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Published on 27.06.18 in Vol 20, No 6 (2018): June

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/9458, first published Nov 20, 2017.

This paper is in the following e-collection/theme issue:

    Review

    Applications of Space Technologies to Global Health: Scoping Review

    Hopitaux Universitaires de Genève, eHealth and Telemedicine Division, Geneva, Switzerland

    *these authors contributed equally

    Corresponding Author:

    Damien Dietrich, MD

    Hopitaux Universitaires de Genève

    eHealth and Telemedicine Division

    Rue Gabrielle-Perret-Gentil 4

    Geneva, 1205

    Switzerland

    Phone: 41 22 372 62 01

    Email: Damien.Dietrich@hcuge.ch


    ABSTRACT

    Background: Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

    Objective: The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

    Methods: We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

    Results: A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

    Conclusions: Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

    J Med Internet Res 2018;20(6):e230

    doi:10.2196/jmir.9458

    KEYWORDS



    Introduction

    Background

    The space-earth frontier is no longer afforded to a narrow niche of individuals. Compared with over 50 years ago when the first humans reached outer space, and satellite function only concerned a small number of scientists, today many programs and research projects in multiple fields exist that make use of outer space technologies. The field of global health too—interdisciplinary by definition—has innovated over the years and has made strides in the advancement of health aims using space technologies. Examples include using remote sensing technology to detect environmental changes that have a significant effect on local population health, satellite communication for medical endeavors and management of natural disasters, advancing medical knowledge through space medicine programs, and tapping into the benefits of localization through global navigation satellite systems (GNSSs). The UNISPACE+50 conference, taking place in 2018, marks the 50th anniversary of the start of the United Nations (UN) conferences that engaged states to cooperate in their outer space engagements. After half a century of cooperation and innovation, it is an appropriate time to take stock of where the global health field has ventured into its use of space technologies.

    Objectives

    The Expert Group on Space and Global Health of the UN Office for Outer Space Affairs (UNOOSA), in its 2016 work plan, mandated one of its members, Antoine Geissbühler, to produce a compilation of practices and initiatives [1] in the form of a scoping review, including both a literature review and stakeholders’ involvement, via a self-administered questionnaire to identify (1) The main stakeholders in the field, (2) The key applications of space technologies to global health, and (3) The gaps, challenges, and perspectives.

    This work uses a scoping review methodology, including both a literature review and stakeholders’ involvement via a self-administered questionnaire. These are used to identify (1) The main stakeholders in the field, (2) The key applications of space technologies to global health, and (3) The gaps, challenges, and perspectives.

    Key stakeholders of the fields are first briefly presented. Then, main themes in which space technologies are of benefit to global health are identified and illustrated in four technological domains. Finally, findings are summarized, and gaps, challenges, and perspectives are discussed.


    Methods

    Scoping Review

    The general aim of a scoping review is to “map rapidly the key concepts underpinning a research area and the main sources and types of evidence available and can be undertaken as [a] stand-alone project in [its] own right, especially where an area is complex or has not been reviewed comprehensively before” [2]. As opposed to systematic reviews, scoping reviews can include a diversity of sources and, in particular, are not necessarily limited to scientific articles. This allows researchers to gain a better overview on a broad subject but prevents precisely answering a well-defined question.

    Accordingly, the scoping review methodology matches our objectives and was chosen for this work [3,4]. The Expert Group on Space and Global Health identified four key technological domains that are applied or could be applied to global health [5,1]: domain A: remote sensing, domain B: GNSS, domain C: satellite communications, and domain D: human space flight. Our scoping work was conducted using these domains as a framework. A distinct literature search was conducted for each of the four key technological domains on PubMed, with eventual further insights gathered from RERO, the Western Switzerland online network for libraries, and Google Scholar. Additionally, stakeholders’ insights were collected through an emailed, self-administered questionnaire.

    Literature Review

    Search Strategy

    Searches were conducted per technological domain. PubMed was the main search engine used. Complementary searches were performed on RERO and Google Scholar. Resources retrieved by these search engines were included only if they brought insights that were not identified in the original search. The keywords used for each domain are listed in Table 1. For each domain, the basic search structure was “domain-associated technology” AND “health.” Medical Subject Headings (MeSH) terms were not systematically used as some did not properly refer to the technology we were searching for. For Domain C, the search term “eHealth” was used in addition to “health” as it is a MeSH entry term for “telemedicine.” The year-parameter of the search was unbounded to access published material that could date back to the start of outer space technology and its application to global health activities. The “Similar Articles” feature of PubMed, as well as the list of references of included articles were used to identify additional resources. Finally, key stakeholders’ websites were assessed for ongoing projects (listed in Multimedia Appendix 1).

    Material Inclusion

    Presentations, books, websites, and articles identified by the searches were included if they satisfied all of the following criteria:

    1. Reporting research, or an applied program related to health
    2. Use of space technology based on one of the four domains (remote sensing, GNSS, satellite communication, and inhabited space flight)
    3. Only for RERO and Google Scholar: global health application not already described in a resource identified through the PubMed search

    In each domain, duplicates found across the various search engines were excluded. Included resources were entered in Endnote (Clarivate Analytics) by domain and exported on spreadsheets (one for each domain). Importantly, resources written in languages other than English but whose abstracts were translated to English were included in the review. However, for these, full texts were not read.

    Analysis and Reporting

    On the spreadsheets described above, global health applications were identified for each resource. Then, main themes of global health applications per technological domain were identified. The numbers of resources per theme were counted in an attempt to weight the different themes (Tables 2-5) for a particular technological domain. Articles dealing with more than one theme were allocated according to the dominant theme. If this was not possible, they were classified as “miscellaneous.” The different themes were then described by domain in the main text and illustrated by the citation of relevant articles.

    Table 1. Search keywords used in this study.
    View this table
    Stakeholder Involvement

    To gather additional insights, a brief semistructured, self-administered questionnaire (Multimedia Appendix 2) was created and sent by email to 16 stakeholders identified after the initial literature searches. Two reminders were eventually sent to nonresponders. The questionnaire was created following the same structure and logic as our overall work. Four open-ended questions were used, asking participants about:

    1. Key applications of space technologies to global health for each domain and eventual other domains
    2. Gaps, challenges, and opportunities
    3. Key events related to the topic
    4. Other important remarks they may have

    Comments on the current state of the space-technology- global-health interface are included at the end of the Results section, whereas gaps and potential solutions are presented in the Discussion section.


    Results

    Data Collected

    After the whole literature review process, 222 articles were included for domain A, 82 articles for domain B, 144 articles for domain C, and 31 articles for domain D. In total, 473 articles were included (6 of those were included in 2 domains). Most of the included resources were peer-reviewed scientific articles (96%, 213/222 for domain A; 99%, 81/82 for domain B; 84%, 121/144 for domain C; and 100%, 31/31 for domain D), and other types of sources included mainly book sections and Web pages. The mean publication year and the minimal and maximal publication years were 2010 (1985; 2017) for domain A, 2010 (1996; 2016) for domain B, 2004 (1986; 2016) for domain C, and 1999 (1981; 2011) for domain D. Of note, in accordance with the scoping methodology used for this work and described in the Methods section, we used different combinations of keywords; included resources via the “Similar Articles” feature of PubMed and the list of references of included articles and navigation on stakeholders’ websites.

    Regarding questionnaires, 3 out of 16 sent were answered and included for the analysis.

    Presentation of the Stakeholders

    Using insights from the literature review and the questionnaires, we performed a nonexhaustive listing of stakeholders implicated in the space and global health fields.

    We categorized stakeholders per their nature: National Space Institutes; UN entities and specialized agencies; entities fostering data availability, usage, analysis and exchange; and journals, other consortia, and associations. These stakeholders are depicted in Figure 1.

    National Space Institutes are usually public institutes that are responsible for applying their countries' spatial programs. Their missions are space exploration, education, research, and development that can sometimes be translated into commercial applications, or eventually for terrestrial use. Nonexhaustively, we identified the US’s National Aeronautics and Space Administration (NASA), the Russian Federal Space Agency, the Japan Aerospace Exploration Agency, the French Centre National d’Etudes Spatiales, and the Canadian Aeronautics and Space Institute as being engaged at the space and global health interface.

    The UN comprises several entities that deal with space and global health. The UN platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER) and the UN Operational Satellite Applications Program (UNOSAT) aims at providing all countries and international organizations with space-based information useful for disaster risk management and emergency response. This is also one of the goals of the UN Economic and Social Commission for Asia and the Pacific. UN-Space is an interagency coordinating body aiming at fostering collaboration and synchronization between the various agencies implicated in space and global health. The Committee on the Peaceful Use of Outer Space (COPUOS) was set up by the general assembly in 1959 to govern the exploration and use of space for the benefit of all humanity: for peace, security, and development. The Expert Group on Space and Global Health that guided this review is part of COPUOS and has a focused scope on global health applications of space technologies. Of note, UNOOSA is a governing office that comprises UN-SPIDER, UN-Space, and COPUOS. It is also in charge of organizing the UNISPACE+50 conference that will mark the 50th anniversary of the first UN conference on the peaceful uses of outer space that engaged states to cooperate in their outer space uses. Applications of space technologies to global health is also an important interest of the World Health Organization (WHO), a specialized UN agency.

    In addition, we identified entities aiming at fostering satellite data availability, analysis, visualization, interoperability, and exchange. As an example, the Group on Earth Observations (GEO) is a partnership of governments and organizations whose one activity among others is to build the Global Earth Information System of Systems. This platform offers access via a Web-based interface to earth-observation data coming from multiple sources, including satellites. It acts as a connector between different data sources and thus, increases data availability for researchers, public health professionals, and international organizations. The Global Disaster Alert and Coordinating System is a cooperative framework under the UN umbrella that connects to various services and platforms (the majority of which are listed in this section) to create a comprehensive solution that aims to create early alerts in the case of a disaster, to assess the impact of the disaster, to coordinate the response, and to provide disaster maps and satellite images. Black Sky is a service of Spaceflights Industries (a private company) that provides access to satellite imagery in addition to other sources of data (eg, radio communication and social media). It also offers spatial analysis based on those datasets and several algorithms. Humanitarian Data Exchange is an open platform for data sharing in the humanitarian context. The OSGeo foundation is a foundation that supports the creation and usage of an open source geospatial software. Finally, the National Oceanic and Atmospheric Administration provides environmental data, some of which are acquired via satellites. It is to be noted that most of the national space institutes listed previously are data providers too.

    Some stakeholders are consortiums or associations active in the field of space and global health. We included the University Corporation for Atmospheric Research that regroups North American colleges and universities focused on research and training in the atmospheric and related Earth system sciences. The Space Generation Advisory Council is a nongovernmental organization that promotes the access of students and young professionals to UN agencies and National Space Institutes.

    Finally, we included as part of Figure 1 a nonexhaustive list of journals that are implicated in the field of space and global health.

    Domain A: Remote Sensing

    Definition

    Remote sensing refers to data collection at distance, usually from a satellite or an aircraft, as opposed to on-site sensing.

    How It Works

    A sensor, carried by a satellite or an aircraft, detects electromagnetic radiation coming from Earth and its characteristics. The electromagnetic radiation may be the reflection of an external source of energy (usually the sun) or of a source of energy carried by the satellite or aircraft itself. The terms passive or active remote sensing are used, respectively [6].

    The detected signal is then processed through algorithms of various complexities to derive the parameters of interest. Example of parameters that can be derived via remote sensing include land temperature, altitude, humidity, rainfall, cloud coverage, air pollutants, livestock density, vegetation indices, sea temperature, sea salinity, sea nutrient concentration, sea algae concentration, sea bacteria concentration, urbanization, population density, and bare soil coverage. This list is nonexhaustive.

    Insights From the Literature Review

    Overall, remote sensing was useful for global health in three major ways:

    • Identifying associations between diseases (or disease vectors) and remotely sensed parameters
    • On the basis of these associations, model development and forecasting of the spatio-temporal evolution of diseases, thus allowing rational public health strategies
    • Direct monitoring of certain microorganisms

    Two major themes and two secondary themes were identified and are presented in Table 2.

    Main themes of global health applications in the remote sensing domain were identified, and the total number of resources per theme were counted as described in the Methods section.

    Remote sensing was most used to identify determinants of infectious diseases and to develop models to predict their evolution (Theme A-1). For example, Midekisa et al [99] quantified the degree of association between malaria cases and remotely sensed environmental parameters such as rainfall, vegetation indices, and temperature. On this basis, they developed and tested a model able to predict malaria evolution and thus, guide public health decisions. Applications of spatial technologies for malaria transmission modeling and control were reviewed in 2015 by Gebreslasie [48]. In addition to malaria [8-10,17,24,26,31,33-35,38-42,46,48,49,55,56,76,87, 90-93,99,100,102,103,105,111-115,118,119,122, 129-132,141] and schistosomiasis [15,37,53,54,63,95,124,126, 127,142-145,153,154,156,158], dengue fever [7,12,13,16,23, 44,89,94,98,101,117,140], cholera [43,71,72,74,75,80,88], and cyanobacterias [28,81,82,123,137,138,148,150,155] were the most studied.

    Figure 1. Nonexhaustive collection of stakeholders and journals in the intersection of space technology and global health.
    View this figure
    Table 2. Main themes of global health application for the remote sensing domain.
    View this table

    Other studied diseases or pathogens included meningitis [225]; brucellosis [70]; C. imicola [67]; avian pathogens [25,134,136,50]; V. vulnifucus [52]; V. parahaemoliticus [52]; Fasciola hepatica [36]; hand, foot, and mouth disease [20]; Helminth infections (not limited to schistosomiasis) [120,85,21,22]; Lyme disease [108,45,79,110]; Guinea worm [30]; Nipah virus [133]; onchocerciasis [68]; opistorchiasis [146]; rotavirus [69]; typhoid fever [32]; Rift Valley fever [139,125,84]; Murray Valley encephalitis virus [121]; West Nile fever virus [96,159]; and hanta virus [149,152].

    In an important number of studies, disease vectors (and not disease cases) were the outcomes predicted based on sensed environmental parameters. These vectors included Anopheles [10,55,103,141] (transmitting malaria) and Aedes [16,44,101,117] (transmitting dengue) mosquitos, as well as ticks [45,64,77,79,110,147] (transmitting Lyme’s disease among other tick-borne diseases).

    Of note, remote sensing techniques allow to directly derive the concentrations of some bacteria. Cyanobacteria produce various toxins that have been linked to the occurrence of amyotrophic lateral sclerosis and nonalcoholic liver disease [155,138]. They also have distinct fluorescent properties that can be exploited in active remote sensing to monitor their concentration [28,81,82,123,137,138,148,150,155].

    The second main theme (A-2) was the use of remote sensing to monitor air pollutants and eventually link them to noncommunicable diseases (NCDs) such as respiratory diseases (asthma [161,188,191] and others [185,162,208]), coronary artery disease [165], premature birth [195], and low birth weight [181]. Particulate matter (PM2.5 and PM10) [188,181,161,195, 196,175,194,193,164,189,174,170,165,163,173,171,172,160,178,183], O3[218], NO2[180,225,218], pollens [209], asbestos [202], volcanic ash [184], and wildfire smoke [168,190,176] are among the air pollutants that can be effectively detected by remote sensing. Temperature and humidity are usually included as additional parameters when monitoring air pollutants as they may affect both respiratory diseases and air pollutants behavior. If many articles successfully describe the use of remote sensing for the monitoring of air quality, only a few establish a direct link between air pollutants and health outcomes [188,162,181,191,161,195,165,185,208]. Moreover, results may be controversial, such as for asthma, where one study found a correlation between childhood asthma hospital admission that disappears after multivariate analysis [161], another one finds no correlation between air pollution and asthma prevalence [188], and a last one finds a correlation between PM2.5 concentration and salbutamol (treatment used in asthma and chronic obstructive pulmonary disease) use [191].

    The remaining articles identified for remote sensing dealt with monitoring environmental pollutants (B-1) or parameters (B-2) and their links with NCDs. For example, studies investigated the link between urban greenness and birth outcomes [212] or cardiovascular diseases [206]. Others investigated the link between drought and respiratory illnesses [208] or between heat and elderly health [215,211] or childhood diarrhea [205]. Additional parameters or pollutants that can be sensed by remote sensing include artificial lights [207], soil contaminants (heavy metals [203], nitrates, nitrogens [197]), water quality [128,97,14], and arsenic [198].

    Domain B: Global Navigation Satellite System

    Definition

    GNSS is the generic term for satellite navigation systems that provide autonomous geo-spatial positioning with global coverage [228]. GNSSs are satellite ensembles that allow any user on or near the Earth to determine their position with a precision from some meters to some centimeters.

