In this section, we start by reviewing some of the recipes and recommendations provided by various specialist groups and researchers from around the world for a successful implementation of a national geo-information infrastructure that can also support real-time GIS applications in public health. We then present a Canadian case study that emphasises the importance of data modelling and community/university collaboration among other elements involved in the development of community health geo-information systems. The reader will notice that there are many recurring themes, and elements and ingredients which are common to all these recipes and the presented case study. The section concludes with a detailed discussion of some of these elements and others that are crucial for properly building a national spatial health information infrastructure.
The Nairobi statement – International Federation of Surveyors and the United Nations-Habitat, 2002
The International Conference on Spatial Information for Sustainable Development held in Nairobi, Kenya, in 2001 recognised that development and implementation of a National Spatial Data Infrastructure (NSDI) is a prerequisite for promoting sustainable development in any country. The conference also recognised that although every NSDI is different due to a variety of cultural, social and economic factors unique within each country, there are a significant number of common elements that can be shared, and which countries should avoid re-inventing; these elements include [6]:
(1) Fostering a culture of data sharing that considers spatial information an asset: A key success factor of NSDI implementation is the management of information (including spatial information) as an asset, e.g., only capture data that are needed and can be maintained, as in the case with finance and human resources. A NSDI requires a culture of data sharing to exist within a country. The benefits associated with data sharing should be researched to encourage wide participation [6].
(2) Education, training, and capacity building: The sharing of education and training resources and experiences by organisations is important for capacity building. Universities should be encouraged to work with local organisations in the provision of Continuing Professional Development [6]. (Of interest in this context is UNIGIS International http://www.unigis.org/, a worldwide network of educational institutions offering distance-learning courses in GIS.)
(3) Addressing crucial legal issues: Experience has shown that issues associated with national security, data privacy and associated liability are potential obstacles for NSDI initiatives. Unambiguous legal frameworks to address these crucial legal issues must be established as early as possible. Ordinary citizens must be considered one of the main NSDI beneficiaries and allowed access to NSDI information and services (where appropriate) [6].
(4) Development of effective partnerships, and involvement of all stakeholders and users: Mature NSDIs are complex solutions involving many stakeholders (including the health sector with all its organisations). NSDIs are underpinned by effective partnerships and cooperation amongst a wide variety of multi-disciplinary stakeholders in the public and private sectors and end-user communities. Appropriate business models must be agreed to support these partnerships at an early stage. The success of a NSDI depends upon delivering products and services that are acceptable and desired by end users (within the government and the private sector, and also citizens). It is essential that all users are involved when defining (user) requirements and testing the associated products and services. NSDI policy must be flexible to address rapidly changing user needs and adapt to changing technologies. NSDI Steering Groups (with end-user representation) should be formed to formulate appropriate policy and institutional frameworks and facilitate multi-stakeholder cooperation. However, complete policy and institutional frameworks need not be in place before implementation of a NSDI can begin. Roles and responsibilities among stakeholders must be clarified at an early stage, including the lead role – this should be an initial activity of a NSDI Steering Group [6].
(5) Adopting common standards and data models: ISO http://www.iso.org/ and the Open GIS Consortium http://www.opengis.org/ produce data and interoperability standards that should be adopted by NSDI stakeholders (see later). To be able to integrate and share data we need to understand and resolve different semantics in data. All NSDI datasets from different sources should adopt the same overarching philosophy and same/compatible data models to achieve multi-purpose data integration, both vertically and horizontally (within organisations, and across organisations and different administrative levels) [6].
(6) A combined top-down and bottom-up incremental implementation approach: It is recommended that a top down approach is combined with a pragmatic bottom up approach. A mature NSDI can only be achieved through simpler and smaller solutions that start with realistic and clear short-term objectives, and grow incrementally through political and market needs. Short-term bottom up projects will provide valuable experience that can feed into the formulation of NSDI policy and strategy. By creating "proof of concept and benefits applications", these projects can be also used to gain and sustain political support, and convince further funding of NSDI [6].
(7) Do not just focus on data; develop applications: Varied applications and services through a project-oriented approach will bring reality to the NSDI. An overemphasis on data acquisition, without a market-linked application, will not provide any momentum for further development. Visualisation, modelling and analysing activities will be the focus of value-added services in the coming years [6].
EIS-AFRICA – Gavin et al, 2002
EIS-AFRICA is a network for the cooperative management of environmental information in Africa. It is a pan-African, non-profit, non-governmental organisation, registered in South Africa, and born out of the World Bank's Environmental Information Systems in Sub-Saharan Africa programme (EIS-SSA). Building infrastructure for geo-information use is becoming as important to African countries as the building of roads and telecommunication networks. As with the investment in other basic infrastructures, investing in a Spatial Data Infrastructure (SDI) underpins the provision of many essential services. In a recent EIS-AFRICA position paper published in 2002, Gavin et al describe the following SDI components [9]:
(1) Up-to-date core digital geo-datasets: A country's ability to use geo-information effectively depends on the existence and proper investment into the provision and maintenance of up-to-date core digital geo-datasets, e.g., locations of river networks, roads, land cover, administrative boundaries, and populated places. The existence of these commonly used datasets facilitates the use of other geodata, such as demographic, socio-economic, epidemiological, environmental, and water quality data, which must be also available, accessible and up-to-date [9].
(2) Standards: Geodata must adhere to accepted standards to enable the unambiguous interpretation, integration, and comparison of related datasets from different sources. Stakeholders should work together locally and with international bodies to develop/adopt standards for geodata collection and documentation [9]. (Adopting international standards will also ensure that future collaboration is possible at regional and global levels.)
