The paper builds on contributions from OGC Health Domain Working Group (DWG) members and is informed by OGC Health DWG sessions from 2012-2019, including Summits in 2016 and 2017. The OGC Health DWG provides a forum for discussion of key geospatial interoperability requirements, issues and potential solutions relating to the health domain. The following key application areas are addressed providing a broad scope of market requirements and functional areas:

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.

Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation.

Experts under the Group on Earth Observations (GEO) supported the development of a study to identify Earth Observations required to support a Health Societal Benefit Area (Health SBA) under Task US-09-01a [3]. The Health SBA was separated into three areas dealing with:

Three teams documented requirements for Earth Observation in each of these areas.

Earth Observations for Health (EO4HEALTH) is a community activity under the GEO 2017-2019 Work Program. Its goal is the advancement of integrated information systems to reduce environmental health related risks, focusing on:

An Implementation Plan for years 2020-2022 was developed in March 2019.

EO2HEAVEN was funded by European Commission 7th Framework Program to advance understanding of the complex relationships between environmental changes and their impact on human health. The project advanced a system architecture and developed applications related to changes induced by human activities, with emphasis on atmospheric, river, lake, and coastal marine pollution. Recommendations on standards-based Spatial Information Infrastructure (SII) to support research of human exposure and early detection of infections were provided [4].

The project started on February 1, 2010 and ended on May 31, 2013 with results published in a publication called EO2HEAVEN - Mitigating Environmental Health Risks. The case studies that were examined were the impact of air quality on respiratory and cardiovascular diseases; relationship between industrial pollutant exposure and adverse respiratory outcomes; and the links between environmental variables and cholera.

The Canadian Geospatial Data Infrastructure (CGDI) implements a framework for data sharing and data integration by using standard based technologies. It has adopted many specifications addressed by the OGC, the Federal Geographic Data Committee (FGDC), the World Wide Web Consortium (W3C), and the ISO/TC211 standards committee on Geographic Information/Geomatics in describing, publishing, visualizing, accessing and manipulating geospatial resources, such as Catalog services interface, Web Map Service (WMS), Styled Layer Descriptor (SLD), Web Feature Service (WFS), and Web Processing Service (WPS) among others. These services can be chained together to implement complex tasks by defining a workflow, as was done in a pandemic simulation in 2007 funded by GeoConnections – Natural Resources Canada and the U.S. Geological Survey (USGS).

The collaboration between different governmental entities ensures interoperability for the CGDI. The GeoConnections program’s 3 phases, listed below, were completed as of 2015:

In 2015, an assessment was done to see how the CGDI presented data to various government agencies. The finding determined that there was an increased use in the CGDI but with only an 80% implementation success rate. The goal was to implement it to a full 100% success rate. In 2018, another study was conducted to establish the improvement potential of the CGDI. The results presented that many organizations were not using complete CGDI services. Going forward, many organizations will develop a workflow to incorporate CGDI into their data infrastructure.

In earlier work supported by the CGDI, a Health Representation XML (HERXML) schema was designed that consists of semantic, geometric, and cartographic representation of health data. HERXML enables web-based visual representation of health data to support, among other applications, policy makers and health planning efforts.

The Infrastructure for spatial information in Europe has defined the Human Health and Safety theme, including technical guidelines for data specifications, as shown in Figure 1 [5]. INSPIRE is a directive of the European Union (EU) that has been implemented across the 27 current member countries of the EU; European Free Trade Association member countries Iceland, Norway, Switzerland, and Liechtenstein; as well as the non-member states of the United Kingdom, North Macedonia, Serbia, and Turkey.

The Data Access for Risk Management (DARMA) initiative aims to increase the availability and accuracy of risk related information to allow decision-makers to simulate the impact of risk reduction measures and make informed decisions about risk reduction investment. The type of risk information useful to decision makers depends on the geographical location, the type of risk affecting the region (e.g., weather, natural disaster, and rapid urbanization), local policies, and more. GEO-DARMA addresses several articles of the Sendai Framework such as articles 24 and 25 that call for the "promotion and enhancement through International cooperation, including technology transfer, (of) access to and use of non-sensitive data, information, as appropriate, communications and geospatial and space-based technologies and related services." GEO-DARMA will define end-to-end solutions fostering the use of accurate Earth Observation data risk information products and services for evidence-based decision making.

