Literature DB >> 8888482

Protecting confidentiality in small population health and environmental statistics.

L H Cox1.   

Abstract

Public policy decisions are fuelled by information. Often, this information is in the form of statistical data. Questions stemming from public health and environmental concerns often arise or are studied within small subgroups of a population. Continuing improvements in the performance and availability of computing resources, including geographic information systems, and the need to better understand environmental exposures and consequent health effects create increasing demand for small population health and environmental data. These demands are at odds with the need to preserve the privacy and data confidentiality of persons, groups or organizations covered by the data. Although confidentiality issues for demographic and economic data are well-studied and are gaining maturity for health data, these issues are only beginning to emerge for environmental data and combined environmental-health data. The aim of this paper is to provide a framework for that examination. Herein we examine confidentiality problems posed by small population health and environmental data, summarize available statistical methods, and propose avenues for the solution of new problems.

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Year:  1996        PMID: 8888482     DOI: 10.1002/(SICI)1097-0258(19960915)15:17<1895::AID-SIM401>3.0.CO;2-W

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  8 in total

1.  Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule.

Authors:  Bradley Malin; Kathleen Benitez; Daniel Masys
Journal:  J Am Med Inform Assoc       Date:  2011 Jan-Feb       Impact factor: 4.497

Review 2.  Disproportionate exposures in environmental justice and other populations: the importance of outliers.

Authors:  Michael Gochfeld; Joanna Burger
Journal:  Am J Public Health       Date:  2011-05-06       Impact factor: 9.308

3.  Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.

Authors:  Meng Wang; Zhanglong Ji; Hyeon-Eui Kim; Shuang Wang; Li Xiong; Xiaoqian Jiang
Journal:  IEEE Trans Knowl Data Eng       Date:  2017-11-14       Impact factor: 6.977

4.  Power to detect spatial disturbances under different levels of geographic aggregation.

Authors:  Caroline Jeffery; A Ozonoff; Laura F White; Miriam Nuño; Marcello Pagano
Journal:  J Am Med Inform Assoc       Date:  2009-08-28       Impact factor: 4.497

5.  Revealing the spatial distribution of a disease while preserving privacy.

Authors:  Shannon C Wieland; Christopher A Cassa; Kenneth D Mandl; Bonnie Berger
Journal:  Proc Natl Acad Sci U S A       Date:  2008-11-17       Impact factor: 11.205

6.  Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom.

Authors:  Maged N Kamel Boulos
Journal:  Int J Health Geogr       Date:  2004-01-28       Impact factor: 3.918

7.  A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data.

Authors:  Ourania Kounadi; Bernd Resch
Journal:  J Empir Res Hum Res Ethics       Date:  2018-04-23       Impact factor: 1.742

8.  Effect of spatial resolution on cluster detection: a simulation study.

Authors:  Al Ozonoff; Caroline Jeffery; Justin Manjourides; Laura Forsberg White; Marcello Pagano
Journal:  Int J Health Geogr       Date:  2007-11-27       Impact factor: 3.918

  8 in total

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