(3) Metadata: The accessibility of proper documentation (metadata) about existing geodata is also extremely important. The mere existence of geodata is not sufficient. Information about datasets is needed for the purposes of generating awareness of the data's existence among potential users, and for helping these users assess the reliability and/or relevance of available datasets for selected uses. This in turn requires that data providers publicise their metadata to the public and targeted users in a suitable catalogue, directory or clearinghouse to enable searching and retrieving documentation on available datasets. Metadata, too, should be standardised [9] (Figure 3).
(4) Policies and practices actively promoting the exchange and reuse of information, and greater public access to geodata are also needed. Policies should start by removing barriers to access, e.g., excessive costs to use an information product or lack of clarity concerning copyright. The absence of a policy concerning data access and sharing can often be as handicapping as the presence of an inhibiting policy. Existing policies need to be revised and new policies developed as necessary. Broad-based national committees of data producers, users, and other stakeholders should be created to oversee the development of geo-information policy and standards and ensure compliance [9].
(5) Appropriate human and other resources: Sufficient human and technical resources are required to collect, manipulate, interpret, and distribute geo-information. Without appropriate human resources, geo-information will remain unexploited. Sufficient financial resources must be available to invest in training people. Retaining technical expertise should be also a priority within institutions using geo-information. Adequate investments must be also made in technologies for digital data management and storage, and in improving communications infrastructure [9].
(6) Coordination between various stakeholders: The cost-effective development of SDI requires the coordinated harnessing of resources and expertise in many different government agencies, the private sector, universities, non-governmental organisations, and regional and international bodies. Collaborative frameworks (partnerships) are required to prevent duplication of effort (which would occur if various institutions pursue singular, uncoordinated agendas), and ensure that all captured and generated data and information conform to common standards, so that they can be easily combined and effectively analysed. Such frameworks should specify which organisations are gathering which kinds of information, how the information will be captured, and arrangements for data sharing [9].
(7) Raising awareness: Establishing a formal national programme can help heighten awareness and generate support. Policymakers need to be engaged in the process through awareness training, briefings, and policy dialogue. Organising conferences on geo-information, conducting studies on implementing SDI, and supporting professional development are all important ingredients [9].
WHO-AFRO – Briggs, 2000
In a 140-page report commissioned by the World Health Organisation – Regional office for Africa (WHO-AFRO), Briggs proposes a programme of action to advance environmental health hazard mapping in Africa that includes the following elements [54]:
(1) Data modelling: This involves developing and adapting health indicators according to specific local user needs [54] (see "Common semantics, data models and health indicators" below).
(2) Awareness-raising campaigns: These should be based on real-world examples and demonstrations of environmental health hazard mapping, and aimed at key decision makers in concerned organisations [54].
(3) Joint working in partnerships: This involves adoption of a multi-sectoral approach to environmental health hazard mapping, encouragement and support for the sharing of experience and facilities, and support for training and long-term capacity-building, e.g., by building up expert/national networks (partnerships) and organising workshops, seminars and study visits [54].
(4) An incremental approach aimed at making the best possible use of available data and expertise to address local needs [54].
Richards et al, 1999
In a paper published in 1999 in Public Health Reports, Richards et al stress the following points [7]:
(1) There is a need for intelligent tools specifically designed for public health, and seamlessly integrated into routine workflows [7].
(2) Training, its costs, and time needed for it should be all considered: Training should cover epidemiological methods to ensure appropriate use of GIS technology in public health. The cost of training programs offered by commercial GIS vendors and solution providers can be a financial burden, and GIS training programmes specifically designed for public health professionals are still relatively limited. The time required for training can be also a challenge for organisations in which demands on personnel are already high. Training materials should be offered in a variety of formats to facilitate distance learning (e.g., CD-ROMs and self-instruction Web-based courses). Public health professional specialties/bodies need to recognise continuing education credit for individuals participating in GIS software training [7].
(3) Current and accurate base data must be made available [7].
(4) Software and data acquisition, maintenance and upgrade costs should be secured [7]. (In the case of the UK, reaching an agreement to enable the whole NHS for example to access Ordnance Survey (OS) geographic information would be economically much better than asking each NHS organisation to strike a separate deal with OS. It is noteworthy that the business case outlining a proposed pilot agreement between OS and the NHS was approved by the NHSIA board in September 2003, and it now remains for the NHSIA and OS to determine the scope and funding of the pilot agreement, which is expected soon.)
(5) Confidentiality issues must be addressed [7] (see "Individual privacy, national security, and data confidentiality issues" below).
(6) Standards must be adopted and partnerships promoted at all levels [7].
(7) An incremental approach is needed: Longer-term solutions usually require a series of small successes, carefully built upon in incremental fashion over time [7].
In fact, much of the wider vision of a national public health spatial data infrastructure can be gradually and incrementally achieved through disparately funded and managed short-term projects, as long as we can ensure that these short-term projects make a useful and lasting contribution towards this wider vision.
Davenhall, 2002
In a recent ArcUser Online article, William F. Davenhall, ESRI Health and Human Service Solutions Manager http://www.esri.com/industries/health/index.html, describes an ambitious vision of a Community Health Surveillance System (CHSS – see later) spanning wide geographic areas, and mentions the following success factors [2]:
(1) Community data sharing must be systematic and regular [2].
(2) Adopting data standards and sharing agreements will ensure a CHSS works effectively in real time [2].
(3) Data have to be collected uniformly and include specifications for update frequency and allowed dissemination in different emergency and non-emergency situations, and for purposes other than those for which they were originally collected [2].
(4) CHSS also requires robust, epidemiologically sound analytical software, well-trained staff, full system redundancy/fault tolerance, standardised database replication and off-site backups, among other ingredients for success [2].