GEO-DARMA is one of the major initiatives that are supported by space agencies as a follow-on action to the Sendai Framework for Disaster Risk Reduction 2015-2030. Their long-term goal is to use EO data and EO-based risk information to increase awareness with donor agencies like The World Bank, and to foster the promise of EO solutions. There are 3 phases beginning with a Concept Phase and continuing to a Prototyping Phase, that aim to establish demonstrations in representative areas of the added value of using satellite data for multi-hazard Disaster Risk Reduction (DRR) in an international context. The third and final phase is an Operational Phase in which GEO-DARMA is a selecting on a case-by-case basis the projects to move towards operations based on their benefits to stakeholders and sustainability.

The Linked Open Data for Global Disaster Risk (LODGD) group, as part of the interdisciplinary Committee on Data for Science and Technology (CODATA), aims to address the challenge of management and integration of disaster-related data for research and policy making. This Task Group has produced three white papers on this topic. In its first white paper publication [6], the group highlighted the importance of data interconnectivity from different scientific disciplines such as hydrology, meteorology, climate, civil engineering, land use, and public health. CODATA produced a second white paper, titled “Next Generation Disaster Data Infrastructure” stating the importance of developing an infrastructure that includes information systems and services that a region can depend on to gather, process, and display disaster data to reduce the impact of natural disasters [7]. A third expected white paper on “National Policy Study on Disaster Data around the World” was set to be published sometime in 2019.

The Disaster Loss Data (DATA) project, under the umbrella of the Integrated Research on Disaster Risk (IRDR) program, brings together stakeholders from different disciplines and sectors to study issues related to the collection, storage, and dissemination of disaster of loss data. A white paper was published established the need of disaster data collection and a data base to visualize the data.

The Medical Subject Headings (MeSH) is the US National Library of Medicine’s thesaurus. It provides a controlled vocabulary of terminology useful for indexing and cataloging biomedical and pharmaceutical information.

The Systematized Nomenclature of Medicine (SNOMED) provides a comprehensive controlled vocabulary for terms related to anatomy, diseases, findings, procedures, microorganisms, substances, and other topics. It is used by the U.S. Federal Government systems for the electronic exchange of clinical health information.

The Unified Medical Language System (UMLS) provides controlled vocabularies for biomedical information and health records. Useful applications that are built with UMLS can enable linking of records (via codes or terms) between doctor’s, care centers, pharmacies, and insurance companies.

Climate variability can pose significant threats to human health and well-being in the form of higher temperatures, increased extreme weather events, such as hurricanes and wildfires, decreased air quality, water shortages, and illnesses transmitted by food, water, and disease carriers such as mosquitoes and ticks. Extreme storms and temperatures can also disrupt the delivery of health services by causing damage to roads and transportation infrastructure, hospitals, clinics, wastewater treatment plants, and other facilities. Climate variability can also impact economic sectors that support health, such as energy, transportation, and agriculture.

As a case in point, there are many individuals in the U.S. who depend on electrically powered, life-saving medical devices, such as ventilators, cardiac devices, and home dialysis machines. These individuals depend on the medical devices to function properly, and therefore also depend on available and continuous electrical power. The U.S. Department of Health and Human Services (HHS) produces an emPOWER map (shown below in Figure 2) that provides a visualization by region (state, county, or ZIP Code level) as well as the specific number of people in tabular form of those who depend on such devices. For instance, the U.S. state of Georgia is home to 77,930 such individuals.

A Health SDI could help in the development of new ways to monitor, prevent, and respond to climate impacts on human health. Steps taken to prepare for climate impacts can improve health and provide other societal benefits, such as sustainable development, disaster risk reduction, and improvements in quality of life.

