CORD-19:06559dd625491d6474ee88f08c12fc17c1830995 JSONTXT 9 Projects

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T1 411-556 Epistemic_statement denotes From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving.
T2 958-1067 Epistemic_statement denotes Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS).
T3 1068-1717 Epistemic_statement denotes This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide.
T4 1910-2059 Epistemic_statement denotes The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels.
T5 2964-3021 Epistemic_statement denotes Interactions within this triad can also change with time.
T6 3022-3371 Epistemic_statement denotes Today's health planners aim at developing health policy and services that address geographical and social inequalities in health, and therefore should benefit from evidence-based approaches that can be used to investigate spatial aspects of health policy and practice, and evaluate geographical equity (or inequity) in health service provision [3] .
T7 3971-4138 Epistemic_statement denotes Evidence-based approaches can also highlight areas where the evidence may be less than reliable, requiring further assessment before expending large funds and efforts.
T8 4139-4357 Epistemic_statement denotes Ideally, the tools to achieve this goal should be accessible and usable by mainstream practitioners, transparently embedded into routine workflows, and seamlessly incorporated into existing busy work environments [4] .
T9 4496-4633 Epistemic_statement denotes The same can be also said about government data in other countries, including data generated by the health sector in different countries.
T10 4634-4830 Epistemic_statement denotes This locational or spatial reference is a "main key" in the transformation of data into information, and for linking and integrating different datasets covering same and contiguous locations [6] .
T11 4896-5005 Epistemic_statement denotes Use of spatial information opens up the possibility to increase efficiency in the public and private sectors.
T12 6448-6792 Epistemic_statement denotes GIS are potentially powerful resources for community health for many reasons including their ability to integrate data from disparate sources to produce new information, and their inherent visualisation (mapping) functions, which can promote creative problem solving and sound decisions with lasting, positive impacts on people's lives [8, 9] .
T13 6890-7201 Epistemic_statement denotes However, GIS have been usually applied to time-limited, single, isolated aetiological research or surveillance issues processing mainly retrospective data rather than to ongoing, broad efforts and wide-scale applications processing real-time or near-real-time data for health planning, promotion and protection.
T14 7202-7483 Epistemic_statement denotes This may be due to the problems encountered in identifying, acquiring and integrating a wide range of geo-referenced data relevant to community health in order to support decision-making and problem solving in community health planning, service delivery, and health promotion [8] .
T15 8241-8600 Epistemic_statement denotes Spatial information management is based on the idea that data, people, software and hardware interact, and that it is practicable to obtain synergy by coordinating changes and development to help users have a better overview of both simple and complex problems, and give them the possibility to create comprehensible, acceptable solutions and/ or compromises.
T16 8992-9075 Epistemic_statement denotes To conclude this introduction, we indicate how the rest of this paper is organised.
T17 9686-10020 Epistemic_statement denotes This is followed by a section on "Geo-information and real-time GIS infrastructure requirements" in which we review the most important technical and organisational elements that are required for a successful implementation of a national geo-information infrastructure that can also support real-time GIS applications in public health.
T18 10021-10266 Epistemic_statement denotes The section that follows, titled "Problematic issues and solutions", is a direct continuation of the one preceding it, and discusses tricky issues like data confidentiality and data/analysis errors, together with solutions that can address them.
T19 10724-10883 Epistemic_statement denotes Such applications currently involve limited SDI-like arrangements, and would certainly benefit from the development of mature SDIs in their respective regions.
T20 10884-11106 Epistemic_statement denotes The final section titled "Discussion, recommendations and concluding remarks" very briefly reiterates and wraps up the main points made in this paper, and provides some final recommendations and directions for future work.
T21 11690-11832 Epistemic_statement denotes These GIS methods should be coupled with proper spatio-temporal statistical methods to ensure valid analyses and robust conclusions [11, 12] .
T22 11932-12450 Epistemic_statement denotes In GIS, geographic boundaries of study areas can be accessed and modified, data class intervals and symbologies restructured, map layers (variables) vertically overlayed and integrated, new independent map variables added for multivariate spatial statistical analysis, spatial weights computed, spatial autocorrelation on predictor variables assessed, and probability scenarios of mapped variables explored based on modelled changes in regression coefficients over time, with unparalleled computational speed and ease.
T23 12594-12732 Epistemic_statement denotes The mathematical treatment of topographic or surface statistical values can be used as a filter against other variables or other surfaces.
T24 12733-12934 Epistemic_statement denotes A range of statistical techniques have evolved that are well suited to GIS analysis, including density kernel estimation, grid and probability estimation, and kriging (see "Smoothed maps" below) [13] .
T25 12935-13337 Epistemic_statement denotes Rushton suggests that GIS provide the capability to perform two types of spatial analysis that could not be performed without GIS: finding areas of high disease incidence that can be labelled as statistically significant and worthy of further investigation, and examining the spatial relationship between disease incidence and information that is geo-referenced differently from the disease data [14] .
T26 13338-13551 Epistemic_statement denotes Rushton also argues that GIS are useful for exploratory spatial analysis but are less useful for confirmatory analysis [14] , although it is clearly possible to integrate confirmatory statistical methods with GIS.
T27 13552-13868 Epistemic_statement denotes By combining health datasets with other sources, such as census data for small areas, GIS can be used to investigate spatial patterns in health outcomes in relation to socioeconomic characteristics of areas, in identifying gaps in healthcare provision, as well as in monitoring the impacts of changes in policy [3] .
T28 13869-14102 Epistemic_statement denotes GIS point-in-polygon analysis, which overlays points on area features, can be used to attach census data relating to small areas such as enumeration districts (in the UK) to individual point level data such as patient postcodes [3] .
T29 14225-14457 Epistemic_statement denotes By working at the individual patient (point) level, they have demonstrated the potential for GIS to work with spatially disaggregate data to address key concerns of policy makers towards, for example, equity of healthcare provision.
T30 14458-14660 Epistemic_statement denotes Their study also highlighted the importance of maintaining high quality (i.e., up-to-date, complete, accurate, fully postcoded, and one could also add clinicallycoded) health registers and records [3] .
T31 14928-15063 Epistemic_statement denotes Different deprivation indices have different points of strength and weakness, and can yield different results in some studies [3, 15] .
T32 15248-15322 Epistemic_statement denotes Choropleth maps are commonly used to depict the patterns of disease rates.
T33 15323-15480 Epistemic_statement denotes Disease incidence and other spatio-temporal epidemiological events are portrayed on these maps as shaded polygons (each representing an administrative area).
T34 15632-15748 Epistemic_statement denotes Visual communication of disease risk is over-simplified since all values appear evenly distributed within a polygon.
T35 15749-16149 Epistemic_statement denotes Moreover, values among contiguous areas (polygons) in a choropleth map can differ abruptly at adjoining borders, while in reality disease incidence and most other spatio-temporal events and phenomena such as deprivation levels are continuous variables distributed continuously across space and do not change abruptly at arbitrarily defined administrative, census and political boundaries (Figure 1 ).
T36 16150-16266 Epistemic_statement denotes Other limitations of the choropleth design include the visual dominance of larger areas over smaller ones [14, 16] .
T37 16267-16625 Epistemic_statement denotes Yet, despite all these limitations, the choropleth design remains in many cases the method of choice to communicate estimated spatial density of reported disease incidence, being quite easy and straightforward to construct compared to the use of geostatistics like kriging (see "Smoothed maps" below), which requires more complex computational choices [16] .
T38 16626-16865 Epistemic_statement denotes The choropleth map could be considered a filtered map using a non-overlapping, variable-size, spatial filter with filter shapes selected from available political or administrative regions (hence its limitations -see "Smoothed maps" below).
T39 16866-17534 Epistemic_statement denotes Rushton mentions three factors to explain why data is commonly made available for such oddshaped and different sized regions: (1) data for such areas can be easily encoded from the information provided; (2) information is often requested for such areas as people are familiar with them and use them to convey the spatial limits of their interest, and also to enable comparisons between different administrative regions, e.g., regarding success in implementing a particular directive, health promotion programme or other intervention; and (3) aggregating health data to areas is one easy method to reduce the risk of disclosure and protect privacy of individuals [14] .
T40 17694-17865 Epistemic_statement denotes One can display data collected at smaller geographic areas (with fewer individuals) and still maintain the stability of the estimated rates by constructing a smoothed map.
T41 18480-18592 Epistemic_statement denotes A spatial filter can be applied to individual point data, as well as to data aggregated into small census areas.
T42 18868-19041 Epistemic_statement denotes After assigning estimated rates to each grid point, contouring software is used to create isarithmic maps in which regions with a constant range of values can be recognised.
T43 19116-19326 Epistemic_statement denotes Talbot et al propose a modified spatial filter for creating smoothed disease maps, where the spatial filter is defined in terms of constant or near constant population size rather than constant geographic size.
T44 19483-19562 Epistemic_statement denotes Kriging can be also used to produce continuous map surfaces from sample points.
T45 20456-20631 Epistemic_statement denotes Kriged smoothed maps may strengthen our ability to visually communicate event patterns, especially over time (also possibly through the combined use of kriging and animation).
T46 20814-21044 Epistemic_statement denotes Statistically optimal estimates and their standard errors for locations with missing data (unsampled locations) may be derived, and the actual and estimated data represented together as a smoothed surface or raster data structure.
T47 21045-21151 Epistemic_statement denotes Kriging can also take into consideration associative covariates when producing the final smoothed surface.
T48 21152-21307 Epistemic_statement denotes However, the accuracy of kriging results depends on the aggregation level of the data used (e.g., state-level vs. finer county-level data in the US) [16] .
T49 21308-21380 Epistemic_statement denotes Trend surface analysis is another technique for producing smoothed maps.
T50 21381-21533 Epistemic_statement denotes Trend surface maps are commonly used to report the spatial diffusion process of disease epidemics (the movement of epidemics across geographical space).
T51 21534-21751 Epistemic_statement denotes In their GIS-driven Drug Incidence and Prevalence Estimation Program (DIPEP), Field et al used trend surface maps to overcome the drawbacks of administrative boundary choropleth maps (e.g., ward-based maps in the UK).
T52 21752-21879 Epistemic_statement denotes They also used animated sequences of trend surface maps to study the waves of diffusion of problematic drug misuse across time.
T53 21880-22152 Epistemic_statement denotes Animated trend surface maps could be considered as illustrating a more accurate picture of the spatio-temporal characteristics of mapped events and phenomena, when compared to administrative boundary maps, since populations are distributed continuously across space [18] .
T54 22587-22763 Epistemic_statement denotes (ArcGIS 3D Analyst also supports Triangulated Irregular Networks (TINs) and three-dimensional (3D) data visualisation giving users completely new perspectives about their data.
T55 24426-24635 Epistemic_statement denotes This leads to better strategic insight, input for state and government policy and programmes, information for more effectively assigning finite resources and last but not least: more crimes being solved [22] .