RODS – Tsui et al, 2003 and Wagner et al, 2003
RODS, the Real-time Outbreak and Disease Surveillance system, is a computerised public health surveillance system for early detection of disease outbreaks, including those caused by bioterrorism. RODS processes clinical encounter data from participating hospitals and sales data of over-the-counter (OTC) healthcare products from participating stores and pharmacies. The system was used during the 2002 Winter Olympics and currently operates in two US states – Pennsylvania and Utah (more details about RODS are presented below under "Proactive, real-time, GIS-enabled health and environmental surveillance services") [55, 56]. RODS researchers identified the following key elements for success:
(1) Data-sharing agreements: These were executed in the case of RODS with every participating health system and OTC healthcare product retailer, and addressed confidentiality and other concerns. Data sharing agreements should allow redistribution of data to any public health authority and permit data to be used in research [55, 56].
(2) National data utilities/services: Data sources that are amenable to a "national" approach should be formed into industry-based data utilities (services independent of any particular user interface) [56].
(3) A deep understanding of data and industry: Wagner et al found that a key element for success included the deep understanding of the OTC healthcare products industry provided to them by an industry expert [56].
(4) Official/governmental support: Equally key was a personal invitation, sent to the CEO of relevant corporations, for participation (sharing of otherwise proprietary data), authored by a highly respected government or public health official [56].
(5) Development of an interdisciplinary team with expertise in medical informatics, computer science, law, and engineering [56].
Morris and Henton, 2003
Morris and Henton list several key factors for progress and success of an environmental health surveillance system for Scotland (see "Large-scale environmental surveillance projects in the UK" below), including ensuring joint ownership of the project, successful partnerships and shared commitment among the disparate agencies that are involved in the project, adopting a phased approach, reaching a consensus on inputs and outputs, and having realistic expectations [57].
Higgs et al, 2003
In a recent study published in 2003 (see "Most recent published survey of levels of GIS use in the NHS" above), Higgs et al point to the following ingredients [35]:
(1) Establishing networks of GIS users from both the NHS and local authorities at local and higher levels to encourage more joined-up working, share expertise and experiences, as well as establish contacts and trust, and raise the awareness of the types of data that are held by different organisations [35].
(2) Raising awareness: A substantial proportion of respondents in Higgs et al's study from health authorities (90%) and trusts (74%) stated that a dedicated Web site giving advice on GIS matters for NHS organisations would be helpful in providing a forum or virtual network on the Web for the exchange of information and experiences, as well as in promoting and disseminating good practice examples of GIS use in healthcare, and identifying other suitable Web resources. Successful examples of collaborative projects between NHS and local authorities that have involved the use of GIS should be also highlighted. Other factors considered important in raising awareness include an annual GIS conference aimed at professionals from NHS organisations and local authorities, and the provision of seminars, workshops, and road shows. According to Higgs et al, such "raising awareness" activities are vital given the need to build business cases for the development of GIS within NHS organisations and to show the capabilities and "business benefits" of GIS to directors [35].
Croner, 2003
Croner points to several elements and tasks required to develop a nation's public health geospatial infrastructure and realise comprehensive Internet geospatial readiness in public health; these elements include [58]:
(1) Vision and leadership at the highest levels (e.g., departments of health): This is necessary to ensure national public health geospatial mobilisation and readiness. A suitable policy and funding must be established, including the provision of support to organisations lacking the resources to join in a common, coherent national initiative [58].
(2) Assessing current state of geospatial readiness to respond to normal and emergency community health needs, and identifying beacon sites as resources for guidance and other forms of assistance to those agencies and departments not yet or in early formative stages of involvement [58].
(3) Technology introduction; training and education programmes: This implies the provision of necessary budgets for these activities [58].
(4) Promoting collaboration with and between all sectors to share data, applications and expertise [58] (see "On partnerships" below).
(5) Moving to the Web and building all necessary critical connectivity/geospatial infrastructure that should not be independently recreated by all [58].
(6) Geospatial readiness also requires that geospatial data holdings be identified, described and made Web-searchable in a standardised manner forming a truly uniform, integrated, navigable and shareable national inventory of existing public health geospatial data resources. Best standards, rules, designs, and practices must be created/agreed upon and published (covering spatial metadata, geocoding, accessibility for visually and manually impaired data users, and data access restrictions among other things) for uniform Internet-enabled GIS services, in which standards, definitions, and look-and-feel of the data and Web-based technology are the same throughout the nation [58].
Case study – Buckeridge et al, 2002
Community/university research collaboration is a relatively new research paradigm that has recently become a major strategic theme of health funding agencies in Canada and elsewhere. Buckeridge and his colleagues present their experience in conducting a collaborative community/university respiratory health GIS research project in Canada. Their experience is a good example of research into the kind of partnerships (community/academia) that are also required to realise the envisaged national spatial health information infrastructure in the UK. Their specific project objectives were to: (1) develop and iteratively refine via active community/university collaboration a GIS for ready access to routinely collected health data (focusing on respiratory health), and to study logistical, conceptual and technical problems encountered during system development; and (2) to conduct a qualitative ethnographic study to document and analyse issues that can emerge in the process of community/university research collaboration [8].
Buckeridge et al adopted user-centred and rapid prototyping/iterative design methods. User feedback was gathered via questionnaires and discussions [8].
In an initial step, university and community partners jointly developed a conceptual data model (or ontology) to facilitate data integration and enable participants from different backgrounds to share a common vocabulary and dialogue (see also "Common semantics, data models and health indicators" below). The data model described by Buckeridge et al was based on a "determinants of health" model that explicitly acknowledges the influence of non-medical determinants (e.g., income, occupation, and environment) on population health status, and qualitatively relates these determinants to health outcomes [8]. Such models have been used successfully as the basis for other population health information system approaches, e.g., POPULIS (see "Caring for population demographics and socio-economic factors" below) [59, 60].
