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Exploring sources of variability in adherence to guidelines across hospitals in low-income settings: a multi-level analysis of a cross-sectional survey of 22 hospitals

Overview of attention for article published in Implementation Science, April 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

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18 tweeters

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65 Mendeley
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Title
Exploring sources of variability in adherence to guidelines across hospitals in low-income settings: a multi-level analysis of a cross-sectional survey of 22 hospitals
Published in
Implementation Science, April 2015
DOI 10.1186/s13012-015-0245-x
Pubmed ID
Authors

David Gathara, Mike English, Michael Boele van Hensbroek, Jim Todd, Elizabeth Allen

Abstract

Variability in processes of care and outcomes has been reported widely in high-income settings (at geographic, hospital, physician group and individual physician levels); however, such variability and the factors driving it are rarely examined in low-income settings. Using data from a cross-sectional survey undertaken in 22 hospitals (60 case records from each hospital) across Kenya that aimed at evaluating the quality of routine hospital services, we sought to explore variability in four binary inpatient paediatric process indicators. These included three prescribing tasks and use of one diagnostic. To examine for sources of variability, we examined intra-class correlation coefficients (ICC) and their changes using multi-level mixed models with random intercepts for hospital and clinician levels and adjusting for patient and clinician level covariates. Levels of performance varied substantially across indicators and hospitals. The absolute values for ICCs also varied markedly ranging from a maximum of 0.48 to a minimum of 0.09 across the models for HIV testing and prescription of zinc, respectively. More variation was attributable at the hospital level than clinician level after allowing for nesting of clinicians within hospitals for prescription of quinine loading dose for malaria (ICC = 0.30), prescription of zinc for diarrhoea patients (ICC = 0.11) and HIV testing for all children (ICC = 0.43). However, for prescription of correct dose of crystalline penicillin, more of the variability was explained by the clinician level (ICC = 0.21). Adjusting for clinician and patient level covariates only altered, marginally, the ICCs observed in models for the zinc prescription indicator. Performance varied greatly across place and indicator. The variability that could be explained suggests interventions to improve performance might be best targeted at hospital level factors for three indicators and clinician factors for one. Our data suggest that better understanding of performance and sources of variation might help tailor improvement interventions although further data across a larger set of indicators and sites would help substantiate these findings.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Master 11 17%
Student > Ph. D. Student 7 11%
Student > Postgraduate 6 9%
Student > Bachelor 6 9%
Other 12 18%
Unknown 9 14%
Readers by discipline Count As %
Medicine and Dentistry 26 40%
Social Sciences 10 15%
Nursing and Health Professions 6 9%
Agricultural and Biological Sciences 4 6%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 4 6%
Unknown 13 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 12 May 2015.
All research outputs
#2,813,073
of 22,053,897 outputs
Outputs from Implementation Science
#647
of 1,696 outputs
Outputs of similar age
#37,568
of 245,703 outputs
Outputs of similar age from Implementation Science
#1
of 1 outputs
Altmetric has tracked 22,053,897 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,696 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one has gotten more attention than average, scoring higher than 61% of its peers.
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