Land use as an effective factor on the occurrence of chromosomal diseases in Brazil.

International journal of molecular epidemiology and genetics Pub Date : 2021-10-15 eCollection Date: 2021-01-01
Marcos Roberto Cochak, Marília Melo Favalesso, Rose Meire Costa, Ana Tereza Bittencourt Guimarães, Lucinéia Fátima Chasko Ribeiro
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Abstract

Background: 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.

Objectives: 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.

Methods: 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 global forest canopy height, distance from the built-up area, and solar radiation. We characterized the predictive areas as potential occurrence of chromosomal diseases by land use and occupation.

Results: 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).

Discussion: 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.

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土地利用是巴西染色体疾病发生的一个有效因素。
背景:染色体疾病的发生是一个世界性的健康问题。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。目的:本研究旨在描述2005年至2015年巴西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。方法:我们使用机器学习算法使用染色体患病率来模拟染色体疾病的潜在分布。我们使用全球森林冠层高度、与建成区的距离和太阳辐射作为模型的预测变量。我们将染色体疾病的预测区描述为土地利用和占用的潜在发生区域。结果:43,672个核型的地理参考数据检测到染色体疾病7,237例,并利用5,362例建立模型。生成的模型准确(TSS>0.5)。讨论:染色体疾病发生率较高的地区与牧区、作物和农林复合系统以及城市化地区存在显著关联。这项研究是巴西第一个采用这种方法的研究,似乎有希望预测染色体疾病的潜在分布。因此,它可以成为一个很好的公共卫生管理工具。
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