Marcos Roberto Cochak, Marília Melo Favalesso, Rose Meire Costa, Ana Tereza Bittencourt Guimarães, Lucinéia Fátima Chasko Ribeiro
{"title":"土地利用是巴西染色体疾病发生的一个有效因素。","authors":"Marcos Roberto Cochak, Marília Melo Favalesso, Rose Meire Costa, Ana Tereza Bittencourt Guimarães, Lucinéia Fátima Chasko Ribeiro","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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 <i>global forest canopy height, distance from the built-up area</i>, and <i>solar radiation</i>. We characterized the predictive areas as potential occurrence of chromosomal diseases by land use and occupation.</p><p><strong>Results: </strong>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).</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":73460,"journal":{"name":"International journal of molecular epidemiology and genetics","volume":"12 5","pages":"102-111"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611229/pdf/ijmeg0012-0102.pdf","citationCount":"0","resultStr":"{\"title\":\"Land use as an effective factor on the occurrence of chromosomal diseases in Brazil.\",\"authors\":\"Marcos Roberto Cochak, Marília Melo Favalesso, Rose Meire Costa, Ana Tereza Bittencourt Guimarães, Lucinéia Fátima Chasko Ribeiro\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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 <i>global forest canopy height, distance from the built-up area</i>, and <i>solar radiation</i>. We characterized the predictive areas as potential occurrence of chromosomal diseases by land use and occupation.</p><p><strong>Results: </strong>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).</p><p><strong>Discussion: </strong>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.</p>\",\"PeriodicalId\":73460,\"journal\":{\"name\":\"International journal of molecular epidemiology and genetics\",\"volume\":\"12 5\",\"pages\":\"102-111\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611229/pdf/ijmeg0012-0102.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of molecular epidemiology and genetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of molecular epidemiology and genetics","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Land use as an effective factor on the occurrence of chromosomal diseases in Brazil.
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.