Applications arising from a Health SDI could help health authorities publish up-to-date maps showing various dimensions of disease, population health, and environment. Economies of scale can be leveraged when epidemiological research and health planning communities utilize a coordinated system to address inequalities in and stresses on health systems and healthcare provision, access, and promotion. Applications can be scaled up to support more sophisticated climate health, flood risk, and heat-event scenarios, particularly during health emergencies. The OGC Disaster Resilience pilot conducted in 2019 addressed these scenarios in part [8].

Research collaboratives such as the Lancet Countdown can also inform framework development. The Lancet Countdown tracks the world’s response to climate change and health benefits that may emerge from this response. This reporting is done annually, and different indicators are evaluated and changed as needed [9]. The Countdown proposes five thematic breakdowns:

These thematic breakdowns, along with associated indicator domains, could be used to provide organizing principles for a health SDI or perhaps even for the overall spatial information framework itself. There were 40 indicators in 2017. In 2018, a revision of the indicators led to the inclusion of a new indicator under the Public and Political Engagement breakdown called Corporate Sector Engagement in Health and Climate Change. The list of indicators from 2017 and 2018 appear in the Appendix.

Some populations are more vulnerable than others to changes in health status. Vulnerable populations of concern include children, pregnant women, those with low income, immigrant groups, indigenous peoples, communities of color, as well as older adults. It is well known that the bulk of the healthcare expenditure goes towards care for senior populations. In the U.S., 36% of healthcare spending is attributed to the 65 and over population, even though they make up only 16% of the U.S. population.

In order to address and reduce the vulnerability of aging populations, health organizations are increasingly turning to geospatial analysis to assess and prioritize areas for intervention based on location.

Additional efforts to enable healthy aging were discussed in the Health DWG Summit in 2016 and include, for example, efforts that can take advantage of indoor sensors to improve the mobility of elders. Further details are available on the DWG’s Wiki page.

Growth in the proportion of urban residents making up the global population can impact environmental sustainability, the availability and effectiveness of public services, economic growth and social resilience. Effective integration of human, physical, and digital systems would enable cities to be more prosperous, sustainable and resilient.

The World Health Organization (WHO) Healthy Cities project brings together hundreds of cities under its network to make health a priority on economic, social, and political agendas. Boulus and Al-Shorbaji [11] discussed the importance of Internet of Things (IoT) and geospatial analytics empowering healthy city decisions. One example is the wireless sensors connected to garbage bins to monitor trash levels in Barcelona. This not only provides data for optimization of data collection but can help minimize pollution and sanitation related illnesses. One could theorize that trash routes could be optimized to address the heaviest use garbage bins and best avoid any overflow. Overflowing waste containers can cause bacteria to grow, pollute air and water sources, and cause respiratory diseases, salmonella, and fever among others.

Mobile Health in an information technology field that advances the use of mobile devices to support health services and information. Information from mobile health and smart cities can improve healthcare and overall quality of life.

Smart Cities provide an exciting opportunity to continue and further drive advancements in the delivery of public services improving human life. Distributed Internet-of-Things and Internet-of-Medical-Things sensors on wearables, mobile medical devices, and even perhaps implantables together with a Smart City infrastructure allow for new means of gathering data on all aspects of life-in-the-city and provide health systems new and innovative means of responding to the specific needs of individual patients.

As another example, a Smart City can provide real-time monitoring of occupancy levels of inpatient and emergency room hospital beds, current stock of key medical supplies, and current clinical staffing levels at hospitals and other healthcare facilities, as well as contact tracing. Contact tracing has emerged as a crucial capability for tracking and controlling the transmission of an infectious disease, especially as the alternative being to lock down entire communities. The sensor infrastructure of a Smart City can allow not only tracking movement of a potentially infected individual, but also the identification of everyone who may have either come into direct contact with that individual or have been where that individual was and therefore possibly exposed to the disease.

