Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 365

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Cost of Prescription Drug–Related Morbidity and Mortality

Open access
Watanabe, Jonathan H., McInnis, Terry, Hirsch, Jan D.·2018·Annals of Pharmacotherapy

Abstract

Background: Public attention and recent US Congressional activity has intensified focus on escalating medication prices. However, the actual cost of medication use extends beyond the up-front cost of purchasing medicines. It also encompasses the additional medical costs of morbidity and mortality resulting from nonoptimized medication regimens, including medication nonadherence. Objectives: Applying the most current nationally representative data sources, our goal was to estimate the cost of prescription drug–related morbidity and mortality in the United States. Methods: Total costs of nonoptimized prescription drug use and average pathway costs for a patient who experienced a treatment failure (TF), a new medical problem (NMP), or a TF and NMP were modeled in Microsoft Excel (Microsoft Corporation, Redmond, WA) and TreeAge Pro Healthcare, v2014 (TreeAge Software, Inc, Williamstown, MA), respectively. Results: The estimated annual cost of prescription drug–related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion in 2016 US dollars, with a plausible range of $495.3 billion to $672.7 billion. The average cost of an individual experiencing TF, NMP, or TF and NMP after initial prescription use were $2481 (range: $2233, $2742), $2610 (range: $2374, $2848) and $2572 (range: $2408, $2751), respectively. Conclusions: The estimated annual cost of drug-related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion, equivalent to 16% of total US health care expenditures in 2016. We propose expansion of comprehensive medication management programs by clinical pharmacists in collaborative practices with physicians and other prescribers as an effective and scalable approach to mitigate these avoidable costs and improve patient outcomes.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/1060028018765159

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

Metadata

Authors
Watanabe, J. H., McInnis, T., & Hirsch, J. D.
Venue
Annals of Pharmacotherapy
Published
2018-03-26
Volume
52
Issue
9
Pages
829-837
ISSN
  • 0012-6578 (ISSN-L)
  • 1060-0280
  • 1542-6270
OpenAlex
W2792631875
Cited by
365
References
3
FWCI
18.32
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.