RESUMO
OBJECTIVES: To investigate a potential association between exposure to different concentrations of environmental fine particulate matter (PM2.5) during early pregnancy and maternal thyroid hormone levels. METHODS: The Midong District of Urumqi City was selected as the site for PM2.5 exposure, while Bole City served as the non-exposed group. Participants were women volunteers enrolled before the 28th week of gestation. Basic data were collected, and levels of FT3, FT4, TSH, and urinary iodine were measured. Generalized linear models were used to investigate associations between different environmental exposures to PM2.5 and maternal thyroid hormone levels in early pregnancy. A restricted cubic spline was employed to examine exposure-response relationships between PM2.5 pollution and maternal thyroid hormone levels. RESULTS: The mean daily indoor, outdoor, and multi-environmental PM2.5 exposure of pregnant women in early pregnancy was significantly different between the two sites (p < 0.05). The average daily exposure concentrations of PM2.5 in different environments during the first weeks of pregnancy in the two regions were negatively correlated with maternal levels of FT3 and FT4. The risk of abnormal thyroid hormone levels was higher in pregnant women living in the Midong District compared to those from Bole City. CONCLUSIONS: PM2.5 exposure during early pregnancy was associated with decreased maternal levels of FT3 and FT4. The adverse health effects of exposure to PM2.5 during early pregnancy on both pregnant women and their offspring should be continually emphasized.
RESUMO
Background: With the rapid advancement of the One Health approach, the transmission of human infectious diseases is generally related to environmental and animal health. Coronavirus disease (COVID-19) has been largely impacted by environmental factors regionally and globally and has significantly disrupted human society, especially in low-income regions that border many countries. However, few research studies have explored the impact of environmental factors on disease transmission in these regions. Methods: We used the Xinjiang Uygur Autonomous Region as the study area to investigate the impact of environmental factors on COVID-19 variation using a dynamic disease model. Given the special control and prevention strategies against COVID-19 in Xinjiang, the focus was on social and environmental factors, including population mobility, quarantine rates, and return rates. The model performance was evaluated using the statistical metrics of correlation coefficient (CC), normalized absolute error (NAE), root mean square error (RMSE), and distance between the simulation and observation (DISO) indices. Scenario analyses of COVID-19 in Xinjiang encompassed three aspects: different population mobilities, quarantine rates, and return rates. Results: The results suggest that the established dynamic disease model can accurately simulate and predict COVID-19 variations with high accuracy. This model had a CC value of 0.96 and a DISO value of less than 0.35. According to the scenario analysis results, population mobilities have a large impact on COVID-19 variations, with quarantine rates having a stronger impact than return rates. Conclusion: These results provide scientific insight into the control and prevention of COVID-19 in Xinjiang, considering the influence of social and environmental factors on COVID-19 variation. The control and prevention strategies for COVID-19 examined in this study may also be useful for the control of other infectious diseases, especially in low-income regions that are bordered by many countries.