T56 24806-25035 Epistemic_statement denotes High-resolution satellite imagery provides timely and detailed digital representations of existing landscapes and land covers, which can be spectrally classified and statistically correlated with disease host and vector habitats.
T57 26223-26428 Epistemic_statement denotes Mobile phones and other digital devices are rapidly gaining location awareness and Web connectivity, promising new spatial technology applications that will yield vast amounts of spatial information [24] .
T58 26550-26870 Epistemic_statement denotes However, according to RSA Security Inc. http://www.rsasecu rity.com/, wireless and mobile telecommunications also pose the following security challenges: more connectivity resulting in more points of vulnerability; information is more easily intercepted; and devices, being more portable, are more easily lost or stolen.
T59 26871-27110 Epistemic_statement denotes Through multivariate spatial statistical modelling of disease processes, GIS enable the evaluation of potentially true disease outbreaks and a more effective allocation of sparse remedial resources towards their containment and prevention.
T60 27111-27327 Epistemic_statement denotes GIS also assist users in better understanding the potential harmful effects of environmental pollutants, e.g., toxic waste sites, and even in understanding the occurrence of pedestrian and other injuries, and crimes.
T61 27328-27486 Epistemic_statement denotes Today, environmental monitors measure air and water quality, solar irradiation, radon gas levels, and other exposures potentially deleterious to human health.
T62 27487-27635 Epistemic_statement denotes These measurements can be brought into GIS, spatially referenced and integrated analytically with other health predictor variables and outcome data.
T63 27636-27796 Epistemic_statement denotes In fact, any adverse (or positive) health-related phenomenon that can be defined spatially (atmospheric, aquatic or terrestrial) can lead to GIS analysis [13] .
T64 28020-28264 Epistemic_statement denotes GIS can also help promote healthy behaviours by documenting where the populations are located that have the greatest need of improved information, then using GISenabled Internet sites as an outreach vehicle for community health education [27] .
T65 28265-28570 Epistemic_statement denotes For this reason, it is always encouraged to consider the public as one of the main beneficiaries of any national spatial health information infrastructure (see later), and they should be offered full access to data and information (subject to appropriate confidentiality and national security safeguards).
T66 29369-29686 Epistemic_statement denotes Richards et al also describe a feasible scenario for geographically enabled electronic medical records wherein all electronic inpatient and outpatient medical records in a given community are regularly scanned to map asthma cases (in the example given) and compare current week maps with those for prior time periods.
T67 29827-29916 Epistemic_statement denotes Such patterns can be further and more closely investigated and appropriate actions taken.
T68 30076-30296 Epistemic_statement denotes Using GIS technology linked to a database about workplace chemical exposures, the potential exposures at the factory in question were reviewed and the agents associated with asthma-related hospital admissions identified.
T69 30440-30753 Epistemic_statement denotes Gavin and her colleagues provide examples of how developing African countries are currently using geo-information to produce enhanced capacity for emergency response, more effective and efficient government operations, increased transparency of public decision-making and better addressing of social inequalities.
T70 30977-31218 Epistemic_statement denotes They also describe how geo-information used in a poverty mapping initiative in South Africa was combined with information on sanitation and safe water supplies to create a strategy for containing a cholera outbreak in KwaZulu Natal province.
T71 32014-32294 Epistemic_statement denotes Traditionally, two broad types of GIS applications can be distinguished which also reflect the two traditions in health geography (geography of disease and geography of healthcare systems), namely health outcomes and epidemiology applications and healthcare delivery applications.
T72 32491-32767 Epistemic_statement denotes A number of studies have used GIS to study disease patterns (e.g., identify leukaemia clusters), spatio-temporal variations in health outcomes, and identify possible causes of mapped patterns (e.g., the relationship between cancer incidence and various environmental factors).
T73 32870-33110 Epistemic_statement denotes GIS can also be used to target resources for disease prevention by highlighting areas with significantly high rates, and to predict which areas might be at future risk and which may benefit most from future local population screening [28] .
T74 33111-33548 Epistemic_statement denotes Examples of health outcomes and epidemiology applications using GIS include research carried in the UK at the West Midlands Cancer Intelligence Unit and the Small Area Health Statistics Unit (SAHSU) [10] , and also the work published by Dunn et al in which they have examined the association between asthma incidence and proximity to industrial sites in North East England and suggested relationships with prevailing wind patterns [29] .
T75 33630-33840 Epistemic_statement denotes Current methods for estimating the incidence, prevalence, and spread of drug misuse tend to be retrospective (delivering information about past events) and are not capable of forecasting spatio-temporal trends.
T76 34185-34539 Epistemic_statement denotes Their approach provides the basis for examining more complex geographic diffusion scenarios such as the introduction of new practices by new users, the development of education and remedial initiatives, impacts of tourism and migration, cross-border contact, drug transportation, and increasing opportunities for economic and international contact [18] .
T77 35222-35532 Epistemic_statement denotes Even if the WHO keeps publishing updated versions of this atlas, it will always lack (in its current form) the interactivity, realtime or near-real-time processing of current data, and the proactive features desirable in a true regional/community public health surveillance and spatial decision support system.
T78 36095-36254 Epistemic_statement denotes Like the WHO's Atlas of Health in Europe, this Swedish atlas remains a collection of pre-drawn, static maps (still very valuable, but limited in many aspects).
T79 37258-37558 Epistemic_statement denotes GIS have been used in a number of studies to estimate the best/optimal location for a new clinic, hospital or GP surgery to minimise distances potential patients need to travel taking into account existing facilities, transport provision, hourly variations in traffic volumes, and population density.
T80 37848-37963 Epistemic_statement denotes Another remarkable application involves the use of GIS to improve hospital bed availability and avoid access block.
T81 38492-38681 Epistemic_statement denotes Access block may result in ambulance bypass, increased ED waiting time and casualty queues, increased frequency of adverse events, increased patient complaints, and adverse media attention.
T82 39754-40135 Epistemic_statement denotes It is noteworthy that Downey Regional Medical Centre (DRMC) in California, US, is currently using a large, multi-layered, GIS-enabled patient care and room management system that leverages digital floor plans, workflow analysis, and data visualisation for a better solution to how DRMC assigns patients to rooms, monitors the discharge process, and prepares rooms for new patients.
T83 40326-40507 Epistemic_statement denotes In a recent review paper, Higgs and Gould highlighted the gap between academic health-related applications of GIS and their everyday use within the UK National Health Service (NHS).
T84 40628-40823 Epistemic_statement denotes GIS have been used in the UK health sector for over a decade, but their greatest contribution so far has been in low-level operational tasks (see "surveys of levels of GIS use in the NHS" below).
T85 40824-40993 Epistemic_statement denotes There is little evidence that GIS are being formally considered or regularly used in strategic decision-making, e.g., major healthcare planning within the NHS [28, 35] .
T86 41576-41739 Epistemic_statement denotes Linkages between, for example, poor health and unemployment, housing, crime, and education are major drivers for partnership approaches between such organisations.
T87 41740-41874 Epistemic_statement denotes The Acheson Report published in 1998 also recognised the need to adopt cross-governmental approaches to address health concerns [37] .
T88 41875-42656 Epistemic_statement denotes Considering all of this, and given the recent media attention to geographical variations in healthcare service provision, which often revolve around the so-called "postcode lottery" in treatment levels, the fact that a considerable majority of the datasets used in UK primary and secondary care are geo-referenced, and the recent increase in the number of articles (e.g., [10] ), books (e.g., [38] [39] [40] [41] ), and conferences (e.g., [42] ) about the potentials and use of GIS in health applications, it is surprising there has been no mention in Information for Health or other more recent follow-up documents (e.g., "Building the Information Core: Implementing the NHS Plan" published in 2001 -[43]) of the role that spatial data and GIS could play in the new NHS [28, 35] .
T89 42657-43041 Epistemic_statement denotes The role of spatial information in the health sector in relation to, for example, local health improvement programmes or performance management is not identified in any of the core UK national strategy and policy documents, although the potential for using information from primary care systems to support needs assessment and resource targeting is one of the principal action points.
T90 43042-43223 Epistemic_statement denotes There is also no mention of the potential for GIS to support partnership approaches for providing and exchanging information on such issues at either national or local scales [35] .
T91 43224-43676 Epistemic_statement denotes The NHS Information Authority (NHSIA), established as a special Health Authority in 1999, states as one of its strategic objectives the need "to contribute to the implementation of Information for Health by establishing, maintaining, developing and supporting a national information infrastructure, national products, national standards, national services and working with the NHS and others to make effective use of these products and services" [44] .
T92 43677-43805 Epistemic_statement denotes Again it is astonishing that there is no explicit mention of the potential for geo-information and GIS in addressing these aims.
T93 44028-44142 Epistemic_statement denotes However, this author was able to spot several local implementation documents on the Web mentioning the use of GIS.
T94 44569-44832 Epistemic_statement denotes It is also noteworthy that a GIS special interest group has been set up in 2003 within the NHS Online Health Informatics Community Portal http://www.informat ics.nhs.uk/ to disseminate information and provide support to users of GIS within the UK health industry.
T95 45014-45485 Epistemic_statement denotes Unlike the UK national strategy documents and plans, the US National Health Information Infrastructure Strategy document (also known as "Information for Health") refers explicitly to GIS and real-time health and disease monitoring and states that "public health will need to include in its toolkit integrated data systems; high-quality community-level data; tools to identify significant health trends in real-time data streams; and geographic information systems" [48] .
T96 47301-47521 Epistemic_statement denotes Typically, maps were being included in the annual reports of the Directors of Public Health to illustrate the health priorities of individual health authorities, with very little emphasis on using GIS in strategic tasks.
T97 47984-48295 Epistemic_statement denotes Smith and Jarvis surveyed changes in the use of GIS within the NHS following the reforms of the early 1990s and found that GIS use has again tended to be uncoordinated and low-level in nature, because of a lack of policy directives concerning appropriate systems, as well as a general lack of high quality data.
T98 48731-48956 Epistemic_statement denotes A number of dimensions could be measured such as improving the health of the general population, ensuring fair access to services, maintaining the effective delivery of appropriate care and analysing the outcomes of NHS care.
T99 48957-49102 Epistemic_statement denotes GIS have a potential role in evaluating performance and could be used to enable comparisons to be made between health authorities and NHS trusts.
T100 50159-50349 Epistemic_statement denotes These regional services could be responsible for coordinating data collection at the regional level, and preventing any duplication of efforts in spatial data collection or processing [50] .
T101 50350-50639 Epistemic_statement denotes Public health observatories (PHOs), as proposed in the government White Paper "Saving Lives: Our Healthier Nation" [51] , have been set up in each NHS region to draw information together from a range of sources with which to monitor health trends and to identify gaps in information [10] .
T102 50640-50774 Epistemic_statement denotes Looking at their objectives and published agenda, PHOs could have easily undertaken the tasks suggested by Cooper in [50] (see above).
T103 50775-50991 Epistemic_statement denotes However, after almost three years in existence now, it seems PHOs have failed to fulfil this task (or never thought of fulfilling it), though there are certainly some very good, but sporadic GIS activity within PHOs.