This also demonstrates the integration of indoor location models with sensed occupancy data leveraging the IndoorGML and SensorThings API standards. This information can aid in balancing patient counts such as from emergency ambulance transport and, further, can be used by emergency medical services providers, such as 911 systems, to direct patients to the hospital with the clinical staff best suited to treat the patient’s current condition. Up-to-date information on the status of medical facilities will also aid in transferring patients between facilities. Enhanced visibility can also predict when medical supplies will run out, based on current stock and anticipated patient census, to automatically re-order supplies to ensure a safe, minimum level of supplies remain available. Also, such monitoring and visibility will aid in balancing patient counts during non-disaster scenarios for hospitals with overlapping service delivery areas.

Emergency and disaster response efforts will also benefit as Emergency Operations Managers will have knowledge of the specific medical supplies, clinicians, and pharmaceuticals to send and to where – greatly improving Emergency Medical Responders ability to provide medical care during a disaster.

Leveraging Smart Cities for health applications can be a key step in advancing OGC standards towards building a Health Spatial Data Infrastructure (SDI) that can be applicable to broad healthcare applications, including health system cost reduction efforts, disaster response scenarios, as well as improved health infrastructures in smart cities.

Natural and man-made disasters pose significant threats to human health in numerous ways – loss of housing, contamination of the food and water supplies, decreased air quality, exposure to pollutants, general environmental degradation, damage or force the closure of roads, airports, and other transportation infrastructure, as well as other potential impacts. Disasters can also disrupt the delivery of health services by damaging or causing the closure of hospitals, community clinics, and pharmacies or making them unavailable due to unpassable roads. An often-overlooked consequence of this is the reduced availability of prescription drugs. For many individuals, even a 1-3 day gap in medications can lead to significant and life threatening health complications. As of 2017, over 60% of Americans were on daily medications for one and often multiple chronic conditions ^[1]. Therefore, maintaining channels for healthcare delivery needs to be a key part of an emergency response capability.

A Health SDI can support disaster resilience effort by leveraging OGC standards and geocoding health data for secure sharing among appropriate emergency response team members, health complications can be avoided, and lives can be saved that may otherwise be lost to the disaster.

A Health SDI can identify and bring together data that can potentially aid disaster response effort, including:

A disaster response effort will be benefited by well-defined protocols for using health information in mapping applications while protecting privacy, to better understand the impact to human health caused by the disaster, as well as to understand the health interventions and responses needed to address that impact among the displaced and affected population. Applications arising from a Health SDI will help emergency response teams and health authorities publish up-to-date maps showing various dimensions of the disaster, such as disease prevalence or outbreak, changes to population health, and potentially the identification of baseline healthcare services that should be provided to all displaced members of the population. Economies of scale can be leveraged when all members of a disaster response team – both teams on the ground, as well as support teams away from the disaster site(s) utilize a coordinated system to address inequalities in health care provision, access, and promotion. Applications can be scaled up (down) to support larger (smaller) disaster scenarios, such as by geographic region and/or population size impacted.

Various global initiatives have provided guidance towards global indicators to help monitor the status of health in populations. The initiatives include Sustainable Development Goals (SDGs) developed by the U.N. and the Sendai Framework for Disaster Risk Reduction.

Maternal mortality is a significant public health issue and a strong indicator of a nation’s health status both nationally and internationally. The death of a mother has lasting consequences on family members and the larger society, ultimately representing one of the largest failures of a nation. The U.S. leads the developed world in its rates of both maternal and infant mortality. One of the issues the U.S. faces in its efforts in improving maternal care is the quality of data surrounding the incidents of maternal and infant mortality in the first place. The U.S National Vital Statistics System (NVSS) is the source of official maternal mortality statistics used for both subnational and international comparisons. However, this database utilizes statistics for which there is no gold standard in how death records are reported or collected.

Until the early 1990s, there was no systematic way to collect maternal mortality data in the country. Pregnancy-related death classifications were limited to a narrow classification listed on death certificates at the time, i.e. complications of pregnancy, childbirth, and the puerperium. The certificates used by states collected no information on whether a woman was pregnant at the time of death or had recently given birth. This means that the deaths which occurred during pregnancy for non-obstetric causes, like, high blood pressure or depression, as well as those that happen after birth, were not counted as maternal related deaths under local level reporting.

