Severe acute malnutrition, Parametric frailty, Accelerated failure time model
DOI:
Abstract :
Background: In developing countries about 3.5% of children aged 0–5 years are victims of severe acute malnutrition
(SAM). Once the morbidity has developed the cure process takes variable period depending on various factors.
Knowledge of time-to-cure from SAM will enable health care providers to plan resources and monitor the progress of
cases with SAM. The current analysis presents modeling time-to-cure from SAM starting from the day of diagnosis in
Wolisso St. Luke Catholic hospital, southwest Ethiopia.
Methods: With the aim of coming up with appropriate survival (time-to-event) model that describes the SAM
dataset, various parametric clustered time-to-event (frailty) models were compared. Frailty model, which is an
extension of the proportional hazards Cox survival model, was used to analyze time-to-cure from SAM. Kebeles
(villages) of the children were considered as the clustering variable in all the models. We used exponential, weibull
and log-logistic as baseline hazard functions and the gamma as well as inverse Gaussian for the frailty distributions
and then based on AIC criteria, all models were compared for their performance.
Results: The median time-to-cure from SAM cases was 14 days with the maximum of 63 days of which about 83%
were cured. The log-logistic model with inverse Gaussian frailty has the minimum AIC value among the models
compared. The clustering effect was significant in modeling time-to-cure from SAM. The results showed that age of a
child and co-infection were the determinant prognostic factors for SAM, but sex of the child and the type of
malnutrition were not significant.
Conclusions: The log-logistic with inverse Gaussian frailty model described the SAM dataset better than other
distributions used in this study. There is heterogeneity between the kebeles in the time-to-cure from SAM, indicating
that one needs to account for this clustering variable using appropriate clustered time-to-event frailty models.