Prematuridade na Bahia: análise temporal, espacial e de fatores de risco associados

Abstract

Prematurity is the leading cause of morbidity and mortality in the first four weeks of a newborn's life, influenced by sociodemographic, healthcare, maternal, and territorial factors. In Bahia, the occurrence of premature births appears to be stable over time, and the determinants of this condition remain poorly understood. In this context, a cohort study of live births was conducted between 2012 and 2024, with the objective in research 1 of analyzing the temporal trend and identifying risk factors associated with prematurity. For the temporal trend analysis, the annual percentage change (APC) and the average period change (AAPC) were calculated using Jointpoint regression analysis. To assess the association of sociodemographic, healthcare, and maternal-fetus/child related variables, Poisson regression with robust variance was performed, considering prematurity (gestational age less than 37 weeks of gestation) as the outcome variable. In study 2, the objective was to analyze the spatial distribution and identify the spatial autocorrelation patterns of prematurity in the state of Bahia, from 2012 to 2024. To analyze the spatial association of prematurity, the Univariate Global Moran's I index and the Univariate Local Moran's I index (LISA) were used to identify the clusters. In article 1, the prematurity rate remained stationary during the period 2012-2024. During the same total period, an increasing trend in prematurity was observed for the following categories: low maternal education; having fewer than six prenatal appointments; being over 35 years old; having a cesarean delivery; and a history of one or more stillbirths. The factors associated with the occurrence of prematurity were: low maternal education; single marital status; fewer than six prenatal consultations; extremes of maternal age; cesarean delivery; one or more stillbirths; and male newborn sex. In article 02, prematurity rates showed a heterogeneous spatial distribution among the municipalities of Bahia. Positive spatial autocorrelation was identified in all analyzed periods, with a decrease in I in the analyzed periods. Additionally, LISA identified a High-High (HA) type cluster, indicating that municipalities with high prematurity rates are surrounded by municipalities also with high rates, persistent in the West and Southwest macro-regions of health in all analyzed periods. From the second three-year period onwards, the Northeast and South macro-regions began to integrate these clusters. On the other hand, the East macro-region presented low-low (LL) type clusters, corresponding to municipalities with low prematurity rates surrounded by municipalities with equally low rates, maintained throughout all analyzed periods. The findings of these studies update the panorama of prematurity in Bahia, identifying a stationary prematurity rate over time. Sociodemographic, healthcare, and maternal and fetal/child factors were associated with higher prematurity rates, and the spatial distribution of premature birth was heterogeneous among municipalities, with the identification of persistent AA-type clusters, highlighting territorial inequalities in the occurrence of premature birth. These results reinforce the need for local and targeted strategies that consider the social and maternal health determinants to reduce prematurity and its impacts in the state of Bahia.