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Bariatric surgery implementation trends in the USA from 2002 to 2012

Overview of attention for article published in Implementation Science, January 2016
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)

Mentioned by

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4 tweeters

Citations

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19 Dimensions

Readers on

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42 Mendeley
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Title
Bariatric surgery implementation trends in the USA from 2002 to 2012
Published in
Implementation Science, January 2016
DOI 10.1186/s13012-016-0382-x
Pubmed ID
Authors

Emily E. Johnson, Emily E. Johnson, Annie N. Simpson, Jillian B. Harvey, Kit N. Simpson

Abstract

Many beneficial health care interventions are either not put into practice or fail to diffuse over time due to complex contextual factors that affect implementation and diffusion. Bariatric surgery is an example of an effective intervention that recently experienced a plateau and decrease in rates, with minimal documented justification for this trend. While there are conceptual models that provide frameworks of general innovation implementation and diffusion, few studies have tested these models with data to measure the relative effects of factors that affect diffusion of specific health care interventions. A literature review identified factors associated with implementation and diffusion of health care innovations. These factors were utilized to construct a conceptual model of diffusion to explain changes in bariatric surgery over time. Six data sources were used to construct measures of the study population and factors in the model that may affect diffusion of surgery. The population included obese and morbidly obese patients from 2002 to 2012 who had bariatric surgery in 15 states. Multivariable models were used to identify environmental, population, and medical practice factors that facilitated or impeded diffusion of bariatric surgery over time. It was found that while bariatric surgery rates increased over time, the speed of growth in surgeries, or diffusion, slowed. Higher cumulative number of surgeries and higher proportion of the state population in age group 50-59 slowed surgery growth, but presence of Medicare centers of excellence increased the speed of surgery diffusion. Over time, the factors affecting the diffusion of bariatric surgery fluctuated, indicating that diffusion is affected by temporal and cumulative effects. The primary driver of diffusion of bariatric surgery was the extent of centers of excellence presence in a state. Higher cumulative surgery rates and higher proportions of older populations in a state slowed diffusion. Surprisingly, measures of the presence of champions were not significant, perhaps because these are difficult to measure in the aggregate. Our results generally support the conceptual model of diffusion developed from the literature, which may be useful for examining other innovations, as well as for designing interventions to support rapid diffusion of innovations to improve health outcomes and quality of care.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Student > Master 8 19%
Other 5 12%
Researcher 4 10%
Student > Bachelor 3 7%
Other 10 24%
Unknown 4 10%
Readers by discipline Count As %
Medicine and Dentistry 13 31%
Nursing and Health Professions 8 19%
Social Sciences 3 7%
Computer Science 2 5%
Business, Management and Accounting 1 2%
Other 6 14%
Unknown 9 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 February 2016.
All research outputs
#3,161,811
of 7,269,211 outputs
Outputs from Implementation Science
#749
of 979 outputs
Outputs of similar age
#121,285
of 284,355 outputs
Outputs of similar age from Implementation Science
#45
of 48 outputs
Altmetric has tracked 7,269,211 research outputs across all sources so far. This one has received more attention than most of these and is in the 55th percentile.
So far Altmetric has tracked 979 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 284,355 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.