• 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

从自然资源评估到空间流行病学:二十五年磨一剑。

From Natural Resources Evaluation to Spatial Epidemiology: 25 Years in the Making.

作者信息

Goovaerts P

机构信息

BioMedware, 167 Little Lake Drive, Ann Arbor, MI 48106, USA.

出版信息

Math Geosci. 2021 Feb;53(2):239-266. doi: 10.1007/s11004-020-09886-x. Epub 2020 Aug 28.

DOI:10.1007/s11004-020-09886-x
PMID:33767799
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7987064/
Abstract

When in the winter of 1994, under the supervision of my post-doc adviser André Journel, I started writing "" in the bedroom of a tiny Palo Alto apartment, little did I know that 25 years later I would be conducting NIH-funded research on medical geostatistics from a lakefront office nestled in the Irish Hills of Michigan. The professional and personal path that led me to trade the mapping of heavy metal concentrations in the topsoil of the Swiss Jura for the geostatistical analysis of cancer data was anything but planned, yet André's help and guidance were instrumental early on. Looking back, shifting scientific interest from the characterization of contaminated sites to human health made sense as the field of epidemiology is increasingly concerned with the concept of exposome, which comprises all environmental exposures (e.g., air, soil, drinking water) that a person experiences from conception throughout the life course. Although both environmental and epidemiological data exhibit space-time variability, the latter has specific characteristics that required the adaptation of traditional geostatistical tools, such as semivariogram and kriging. Challenges include: (i) the heteroscedasticity of disease rate data (i.e., larger uncertainty of disease rates computed from small populations), (ii) their uneven spatial support (e.g., rates recorded for administrative units of different size and shape), and (iii) the limitations of Euclidean metrics to embody proximity when dealing with data that pertain to human mobility. Most of these challenges were addressed by borrowing concepts developed in adjacent fields, stressing the value of interdisciplinary research and intellectual curiosity, something I learned as a fresh PhD in agronomical sciences joining André's research group at the Stanford Center for Reservoir Forecasting in the early nineties.

摘要

1994年冬天,在我的博士后导师安德烈·朱内尔的指导下,我在帕洛阿尔托一套狭小公寓的卧室里开始撰写《》。当时我怎么也想不到,25年后我会在密歇根州爱尔兰山湖畔的办公室里开展由美国国立卫生研究院资助的医学地理统计学研究。我从绘制瑞士汝拉州表土中的重金属浓度转而进行癌症数据的地理统计分析,这条职业和个人道路完全不在计划之中,但安德烈早期的帮助和指导起到了关键作用。回首往事,随着流行病学领域越来越关注暴露组的概念,将科学兴趣从受污染场地的特征描述转向人类健康是有意义的。暴露组包括一个人从受孕到生命历程中所经历的所有环境暴露(如空气、土壤、饮用水)。尽管环境数据和流行病学数据都表现出时空变异性,但后者具有特定特征,需要对传统地理统计工具(如半变异函数和克里金法)进行调整。挑战包括:(i)疾病发生率数据的异方差性(即从小群体计算出的疾病发生率不确定性更大),(ii)它们不均匀的空间支持(例如,为不同大小和形状的行政单位记录的发生率),以及(iii)在处理与人类流动性相关的数据时,欧几里得度量在体现邻近性方面的局限性。这些挑战大多通过借鉴相邻领域发展出的概念得以解决,这凸显了跨学科研究和求知欲的价值。这是我在90年代初作为一名刚获得农学博士学位的新人加入安德烈在斯坦福水库预测中心的研究小组时学到的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/870239eb4f1f/nihms-1624715-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/0aa95373615b/nihms-1624715-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/0de101fbdf14/nihms-1624715-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/4f36004f426e/nihms-1624715-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/272ccf4153d5/nihms-1624715-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/045db86b4722/nihms-1624715-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/36307d2ef6d8/nihms-1624715-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/c7fbdd6ee4cc/nihms-1624715-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/f3e70545095e/nihms-1624715-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/870239eb4f1f/nihms-1624715-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/0aa95373615b/nihms-1624715-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/0de101fbdf14/nihms-1624715-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/4f36004f426e/nihms-1624715-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/272ccf4153d5/nihms-1624715-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/045db86b4722/nihms-1624715-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/36307d2ef6d8/nihms-1624715-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/c7fbdd6ee4cc/nihms-1624715-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/f3e70545095e/nihms-1624715-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63c/7987064/870239eb4f1f/nihms-1624715-f0009.jpg

