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

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 occurs 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 pediatric pneumonia care. Methods: We analyzed 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 pediatric care. The remaining six in control arm received standard A&F alone. We derived and analyzed a composite outcome known as Pediatric 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. Results: Overall, 2,299 children aged 2 to 59 months were admitted with childhood pneumonia in 12 hospitals during the trial period. 2,127 (92%) of the children (level 1) were admitted by 378 clinicians across the 12 hospitals. Enhanced A&F led to improved inpatient pediatric pneumonia care over time compared to standard A&F. Female clinicians and hospitals with low admission workload were associated with higher uptake of the new pneumonia guidelines during the trial period. In both random effects and marginal model, parameter estimates were biased and inefficient under complete case analysis. Conclusions: Enhanced A&F improved the uptake of WHO recommended pediatric pneumonia guidelines over time compared to standard audit and feedback. When imputing missing data, it is important to account for the hierarchical structure to ensure compatibility with analysis models of interest to alleviate bias.

Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

“…We found that disagreement between the reabstracted and original data were mainly due to missing maternal data and incomplete records in the patient medical charts rather than poor abstraction of data. Missing data secondary to poor patient medical chart documentation by healthcare providers is a documented phenomenon ( 15 ). In our study, many outborn infants in Children's Hospitals in CHNN lack maternal information such as the use of AC (including timing and course) and chorioamnionitis, because maternal information is often not routinely provided when the infant is transferred from the maternity hospital.…”

Handling missing data in a composite outcome with partially observed components: Application in clustered paediatric routine data.

“…Multiple imputation of both PAQC score subcomponents (treatment domain) and missing covariates led to slight changes in regression coe cients estimates and standard errors. This was in comparison to results from previous analysis of the trial [45] where we only imputed missing covariates and handled all missing PAQC score subcomponents using the conventional method. Although the proportion of missingness in PAQC score subcomponents of interest (treatment domain) was small, the observed difference is an indication of MI superiority in handling missing PAQC score subcomponents over the conventional approach.…”

The effect of introduction of routine immunization for rotavirus vaccine on paediatric admissions with diarrhoea and dehydration to Kenyan Hospitals: an interrupted time series study

“…We did fifteen imputations and ten iterations under Missing At Random (MAR) assumption (Schafer, 1999). Previous analysis of data from CIN hospitals have shown consistency with MAR assumption (Gachau et al, 2019;Malla et al, 2019). On each of the imputed datasets, we proceeded to (i) sum the number of patients with diarrhoea and dehydration per month, both as classified by the clinicians and identified by the algorithms, and (ii) fit segmented mixed effects model with autoregressive covariance structure and with the counts following negative binomial distribution.…”