dc.description.abstract | Health stakeholders usually need complete, accurate and reliable estimates of various
health outcomes to make decisions on improving health care delivery. Missing observations
especially in clinical routine data is one of major setbacks in evaluating public health
problems efficiently. One of the tools used to measure clinical quality is the Paediatric
Admission Quality of Care (PAQC) score. We seek to identify factors that in uence clinical
quality in this study after dealing with missing values.The main objective of this study is
to identify key determinants of Pediatric Admission Quality of Care (PAQC) score using
Random Forests.
Data on a total of 2027 children between 2 and 59 months who were admitted in selected
county hospitals in Kenya was used. The data contained clinical data from admission to
treatment. Random forests missForest package was used to impute missing data. Cumulative
logit mixed models were fit with PAQC score as an outcome and age, sex, comorbidity,
weight, clinician sex and cadre, hospital workload, malaria prevalence, intervention arm
and time of admission as predictors to determine the signi cant determinants of clinical
quality. The models were nested within both hospital and clinician levels. Both Random
forests and conditional random forests were used to determine variable importance.
The cumulative logit mixed model nested within both clinician and hospital level was
selected based on AIC. Weight of the child, clinician sex, cadre and the time of admission
were significant determinants of PAQC score based on the P values at 0.05 level of
signficance. A unit increase in weight increases the probability of a higher PAQC score
by 0.06, while being attended by a medical o cer relative to a clinical officer increases
the probability by 0.27. The time of admission increases the probability by 0.11. On the
other hand, PAQC scores would be lower if the clinician was male. The probability of a
reduced PAQC score if a clinician is male is 0.49. Month, weight, intervention arm and
hospital workload were the most important variables in predicting the quality of care while
age and the number of comorbiditieswere the least important using Random forests models.
Based on the cumulative logit mixed models, the study concludes that hospital level, weight
of the child, clinician sex, cadre and the time of admission are key determinants of PAQC
score. On the other hand, age and the number of comorbidities for a given patient may not
strongly influence the quality of care provided based on the random forests models. The
mechanisms around these associations however need to be studied extensively. Pneumonia | en_US |