相似文献

1
From Natural Resources Evaluation to Spatial Epidemiology: 25 Years in the Making.从自然资源评估到空间流行病学:二十五年磨一剑。
Math Geosci. 2021 Feb;53(2):239-266. doi: 10.1007/s11004-020-09886-x. Epub 2020 Aug 28.
2
Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography.地统计插值中面状数据与点状数据的结合:在土壤科学和医学地理学中的应用
Math Geosci. 2010 Jul 1;42(5):535-554. doi: 10.1007/s11004-010-9286-5.
3
Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging.疾病数据的地统计分析:在使用面积到点的泊松克里金法绘制癌症死亡风险等值线图时考虑空间支持和人口密度。
Int J Health Geogr. 2006 Nov 30;5:52. doi: 10.1186/1476-072X-5-52.
4
Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).大分子拥挤现象:化学与物理邂逅生物学(瑞士阿斯科纳,2012年6月10日至14日)
Phys Biol. 2013 Aug;10(4):040301. doi: 10.1088/1478-3975/10/4/040301. Epub 2013 Aug 2.
5
Geostatistics: a common link between medical geography, mathematical geology, and medical geology.地质统计学:医学地理学、数学地质学和医学地质学之间的共同纽带。
J South Afr Inst Min Metall. 2014 Aug;114:605-612.
6
How does Poisson kriging compare to the popular BYM model for mapping disease risks?与用于绘制疾病风险图的流行的贝叶斯层次模型(BYM)相比,泊松克里金法如何?
Int J Health Geogr. 2008 Feb 4;7:6. doi: 10.1186/1476-072X-7-6.
7
A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties.一种在土壤属性空间插值中结合分级统计图和实地数据的连贯地质统计学方法。
Eur J Soil Sci. 2011 Jun;62(3):371-380. doi: 10.1111/j.1365-2389.2011.01368.x.
8
From the History of the Croatian Dermatovenereological Society - The Croatian Medical Association and an Overview of Important Information Regarding the Journal Acta Dermatovenerologica Croatica.克罗地亚皮肤性病学会史——克罗地亚医学协会及《克罗地亚皮肤性病学学报》重要信息概述
Acta Dermatovenerol Croat. 2018 Dec;26(4):344-348.
9
Development and Evaluation of Geostatistical Methods for Non-Euclidean-Based Spatial Covariance Matrices.基于非欧几里得空间协方差矩阵的地质统计学方法的开发与评估
Math Geosci. 2019 Aug;51(6):767-791. doi: 10.1007/s11004-019-09791-y. Epub 2019 Mar 14.
10
Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging.疾病数据的地质统计学分析:使用泊松克里金法从经验频率估计癌症死亡风险。
Int J Health Geogr. 2005 Dec 14;4:31. doi: 10.1186/1476-072X-4-31.

引用本文的文献

1
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.理解葡萄牙 COVID-19 发病率的时空模式:2020 年 8 月至 2022 年 3 月的功能数据分析。
PLoS One. 2024 Feb 1;19(2):e0297772. doi: 10.1371/journal.pone.0297772. eCollection 2024.

本文引用的文献

1
Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography.地统计插值中面状数据与点状数据的结合:在土壤科学和医学地理学中的应用
Math Geosci. 2010 Jul 1;42(5):535-554. doi: 10.1007/s11004-010-9286-5.
2
Combining area-based and individual-level data in the geostatistical mapping of late-stage cancer incidence.在晚期癌症发病率的地理统计绘图中结合基于区域和个体层面的数据。
Spat Spatiotemporal Epidemiol. 2009 Oct-Dec;1(1):61-71. doi: 10.1016/j.sste.2009.07.001.
3
Medical Geography: a Promising Field of Application for Geostatistics.
医学地理学:地统计学一个颇具前景的应用领域。
Math Geol. 2009;41:243-264. doi: 10.1007/s11004-008-9211-3.
4
Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units.存在不规则地理单元时的克里金法和半变异函数反褶积
Math Geol. 2008;40(1):101-128.
5
Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging.疾病数据的地统计分析:在使用面积到点的泊松克里金法绘制癌症死亡风险等值线图时考虑空间支持和人口密度。
Int J Health Geogr. 2006 Nov 30;5:52. doi: 10.1186/1476-072X-5-52.
6
Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation.疾病数据的地质统计学分析:利用泊松克里金法和p场模拟对癌症死亡风险的空间不确定性进行可视化和传播
Int J Health Geogr. 2006 Feb 9;5:7. doi: 10.1186/1476-072X-5-7.
7
Analyzing geographic patterns of disease incidence: rates of late-stage colorectal cancer in Iowa.分析疾病发病率的地理模式:爱荷华州晚期结直肠癌的发病率
J Med Syst. 2004 Jun;28(3):223-36. doi: 10.1023/b:joms.0000032841.39701.36.
8
Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York.在使用地理统计滤波和空间中性模型检测空间聚类和离群值时考虑区域背景和人口规模:以纽约长岛的肺癌为例。
Int J Health Geogr. 2004 Jul 23;3(1):14. doi: 10.1186/1476-072X-3-14.
9
Spatial contouring of risk: a tool for environmental epidemiology.风险的空间轮廓分析:环境流行病学的一种工具
Epidemiology. 2004 May;15(3):287-92. doi: 10.1097/01.ede.0000121379.57583.84.
10
Using a GIS-based floating catchment method to assess areas with shortage of physicians.使用基于地理信息系统的浮动集水区方法评估医生短缺地区。
Health Place. 2004 Mar;10(1):1-11. doi: 10.1016/s1353-8292(02)00067-9.