    The term global positioning system (GPS) is specific to the US’ GNSS, the NAVSTAR GPS. The Global Orbiting Navigation Satellite System (GLONASS) is the Russian Federation’s GNSS. As of 2013, these two are the only fully operational GNSSs.

    Other GNSSs in various stages of development and deployment include:

    • Galileo, the European Union’s positioning system
    • IRNSS, India’s next generation regional system
    • QZSS, the Japanese regional system
    • China’s BeiDou (COMPASS) GNSS
    Table 3. Main themes in the global navigation satellite systems (GNSS) domain.
    View this table
    How It Works

    Each system (GPS, GLONASS, Galileo, COMPASS, etc) consists of a constellation of satellites that send a continuous signal toward the Earth. Individuals wanting to use GNSS to determine their position must have an antenna that receives the signals coming from the satellites and a receiver that translates these signals. The antenna position will be deduced from the measurements of the time delay between the emission time (satellite) and the reception time (receiver) for at least four signals coming from different satellites [229]. Most importantly, the atomic clocks onboard the satellites are all synchronized so that the signals coming from the different satellites of the same constellation share the same reference time scale.

    Although a GNSS is the space technology that is highlighted in this review, often mentioned in global health applications is the use of a geographic information system (GIS). A GNSS allows a user to determine the location of an object or individual, whereas a GIS is the system for storing, combining, and displaying data (partly coming from GNSS) on a map. It allows users to easily visualize spatial data, analyze them, and interpret trends or patterns.

    Insights From the Literature Review

    Seven themes were identified after the literature review and are shown in Table 3.

    Main themes of global health applications in the GNSS domain were identified, and the total number of resources per theme were counted as described in the Methods section.

    GNSS was used in epidemiological studies, often in combination with GIS and remote sensing. NCDs were the focus of many studies, whether directly as a measured outcome, or because of their risk factors being studied [239,236,232,245]. Physical activity (PA) was a very popular research area [243,237,247,233,250], most notably in children and adolescents [230,242,240,248,234,244,231]. Edwards and authors [234] assessed adolescents’ use of public parks with regards to the features of the parks. The parks were characterized using GIS and a desktop auditing tool that uses remote sensing techniques, whereas the adolescents were surveyed to assess their activities. In two other studies in the United States and Switzerland [244,231], participants wore GPS receivers and accelerometers, enabling researchers to assess and compare the intensity and location of the PAs. Links between different locations (home, playground, sidewalk, and more) and the intensity of PA were identified. In addition to PA, the built and natural environment were studied for their associations with NCDs. Researched environmental determinants of health ranged from air pollution [291,251] and water quality monitoring [246,241] to the complex ways in which climate change impacts global health [259]. For this purpose, researchers used GNSS and satellite imagery in a variety of ways. Interestingly, happiness was also studied as a health outcome. MacKerron and Mourato (2013) [238] used GPS to locate individuals at various, spontaneous moments while they answered questions about their subjective well-being. They found that participants were substantially happier in natural rather than urban environments. The variety of ways in which GIS can be used in environmental epidemiological studies was reviewed by Nuckols et al (2004) [282], who concluded that GIS and GPS are useful tools in providing precise locations of subjects and studying proximity and level of exposure to environmental contaminants.

    GNSSs have been used often in the field of communicable diseases too, including person-to-person transmissible varieties [253,255], vector-borne diseases [21,48,38,252,258,260, 257,264,256,95,263], and zoonoses [262,260,254,261,25]. In our search, the most studied communicable disease was malaria. Predicting vector breeding sites [21,38,257], malaria incidence, and adherence to medication [263] using GNSS, often in combination with GIS and remote sensing, were some practical applications. Additionally, distance to health facility was also used for malaria risk mapping [265]. Studies of zoonotic communicable diseases were limited to avian pathogens in this domain. Newman et al [260] marked two hosts of H5N1, a highly pathogenic avian influenza, with GPS transmitters and found links between flu outbreaks in humans and the hosts’ travel patterns.

    The use of GNSS as a new tool for epidemiological research was discussed in a variety of articles [282,275,270,274, 280,279,281,287,288,271,269,284,268]. GNSS use was reported to construct random sampling frames for surveys, mapping households, or determining population estimates [267,278, 272,277,273,266,285,289,276]. The potential future impacts of GPS devices on medicine is discussed in Pager’s article, Impacts for medicine of global monitoring [283].

    Geolocation of individuals has been used is in the assistance of mentally or physically impaired individuals to improve their autonomy [294,297]. Alisky [293] presents hypothetical scenarios whereby GPS devices can be of assistance. For instance, in the case of an individual with partial complex seizure disorder, the individual can wear a GPS-enabled watch that will notify a health management center in the case of a seizure. Gallay et al [292] give a review of GPS technologies that have already been available to aid visually impaired individuals to navigate their surroundings. They discuss several limitations, eg, that the GPS receiver does not work well unless satellite coverage is satisfactory, and this is affected by climatic conditions as well as the user’s location. GPS can also be of assistance for persons suffering from dementia. This could be achieved through orientation and safety cues, daily reminders of activities, protection against wandering, and direct links to medical assistance in case of incapacitation. Potential benefits are decreased stress and workload for formal and informal caregivers, decreased institutionalization, and thus, lower costs.

    Geolocation is also helpful in promoting health care access in different settings [306,303]. In Bolivia, Perry et al [305] used GPS techniques and satellite imagery of the remote, impoverished, and mountainous region of Andean Bolivia to create a GIS that enabled them to assess the physical accessibility of several populations to health care services and auxiliary nurses [305]. Their findings demonstrate how medical geography can be used for better informed health care policy and planning decisions. Tassetto et al [286] tested a novel method to locate victims of disaster by using their existing portable devices (such as simple mobile phones or laptops) and the existing cellular network. Their proposed technology is mediated by a satellite system and requires little action by victims. Although this new system has been tested in experimental settings, it is yet to be used in real-life scenarios. In northern Nigeria, polio vaccination teams were tracked with handheld GPS devices, and their movements were overlaid on catchment area maps [304]. This method allowed the identification of low vaccine coverage areas and was identified as a tool to improve microplanning of global health projects.

    The use of GNSS to improve transportation for improved public health appears as one area in which there is a huge potential for growth, for instance by preventing road accidents. Guo et al [301], working under the current constraints of suboptimal space-time reference for vehicles, conducted research with the aim of locating vehicles with high precision, down to the lane in which the vehicle is moving. This has immense safety implications which, in addition to a safety notification system, can provide information on high-risk vehicles (eg, trucks carrying chemicals) or high-priority-of-way vehicles (eg, school busses) and can also track illegal or dangerous vehicle movements [301]. Other transportation-related GPS studies have focused on speeding [300], commute routes, and daily mobility [299,298], as well as emergency patient transportation [302].

    Finally, in a category of its own, accurate timekeeping using GPS was a proposal brought forth by Aljewari et al, especially in settings where time is of utmost importance, such as in hospitals [307].

    Domain C: Satellite Communication

    Definition

    Satellite communication is the ability of information to travel from one area to another via a communication satellite that is in orbit around the Earth. It is often performed with mobile satellite phones and is distinct from cellular phones that use earth-based towers that form a cellular network. “Wide area coverage, reliable data delivery, and robustness and broadcast or multicast are the unique features of satellite systems” [308].

    How It Works

    Satellite communication has two main components: the ground segment, which consists of fixed or mobile transmission, reception, and ancillary equipment, and the space segment, which primarily is the satellite itself. A typical satellite link involves the transmission (uplinking) of a signal from an Earth station to a satellite. The satellite then receives and amplifies the signal and retransmits it back to Earth (downlinking). Satellite receivers on the ground include direct-to-home satellite equipment, mobile reception equipment in aircraft, satellite telephones, and handheld devices [309].

    Insights From the Literature Review

    This domain was largely centered on telemedicine, often combined with tele-education. Health-on-the-go is defined below with several examples from the literature, and there are a handful of demonstrations of how satellite communication can be of importance in disaster situations. Main themes are presented in Table 4.

    Main themes of global health applications in the satellite communication domain were identified, and the total number of resources per theme were counted as described in the Methods section.

    Telemedicine is the application of communication technologies to the field of health in instances where medical expertise or resources are not available on site for different reasons. These reasons, nonexhaustively, include the geographical distance; physical barriers (mountains, space, desert, etc) and insufficient time or resources to transfer a patient. Often, the patient may be in the physical presence of a health care provider (HCP), but telemedicine could mean connecting the two parties to a third party at a distance, such as a medical specialist or a general practitioner (GP) if the HCP is a nonphysician. Telemedicine is possible via satellite and cellular network. This review is limited to telemedicine by means of satellite communications. More in-depth assessment of the definition and breadth of telemedicine can be found in several review and discussion references [390,386,337,393,441,383,352,353], some theoretical articles linking satellite communication with health [441,415, 446,448,372,444,451,453,449,445,442,454,447,364,450], as well as country reports [330,387,322,378,343,355,359,325, 321,399].

    Table 4. Main themes in the satellite communication domain.
    View this table

    A first example is in Thailand, where the country’s first communication satellite, THAICOM, was launched in 1993. HCPs in rural areas were connected with specialists in urban areas, and consultations became possible, with two main components: videoconferencing and exchange of medical images. Thailand’s telemedicine network is housed in its Ministry of Public Health, with all hospitals that are in the telemedicine network also having a direct communication link with the government base. The Thai example illustrates a common model of telemedicine and teleconsultation: access to expert opinion by GPs, nurses, or paramedics via videoconferencing or textual exchange [373,368, 315,318,316,397,351,314]. These are often accompanied by still images from radiography [332,361,311] or dermoscopy [356], but innovative advances have made possible the transfer of 3D images [381] and live ultrasound feed [331,374,317,326]. Use of telemedicine methods has been reported in various medical fields including dermatology [345], pediatrics [327], and surgery. Telesurgery [376] has been trialed on internal mammary artery dissection in pigs with robotic technology to determine feasibility and bandwidth requirements. The authors concluded that telesurgery via satellite communication is feasible and also identified the limit of satellite bandwidth below which it cannot be performed (3 Mb/s).

    Telemedicine using satellite communication may also be useful for a country’s defense system. By equipping more than 300 US Navy ships with telemedicine capabilities, researchers estimated that 17% of medical evacuations could be avoided, representing US $4400 savings per single medical evacuation [384]. Similarly, German defense units have access to a telemedicine workstation, accompanied by a medical officer present on-board the ship or at the unit [371]. This station has the possibility of being equipped with various medical devices (X-ray film digitizer, dermatoscope, otoscope) and can also contain other imaging methods (eg, videocamera and ultrasound). The authors propose cooperation not only between civilian and military health service providers but also military-military cooperation between the medical services of allied armed forces.

    As the field of telemedicine is both broadly defined and applied, as well as having fluid borders with tele-education and health-on-the-go, further sources were found in this search that do not fall under the broader categories discussed above [310,312,313,319,320,323,324,328,329,333-335,338,339,341,342,344, 347-350,354,357,358,360,362,363,365,369,370,375,377,379,380,382, 385,388,389,412,391,392,394,452,395,398].

    Medical tele-education, the practice of providing new or continuing medical education via distance learning, often uses the same networks and infrastructures as telemedicine does. It is especially useful for HCPs who are located far from teaching facilities [321,400,401,403-411]. The Réseau en Afrique Francophone pour la Télémédecine network is one such example of successful implementation of tele-education; a model that has expanded into multiple countries and continents [402]. Health educators, usually located in teaching universities of larger cities of the region, teach courses to HCPs in peripheral areas in real time. Two-way communication enables students to ask questions and collaborate with the lecturer. Exchanges in the same country or between neighboring countries are promoted as much as possible to build capacity and collaboration. Another application of tele-education is implemented in Japan, where 39 universities and institutes were connected by satellite for a joint radiology conference [413]. Participants engaged in discussions around various images, and the results of a survey to radiologists after the conference showed that while the technology used may not be good for diagnosing purposes, it is useful for discussion and educational purposes.

    The third broadly studied area of satellite communication and global health is what we refer to as health-on-the-go. In this theme, which can be considered as subcategory of telemedicine, mobile medical units can provide treatment and can transmit health information (text, health parameters, images, laboratory exams) using satellite communication [426,427,421,419,422,414,418]. This gives the ability to provide health care services to individuals over a large area that may be deprived of traditional communication systems. The TraumaStation is one such device, a portable and lightweight suitcase that carries ultrasound, electrocardiogram, blood pressure, and oxygen meter apparatus [425]. The TraumaStation allows for telecommunication with instant messaging and real-time video stream through satellite and a variety of other gateways. Alternatively, the HOPEmobile provides biometric measurement (body mass index, cholesterol, glycosylated hemoglobin, and retinal screening) from a mobile unit [416]. The study reported a return on investment of US $15 for every US $1 spent and a significant reduction in overall cholesterol at the second screening of a patient. Finally, Guo and colleagues (2015) [417] describe a portable, robust, and low-power device that performs all essential functions of enzyme-linked immunosorbent assay and can thus diagnose diseases in remote, mobile contexts. The results can then be sent via cell phone short message service (SMS) messaging or in email format via satellite. The authors describe how patient confidentiality is taken into account through the usage of this device. Another area of health-on-the-go is emergency patient transportation. The transmission of the patient’s medical history, vital signs, and laboratory exams (for instance electrocardiogram) during the transport can allow a remotely based medical expert to guide the management of the patient. Nakajima et al [424] explain that 3G mobile networks tend to be sensitive to congestion in urban areas and that the satellite provides a good solution to counter this. One technical innovation in this area includes the Emergency Medical Video Multiplexing Transport System. This divides a patient’s live video stream from a medical vehicle into four pieces, and these translate to high-quality videos that can be viewed by emergency doctors in a remote location [423].

    Satellite communication is also valuable in emergency situations arising from natural disasters, man-made disasters (eg, terrorism and war), highly contagious diseases, or large-scale epidemics [431,440,436,437,433,432,430,429,435,439,434]. Satellites for Epidemiology (SAFE) is a system for early health warnings in a postdisaster period. It is a system that combines satellite, radio, wireless networks, and GIS to promptly identify and respond to a disease outbreak. SAFE’s added value is reported to be its integration into already-existing national, regional, and international preparedness plans [428]. Existing cellular and telephone networks almost always become overloaded or disabled following disasters, so satellite communication methods are superior in these instances. For this reason, East Carolina University tested the time it would take to set up a fault-tolerant communications infrastructure from scratch; one component of several being the satellite connection. They concluded that the time it took to mount the network by technically trained personnel made it a feasible and valuable contribution to disaster response operations. Potential drawbacks of this are that technical experts of the system may need to be made a part of the team of emergency responders and that in case of loss of electrical power, alternative methods need to be used [438].

    Domain D: Human Space Flight

    Definition

    We looked for evidence on how inhabited space flight-associated technologies and procedures may promote global health.

    Literature Review

    Two main themes and one secondary theme were identified and are represented in Table 5. Main themes of global health applications in the inhabited space flight domain were identified, and the total number of resources per theme were counted as described in the Methods section.

    Telemedicine seems to be one of the dominant theme at the crossings of inhabited space flights and global health. Indeed, providing health care for an astronaut needing medical assistance onboard a space station, or an individual living far from medical expertise in a desolated rural area, may pose similar problems. In both cases, one must deal with the restriction of not being able to quickly transfer the patient and limited medical resources and expertise in the patient’s vicinity [366,462,463,465,467,466,468]. Telemedicine thus provides a possible solution in both cases. Interestingly, challenges for successful implementation are similar in space and on earth. They include dealing with low bandwidth connection, maintaining stable electrical power, assuring data storage, developing intelligent software, and training users.

    Going further in the similarities between space and earth telemedicine, tele-ultrasound has been extensively designed and tested in space [460,458,459,457,455,456,461,480] but is also used on earth [484]. In addition, tele-surgery has been developed and practiced on earth [376] and is foreseen to be a requirement to medical support in extraplanetary human outposts [469]. Challenges for this particular implementation notably include the latency between the command and the robot movement, induced by the long distance [469]. Taken together, telemedicine in space and telemedicine on earth are facing similar yet complementary challenges that are potential synergies for researches in the development, implementation, and testing phases.

    Among included articles, technology transfer of space technologies to earth appears to be an important topic [470,429,471-479,481]. An example is the successful reprogramming of neural networks initially trained to identify craters or incoming missiles in space toward the detection of cancer-associated breast microcalcifications on mammograms [478,479,474]. The potential use, on Earth, of miniature or implantable biometric sensors developed by the NASA sensors 2000! program (S2K!) is another example of technology transfer [472].

    The space scientific community is actively conducting research on how to provide adequate life support for long extraterrestrial missions or on extraplanetary outposts. In addition to new technology transfers, outputs from this research should lead to development of new medical procedures that may be applicable on earth [483].

