Introduction:

there is evidence of methodological progress towards the optimization of study designs and analytical methods in genome-environment interactions in asthma, including logistic regression models.

Objective:

to determine a binary logistic regression model to identify the risk of asthma in pediatric patients.

Methods:

an observational, analytical, case-control study was conducted in asthmatic patients in Pinar del Río province, Cuba, in the period 2015-2019. The sample comprised 735 cases, establishing a ratio of cases/controls of 1:2. From the genetic, infectious and environmental variables, a binary logistic regression model was completed.

Results:

the correspondence between the current and predicted values of the dependent variable was statistically proven (p=0.602). Nagelkerke's R2 indicated that the predictors used explained 87% of the variability of the dependent variable. The estimated model correctly classified 94% of the cases, with a sensitivity of 93% and a specificity of 95%. The model resulted from the use of 11 variables, of which four corresponded to family history of asthma, four to infectious variables and three to environmental variables.

Conclusions:

the multivariate analysis allowed designing a model for the calculation of the probability of asthma using the tables of risks in Pinar del Río, which leads to the implementation of personalized medicine and the improvement of preventive medicine in primary health care.

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