Medicaid Outpatient Prescription Drugs: Second Quarter 2008 Federal Upper Limits for Reimbursement Compared with Average Retail Pharmacy Acquisition Costs
Bibliographic record
Abstract
Correspondence issued by the Government Accountability Office with an abstract that begins "Medicaid--the joint federal-state program that finances medical services for certain low-income adults and children--spent $15.0 billion on outpatient prescription drugs in fiscal year 2007. Instead of directly purchasing drugs, state Medicaid programs reimburse retail pharmacies for dispensing them to Medicaid beneficiaries. The federal government provides matching funds to states to help cover the costs of their Medicaid programs, and states must pay the remaining costs to qualify for these federal funds. For certain outpatient prescription drugs, state Medicaid programs may only receive federal matching funds for reimbursements up to a maximum amount known as a federal upper limit (FUL). Designed to control drug spending, FULs are currently calculated as 150 percent of a drug's lowest published price in three national drug pricing compendia. State Medicaid programs can determine reimbursements to retail pharmacies for each drug, but the federal government will only provide matching funds to the extent that reimbursements for all drugs subject to FULs do not exceed established FULs in the aggregate. A 2005 report by the Department of Health and Human Services' (HHS) Office of Inspector General (OIG) found that FULs were ineffective at controlling outpatient Medicaid prescription drug spending. The Deficit Reduction Act of 2005 (DRA) included provisions--the implementation of which has been delayed by judicial and legislative action--that would change the methodology for calculating FULs. Under the DRA, FULs would be calculated as 250 percent of the average manufacturer price (AMP) for a drug's least costly therapeutically equivalent version. In 2006, the Congressional Budget Office estimated that the implementation of AMP-based FULs would reduce total Medicaid spending for prescription drugs by $11.8 billion from 2007 through 2015. However, retail pharmacies have raised concerns that AMP-based FULs would not be sufficient to cover their costs of acquiring drugs dispensed to Medicaid beneficiaries. Two retail pharmacy industry groups, the National Association of Chain Drug Stores (NACDS) and the National Community Pharmacists Association (NCPA), have claimed that AMP-based FULs would make some retail pharmacies unprofitable and thus limit certain Medicaid beneficiaries' access to retail pharmacies. A 2006 GAO report and a 2007 report by the HHS OIG both found that AMP-based FULs would have been lower than average pharmacy acquisition costs, on a drug-by-drug basis, for most drugs included in the respective samples. To implement the DRA provisions pertaining to prescription drugs in Medicaid, CMS published a final rule in July 2007. This rule includes provisions regarding the calculation of AMP-based FULs that might also affect how they compare to pharmacy acquisition costs. For example, FULs apply only to certain outpatient prescription drugs--known as multiple-source drugs--and the rule changed the definition of multiple-source drugs. Additionally, to minimize the effect of outliers, the final rule included a provision which would use the second-lowest AMP for a multiple-source drug to set the FUL if the lowest AMP is less than 40 percent of the second-lowest AMP. The final rule requires drug manufacturers to report AMP data on a monthly basis, and drug manufacturers and state Medicaid programs were expected to begin complying with the provisions of the final rule by October 1, 2007."
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".