Spatiotemporal distribution and ecological factors of brucellosis among children from 2016 to 2020 in Inner Mongolia, China.

OBJECTIVE

In recent years, the increasing incidence of brucellosis in children has become more serious. However, relatively few studies have been conducted to characterize the spatialtemporal distribution of brucellosis in children. This study aimed to analyze the spatiotemporal distribution characteristics and ecological influencing factors of brucellosis incidence among children in Inner Mongolia.

METHODS

This study used data on brucellosis incidence in children aged 0-14 years reported in Inner Mongolia from 2016 to 2020. A Bayesian model was used to analyze the spatial and temporal distribution of brucellosis in children from 2016 to 2020 in Inner Mongolia. Geographical weighted regression model was used to analyze the ecological factors related to the incidence of brucellosis in children.

RESULT

Bayesian spatiotemporal analysis indicated that the highest brucellosis risk and increased disease incidence were observed in Hinggan, Inner Mongolia, in children aged 0-14 years. Alxa had the lowest risk but the incidence rate increased rapidly. The incidence of childhood brucellosis was positively associated with the number of sheep at the year-end (β: 2.5909 ~ 2.5926, P < 0.01), average temperature (β: 2.8978 ~ 2.9030, P < 0.05), and precipitation level (β: 3.3261 ~ 3.3268, P < 0.01).

CONCLUSION

From 2016 to 2020, the overall incidence of brucellosis in children in Inner Mongolia showed an upward trend, with cases exhibiting spatial aggregation. We should focus on areas where the incidence of brucellosis in children is rising rapidly. The incidence of childhood brucellosis was associated with the number of sheep at the year-end, average temperature and precipitation level.

IMPLICATIONS AND CONTRIBUTION

The findings suggest that brucellosis in children is not to be taken lightly. For children should also focus on protection, take corresponding protective measures. While we focus on high-risk areas, we must also monitor areas where the risk of disease is low, but the incidence is rising fast, to prevent outbreaks in low-risk areas from becoming high-risk areas.