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Effects of restrictions on reimbursement for pharmaceutical drugs

2010· article· en· W2068393204 on OpenAlexaff
Carolyn J. Green

Bibliographic record

VenueJournal of Evidence-Based Medicine · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsReimbursementIncentiveHealth carePharmaceutical careSystematic reviewBusinessActuarial sciencePharmaceutical industryMEDLINEMedicinePublic economicsPublic relationsFamily medicineEconomicsPolitical sciencePharmacologyPharmacyLawEconomic growth

Abstract

fetched live from OpenAlex

Our review on restrictions to reimbursement by drug benefit plans (1) is part of an emerging series on pharmaceutical policies from the Cochrane Effective Practice and Organization of Care group (2–5). As expected, we found strong evidence that these restrictions do lead to lower spending on drugs but the more important question for us to answer was whether the savings associated with the restrictions can be obtained without unintended health consequences or shifting costs to other parts of the health care system. The answer is yes, but not always. The Cochrane Collaboration has taken up the challenge of Archie Cochrane's vision of systematically reviewing the best available evidence in all areas of health care, to support clinical practice. We have a similar vision for a collection of reviews to support informed decision making on pharmaceutical policies. Many people are not aware of how much the science of pharmaceutical policy has advanced, with the availability of large administrative datasets and the ability of researchers to process anonymous data from millions of routine transactions. Our review is the fourth to be completed in the planned series of 13 Cochrane reviews of pharmaceutical policy. The other three completed reviews looked at reference pricing, copayments and caps, and physician incentives; and have all yielded insights that can be used to formulate better pharmaceutical policies. As we brought together the data for our review, we were surprised to find how much the results varied by drug class. In the 29 included studies, nine drug classes had been targeted for restriction. Most looked at policies restricting gastric acid suppressants and non steroidal anti inflammatory drugs, or NSAIDS. For these, there is good evidence that restrictive policies lead to savings without increasing the use of other health services. We also found evidence supporting restrictions for wet nebulizer respiratory drugs and fluroquinolone antimicrobials. However, second generation antipsychotic medications provided an exception to the generally positive findings. There was good evidence that restrictions for these were associated with treatment discontinuity and increased out-patient visits. This makes them poor targets for restrictive policies. We also found that the available evidence does not support restrictions for anti-platelets and angiotensin receptor blocker medications. The global nature of our review made us aware of European strategies to encourage the use of cost effective medications to prevent secondary complications of high blood pressure and blood cholesterol. We found a small but high quality body of evidence supporting the lifting or exemption of restrictions, when these drugs are used for secondary prevention. The evidence available for our review applies mostly to older or low income populations, because the research was conducted using data from publicly funded drug benefit plans. However, the policies are also used by employer or private insurance plans and our findings might be relevant in those settings. Spending on prescription medications has increased dramatically in recent years, while public funding has contracted, so the use of policies to control costs may be essential to ensure that drug benefit plans can be sustained. We are excited about our early steps in realizing the vision of providing policy makers with systematic reviews of pharmaceutical policy across all 13 major areas. These policies can have enormous ramifications for the health of populations and the sustainability of health systems. We will be moving on to investigate policies that determine which drugs are reimbursed but the body of research in this area is growing so quickly that we also need to develop strategies to ensure the regular updating of the existing reviews.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.203
GPT teacher head0.400
Teacher spread0.197 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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