T104 52022-52151 Epistemic_statement denotes However, only 54% of health authorities and 56% of health trusts within this active subset reported having fully operational GIS.
T105 52380-52903 Epistemic_statement denotes Factors such as historical precedent, the presence of dedicated GIS-able individuals or teams, and the presence of an effective infrastructure of GIS advice, guidance, and support available to NHS organisations (e.g., in West Midlands and Trent -for some examples, see http://www.sheffield.nhs.uk/health data/gis.htm and http://gis.sheffield.ac.uk/) could explain the observed patterns of health organisations that are GIS users or nonusers, and those that show higher degrees of collaboration with local authorities [35] .
T106 52904-53036 Epistemic_statement denotes The production of maps was undertaken in 96% of the health authorities and 67% of the health trusts that reported using GIS in 2001.
T107 54373-54448 Epistemic_statement denotes Internet and Intranet GIS were found to be still rare within the NHS [35] .
T108 54449-54797 Epistemic_statement denotes Higgs et al also attempted to measure the levels of joinedup working within NHS organisations and with external agencies (e.g., Police, local authorities, utilities, and other central government departments), which has the potential to address a wider range of cross departmental or governmental issues (e.g., health, poverty and social exclusion).
T109 55135-55399 Epistemic_statement denotes Despite these uses of GIS in operational and policyrelated tasks, many respondents identified factors they perceived to be hindering the wider use of GIS within their organisation, and data exchange and collaboration with other organisations and local authorities.
T110 55968-56131 Epistemic_statement denotes The lack of a clear organisational policy for exchanging data was among the most significant data exchange constraints identified by health authorities and trusts.
T111 56447-56566 Epistemic_statement denotes Another important problem reported was that of organisations not being aware of data held by other organisations [35] .
T112 56567-56938 Epistemic_statement denotes Higgs et al suggest raising awareness of the benefits of joined-up working arrangements, and introducing significant organisational and cultural changes to facilitate enabling contexts for enhanced collaborative use of GIS between NHS organisations and local authorities, in order to support the wider joined-up government agenda currently being promoted in the UK [35] .
T113 56939-57371 Epistemic_statement denotes It should be noted that Higgs et al carried their study and reported differences between health authorities and trusts in 2001 some time before the start of the current changes in the UK health system where Primary Care Trusts are now taking over many of the classical functions of health authorities and a smaller number of Strategic Health Authorities are taking an increasingly strategic role in performance management of trusts.
T114 57372-57550 Epistemic_statement denotes Higgs et al's survey could be used as a baseline with which to monitor the impacts of current and future organisational restructuring on the uses of GIS within the NHS [35, 53] .
T115 57551-57849 Epistemic_statement denotes 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.
T116 58228-58406 Epistemic_statement denotes 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.
T117 58687-58987 Epistemic_statement denotes 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] :
T118 58988-59306 Epistemic_statement denotes (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.
T119 59376-59476 Epistemic_statement denotes The benefits associated with data sharing should be researched to encourage wide participation [6] .
T120 59642-59770 Epistemic_statement denotes Universities should be encouraged to work with local organisations in the provision of Continuing Professional Development [6] .
T121 59940-60128 Epistemic_statement denotes (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.
T122 60129-60237 Epistemic_statement denotes Unambiguous legal frameworks to address these crucial legal issues must be established as early as possible.
T123 60388-60598 Epistemic_statement denotes (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).
T124 60781-60872 Epistemic_statement denotes Appropriate business models must be agreed to support these partnerships at an early stage.
T125 61053-61180 Epistemic_statement denotes It is essential that all users are involved when defining (user) requirements and testing the associated products and services.
T126 61285-61460 Epistemic_statement denotes NSDI Steering Groups (with end-user representation) should be formed to formulate appropriate policy and institutional frameworks and facilitate multi-stakeholder cooperation.
T127 61461-61578 Epistemic_statement denotes However, complete policy and institutional frameworks need not be in place before implementation of a NSDI can begin.
T128 61579-61752 Epistemic_statement denotes 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] .
T129 61753-61977 Epistemic_statement denotes (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).
T130 61978-62079 Epistemic_statement denotes To be able to integrate and share data we need to understand and resolve different semantics in data.
T131 62080-62364 Epistemic_statement denotes 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] .
T132 62365-62531 Epistemic_statement denotes (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.
T133 62532-62722 Epistemic_statement denotes 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.
T134 62723-62849 Epistemic_statement denotes Short-term bottom up projects will provide valuable experience that can feed into the formulation of NSDI policy and strategy.
T135 62850-63021 Epistemic_statement denotes 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] .
T136 63022-63175 Epistemic_statement denotes (7) Do not just focus on data; develop applications: Varied applications and services through a project-oriented approach will bring reality to the NSDI.
T137 63176-63304 Epistemic_statement denotes An overemphasis on data acquisition, without a market-linked application, will not provide any momentum for further development.
T138 64873-65018 Epistemic_statement denotes Stakeholders should work together locally and with international bodies to develop/adopt standards for geodata collection and documentation [9] .
T139 65019-65139 Epistemic_statement denotes (Adopting international standards will also ensure that future collaboration is possible at regional and global levels.)
T140 65766-65820 Epistemic_statement denotes Metadata, too, should be standardised [9] (Figure 3 ).
T141 65960-66106 Epistemic_statement denotes Policies should start by removing barriers to access, e.g., excessive costs to use an information product or lack of clarity concerning copyright.
T142 66107-66235 Epistemic_statement denotes The absence of a policy concerning data access and sharing can often be as handicapping as the presence of an inhibiting policy.
T143 66236-66315 Epistemic_statement denotes Existing policies need to be revised and new poli- cies developed as necessary.
T144 66316-66508 Epistemic_statement denotes Broad-based national committees of data producers, users, and other stakeholders should be created to oversee the development of geoinformation policy and standards and ensure compliance [9] .
T145 66673-66750 Epistemic_statement denotes Without appropriate human resources, geo-information will remain unexploited.
T146 66751-66829 Epistemic_statement denotes Sufficient financial resources must be available to invest in training people.
T147 66830-66928 Epistemic_statement denotes Retaining technical expertise should be also a priority within institutions using geo-information.
T148 67368-67692 Epistemic_statement denotes 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.
T149 67693-67863 Epistemic_statement denotes Such frameworks should specify which organisations are gathering which kinds of information, how the information will be captured, and arrangements for data sharing [9] .
T150 67864-67977 Epistemic_statement denotes (7) Raising awareness: Establishing a formal national programme can help heighten awareness and generate support.
T151 67978-68084 Epistemic_statement denotes Policymakers need to be engaged in the process through awareness training, briefings, and policy dialogue.
T152 68677-68880 Epistemic_statement denotes (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] .
T153 69629-69810 Epistemic_statement denotes (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.
T154 69811-70036 Epistemic_statement denotes 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.
T155 70037-70157 Epistemic_statement denotes The time required for training can be also a challenge for organisations in which demands on personnel are already high.
T156 70158-70306 Epistemic_statement denotes Training materials should be offered in a variety of formats to facilitate distance learning (e.g., CD-ROMs and self-instruction Web-based courses).
T157 70460-70523 Epistemic_statement denotes (3) Current and accurate base data must be made available [7] .
T158 70524-70612 Epistemic_statement denotes (4) Software and data acquisition, maintenance and upgrade costs should be secured [7] .
T159 70613-70851 Epistemic_statement denotes (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.
T160 70852-71126 Epistemic_statement denotes 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.)
T161 71262-71337 Epistemic_statement denotes (6) Standards must be adopted and partnerships promoted at all levels [7] .
T162 71504-71819 Epistemic_statement denotes 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.
T163 72212-72318 Epistemic_statement denotes (2) Adopting data standards and sharing agreements will ensure a CHSS works effectively in real time [2] .
T164 73428-73495 Epistemic_statement denotes RODS researchers identified the following key elements for success:
T165 73496-73690 Epistemic_statement denotes (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.
T166 73691-73831 Epistemic_statement denotes Data sharing agreements should allow redistribution of data to any public health authority and permit data to be used in research [55, 56] .
T167 73832-74040 Epistemic_statement denotes (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] .
T168 75586-76056 Epistemic_statement denotes (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.
T169 76057-76198 Epistemic_statement denotes Successful examples of collaborative projects between NHS and local authorities that have involved the use of GIS should be also highlighted.
T170 77046-77227 Epistemic_statement denotes 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] .
T171 77228-77516 Epistemic_statement denotes (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] .
T172 77792-77945 Epistemic_statement denotes (5) Moving to the Web and building all necessary critical connectivity/geospatial infrastructure that should not be independently recreated by all [58] .
T173 79181-79661 Epistemic_statement denotes 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] .
T174 80426-80638 Epistemic_statement denotes 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] .
T175 80639-80783 Epistemic_statement denotes The next steps involved identifying, evaluating, and acquiring potentially relevant datasets based on data needs identified from the data model.
T176 81673-81897 Epistemic_statement denotes 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] .
T177 82195-82383 Epistemic_statement denotes 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] .
T178 82384-82591 Epistemic_statement denotes (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] .
T179 82867-82952 Epistemic_statement denotes Web accessible directories of data would greatly facilitate identifying data sources.
T180 82953-83102 Epistemic_statement denotes In addition, action should be taken to improve data documentation (metadata), develop data standards, and enhance compliance with existing standards.
T181 83411-83537 Epistemic_statement denotes The difficulties encountered in acquiring data indicate that privacy concerns present a serious barrier to system development.
T182 83538-83673 Epistemic_statement denotes A wide range of stakeholders in society must collectively address the issues of privacy and stewardship of population health data [8] .
T183 83674-83875 Epistemic_statement denotes (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.
T184 84413-84531 Epistemic_statement denotes (6) Users of community health information systems will nearly always have variable skills and organisational contexts.
T185 84698-84858 Epistemic_statement denotes Another approach would be to use artificial intelligence, as employed in decision support systems, to facilitate user control of information visualisation [8] .
T186 85068-85344 Epistemic_statement denotes 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] .
T187 85345-85585 Epistemic_statement denotes 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.
T188 85941-86108 Epistemic_statement denotes 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] .
T189 86748-86941 Epistemic_statement denotes 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] .
T190 87206-87405 Epistemic_statement denotes 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] .
T191 87406-87517 Epistemic_statement denotes Community partners engaged in collaborative research with universities should see themselves as equal partners.
T192 87518-87706 Epistemic_statement denotes 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).
T193 87707-88006 Epistemic_statement denotes 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] .
T194 88186-88596 Epistemic_statement denotes 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.
T195 88597-88933 Epistemic_statement denotes 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] .
T196 88934-89191 Epistemic_statement denotes 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] .
T197 89679-89791 Epistemic_statement denotes Thus each participating agency manages its own data and its timeliness, which can be current and even real-time.
T198 89841-89917 Epistemic_statement denotes Any agency can participate by adding its own data layer(s) to existing ones.
