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.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
KEMRI Wellcome Trust Research Programme, 43640 - 00100, Nairobi, Kenya. dgathara@kemri-wellcome.org.
David
D
Gathara
KEMRI Wellcome Trust Research Programme, 43640 - 00100, Nairobi, Kenya. menglish@kemri-wellcome.org.|Nuffield Department of Medicine, University of Oxford, Oxford, UK. menglish@kemri-wellcome.org.
Mike
M
English
Department of Global Health, Academic Medical Centre, University of Amsterdam, Amsterdam, UK. mbvh04@gmail.com.
Michael Boele
MB
van Hensbroek
Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK. jim.todd@lshtm.ac.uk.
Jim
J
Todd
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK. Elizabeth.allen@lshtm.ac.uk.
Elizabeth
E
Allen
D003430:Cross-Sectional Studies; D003967:Diarrhea / Q000188:drug therapy; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D015658:HIV Infections / Q000175:diagnosis; D006739:Hospital Administration / Q000592:standards*; D006801:Humans; D007630:Kenya; D008288:Malaria / Q000188:drug therapy; D011014:Pneumonia / Q000188:drug therapy; D011204:Poverty Areas; D017410:Practice Guidelines as Topic; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data*