SEEDNet:用于网络分析的无协变量多国定居水平流行病学估计数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Amir Hossein Darooneh, Jean-Luc Kortenaar, Céline M Goulart, Katie McLaughlin, Sean P Cornelius, Diego G Bassani
{"title":"SEEDNet:用于网络分析的无协变量多国定居水平流行病学估计数据集。","authors":"Amir Hossein Darooneh, Jean-Luc Kortenaar, Céline M Goulart, Katie McLaughlin, Sean P Cornelius, Diego G Bassani","doi":"10.1038/s41597-025-05143-0","DOIUrl":null,"url":null,"abstract":"<p><p>The study of population health through network science is promising but suitable population health datasets covering low- and middle-income countries (LMICs) are not available. Covariate-based methods used to produce small-area estimates (SAEs) combine national health surveys with covariates from varied sources through various methods limiting their use for producing network representations of populations by injecting unquantifiable uncertainty into estimates of node attributes, affecting the comparability of representations across countries and time. Here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis), a multi-country data library of population health indicators across human settlements. Our datasets are produced through a covariate-free method that uses georeferenced national surveys to produce SAEs of health indicators and include complete mapping of population settlements of all sizes. Our open-access library is intended to be used as the basis for network representations of population health in LMICs. Novel aspects include automated estimation process, harmonized data inputs, complete settlement mapping and the adoption of settlements as the functional units for network-based analysis of epidemiological data.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"975"},"PeriodicalIF":6.9000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12152140/pdf/","citationCount":"0","resultStr":"{\"title\":\"SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis.\",\"authors\":\"Amir Hossein Darooneh, Jean-Luc Kortenaar, Céline M Goulart, Katie McLaughlin, Sean P Cornelius, Diego G Bassani\",\"doi\":\"10.1038/s41597-025-05143-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The study of population health through network science is promising but suitable population health datasets covering low- and middle-income countries (LMICs) are not available. Covariate-based methods used to produce small-area estimates (SAEs) combine national health surveys with covariates from varied sources through various methods limiting their use for producing network representations of populations by injecting unquantifiable uncertainty into estimates of node attributes, affecting the comparability of representations across countries and time. Here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis), a multi-country data library of population health indicators across human settlements. Our datasets are produced through a covariate-free method that uses georeferenced national surveys to produce SAEs of health indicators and include complete mapping of population settlements of all sizes. Our open-access library is intended to be used as the basis for network representations of population health in LMICs. Novel aspects include automated estimation process, harmonized data inputs, complete settlement mapping and the adoption of settlements as the functional units for network-based analysis of epidemiological data.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"975\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12152140/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05143-0\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05143-0","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

通过网络科学研究人口健康是有前途的,但没有涵盖低收入和中等收入国家(LMICs)的合适人口健康数据集。用于产生小区域估计(SAEs)的基于协变量的方法通过各种方法将国家健康调查与来自不同来源的协变量结合起来,通过向节点属性的估计中注入不可量化的不确定性,从而影响不同国家和时间表征的可比性,限制了它们用于产生人口网络表征的使用。在这里,我们提出了SEEDNet(用于网络分析的定居级流行病学估计数据集),这是一个跨越人类住区的人口健康指标的多国数据库。我们的数据集是通过无协变量方法生成的,该方法使用地理参考的国家调查来生成健康指标的sae,并包括各种规模的人口住区的完整地图。我们的开放存取库旨在作为中低收入国家人口健康网络表示的基础。新的方面包括自动化估计过程、统一的数据输入、完整的住区制图和采用住区作为基于网络的流行病学数据分析的功能单位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis.

The study of population health through network science is promising but suitable population health datasets covering low- and middle-income countries (LMICs) are not available. Covariate-based methods used to produce small-area estimates (SAEs) combine national health surveys with covariates from varied sources through various methods limiting their use for producing network representations of populations by injecting unquantifiable uncertainty into estimates of node attributes, affecting the comparability of representations across countries and time. Here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis), a multi-country data library of population health indicators across human settlements. Our datasets are produced through a covariate-free method that uses georeferenced national surveys to produce SAEs of health indicators and include complete mapping of population settlements of all sizes. Our open-access library is intended to be used as the basis for network representations of population health in LMICs. Novel aspects include automated estimation process, harmonized data inputs, complete settlement mapping and the adoption of settlements as the functional units for network-based analysis of epidemiological data.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信