T199 90014-90121 Epistemic_statement denotes Public health databases are not yet included in WME, but there are no specific barriers to inclusion [58] .
T200 90122-90249 Epistemic_statement denotes As information systems increase in complexity, models of the relationships between data elements become increasingly important.
T201 90250-90369 Epistemic_statement denotes Data models, more correctly called ontologies, explicitly define how concepts within data sources relate to each other.
T202 90370-90514 Epistemic_statement denotes They are conceptual models that facilitate integration of data by information systems and support a common understanding of data by people [8] .
T203 90515-90895 Epistemic_statement denotes 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.
T204 91686-91730 Epistemic_statement denotes Three types of indicator are proposed [54] :
T205 91731-91925 Epistemic_statement denotes (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;
T206 91926-92026 Epistemic_statement denotes (2) Risk indicators: describe the hazard in terms of the number or percentage of people exposed; and
T207 92027-92164 Epistemic_statement denotes (3) Health impact indicators: describe the hazard in terms of the actual health outcome, measured as either morbidity or mortality [54] .
T208 92165-92266 Epistemic_statement denotes Which type of indicator is most appropriate is likely to depend on the specific question being asked.
T209 92267-92454 Epistemic_statement denotes 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] .
T210 92533-92752 Epistemic_statement denotes 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.
T211 92753-93015 Epistemic_statement denotes 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] .
T212 93784-93895 Epistemic_statement denotes The area across which the indicator can be used (scale of application or aggregation level) must be determined.
T213 93896-94013 Epistemic_statement denotes Finally, the ways in which the indicator may be interpreted in relation to the hazard(s) it covers must be described.
T214 94014-94287 Epistemic_statement denotes 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] .
T215 94844-95017 Epistemic_statement denotes 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.
T216 95018-95291 Epistemic_statement denotes 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.
T217 95292-95518 Epistemic_statement denotes 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].
T218 96003-96439 Epistemic_statement denotes (NAC-CHO developed and tested the Local Public Health System Performance Assessment Instrument for NPHPSPhttp://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.
T219 97091-97157 Epistemic_statement denotes The outcome will be a basic data model with three components [64]:
T220 97158-97292 Epistemic_statement denotes (1) A conceptual object model of health application features, building relationships between health application geographies and users;
T221 97293-97389 Epistemic_statement denotes (2) UML (Unified Modelling Language) code which is easily transformed into an ESRI geo-database.
T222 97390-97997 Epistemic_statement denotes 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 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.
T223 97998-98344 Epistemic_statement denotes 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).
T224 98458-98744 Epistemic_statement denotes 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?"
T225 98906-99033 Epistemic_statement denotes [59, 65] POPULIS reports on the health of a population, and the relationship between health and the use of healthcare services.
T226 99608-99956 Epistemic_statement denotes 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] .
T227 99957-100024 Epistemic_statement denotes However, POPULIS has missed a lot by not being a GISenabled system.
T228 100391-100642 Epistemic_statement denotes 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.
T229 100974-101147 Epistemic_statement denotes 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.
T230 101148-101332 Epistemic_statement denotes 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?
T231 101333-101499 Epistemic_statement denotes 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.
T232 102693-102995 Epistemic_statement denotes 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.
T233 102996-103350 Epistemic_statement denotes 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] .
T234 103351-103495 Epistemic_statement denotes XML is also used for encoding spatial metadata (metadata are essential to aid the discovery of spatial data in a distributed environment) [58] .
T235 103960-104079 Epistemic_statement denotes Any data store can be used -users no longer need to care whether the underlying store is from ESRI, Oracle or IBM [69].
T236 104218-104425 Epistemic_statement denotes 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/.
T237 104499-104681 Epistemic_statement denotes 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.
T238 104682-104893 Epistemic_statement denotes 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] .
T239 104894-105005 Epistemic_statement denotes By separating presentation from content, powerful maps can be made that offer enhanced functionality for users.
T240 105006-105187 Epistemic_statement denotes 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.
T241 105188-105337 Epistemic_statement denotes 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] .
T242 105338-105521 Epistemic_statement denotes 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 ).
T243 105802-105980 Epistemic_statement denotes GML contains map "content" only (e.g., where features are, their geometry, type and attributes), but does not provide any information about how that map data should be displayed.
T244 105981-106096 Epistemic_statement denotes This allows different "stylesheets" to be applied to the geographic data to make it appear however the user wishes.
T245 106097-106283 Epistemic_statement denotes By combining a selected map stylesheet with a Web Feature Service (WFS) query, users are presented with a fully interactive and editable vector map that can be viewed in any Web browser.
T246 106306-106673 Epistemic_statement denotes 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.
T247 106863-106983 Epistemic_statement denotes However, it should be noted that GML and Web Services are only part of the solution to integration and interoperability.
T248 107883-108177 Epistemic_statement denotes 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.
T249 108242-108404 Epistemic_statement denotes 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.
T250 108405-108568 Epistemic_statement denotes 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.
T251 108660-108899 Epistemic_statement denotes 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.
T252 109243-109429 Epistemic_statement denotes 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.
T253 109430-109601 Epistemic_statement denotes 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.
T254 109946-110211 Epistemic_statement denotes 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] .
T255 110579-110748 Epistemic_statement denotes 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] .
T256 110749-110897 Epistemic_statement denotes 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] .
T257 111468-111555 Epistemic_statement denotes This can be crucial in emergency situations such as terrorist and bioterrorist attacks.
T258 112289-112373 Epistemic_statement denotes Bandwidth is not only a problem of developing countries, but developed ones as well.
T259 112374-112607 Epistemic_statement denotes 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).
T260 112786-112986 Epistemic_statement denotes 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] .
T261 112987-113262 Epistemic_statement denotes 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.
T262 113418-113587 Epistemic_statement denotes 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.
T263 114571-114879 Epistemic_statement denotes 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.
T264 114880-115019 Epistemic_statement denotes To maximise their utility, these wizards should also be fully integrated into everyday public health workflows and decision-making process.
T265 115020-115242 Epistemic_statement denotes 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.
T266 115243-115586 Epistemic_statement denotes 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/soft ware/section508/index.html).
T267 115587-115679 Epistemic_statement denotes The UK/EU equivalents of these accessibility requirements can be consulted online [77, 78] .
T268 115680-115927 Epistemic_statement denotes 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.can cer.gov/atlasplus/index.html.
T269 115928-116093 Epistemic_statement denotes 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.
T270 116094-116314 Epistemic_statement denotes 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 ).
T271 116423-116807 Epistemic_statement denotes Critical information infrastructures are potentially vulnerable to Screenshot of Section 508-compliant NCI cancer mortality maps and graphs Figure 5 Screenshot of Section 508-compliant NCI cancer mortality maps and graphs Screenshot of the customisable cancer mortality maps and graphs developed by the US National Cancer Institute (NCI -http://www3.cancer.gov/atlasplus/ index.html).
T272 116808-116970 Epistemic_statement denotes These maps (upper part of screenshot) and the associated charts and graphs (lower part of screenshot) are compliant with Section 508 of the US Rehabilitation Act.
T273 117171-117333 Epistemic_statement denotes 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.
T274 117448-117596 Epistemic_statement denotes Kevin Coleman suggests several measures that can be taken for thwarting cyber terrorism; interested readers are urged to refer to his article [79] .
T275 118374-118797 Epistemic_statement denotes SARS (Severe Acute Respiratory Syndrome) mapping in Hong Kong using disaggregate case data at individual building level in near real time was another noticeable exception to this well-established public health confidentiality rule, and also a unique and rare GIS opportunity that resulted in some very comprehensive public Internet mapping services (see "Real-time/near-real-time GIS for epidemics management" below) [80] .
T276 118798-118846 Epistemic_statement denotes Spatial data confidentiality is a complex issue.
T277 118847-119030 Epistemic_statement denotes Even if a single database may appear to have effective confidentiality safeguards, when several databases are linked within GIS, the "sum" may be less well protected than the "parts".
T278 119031-119158 Epistemic_statement denotes A false identification may be just as damaging to an individual as a correct identification that is not kept confidential [7] .
T279 119159-119365 Epistemic_statement denotes On the other hand, confidentiality constraints often preclude the release of disaggregate data about individuals, which limits the types and accuracy of the results of the analyses that could be done [81] .
T280 119366-119538 Epistemic_statement denotes Individual agencies holding micro-data (small population/individual health and environmental data) often impose restrictions on the level of geography that can be reported.
T281 119851-119999 Epistemic_statement denotes Traditional ecological analysis based on choropleth mapping and the analysis of aggregate data for administrative areas has been heavily criticised.
T282 120000-120491 Epistemic_statement denotes It is increasingly becoming clear in the field of public health that individual-level health information aggregated to pre-existing political or other administrative areas to protect individual privacy often destroys information needed for geographical analyses making it impossible to address many important public health concerns, e.g., accident risk of particular environments, hazards of living close to hazardous waste sites, exposure risk from lead associated with urban highways, etc.
T283 120492-120545 Epistemic_statement denotes Such concerns can only be addressed using micro-data.
T284 120546-120735 Epistemic_statement denotes The lack of spatially-disaggregate data on healthcare utilisation and clinical activity also limits the types and power of healthcare delivery studies that can be carried [13, 28, 82, 83] .
T285 120869-120962 Epistemic_statement denotes Moreover, using area centroids instead of exact locations can yield misleading results [12] .
T286 120963-122208 Epistemic_statement denotes According to Armstrong et al, when data are spatially aggregated to large areas, the ability of researchers to detect disease clusters or to investigate suspected relationships between environmental exposures and disease events is affected in four ways: (1) absolute and relative locations within the geographical extent of each area are unobservable making it impossible to perform tests of clustering, except for those designed to operate specifically on data aggregated to areas; (2) the effect of the geographic scale of the aggregation with respect to the geographic scale of the clusters means that the aggregation level used in an analysis limits the size of clusters that could be detected; (3) the shape and placement of aggregation areas in relation to the realworld distribution of the disease or clusters under study, e.g., when a disease cluster straddles two or more aggregation areas, may result in ambiguous or negative results; and (4) accurate analyses are only possible when health data are spatially encoded to the boundaries of areas with common levels of environmental exposure, which is usually not the case since exposure assessment data are generally collected for different areas than health and demographic data [83] .
T287 122209-122339 Epistemic_statement denotes Fortunately, solutions exist that can preserve data confidentiality while still enabling fine-level analyses and reliable results.
T288 122340-122944 Epistemic_statement denotes These solutions involve (1) the use of statistical and epidemiological methods to mask the geographic location of data in a way that can still permit meaningful analysis, e.g., special types of spatial and temporal aggregation of data; (2) the creation of secure (networked) environments with limited and multiple levels of access (to confidential data) in which public health researchers can be carefully monitored to ensure protection of individual and household confidentiality; and (3) the development, publication and strict enforcement of appropriate, unambiguous policies and regulations [7, 58] .
