Multi-Perspective Interpretation of Environmental & Health-Data: A Search for Good Quality Action

2009 ◽  
Vol 99 (10) ◽  
pp. 1739-1741 ◽  
Author(s):  
Natalya Dymova ◽  
R. Choudary Hanumara ◽  
Richard T. Enander ◽  
Ronald N. Gagnon

2016 ◽  
Vol 78 (6-3) ◽  
Author(s):  
Samsul Huda ◽  
Nurul Fahmi ◽  
Amang Sudarsono ◽  
M. Udin Harun Al Rasyid

In Internet of Things (IoT) era, The limitation storage on Wireless Sensor Network (WSN) can be solved by synchronized data sensors from the gateway node to the data center server. Data in the data center can be remotely accessed by the user at any time and anywhere from end user devices such as PCs, laptop PCs, and smart phones., and data should be accessed securely. The Only legitimated user can access the data sensor from an environmental health data center. CP-ABE (Ciphertext-Policy Attribute-Based Encryption) is becoming a robust cryptographic scheme solution to this issue. To enable a secure data sensor sharing and access on an environmental health data center, we propose a secure system model using CP-ABE which ensures confidentiality, integrity, and user privacy features. Experimental results prove that the implementation of CP-ABE does not overload the system.


2020 ◽  
Vol 60 (2) ◽  
pp. 159-182
Author(s):  
Kimberly B. Nelson ◽  
Jennifer D. Runkle ◽  
Margaret M. Sugg

2006 ◽  
Vol 17 (4) ◽  
pp. 363-385 ◽  
Author(s):  
Gangqiang Xia ◽  
Marie Lynn Miranda ◽  
Alan E. Gelfand

Author(s):  
Dany Doiron ◽  
Eleanor Setton ◽  
Evan Seed ◽  
Mahdi Shooshtari ◽  
Jeffrey Brook

IntroductionHealth and environmental exposure databases are generally siloed in different research institutions across Canada and integrating them for environmental health research is a considerable challenge. Facilitating the linkage of these databases is essential to provide new analytical opportunities and help create efficiencies for research on environmental determinants of health. Objectives and ApproachCANUE is a Canadian Institutes of Health Research-funded platform for supporting environmental health research. CANUE collates and generates standardized environmental data on air and noise pollution, land use, green/natural spaces, climate change/extreme weather, and socioeconomic conditions for every postal code in Canada and makes them freely available to researchers. Systems and procedures are being developed by CANUE to facilitate the sharing and integration of these extensive geospatial exposures with existing observational cohorts and administrative health databases across Canada. This linkage will enable investigators to test hypotheses on the interdependent associations of environmental features with health impacts or benefits. ResultsCANUE now hosts a dozen national exposure databases and related metadata files, and actively adds new regional and national datasets. Streamlined processes for data sharing have been developed to facilitate easy merging with health data. Substantial consultation has also taken place with a wide range of health data holders to establish appropriate processes for receiving and managing environmental data, with particular focus on addressing challenges presented by differing ethics, consent and confidentiality requirements. These processes help accelerate the research process by making analysis-ready data available to investigators, create opportunities to study how multiple environmental factors are linked to a wide range of health outcomes, and generally increase the use of health and population databases for environmental health research. Conclusion/ImplicationsThe CANUE collaborative model illustrates how the production of policy-relevant evidence can be advanced through better coordination among environmental health researchers and linkage with health databases. CANUE is improving the scientific potential and cost-effectiveness of research in environmental epidemiology through streamlining linkage and access to standardized exposure datasets.


First Monday ◽  
2020 ◽  
Author(s):  
Maria João Silva

The Eco-sensors4Health Project (Eco-sensors for health: Supporting children to create eco-healthy schools) is centered on the use of electronic sensors by children to become agents in the creation of healthy and sustainable environments in schools. In this Project, the environmental health data, acquired by children with the sensors, with tablets or mobile phones, is managed with the support of a collaborative platform that allows entering, searching and visualizing data of the different schools. The Eco-sensors4Health Toolkit is a guide to the implementation of the environmental health activities in schools, which include the exploratory sensorial tasks, the environmental data acquisition, organization and interpretation, and the decision making to improve schools’ environmental health. The iterative development processes of the Eco-sensors4Health Platform and Toolkit are presented in this paper as well as illustrative results of its uses in different schools. Those results indicate that the use of sensors by children in the context of authentic environmental health activities makes it possible to children to create and apply knowledge to solve schools’ environmental health problems.


2019 ◽  
Vol 32 (1) ◽  
pp. 233-256
Author(s):  
Donald Brown

This paper explains the methods that were used to study environmental health problems in Karonga, a rapidly growing secondary urban centre in Malawi. The study used existing information from hospital records and consulted local health officials and academics on how best to use it. The aim was to position the hospital as a disease surveillance site by using its records to generate disaggregated health data at the intra-urban scale. This paper identifies the strengths and limitations of using hospital data to inform joint urban planning and public health interventions. It also provides a summary of the key findings, including a discussion of the implications for enhancing urban health intelligence and urban policy formulation in Malawi and other rapidly urbanizing countries. This paper is intended to show researchers how existing information in low-resource settings can be used to generate needed health data for urban populations, with a particular interest in secondary centres.


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