Analyzing composition and generation rates of biomedical waste in selected hospitals of Uttarakhand, India

Abstract

In India, a few studies have been conducted for analyzing the generation rates and composition of medical waste (MW). Inadequate information about the amount and composition of MW results in ineffective management practices. The present study seeks to evaluate healthcare waste (HCW) generation rates by healthcare facilities (HCFs) available in Uttarakhand, a northern state of India. Study also focuses on modeling the quantity of different types of MW generated at various HCFs and determining significant factors contributing towards MW generation. Seasonal variation in amount of MW generated from various HCFs has also been considered. To achieve these objectives, cross-sectional as well as longitudinal data have been collected from various HCFs in Uttarakhand, India. The survey revealed that around 36% of the total HCFs did not segregate their MW as per policy guidelines. Cross-sectional data for May 2015 were collected from 75 HCFs to analyze and model the composition and quantity of HCW generated. Multiple Linear Regression and Artificial Neural Network techniques were applied to model cross-sectional data. In the composition of the overall MW, ‘yellow waste’ carries the maximum share, followed by ‘red waste’ and then the ‘blue waste’. In addition, the ‘type of HCF’ and ‘bed occupancy’ have been modeled as the important factors, contributing towards the MW generations rates. Longitudinal data for 2 years (2013 and 2014) were collected to examine seasonal variation in HCW generation rates using polynomial regression analysis. Result shows that MW quantity also varies with the change in the season. Findings of the study will help hospitals and waste treatment facilities to predict amount of waste that may be generated, and plan resources towards efficient handling and disposal of MW.