    This review focuses on how inhabited space flight-associated technologies and procedures may promote global health. It is important to note that, in addition to global health, the space research community has also been very active in various domains of life sciences. These domains include microgravity physiology, microgravity microbiology, microgravity surgery, radiation medicine, and the study of the psychological effects because of space travel and isolation.

    Insights From the Questionnaires

    Respondents’ insights were collated and are reported below.

    Domain A: Remote Sensing

    Stakeholders believe that incorporating environmental exposure data into clinical practice will improve the quality of care. Indeed, diagnostic accuracy may be improved via integration of remotely sensed parameters into decision support tools. For example, knowing that the allergens concentration was high over the last days will increase the probability of asthma when a patient consults for breathlessness.

    Table 5. Main themes in the inhabited space flight domain.
    View this table
    Domain B: Global Navigation Satellite Systems

    No insights were provided by respondents.

    Domain C: Satellite Communications

    Stakeholders provided more examples about situations in which telemedicine is used through satellite networks. These situations include people onboard a plane, on a boat, working on an off-shore platform, or on construction sites. Satellite communication may also be necessary to provide telemedicine in remote areas of developed countries. Examples include communications between French overseas territories and the mainland, or locally between islands. In all these cases, satellite communications can be used to compensate for the unavailability of the cellular network.

    Domain D: Human Space Flight

    In this domain, in particular, stakeholder questionnaires provided insightful additions. Physical inactivity is a major determinant of NCDs such as cardiovascular diseases and osteoporosis. It is thus of particular interest to global health. Despite this fact, studies on the physiological effects of physical inactivity are lacking. In space, astronauts are exposed to microgravity, and accordingly, the space research and development area has been very active in studying the physiological effects of microgravity, notably by using ground-based bed rest analog. As microgravity partly mimics physical inactivity and aging, space-associated study results may help us to understand the deleterious physiological effects behind those processes. Future joint research programs should thus be encouraged.

    As another example of space technology transfer, bone quality measurement tools were initially developed by the space industry. A NASA review of spin-offs of space research can be found on their website.

    Long-term missions will require the development of “integrated countermeasures” to prevent the adverse effects of the space environment, including radiation. These countermeasures may find applications on earth, such as in radiation medicine.

    Another challenge is to be able to personalize space medicine, which is a major trend in nowadays medicine. Moreover, when thinking about long flight duration, space health systems will need to achieve some level of autonomy, which imply the development of decision algorithms and consistent procedures that may be of benefit to global health, especially in isolated settings.

    Finally, another big challenge of human space exploration is to develop a closed-loop environmental system technology to maintain, at low cost, an environment suitable for human life. These technologies include monitoring and control of physical, chemical, and biological environments; waste recycling; and food production. Results from such research may contribute to the development of sustainable and green solutions of benefit to global health.


    Discussion

    Principal Findings

    Using a scoping review methodology, including a literature review and questionnaires to stakeholders, we identified, described, and illustrated key areas in which space technology is, or may be, of benefit to global health. Remote sensing of environmental parameters allows the prediction of communicable and NCD evolution, often in association with GIS and GNSS. GIS and GNSS are also used to bring new insights to epidemiological research, to improve access to health care, to develop autonomy assistance for the disabled, and to assist in disaster response. For this last task, space communications are also used, as well as in telemedicine and tele-education. Finally, some technologies and procedures developed by the space industry for inhabited-space flights are applied on earth. Overall, our results strengthen the vision that space technologies and global health are two synergistic fields, and they help us to identify perspectives and issues for the coming years that will be discussed in this section.

    Remote Sensing

    Remote sensing brings new tools for monitoring diseases, investigating their association with multiple sensed parameters, and ultimately creating an intelligent alert system. The literature is particularly abundant on infectious diseases and air pollutants. One limit is that most studies do not link directly sensed parameters to health outcomes but rather to some disease determinants (disease vectors, air pollutants). This is an interesting first step as it gives insight to more than one disease. Yet, more studies investigating direct health outcomes are needed to allow the creation of relevant models that will guide public health decisions. Importantly, the limited presence of environmental monitoring systems in low-income countries is an obstacle. Moreover, achieving high spatial and temporal resolution either by hardware improvement or through the development of numerical models is an important challenge in remote sensing. Finally, the sustainability of the developed alert systems, as well as their reproducibility across different geographical areas, must be evaluated. In addition to adding value at the population level, remote sensing used in combination with GNSS holds great potential to assist caregivers in their routine decision making for individual patients. This could be done, eg, by assessing relevant environmental data for each patient.

    Global Navigation Satellite Systems

    The last example illustrates the synergy between remote sensing, GIS, and GNSS applied to global health. Indeed, most of the epidemiological studies identified in this review and aiming at predicting disease evolution based on environmental parameters are using GIS and GNSS, in addition to remote sensing. GNSS and GIS are also used in innovative epidemiological methodologies for activity tracking (eg, movement or localization as an outcome or a determinant), randomization, or population estimation. Disaster response and autonomy improvement of disabled patients constitute two other fields in which these technologies are used. Requirements for the successful use of GNSSs are stable and easily accessible signals, as well as procedures preventing power failure. Combining space-, cyber-, and ground-data thus holds a great potential. The use of big data analytics and machine learning may lead to further applications that are not even suspected nowadays. Creating a platform warranting availability, interoperability, and quality of data issued from different sources is a requirement to go further in this direction.

    Satellite Communications

    Satellite communications are mainly used when standard telecommunications using landlines and antenna are not available, such as in disaster situations. Through these networks, telemedicine and health tele-education are possible. Bringing medical expertise at distance is useful in various places such as in isolated rural areas, areas affected by natural disaster, but also elderly homes, isolated places in high-income countries (northern Canada, Alps), ambulances, and remote work places (off-shore platforms, boats, airplanes). In addition to information exchanges, telemedicine encompasses laboratory exams and medical procedures at distance and sometimes in real time. Examples include tele-echography, tele-electrocardiogram, tele-dermatoscopy, and tele-surgery.

    Human Space Flight

    In parallel, research in outer space has been very active in developing telemedicine, including tele-echography and tele-surgery. In space and on earth, challenges for the development, implementation, and testing of telemedicine are similar and complementary. Strengthening existing collaborations in the field and creating new ones thus appear particularly relevant. In addition to telemedicine, we retrieved from the literature evidence of technology and medical procedure transfers from the space industry to the health sector. However, the number of articles retrieved was small and is probably not a true reflection of all ongoing synergies. This was confirmed by the questionnaire results that identified additional examples and themes such as the use of microgravity to study the physiology of physical inactivity, which is a major and frequent determinant of NCDs. Overall, it seems that encouraging collaborations between the space and health sectors is of particular interest for this domain (inhabited space flights). Moreover, reinforcement of the scientific publishing and public communication is needed to strengthen the scientific community awareness of the existing synergies.

    Value Added From Questionnaires

    In all collected questionnaires, the potential of space applications to improve global health was reported to suffer from a lack of awareness among health workers and space researchers. Moreover, a deficit in space-associated skills and knowledge was also reported for health researchers. More interdisciplinary collaboration and an easier access for health researchers to space technologies was expressed. Finally, a gap in organizational level activities was identified. Accordingly, efforts are reported to be necessary to:

    • Raise awareness on the potential global health applications of space technologies
    • Train researchers interested in the field
    • Promote interdisciplinary collaborations
    • Improve the organizational-level governance

    Results from the questionnaires suggested the reinforcement of public communication and the organization of dedicated conferences and training sessions as a first step toward a more comprehensive solution. Moreover, early involvement of end users and policy makers in the various projects has been suggested to improve their relevance.

    Implications of the Research

    By providing a thorough review of the published literature on space and global health, as well as the identification of key stakeholders, this work presents a solid base for improving mutual understanding between the two domains. This should lead to more synergies among the various actors, including the development of formal interagency coordination mechanisms. Comprehensive strategies to address sustainable development goals must indeed leverage the complementary competencies from UN agencies such as the WHO, the UNOOSA, UNOSAT, as well as other organizations such as the GEO.

    Limitations

    This review has several limitations. A scoping review is a methodology useful to gather as many insights as possible on a broad subject, such as this one, to achieve a better awareness of the question and its past and ongoing research, practices, and initiatives. We chose this methodology as it matches our objectives well. The searches that we ran gave us thousands of hits but only came from two search engines: PubMed and RERO Western Swiss database. Moreover, only 3 of the 16 questionnaires sent were answered despite two reminders. Accordingly, we can’t exclude that eventual supplementary themes were missed. In addition, the low response rate to the questionnaire may have introduced biases in the insights that were reported. Insights gathered from the questionnaires should thus be considered as expert opinions. Another limitation to our study is that we cannot draw definitive conclusions on precise subthemes and questions (eg, is remote sensing effective in predicting malaria outbreaks in Africa?). For this purpose, systematic reviews are needed. The different domains that guided the searches were suggested by the Expert Group on Space and Global Health. This group is mainly constituted by key stakeholders of various national space agencies and public health authorities. Accordingly, it is unlikely that an important domain was missed, but it constitutes a limitation to our study. The language barrier is another one. Indeed, the space literature in Russian or Japanese is abundant and not always available in an English translation, save for the abstract. Accordingly, key concepts may have been missed.

    As the paper has technology at its core, one must note that articles used in the review date back to 1981. Space technology and access to it has improved significantly since then, but to remain aligned with the goal of the review, we reference all relevant articles. Nevertheless, as ease of use and access to space technology has improved in recent times, as well as an increased human presence in outer space, the themes will be largely shaped by more recent articles, simply as there are more of them.

    Acknowledgments

    The authors would like to thank Dr Alban Duverdier, Centre National d’Etudes Spatiales; Prof Fazlay S Faruque, The University of Mississippi Medical Center; and Mr Hajime Shinomiya, Japan Aerospace Exploration Agency for their answers to the questionnaires and their insights for the review.

    In addition, they thank all the members from the Expert Group on Space and Global Health for their valuable insights and comments to this review.

    Authors' Contributions

    DD and RD participated in the literature review, the stakeholder involvement, and wrote the manuscript. SD participated in the literature review and reviewed the manuscript. GF participated in the literature review. AG supervised the work and reviewed the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    List of stakeholder websites consulted for projects and meetings.

    PDF File (Adobe PDF File), 32KB

    Multimedia Appendix 2

    Questionnaire sent to stakeholders.