Death registration is based on state law where death certificates are filed and maintained in the state vital statistics offices. The states have recommended the use of the U.S. Standard Certificate of Death, which is revised once every 10 years in collaboration with states, National Center for Health Statistics (NCHS), and other federal agencies and subject matter experts. However, each state issues its own death certificate. States like West Virginia didn’t even introduce a pregnancy classification on their death certificates until 2017.

U.S. states continue to have different mechanisms for reporting maternal deaths. The very information on the death certificate is provided by two groups of persons: 1) the certifying physician, medical examiner, or coroner and 2) the funeral director. The cause of death, critical in understanding and responding to maternal and infant mortality, is supplied by either the certifying physician, the medical examiner, or the coroner.

However, state-by-state data is reviewed by Maternal Mortality Review Committees (MMRCs)—review groups in only around half of U.S states. Their role is to filter through the death certificates to determine if the cause of death is pregnancy or child-birth related, at times without access to the patient’s complete medical record. Each state then sends their statistics to the U.S. Centers for Disease Control and Prevention (CDC), who produce and release national-level data, after their own epidemiologists review the data to assess cause of death. While the review committee is a great step towards ensuring quality control, the official CDC data are not updated to reflect the findings of the committees, which means the national numbers on record aren’t just likely to be inaccurate—they are known to be wrong. Additionally, copies of the matching birth and fetal death certificates are sent – if they can be matched.

Producing reliable data on maternal mortality should not be an issue considering growths in the field of technology and healthcare. Yet for some reason, quality assurance and reporting methodologies remain a challenge. When conducting a review of the literature, we find that there is no consensus in reported death rates on a national level, with the reported range being quite large. The U.S. maternal mortality rate, considered to be 23.8 deaths per 100,000 live births, is anywhere between a range of 16 to 26.5 maternal deaths. Failure in producing an official maternal mortality rate stunts prevention effort.

The U.S shares its reporting challenges with Mexico, a country where misclassification of the cause of death is a major component of the maternal mortality problem. In fact, in Mexico, no single number exists for its maternal mortality rate. Because of the inconsistencies in reporting, the rate is a range, as shown in Figure 3 below. It is no coincidence that the United States parallels Mexico’s maternal mortality rates.

The different reporting methods and standards make it challengingly difficult to integrate all reported data to develop one concise, accurate view of Maternal mortality or any health indicator. This makes it nearly impossible to learn the posture of the overall health & wellbeing of a nation. If the United Nations Sustainable Development goals are to be successful, a necessary start would be to have a detailed and universally consistent reporting on the medical conditions underlying the indicators.

Currently, without a universal standard for reporting, healthcare systems lack in uniformity with respect to reporting requirements. This can lead to challenges in patient care, confusion in expectations, the inability to communicate lessons learned and adopt best practices, which can lead to decreased national health posture. This can be easily seen through Maternal Mortality reporting, as the process of reporting deaths is significantly different both between and within countries.

The most well-known pandemic of the last Century, as well as the most devastating in terms of both loss of life and economic costs was the Spanish Flu of 1918. However, the world has experienced a handful of pandemics since (see the table below) and as human mobility and interconnectedness grows, the ability for a novel viral strain to spread across the globe and infect populations with no previous immunity grows exponentially.

Adding to this list, on March 11, 2020, the World Health Organization (WHO) declared the outbreak caused by the novel strain of Coronavirus, which emerged in late 2019 and is known officially as COVID-19, as a pandemic [12]. National and international response to this pandemic have included international travel restrictions, the cancelation of nearly all large gatherings of people across the globe including public, entertainment, and sporting events – as well as religious pilgrimages and Sunday Mass at the Vatican, as well as the quarantine and social isolation of over a billion people across a majority of the world’s countries. Addressing COVID-19 will require concerted effort from government at all levels, the private sector, as well as the public.

