Cochak Marcos Roberto, Favalesso Marília Melo, Costa Rose Meire, Guimarães Ana Tereza Bittencourt, Ribeiro Lucinéia Fátima Chasko
Postgraduate Program in Bioscience and Health, Universidade Estadual do Oeste do Paraná Cascavel, PR-Brazil.
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto Nacional de Medicina Tropical (INMeT) Argentina.
Int J Mol Epidemiol Genet. 2021 Oct 15;12(5):102-111. eCollection 2021.
The occurrence of chromosomal diseases is a worldwide health problem. The use of agrochemicals, urbanization processes, and solar radiation can be predictive factors of the elevated risk of congenital malformations. In this sense, predicting the geographical potential of the distribution of chromosomal diseases has high relevance for public health.
This study aimed to describe chromosomal prevalence in Brazil regions, from 2005 to 2015, to model a potential distribution of chromosomal disease occurrence probability associated with land use.
We used chromosomal prevalence to model a potential distribution of chromosomal diseases using machine learning algorithms. As the predictors of the models, we used the variables , and . We characterized the predictive areas as potential occurrence of chromosomal diseases by land use and occupation.
Georeferenced data of 43,672 karyotypes detected 7,237 cases of chromosomal diseases and used 5,362 to build the models. The models generated were accurate (TSS>0.5).
The areas with greater occurrence of chromosomal diseases present a significant association with pasture areas, crops and agroforestry systems, and urbanized areas. This research is the first Brazilian study with this approach that seems promising in predicting the potential distribution of chromosomal diseases. Therefore, it can be an excellent management tool in public health.
染色体疾病的发生是一个全球性的健康问题。农用化学品的使用、城市化进程和太阳辐射可能是先天性畸形风险升高的预测因素。从这个意义上说,预测染色体疾病分布的地理潜力对公共卫生具有高度相关性。
本研究旨在描述2005年至2015年巴西各地区的染色体患病率,以模拟与土地利用相关的染色体疾病发生概率的潜在分布。
我们使用染色体患病率,通过机器学习算法模拟染色体疾病的潜在分布。作为模型的预测因子,我们使用了变量 、 和 。我们根据土地利用和占用情况将预测区域表征为染色体疾病的潜在发生区域。
43672个核型的地理参考数据检测到7237例染色体疾病病例,并使用5362例来构建模型。生成的模型准确(TSS>0.5)。
染色体疾病发生率较高的地区与牧场、农作物和农林业系统以及城市化地区存在显著关联。这项研究是巴西首次采用这种方法进行的研究,在预测染色体疾病的潜在分布方面似乎很有前景。因此,它可以成为公共卫生领域的一个优秀管理工具。