Evidence map›Paper›PMID 33196286›Full record

Observational studyThe American journal of managed care2020

Variation in generic dispensing rates in Medicare Part D.

Christine Buttorff, Yifan Xu, Geoffrey Joyce

Abstract readObservational Study
In one paragraph

Observational study in The American journal of managed care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christine ButtorffRAND Corporation, 1200 S Hayes St, Arlington, VA 22202. Email: buttorff@rand.org.
Yifan Xu
Geoffrey Joyce

Funding

Evidence for Value-based Prescription Drug Plan for Diabetes, CVD and Chronic IllnessR01AG059234 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JOYCE, GEOFFREY F · 2019 to 2021
$1.3M
NIA NIH HHS R01 AG059234
6 · The paper itself

Abstract

objectivesThe use of generics in Medicare Part D generates cost savings for plan sponsors, beneficiaries, and the federal government. However, there is considerable variation in generic use across plans, even within a therapeutic class. Our objective is to understand the extent of variation in generic use in Part D and to understand factors associated with generic use. STUDY

designWe used an observational study design using Medicare Part D claims from 2006 to 2016.

methodsWe used descriptive statistics and regression analysis to examine the variation in generic and brand use across plans and the extent to which patient, plan, and area characteristics are associated with the choice of medication within a therapeutic class.

resultsAlthough generic use has increased markedly over time in Part D, substantial variation across plans persists in a number of common therapeutic classes. Beneficiary characteristics such as gender and health status are associated with higher/lower generic use, as are plan characteristics such as plan type (stand-alone prescription drug plan or Medicare Advantage), premium, and parent company.

conclusionsBecause we cannot study the impact of brand-name drug rebates on generic use, we can study the variation in generic use across Part D plans as an indirect way to assess pharmacy benefit manager and plan incentives. We find circumstantial evidence that, in certain classes, rebates may play a role in influencing brand over generic use, although the exact relationship is unknowable given the proprietary nature of rebates.

Indexed as

Medicare Part DPrescription DrugsAgedCost SavingsDrug CostsDrugs, GenericHumansUnited StatesDrugs, GenericPrescription Drugs

Identifiers

PMID33196286
PMCPMC8577616

What OpenQuestion holds

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LicenceTDM
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.