The next steps involved identifying, evaluating, and acquiring potentially relevant datasets based on data needs identified from the data model. Data describing determinants of respiratory health included census, cartographic files, land use, traffic volume, air monitoring and emissions, and consumer spending patterns. Data describing outcomes of respiratory health included hospital separations (similar to Hospital Episode Statistics in England – see http://www.doh.gov.uk/hes/), ambulatory physician visits and procedures, and prescription drug sales. Once data were acquired, they were integrated into the GIS using the developed data model and the spatial unit of the enumeration area, a Canadian census sampling area with a median population of 400 in the study area (Southeast Toronto) to relate datasets to one another (in this case the enumeration area acted as a common high resolution geographical unit for linkage – the data model facilitated data integration around the common geographical unit of the enumeration area). The limited and inconsistent descriptions (metadata) of existing data were partially addressed by adopting a "standard" ad hoc metadata model within the system to represent available descriptions in an organised manner [8].
Buckeridge et al highlighted some important issues they have encountered during the development of their system, and which are also generalizable to other community health information systems [8]:
(1) Early and continued involvement of users in system development is important, if not essential. However, maintaining and coordinating consistent user involvement, especially across a number of organisations, is a difficult and resource-intensive task that should be well planned [8].
(2) All relevant system stakeholders should be involved in the development of a data model or ontology to facilitate data selection and integration, and support a common understanding of data by people [8].
(3) Challenges met while bringing disparate data together included lack of directories or catalogues for locating existing data, generally poor descriptions (metadata) for existing data, non-standard encoding of data, and concern over data "ownership" and/or privacy issues. Web accessible directories of data would greatly facilitate identifying data sources. In addition, action should be taken to improve data documentation (metadata), develop data standards, and enhance compliance with existing standards. Many data holders did not have an established protocol for access to their data, or a clearly identified person with the authority to release data. In the absence of these, data holders were reluctant to release data, and acquisition of some data required a considerable amount of negotiation and follow-up. The difficulties encountered in acquiring data indicate that privacy concerns present a serious barrier to system development. A wide range of stakeholders in society must collectively address the issues of privacy and stewardship of population health data [8].
(4) The potential for data display to be misleading and for misinterpretation of data was addressed by providing users with descriptions (metadata) of datasets and constraining map types by data types. Methods to allow only valid visualisation and analysis of data from a variety of sources across space and time must be developed and evaluated [8].
(5) Problems from an interface design perspective included the need to constantly change the interface to accommodate a refined understanding of user needs and changes in the underlying data structure (because of the iterative nature of the development process). Standard software engineering methods, such as design models and modular programming, helped to address these problems [8].
(6) Users of community health information systems will nearly always have variable skills and organisational contexts. The range of user skills and knowledge was partially addressed by developing a graphical user interface with multiple levels, each supporting a different user level. Another approach would be to use artificial intelligence, as employed in decision support systems, to facilitate user control of information visualisation [8].
(7) Community/university collaboration issues: As organisations and individuals are brought together to form research partnerships, differences in their organisational/institutional cultures become apparent. Community partners tend to see potential conflicts between service provision and research demands, while university partners tend to see the collaboration as posing threats to research rigor, control over the research process and constraints on publication opportunities [8].
Leadership style, vision, commitment to the idea of community/university collaboration, at least small amounts of "seed funding", and the willingness to learn from failures all appear to be significant features in successful collaborations. Issues that shaped and influenced the collaborative process and partnership that developed during the course of Buckeridge et al's project revolved around three major themes: separate cultures (differences in expectations, values, outcomes, reward systems and work styles), time, and uncertainty/ambiguity. These issues are neither positive nor negative. Rather, they represent challenges which, depending on how they are met, have the potential to shape the collaborative process in either positive or negative ways [8].
Sub-themes within the "separate cultures" category included issues around language, trust, and power. Language differences (different knowledge backgrounds and ways of understanding the world) occurred as frequently between university partners from different academic disciplines as between university and community partners. Trust developed gradually with time, as co-investigators came to recognise the strengths, commitment and knowledge of each other, and as the group worked to make joint decisions and solve conflicts. Issues of power arose from differences in status, resources, skills, and personal commitment to the project [8].
Time was a burden for individuals, but an asset to the collaborative project as a whole, as it supported the development of trust, mutual understanding and effective working relationships [8].
Many co-investigators pointed out that the most difficult aspect of their collaboration was to learn to accept and work with the uncertainty and ambiguity about where the project was going as it developed and unfolded (despite clear project goals and objectives). Nevertheless, uncertainty and ambiguity were found to be essential to the shared positive experience of exploration, debate, and reflection, and also created the space to ask critical questions [8].
Community partners engaged in collaborative research with universities should see themselves as equal partners. This could be achieved in part by making an organisational commitment to research (e.g., supporting staff involved in research and advocating with funding agencies for research resources). On the other hand, universities should foster community/university research partnerships by developing university structures that support such collaboration, and inducing positive changes in the current academic culture, which places more value on individual rather than collaborative research [8].
On partnerships
In the case study presented above, Buckeridge et al stressed the importance of community/university collaboration when developing a community health geo-information service [8]. Public health also needs to be an integral part of a larger structural, multi-agency whole, where government and other relevant agencies at all levels are brought together to build, integrate, leverage through sharing and partnerships, and optimise spatial information, both vertically within and horizontally across organisations, for comprehensive routine as well as emergency planning and response services. Intranet and Internet environments can help facilitating public health spatial data accessibility and integration at local, national and regional levels, and can support a physical and virtual "situation room" for both emergency and day-to-day management of operations for safeguarding the environment and protecting human health [58].