T289 122992-123152 Epistemic_statement denotes (1) Statistical and epidemiological methods: Armstrong et al describe different promising types of geographical masks to encode the geography of health records.
T290 123153-123477 Epistemic_statement denotes These masks not only preserve the confidentiality of individual health records, but also preserve, to the maximum degree possible, the geographic properties of the data, thus permitting the investigation of questions that can be validly answered only with some (adequate) knowledge about the location of health events [83] .
T291 123478-123676 Epistemic_statement denotes The geographic coordinates of data collected at discrete locations can be subjected to a family of affine point transformations that move these locations deterministically to a new set of locations.
T292 124169-124401 Epistemic_statement denotes In the latter case, regions could be represented by their geographic centroids, or surrogate locations could be computed that are optimised regarding some defined relationship to the original locations (location-allocation methods).
T293 124473-124699 Epistemic_statement denotes It is also possible to aggregate for non-conterminous "regions" of interest like releasing health data for all areas within a given distance of a specified hazard, e.g., all children's accidents within 20 metres of stop signs.
T294 124700-124910 Epistemic_statement denotes Another possible approach to limiting disclosure is to remove all explicit geographic identifiers from the health record and replace them with contextual information of specific interest to the data user [83] .
T295 125058-125219 Epistemic_statement denotes Preliminary research suggests that random perturbation of data, up to some limit, is superior to affine and aggregation masks for many analytical purposes [83] .
T296 125220-125331 Epistemic_statement denotes Areal aggregation is perhaps the most commonly adopted approach among those suggested by Armstrong et al [83] .
T297 126211-126343 Epistemic_statement denotes SCDHEC chose census tracts because they contain useful socio-economic data that could be combined with the aggregated vital records.
T298 127832-127945 Epistemic_statement denotes Armstrong et al also mention another possible solution to data confidentiality problems based on software agents.
T299 128011-128158 Epistemic_statement denotes If an agent were designed to support the analysis of public health data, users would not be required to have access to confidential health records.
T300 128159-128406 Epistemic_statement denotes Rather, they would submit a request to an intelligent analysis agent that would assess the request, and if found appropriate, would complete the analysis and return a result to the data user without exposing any individual-level health data [83] .
T301 128592-128871 Epistemic_statement denotes HSRC is located within the firewall of a health system, and its purpose is to provide RODS with additional public health surveillance functions that would not be possible if it were located outside of the firewall due to restrictions on the release of identifiable clinical data.
T302 129456-129570 Epistemic_statement denotes Access to confidential data can be accommodated for qualified users in secure Intranet or Internet settings [58] .
T303 130176-130477 Epistemic_statement denotes The latest Microsoft Windows Server 2003 Rights Management Services (RMS) technology offers the possibility to create multiple detail/data levels of data categorised according to sensitivity, and match them to multiple levels of access according to user credentials (see http:// www.microsoft.com/rm).
T304 130793-131072 Epistemic_statement denotes Each participating organisation in a community health surveillance system (CHSS -see below) can run its own PSGN and geoserver behind its firewall, and directly control information content and access by internal and external entities and maintain the confidentiality of its data.
T305 131073-131481 Epistemic_statement denotes While each participating organisation maintains its data securely, perhaps generating/holding different classes of data/levels of detail (e.g., anonymised vs. personal identifiable information) at a variety of security levels, all data can be automatically and quickly integrated when required, e.g., in the event of outbreak or epidemic, and released to only those who have proper access authorisation [2] .
T306 131942-132071 Epistemic_statement denotes On the other hand, data, information, maps and software that have been approved for public dissemination are available to anyone.
T307 132414-132564 Epistemic_statement denotes This security model allows different levels of access to the data depending on the likelihood that an individual's privacy could be compromised [58] .
T308 132817-133130 Epistemic_statement denotes Lack of sufficient or clear laws regarding privacy, and variations in protections of health data across different organisations and agencies may preclude or delay data sharing across regional lines and organisational boundaries, or involve unacceptable risks to the privacy of data that are transmitted [13, 58] .
T309 133131-133323 Epistemic_statement denotes Confidentiality guidelines and accessibility restrictions to the public and research community should be Web documented in searchable metadata that describe essential elements of the database.
T310 133324-133469 Epistemic_statement denotes Through metadata all public health agencies can inform others of their spatial data holdings and any limitations associated with their use [58] .
T311 133470-133718 Epistemic_statement denotes In the UK, the implications of recent legislation, such as the 1998 Data Protection Act [86] , which came into force in March 2000, on the use of geocoded patient information in medical research are somewhat unclear and need to be closely examined.
T312 133719-134010 Epistemic_statement denotes Potential changes in the provision of patient data to cancer registries such as the ethical requirement to obtain patient consent prior to information being passed to registries could, for example, have major implications for researchers examining spatial patterns in cancer incidence [28] .
T313 134312-134643 Epistemic_statement denotes This covers the processing of confidential patient information that relates to the present or past geographical locations of patients (including where necessary information from which patients may be identified) which is required for medical research into the locations at which disease or other medical conditions may occur [87] .
T314 135460-135755 Epistemic_statement denotes On another level, following the September 2001 events in the US, many federal and local spatial databases, e.g., "critical infrastructure" spatial data, were assessed by their holding agencies as a potential liability to national security and withdrawn from the Internet or public dissemination.
T315 135756-136007 Epistemic_statement denotes The current concern is to find an appropriate balance between public access to spatial information and protection of information considered a priority for national security (this is another important aspect of data security and confidentiality) [58] .
T316 136008-136138 Epistemic_statement denotes GIS integration of complex data into visually easy-tounderstand pictures can sometimes be a setup for misunderstanding and misuse.
T317 136139-136284 Epistemic_statement denotes Richards et al call for sound epidemiological principles and methods to provide the foundation for the data analyses to be displayed on GIS maps.
T318 136285-136701 Epistemic_statement denotes To avoid drawing false conclusions from maps, GIS users need to understand and apply epidemiological principles and methods in formulating study questions, testing hypotheses about cause-and-effect relationships, and critically evaluating how the chosen dataset(s) and GIS method(s), data quality, confounding factors, and bias may influence the interpretation of results, and hence any decisions based on them [7] .
T319 136702-136782 Epistemic_statement denotes According to Monmonier, it is not just easy but also essential to lie with maps.
T320 136783-136940 Epistemic_statement denotes The cartographer's paradox is that to avoid hiding critical information in a fog of detail, the map must offer a selective, incomplete view of reality [95] .
T321 136941-137271 Epistemic_statement denotes Public health practitioners need to be alert for "lies" that can range from legitimate and appropriate suppression of some details selectively to help the user focus on what needs to be seen to more serious distortions in which the visual image suggests conclusions that would not be supported by careful epidemiological analysis.
T322 137272-137486 Epistemic_statement denotes For example, when some geographic units of analysis have small denominators, disease rates computed for these areas may appear extremely high if any cases have occurred in these areas (the "small numbers" problem).
T323 137487-137669 Epistemic_statement denotes When the rates for these geographic locations are displayed on a map, readers may incorrectly conclude that these are "hot spots", high priority locations for targeted interventions.
T324 137670-137821 Epistemic_statement denotes More appropriately, these areas should be labelled to indicate that rates are statistically unstable due to small numbers and therefore not shown [7] .
T325 137822-138365 Epistemic_statement denotes Along similar lines, in 1998, Jacquez defined the "gee whiz" effect as "the formulation of hypotheses to explain an apparent (visual) pattern whose existence has not been confirmed", and stressed the importance that appropriate and robust statistical methods be used to support the thematic data layers being displayed and analysed in order to avoid the consequences of visual bias in GIS processes, in which spatial patterns might seem to appear where none actually exists, and inferences might sometimes be made on invalid assumptions [12] .
T326 138366-138678 Epistemic_statement denotes In a personal e-mail communication with Dr. Geoffrey Jacquez five years after his original definition of the "gee whiz" effect, he affirmed that he still stands by the idea that pattern recognition (both spatial and spatio-temporal) requires objective approaches that transcend the subjectivity of the human eye.
T327 138777-138941 Epistemic_statement denotes He continues: "Especially within the exploratory framework, one must be able to discriminate true patterns from apparent patterns that could be explained by chance.
T328 138942-139138 Epistemic_statement denotes In the absence of such capability, both confirmatory and exploratory analyses spin their wheels because they lack an objective mechanism for identifying and quantifying relationships in the data."
T329 139400-139672 Epistemic_statement denotes One of these projects, NetSurv, will link diverse databases in real time, will support dynamic visualisation (linked windows and cartographic and statistical brushing), and will include surveillance and pattern recognition statistics for separating true signal from noise.
T330 139673-139844 Epistemic_statement denotes This will enable prospective analysis of incoming health data (the continuous monitoring of health data, combining historical data with new information as it is received).
T331 140069-140289 Epistemic_statement denotes An early version of the architecture, but one that is linked only to static cancer mortality outcomes has been developed for the US NCI and may be downloaded from https://www.terraseer.com/atlas viewer.html ( Figure 6 ).
T332 141664-141810 Epistemic_statement denotes The third project is Daniel Carr's micromap plots on the NCI/CDC State Cancer Profiles Web site http://statecancerpro files.cancer.gov/micromaps/.
T333 141811-142093 Epistemic_statement denotes In the future, it may become possible to incorporate BioMedware's disease trend monitoring techniques and novel visualisation approaches that are currently being developed within the NetSurv project (as well as tools like GeoDa) as analytic components in other surveillance systems.
T334 142094-142216 Epistemic_statement denotes However, early NetSurv pilot results showed that its Web-based interface was difficult, slow, and not user friendly [96] .
T335 142217-142476 Epistemic_statement denotes Though we definitely need rigorous, "objective approaches that transcend the subjectivity of the human eye", we also equally need easy and reliable tools suitable for use by non-expert statisticians (mainstream public health practitioners and informaticians).
T336 142477-142645 Epistemic_statement denotes Users, including policy makers, may be tempted to infer causation from correlation and to make inferences about individuals from population data (the ecologic fallacy).
T337 142646-143059 Epistemic_statement denotes While conclusions based on an analysis at the aggregate level are likely to be limited by aggregation bias and by the ecologic fallacy (failing to identify the true nature of cause-effect relationships at the level of the individual), conclusions based on analysis at the individual level may be also limited by the atomistic fallacy (failing to consider the broader context in which individual behaviour occurs).
T338 143060-143090 Epistemic_statement denotes A balanced approach is needed.
T339 143091-143427 Epistemic_statement denotes GIS technology could be used to link data for an individual (individual predictors) with contextual information and ecologic predictors aggregated at a variety of geographic (community) levels, enabling the preparation of multi-level spatial models to better evaluate and distinguish biological, contextual, and ecological effects [7] .
T340 143428-143621 Epistemic_statement denotes The potential discrepancy between the place of diagnosis and that of the exposure to environmental variables influencing the particular health outcome(s) in question must be taken into account.
T341 143622-143680 Epistemic_statement denotes We need to consider the daily activity spaces of patients.