    PDF File (Adobe PDF File), 15KB

    References

    1. Pascal M. Second meeting of the expert group on space and global health, 18-19 February 2016. 2016 Feb 18 Presented at: 53rd session of the Scientific and Technical Subcommittee (STSC, or the Subcommittee) of the Committee on the Peaceful Uses of Outer Space (COPUOS, or the Committee); 15-26 February 2016; Vienna   URL: http:/​/www.​unoosa.org/​res/​oosadoc/​data/​documents/​2016/​aac_105c_12016crp/​aac_105c_12016crp_21_0_html/​AC105_C1_2016_CRP21E.​pdf
    2. Mays NR, Roberts E, Popay J. Synthesising research evidence. In: Fulop N, Allen P, Clarke A, Black N, editors. Methods for Studying the Delivery and Organisation of Health Services: research methods. London: Routledge; 2001:188-220.
    3. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2007;8(1):19-32. [CrossRef]
    4. Dijkers M. KT update. Vol 4. 2015. What is a scoping review   URL: http://ktdrr.org/products/update/v4n1/ [accessed 2018-06-05] [WebCite Cache]
    5. Pascal M. Report on the proposed mandate, workplan and initial considerations. 2015 Presented at: First meeting of the expert group on space and global health held on 5 February; February 2015; Vienna   URL: http://www.unoosa.org/pdf/limited/c1/AC105_C1_2015_CRP29E.pdf
    6. Graham S.. earthobservatory.nasa.gov. 1999 Sep 17. Remote Sensing : Feature Articles   URL: https://earthobservatory.nasa.gov/Features/RemoteSensing/remote_08.php [accessed 2017-10-17] [WebCite Cache]
    7. Adde A, Roucou P, Mangeas M, Ardillon V, Desenclos JC, Rousset D, et al. Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators. PLoS Negl Trop Dis 2016 Apr;10(4):e0004681 [FREE Full text] [CrossRef] [Medline]
    8. Adde A, Roux E, Mangeas M, Dessay N, Nacher M, Dusfour I, et al. Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data. PLoS One 2016;11(10):e0164685 [FREE Full text] [CrossRef] [Medline]
    9. Adimi F, Soebiyanto RP, Safi N, Kiang R. Towards malaria risk prediction in Afghanistan using remote sensing. Malar J 2010 May 13;9:125 [FREE Full text] [CrossRef] [Medline]
    10. Ageep TB, Cox J, Hassan MM, Knols BGJ, Benedict MQ, Malcolm CA, et al. Spatial and temporal distribution of the malaria mosquito Anopheles arabiensis in northern Sudan: influence of environmental factors and implications for vector control. Malar J 2009 Jun 07;8:123 [FREE Full text] [CrossRef] [Medline]
    11. Almeida AS, Werneck GL. Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data. Int J Health Geogr 2014 May 20;13:13 [FREE Full text] [CrossRef] [Medline]
    12. Anno S, Imaoka K, Tadono T, Igarashi T, Sivaganesh S, Kannathasan S, et al. Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka. Geospat Health 2015 Nov 26;10(2):376 [FREE Full text] [CrossRef] [Medline]
    13. Arboleda S, Jaramillo- O, Peterson AT. Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. Int J Environ Res Public Health 2009 Dec;6(12):3040-3055 [FREE Full text] [CrossRef] [Medline]
    14. Asadi SS, Vuppala P, Reddy MA. Remote sensing and GIS techniques for evaluation of groundwater quality in municipal corporation of Hyderabad (Zone-V), India. Int J Environ Res Public Health 2007 Mar;4(1):45-52 [FREE Full text] [Medline]
    15. Assaré RK, Lai Y, Yapi A, Tian-Bi YT, Ouattara M, Yao PK, et al. The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d'Ivoire. Geospat Health 2015 Jun 03;10(1):345 [FREE Full text] [CrossRef] [Medline]
    16. Aziz S, Aidil RM, Nisfariza MN, Ngui R, Lim YAL, Yusoff WSW, et al. Spatial density of Aedes distribution in urban areas: a case study of breteau index in Kuala Lumpur, Malaysia. J Vector Borne Dis 2014 Jun;51(2):91-96 [FREE Full text] [Medline]
    17. Baeza A, Bouma MJ, Dhiman RC, Baskerville EB, Ceccato P, Yadav RS, et al. Long-lasting transition toward sustainable elimination of desert malaria under irrigation development. Proc Natl Acad Sci U S A 2013 Sep 10;110(37):15157-15162 [FREE Full text] [CrossRef] [Medline]
    18. Beck LR, Lobitz BM, Wood BL. Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 2000;6(3):217-227 [FREE Full text] [CrossRef] [Medline]
    19. Bhunia GS, Kesari S, Jeyaram A, Kumar V, Das P. Influence of topography on the endemicity of Kala-azar: a study based on remote sensing and geographical information system. Geospat Health 2010 May;4(2):155-165. [CrossRef] [Medline]
    20. Bo Y, Song C, Wang J, Li X. Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China. BMC Public Health 2014 Apr 14;14:358 [FREE Full text] [CrossRef] [Medline]
    21. Brooker S, Beasley M, Ndinaromtan M, Madjiouroum EM, Baboguel M, Djenguinabe E, et al. Use of remote sensing and a geographical information system in a national helminth control programme in Chad. Bull World Health Organ 2002;80(10):783-789 [FREE Full text] [Medline]
    22. Brooker S, Michael E. The potential of geographical information systems and remote sensing in the epidemiology and control of human helminth infections. Advances in Parasitology 2000;47:245-288. [Medline]
    23. Buczak AL, Koshute PT, Babin SM, Feighner BH, Lewis SH. A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data. BMC Med Inform Decis Mak 2012 Nov 05;12:124 [FREE Full text] [CrossRef] [Medline]
    24. Caldas de Castro M, Yamagata Y, Mtasiwa D, Tanner M, Utzinger J, Keiser J, et al. Integrated urban malaria control: a case study in dar es salaam, Tanzania. Am J Trop Med Hyg 2004 Aug;71(2 Suppl):103-117. [Medline]
    25. Cappelle J, Gaidet N, Iverson SA, Takekawa JY, Newman SH, Fofana B, et al. Characterizing the interface between wild ducks and poultry to evaluate the potential of transmission of avian pathogens. Int J Health Geogr 2011 Nov 15;10:60 [FREE Full text] [CrossRef] [Medline]
    26. Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk. Parassitologia 2005 Mar;47(1):81-96. [Medline]
    27. Chan L, Cheung GTY, Lauder IJ, Kumana CR, Lauder IJ. Screening for fever by remote-sensing infrared thermographic camera. J Travel Med 2004;11(5):273-279 [FREE Full text] [Medline]
    28. Cheng C, Wei Y, Sun X, Zhou Y. Estimation of chlorophyll-a concentration in Turbid Lake using spectral smoothing and derivative analysis. Int J Environ Res Public Health 2013 Jul 16;10(7):2979-2994 [FREE Full text] [CrossRef] [Medline]
    29. Chiang M, Lin P, Lin L, Chiou H, Chien C, Chu S, et al. Mass screening of suspected febrile patients with remote-sensing infrared thermography: alarm temperature and optimal distance. J Formos Med Assoc 2008 Dec;107(12):937-944 [FREE Full text] [CrossRef] [Medline]
    30. Clarke K, Osleeb J, Sherry J, Meert J, Larsson R. The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm) eradication effort. Preventive Veterinary Medicine 1991 Dec;11(3-4):229-235. [CrossRef] [Medline]
    31. Clennon JA, Kamanga A, Musapa M, Shiff C, Glass GE. Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia. Int J Health Geogr 2010 Nov 05;9:58. [CrossRef] [Medline]
    32. Corner RJ, Dewan AM, Hashizume M. Modelling typhoid risk in Dhaka metropolitan area of Bangladesh: the role of socio-economic and environmental factors. Int J Health Geogr 2013 Mar 16;12:13 [FREE Full text] [CrossRef] [Medline]
    33. Dambach P, Machault V, Lacaux J, Vignolles C, Sié A, Sauerborn R. Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa. Int J Health Geogr 2012 Mar 23;11:8 [FREE Full text] [CrossRef] [Medline]
    34. Dambach P, Sié A, Lacaux JP, Vignolles C, Machault V, Sauerborn R. Using high spatial resolution remote sensing for risk mapping of malaria occurrence in the Nouna district, Burkina Faso. Glob Health Action 2009 Nov 11;2:1 [FREE Full text] [CrossRef] [Medline]
    35. de Oliveira ES, dos Santos ES, Zeilhofer P, Souza-Santos R, Atanaka-Santos M. Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon. Malar J 2013 Nov 15;12:420 [FREE Full text] [CrossRef] [Medline]
    36. De Roek E, Van Coillie F, De Wulf R, Soenen K, Charlier J, Vercruysse J, et al. Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study. Geospat Health 2014 Dec 01;8(3):S671-S683 [FREE Full text] [CrossRef] [Medline]
    37. de Souza Gomes E, Leal-Neto OB, Albuquerque J, Pereira da Silva H, Barbosa CS. Schistosomiasis transmission and environmental change: a spatio-temporal analysis in Porto de Galinhas, Pernambuco--Brazil. Int J Health Geogr 2012 Nov 20;11:51 [FREE Full text] [CrossRef] [Medline]
    38. Diuk-Wasser MA, Dolo G, Bagayoko M, Sogoba N, Toure MB, Moghaddam M, et al. Patterns of irrigated rice growth and malaria vector breeding in Mali using multi-temporal ERS-2 synthetic aperture radar. Int J Remote Sens 2006 Feb;27(3):535-548 [FREE Full text] [CrossRef] [Medline]
    39. Dlamini SN, Franke J, Vounatsou P. Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data. Geospat Health 2015 Jun 03;10(1):302 [FREE Full text] [CrossRef] [Medline]
    40. Ebhuoma O, Gebreslasie M. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa. Int J Environ Res Public Health 2016 Dec 14;13(6):pii: E58 [FREE Full text] [CrossRef] [Medline]
    41. Ehlkes L, Krefis AC, Kreuels B, Krumkamp R, Adjei O, Ayim-Akonor M, et al. Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana. Int J Health Geogr 2014 Sep 30;13:35 [FREE Full text] [CrossRef] [Medline]
    42. Eisele TP, Keating J, Swalm C, Mbogo CM, Githeko AK, Regens JL, et al. Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya. Malar J 2003 Dec 10;2(1):44 [FREE Full text] [CrossRef] [Medline]
    43. Eisenberg MC, Kujbida G, Tuite AR, Fisman DN, Tien JH. Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches. Epidemics 2013 Dec;5(4):197-207 [FREE Full text] [CrossRef] [Medline]
    44. Espinosa M, Weinberg D, Rotela CH, Polop F, Abril M, Scavuzzo CM. Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina). PLoS Negl Trop Dis 2016 Dec;10(5):e0004621 [FREE Full text] [CrossRef] [Medline]
    45. Estrada-Peña A. Increasing habitat suitability in the United States for the tick that transmits Lyme disease: a remote sensing approach. Environ Health Perspect 2002 Jul;110(7):635-640 [FREE Full text] [Medline]
    46. Franke J, Gebreslasie M, Bauwens I, Deleu J, Siegert F. Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches. Geospat Health 2015 Jun 03;10(1):335 [FREE Full text] [CrossRef] [Medline]
    47. Fuentes MV. Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project. Geospat Health 2006 Nov;1(1):59-70. [CrossRef] [Medline]
    48. Gebreslasie MT. A review of spatial technologies with applications for malaria transmission modelling and control in Africa. Geospat Health 2015 Nov 26;10(2):328 [FREE Full text] [CrossRef] [Medline]
    49. Giardina F, Franke J, Vounatsou P. Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery. Geospat Health 2015 Nov 26;10(2):333 [FREE Full text] [CrossRef] [Medline]
    50. Gilbert M, Newman SH, Takekawa JY, Loth L, Biradar C, Prosser DJ, et al. Flying over an infected landscape: distribution of highly pathogenic avian influenza H5N1 risk in South Asia and satellite tracking of wild waterfowl. Ecohealth 2010 Dec;7(4):448-458 [FREE Full text] [CrossRef] [Medline]
    51. Goetz S, Prince SD, Small J. Advances in satellite remote sensing of environmental variables for epidemiological applications. Adv Parasitol 2000;47:289-307. [Medline]
    52. Grimes DJ, Ford TE, Colwell RR, Baker-Austin C, Martinez-Urtaza J, Subramaniam A, et al. Viewing marine bacteria, their activity and response to environmental drivers from orbit: satellite remote sensing of bacteria. Microb Ecol 2014 Apr;67(3):489-500 [FREE Full text] [CrossRef] [Medline]
    53. Guimarães RJDPS, Freitas CC, Dutra LV, Scholte RGC, Martins-Bedé FT, Fonseca FR, et al. A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil. Mem Inst Oswaldo Cruz 2010 Jul;105(4):524-531 [FREE Full text] [Medline]
    54. Guo J, Vounatsou P, Cao C, Utzinger J, Zhu H, Anderegg D, et al. A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. Acta Trop 2005;96(2-3):213-222. [CrossRef] [Medline]
    55. Hanafi-Bojd AA, Vatandoost H, Oshaghi MA, Charrahy Z, Haghdoost AA, Sedaghat MM, et al. Larval habitats and biodiversity of anopheline mosquitoes (Diptera: Culicidae) in a malarious area of southern Iran. J Vector Borne Dis 2012 Jun;49(2):91-100 [FREE Full text] [Medline]
    56. Haque U, Magalhães RJS, Reid HL, Clements ACA, Ahmed SM, Islam A, et al. Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malar J 2010 May 09;9:120 [FREE Full text] [CrossRef] [Medline]
    57. Hassan AN, Onsi HM. Remote sensing as a tool for mapping mosquito breeding habitats and associated health risk to assist control efforts and development plans: a case study in Wadi El Natroun, Egypt. J Egypt Soc Parasitol 2004 Aug;34(2):367-382. [Medline]
    58. Hay SI, Lennon JJ. Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate. Trop Med Int Health 1999 Jan;4(1):58-71 [FREE Full text] [Medline]
    59. Hay S, Packer M, Rogers D. Review article The impact of remote sensing on the study and control of invertebrate intermediate hosts and vectors for disease. International Journal of Remote Sensing 1997 Sep;18(14):2899-2930. [CrossRef]
    60. Hayes RO, Maxwell EL, Mitchell CJ, Woodzick TL. Detection, identification, and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites. Bull World Health Organ 1985;63(2):361-374 [FREE Full text] [Medline]
    61. Herbreteau V, Salem G, Souris M, Hugot JP, Gonzalez JP. Sizing up human health through remote sensing: uses and misuses. Parassitologia 2005 Mar;47(1):63-79. [Medline]
    62. Herbreteau V, Salem G, Souris M, Hugot J, Gonzalez J. Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health Place 2007 Jun;13(2):400-403. [CrossRef] [Medline]
    63. Hu Y, Li R, Bergquist R, Lynn H, Gao F, Wang Q, et al. Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China. PLoS Negl Trop Dis 2015 Feb;9(2):e0003470 [FREE Full text] [CrossRef] [Medline]
    64. Hugh-Jones M. Applications of remote sensing to the identification of the habitats of parasites and disease vectors. Parasitol Today 1989 Aug;5(8):244-251. [Medline]
    65. Hunter P, Tyler A, Carvalho L, Codd G, Maberly S. Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment 2010 Nov;114(11):2705-2718. [CrossRef]
    66. Hunter PD, Tyler AN, Gilvear DJ, Willby NJ. Using remote sensing to aid the assessment of human health risks from blooms of potentially toxic cyanobacteria. Environ Sci Technol 2009 Apr 01;43(7):2627-2633. [Medline]
    67. Ippoliti C, Gilbert M, Vanhuysse S, Goffredo M, Satta G, Wolff E, et al. Can landscape metrics help determine the Culicoides imicola distribution in Italy? Geospat Health 2013 Nov;8(1):267-277 [FREE Full text] [CrossRef] [Medline]
    68. Jacob BG, Novak RJ, Toe LD, Sanfo M, Griffith DA, Lakwo TL, et al. Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa. PLoS Negl Trop Dis 2013;7(7):e2342 [FREE Full text] [CrossRef] [Medline]
    69. Jagai J, Sarkar R, Castronovo D, Kattula D, McEntee J, Ward H, et al. Seasonality of rotavirus in South Asia: a meta-analysis approach assessing associations with temperature, precipitation, and vegetation index. PLoS One 2012;7(5):e38168 [FREE Full text] [CrossRef] [Medline]
    70. Jia P, Joyner A. Human brucellosis occurrences in inner mongolia, China: a spatio-temporal distribution and ecological niche modeling approach. BMC Infect Dis 2015 Feb 03;15:36 [FREE Full text] [CrossRef] [Medline]
    71. Jutla A, Akanda AS, Huq A, Faruque ASG, Colwell R, Islam S. A water marker monitored by satellites to predict seasonal endemic cholera. Remote Sens Lett 2013;4(8):822-831 [FREE Full text] [CrossRef] [Medline]
    72. Jutla A, Aldaach H, Billian H, Akanda A, Huq A, Colwell R. Satellite Based Assessment of Hydroclimatic Conditions Related to Cholera in Zimbabwe. PLoS One 2015;10(9):e0137828 [FREE Full text] [CrossRef] [Medline]
    73. Jutla A, Huq A, Colwell RR. Diagnostic approach for monitoring hydroclimatic conditions related to emergence of west nile virus in west virginia. Front Public Health 2015;3:10 [FREE Full text] [CrossRef] [Medline]
    74. Jutla A, Akanda AS, Islam S. Tracking Cholera in Coastal Regions using Satellite Observations. J Am Water Resour Assoc 2010 Aug;46(4):651-662 [FREE Full text] [CrossRef] [Medline]