A San Diego Association of Governments report titled "Guidelines for Data Development Partnership Success" is based on many years of GIS partnering experience and cites guidelines that may help other agencies develop successful partnership activities [61].
A good example of successful partnerships is the online GIS service known as "Window to My Environment" (WME – http://www.epa.gov/enviro/wme/), which is offered by the US Environmental Protection Agency (EPA). WME is designed to provide public accessibility to a wide range of federal, state, and local geospatial data about environmental conditions and features in any US location. The data available in the WME application are distributed and reside at their respective agency servers. Thus each participating agency manages its own data and its timeliness, which can be current and even real-time. There is no limit on the number of WME partners. Any agency can participate by adding its own data layer(s) to existing ones. Participants can also create a reciprocal interface on their home server with WME connectivity. Public health databases are not yet included in WME, but there are no specific barriers to inclusion [58].
Common semantics, data models and health indicators
As information systems increase in complexity, models of the relationships between data elements become increasingly important. Data models, more correctly called ontologies, explicitly define how concepts within data sources relate to each other. They are conceptual models that facilitate integration of data by information systems and support a common understanding of data by people [8].
To explain the importance of adopting common semantics when developing health geo-information services that span administrative boundaries, Richards et al provide the example of two neighbouring public health departments that are addressing a common infectious disease problem and would like to join their independently developed GIS maps into a common map for both jurisdictions. Doing so requires consensus on a range of technical, GIS-related issues and public health-related issues. The latter for example include case definitions, sources for case reports, and the time period for the study [7].
In his report commissioned by WHO-AFRO, Briggs classifies environmental health hazards into eight categories: land/climate-related hazards, atmospheric hazards (outdoor air pollution), water-related hazards, food-borne hazards, vector-borne hazards, domestic hazards, occupational hazards, and infrastructural hazards. Briggs' report stresses the importance of indicators as essential tools for environmental health hazard mapping. Indicators provide the means of describing, monitoring, managing, and comparing hazards in terms that are relevant to information users. Three types of indicator are proposed [54]:
(1) Hazard indicators: define the hazard in terms of its extent, magnitude, duration, frequency or probability of occurrence, without reference either to the exposed population or health effect;
(2) Risk indicators: describe the hazard in terms of the number or percentage of people exposed; and
(3) Health impact indicators: describe the hazard in terms of the actual health outcome, measured as either morbidity or mortality [54].
Which type of indicator is most appropriate is likely to depend on the specific question being asked. Natural hazards, for example, can be readily described by hazard indicators, while hazards like suicides and domestic violence are more easily described by health impact indicators [54].
Unfortunately, there are no one-size-fits-all indicators that suit all users. Indicators need to be customised according to specific and local user circumstances and needs, the specific hazard of interest, the type of question being asked, the scale of analysis, and data availability and quality. For this reason, the emphasis in Briggs report was not on providing a core or generic set of environmental health hazard indicators, but on providing indicator profiles that show, for a sample of indicators, how they can be constructed/customised and used [54].
An indicator profile specifies the environmental health hazard(s) to which the indicator relates, the indicator's rationale and role, any alternative methods and definitions, any related indicator sets, sources of further information, and a listing of involved agencies. Each indicator must be clearly defined alongside all underlying terms and concepts involved in describing and constructing it. Data needed to construct an indicator must be identified and assessed regarding availability, quality, and characteristics in terms of the indicator in question. The ways in which the indicator is computed (e.g., a mathematical formula) and units of measurements used in presenting it (e.g., percentage or number per thousand head of population) must be also specified. The area across which the indicator can be used (scale of application or aggregation level) must be determined. Finally, the ways in which the indicator may be interpreted in relation to the hazard(s) it covers must be described. This includes determining what inferences can be made from apparent trends or patterns in the indicator, and any constraints on the interpretation of the indicator, due for example to data limitations or complexities in the relationships implied by the indicator [54, 62].
Indicators are not limited to environmental health hazard mapping. In 2000, the US National Association of County and City Health Officials – NACCHO has produced a comprehensive list of core and extended health indicators as part of their Community Health Status Assessment (CHSA) Toolbox. CHSA collects data under eleven indicator categories (formatted in bold below) to answer three main questions [63]:
(1) Who are we and what do we bring to the table? (demographic characteristics; socio-economic characteristics; and health resource availability)
(2) What are the strengths and risks in our community that contribute to health? (quality of life; behavioural risk factors, e.g., substance abuse, lifestyle, and screening programmes; and environmental health indicators, e.g., air and water quality, workplace hazards, food safety, etc.)
(3) What is our health status? (social and mental health; maternal and child health; death, illness and injury; infectious disease; and sentinel events)
CHSA also calls for establishing a system to monitor these indicators over time, e.g., to detect sentinel events. The latter are cases of unnecessary disease, disability, or untimely death that could be avoided if appropriate and timely preventive services or medical care were provided. These include vaccine-preventable illness, avoidable hospitalisations (those patients admitted to the hospital in advanced stages of disease which potentially could have been detected or treated earlier), late stage cancer diagnosis, and unexpected syndromes or infections. Sentinel events may alert the community to health system problems such as inadequate vaccine coverage or lack of primary care and/or screening, a bioterrorist event, or the introduction of globally transmitted infections [63].
