Air pollution exposure during pregnancy and preterm birth in Brazil.

Ambient air pollution is a significant environmental risk factor for adverse pregnancy outcomes, including preterm birth. However, the impact of different pollutants across various regions and trimesters of pregnancy has not been fully investigated in Brazil. This study aimed to examine the associations between exposure to PM2.5, NO2, and O3 during different trimesters of pregnancy and the risk of preterm birth across five regions of Brazil. We used logistic regression models to estimate the odds ratios (OR) of preterm birth associated with PM2.5, NO2, and O3 adjusting for potential confounders such as maternal age, education, and socioeconomic status. Our study included over 9.9 million live births from 2001 to 2018, with data obtained from the Ministry of Health in Brazil. On average, for each 1-µg/m3 increase in PM2.5, we estimated a 0.26 % (95 % CI: 0.08-0.44 %) increase in the risk of preterm birth nationally in the first trimester. For NO2, each 1ppb increase was associated with a percentage increase in preterm birth risk of 7.26 % (95 % CI: 4.77-9.74 %) in the first trimester, 8.05 % (95 % CI: 5.73-10.38 %) in the second trimester, and 7.48 % (95 % CI: 5.25-9.72 %) in the third trimester. For O3, each 1ppb increase was associated with a percentage increase in preterm birth risk of 1.24 % (95 % CI: 0.29-2.18 %) in the first trimester, 1.51 % (95 % CI: 0.60-2.41 %) in the second trimester, and 0.72 % (95 % CI: -0.18-1.62 %) in the third trimester. This study highlights the significant impact of ambient air pollution on preterm birth risk in Brazil, with significant regional variations. Our findings underscore the need for targeted public health interventions to mitigate the effects of air pollution on pregnancy outcomes, particularly in the most affected regions.

Ambient air pollution and low birth weight in Brazil: A nationwide study of more than 10 million births between 2001 and 2018.

Low birth weight (LBW) is a global health concern. While it is commonly associated with maternal health and behavior, exposure to ambient air pollution, can also play a role in contributing to LBW. In Brazil, where diverse environmental conditions and regional disparities exist, assessing the impact of ambient air pollution on LBW becomes particularly pertinent. To our knowledge, there is a gap in the existing literature, as no previous study has specifically investigated the relationship between ambient air pollution and LBW nationwide in Brazil. This study aims to fill this gap by examining the association between ambient air pollution and LBW in each trimester of pregnancy across the Brazilian states. In this work, birth data from January 1, 2001, to December 31, 2018 has been used. We utilized logistic regression models to estimate the odds ratio (OR) for low birth weight (LBW) associated with ambient air pollution (PM2.5, NO2, and O3) during each trimester of pregnancy (1st to 3rd trimester) across all 27 Brazilian states in our nationwide case-control study. We adjusted our model for several variables, including ambient temperature, relative humidity, and socioeconomic status (SES) variables at the individual level. We also conducted effect modification analyses by infant sex, mother's age, and the number of prenatal visits. Our study comprises over 10,213,144 birth records nationwide. Of these, 479,204 (4.92%) infants were included as cases of LBW. Our results indicate positive associations between PM2.5 and LBW, mainly in the Southern region. For example, in the state of Santa Catarina (South region), ORs were 1.003 (95% CI: 1.002, 1.004), 1.003 (95% CI: 1.002, 1.004), and 1.005 (95% CI: 1.003, 1.007) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. NO2 had a robust association with LBW in the Northern and Northeastern states, including the state of Amapá (North region, where the Amazon Forest is located) with ORs of 1.377 (95% CI: 1.010, 1.878), 1.390 (95% CI: 1.020, 1.894), and 1.747 (95% CI: 1.297, 2.352) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. Similarly, O3 had a robust association in the North and Midwest states, as observed in the state of Amapá with ORs of 1.033 (95% CI: 1.012, 1.054), and 1.033 (95% CI: 1.013, 1.053) for the 2nd, and 3rd trimesters, respectively. In the stratified analysis, boys were more vulnerable than girls, and the lower number of prenatal visits was associated with higher OR. Our findings are essential to the development of guidelines to prevent maternal exposure and protection of newborns in Brazil. This study provides valuable insights for region-specific strategies to improve maternal and neonatal health.

Thermal stress and hospital admissions for cardiorespiratory disease in Brazil.

The growing body of scientific literature underscores the intricate relationship between meteorological conditions and human health, particularly in the context of extreme temperatures. However, conventional temperature-centric approaches often fall short in capturing the complexity of thermal stress experienced by individuals. Temperature alone, as a metric, fails to encompass the entirety of the thermal stress individuals face, necessitating a more nuanced understanding. In response to this limitation, climatologists have devised thermal indices-composite measures meticulously crafted to reflect the intricate interplay of meteorological factors influencing human perception of temperature. Recognizing the inadequacy of simplistic temperature-focused methodologies, our study aims to address the multifaceted nature of thermal stress. In this study, we explored the association between thermal indices and hospital admissions for circulatory and respiratory diseases in Brazil. We used an extensive dataset spanning 11 years (2008-2018) from the Brazilian Ministry of Health, encompassing a total of 23,791,093 hospitalizations for circulatory and respiratory diseases. We considered four distinct thermal indices-Discomfort Index (DI), Net Effective Temperature (NET), Humidex (H), and Heat Index (HI). We used an extension of the two-stage design with a case time series to assess this relationship. In the first stage, we applied a distributed lag non-linear modeling framework to create a cross-basis function. We next applied quasi-Poisson regression models adjusted by time-varying confounders. In the second stage, we applied meta-analysis with random effects to estimate the national relative risk (RR). Our findings suggest robust variations among the thermal indices under examination. These variations underscore the intricate nature of associations between temperature and health, with each index capturing distinct aspects of thermal conditions. Our results indicate that extreme thermal conditions, both at the low and high ends, are associated with increased risks of hospital admissions. The diverse impact observed among different indices emphasizes the complex interplay between various meteorological factors and their specific physiological consequences. This underscores the necessity for a comprehensive comprehension of temperature metrics to guide precise public health interventions, recognizing the multifaceted nature of temperature-health relationships.