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Record W2117516569 · doi:10.18553/jmcp.2007.13.8.677

Factors Associated With Choice of Pharmacy Setting Among DoD Health Care Beneficiaries Aged 65 Years or Older

2007· article· en· W2117516569 on OpenAlexfundno aff
Andrea Linton, Mathew C. Garber, Nancy K. Fagan, Michael R. Peterson

Bibliographic record

VenueJournal of Managed Care Pharmacy · 2007
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersAlberta Machine Intelligence InstituteU.S. Department of Defense
KeywordsMedicinePharmacyGerontologyFamily medicineHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Department of Defense (DoD) health care planners want to stimulate a voluntary migration of prescription fills from military and community pharmacies to its mail-order pharmacy, a lower-cost dispensing option for the department. Beneficiary cost share for a 90-day supply of generic/ brand medication is $0/$0 at military (DoD) pharmacies, $3/$9 at the DoD mail-order pharmacy, and $9/$27 at network community pharmacies. OBJECTIVE: To examine the pharmacy use patterns among the beneficiary population age 65 years or older, traditionally the heaviest users of the TRICARE DOD prescription drug benefit, to identify factors that are associated with beneficiary use of pharmacy setting(s). METHODS: Outpatient prescription fill records were examined for TRICARE beneficiaries age 65 years or older (N = 300,084) residing in North Carolina, Texas, and California for dates of service from December 1, 2004 through February 28, 2005. Binary logistic regression models were run for each type (military, community, and mail order) and number of pharmacy settings used by beneficiary gender, age group, catchment area status (located either within or outside a 40-mile radius of each military pharmacy), state, and number of medications obtained (defined as count of unique combinations of strength, and route of administration). The mean number of medications per beneficiary and cost per medication were tabulated for each type and number of settings used. RESULTS: In the 3-month period from December 1, 2004 through February 28, 2005, beneficiary use of military, community, and mail-order pharmacies was 45.4%, 67.6%, and 22.1%, respectively. About 67% of the study population used 1 setting exclusively and 2.4% used all 3 settings. Noncatchment residents were significantly less likely (adjusted odds ratio [AOR]= 0.080; 95% confidence interval [CI], 0.078-0.082) to use a military pharmacy exclusively and significantly more likely to use a community pharmacy (AOR = 4.64; 95% CI, 4.55-4.73) or the mail-order pharmacy (AOR = 3.92; 95% CI, 3.80-4.05) exclusively than were catchment residents. Beneficiaries taking 10 or more medications were more likely (AOR = 8.43; 95% CI, 8.21-8.65) to use multiple settings than were those who obtained 3 or fewer medications. Single-setting users obtained a median of 4 (interquartile range [IQ]) 2-7) medications with a median copayment of $7.00 (IQ $0-$13.19) per medication. Those who used all 3 settings obtained a median of 9 (IQ 7-12) medications with a median copayment of $4.33 (IQ $3.00-$6.00) per medication. Among beneficiaries who obtained 6 or more unique medications during the 90-day study period, approximately 25% used the mail-order pharmacy to obtain 1 or more prescription fills. CONCLUSION: A significant portion of the study population did not use the mail-order pharmacy despite the financial incentive to use mail-order rather than community pharmacies. Relatively small financial incentives alone may be inadequate for promoting a switch to the mail-order option among those beneficiaries not already using it in a pharmacy benefit plan with low copayments. Larger monetary and other incentives may be necessary to achieve the desired transfer of prescriptions fills to the mail-order pharmacy and the associated reduction in military pharmacy workload.

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)0.001
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 score0.067
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.079
GPT teacher head0.391
Teacher spread0.312 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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