The CDC National Public Health Performance Standards Programme (NPHPSP – http://www.phppo.cdc.gov/nphpsp/index.asp) is a more current partnership effort to improve the practice of public health, the performance of public health systems, and the infrastructure supporting public health actions in the US. To achieve its goals, NPHPSP developed performance standards and matching assessment instruments for state and local public health systems, and for public health governing bodies. (NACCHO developed and tested the Local Public Health System Performance Assessment Instrument for NPHPSP – http://www.phppo.cdc.gov/nphpsp/Documents/Local_v_1_OMB_0920-0555.pdf) NPHPSP describes ten "Essential Public Health Services" that provide the fundamental framework for NPHPSP instruments by defining public health activities that should be undertaken in all communities http://www.phppo.cdc.gov/nphpsp/10EssentialPHServices.asp.
The Health Data Model (HDM) is a conceptually related collaborative project to develop a generic data model for health applications, using ESRI software. ESRI staff and researchers at the University of California at Santa Barbara (UCSB) are leading this consortium. The user members represent public health planning and research organisations, public health consulting firms, and GIS coordinators from medical centres around the US. The current phase of this work has assigned top priority to service site selection, emergency response, facility emergency response, campus facility management, regional environmental health, and disease surveillance. The outcome will be a basic data model with three components [64]:
(1) A conceptual object model of health application features, building relationships between health application geographies and users;
(2) UML (Unified Modelling Language) code which is easily transformed into an ESRI geo-database. The average user can immediately begin to populate the geo-database rather than to design it, and the inherent commonality between users and sites adopting the resultant geo-database(s) should facilitate exchange of data; and
(3) Documentation in the form of a book on GIS Health Applications [64].
In October 2003, this author contacted Dr. Mike Goodchild, HDM project leader, and asked him how does/will their conceptual object model relate/link to health indicators, e.g., those produced by NACCHO as part of their Community Health Status Assessment (CHSA) Toolbox, and those produced by WHO-AFRO as part of their consultation on environmental health hazard mapping for Africa. Goodchild replied that he thinks they should include health indicators, and that they will start investigating NACCHO and WHO-AFRO's indicators to see if they can come up with a suitable way of including them in their HDM (Mike Goodchild, HDM project leader at the University of California at Santa Barbara, personal communication – October 2003).
Caring for population demographics and socio-economic factors
More beds, more physicians and nurses, and more procedures do not always translate into better community health. Departing from this premise, the Manitoba Centre for Health Policy (MCHP – http://www.umanitoba.ca/centres/mchp/) has developed POPULIS, a POPULation health Information System, to answer questions like: "What factors – beyond access to medical care – determine the health of populations?" and "Would healthcare money have a greater impact on health if some were spent in other areas such as education, housing, nutrition, job creation and training?" [59, 65]
POPULIS reports on the health of a population, and the relationship between health and the use of healthcare services. It also relates these to socio-economic factors like education, unemployment, housing, and single parent households. These factors are key components of the Socio-Economic Risk Index (SERI), a measure developed by MCHP. The higher a region's score on this index, the higher the death rate is among its residents – death rate being a key and rather obvious indicator of a population's health status [59, 65].
POPULIS has been conceived to help policy makers avoid a "knee-jerk" reaction to one set of negative indicators or to pressures generated by one-sided media stories. It builds on data that are available but somewhat underused in today's healthcare systems, e.g., vital statistics, census, and healthcare service utilisation data, to provide healthcare decision makers with the continuously updated and localised detail essential for planning and managing a more effective and efficient healthcare system [59, 65].
However, POPULIS has missed a lot by not being a GIS-enabled system. The original POPULIS (based on Statistical Analysis System (SAS), a very popular statistical package) proved to be hard to maintain and not scalable, and a more recent publication by Roos in 1999 [65] has moved from describing POPULIS as a SAS front-end or software program to presenting it as a framework or approach of concepts, methods, procedures and databases. GIS are excellent integrative, multidisciplinary knowledge management tools capable of linking and spatio-temporally analysing disparate, continuously changing datasets, and as such could have helped POPULIS achieve its vision in far much better ways.
Demographic shifts, e.g., the forecast rise in the number of elderly people in developed countries over the next decades, also have their impacts on healthcare services and expenditure, and must be carefully considered and modelled [66].
Integration and interoperability issues: GML and other technologies
Aggregating disparate data sources to a common geography has always been a strength of GIS. The challenge of nationwide, regional and global coordinated efforts in case of natural or man-made disasters, however, calls for aggregating the aggregates on short notice. For instance, if a disaster hits at the border of two cities or two EU countries, will their two information silos be able to work together, sharing and combining data instantaneously? Today, many systems are based on closed or proprietary interfaces and formats, and are difficult to integrate with brands and platforms in use by other organisations. Embracing open standards is the key to interoperability [67].
Interoperability allows spatial data silos distributed anywhere on the Web to be searched, located, retrieved and compiled, either by a Web GIS service provider or at an individual's desktop. The OpenGIS Consortium (OGC – http://www.opengis.org/) develops specifications to accommodate any operational differences and allow disparate Web GIS clients and desktop users to fully integrate Web accessible spatial data resources [58]. OGC's ultimate goal is to enable the "spatial Web" with products that plug and play across different processing platforms, vendor brands, networks, and programming languages [67].
Founded in 1994, OGC is an international industry consortium of 258 companies, government agencies and universities participating in a consensus process to develop publicly available geoprocessing specifications.
Geography Markup Language (GML) is the base language developed by OGC. GML is becoming the world standard for eXtensible Markup Language (XML) encoding of geographic features and geoprocessing service requests. The relevance of Web Services to spatial integration of disparate data sources is also obvious. XML encoding of geodata, using GML and Web Services http://www.opengis.org/initiatives/?iid=7 specifications and recommendations, makes it possible to display, overlay, and analyse geodata on any Web browser, even if the browser obtains views of different map layers from different remote map servers. For example, layering Web Services from two politically/administratively separate but geographically contiguous cities or regions would allow the integration of their independent data silos to answer questions about an emergency involving both (provided that issues of common semantics, data models and case definitions have been resolved) [58, 67, 68].
