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Risk factors for low birth weight according to the multiple logistic regression model. A retrospective cohort study in José María Morelos municipality, Quintana Roo, Mexico

2018; Medwave Estudios Limitada; Volume: 18; Issue: 01 Linguagem: Espanhol

10.5867/medwave.2018.01.7143

0717-6384

José Franco Monsreal, Miriam Del Ruby Tun Cobos, José Ricardo Hernández Gómez, Lidia Esther Del Socorro Serralta Peraza,

Maternal and Neonatal Healthcare

Low birth weight has been an enigma for science over time. There have been many researches on its causes and its effects. Low birth weight is an indicator that predicts the probability of a child surviving. In fact, there is an exponential relationship between weight deficit, gestational age, and perinatal mortality. Multiple logistic regression is one of the most expressive and versatile statistical instruments available for the analysis of data in both clinical and epidemiology settings, as well as in public health.To assess in a multivariate fashion the importance of 17 independent variables in low birth weight (dependent variable) of children born in the Mayan municipality of José María Morelos, Quintana Roo, Mexico.Analytical observational epidemiological cohort study with retrospective temporality. Births that met the inclusion criteria occurred in the "Hospital Integral Jose Maria Morelos" of the Ministry of Health corresponding to the Maya municipality of Jose Maria Morelos during the period from August 1, 2014 to July 31, 2015. The total number of newborns recorded was 1,147; 84 of which (7.32%) had low birth weight. To estimate the independent association between the explanatory variables (potential risk factors) and the response variable, a multiple logistic regression analysis was performed using the IBM SPSS Statistics 22 software.In ascending numerical order values of odds ratio > 1 indicated the positive contribution of explanatory variables or possible risk factors: "unmarried" marital status (1.076, 95% confidence interval: 0.550 to 2.104); age at menarche ≤ 12 years (1.08, 95% confidence interval: 0.64 to 1.84); history of abortion(s) (1.14, 95% confidence interval: 0.44 to 2.93); maternal weight < 50 kg (1.51, 95% confidence interval: 0.83 to 2.76); number of prenatal consultations ≤ 5 (1.86, 95% confidence interval: 0.94 to 3.66); maternal age ≥ 36 years (3.5, 95% confidence interval: 0.40 to 30.47); maternal age ≤ 19 years (3.59, 95% confidence interval: 0.43 to 29.87); number of deliveries = 1 (3.86, 95% confidence interval: 0.33 to 44.85); personal pathological history (4.78, 95% confidence interval: 2.16 to 10.59); pathological obstetric history (5.01, 95% confidence interval: 1.66 to 15.18); maternal height < 150 cm (5.16, 95% confidence interval: 3.08 to 8.65); number of births ≥ 5 (5.99, 95% confidence interval: 0.51 to 69.99); and smoking (15.63, 95% confidence interval: 1.07 to 227.97).Four of the independent variables (personal pathological history, obstetric pathological history, maternal stature 1 señalaron la contribución positiva de las variables explicativas o factores de riesgo: estado civil no–casada (1,08, intervalo de confianza del 95%: 0,55 a 2,10); edad a la menarca ≤ 12 años (1,08, intervalo de confianza del 95%: 0,64 a 1,84); antecedentes de aborto (1,14, intervalo de confianza del 95%: 0,44 a 2,93); peso materno < 50 kilogramos (1,51, intervalo de confianza del 95%: 0,83 a 2,76); número de consultas prenatales ≤ 5 (1,86, intervalo de confianza del 95%: 0,94 a 3,66); edad materna ≥ 36 años (3,5, intervalo de confianza del 95%: 0,40 a 30,47); edad materna ≤ 19 años (3,59, intervalo de confianza del 95%: 0,43 a 29,87); primiparidad (3,86, intervalo de confianza del 95%: 0,33 a 44,85); antecedentes personales patológicos (4,78, intervalo de confianza del 95%: 2,16 a 10,59); antecedentes obstétricos patológicos (5,01, intervalo de confianza del 95%: 1,66 a 15,18); estatura materna < 150 centímetros (5,16, intervalo de confianza del 95%; 3,08 a 8,65); número de partos ≥ 5 (5,99, intervalo de confianza del 95%: 0,51 a 69,99); y tabaquismo (15,63, intervalo de confianza del 95%: 1,07 a 227,97). El modelo de regresión logística mostró ajuste aceptable (Hosmer-Lemeshow con p=0,873).Se demuestra que cuatro de las variables independientes (antecedentes personales patológicos, antecedentes obstétricos patológicos, estatura materna < 150 centímetros y tabaquismo) resultaron con contribución positiva significativa por lo que pueden considerarse claros factores de riesgo de bajo peso al nacer. El uso de este modelo de regresión logística en municipio maya de José María Morelos, permitirá estimar la probabilidad de peso al nacer de cada embarazada en el futuro lo que será de utilidad para las autoridades sanitarias de la región.