T342 143681-144041 Epistemic_statement denotes Understanding the individual's time-space history can provide important (aetiological) information not only for the epidemiologist, but also for the clinician, and should be considered in order to address the effect of individuals' high mobility/activity space on any identified disease patterns, and to avoid erroneous aetiological hypotheses and conclusions.
T343 144042-144128 Epistemic_statement denotes The problem is particularly acute for diseases that have a long lag or latency period.
T344 144250-144317 Epistemic_statement denotes Clearly, complete datasets of this nature are currently rare [97] .
T345 144318-144596 Epistemic_statement denotes Back in 1992, Openshaw (cited in [81] ) identified the following sources of GIS data error: errors in the positioning of objects, errors in the attributes associated with objects, and errors in modelling spatial variation (e.g., by assuming spatial homogeneity between objects).
T346 144856-145022 Epistemic_statement denotes The scale level should be appropriate for the issues being investigated in an analysis, otherwise the results will not be meaningful and may be even misleading [11] .
T347 145023-145187 Epistemic_statement denotes Different diseases have patterns that are interesting at different spatial scales, and the optimum scale is the one that reveals the most interesting pattern [14] .
T348 145188-145358 Epistemic_statement denotes Moreover, because accuracy is scale-dependent, users should always determine if any resultant error at the currently selected scale is acceptable for a given application.
T349 145359-145551 Epistemic_statement denotes Users also need to be continually aware of the errors that could arise when map data compiled for different purposes, and frequently, at different scales are merged into one application [81] .
T350 146358-146573 Epistemic_statement denotes Users can view the data in the form of maps, animated (slideshow) maps, tables, scatterplots, boxplots, and/or histograms, and can also use the software to perform statistics to evaluate spatial pattern in the data.
T351 146617-146802 Epistemic_statement denotes Such variations are often encountered in cancer research (to give an example), and can result in serious problems when pooling data from different locations for a common analysis [81] .
T352 146893-147128 Epistemic_statement denotes National data reported to WHO is problematic because of differences between countries in adequacy of testing facilities and reporting practices, varying definitions of what constitutes a case of AIDS, and political distortions of data.
T353 147280-147393 Epistemic_statement denotes Paucity of biomedical facilities in rural areas usually means many health conditions there pass unreported [81] .
T354 147394-147712 Epistemic_statement denotes Since it is impossible (in practice) to perform error-free spatial analysis, users must develop increased sensitivity to and awareness of the various types of data errors and uncertainty, as well as competency in techniques for recognising and reducing their negative impact on conclusions drawn from spatial analysis.
T355 147713-147998 Epistemic_statement denotes For example, the MARA (Mapping Malaria Risk in Africa -http:// www.mara.org.za/ project resorted to establishing a malaria risk atlas instead of an incidence atlas due to the lack of reliable data for determining the level of malaria incidence and mortality in African countries [81] .
T356 147999-148213 Epistemic_statement denotes Spatial data are strategically important to decision makers at all levels and thus should be an indispensable part of the basic infrastructure in the individual country, in line with roads, hospitals, schools, etc.
T357 148344-148887 Epistemic_statement denotes An infrastructure has the following characteristics: (1) users are aware that "somebody" maintains the infrastructure, but do not regard this maintainer as an owner; (2) users expect it to always be available, even if there is a fee or other requirement for its use; (3) the delivery or provision of the service is largely standardised, and as a result of this, users take it for granted because of the ease of use; and (4) an infrastructure is expensive to develop and maintain, and the returns from the investment are usually long term [6] .
T358 149517-149751 Epistemic_statement denotes The contents of a distributed geolibrary are not limited to information normally associated with location maps or images of the Earth's surface, but also include any other information that can be associated with a geographic location.
T359 149752-149891 Epistemic_statement denotes A geolibrary is distributed if its users, services, metadata, and information assets can be integrated among many distinct locations [98] .
T360 149892-150042 Epistemic_statement denotes A distributed geolibrary would support collaborative work, such as multidisciplinary research by teams, and decision-making by groups of stakeholders.
T361 150043-150245 Epistemic_statement denotes It should be also possible to access a distributed geolibrary right in the field where information is needed most (especially in emergency management) using portable systems and wireless communications.
T362 150246-150396 Epistemic_statement denotes Moreover, specialised sensors may be brought to the field, supplying new data that will have to be integrated with existing data in the library [98] .
T363 150704-150863 Epistemic_statement denotes In addition, there are a variety of social and organisational issues, privacy concerns and intellectual property rights that also need to be catered for [98] .
T364 150864-151254 Epistemic_statement denotes To demonstrate how important the concept of geolibraries is, reference [98] provides some very realistic example scenarios (see http://www.nap.edu/html/geolibraries/ ch1.html), including one about a public health researcher who wants to analyse the complex associations of environment and disease in a particular urban area, and another one dealing with a chemical spill emergency response.
T365 151255-151425 Epistemic_statement denotes Information resources through distributed geolibraries could greatly assist rapid response to such emergencies and longer-term efforts aimed at prevention and mitigation.
T366 152655-152807 Epistemic_statement denotes Today more than any time before, the US federal government is fully supporting the premise that digital spatial data constitute a federal capital asset.
T367 152808-153063 Epistemic_statement denotes The return on spatial investment can be highly cost effective through the onetime development of spatial data, and the subsequent sharing of that data among many users, at all levels of government and all sectors, over time ("build once, use many times").
T368 153319-153687 Epistemic_statement denotes This will enable immediate discovery and "one-stop" access to spatial metadata and data via a single Internet location/interface for different kinds of analyses and improved decision-making, and will eliminate the redundancies of costs associated with (duplicate efforts of) spatial data collection, conversion between formats, production and dissemination [58, 100] .
T369 153688-154042 Epistemic_statement denotes To achieve its vision, the Geospatial One-Stop initiative has launched Geodata.gov http://www.geodata.gov/, a Web-based portal for one-stop access to maps, data and other spatial services that will simplify the ability of all levels of government, private sector, academia and citizens to find spatial data and learn more about spatial projects underway.
T370 154602-154858 Epistemic_statement denotes However, it is expected that all current national metadata specifications, e.g., the US FGDC-STD-001-1998 and the UK GIgateway Discovery Metadata Specifications (see below), will ultimately converge to ISO 19115/19139 in the near future [102] [103] [104] .
T371 155687-156041 Epistemic_statement denotes However, many of the returned metadata records had incomplete/empty fields, and no instant access over the Internet to the actual datasets they are describing, or to a license agreement/payment form to access these datasets, as one would expect from a comprehensive "onestop" Web-based clearinghouse (e-mail contact details are usually provided instead).
T372 157163-157313 Epistemic_statement denotes INSPIRE is founded on the following principles: (1) data should be collected once and maintained at the level where this can be done most effectively;
T373 157314-157873 Epistemic_statement denotes (2) it must be possible to combine seamlessly spatial data from different sources across the EU and share it between many users and applications; (3) it must be possible for spatial data collected at one level of government to be shared between all levels of government; (4) spatial data needed for good governance should be available on conditions that are not restricting its extensive use; and (5) it should be easy to discover which spatial data are available, to evaluate their fitness for purpose and to know which conditions apply for their use [105] .
T374 157874-157969 Epistemic_statement denotes A common infrastructure for spatial information in Europe can only be realised in the long run.
T375 158045-158218 Epistemic_statement denotes It is noteworthy that the US NSDI development activities, which started nearly ten years ago, are not yet complete with some serious gaps still needing to be addressed [5] .
T376 158530-158625 Epistemic_statement denotes It features six search methods that can be used any combination to retrieve the results needed.
T377 160782-160872 Epistemic_statement denotes (3) Framework: includes base layers, which will probably differ from location to location.
T378 161625-161871 Epistemic_statement denotes A free how-to book, "Developing Spatial Data Infrastructures: the SDI Cookbook", is also available for downloading from GSDI Web site in several languages; the English version is available from http://www.gsdi.org/pubs/cook book/cookbook0515.pdf.
T379 162025-162152 Epistemic_statement denotes It includes recommended existing and emerging standards and specifications, as well as business case examples of best practice.
T380 162212-162470 Epistemic_statement denotes The vision and services presented in this section involve SDI-like structures and arrangements or rely on early "small-scale" SDI implementations, and would certainly benefit from the presence of mature SDIs covering the regions where these services operate.
T381 162471-162769 Epistemic_statement denotes The US CDC define public health surveillance as "the ongoing systematic collection, analysis, and interpretation of health data essential to planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those who need to know.
T382 163188-163280 Epistemic_statement denotes This should ideally be linked to public health action, which is the problem-solving process.
T383 163281-163565 Epistemic_statement denotes Traditionally, surveillance was used for acute infectious diseases, but over the past decades there has been a significant expansion of surveillance into new areas of public health concern including injuries, environmental health, occupational safety and health, and chronic diseases.
T384 163831-164040 Epistemic_statement denotes It is unlikely that without an event or alert to raise his or her index of suspicion, a physician will attribute the early symptoms and signs of disease in a bioattack victim appropriately and report the case.
T385 164041-164309 Epistemic_statement denotes A key limitation of the current system is that the lone physician is blind to the cases his or her colleagues in a nearby hospital are seeing -knowledge that might lead the physician to consider uncommon diseases more strongly in his or her diagnostic reasoning [55] .
T386 164620-164828 Epistemic_statement denotes The question remains, if we build such systems (some early examples already existsee below), what data should we monitor in real or near real time in order to be able to identify a covert bioterrorist attack.
T387 164829-164993 Epistemic_statement denotes Syndromic surveillance methods that can detect disease at an earlier stage are increasingly becoming an important research direction for public health surveillance.
T388 164994-165267 Epistemic_statement denotes Because the data used by syndromic surveillance systems cannot be used to establish a specific diagnosis in any particular individual, syndromic surveillance systems must be designed to detect signature patterns of disease in a population to achieve sufficient specificity.
T389 165268-165683 Epistemic_statement denotes For example, it would be irrational to use only the symptom of fever to attempt to establish a working diagnosis of inhalational anthrax in an individual, but it would be very sensible to consider anthrax release in a community if we were to observe a pattern of 1,000 individuals with fever distributed in a linear streak across an urban region consistent with the prevailing wind direction two days earlier [55] .
T390 166380-166682 Epistemic_statement denotes Mandl et al also suggest using data already collected for other purposes whenever this is possible, since implementing new data collection processes can have prohibitive costs, and healthcare workers have repeatedly demonstrated poor compliance with additional data collection and administrative tasks.
T391 166683-166902 Epistemic_statement denotes They also recommend designing "dual use" systems and not only focusing on the detection of bioterrorism or very rare outbreaks in order to boost the sustainability and long-term funding viability of such systems [109] .
T392 167126-167463 Epistemic_statement denotes The dream now is to develop a universal multivariate surveillance system that can collect, analyse and interpret health-related information worldwide using modern information infrastructures for the global prevention of a wide range of health problems, or at least the early detection of such problems in order to mitigate their effects.