    75. Jutla A, Akanda AS, Islam S. Satellite Remote Sensing of Space-Time Plankton Variability in the Bay of Bengal: Connections to Cholera Outbreaks. Remote Sens Environ 2012 Aug;123:196-206 [FREE Full text] [CrossRef] [Medline]
    76. Kabaria C, Molteni F, Mandike R, Chacky F, Noor AM, Snow RW, et al. Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam. Int J Health Geogr 2016 Dec 30;15(1):26 [FREE Full text] [CrossRef] [Medline]
    77. Kalluri S, Gilruth P, Rogers D, Szczur M. Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 2007 Oct 26;3(10):1361-1371 [FREE Full text] [CrossRef] [Medline]
    78. Kesari S, Bhunia G, Chatterjee N, Kumar V, Mandal R, Das P. Appraisal of Phlebotomus argentipes habitat suitability using a remotely sensed index in the kala-azar endemic focus of Bihar, India. Mem Inst Oswaldo Cruz 2013 Apr;108(2):197-204 [FREE Full text] [CrossRef] [Medline]
    79. Kiewra D, Lonc E. Epidemiological consequences of host specificity of ticks (Ixodida). Ann Parasitol 2012;58(4):181-187 [FREE Full text] [Medline]
    80. Kirpich A, Weppelmann TA, Yang Y, Ali A, Morris JG, Longini IM. Cholera Transmission in Ouest Department of Haiti: Dynamic Modeling and the Future of the Epidemic. PLoS Negl Trop Dis 2015;9(10):e0004153 [FREE Full text] [CrossRef] [Medline]
    81. Kutser T. Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing. Limnol. Oceanogr 2004 Nov 12;49(6):2179-2189. [CrossRef]
    82. Kutser T, Metsamaa L, Strömbeck N, Vahtmäe E. Monitoring cyanobacterial blooms by satellite remote sensing. Estuarine, Coastal and Shelf Science 2006 Mar;67(1-2):303-312. [CrossRef]
    83. Kzar A, Mat JMZ, Mutter KN, Syahreza S. A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping. Int J Environ Res Public Health 2015 Dec 30;13(1):pii-E92 [FREE Full text] [CrossRef] [Medline]
    84. Lafaye M, Sall B, Ndiaye Y, Vignolles C, Tourre YM, Borchi FO, et al. Rift Valley fever dynamics in Senegal: a project for pro-active adaptation and improvement of livestock raising management. Geospat Health 2013 Nov;8(1):279-288 [FREE Full text] [CrossRef] [Medline]
    85. Lai Y, Zhou X, Utzinger J, Vounatsou P. Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People's Republic of China. Parasit Vectors 2013 Dec 18;6:359 [FREE Full text] [CrossRef] [Medline]
    86. Lauer A, Talamantes J, Castañón Olivares L, Medina L, Baal J, Casimiro K, et al. Combining Forces - The Use of Landsat TM Satellite Imagery, Soil Parameter Information, and Multiplex PCR to Detect Coccidioides immitis Growth Sites in Kern County, California. PLoS ONE 2014 Nov 7;9(11):e111921. [CrossRef]
    87. Li Z, Roux E, Dessay N, Girod R, Stefani A, Nacher M, et al. Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil. Remote Sensing 2016 Apr 11;8(4):319. [CrossRef]
    88. Lobitz B, Beck L, Huq A, Wood B, Fuchs G, Faruque AS, et al. Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc Natl Acad Sci U S A 2000 Feb 15;97(4):1438-1443 [FREE Full text] [Medline]
    89. Louis VR, Phalkey R, Horstick O, Ratanawong P, Wilder-Smith A, Tozan Y, et al. Modeling tools for dengue risk mapping - a systematic review. Int J Health Geogr 2014 Dec 09;13:50 [FREE Full text] [CrossRef] [Medline]
    90. Machault V, Orlandi-Pradines E, Michel R, Pagès F, Texier G, Pradines B, et al. Remote sensing and malaria risk for military personnel in Africa. J Travel Med 2008;15(4):216-220 [FREE Full text] [CrossRef] [Medline]
    91. Machault V, Vignolles C, Borchi F, Vounatsou P, Pages F, Briolant S, et al. The use of remotely sensed environmental data in the study of malaria. Geospat Health 2011 May;5(2):151-168. [CrossRef] [Medline]
    92. Machault V, Vignolles C, Pagès F, Gadiaga L, Gaye A, Sokhna C, et al. Spatial heterogeneity and temporal evolution of malaria transmission risk in Dakar, Senegal, according to remotely sensed environmental data. Malar J 2010 Sep 03;9:252 [FREE Full text] [CrossRef] [Medline]
    93. Machault V, Vignolles C, Pagès F, Gadiaga L, Tourre YM, Gaye A, et al. Risk mapping of Anopheles gambiae s.l. densities using remotely-sensed environmental and meteorological data in an urban area: Dakar, Senegal. PLoS One 2012;7(11):e50674 [FREE Full text] [CrossRef] [Medline]
    94. Machault V, Yébakima A, Etienne M, Vignolles C, Palany P, Tourre Y, et al. Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data. ISPRS International Journal of Geo-Information 2014;3(4):1352.
    95. Manyangadze T, Chimbari MJ, Gebreslasie M, Mukaratirwa S. Application of geo-spatial technology in schistosomiasis modelling in Africa: a review. Geospat Health 2015 Nov 04;10(2):326 [FREE Full text] [CrossRef] [Medline]
    96. Marcantonio M, Rizzoli A, Metz M, Rosà R, Marini G, Chadwick E, et al. Identifying the environmental conditions favouring West Nile Virus outbreaks in Europe. PLoS One 2015;10(3):e0121158 [FREE Full text] [CrossRef] [Medline]
    97. Mattikalli N, Richards K. Estimation of Surface Water Quality Changes in Response to Land Use Change: Application of The Export Coefficient Model Using Remote Sensing and Geographical Information System. Journal of Environmental Management 1996 Nov;48(3):263-282. [CrossRef]
    98. Méndez-Lázaro P, Muller-Karger FE, Otis D, McCarthy MJ, Peña-Orellana M. Assessing climate variability effects on dengue incidence in San Juan, Puerto Rico. Int J Environ Res Public Health 2014 Sep 11;11(9):9409-9428 [FREE Full text] [CrossRef] [Medline]
    99. Midekisa A, Senay G, Henebry GM, Semuniguse P, Wimberly MC. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malar J 2012 May 14;11:165 [FREE Full text] [CrossRef] [Medline]
    100. Midekisa A, Senay GB, Wimberly MC. Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia. Water Resour Res 2014 Nov;50(11):8791-8806 [FREE Full text] [CrossRef] [Medline]
    101. Moloney JM, Skelly C, Weinstein P, Maguire M, Ritchie S. Domestic Aedes aegypti breeding site surveillance: limitations of remote sensing as a predictive surveillance tool. Am J Trop Med Hyg 1998 Aug;59(2):261-264. [Medline]
    102. Moss WJ, Hamapumbu H, Kobayashi T, Shields T, Kamanga A, Clennon J, et al. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey. Malar J 2011 Jun 10;10:163 [FREE Full text] [CrossRef] [Medline]
    103. Mushinzimana E, Munga S, Minakawa N, Li L, Feng C, Bian L, et al. Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malar J 2006 Feb 16;5:13 [FREE Full text] [CrossRef] [Medline]
    104. Myers M, Rogers DJ, Cox J, Flahault A, Hay SI. Forecasting disease risk for increased epidemic preparedness in public health. Adv Parasitol 2000;47:309-330 [FREE Full text] [Medline]
    105. Nygren D, Stoyanov C, Lewold C, Månsson F, Miller J, Kamanga A, et al. Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study. Malar J 2014 Jun 13;13:231 [FREE Full text] [CrossRef] [Medline]
    106. Oliveira EFD, Silva EAE, Fernandes CEDS, Paranhos FAC, Gamarra RM, Ribeiro AA, et al. Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil. Mem Inst Oswaldo Cruz 2012 May;107(3):396-401 [FREE Full text] [Medline]
    107. Omumbo JA, Hay SI, Goetz SJ, Snow RW, Rogers DJ. Updating Historical Maps of Malaria Transmission Intensity in East Africa Using Remote Sensing. Photogramm Eng Remote Sensing 2002 Feb;68(2):161-166 [FREE Full text] [Medline]
    108. Ozdenerol E. GIS and Remote Sensing Use in the Exploration of Lyme Disease Epidemiology. Int J Environ Res Public Health 2015 Dec 01;12(12):15182-15203 [FREE Full text] [CrossRef] [Medline]
    109. Masimalai P. Remote sensing and Geographic Information Systems (GIS) as the applied public health and environmental epidemiology. Int J Med Sci Public Health 2014;3(12):1430. [CrossRef]
    110. Polo G, Labruna MB, Ferreira F. Satellite Hyperspectral Imagery to Support Tick-Borne Infectious Diseases Surveillance. PLoS One 2015;10(11):e0143736 [FREE Full text] [CrossRef] [Medline]
    111. Pope K, Rejmankova E, Savage HM, Arredondo-Jimenez JI, Rodriguez MH, Roberts DR. Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecol Appl 1994 Feb;4(1):81-90. [Medline]
    112. Rahman A, Kogan F, Roytman L. Analysis of malaria cases in Bangladesh with remote sensing data. Am J Trop Med Hyg 2006 Jan;74(1):17-19. [Medline]
    113. Rahman A, Krakauer N, Roytman L, Goldberg M, Kogan F. Application of advanced very high resolution radiometer (AVHRR)-based vegetation health indices for estimation of malaria cases. Am J Trop Med Hyg 2010 Jun;82(6):1004-1009 [FREE Full text] [CrossRef] [Medline]
    114. Rai PK, Nathawat MS, Rai S. Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India. Inform Prim Care 2013;21(1):43-52 [FREE Full text] [CrossRef] [Medline]
    115. Rakotomanana F, Ratovonjato J, Randremanana RV, Randrianasolo L, Raherinjafy R, Rudant J, et al. Geographical and environmental approaches to urban malaria in Antananarivo (Madagascar). BMC Infect Dis 2010 Jun 16;10:173 [FREE Full text] [CrossRef] [Medline]
    116. Samson DM, Archer RS, Alimi TO, Arheart KL, Impoinvil DE, Oscar R, et al. New baseline environmental assessment of mosquito ecology in northern Haiti during increased urbanization. J Vector Ecol 2015 Jun;40(1):46-58 [FREE Full text] [CrossRef] [Medline]
    117. Sarfraz M, Tripathi NK, Faruque FS, Bajwa UI, Kitamoto A, Souris M. Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters. Geospat Health 2014 Dec 01;8(3):S685-S697 [FREE Full text] [CrossRef] [Medline]
    118. Saxena R, Das MK, Nagpal BN, Srivastava A, Gupta SK, Kumar A, et al. Identification of risk factors for malaria control by focused interventions in Ranchi district, Jharkhand, India. J Vector Borne Dis 2014 Dec;51(4):276-281 [FREE Full text] [Medline]
    119. Saxena R, Nagpal BN, Singh VP, Srivastava A, Dev V, Sharma MC, et al. Impact of deforestation on known malaria vectors in Sonitpur district of Assam, India. J Vector Borne Dis 2014 Sep;51(3):211-215 [FREE Full text] [Medline]
    120. Scholte RGC, Schur N, Bavia ME, Carvalho EM, Chammartin F, Utzinger J, et al. Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models. Geospat Health 2013 Nov;8(1):97-110 [FREE Full text] [CrossRef] [Medline]
    121. Schuster G, Ebert EE, Stevenson MA, Corner RJ, Johansen CA. Application of satellite precipitation data to analyse and model arbovirus activity in the tropics. Int J Health Geogr 2011 Jan 22;10:8 [FREE Full text] [CrossRef] [Medline]
    122. Sewe M, Ahlm C, Rocklöv J. Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya. PLoS One 2016;11(4):e0154204 [FREE Full text] [CrossRef] [Medline]
    123. Simis S, Ruiz-Verdú A, Domínguez-Gómez J, Peña-Martinez R, Peters S, Gons H. Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass. Remote Sensing of Environment 2007 Feb;106(4):414-427. [CrossRef]
    124. Simoonga C, Utzinger J, Brooker S, Vounatsou P, Appleton CC, Stensgaard AS, et al. Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa. Parasitology 2009 Nov;136(13):1683-1693 [FREE Full text] [CrossRef] [Medline]
    125. Soti V, Chevalier V, Maura J, Bégué A, Lelong C, Lancelot R, et al. Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery. Int J Health Geogr 2013 Mar 01;12:10 [FREE Full text] [CrossRef] [Medline]
    126. Spear RC, Hubbard A, Liang S, Seto E. Disease transmission models for public health decision making: toward an approach for designing intervention strategies for Schistosomiasis japonica. Environ Health Perspect 2002 Sep;110(9):907-915 [FREE Full text] [Medline]
    127. Spear RC, Seto E, Liang S, Birkner M, Hubbard A, Qiu D, et al. Factors influencing the transmission of Schistosoma japonicum in the mountains of Sichuan Province of China. Am J Trop Med Hyg 2004 Jan;70(1):48-56. [Medline]
    128. Stadler H, Skritek P. Remote water quality monitoring “on-line” using LEO satellites. Water Sci Technol 2003;47(2):197-204. [Medline]
    129. Stefani A, Dusfour I, Corrêa APSA, Cruz MCB, Dessay N, Galardo AKR, et al. Land cover, land use and malaria in the Amazon: a systematic literature review of studies using remotely sensed data. Malar J 2013 Jun 08;12:192 [FREE Full text] [CrossRef] [Medline]
    130. Stefani A, Roux E, Fotsing J, Carme B. Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations. Int J Health Geogr 2011 Dec 13;10:65 [FREE Full text] [CrossRef] [Medline]
    131. Tatem AJ, Hay SI. Measuring urbanization pattern and extent for malaria research: a review of remote sensing approaches. J Urban Health 2004 Sep;81(3):363-376 [FREE Full text] [CrossRef] [Medline]
    132. Texier G, Machault V, Barragti M, Boutin JP, Rogier C. Environmental determinant of malaria cases among travellers. Malar J 2013 Mar 04;12:87 [FREE Full text] [CrossRef] [Medline]
    133. Thanapongtharm W, Linard C, Wiriyarat W, Chinsorn P, Kanchanasaka B, Xiao X, et al. Spatial characterization of colonies of the flying fox bat, a carrier of Nipah virus in Thailand. BMC Vet Res 2015 Mar 28;11:81 [FREE Full text] [CrossRef] [Medline]
    134. Thanapongtharm W, Van BTP, Biradar C, Xiao X, Gilbert M. Rivers and flooded areas identified by medium-resolution remote sensing improve risk prediction of the highly pathogenic avian influenza H5N1 in Thailand. Geospat Health 2013 Nov;8(1):193-201 [FREE Full text] [CrossRef] [Medline]
    135. Thomas CJ, Lindsay SW. Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Trans R Soc Trop Med Hyg 2000;94(2):159-163. [Medline]
    136. Tian H, Cui Y, Dong L, Zhou S, Li X, Huang S, et al. Spatial, temporal and genetic dynamics of highly pathogenic avian influenza A (H5N1) virus in China. BMC Infect Dis 2015 Feb 13;15:54 [FREE Full text] [CrossRef] [Medline]
    137. Torbick N, Corbiere M. A Multiscale Mapping Assessment of Lake Champlain Cyanobacterial Harmful Algal Blooms. Int J Environ Res Public Health 2015 Sep 15;12(9):11560-11578 [FREE Full text] [CrossRef] [Medline]
    138. Torbick N, Hession S, Stommel E, Caller T. Mapping amyotrophic lateral sclerosis lake risk factors across northern New England. Int J Health Geogr 2014 Jan 02;13:1 [FREE Full text] [CrossRef] [Medline]
    139. Tourre Y, Lacaux JP, Vignolles C, Lafaye M. Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal. Glob Health Action 2009 Nov 11;2:e [FREE Full text] [CrossRef] [Medline]
    140. Tran A, L'Ambert G, Lacour G, Benoît R, Demarchi M, Cros M, et al. A rainfall- and temperature-driven abundance model for Aedes albopictus populations. Int J Environ Res Public Health 2013 Apr 26;10(5):1698-1719 [FREE Full text] [CrossRef] [Medline]
    141. Tran A, Ponçon N, Toty C, Linard C, Guis H, Ferré J, et al. Using remote sensing to map larval and adult populations of Anopheles hyrcanus (Diptera: Culicidae) a potential malaria vector in Southern France. Int J Health Geogr 2008 Feb 26;7:9 [FREE Full text] [CrossRef] [Medline]
    142. Walz Y, Wegmann M, Dech S, Raso G, Utzinger J. Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook. Parasit Vectors 2015 Mar 17;8:163 [FREE Full text] [CrossRef] [Medline]
    143. Walz Y, Wegmann M, Dech S, Vounatsou P, Poda JN, N'Goran EK, et al. Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing. PLoS Negl Trop Dis 2015 Nov;9(11):e0004217 [FREE Full text] [CrossRef] [Medline]
    144. Walz Y, Wegmann M, Leutner B, Dech S, Vounatsou P, N'Goran EK, et al. Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling. Geospat Health 2015 Nov 30;10(2):398 [FREE Full text] [CrossRef] [Medline]
    145. Wang Y, Zhuang D. A Rapid Monitoring and Evaluation Method of Schistosomiasis Based on Spatial Information Technology. Int J Environ Res Public Health 2015 Dec 12;12(12):15843-15859 [FREE Full text] [CrossRef] [Medline]
    146. Wang Y, Feng C, Sithithaworn P. Environmental determinants of Opisthorchis viverrini prevalence in northeast Thailand. Geospat Health 2013 Nov;8(1):111-123 [FREE Full text] [CrossRef] [Medline]
    147. Washino RK, Wood BL. Application of remote sensing to arthropod vector surveillance and control. Am J Trop Med Hyg 1994;50(6 Suppl):134-144. [Medline]
    148. Watanabe F, Alcântara E, Rodrigues TWP, Imai NN, Barbosa CCF, Rotta LHDS. Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images. Int J Environ Res Public Health 2015 Aug 26;12(9):10391-10417 [FREE Full text] [CrossRef] [Medline]
