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Bariatric surgery implementation trends in the USA from 2002 to 2012

Overview of attention for article published in Implementation Science, February 2016
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Bariatric surgery implementation trends in the USA from 2002 to 2012
Published in
Implementation Science, February 2016
DOI 10.1186/s13012-016-0382-x
Pubmed ID

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


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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 17%
Student > Master 8 17%
Other 5 10%
Researcher 4 8%
Student > Bachelor 3 6%
Other 10 21%
Unknown 10 21%
Readers by discipline Count As %
Medicine and Dentistry 13 27%
Nursing and Health Professions 8 17%
Social Sciences 3 6%
Computer Science 2 4%
Agricultural and Biological Sciences 1 2%
Other 6 13%
Unknown 15 31%
Attention Score in Context

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
of 25,373,627 outputs
Outputs from Implementation Science
of 1,809 outputs
Outputs of similar age
of 312,187 outputs
Outputs of similar age from Implementation Science
of 46 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,809 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one is in the 13th percentile – i.e., 13% 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 312,187 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 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.