• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

应用数据科学方法识别纽约儿童哮喘和过敏相关症状的学校和家庭风险因素。

Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York.

机构信息

Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY 12144, USA.

Department of Environmental Medicine, School of Medicine, New York University, New York, NY 12144, USA.

出版信息

Sci Total Environ. 2021 May 20;770:144746. doi: 10.1016/j.scitotenv.2020.144746. Epub 2021 Jan 23.

DOI:10.1016/j.scitotenv.2020.144746
PMID:33736384
Abstract

OBJECTIVES

Few studies have comprehensively assessed multiple environmental exposures affecting children's health. This study applied machine-learning methods to evaluate how indoor environmental conditions at home and school contribute to asthma and allergy-related symptoms.

METHODS

We randomly selected 10 public schools representing different socioeconomic statuses in New York State (2017-2019) and distributed questionnaires to students to collect health status and home-and school-environmental exposures. Indoor air quality was measured at school, and ambient particle exposures (PM and components) were measured using real-time personal monitors for 48 h. We used random forest model to identify the most important risk factors for asthma and allergy-related symptoms, and decision tree for visualizing the inter-relationships among the multiple risk factors with the health outcomes.

RESULTS

The top contributing factors identified for asthma were family rhinitis history (relative importance: 10.40%), plant pollen trigger (5.48%); bedroom carpet (3.58%); environmental tobacco smoke (ETS) trigger symptom (2.98%); and ETS exposure (2.56%). For allergy-related symptoms, plant pollen trigger (10.88%), higher paternal education (7.33%), bedroom carpet (5.28%), family rhinitis history (4.78%), and higher maternal education (4.25%) were the strongest contributing factors. Conversely, primary heating with hot water radiator was negatively (-6.86%) associated with asthma symptoms. Younger children (<9 years old) with family history of rhinitis and carpeting in the bedroom were the prominent combined risk factors for asthma. Children jointly exposed to pollen, solvents, and carpeting in their home tended to have greater risks of allergy-related symptoms, even without family history of rhinitis.

CONCLUSION

Family rhinitis history, bedroom carpet, and pollen triggers were the most important risk factors for both asthma and allergy-related symptoms. Our new findings included that hot-water radiator was related to reduced asthma symptoms, and the combination of young age, rhinitis history, and bedroom carpeting was related to increased asthma symptoms. Further studies are needed to confirm our findings.

摘要

目的

很少有研究全面评估影响儿童健康的多种环境暴露因素。本研究应用机器学习方法评估家庭和学校室内环境条件如何导致哮喘和过敏相关症状。

方法

我们在纽约州随机选择了 10 所代表不同社会经济地位的公立学校(2017-2019 年),向学生发放问卷收集健康状况和家庭及学校环境暴露情况。在学校测量室内空气质量,并使用实时个人监测仪测量 48 小时环境颗粒物暴露(PM 及其成分)。我们使用随机森林模型确定哮喘和过敏相关症状的最重要危险因素,并使用决策树可视化多个危险因素与健康结果之间的相互关系。

结果

确定的哮喘最重要影响因素为家族性鼻炎史(相对重要性:10.40%)、植物花粉触发(5.48%)、卧室地毯(3.58%)、环境烟草烟雾(ETS)触发症状(2.98%)和 ETS 暴露(2.56%)。对于过敏相关症状,植物花粉触发(10.88%)、父亲教育程度较高(7.33%)、卧室地毯(5.28%)、家族性鼻炎史(4.78%)和母亲教育程度较高(4.25%)是最强的影响因素。相反,主要使用热水散热器供暖与哮喘症状呈负相关(-6.86%)。有家族性鼻炎史和卧室地毯的年龄较小(<9 岁)的儿童是哮喘的突出综合危险因素。在家庭中同时暴露于花粉、溶剂和地毯的儿童患过敏相关症状的风险更大,即使没有家族性鼻炎史也是如此。

结论

家族性鼻炎史、卧室地毯和花粉触发是哮喘和过敏相关症状的最重要危险因素。我们的新发现包括热水散热器与哮喘症状减少有关,以及年龄较小、鼻炎史和卧室地毯相结合与哮喘症状增加有关。需要进一步研究来证实我们的发现。