    149. Wei L, Qian Q, Wang Z, Glass GE, Song S, Zhang W, et al. Using geographic information system-based ecologic niche models to forecast the risk of hantavirus infection in Shandong Province, China. Am J Trop Med Hyg 2011 Mar;84(3):497-503 [FREE Full text] [CrossRef] [Medline]
    150. Wynne TT, Stumpf RP. Spatial and temporal patterns in the seasonal distribution of toxic cyanobacteria in Western Lake Erie from 2002-2014. Toxins (Basel) 2015 May 12;7(5):1649-1663 [FREE Full text] [CrossRef] [Medline]
    151. Xu M, Cao C, Wang D, Kan B. Identifying environmental risk factors of cholera in a coastal area with geospatial technologies. Int J Environ Res Public Health 2014 Dec 29;12(1):354-370 [FREE Full text] [CrossRef] [Medline]
    152. Yan L, Fang L, Huang H, Zhang L, Feng D, Zhao W, et al. Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People's Republic of China. Emerg Infect Dis 2007 Sep;13(9):1301-1306 [FREE Full text] [CrossRef] [Medline]
    153. Yang G, Vounatsou P, Tanner M, Zhou X, Utzinger J. Remote sensing for predicting potential habitats of Oncomelania hupensis in Hongze, Baima and Gaoyou lakes in Jiangsu province, China. Geospat Health 2006 Nov;1(1):85-92. [CrossRef] [Medline]
    154. Yang G, Vounatsou P, Zhou X, Utzinger J, Tanner M. A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. Acta Trop 2005;96(2-3):117-129. [CrossRef] [Medline]
    155. Zhang F, Lee J, Liang S, Shum CK. Cyanobacteria blooms and non-alcoholic liver disease: evidence from a county level ecological study in the United States. Environ Health 2015 May 07;14:41 [FREE Full text] [CrossRef] [Medline]
    156. Zhang Z, Bergquist R, Chen D, Yao B, Wang Z, Gao J, et al. Identification of parasite-host habitats in Anxiang county, Hunan Province, China based on multi-temporal China-Brazil earth resources satellite (CBERS) images. PLoS One 2013;8(7):e69447 [FREE Full text] [CrossRef] [Medline]
    157. Zhang Z, Ward M, Gao J, Wang Z, Yao B, Zhang T, et al. Remote sensing and disease control in China: past, present and future. Parasit Vectors 2013 Jan 11;6:11 [FREE Full text] [CrossRef] [Medline]
    158. Zhu H, Liu L, Zhou XN, Yang GJ. Ecological Model to Predict Potential Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonicum in the Mountainous Regions, China. PLoS Negl Trop Dis 2015;9(8):e0004028 [FREE Full text] [CrossRef] [Medline]
    159. Zou L, Miller SN, Schmidtmann ET. Mosquito larval habitat mapping using remote sensing and GIS: implications of coalbed methane development and West Nile virus. J Med Entomol 2006 Sep;43(5):1034-1041. [Medline]
    160. Wang Z, Liu Y, Hu M, Pan X, Shi J, Chen F, et al. Acute health impacts of airborne particles estimated from satellite remote sensing. Environ Int 2013 Jan;51:150-159 [FREE Full text] [CrossRef] [Medline]
    161. Higgs G, Sterling DA, Aryal S, Vemulapalli A, Priftis KN, Sifakis NI. Aerosol optical depth as a measure of particulate exposure using imputed censored data, and relationship with childhood asthma hospital admissions for 2004 in athens, Greece. Environ Health Insights 2015;9(Suppl 1):27-33 [FREE Full text] [CrossRef] [Medline]
    162. Andrade FV, Artaxo P, Hacon S, Carmo CND, Cirino G. Aerosols from biomass burning and respiratory diseases in children, Manaus, Northern Brazil. Rev Saude Publica 2013 Apr;47(2):239-247 [FREE Full text] [CrossRef] [Medline]
    163. Shi W, Wong MS, Wang J, Zhao Y. Analysis of airborne particulate matter (PM2.5) over Hong Kong using remote sensing and GIS. Sensors (Basel) 2012;12(6):6825-6836 [FREE Full text] [CrossRef] [Medline]
    164. Lee H, Kang CM, Coull BA, Bell ML, Koutrakis P. Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States. Environ Res 2014 Aug;133:103-110 [FREE Full text] [CrossRef] [Medline]
    165. McGuinn L, Ward-Caviness CK, Neas LM, Schneider A, Diaz-Sanchez D, Cascio WE, et al. Association between satellite-based estimates of long-term PM2.5 exposure and coronary artery disease. Environ Res 2016 Feb;145:9-17 [FREE Full text] [CrossRef] [Medline]
    166. Jerrett M, Turner MC, Beckerman BS, Pope CA, van DA, Martin RV, et al. Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates. Environ Health Perspect 2017 Dec;125(4):552-559 [FREE Full text] [CrossRef] [Medline]
    167. Lee SJ, Serre ML, van DA, Martin RV, Burnett RT, Jerrett M. Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States. Environ Health Perspect 2012 Dec;120(12):1727-1732 [FREE Full text] [CrossRef] [Medline]
    168. Yao J, Henderson SB. An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data. J Expo Sci Environ Epidemiol 2014;24(3):328-335 [FREE Full text] [CrossRef] [Medline]
    169. Al-Hamdan MZ, Crosson WL, Economou SA, Estes MG, Estes SM, Hemmings SN, et al. Environmental Public Health Applications Using Remotely Sensed Data. Geocarto Int 2014 Jan 01;29(1):85-98 [FREE Full text] [CrossRef] [Medline]
    170. Liu Y, Sarnat JA, Kilaru V, Jacob DJ, Koutrakis P. Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. Environ Sci Technol 2005 May 01;39(9):3269-3278. [Medline]
    171. Sotoudeheian S, Arhami M. Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran. J Environ Health Sci Eng 2014;12(1):122 [FREE Full text] [CrossRef] [Medline]
    172. van Donkelaar A, Martin RV, Park RJ. Estimating ground-level PM using aerosol optical depth determined from satellite remote sensing. J. Geophys. Res 2006 Nov 02;111(D21):e. [CrossRef]
    173. Song Y, Yang HL, Peng JH, Song YR, Sun Q, Li Y. Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data. PLoS One 2015;10(11):e0142149 [FREE Full text] [CrossRef] [Medline]
    174. Liu Y, Paciorek CJ, Koutrakis P. Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information. Environ Health Perspect 2009 Jun;117(6):886-892 [FREE Full text] [CrossRef] [Medline]
    175. Lary D, Faruque FS, Malakar N, Moore A, Roscoe B, Adams ZL, et al. Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospat Health 2014 Dec 01;8(3):S611-S630 [FREE Full text] [CrossRef] [Medline]
    176. Yao J, Brauer M, Henderson SB. Evaluation of a wildfire smoke forecasting system as a tool for public health protection. Environ Health Perspect 2013 Oct;121(10):1142-1147 [FREE Full text] [CrossRef] [Medline]
    177. Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, et al. Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. Environ Sci Technol 2012 Jan 17;46(2):652-660 [FREE Full text] [CrossRef] [Medline]
    178. Xu L, Yin H, Xie XD. Health risk assessment of inhalable particulate matter in Beijing based on the thermal environment. Int J Environ Res Public Health 2014 Nov 28;11(12):12368-12388 [FREE Full text] [CrossRef] [Medline]
    179. Kloog I, Ridgway B, Koutrakis P, Coull BA, Schwartz JD. Long- and short-term exposure to PM2.5 and mortality: using novel exposure models. Epidemiology 2013 Jul;24(4):555-561 [FREE Full text] [CrossRef] [Medline]
    180. Geddes JA, Martin RV, Boys BL, van Donkelaar A. Long-Term Trends Worldwide in Ambient NO2 Concentrations Inferred from Satellite Observations. Environ Health Perspect 2016 Mar;124(3):281-289 [FREE Full text] [CrossRef] [Medline]
    181. Coker E, Ghosh J, Jerrett M, Gomez-Rubio V, Beckerman B, Cockburn M, et al. Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County. Environ Res 2015 Oct;142:354-364 [FREE Full text] [CrossRef] [Medline]
    182. Olaguer EP. Overview of the Benzene and Other Toxics Exposure (BEE-TEX) Field Study. Environ Health Insights 2015;9(Suppl 4):1-6 [FREE Full text] [CrossRef] [Medline]
    183. Yao L, Lu N. Particulate matter pollution and population exposure assessment over mainland China in 2010 with remote sensing. Int J Environ Res Public Health 2014 May 14;11(5):5241-5250 [FREE Full text] [CrossRef] [Medline]
    184. Mackie S, Watson M. Probabilistic detection of volcanic ash using a Bayesian approach. J Geophys Res Atmos 2014 Mar 16;119(5):2409-2428 [FREE Full text] [CrossRef] [Medline]
    185. Rashidi M, Ramesht MH, Zohary M, Poursafa P, Kelishadi R, Rashidi Z, et al. Relation of air pollution with epidemiology of respiratory diseases in isfahan, Iran from 2005 to 2009. J Res Med Sci 2013 Dec;18(12):1074-1079 [FREE Full text] [Medline]
    186. Hoff R, Christopher SA. Remote sensing of particulate pollution from space: have we reached the promised land? J Air Waste Manag Assoc 2009 Jun;59(6):645-75; discussion 642. [Medline]
    187. Gupta P, Christopher S, Wang J, Gehrig R, Lee Y, Kumar N. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment 2006 Sep;40(30):5880-5892. [CrossRef]
    188. Anderson HR, Butland BK, van Donkelaar DA, Brauer M, Strachan DP, Clayton T, et al. Satellite-based estimates of ambient air pollution and global variations in childhood asthma prevalence. Environ Health Perspect 2012 Sep;120(9):1333-1339 [FREE Full text] [CrossRef] [Medline]
    189. Lin G, Fu J, Jiang D, Wang J, Wang Q, Dong D. Spatial Variation of the Relationship between PM 2.5 Concentrations and Meteorological Parameters in China. Biomed Res Int 2015;2015:684618 [FREE Full text] [CrossRef] [Medline]
    190. Liu JC, Pereira G, Uhl SA, Bravo MA, Bell ML. A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke. Environ Res 2015 Jan;136:120-132 [FREE Full text] [CrossRef] [Medline]
    191. Elliott CT, Henderson SB, Wan V. Time series analysis of fine particulate matter and asthma reliever dispensations in populations affected by forest fires. Environ Health 2013 Jan 28;12:11 [FREE Full text] [CrossRef] [Medline]
    192. van Donkelaar A, Martin RV, Brauer M, Boys BL. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ Health Perspect 2015 Feb;123(2):135-143 [FREE Full text] [CrossRef] [Medline]
    193. Lee HJ, Coull BA, Bell ML, Koutrakis P. Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations. Environ Res 2012 Oct;118:8-15 [FREE Full text] [CrossRef] [Medline]
    194. Lary DJ, Lary T, Sattler B. Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies. Environ Health Insights 2015;9(Suppl 1):41-52 [FREE Full text] [CrossRef] [Medline]
    195. Kloog I, Melly SJ, Ridgway WL, Coull BA, Schwartz J. Using new satellite based exposure methods to study the association between pregnancy PM₂.₅ exposure, premature birth and birth weight in Massachusetts. Environ Health 2012 Jun 18;11:40 [FREE Full text] [CrossRef] [Medline]
    196. Konkel L. The view from afar: satellite-derived estimates of global PM2.5. Environ Health Perspect 2015 Feb;123(2):A43 [FREE Full text] [CrossRef] [Medline]
    197. Witheetrirong Y, Tripathi NK, Tipdecho T, Parkpian P. Estimation of the effect of soil texture on nitrate-nitrogen content in groundwater using optical remote sensing. Int J Environ Res Public Health 2011 Dec;8(8):3416-3436 [FREE Full text] [CrossRef] [Medline]
    198. Bandaru V, Daughtry CS, Codling EE, Hansen DJ, White-Hansen S, Green CE. Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants to Monitor Arsenic Contamination. Int J Environ Res Public Health 2016 Dec 18;13(6):pii:-E606 [FREE Full text] [CrossRef] [Medline]
    199. Kamińska IA, Ołdak A, Turski WA. Geographical Information System (GIS) as a tool for monitoring and analysing pesticide pollution and its impact on public health. Ann Agric Environ Med 2004;11(2):181-184 [FREE Full text] [Medline]
    200. Ward MH, Nuckols JR, Weigel SJ, Maxwell SK, Cantor KP, Miller RS. Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System. Environ Health Perspect 2000 Jan;108(1):5-12 [FREE Full text] [Medline]
    201. Yang K, Zhou X, Yan W, Hang D, Steinmann P. Landfills in Jiangsu province, China, and potential threats for public health: leachate appraisal and spatial analysis using geographic information system and remote sensing. Waste Manag 2008 Dec;28(12):2750-2757. [CrossRef] [Medline]
    202. Frassy F, Candiani G, Rusmini M, Maianti P, Marchesi A, Rota NF, et al. Mapping asbestos-cement roofing with hyperspectral remote sensing over a large mountain region of the Italian Western Alps. Sensors (Basel) 2014 Aug 27;14(9):15900-15913 [FREE Full text] [CrossRef] [Medline]
    203. Chen Y, Liu Y, Liu Y, Lin A, Kong X, Liu D, et al. Mapping of Cu and Pb contaminations in soil using combined geochemistry, topography, and remote sensing: a case study in the Le'an River floodplain, China. Int J Environ Res Public Health 2012 Dec;9(5):1874-1886 [FREE Full text] [CrossRef] [Medline]
    204. Maxwell SK, Airola M, Nuckols JR. Using Landsat satellite data to support pesticide exposure assessment in California. Int J Health Geogr 2010 Sep 16;9:46 [FREE Full text] [CrossRef] [Medline]
    205. Xu Z, Liu Y, Ma Z, Sam TG, Hu W, Tong S. Assessment of the temperature effect on childhood diarrhea using satellite imagery. Sci Rep 2014 Jun 23;4:5389 [FREE Full text] [CrossRef] [Medline]
    206. Pereira G, Foster S, Martin K, Christian H, Boruff BJ, Knuiman M, et al. The association between neighborhood greenness and cardiovascular disease: an observational study. BMC Public Health 2012 Jun 21;12:466 [FREE Full text] [CrossRef] [Medline]
    207. Bennie J, Davies TW, Duffy JP, Inger R, Gaston KJ. Contrasting trends in light pollution across Europe based on satellite observed night time lights. Sci Rep 2014 Jan 21;4:3789 [FREE Full text] [CrossRef] [Medline]
    208. Smith LT, Aragão LEOC, Sabel CE, Nakaya T. Drought impacts on children's respiratory health in the Brazilian Amazon. Sci Rep 2014 Jan 16;4:3726 [FREE Full text] [CrossRef] [Medline]
    209. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, et al. Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach. Environ Health Perspect 2016 Dec;124(5):619-626 [FREE Full text] [CrossRef] [Medline]
    210. Liss A, Koch M, Naumova EN. Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications. Geospat Health 2014 Dec 01;8(3):S647-S659 [FREE Full text] [CrossRef] [Medline]
    211. Johnson D, Vijay L, Stanforth A, Webber J. Remote Sensing of Heat-Related Health Risks: The Trend Toward Coupling Socioeconomic and Remotely Sensed Data. Geography Compass 2011;5(10):767-780. [CrossRef]
    212. Hystad P, Davies HW, Frank L, Van Loon J, Gehring U, Tamburic L, et al. Residential greenness and birth outcomes: evaluating the influence of spatially correlated built-environment factors. Environ Health Perspect 2014 Oct;122(10):1095-1102 [FREE Full text] [CrossRef] [Medline]
    213. Johnson D, Wilson JS, Luber GC. Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. Int J Health Geogr 2009 Oct 16;8:57 [FREE Full text] [CrossRef] [Medline]
    214. Song D, Jiang D, Wang Y, Chen W, Huang Y, Zhuang D. Study on association between spatial distribution of metal mines and disease mortality: a case study in Suxian District, South China. Int J Environ Res Public Health 2013 Oct 16;10(10):5163-5177 [FREE Full text] [CrossRef] [Medline]
    215. Morabito M, Crisci A, Gioli B, Gualtieri G, Toscano P, Di SV, et al. Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities. PLoS One 2015;10(5):e0127277 [FREE Full text] [CrossRef] [Medline]
    216. Brown ME, Grace K, Shively G, Johnson KB, Carroll M. Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Popul Environ 2014;36:48-72 [FREE Full text] [CrossRef] [Medline]
    217. Hay SI. An overview of remote sensing and geodesy for epidemiology and public health application. Advances in Parasitology 2000;47:1-35. [CrossRef]
    218. Igarashi T, Kuze A, Sobue S, Yamamoto A, Yamamoto K, Oyoshi K, et al. Japan's efforts to promote global health using satellite remote sensing data from the Japan Aerospace Exploration Agency for prediction of infectious diseases and air quality. Geospat Health 2014 Dec 01;8(3):S603-S610 [FREE Full text] [CrossRef] [Medline]
    219. Jovanović P. Satellite remote sensing imagery in public health. Acta Astronautica 1987 Nov;15(11):951-953. [CrossRef]
    220. Boulos MN, Roudsari AV, Carson ER. Health geomatics: an enabling suite of technologies in health and healthcare. J Biomed Inform 2001 Jun;34(3):195-219 [FREE Full text] [Medline]
    221. Kistemann T, Dangendorf F, Schweikart J. New perspectives on the use of Geographical Information Systems (GIS) in environmental health sciences. Int J Hyg Environ Health 2002 Apr;205(3):169-181. [CrossRef] [Medline]
    222. Li Z, Xu D, Guo X. Remote sensing of ecosystem health: opportunities, challenges, and future perspectives. Sensors (Basel) 2014 Nov 07;14(11):21117-21139 [FREE Full text] [CrossRef] [Medline]
