Statistical Models of Economic Burden: A Case Study in Medicine
Purpose: The main aim of this article is to use statistical methods for the estimation of the economic burden and the survival rate of deeply premature babies. Design/Methodology/Approach: The results of a survey of 2.222 children with a birth weight of 501-1500 grams and a gestational age of 23-37 weeks were used as input data. Cox’s proportional hazards model was used as a survival tool. Findings: The results of Cox survival regression model showed a series of statistically significant predictors of survivability (p<0.05) for three age cohorts: neonatal, postnatal and pediatric (until 2 years). One of the statistically significantly predictors of survivability of premature infants with very low birth weight (VLBW) and extremely low birth weight (ELBW) in every age cohort is the volume of primary resuscitation measure and the length of stay in the neonatal pathology unit (NPU). Practical Implications: The results permitted to assess the amount of nursing care measures, the duration of care in a neonatal pathology unit, the rehabilitation of children with VLBW and ELBW in the long run. The assessment will ultimately help to estimate the overall economic burden associated with maintaining health and quality of life of premature babies. Originality/Value: The scientific contribution of the study consists in the use of an integrated approach to the problem of estimating the economic burden of nursing very premature babies, taking into account their survival and subsequent disability risks in the neonatal, postnatal, and pediatric periods.