The Health Risk Index discussed above that was developed through the Open Geospatial Consortium’s Disaster Resilience Pilot and supported by U.S. Federal Emergency Management Agency (FEMA), is an example of an application that resides on a Health SDI that can aid a pandemic response. By leveraging the diverse sets of data within an SDI’s library, the Health Risk Index can track the Transmission Risk of the pandemic to help Predict the next Hot Zones and Outbreaks of Coronavirus at the County and ZIP Codes levels within a country (pictured here for the U.S.) as well as Globally. The prediction can leverage data such as:

A Transmission Risk Index will enable deployment of medical resources in advance of the virus, rather than chasing the virus, and can significantly improve the ability to halt the spread of the contagion and treat the infected.

In addition, such an index can also guide eventual De-Quarantine efforts, allowing the resumption of normal daily and economic activity in ‘safe’ zones – or at least zones with the least risk of a ‘second bump’ of Coronavirus cases once social interaction is resumed. Speeding the safe resumption of normal economic activity will benefit the economy, as well as reduce the risks of mental health issues associated with social isolation.

The Health Risk Index can also provide guidance on where risk or mortality from the novel Coronavirus is high at multiple geographic resolutions (and shown in Figure 5 for the U.S.). By considering the underlying health status of the population, available medical resources, potential supply chain disruptions for medical equipment and resources, a Mortality Risk Index can be established that identifies both where emergency medical facilities and resources are needed as well as what contingencies need to be established to address shortcomings.

These resources provide health systems, local governments, and Emergency Management Agencies data analytic resources to help stem the tide of Coronavirus. As another benefit, by chaining together implementations of OGC standards (such as WMS, WFS, WPS, SOS, SensorThings API or OGC API - Features etc), scalable visualization of case counts, disease progression, and health resources can be displayed ensuring all users have the same view of the current state of the pandemic – this enables collaboration across the globe enabling coordinated response.

A Health SDI can be leveraged to address challenges in healthcare today, such as the issuing of reports surrounding the maternal mortality rate. An ideal approach to rectify the problem can be to develop a spatial data infrastructure that enables health data to be exchanged in Geography Markup Language (GML) in order to standardize the semantics and definitions of data being captured and reported. This will facilitate both integration of data from different sources and comparison of results reported from different health systems and nations.

A spatial data infrastructure will also allow the health data to be easily mapped – and layered against social determinants of health, including but not limited to population density, education, income, transportation, environmental factors, climate change, and a variety of other factors. In addition, an SDI will enable the inclusion of new data sources that currently aren’t considered when analyzing health outcomes, such as environmental sensors, smart city sensors, and satellite data. This will allow more data and more diverse data to be brought to bear on health data analytics that was previously possible – leading to more comprehensive research into and discovery of both the root causes and solutions to adverse outcomes that plague our global community.

The INSPIRE report A Conceptual Model for Developing Interoperability Specifications in Spatial Data Infrastructures, especially on pages 40, 46, and 52 of the report provides important mode recommendations for services, data and data harmonization:

Building a framework requires to partition the data needs in functional requirements. The suggested functional areas are as follows:

For the data requirements the following types of data are assumed:

The following table provides information about the data, its type and application area.

This document advocates for the development of Global Health Spatial Data Infrastructure, discussing specific applications where such a solution can be helpful. In the process, the current market requirements as well as several use cases are also identified. Also discussed are additional challenges that are likely to be encountered in the process of establishing a Health SDI that can serve the world. Future work towards this end can involve:

Use cases of a Health SDI are expected to grow. For instance, disruptions in the supply chain of medical resources has been identified as a key challenge in pandemic response. A Health SDI can be leveraged to track – as well as potentially anticipate such disruptions in advance so contingencies can be planned.

In addition, a key next step can be the identification of the open data standards that either need to be updated to accommodate and support a Health SDI or the creation of a documented profile to guide the implementation of a Health SDI. The OGC’s concept development studies, pilots, and testbeds are effective mechanisms to accomplishing these tasks and identify additional potential obstacles and solutions towards the construction of Health SDI.