    223. Patz J. Satellite remote sensing can improve chances of achieving sustainable health. Environ Health Perspect 2005 Feb;113(2):A84-A85 [FREE Full text] [Medline]
    224. Robinson T. Spatial statistics and geographical information systems in epidemiology and public health. Adv Parasitol 2000;47:81-128. [Medline]
    225. Seltenrich N. Remote-sensing applications for environmental health research. Environ Health Perspect 2014 Oct;122(10):A268-A275 [FREE Full text] [CrossRef] [Medline]
    226. Sprigg W. Public-health applications in remote sensing. SPIE Newsroom 2009:1-3. [CrossRef]
    227. Wood B, Beck LR, Lobitz BM, Bobo MR. Education, outreach and the future of remote sensing in human health. Adv Parasitol 2000;47:331-344. [Medline]
    228. What is the difference between GPS and GNSS?. 2013 Sep. Retrieved November 20, 2016, from   URL: http://kb.unavco.org/kb/article/what-is-the-difference-between-gps-and-gnss-167.html [accessed 2017-11-19] [WebCite Cache]
    229. Rob. How GNSS Works. (October 10, 2011)   URL: http://gnss.be/how_tutorial [accessed 2017-11-19] [WebCite Cache]
    230. Almanza E, Jerrett M, Dunton G, Seto E, Pentz MA. A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place 2012 Jan;18(1):46-54 [FREE Full text] [CrossRef] [Medline]
    231. Bürgi R, Tomatis L, Murer K, de Bruin ED. Localization of Physical Activity in Primary School Children Using Accelerometry and Global Positioning System. PLoS One 2015;10(11):e0142223 [FREE Full text] [CrossRef] [Medline]
    232. Carvalho LFR, de Melo CB, McManus C, Haddad JPA. Use of satellite images for geographical localization of livestock holdings in Brazil. Prev Vet Med 2012 Jan 01;103(1):74-77 [FREE Full text] [CrossRef] [Medline]
    233. Chaix B, Kestens Y, Duncan S, Merrien C, Thierry B, Pannier B, et al. Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. Int J Behav Nutr Phys Act 2014 Sep 27;11:124 [FREE Full text] [CrossRef] [Medline]
    234. Edwards N, Hooper P, Knuiman M, Foster S, Giles-Corti B. Associations between park features and adolescent park use for physical activity. Int J Behav Nutr Phys Act 2015 Feb 18;12:21 [FREE Full text] [CrossRef] [Medline]
    235. James P, Banay RF, Hart JE, Laden F. A Review of the Health Benefits of Greenness. Curr Epidemiol Rep 2015 Jun;2(2):131-142 [FREE Full text] [CrossRef] [Medline]
    236. Larson K. A new way to detect volcanic plumes. Geophys. Res. Lett 2013 Jun 13;40(11):2657-2660. [CrossRef]
    237. Li R, Kling SR, Salata MJ, Cupp SA, Sheehan J, Voos JE. Wearable Performance Devices in Sports Medicine. Sports Health 2016;8(1):74-78 [FREE Full text] [CrossRef] [Medline]
    238. MacKerron G, Mourato S. Happiness is greater in natural environments. Global Environmental Change 2013 Oct;23(5):992-1000. [CrossRef]
    239. Marani R, Gelao G, Perri AG. High quality heart and lung auscultation system for diagnostic use on remote patients in real time. Open Biomed Eng J 2010 Nov 03;4:250-256 [FREE Full text] [CrossRef] [Medline]
    240. McCrorie PRW, Fenton C, Ellaway A. Combining GPS, GIS, and accelerometry to explore the physical activity and environment relationship in children and young people - a review. Int J Behav Nutr Phys Act 2014 Sep 13;11:93 [FREE Full text] [CrossRef] [Medline]
    241. Babu PM, Sankar GJ, Sreenivasulu V, Harikrishna DK. Hydrochemical Analysis and Evaluation of Groundwater Quality in Part of Krishna District, Andhra Pradesh – Using Remotesensing and GIS Techniques. IJER 2014 Aug 1;3(8):476-481. [CrossRef]
    242. O'Connor TM, Cerin E, Lee RE, Parker N, Chen TA, Hughes SO, et al. Environmental and cultural correlates of physical activity parenting practices among Latino parents with preschool-aged children: Niños Activos. BMC Public Health 2014 Jul 10;14:707 [FREE Full text] [CrossRef] [Medline]
    243. Oliver M, Badland H, Mavoa S, Duncan MJ, Duncan S. Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors. J Phys Act Health 2010 Jan;7(1):102-108. [Medline]
    244. Oreskovic NM, Perrin JM, Robinson AI, Locascio JJ, Blossom J, Chen ML, et al. Adolescents' use of the built environment for physical activity. BMC Public Health 2015 Mar 15;15:251 [FREE Full text] [CrossRef] [Medline]
    245. Ramesh MNR, Sathish CMR, Undi M, Aravind M, Puthussery YP. Global positioning system: a new tool to measure the distribution of anemia and nutritional status of children (5-10 years) in a rural area in south India. Indian J Med Sci 2012;66(1-2):13-22. [CrossRef] [Medline]
    246. Roelfsema C, Phinn S, Dennison W, Dekker A, Brando V. Monitoring toxic cyanobacteria Lyngbya majuscula (Gomont) in Moreton Bay, Australia by integrating satellite image data and field mapping. Harmful Algae 2006 Jan;5(1):45-56. [CrossRef]
    247. Townshend AD, Worringham CJ, Stewart IB. Assessment of speed and position during human locomotion using nondifferential GPS. Med Sci Sports Exerc 2008 Jan;40(1):124-132. [CrossRef] [Medline]
    248. Tymms P, Curtis SE, Routen AC, Thomson KH, Bolden DS, Bock S, et al. Clustered randomised controlled trial of two education interventions designed to increase physical activity and well-being of secondary school students: the MOVE Project. BMJ Open 2016 Jan 06;6(1):e009318 [FREE Full text] [CrossRef] [Medline]
    249. Walsh S, McCleary AL, Heumann BW, Brewington L, Raczkowski EJ, Mena CF. Community Expansion and Infrastructure Development: Implications for Human Health and Environmental Quality in the Galápagos Islands of Ecuador. Journal of Latin American Geography 2010;9(3):137-159.
    250. Wieters KM, Kim J, Lee C. Assessment of wearable global positioning system units for physical activity research. J Phys Act Health 2012 Sep;9(7):913-923. [Medline]
    251. Wu J, Jiang C, Liu Z, Houston D, Jaimes G, McConnell R. Performances of different global positioning system devices for time-location tracking in air pollution epidemiological studies. Environ Health Insights 2010 Nov 23;4:93-108 [FREE Full text] [CrossRef] [Medline]
    252. Achee NL, Grieco JP, Masuoka P, Andre RG, Roberts DR, Thomas J, et al. Use of remote sensing and geographic information systems to predict locations of Anopheles darlingi-positive breeding sites within the Sibun River in Belize, Central America. J Med Entomol 2006 Mar;43(2):382-392. [Medline]
    253. Barau I, Zubairu M, Mwanza MN, Seaman VY. Improving polio vaccination coverage in Nigeria through the use of geographic information system technology. J Infect Dis 2014 Nov 01;210 Suppl 1:S102-S110. [CrossRef] [Medline]
    254. Cui P, Hou Y, Tang M, Zhang H, Zhou Y, Yin Z, et al. Movement patterns of Bar-headed Geese Anser indicus during breeding and post-breeding periods at Qinghai Lake, China. J Ornithol 2010 Jul 8;152(1):83-92. [CrossRef]
    255. De Souza Dias M, Dias GH, Nobre ML. The use of Geographical Information System (GIS) to improve active leprosy case finding campaigns in the municipality of Mossoró, Rio Grande do Norte State, Brazil. Lepr Rev 2007 Sep;78(3):261-269. [Medline]
    256. Hightower AW, Ombok M, Otieno R, Odhiambo R, Oloo AJ, Lal AA, et al. A geographic information system applied to a malaria field study in western Kenya. Am J Trop Med Hyg 1998 Mar;58(3):266-272. [Medline]
    257. Kaewwaen W, Bhumiratana A. Landscape ecology and epidemiology of malaria associated with rubber plantations in Thailand: integrated approaches to malaria ecotoping. Interdiscip Perspect Infect Dis 2015;2015:909106 [FREE Full text] [CrossRef] [Medline]
    258. Krech T. TBE foci in Switzerland. Int J Med Microbiol 2002 Jun;291 Suppl 33:30-33. [Medline]
    259. Maynard NG, Conway GA. A view from above: use of satellite imagery to enhance our understanding of potential impacts of climate change on human health in the Arctic. Alaska Med 2007;49(3):78-85. [Medline]
    260. Newman S, Hill NJ, Spragens KA, Janies D, Voronkin IO, Prosser DJ, et al. Eco-virological approach for assessing the role of wild birds in the spread of avian influenza H5N1 along the Central Asian Flyway. PLoS One 2012;7(2):e30636 [FREE Full text] [CrossRef] [Medline]
    261. Prosser D, Takekawa JY, Newman SH, Yan B, Douglas DC, Hou Y, et al. Satellite-marked waterfowl reveal migratory connection between H5N1 outbreak areas in China and Mongolia. Ibis 2009;151(3):568-576. [CrossRef]
    262. Shin J, Lee K, Kim S, Hwang J, Woo C, Kim J, et al. Tracking Mallards (Anas platyrhynchos) with GPS Satellite Transmitters Along Their Migration Route Through Northeast Asia. Avian Dis 2016 May;60(1 Suppl):311-315. [CrossRef] [Medline]
    263. Shirayama Y, Phompida S, Shibuya K. Geographic information system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province, Laos. Malar J 2009 Sep 22;8:217 [FREE Full text] [CrossRef] [Medline]
    264. Sithiprasasna R, Ugsang DM, Honda K, Jones JW, Singhasivanon P. Ikonos-derived malaria transmission risk in northwestern Thailand. Southeast Asian J Trop Med Public Health 2005 Jan;36(1):14-22. [Medline]
    265. Silué KD, Raso G, Yapi A, Vounatsou P, Tanner M, N'goran EK, et al. Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach. Malar J 2008 Jun 23;7:111 [FREE Full text] [CrossRef] [Medline]
    266. Ali M, Rasool S, Park J, Saeed S, Ochiai RL, Nizami Q, et al. Use of satellite imagery in constructing a household GIS database for health studies in Karachi, Pakistan. Int J Health Geogr 2004 Sep 28;3(1):20 [FREE Full text] [CrossRef] [Medline]
    267. Bonner M, Han D, Nie J, Rogerson P, Vena JE, Freudenheim JL. Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology 2003 Jul;14(4):408-412. [CrossRef] [Medline]
    268. Boruff BJ, Nathan A, Nijënstein S. Using GPS technology to (re)-examine operational definitions of 'neighbourhood' in place-based health research. Int J Health Geogr 2012 Jun 27;11:22 [FREE Full text] [CrossRef] [Medline]
    269. Croner C, Sperling J, Broome FR. Geographic information systems (GIS): new perspectives in understanding human health and environmental relationships. Stat Med 1996;15(17-18):1961-1977. [Medline]
    270. Elgethun K, Fenske RA, Yost MG, Palcisko GJ. Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument. Environ Health Perspect 2003 Jan;111(1):115-122 [FREE Full text] [Medline]
    271. Graham D, Hipp JA. Emerging technologies to promote and evaluate physical activity: cutting-edge research and future directions. Front Public Health 2014;2:66 [FREE Full text] [CrossRef] [Medline]
    272. Haenssgen MJ. Satellite-aided survey sampling and implementation in low- and middle-income contexts: a low-cost/low-tech alternative. Emerg Themes Epidemiol 2015;12:20 [FREE Full text] [CrossRef] [Medline]
    273. Hillson R, Alejandre JD, Jacobsen KH, Ansumana R, Bockarie AS, Bangura U, et al. Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: a case study of Bo city, Sierra Leone. PLoS One 2014;9(11):e112241 [FREE Full text] [CrossRef] [Medline]
    274. Kerr J, Duncan S, Schipperijn J, Schipperjin J. Using global positioning systems in health research: a practical approach to data collection and processing. Am J Prev Med 2011 Nov;41(5):532-540. [CrossRef] [Medline]
    275. Kleiner K. They can find you: GPS implants will make it easy to pinpoint people. New Sci 2000 Jan 08;165(2220):7. [Medline]
    276. Kondo MC, Bream KDW, Barg FK, Branas CC. A random spatial sampling method in a rural developing nation. BMC Public Health 2014 Apr 10;14:338 [FREE Full text] [CrossRef] [Medline]
    277. Krishnamurthy R, Frolov A, Wolkon A, Vanden EJ, Hightower A. Application of pre-programmed PDA devices equipped with global GPS to conduct paperless household surveys in rural Mozambique. AMIA Annu Symp Proc 2006:991 [FREE Full text] [Medline]
    278. Lin Y, Kuwayama DP. Using satellite imagery and GPS technology to create random sampling frames in high risk environments. Int J Surg 2016 Aug;32:123-128. [CrossRef] [Medline]
    279. Liu H, Skjetne E, Kobernus M. Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment. Environ Health 2013 Nov 04;12:93 [FREE Full text] [CrossRef] [Medline]
    280. Lowther S, Curriero FC, Shields T, Ahmed S, Monze M, Moss WJ. Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia. Trop Med Int Health 2009 Jan;14(1):70-78 [FREE Full text] [CrossRef] [Medline]
    281. Noury-Desvaux B, Abraham P, Mahé G, Sauvaget T, Leftheriotis G, Le FA. The accuracy of a simple, low-cost GPS data logger/receiver to study outdoor human walking in view of health and clinical studies. PLoS One 2011;6(9):e23027 [FREE Full text] [CrossRef] [Medline]
    282. Nuckols JR, Ward MH, Jarup L. Using geographic information systems for exposure assessment in environmental epidemiology studies. Environ Health Perspect 2004 Jun;112(9):1007-1015 [FREE Full text] [Medline]
    283. Pager D. Impacts for medicine of global monitoring. Biomed Sci Instrum 2002;38:283-287. [Medline]
    284. Richardson D, Volkow ND, Kwan MP, Kaplan RM, Goodchild MF, Croyle RT. Medicine. Spatial turn in health research. Science 2013 Mar 22;339(6126):1390-1392. [CrossRef] [Medline]
    285. Stothard JR, Sousa-Figueiredo JC, Betson M, Seto EYW, Kabatereine NB. Investigating the spatial micro-epidemiology of diseases within a point-prevalence sample: a field applicable method for rapid mapping of households using low-cost GPS-dataloggers. Trans R Soc Trop Med Hyg 2011 Sep;105(9):500-506 [FREE Full text] [CrossRef] [Medline]
    286. Tassetto D, Fazli E, Werner M. A novel hybrid algorithm for passive localization of victims in emergency situations. Int. J. Satell. Commun. Network 2010 Oct 17;29(5):461-478. [CrossRef]
    287. Tatem AJ, Noor AM, Hay SI. Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery. Remote Sens Environ 2004 Oct 30;93(1-2):42-52 [FREE Full text] [CrossRef] [Medline]
    288. Thierry B, Chaix B, Kestens Y. Detecting activity locations from raw GPS data: a novel kernel-based algorithm. Int J Health Geogr 2013 Mar 16;12:14 [FREE Full text] [CrossRef] [Medline]
    289. Thomson D, Shitole S, Shitole T, Sawant K, Subbaraman R, Bloom DE, et al. A system for household enumeration and re-identification in densely populated slums to facilitate community research, education, and advocacy. PLoS One 2014;9(4):e93925 [FREE Full text] [CrossRef] [Medline]
    290. Ward M, Nuckols JR, Giglierano J, Bonner MR, Wolter C, Airola M, et al. Positional accuracy of two methods of geocoding. Epidemiology 2005 Jul;16(4):542-547. [Medline]
    291. Wu J, Jiang C, Jaimes G, Bartell S, Dang A, Baker D, et al. Travel patterns during pregnancy: comparison between Global Positioning System (GPS) tracking and questionnaire data. Environ Health 2013 Oct 09;12(1):86 [FREE Full text] [CrossRef] [Medline]
    292. Tenbrink T, Wiener JM, Claramunt C, Gallay M, Denis M, Auvray M. Navigation assistance for blind pedestrians: guidelines for the design of devices and implications for spatial cognition. In: Representing space in cognition : interrelations of behaviour, language, and formal models. Oxford: Oxford University Press; 2014.
    293. Alisky JM. Integrated electronic monitoring systems could revolutionize care for patients with cognitive impairment. Med Hypotheses 2006;66(6):1161-1164. [CrossRef] [Medline]
    294. Lin C, Chiu MJ, Hsiao CC, Lee RG, Tsai YS. Wireless health care service system for elderly with dementia. IEEE Trans Inf Technol Biomed 2006 Oct;10(4):696-704. [Medline]
    295. Milne H, van DPM, McCloughan L, Hanley J, Mead G, Starr J, et al. The use of global positional satellite location in dementia: a feasibility study for a randomised controlled trial. BMC Psychiatry 2014 May 30;14:160 [FREE Full text] [CrossRef] [Medline]
    296. Tenbrink T, Wiener JM, Claramunt C. Representing space in cognition : interrelations of behaviour, language, and formal models. Oxford: Oxford University Press; 2014.
    297. Varshney U. Pervasive Healthcare and Wireless Health Monitoring. Mobile Netw Appl 2007 Jul 12;12(2-3):113-127. [CrossRef]
    298. Badland H, Duncan MJ, Oliver M, Duncan JS, Mavoa S. Examining commute routes: applications of GIS and GPS technology. Environ Health Prev Med 2010 Sep;15(5):327-330 [FREE Full text] [CrossRef] [Medline]