XML is also used for encoding spatial metadata (metadata are essential to aid the discovery of spatial data in a distributed environment) [58]. Standards also exist for metadata (see "Existing SDIs and SDI initiatives worldwide" below).
One of the keys to GML deployment is a companion specification, the OGC Web Feature Service (WFS). To get GML data, users query a Web server with an OGC Web Service Interface, collectively known as a Web Feature Server. The OGC interface enables standardised access to a feature store and enables users to add, update or retrieve GML data locally or across the Internet. Any data store can be used – users no longer need to care whether the underlying store is from ESRI, Oracle or IBM [69].
GML brings an alternative to expensive proprietary software, and an increasing number of companies have already joined the GML bandwagon. Ordnance Survey (OS), the UK's national mapping agency, has adopted GML as the only geospatial data format for its MasterMap of Great Britain http://www.ordnancesurvey.co.uk/oswebsite/products/osmastermap/. OS MasterMap boasts about 400 million geographic features in GML format. Each feature within OS MasterMap is assigned a unique 16-digit "topographic identifier" (TOID) that can be used by OS or its customers to reference any given feature in the database. This makes it much easier for users to associate other information to the spatial feature, to refer unambiguously to a particular feature, and, therefore, to share spatial information with other users [24, 69].
By separating presentation from content, powerful maps can be made that offer enhanced functionality for users. GML contains map "content" only (e.g., where features are, their geometry, type and attributes), but it does not provide any information about how that map data should be displayed. This is actually a benefit because different "stylesheets" can be applied to the geographic data to make it appear however the user wishes [70, 71]. By combining a selected map stylesheet with a WFS query, users are presented with a fully interactive and editable vector map that can be viewed in any Web browser [69] (Figure 4).
Another key feature of GML is its ability to be "self describing" through the use of XML schema. Thanks to this feature, tools have been developed to model and load proprietary databases, e.g., Oracle Spatial databases, with geographic data supplied in GML formats [69].
GML 2 lacked some important features like metadata support and several other geographic information prerequisites [69]. The latest GML version, GML 3.0, was approved by OGC in 2003 and addresses the limitations of GML 2. GML 3 is backwards compatible with GML 2. New additions in GML 3 include support for metadata, units of measure, complex geometries, spatial and temporal reference systems (time information is essential in tracking applications like monitoring ambulance locations and in exploring the movement and growth of natural disasters), topology (the relationships between features, e.g., for use by routing applications popular in location-based services), gridded data, and default styles for feature and coverage visualisation. The new release is modular, allowing users to pick out only the schemas or schema components that apply to their work, which simplifies and minimises the size of implementations [72, 73].
However, it should be noted that GML and Web Services are only part of the solution to integration and interoperability. Other health-related standards like HL7 (Health Level 7 – http://www.hl7.org/) and clinical coding schemes like SNOMED (Systematised Nomenclature of Medicine) – http://www.snomed.org/, LOINC (Logical Observation Identifiers Names and Codes – http://www.loinc.org/, and ICD (International Classification of Diseases – http://www.cdc.gov/nchs/icd9.htm) are also equally important. For example, RODS, the Real-time Outbreak and Disease Surveillance system, uses the HL7 message protocol to receive clinical encounter data from participating hospitals in real time [55], while the US Department of Defense Global Emerging Infections System is basing its seven syndromic surveillance categories on groups of related ICD codes http://www.geis.ha.osd.mil/GEIS/SurveillanceActivities/ESSENCE/ICD9May02.xls (see also "Proactive, real-time, GIS-enabled health and environmental surveillance services" below).
Lowe also stresses the fact that technologies like XML and SOAP (Simple Object Access Protocol – involved in Web Services) are only part of the integration issue, and points to integrating geoprocessing and databases at other levels, and the related issues of optimisers and federated databases. Industry professionals now manage very large spatial databases. Often, client programs will pull a copy of the database spatial data into their own environment to process it instead of asking the database to do the processing. If the client program request happens to involve a very large database table, the copy-and-exchange process may drag on endlessly or even fail because of overload. This same potential problem awaits users of multiple feature-streaming map services [67].
Alternatively, if the spatial processing remains within the database environment, an optimiser program common to all professional databases will internally organise a response to the query that returns results in the fastest possible time. A query from the larger integrated system goes into the database and only the results come out, taking advantage of the database optimiser, reducing processing loads on the client that generated the question, and also reducing transmission loads [67].
Each database vendor's optimiser works best within its own specific database environment. A potential problem arises in case one wants to optimise the use of multiple databases when a query joins data from several different databases (from different vendors) at the same time. In the same spirit as the Web Services model, agencies can keep their existing heterogeneous database technology, and use a federated database technology to unite the mix. IBM, for example, offers a federated database technology that simulates views of any other database tables in IBM DB2 database, offering a master view of all data holdings. Furthermore, the federated technology's optimiser is aware of the available processing resources in other databases and organises query responses appropriately [67, 74].
Grid-based real-time distributed collaborative geoprocessing could also form the basis of a next-generation solution to data and computationally intensive geoprocessing applications that are extremely difficult to execute on conventional systems and networks [75]. Grid computing allows non-collocated computers to work on and process data together, not just communicate and exchange data between each other. It is already a reality with many ongoing projects (see for example http://mapcenter.in2p3.fr/datagrid-s/).
