Abstract
Preterm birth is a global concern with significant adverse effects on neonatal health and long-term outcomes. Brazil, one of the countries with the highest prevalence of preterm birth, presents a unique context for studying this issue due to its diverse climatic conditions, regional disparities in healthcare, and socio-demographic factors. Although several studies have explored the association between ambient temperature and preterm birth, there remains a research gap in investigating this relationship within the Brazilian population. This study aimed to investigate the potential association between ambient temperature exposure during pregnancy and preterm birth in Brazil. We employed a two-stage case-control design. In the first stage, conditional logistic regression models were used to estimate the odds ratios (ORs) for preterm birth associated with ambient temperature exposure during specific trimesters of pregnancy across Brazilian regions. We adjusted the model for PM2.5 levels, mother's race, mother's education level, and number of prenatal consultations. Smoothing spline functions were applied to capture potential nonlinear relationships, while time-stratified sampling controlled for temporal trends. In the second stage, region-specific estimates were pooled using a mixed effects meta-analysis, accounting for intra- and inter-region variability. Our study population includes birth data in Brazil from 2001 to 2018, encompassing a total of 9,906,658 live birth records. Our findings suggest that that, at the national scale, each 1 °C increase in temperature during trimesters 1, 2, and 3 was associated with a higher risk of preterm birth (9.3%, 8.0%, and 8.4% increase in odds, respectively). Regional variations were observed, with the North (where the Amazon is located) and Midwest regions showing higher risks compared to others. These findings provide valuable insights into the relationship between ambient temperature and preterm birth within the Brazilian context, highlighting the importance of considering regional variations. Understanding these associations is critical for developing targeted interventions and policies aimed at reducing the burden of preterm birth.
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