T393 167464-167686 Epistemic_statement denotes GIS technologies and services that can function proactively in real time are extremely and critically important to realise this global public health surveillance vision (and indeed any smaller-scale surveillance services).
T394 169915-170146 Epistemic_statement denotes MAC can produce GIS maps from important prediction model outputs, e.g., a hurricane wind model, a toxic plume model or an earthquake model, coupled with real-time data to provide estimates for projected damages in affected regions.
T395 170147-170326 Epistemic_statement denotes It can also generate maps from damage assessment data after a disaster has occurred to visualise actual damages by analysing collected aerial reconnaissance and ground truth data.
T396 170327-170693 Epistemic_statement denotes This can help emergency managers appreciate the spatial extent of damage, learn who was affected by the disaster and which resources were affected, and make timely, informed decision accordingly (e.g., a plume model can help determine those areas requiring evacuation; early informed interventions almost always result in mitigation of disaster effects) [111, 112] .
T397 170942-171296 Epistemic_statement denotes The WHO has developed a comprehensive Event Management System to manage critical information about outbreaks and to ensure accurate and timely communications between key international public health professionals, including WHO Regional Offices, Country Offices, collaborating centres and partners in the Global Outbreak Alert and Response Network [114] .
T398 171891-172196 Epistemic_statement denotes Web-based maps allow for real-time or near-real-time map updates based on the latest datasets, for interactivity to be incorporated into the maps (desktop GIS-like functionality, e.g., drill-down and zooming), and for wider and more rapid dissemination of information (compared to other publishing media).
T399 172197-172483 Epistemic_statement denotes Some of the best examples of Web-based maps were produced during the latest SARS outbreak, which is considered the first major new infectious disease of the 21 st century and the Internet age that took full advantage of the opportunities for rapid spread along international air routes.
T400 173067-173304 Epistemic_statement denotes This kind of support is vital for improving global vigilance and awareness at all levels, and for making well-informed decisions when designing and following up epidemic control strategies or issuing and updating travel advisories [80] .
T401 173666-173857 Epistemic_statement denotes This proactive, geographically based approach can deal more effectively with and provide early warnings of health threats and disease outbreaks, particularly those caused by bio-weapons [2] .
T402 174020-174304 Epistemic_statement denotes By the time someone is admitted to an acute care hospital with a communicable disease, that person may have been symptomatic for days or weeks and may have already been seen by healthcare professionals repeatedly, and would have already spread the disease to large numbers of persons.
T403 174602-174765 Epistemic_statement denotes From a community health perspective, a spike in the number of prescriptions for Benadryl in one area could be used as an indicator of a possible smallpox outbreak.
T404 174766-174884 Epistemic_statement denotes A CHSS should be able to automatically detect such spike and raise an alarm early enough to contain the outbreak [2] .
T405 174938-175078 Epistemic_statement denotes Human intervention should not be required until preestablished critical levels -in the number and/or clustering of occurrences -are reached.
T406 175193-175327 Epistemic_statement denotes However, the transition from episodic investigation to ongoing monitoring using GIS requires more robust data collection and analysis.
T407 175696-175816 Epistemic_statement denotes To be reliable for the purposes of a CHSS, population-based data must also describe relatively small geographical areas.
T408 177197-177415 Epistemic_statement denotes Real-time (continuous stream) transfer of data is to be preferred to batch transfer of data, as the latter may delay detection of suspicious events by as long as the time interval (periodicity) between batch transfers.
T409 177416-177533 Epistemic_statement denotes For example, a surveillance system with daily batch transfer may delay by one day the detection of an outbreak [55] .
T410 177534-177668 Epistemic_statement denotes Time intervals as small as hours can make a difference when a large cohort is exposed to rapidly progressing diseases such as anthrax.
T411 177847-177962 Epistemic_statement denotes Preliminary studies suggest that sales of OTC healthcare products can be used for the early detection of outbreaks.
T412 178809-178976 Epistemic_statement denotes Soon after such an exposure, the cohort will become symptomatic, and, depending on the symptoms, may begin self-treatment and then either recover or seek medical care.
T413 179321-180444 Epistemic_statement denotes Wagner et al cite the following desiderata for systems like RODS' NRDM: (1) collection and analysis of data in as near as real time as possible; (2) completeness of sales data collection (>=70% is considered an adequate figure) for both early detection and sensitivity to smaller outbreaks; (3) availability of precise spatial information like individual store locations, or at least store Zip Codes to support adequate spatial analysis of sales data; (4) collection of supplemental data, e.g., about retailers' promotions or how day of the week affects local sales volumes; (5) a system for maintaining UPC code masters and mappings to analytic categories (as new product codes are assigned); (6) an effective link with the intended users of the system (public health authorities) to effect the desired actions (e.g., order quarantine); and (7) as most large urban population centres cross jurisdictional health boundaries, a centralised national approach is recommended to provide a complete picture of the health of contiguous regions and prevent any redundant data collection for overlapping nearby jurisdictions [56] .
T414 180746-180831 Epistemic_statement denotes RODS also has a Web-based user interface that supports temporal and spatial analyses.
T415 180832-180988 Epistemic_statement denotes RODS' password-protected, encrypted Web site allows users to review healthcare registration and sales of OTC healthcare products on epidemic plots and maps.
T416 181884-182125 Epistemic_statement denotes A user can quickly spot whether the map is predominantly green with a scattering of blue Zip Codes as would be expected, or whether there are confluent or linear patterns of blue, yellow, orange, or red indicating "unusual" sales activities.
T417 182338-182538 Epistemic_statement denotes This procedure is intended to produce a "normalised" map that is very sensitive to sudden increases in product counts as would be the case in a medium-to large-scale air, food, or water contamination.
T418 182539-182675 Epistemic_statement denotes Alternative data transformations are possible using differ-ent signal processing approaches focused on detecting more gradual increases.
T419 182676-182853 Epistemic_statement denotes RODS researchers plan in the near future to screen the maps automatically with spatial scan statistics to identify those with anomalies suggesting a need for human review [56] .
T420 182994-183122 Epistemic_statement denotes However, deployment of such systems requires skilled network engineers, Oracle database administrators, and interface engineers.
T421 183123-183276 Epistemic_statement denotes An application service provider model for RODS (and similar services) seems more suited for those health organisations with no access to that skills set.
T422 183277-183358 Epistemic_statement denotes Such organisations can form coalitions to share the costs of such services [55] .
T423 183359-183574 Epistemic_statement denotes The relationship between physical environment and health is now accepted as complex, with environment acting not just directly but indirectly and in association with other influences to affect health and well-being.
T424 184164-184407 Epistemic_statement denotes Its purpose will be to collect, hold and, as appropriate, analyse and interpret temporally and spatially tagged environmental and related health data throughout Scotland (e.g., attempt to correlate environmental exposures and health outcomes).
T425 184517-184688 Epistemic_statement denotes EHS3 developers need to determine what information is currently available to begin with, and also need to address the problems of incomplete health and environmental data.
T426 185493-185737 Epistemic_statement denotes In conformity with surveillance principles, data gathering will be ongoing and regular outputs will be agreed which will inform policy and action (as an evidential basis for action) to promote improved environmental standards and public health.
T427 185738-185933 Epistemic_statement denotes With appropriate development, the system will also have potential as a predictive tool for managing environmentally occasioned (including weather-related) fluctuations in demand for NHS services.
T428 186540-186703 Epistemic_statement denotes The data are used to generate the daily updated London urban air pollution maps, which are published on LAQN Web site http://www.erg.kcl.ac.uk/london/asp/home.asp.
T429 187110-187416 Epistemic_statement denotes Accurate and statistically representative locational information along with standardised quality-controlled measurements of environmental exposures, over time, are essential if one is to perform robust spatial statistical analyses of suspected associations between the environment and human diseases [13] .
T430 187557-187702 Epistemic_statement denotes From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving.
T431 188104-188415 Epistemic_statement denotes However, although multiple novel spatial statistical and GIS methods are potentially available, we still need to unambiguously determine which method(s) specifically should be used by practitioners for each specific health condition of interest, and whether the proposed methods are cost-effective and scalable.
T432 188537-188649 Epistemic_statement denotes A good starting point may be the CDC "Guide to Community Preventive Services" http://www.thecommunityguide.org/.
T433 188650-189301 Epistemic_statement denotes Topics identified in this guide (e.g., alcohol abuse, cancer, diabetes, mental health, motor vehicle occupant injury, oral health, physical activity, sexual behaviour, social environment, tobacco product use, vaccine preventable diseases, violence) could be addressed one by one by conducting a focused review of GIS literature on each topic, and then categorising the "nature of the scientific evidence" documenting whether GIS add any value to our understanding and management of the reviewed topic and/or the evidence that it would be feasible and cost-effective for the respective public health programmes tackling the reviewed topic to adopt GIS.
T434 189302-189415 Epistemic_statement denotes This could inform the development of successful GIS business plans for the health conditions under consideration.
T435 189755-189979 Epistemic_statement denotes (However, as is the case with any country-specific GIS research and publications, care should be exercised when extending findings and recommendations to other countries with different health and healthcare system settings.)
T436 190351-190766 Epistemic_statement denotes Also organising focus groups that bring together programme administrators, practitioners and the public is required to complement the expected gaps and deficiencies in current GIS literature, and to define the key questions that decision makers would want to be able to answer with GIS for any health condition under review, and think explicitly about what data and methods should be used to answer those questions.
T437 190767-191047 Epistemic_statement denotes Traditionally, two broad types of GIS applications can be distinguished which also reflect the two traditions in health geography (geography of disease and geography of healthcare systems), namely health outcomes and epidemiology applications and healthcare delivery applications.
T438 191355-191543 Epistemic_statement denotes However, despite all these potentials for GIS, they remain very much under-utilised in the UK NHS in mostly lowlevel, non-strategic tasks and in a largely fragmented and uncoordinated way.
T439 192044-192367 Epistemic_statement denotes This can be achieved by 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.
T440 192486-192705 Epistemic_statement denotes However, this author thinks that a common coherent UK initiative is urgently needed to build a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time.
T441 192883-193028 Epistemic_statement denotes For each health condition amenable to GIS processing within the NHS, the desired information output and ways of using it must be also determined.
T442 193029-193210 Epistemic_statement denotes Tomlison's methodology is targeted at people who have been charged with launching or implementing GIS for their organisation, and is thus strongly recommended in this regard [119] .