Automated geocoding
Automated (even "on-the-fly") geocoding is one of the most essential spatial infrastructure-building tasks [58]. Higgs and Richards mention how different geocoding methods (used to geo-reference UK postcodes) have different levels of accuracy, which could affect study results [3]. Researchers need to determine if the level of error caused by a chosen method of geocoding may affect the results of their particular project [76].
Also of relevance in this context are the North American Association of Central Cancer Registries GIS Handbook http://www.naaccr.org/Standards/GIS Handbook PDF 6-3-03.pdf, which discusses (in its second section) the importance of address geocoding for the spatial analysis of cancer data, and ProADDRESS, an ArcGIS extension that has been made available by ESRI UK for geocoding UK addresses and/or postcodes http://www.esriuk.com/products/ProAddress_products.asp?pid=55.
Automated conflation of geospatial databases
Conflation is the ability to precisely geo-reference variant data layers compiled into one view. This can be crucial in emergency situations such as terrorist and bioterrorist attacks. The need currently exists for the development of automated conflation techniques transparent to the user. Croner gives the example of New York City where lack of automated conflation methods following the fall 2001 World Trade Centre attack resulted in time-consuming problems for emergency response teams. New York City is now building automated conflation capability by modifying all city planning spatial databases to include standardised "hooks" for matching and seamless linkage [58].
Adequate telecommunications infrastructure and bandwidth for spatial data transmission
For public health, a variety of rapid developing emergency-related events, including floods, fires, chemical spills and earthquakes, necessitate timely Web delivery of large spatial databases for responsive disaster intervention and control. Bandwidth is not only a problem of developing countries, but developed ones as well. Again, in the emergency response to the fall 2001 terrorist attack, lack of bandwidth in some areas of New York City resulted in delays in providing processed and urgently needed data for the Emergency Mapping and Data Centre (EMDC). Because of low bandwidth Internet connections, large data files had to be written to CD-ROM and driven by state Police twice daily for delivery to the agencies that needed them. Bandwidth is a key component of the transmission process of spatial data and is rapidly increasing in developed countries, promising improved spatial data transmission speeds in the near future [58].
Seamless integration into routine workflows of tools that are easy-to-use by mainstream public health practitioners
Richards et al call for GIS technology to be linked with community health planning tools through data entry forms and automated procedures (e.g., automated geocoding for vital statistics data) to help public health practitioners map and plan interventions at community level. GIS software tools are needed that are specifically custom-designed for use in public health, especially by organisations with limited staff and resources. Richards et al anticipate that GIS technology may one day become embedded and so deeply "buried" in public health practice to the extent that it is invisible to workers. Future health GIS applications will "know" which data silos are needed and where they are located. After loading the appropriate data and performing relevant analyses, they will offer alternative courses of action ranging from informing other people in the public health system to issuing health advisories [7].
It is noteworthy that Epi Info Version 3 developed by the CDC in the US already fulfils part of this vision. Epi Info Version 3 has been released as public domain software for Microsoft Windows, and is available free of charge on the Internet for anyone to download http://www.cdc.gov/epiinfo/. The program has some GIS functionality allowing public health practitioners to import, utilise, and display map boundary files and data, but there is still room for further improvements. The ultimate system will be one that is fault-tolerant and capable of analysing and presenting assembled data in ways that facilitate only appropriate interpretations of integrated data. This can be achieved by using some form of user friendly, "intelligent", goal-oriented health GIS wizards (based on robust statistical methods where appropriate), so that only valid results and maps are produced, even when users attempt to select inappropriate settings or datasets for a particular analysis. To maximise their utility, these wizards should also be fully integrated into everyday public health workflows and decision-making process. Such seamless integration would let users focus and spend most of their time on what they want to achieve rather than on learning and overcoming the limitations of the tools they are supposed to use to achieve their goals.
User interface accessibility requirements
In the US, Internet-based health GIS services must ensure Section 508 compliance with the Rehabilitation Act Amendments http://www.usdoj.gov/crt/508/508law.html and http://www.section508.gov/ to make complex graphical and mapping files accessible to visually impaired users [58] (see also http://www.esri.com/software/section508/index.html). The UK/EU equivalents of these accessibility requirements can be consulted online [77, 78].
The Web interactive cancer mortality maps developed by the National Cancer Institute (NCI) and the National Institutes of Health (NIH) in the US are a good example of Section 508-compliant GIS services http://www3.cancer.gov/atlasplus/index.html. These maps offer users choices about type of cancer, age, race, sex, geography (e.g., state or county), and selection of class intervals, colour shading and scaling. Charts and graphs associated with the maps translate graphical data into a comparison form accessible by screen readers and are thus compliant with Section 508 for those with visual or manual impairment [58] (Figure 5).
Also of relevance in this context is Cynthia Brewer's ColorBrewer http://www.personal.psu.edu/faculty/c/a/cab38/ColorBrewerBeta.html, a free-to-use online tool available from Pennsylvania State University Web site and designed to help people select good colour schemes for maps and other graphics.
(For other examples of interactive Web maps of health conditions, the reader is referred to CDC's Oral Health Maps http://apps.nccd.cdc.gov/gis/doh/, Heart Disease and Stroke Maps http://www.cdc.gov/cvh/maps/statemaps.htm, and Atlas of Reproductive Health http://www.cdc.gov/reproductivehealth/GISAtlas/.)
Adequate protection measures against cyber terrorism
As the value of our information and computing infrastructure increases so to does the value of disruption. Critical information infrastructures are potentially vulnerable to cyber terrorist attacks. A cyber terrorist attack could be also used in support of a physical attack to cause further confusion and possible delays in proper response with greater losses. Securing any spatial health information infrastructure we build against such attacks is thus extremely important. Kevin Coleman suggests several measures that can be taken for thwarting cyber terrorism; interested readers are urged to refer to his article [79].
