEnvironmental healthHealth insuranceInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Studies have found that 20% to 25% of older patients receive drugs identified as inappropriate by the 1997 Beers criteria. After the addition of 22 new drugs to the 2003 Beers criteria, the National Committee on Quality Assurance convened an expert consensus panel to identify which drugs from the 2003 Beers criteria should always be avoided in the elderly. The resulting list of drugs to avoid was added to the 2006 Health Plan Employer Data and Information Set (HEDIS) to measure the quality of prescribing for the elderly. OBJECTIVE: To use HEDIS 2006 criteria to determine the rate of potentially inappropriate prescribing in the elderly (PIPE) and to determine if patient risk factors are similar to those found using Beers criteria. METHODS: This cross-sectional database study identified older patients receiving drugs included in the HEDIS 2006 criteria using national data from the Veterans Health Administration. Patients aged 65 years or older on October 1, 1999, with at least 2 outpatient visit days during fiscal year 2000, ending September 30, or outpatient visits in fiscal years 1999 and 2000 were included (N=1,096,361). Multivariable logistic regression analyses stratified by gender identified patient characteristics associated with increased risk of HEDIS 2006 drug exposure. Since oral estrogens were considered appropriate at the time of this study, they were excluded from the list of HEDIS 2006 drugs. RESULTS: Overall, 19.6% of older veterans were exposed to HEDIS 2006 drugs. 23.3% of older veteran women and 19.2% of older veteran men. The most commonly prescribed HEDIS 2006 drugs were antihistamines (received by 9.0% of men and 10.7% of women), opioid analgesics (received by 4.6% of men and 5.8% of women), and skeletal muscle relaxants (received by 4.3% of men and 5.3% of women). Propoxyphene was the most commonly used HEDIS 2006 drug, received by 4.5% of men and 5.7% of women, followed by diphenhydramine, received by 3.5% of men and 4.7% of women, and hydroxyzine, received by 3.2% of both men and women. Patients receiving 10 or more medications of any type were at greatest risk of exposure. Men were 8.2 times more likely to receive at least 1 HEDIS 2006 drug than those taking 1 to 3 drugs of any type (95% confidence interval [CI], 8.0-8.4), while women were 9.6 times more likely (95% CI, 8.2-11.2). CONCLUSIONS: Even though we included a slightly different list of drugs to avoid, results for the HEDIS 2006 measure were similar to those of the 1997 Beers criteria. The HEDIS 2006 drugs are commonly prescribed, and there is a distinct need for direct evidence linking HEDIS 2006 PIPE exposure to adverse patient outcomes. To reduce PIPE, it seems necessary to provide additional evidence for clinicians through the conducting of a well-designed study to assess patient outcomes associated with PIPE exposure as defined by the HEDIS criteria.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.446
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations102
Published2006
Admission routes1
Has abstractyes

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