T443 193211-193309 Epistemic_statement denotes Perhaps the NHS should also take a closer look at the three sets of standards published by the US
T444 193310-193526 Epistemic_statement denotes • Work-time constraints, and insufficient staff and financial resources to implement systems fully and to undertake data exchange duties with other organisations • Lack of skills and insufficient training or guidance
T445 193527-194012 Epistemic_statement denotes • Lack of digital data in appropriate formats • Problems ensuring data quality • Data confidentiality issues and the currently ambiguous criteria to conform to data confidentiality requirements • Lack of a service-level agreement with Ordnance Survey (or other providers) for NHS organisations to be able to access base digital data • Organisations not being aware of data held by other organisations (lack of a comprehensive and up-to-date central metadata catalogue or clearinghouse)
T446 194013-194378 Epistemic_statement denotes • Limited awareness of the benefits of geo-information and joined-up working arrangements • Lack of demand from within some organisations to the use of GIS (directors not being aware of value of GIS rather than not being committed to GIS) • Lack of a clear GIS strategy and of a clear organisational policy for exchanging data series http://www.dartmouthatlas.org/.
T447 194863-195174 Epistemic_statement denotes However, GIS have been usually applied to time-limited, single, isolated aetiological research or surveillance issues processing mainly retrospective data rather than to ongoing, broad efforts and wide-scale applications processing real-time or near-real-time data for health planning, promotion and protection.
T448 196016-196229 Epistemic_statement denotes It must be stressed that the contents of a national health spatial data infrastructure are not just any georeferenced health data but, in addition, the foundation spatial data to which health data can be attached.
T449 196434-196821 Epistemic_statement denotes In a personal e-mail communication with Professor Gerard Rushton, he argues PCSAs and Hospital Service Area data layers are spatial data foundation layers because other US health data often collected and maintained locally, are more valuable after they have been linked to these layers (Gerard Rushton, Department of Geography, University of Iowa, personal communication -December 2003).
T450 197244-197556 Epistemic_statement denotes Table 2 presents a summary of the recipes and main recommendations provided by various specialist groups and researchers from around the world for a successful implementation of a national/regional/global spatial data and information infrastructure that can also support real-time GIS public health applications.
T451 197557-197774 Epistemic_statement denotes Raising awareness activities and campaigns are much needed and should put strong emphasis on real-world, practical GIS scenarios and examples to reach out to policy and strategy makers in the health and other sectors.
T452 197847-197954 Epistemic_statement denotes Training should cover epidemiological methods to ensure appropriate use of GIS technology in public health.
T453 197955-198241 Epistemic_statement denotes Public health professional specialties/bodies need to recognise continuing education credit for individuals who participate in GIS software training (perhaps the recently established NHSU, the corporate university for the NHS -http://www.nhsu.nhs.uk/, could play a role in this regard).
T454 198242-198499 Epistemic_statement denotes Some excellent Web-based training material and courses are already available free of charge, but there is still an urgent need for many more training modules to be developed and most importantly to be thoughtfully and coherently integrated in sensible ways.
T455 198754-198983 Epistemic_statement denotes A good example of such gems that should be exposed and disseminated are Boscoe and Pickle's recently published guidelines for choosing geographic units for choropleth rate maps in the context of public health applications [127] .
T456 198984-199114 Epistemic_statement denotes The best, current evidence derived from GIS research should be always embedded (and regularly updated) in all training programmes.
T457 199115-199212 Epistemic_statement denotes This is one important way of linking the academia and research communities to realworld practice.
T458 199330-199610 Epistemic_statement denotes Summary of the recipes and main recommendations provided by various specialist groups and researchers from around the world for a successful implementation of a national/regional/global geo-information infrastructure that can also support real-time GIS public health applications.
T459 199611-200217 Epistemic_statement denotes Developing geospatial culture and awareness/changing people and organisations • Vision and leadership at the highest levels (e.g., departments of health) • Official/governmental support • Fostering a culture of data sharing and joined-up working at all levels (local to global) that considers spatial information an asset • Raising awareness activities and campaigns; reaching out to policy and strategy makers in the health and other sectors • Policies and practices actively promoting the exchange and reuse of geo-information, and greater public access to it • Education, training, and capacity building
T460 200218-200634 Epistemic_statement denotes • Appropriate human, financial and technical resources • Providing support to organisations lacking the necessary resources to join in common, coherent national/regional/global initiatives • Adequate information telecommunications technology infrastructures and bandwidth • Moving to the Web and building all necessary critical connectivity/geospatial infrastructure that should not be independently recreated by all
T461 200635-200918 Epistemic_statement denotes • Developing unambiguous legal frameworks and policies, as well as suitable technical solutions to address the crucial issues of individual privacy, national security, and data confidentiality • Adequate protection measures of networked geo-information assets against cyber terrorism
T462 200919-201359 Epistemic_statement denotes • Up-to-date and accurate core digital geo-datasets • National data utilities/services (industry standard services that are independent of any particular user interface) • Standardised metadata in centralised catalogues or clearinghouses • Adopting common standards to address integration and interoperability issues (GML and other technologies; health-related standards) • Automated geocoding • Automated conflation of geospatial databases
T463 201360-202374 Epistemic_statement denotes • Do not just focus on data; develop applications • Adopting common semantics, data models (ontologies) and health indicators; the latter should also cover population demographics and socio-economic factors • A deep understanding of data and industry; reaching a consensus on the inputs and outputs in different health and healthcare applications • Developing increased sensitivity to and awareness of data problems and errors, as well as competency in techniques for recognising and reducing their negative impact on conclusions drawn from spatial analysis • Appropriate and robust statistical and epidemiological methods must be used to avoid the consequences of visual bias and various data problems in GIS processes • Seamless integration into routine workflows of intelligent software tools that are easy-to-use by mainstream public health practitioners, and which allow only valid visualisations and analyses of data from a variety of sources across space and time • User interface accessibility requirements
T464 202375-202966 Epistemic_statement denotes • Development of effective partnerships (including community/academia collaboration), and involvement of and coordination between all stakeholders and users • Community data sharing must be systematic, uniform and regular, and governed by adequate data-sharing agreements • Building interdisciplinary teams with expertise in public health and epidemiology, medical informatics, medical statistics, health economics, computer science, law, and engineering • Other important points: joint ownership of projects by their respective stakeholders; shared commitment; having realistic expectations
T465 202967-203514 Epistemic_statement denotes • A combined top-down and bottom-up incremental implementation approach • Assessing current state of geospatial readiness to respond to normal and emergency community health needs, and identifying beacon sites as examples to follow • Fault tolerance at all levels (hardware and software) • Full systems redundancy, and standardised database replication measures and off-site backups (these are also important aspects of data security) Sufficient financial resources must be available to invest in training people and retaining technical expertise.
T466 203675-203915 Epistemic_statement denotes Reliable intranet and Internet environments with adequate bandwidth 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.
T467 203999-204123 Epistemic_statement denotes Today, solutions exist that can preserve data confidentiality while still enabling fine-level analyses and reliable results.
T468 204124-204928 Epistemic_statement denotes These solutions involve: (1) the use of statistical and epidemiological methods to mask the geographic location of data in a way that can still permit meaningful analysis, e.g., special types of spatial and temporal aggregation of data; (2) the development and use of software agents and health system resident components that can process an analysis request and return a result to the data user without exposing any individual-level health data; (3) the creation of secure networked environments with limited and multiple levels of access (to confidential data) in which public health researchers can be carefully monitored to ensure protection of individual and household confidentiality; and (4) the development, publication and strict enforcement of appropriate, unambiguous policies and regulations.
T469 205226-205479 Epistemic_statement denotes All relevant infrastructure and systems stakeholders should be involved in the development of appropriate data models (or ontologies) for their various applications to facilitate data selection and integration, and ensure a common understanding of data.
T470 205659-206068 Epistemic_statement denotes Data/analysis problems and errors are not uncommon and include scale issues, the "small numbers" problem, issues of the atomistic and ecologic fallacies, changing activity spaces of mapped subjects, and the frequent variations between different locations in data collection methods and standards, in the recorded items, particularly data on patient residence, and in diagnostic standards and case definitions.
T471 206069-206310 Epistemic_statement denotes Users must develop increased sensitivity to and awareness of the various types of data errors and uncertainty, as well as competency in techniques for recognising and reducing their negative impact on conclusions drawn from spatial analysis.
T472 206311-206696 Epistemic_statement denotes There is also a need for intelligent tools specifically designed for public health, and seamlessly weaved into everyday public health workflows and decision-making processes to enable users to focus and spend the larger part of their work time on what they want to achieve rather than on learning and overcoming the limitations of tools they are supposed to use to achieve their goals.
T473 206697-206847 Epistemic_statement denotes The tools must be able to convey meaningful, bottom-line conclusions that can support the decision maker rather than just outputting bunches of facts.
T474 206848-207026 Epistemic_statement denotes The ideal tools also need to be fault-tolerant and capable of analysing and presenting assembled data in ways that facilitate only appropriate interpretations of integrated data.
T475 207027-207343 Epistemic_statement denotes This can be achieved by using some form of user friendly, "intelligent", goal-oriented health GIS wizards (based on robust statistical and epidemiological methods where appropriate), so that only valid results and maps are produced, even when users attempt to select inappropriate settings for a particular analysis.
T476 207344-207603 Epistemic_statement denotes The tools are also best designed and built to work in modular and nested fashions, so that they may be reused, linked and combined in different ways as needed to serve different scenarios and compound situations with little or no modifications (of the tools).
T477 207811-207978 Epistemic_statement denotes According to Openshaw, the ideal spatial analysis methods should be safe and user friendly for use by people with no higher degrees in statistical or spatial sciences.
T478 207979-208189 Epistemic_statement denotes The methods should also respond to user needs on the ground, be highly automated, explicitly handle spatial data imprecision, and produce self-evident results that can be mapped and communicated to non-experts.
T479 208385-208569 Epistemic_statement denotes Data-sharing agreements are needed that address confidentiality and other concerns, allow redistribution of data to any public health authority, and permit data to be used in research.
T480 208809-208922 Epistemic_statement denotes It is recommended that a combined top-down and bottom-up incremental (phased) implementation approach be adopted.
T481 209044-209391 Epistemic_statement denotes In fact, much of the wider vision of a national/regional/global public health spatial data and information 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.
T482 209392-209525 Epistemic_statement denotes Short-term bottom-up projects can feed valuable experience into the formulation and revision of the relevant policies and strategies.
T483 209526-209735 Epistemic_statement denotes Moreover, by creating "proof of concept and benefits applications", these projects can be also used to gain and continue political support for the wider vision, and secure further funding towards achieving it.
T484 210679-210838 Epistemic_statement denotes Such applications currently involve limited SDI-like arrangements, and would certainly benefit from the development of mature SDIs in their respective regions.
T485 210839-211177 Epistemic_statement denotes The dream remains to develop a universal multivariate surveillance system that can collect, analyse and interpret health-related information worldwide using modern information infrastructures for the global prevention of a wide range of health problems, or at least the early detection of such problems in order to mitigate their effects.
T486 211178-211400 Epistemic_statement denotes GIS technologies and services that can function proactively in real time are extremely and critically important to realise this global public health surveillance vision (and indeed any smaller-scale surveillance services).
T487 211588-211886 Epistemic_statement denotes As the reader might have noticed, there are many requirements, e.g., standards and security, and ingredients of success in common to both the nation-wide implementation of integrated electronic health and social care records and the building of a national spatial health information infrastructure.