Logistic regression analysis of risk factors for pediatric burns: a case-control study in underdeveloped minority areas in China

Abstract

Background: Pediatric burns are common, especially in underdeveloped countries, and these can physically affect the children involved and have an impact on their mental health. The aim of the present study was to assess the effect of pediatric burns in underdeveloped minority areas of China.

Methods: Case information from 192 children was collected from outpatient and inpatient clinics using a survey questionnaire. These included 90 pediatric burn cases and 102 controls who were children without burns. A stepwise logistic regression analysis was used to determine the risk factors for pediatric burns in order to establish a model. The goodness-of-fit for the model was assessed using the Hosmer and Lemeshow test as well as receiver operating characteristic and internal calibration curves. A nomogram was then used to analyze the contribution of each influencing factor to the pediatric burns model.

Results: Seven variables, including gender, age, ethnic minority, the household register, mother's employment status, mother's education and number of children, were analyzed for both groups of children. Of these, age, ethnic minority, mother's employment status and number of children in a household were found to be related to the occurrence of pediatric burns using univariate logistic regression analysis (p < 0.05). After a collinearity diagnosis, a multivariate logistic regression analysis of variables with tolerances of >0.2 and variance inflation factor <5 showed that age was a protective factor for pediatric burns [odds ratio (OR) = 0.725; 95% confidence interval (CI): 0.665-0.801]. Compared with single-child parents, those with two children were at greater risk of pediatric burns (OR = 0.389; 95% CI: 0.158-0.959). The ethnic minority of the child and the mother's employment status were also risk factors (OR = 6.793; 95% CI: 2.203-20.946 and OR = 2.266; 95% CI: 1.025-5.012, respectively). Evaluation of the model used was found to be stable. A nomogram showed that the contribution in the children burns model was age > mother's employment status > number of children > ethnic minority.

Conclusions: This study showed that there are several risk factors strongly correlated to pediatric burns, including age, ethnic minority, the number of children in a household and mother's employment status. Government officials should direct their preventive approach to tackling the problem of pediatric burns by promoting awareness of these findings.

Keywords: burns; children; logistic regression analysis; minority; risk factors; underdeveloped area.