相似文献

1
Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York.应用数据科学方法识别纽约儿童哮喘和过敏相关症状的学校和家庭风险因素。
Sci Total Environ. 2021 May 20;770:144746. doi: 10.1016/j.scitotenv.2020.144746. Epub 2021 Jan 23.
2
Evidence from SINPHONIE project: Impact of home environmental exposures on respiratory health among school-age children in Romania.来自 SINPHONIE 项目的证据:罗马尼亚学龄儿童家庭环境暴露对呼吸健康的影响。
Sci Total Environ. 2018 Apr 15;621:75-84. doi: 10.1016/j.scitotenv.2017.11.157. Epub 2017 Nov 22.
3
Parent-reported environmental exposures and environmental control measures for children with asthma.家长报告的哮喘儿童的环境暴露及环境控制措施。
Arch Pediatr Adolesc Med. 2002 Mar;156(3):258-64. doi: 10.1001/archpedi.156.3.258.
4
[Interactive effects of environmental tobacco smoke and pets ownership on respiratory diseases and symptoms in children].[环境烟草烟雾与宠物饲养对儿童呼吸道疾病及症状的交互作用]
Zhonghua Er Ke Za Zhi. 2013 Feb;51(2):96-100.
5
Assessing associations between indoor environment and health symptoms in Romanian school children: an analysis of data from the SINPHONIE project.评估罗马尼亚学童室内环境与健康症状之间的关联:SINPHONIE 项目数据分析。
Environ Sci Pollut Res Int. 2018 Mar;25(9):9186-9193. doi: 10.1007/s11356-018-1568-3. Epub 2018 Feb 22.
6
Associations between ventilation and children's asthma and allergy in naturally ventilated Chinese homes.自然通风的中国家庭中通风与儿童哮喘和过敏的关系。
Indoor Air. 2021 Mar;31(2):383-391. doi: 10.1111/ina.12742. Epub 2020 Sep 28.
7
Home environmental and lifestyle factors associated with asthma, rhinitis and wheeze in children in Beijing, China.中国北京地区儿童哮喘、鼻炎和喘息与家庭环境及生活方式因素的相关性研究。
Environ Pollut. 2020 Jan;256:113426. doi: 10.1016/j.envpol.2019.113426. Epub 2019 Oct 22.
8
Teacher respiratory health symptoms in relation to school and home environment.教师的呼吸健康症状与学校和家庭环境有关。
Int Arch Occup Environ Health. 2017 Nov;90(8):725-739. doi: 10.1007/s00420-017-1235-x. Epub 2017 Jun 9.
9
Elevated asthma and indoor environmental exposures among Puerto Rican children of East Harlem.东哈莱姆区波多黎各儿童中哮喘发病率升高与室内环境暴露情况
J Asthma. 2003;40(5):557-69. doi: 10.1081/jas-120019028.
10
Predicting environmental risk factors in relation to health outcomes among school children from Romania using random forest model - An analysis of data from the SINPHONIE project.利用随机森林模型预测罗马尼亚在校儿童健康结果相关的环境风险因素——SINPHONIE 项目数据分析。
Sci Total Environ. 2021 Aug 25;784:147145. doi: 10.1016/j.scitotenv.2021.147145. Epub 2021 Apr 16.

引用本文的文献

1
A Comparative Study of CO Forecasting Strategies in School Classrooms: A Step Toward Improving Indoor Air Quality.学校教室中一氧化碳预测策略的比较研究:迈向改善室内空气质量的一步。
Sensors (Basel). 2025 Mar 29;25(7):2173. doi: 10.3390/s25072173.
2
Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.探索机器学习在儿童哮喘管理中的应用:范围综述。
JMIR AI. 2024 Aug 27;3:e57983. doi: 10.2196/57983.
3
Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.
基于详细磁共振成像报告,使用梯度提升树模型预测鼻咽癌远处转移。
World J Radiol. 2024 Jun 28;16(6):203-210. doi: 10.4329/wjr.v16.i6.203.
4
Sociodemographic factors, health behavior, parental or workplace smoking, and adult asthma risk in the United States.美国的社会人口因素、健康行为、父母或工作场所的吸烟情况与成人哮喘风险。
Work. 2024;77(4):1115-1124. doi: 10.3233/WOR-230026.
5
Gas cooking and respiratory outcomes in children: A systematic review.儿童燃气烹饪与呼吸健康结局:一项系统综述
Glob Epidemiol. 2023 Apr 17;5:100107. doi: 10.1016/j.gloepi.2023.100107. eCollection 2023 Dec.
6
Building a predictive model to identify clinical indicators for COVID-19 using machine learning method.利用机器学习方法构建预测 COVID-19 的临床指标的预测模型。
Med Biol Eng Comput. 2022 Jun;60(6):1763-1774. doi: 10.1007/s11517-022-02568-2. Epub 2022 Apr 25.