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Development of a novel algorithm to determine adherence to chronic pain treatment guidelines using administrative claims

Overview of attention for article published in Journal of Pain Research, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
8 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
18 Mendeley
Title
Development of a novel algorithm to determine adherence to chronic pain treatment guidelines using administrative claims
Published in
Journal of Pain Research, February 2017
DOI 10.2147/jpr.s118248
Pubmed ID
Authors

Jay M Margolis, Nicole Princic, David M Smith, Lucy Abraham, Joseph C Cappelleri, Sonali N Shah, Peter W Park, Jay Margolis, David Smith, Joseph Cappelleri, Sonali Shah, Peter Park

Abstract

To develop a claims-based algorithm for identifying patients who are adherent versus nonadherent to published guidelines for chronic pain management. Using medical and pharmacy health care claims from the MarketScan® Commercial and Medicare Supplemental Databases, patients were selected during July 1, 2010, to June 30, 2012, with the following chronic pain conditions: osteoarthritis (OA), gout (GT), painful diabetic peripheral neuropathy (pDPN), post-herpetic neuralgia (PHN), and fibromyalgia (FM). Patients newly diagnosed with 12 months of continuous medical and pharmacy benefits both before and after initial diagnosis (index date) were categorized as adherent, nonadherent, or unsure according to the guidelines-based algorithm using disease-specific pain medication classes grouped as first-line, later-line, or not recommended. Descriptive and multivariate analyses compared patient outcomes with algorithm-derived categorization endpoints. A total of 441,465 OA patients, 76,361 GT patients, 10,645 pDPN, 4,010 PHN patients, and 150,321 FM patients were included in the development of the algorithm. Patients found adherent to guidelines included 51.1% for OA, 25% for GT, 59.5% for pDPN, 54.9% for PHN, and 33.5% for FM. The majority (~90%) of patients adherent to the guidelines initiated therapy with prescriptions for first-line pain medications written for a minimum of 30 days. Patients found nonadherent to guidelines included 30.7% for OA, 6.8% for GT, 34.9% for pDPN, 23.1% for PHN, and 34.7% for FM. This novel algorithm used real-world pharmacotherapy treatment patterns to evaluate adherence to pain management guidelines in five chronic pain conditions. Findings suggest that one-third to one-half of patients are managed according to guidelines. This method may have valuable applications for health care payers and providers analyzing treatment guideline adherence.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 22%
Unspecified 3 17%
Researcher 3 17%
Librarian 3 17%
Student > Doctoral Student 2 11%
Other 3 17%
Readers by discipline Count As %
Medicine and Dentistry 10 56%
Unspecified 4 22%
Pharmacology, Toxicology and Pharmaceutical Science 3 17%
Nursing and Health Professions 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 November 2017.
All research outputs
#2,397,590
of 13,436,295 outputs
Outputs from Journal of Pain Research
#249
of 1,029 outputs
Outputs of similar age
#81,472
of 346,258 outputs
Outputs of similar age from Journal of Pain Research
#16
of 79 outputs
Altmetric has tracked 13,436,295 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,029 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,258 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.