Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution

Wesley Douglas Oliveira Silva (), Danielle Costa Morais, Ketylen Gomes da Silva and P Marques
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Wesley Douglas Oliveira Silva: Escola UNICAP ICAM-TECH, Universidade Católica de Pernambuco (UNICAP), Recife 50050-900, Brazil
Danielle Costa Morais: Management Engineering Department, Universidade Federal de Pernambuco (UFPE), Recife 50740-550, Brazil
Ketylen Gomes da Silva: Escola UNICAP ICAM-TECH, Universidade Católica de Pernambuco (UNICAP), Recife 50050-900, Brazil

Sustainability, 2023, vol. 15, issue 14, 1-18

Abstract: The appropriate management of home medical waste is of paramount importance due to the adverse consequences that arise from improper handling. Incorrect disposal practices can lead to pharmacopollution, which poses significant risks to environmental integrity and human well-being. Involving medicine users in waste management empowers them to take responsibility for their waste and make informed decisions to safeguard the environment and public health. The objective of this research was to contribute to the prevention of pharmacopollution by identifying influential factors that promote responsible disposal practices among medicine users. Factors such as attitude, marketing campaigns, collection points, safe handling, medical prescription, package contents, and public policies and laws were examined. To analyze the complex relationships and interactions among these factors, a dual-staged approach was employed, utilizing advanced statistical modeling techniques and deep learning artificial neural network algorithms. Data were collected from 952 respondents in Pernambuco, a state in northeastern Brazil known for high rates of pharmacopollution resulting from improper disposal of household medical waste. The results of the study indicated that the propositions related to safety in handling and medical prescription were statistically rejected in the structural equation modeling (SEM) model. However, in the artificial neural network (ANN) model, these two propositions were found to be important predictors of cooperative behavior, highlighting the ANN’s ability to capture complex, non-linear relationships between variables. The findings emphasize the significance of user cooperation and provide insights for the development of effective strategies and policies to address pharmacopollution.

Keywords: home waste medicine; pharmacopollution; consumer behavior; structural equation modeling; deep learning; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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