Analysis of Hierarchical Routine Data With Covariate Missingness: Effects of Audit & Feedback on Clinicians' Prescribed Pediatric Pneumonia Care in Kenyan Hospitals

Citation:
Susan Gachau, Nelson Owuor, NJAGI EDMUNDNJERU, Ayieko P, English M. "Analysis of Hierarchical Routine Data With Covariate Missingness: Effects of Audit & Feedback on Clinicians' Prescribed Pediatric Pneumonia Care in Kenyan Hospitals." Frontiers in public health. 2019;7( ):198.

Abstract:

Background:
Routine clinical data are widely used in many countries to monitor quality of care. A limitation of routine data is missing information which occur due to lack of documentation of care processes by health care providers, poor record keeping or limited health care technology at facility level. Our objective was to address missing covariates while properly accounting for hierarchical structure in routine paediatric pneumonia care.
Methods:
We analysed routine data collected during a cluster randomized trial to investigating the effect of audit and feedback (A&F) over time on inpatient pneumonia care among children admitted in 12 Kenyan hospitals between March and November 2016. Six hospitals in the intervention arm received enhance A&F on classification and treatment of pneumonia cases in addition to a standard A&F report on general inpatient paediatric care. The remaining six in control arm received standard A&F alone. We derived and analysed a composite outcome known as Paediatric Admission Quality of Care (PAQC) score. In our analysis, we adjusted for patients, clinician and hospital level factors. Missing data occurred in patient and clinician level variables. We did multiple imputation of missing covariates within the joint model imputation framework. We fitted proportion odds random effects model and generalized estimating equation (GEE) models to the data before and after multilevel multiple imputation.

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