View source for Concepts for integrated monitoring

[[Category:IEHIAS]]
[[Category:IEHIAS models/methods]]
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==What is integrated monitoring?==

Integrated monitoring is defined here as "an ongoing and systematic process to determine, analyze and interpret environmental quality and environment-related health status".

Integrated monitoring of the effects of environmental stressors on human health requires that physical, chemical and biological measurements be taken simultaneously over time of different E & H compartments at the same location.

A good integrated monitoring need to establish mechanism for data sharing, improved data availability, accessibility, comparability, and enhanced exchange of information, between environment and health, across different environmental media, and within health.
Integrated monitoring is scale dependent, both temporal and spatial. The scale to be used will depend upon the project aims and objectives. 

In INTARESE, integrated monitoring is to explore the ways of linking and enhance various sources and technologies in order to provide a more integrated (e.g. EU-wide, multi-agent, multi-pathway, multi-media/receptor) approach to monitoring in the EU.

Integrated monitoring involves:
* Planned and repeated data collection
* Analysis
* Interpretation
* Reporting of the results of monitoring
* Recommendations for action (which usually involves reporting on monitoring), and,
* Taking and reviewing actions

In reality, some of these components may occur in a more iterative manner. The key issue is to consider monitoring as an ongoing process that helps to develop more effective decision-making.

Monitoring is a sort of a systematic use of common sense to tell us if we are on track. The primary benefit of integrated monitoring is to check that   policy statement, plan, or condition on resource consent has resulted in the environmental and environment-related health outcome expected. It provides information to understand the current state of the environment and health, and assess whether things are getting better or worse. Monitoring [http://www.qp.org.nz provides a number of real benefits]:
* Can give early warning of issues or problems before they become serious, costly or irreversible (alert monitoring)
* Prompts organizations to adjust when monitoring shows that current approaches are not working and helps prepare  to respond effectively to any changes (compliance monitoring)
* Provides a better understanding of the key pressures on the environment, the condition or state of the environment and environment-related health, leading to better responses and results (finger on the pulse monitoring)
* Increases policy and plan effectiveness and so reduce costs
* Contributes to a range of feedback systems (including social and economic) and can enable integration of information management systems
* Can lead to better policies, better formulation of policy provisions (including rules) and clearer targets
* Provides accountability
* Protects investment in the plan or policy statement
* Enables more effective participation in resource management and community development and education at the local level
* Enables more targeted consent conditions, more focused rules and standards, and more efficient processing of consents, resulting from a better understanding of the environment and environment-related health, and a smoother process for consent holders.

==Why is integrated monitoring needed?==

* Integrated monitoring forms the backbone of integrated assessment and provides the framework in which any issue can be framed and assessed.
* Integrated monitoring enables the best use of monitoring and surveillance data for integrated environmental health assessment.
* Integrated monitoring brings together different sources of existing information and information systems regarding a certain issue. It generates an added value to these separate pieces of information.
* Integrated monitoring helps generate synergy between information and data in order to tackle the issue at hand.

==What is data integration?==

Data integration is the process of the standardization of data definitions and data structures by using a common conceptual schema across a collection of data sources (Heimbigner and McLeod, 1985; Litwin, et al., 1990). Integrated data will be consistent and logically compatible in different systems or databases, and can use across time and users (Martin, 1986).

Goodhue et al. (1992, p294) defined data integration as "the use of common field definitions and codes across different parts of an organization". According to Goodhue, et al. (1992), data integration will increase along one or both of two dimensions: (1) the number of fields with common definitions and codes, or (2) the number of systems or databases adhering to these standards. Data integration is an example of a highly formalized language for describing the events occurring in an organization's domain. The scope of data integration is the extent to which that formal language is used across multiple organizations or sub-units of the same organization. The objective of data integration is to bring together data from multiple data sources that have relevant information contributing to the achievement of the users' goals (AFT, 1997).

The Advanced Forest Technologies in Canada (AFT, 1997) identified the following factors that must be addressed to integrate data properly:
* identification of an optimal subset of the available data sources for integration
* estimation of the levels of noise and distortions due to sensory, processing, and environmental conditions when the data are collected
* the spatial resolution, the spectral resolution, and the accuracy of the data
* the formats of the data, the archive systems, and the data storage and retrieval
* the computational efficiency of the integrated data sets to achieve the goals of the users

==Why integrate data from multiple sources?==

There are some obvious advantages in integrating information from multiple data sources. Such integration alleviates the burden of duplicating data gathering efforts, and enables the extraction of information that would otherwise be impossible (Subrahmanian et al., 1996).
Subrahmanian, et al. (1996) gives the following examples of benefits of data integration: "... law enforcement agencies such as Interpol benefit from the ability to access databases of various national police forces, to assist their effort in fighting international terrorism, drug trafficking, and other criminal activities. Insurance companies, using data from external sources, including other insurance company and police records, can identify possible fraudulent claims. Medical researchers and epidemiologists, with access to records across geographical and ethnic boundaries, are in a better position to predict the progression of certain diseases. In each case, the information extracted from the integrated sources is not possible when the data sources are viewed in isolation."

Data integration is intended to add value to the data that are already collected and available in variously scattered places within the same system. Data integration is necessary occur before an environmental health impact assessor can conduct a high-level and high-quality analysis. It is common to see multiple units within a Ministry of Environment or health collect and manage large database and not share them with each other. These various sets of data are collected to describe certain element of the system. In general, these multiple sets of data are often designed in varying database applications, organized in different platforms, and coded with self-developed identification code. As a result, the data cannot readily be integrated or used integrative unless a data integration strategy is implemented. Without coordinated management, there cannot be a monitoring and evaluation system, a planning and policy analysis system, or an environmental health impact system that is effective and policy-relevant. Clearly, we must integrate the data from multiple sources so that we can conduct the right data analysis to answer the right policy questions. Multi-level data from multiple sources and years, once centrally integrated and organized, could have a tremendous value for policy-relevant research and analysis and improvement in environmental health management.

In summary, integrated usage of information from multiple environment and health monitoring programs can bridge the gaps between environment and human health. A common framework for the integration of information from environmental monitoring, biomonitoring and health surveillance can facilitate achieving the goals of greater efficiency and quality and of better-informed decisions, in ways that support specific information management needs. The general benefits from a documented, repeatable data integration process are: (i) easy to define; (ii) easy to query; (iii) easy to use; and (iv) eliminate the redundant data.

==References==

* AFT (Advanced Forest Technologies), Canada. 1997. [ http://www.aft.pfc.forestry.ca/Proposal/dataman.html Data Management with Integration of Multiple Data Source.]
* Goodhue, D. L., M. D. Wybo, and L. J. Kirsch. 1992. The Impact of Data Integration on the Costs and Benefits of Information Systems, MIS Quarterly, pp. 293-311.
* Heimbigner, D., and D. McLeod. 1985. A Federated Architecture for Information Management, ACM Transactions on Office Information Systems, Vol. 3, No. 3, pp. 253-278.
* Litwin, W., L. Mark, and N. Roussopoulos. 1990. Interoperability of Multiple Autonomous Databases, ACM Computing Surveys, Vol. 22, No. 3, pp. 267-293.
* Martin, J. 1986. Information Engineering, Savant Research Studies, Carnforth, Lancashire, England.
* Subrahmanian, V. S., Adali, A., Brink, J. J., Lu, A., Rajput, T. J., Rogers, R., Ross, C. Ward. 1996. [http://www.cs.umd.edu/projects/hermes/overview/paper/section1.html HERMES: A Heterogeneous Reasoning and Mediator System.]

==See also==

* [[Methods for data integration]]

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