Multinomial logistic regression model to assess the levels in trans, trans-muconic acid and inferential-risk age group among benzene-exposed group

Abstract

There are only a few studies performed on multinomial logistic regression on the benzene-exposed occupational group. A study was carried out to assess the relationship between the benzene concentration and trans-trans-muconic acid (t,t-MA), biomarkers in urine samples from petrol filling workers. A total of 117 workers involved in this occupation were selected for this current study. Generally, logistic regression analysis (LR) is a common statistical technique that could be used to predict the likelihood of categorical or binary or dichotomous outcome variables. The multinomial logistic regression equations were used to predict the relationship between benzene concentration and t,t-MA. The results showed a significant correlation between benzene and t,t-MA among the petrol fillers. Prediction equations were estimated by adopting the physical characteristic viz., age, experience in years and job categories of petrol filling station workers. Interestingly, there was no significant difference observed among experience in years. Petrol fillers and cashiers having a higher occupational risk were in the age group of ≤24 and between 25 and 34 years. Among the petrol fillers, the t,t-MA levels with exceeding ACGIH TWA-TLV level was showing to be more significant. This study demonstrated that multinomial logistic regression is an effective model for profiling the greatest risk of the benzene-exposed group caused by different explanatory variables.

Cultivating and Sharing Tacit Knowledge in the Medical Field

“…This study employs multinomial logistic regression to analyze survey responses, aiming to evaluate the influence of tacit knowledge sharing on patient care quality within a healthcare organization. Similar techniques have been utilized in prior studies by Mala et al (2010) and Grilo et al (2017). Multinomial logistic regression is a predictive modeling approach used to estimate probabilities associated with different outcomes of a categorically distributed dependent variable.…”

Selection of multinomial logit models via association rules analysis

“…On the basis of Ref 1, early work pertaining to the model includes studies by Gurland et al2 Cox,3 Mantel,4 and Theil 5. Multinomial logit models have been applied in a range of areas including agriculture,6,7 finance,8,9 economics,10 marketing,11,12 nursing and heath care,13–15 and education 16–20…”