    299. Chaix B, Méline J, Duncan S, Merrien C, Karusisi N, Perchoux C, et al. GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? Health Place 2013 May;21:46-51. [CrossRef] [Medline]
    300. Chevalier A, Chevalier AJ, Clarke E, Wall J, Coxon K, Brown J, et al. Naturalistic speeding data: Drivers aged 75 years and older. Data Brief 2016 Sep;8:136-141 [FREE Full text] [CrossRef] [Medline]
    301. Guo C, Guo W, Cao G, Dong H. A lane-level LBS system for vehicle network with high-precision BDS/GPS positioning. Comput Intell Neurosci 2015;2015:531321 [FREE Full text] [CrossRef] [Medline]
    302. Satyanarayana K, Sarma AD, Sravan J, Malini M, Venkateswarlu G. GPS and GPRS Based Telemonitoring System for Emergency Patient Transportation. J Med Eng 2013;2013:363508 [FREE Full text] [CrossRef] [Medline]
    303. de Savigny D, Mayombana C, Mwageni E, Masanja H, Minhaj A, Mkilindi Y, et al. Care-seeking patterns for fatal malaria in Tanzania. Malar J 2004 Jul 28;3:27 [FREE Full text] [CrossRef] [Medline]
    304. Gammino V, Nuhu A, Chenoweth P, Manneh F, Young RR, Sugerman DE, et al. Using geographic information systems to track polio vaccination team performance: pilot project report. J Infect Dis 2014 Nov 01;210 Suppl 1:S98-101. [CrossRef] [Medline]
    305. Perry B, Gesler W. Physical access to primary health care in Andean Bolivia. Soc Sci Med 2000 May;50(9):1177-1188. [Medline]
    306. Satava R, Angood PB, Harnett B, Macedonia C, Merrell R. The physiologic cipher at altitude: telemedicine and real-time monitoring of climbers on Mount Everest. Telemed J E Health 2000;6(3):303-313. [CrossRef] [Medline]
    307. Karim Aljewari Y, Ahmad R, Ahmed A. Providing Complete Precision Timing Solution for Hospitals by GPS Time Synchronized with MCS. JNHC 2015 Mar 01;2(1):1. [CrossRef]
    308. Khan M, Beg S. Transference and retrieval of voice message over low signal strength in satellite communication. Innovations Syst Softw Eng 2012 Aug 22;8(4):293-299. [CrossRef]
    309. Labrador V. www.britannica.com. Satellite Communication   URL: https://www.britannica.com/technology/satellite-communication [accessed 2017-11-19] [WebCite Cache]
    310. www.inmarsat.com. Inmarsat and Global eHealth Foundation join forces to bring healthcare to the world’s poorest communities   URL: http:/​/www.​inmarsat.com/​press-release/​inmarsat-global-ehealth-foundation-join-forces-bring-healthcare-worlds-poorest-communities/​ [accessed 2016-12-20] [WebCite Cache]
    311. Telemammographyinformation M. Telemammography and information management. Academic Radiology 1998 Nov;5:S484-S487. [CrossRef]
    312. Agroyannis B, Fourtounas C, Romagnoli G, Skiadas M, Tsavdaris C, Chassomeris C, et al. Telemedicine technology and applications for home hemodialysis. Int J Artif Organs 1999 Oct;22(10):679-683. [Medline]
    313. Anogianakis G, Maglavera S. Transeuropean network for the provision of added-VAlue Services in Telemedicine--(VAST-Net). Stud Health Technol Inform 1997;39:298-306. [Medline]
    314. Anogianakis G, Maglavera S. MERMAID 1996--report on the implementation of a European Project on “medical emergency aid through telematics”. Stud Health Technol Inform 1997;39:264-270. [Medline]
    315. Anogianakis G, Maglavera S, Pomportsis A. Relief for maritime medical emergencies through telematics. IEEE Trans Inf Technol Biomed 1998 Dec;2(4):254-260. [Medline]
    316. Anscombe DL. Healthcare delivery for oil rig workers: telemedicine plays a vital role. Telemed J E Health 2010;16(6):659-663. [CrossRef] [Medline]
    317. Arbeille P, Ayoub J, Kieffer V, Combes B, Coitrieux A, Herve P, et al. Abdominal and fetal echography tele-operated in several medical centres sites, from an expert center, using a robotic arm & telephone or satellite link. J Gravit Physiol 2007 Jul;14(1):P139-P140. [Medline]
    318. Armstrong IJ, Haston WS. Medical decision support for remote general practitioners using telemedicine. J Telemed Telecare 1997;3(1):27-34. [CrossRef] [Medline]
    319. Ausseresses AD. Telecommunications requirements for telemedicine. J Med Syst 1995 Apr;19(2):143-151. [Medline]
    320. Bagshaw M. Telemedicine in British Airways. J Telemed Telecare 1996;2 Suppl 1:36-38. [Medline]
    321. Bhaskaranarayana A, Satyamurthy LS, Remilla MLN. Indian Space Research Organization and telemedicine in India. Telemed J E Health 2009;15(6):586-591. [CrossRef] [Medline]
    322. Bhaskaranarayana A, Satyamurthy L, Remilla ML, Sethuraman K, Rayappa H. Bridging Health Divide Between Rural and Urban Areas –Satellite Based Telemedicine Networks in India. In: Olla P, editor. Space Technologies for the Benefit of Human Society and Earth. Dordrecht: Springer; 2009.
    323. Chonin A. Telehealth: important concepts for future nursing practice in space environments. Life Support Biosph Sci 1998;5(4):433-435. [Medline]
    324. Cova G, Xiong H, Gao Q, Guerrero E, Ricardo R, Estevez J. A perspective of state-of-the-art wireless technologies for e-health applications. 2009 Presented at: International Symposium on IT in Medicine Education; 14-16 August 2009; Jinan, Shandong, China p. 76-81 Vol 1. [CrossRef]
    325. Dasgupta A, Deb S. Telemedicine: a new horizon in public health in India. Indian J Community Med 2008 Jan;33(1):3-8 [FREE Full text] [CrossRef] [Medline]
    326. Delgorge C, Courrèges F, Al BL, Novales C, Rosenberger C, Smith-Guerin N, et al. A tele-operated mobile ultrasound scanner using a light-weight robot. IEEE Trans Inf Technol Biomed 2005 Mar;9(1):50-58. [Medline]
    327. Ehrlich A, Kobrinsky BA, Petlakh VI, Rozinov VM, Shabanov VE. Telemedicine for a Children's Field Hospital in Chechnya. J Telemed Telecare 2007;13(1):4-6. [CrossRef] [Medline]
    328. Ernst RD, Kawashima A, Shepherd W, Tamm EP, Sandler CM. Distributing digital imaging and communications in medicine data and optimizing access over satellite networks. J Digit Imaging 1999 May;12(2 Suppl 1):195-196 [FREE Full text] [Medline]
    329. Fiadjoe J, Gurnaney H, Muralidhar K, Mohanty S, Kumar J, Viswanath R, et al. Telemedicine consultation and monitoring for pediatric liver transplant. Anesth Analg 2009 Apr;108(4):1212-1214. [CrossRef] [Medline]
    330. Ganapathy K. Telemedicine in the Indian context: an overview. Stud Health Technol Inform 2004;104:178-181. [Medline]
    331. Garawi SA, Courreges F, Istepanian RSH, Zisimopoulos H, Gosset P. Performance analysis of a compact robotic tele-echography E-health system over terrestrial and mobile communication links. 2004 Presented at: Fifth IEE International Conference on 3G Mobile Communication Technologies; 18-20 October, 2004; London, England p. 118-122. [CrossRef]
    332. Gortzis L, Papadopoulos H, Roelofs TA, Rakowsky S, Karnabatidis D, Siablis D, et al. Collaborative work during interventional radiological procedures based on a multicast satellite-terrestrial network. IEEE Trans Inf Technol Biomed 2007 Sep;11(5):597-599. [Medline]
    333. Grant IC. Telemedicine in the British Antarctic survey. Int J Circumpolar Health 2004 Dec;63(4):356-364. [Medline]
    334. Graschew G, Roelofs TA, Rakowsky S, Schlag PM. Interactive telemedical applications in OP 2000 via satellite. Biomed Tech (Berl) 2002;47 Suppl 1 Pt 1:330-333. [Medline]
    335. Graschew G, Roelofs TA, Rakowsky S, Schlag PM. Interactive telemedicine as a tool to avoid a digital divide in the world. Stud Health Technol Inform 2004;103:150-156. [Medline]
    336. Graschew G, Roelofs TA, Rakowsky S, Schlag PM, Heinzlreiter P, Kranzlmüller D, et al. New trends in the virtualization of hospitals--tools for global e-Health. Stud Health Technol Inform 2006;121:168-175. [Medline]
    337. Hartvigsen G, Johansen MA, Hasvold P, Bellika JG, Arsand E, Arild E, et al. Challenges in telemedicine and eHealth: lessons learned from 20 years with telemedicine in Tromsø. Stud Health Technol Inform 2007;129(Pt 1):82-86. [Medline]
    338. Hudson H. Rural telemedicine: lessons from Alaska for developing regions. Telemed J E Health 2005 Aug;11(4):460-467. [CrossRef] [Medline]
    339. Hwang S, Lee J, Kim H, Lee M. Development of a web-based picture archiving and communication system using satellite data communication. J Telemed Telecare 2000;6(2):91-96. [CrossRef] [Medline]
    340. Hwang S, Lee MH. A WEB-based telePACS using an asymmetric satellite system. IEEE Trans Inf Technol Biomed 2000 Sep;4(3):212-215. [Medline]
    341. Jarvis-Selinger S, Chan E, Payne R, Plohman K, Ho K. Clinical telehealth across the disciplines: lessons learned. Telemed J E Health 2008 Sep;14(7):720-725. [CrossRef] [Medline]
    342. Jones R, Clarke M, Kanellopoulos N, Lioupis D, Fowles R. The AIDMAN project – a telemedicine approach to cardiology investigation, referral and outpatient care. J Telemed Telecare 2016 Dec 02;6(1_suppl):32-34. [CrossRef] [Medline]
    343. Junejo ZA. Suparco Telemedicine Pilot Project. 2007 Presented at: 3rd International Conference on Recent Advances in Space Technologies; 14-16 June 2007; Istanbul, Turkey. [CrossRef]
    344. Kanthraj GR. Classification and design of teledermatology practice: what dermatoses? Which technology to apply? J Eur Acad Dermatol Venereol 2009 Aug;23(8):865-875. [CrossRef] [Medline]
    345. Kanthraj GR, Srinivas CR. Store and forward teledermatology. Indian J Dermatol Venereol Leprol 2007;73(1):5-12 [FREE Full text] [Medline]
    346. Kasitipradith N. The Ministry of Public Health telemedicine network of Thailand. Int J Med Inform 2001 May;61(2-3):113-116. [Medline]
    347. Kayser K. Telepathology in Europe. Its practical use. Arch Anat Cytol Pathol 1995;43(4):196-199. [Medline]
    348. Kayser K. [Telemedicine]. Wien Klin Wochenschr 1996;108(17):532-540. [Medline]
    349. Koike K, Takizawa M, Kamiya S, Kamata M, Oleinikova OV, Bogatchenko M, et al. [Medical support for Belarus after Chernobyl accident using a telemedicine system]. Igaku Butsuri 2003;23(1):44-50. [Medline]
    350. Komnakos D, Constantinou P, Vouyioukas D, Maglogiannis I. QoS Study Performance of an Integrated Satellite Telemedicine Platform. 2007 Presented at: 18th International Symposium on Personal, Indoor and Mobile Radio Communications; 3-7 Sept. 2007; Athens, Greece.
    351. Kyriacou E, Pavlopoulos S, Berler A, Neophytou M, Bourka A, Georgoulas A, et al. Multi-purpose HealthCare Telemedicine Systems with mobile communication link support. Biomed Eng Online 2003 Mar 24;2:7 [FREE Full text] [Medline]
    352. Latifi R, editor. Current Principles and Practices of Telemedicine and E-health. USA: IOS Press; 2008.
    353. Lin CF, Lee HW. Wireless Multimedia Communication toward Mobile Telemedicine. 2009 Presented at: 9th WSEAS International Conference on APPLIED INFORMATICS AND COMMUNICATIONS (AIC '09); August 20 - 22, 2009; Moscow, Russia.
    354. Maheu M, Whitten P, Allen A. E-Health, Telehealth, and Telemedicine: A Guide to Startup and Success. New Jersey: John Wiley and Sons; 2002.
    355. Malik AZ. Telemedicine Country Report-Pakistan. 2007 Presented at: 9th International Conference on e-Health Networking, Application and Services; 19-22 June 2007; Taipei, Taiwan p. 90-94.
    356. Massone C, Brunasso AMG, Campbell TM, Soyer HP. Mobile teledermoscopy--melanoma diagnosis by one click? Semin Cutan Med Surg 2009 Sep;28(3):203-205. [CrossRef] [Medline]
    357. Meystre S. The current state of telemonitoring: a comment on the literature. Telemed J E Health 2005 Feb;11(1):63-69. [CrossRef] [Medline]
    358. Min Z. Applications of the satellite communication telemedicine system in our hospital. CNKI Journal 2006 Mar:7th section [FREE Full text]
    359. Mishra SK, Kapoor L, Singh IP. Telemedicine in India: current scenario and the future. Telemed J E Health 2009;15(6):568-575. [CrossRef] [Medline]
    360. Mitka M. Physicians find teleactivity hot near the North Pole. JAMA 1998 Oct 21;280(15):1296. [Medline]
    361. Miyashita T, Takizawa M, Nakai K, Okura H, Kanda H, Murase S, et al. Telemedicine of the heart: real-time telescreening of echocardiography using satellite telecommunication. Circ J 2003 Jun;67(6):562-564 [FREE Full text] [Medline]
    362. Mohr M. Telemedicine in oncology: European perspective. Stud Health Technol Inform 2008;131:255-261. [Medline]
    363. Mougiakakou SG, Kyriacou E, Perakis K, Papadopoulos H, Androulidakis A, Konnis G, et al. A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy. Biomed Eng Online 2011 Jun 07;10:49 [FREE Full text] [CrossRef] [Medline]
    364. Nakajima I, Sastrokusumo U, Mishra SK, Komiya R, Malik AZ, Tanuma T. The Asia Pacific telecommunity's telemedicine activities. 2006 Presented at: 8th International Conference on e-Health Networking, Applications and Services; 17-19 Aug. 2006; New Delhi, India.
    365. Nakajima I, Sawada Y, Ashihara T, Takashima Y. Problems and our solutions for implementing telemedicine systems. J Med Syst 1999 Dec;23(6):425-435. [Medline]
    366. Nicogossian AE, Pober DF, Roy SA. Evolution of telemedicine in the space program and earth applications. Telemed J E Health 2001;7(1):1-15. [CrossRef] [Medline]
    367. Niyato D, Hossain E, Diamond J. IEEE 802.16/WiMAX-based broadband wireless access and its application for telemedicine/e-health services. IEEE Wireless Commun 2007 Feb;14(1):72-83. [CrossRef]
    368. Ohno G, Watanabe K, Okada Y, Higuchi K. Practical experience of telehealth between an Antarctic station and Japan. J Telemed Telecare 2012 Dec;18(8):473-475. [CrossRef] [Medline]
    369. Okada Y, Haruki Y, Ogushi Y. Disaster drills and continuous medical education using satellite-based Internet. Methods Inf Med 2000 Dec;39(4-5):343-347. [Medline]
    370. O'Sullivan UM, Somers J. Southern Health Board--advanced telematic/telemedicine in healthcare services in the south west of Ireland. Stud Health Technol Inform 1999;64:230-240. [Medline]
    371. Otto C, Bolte M, Linneweber F, Weber T. Functional characteristics of the telemedical network for the medical service of the Bundeswehr for support of operations outside Germany and civil-military co-operation. Stud Health Technol Inform 1999;64:270-274. [Medline]
    372. Pak HS, Brown-Connolly NE, Bloch C, Clarke M, Clyburn C, Doarn CR, et al. Global forum on telemedicine: connecting the world through partnerships. Telemed J E Health 2008 May;14(4):389-395. [CrossRef] [Medline]
    373. Paul NL. Telepsychiatry, the satellite system and family consultation. J Telemed Telecare 1997;3 Suppl 1:52-53. [CrossRef] [Medline]
    374. Pyke J, Hart M, Popov V, Harris RD, McGrath S. A tele-ultrasound system for real-time medical imaging in resource-limited settings. In: Conf Proc IEEE Eng Med Biol Soc. 2007 Presented at: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 22-26 Aug. 2007; Lyon, France p. 3094-3097. [CrossRef]
    375. Rao UR. Keynote address: global connectivity through telemedicine. J Med Syst 1995 Jun;19(3):295-304. [Medline]
    376. Rayman R, Croome K, Galbraith N, McClure R, Morady R, Peterson S, et al. Robotic telesurgery: a real-world comparison of ground- and satellite-based internet performance. Int J Med Robot 2007 Jun;3(2):111-116. [CrossRef] [Medline]
    377. Rayman R, Primak S, Patel R, Moallem M, Morady R, Tavakoli M, et al. Effects of latency on telesurgery: an experimental study. Med Image Comput Comput Assist Interv 2005;8(Pt 2):57-64. [Medline]
    378. Reith A, Olsen DR. Teleradiology with satellite units - six years experience at the norwegian radium hospital. Stud Health Technol Inform 2008;134:209-216. [Medline]
    379. Ricke J, Kleinholz L, Hosten N, Zendel W, Lemke A, Wielgus W, et al. Telemedicine in rural areas. Experience with medical desktop-conferencing via satellite. J Telemed Telecare 1995;1(4):224-228. [CrossRef] [Medline]
    380. Ronga L, Jayousi S, Del Re E, Colitta L, Iannone G, Scorpiniti A, et al. TESHEALTH: An integrated satellite/terrestrial system for e-health services. In: International Conference on Communications (ICC). 2012 Presented at: An integrated satellite/terrestrial system for e-health services. Paper presented at the IEEE International Conference on Communications (ICC); 10-15 June 2012; Ottawa, ON, Canada p. 3286-3289.
    381. Samothrakis S, Arvanitis TN, Plataniotis A, McNeill MD, Lister PF. WWW creates new interactive 3D graphics and collaborative environments for medical research and education. Int J Med Inform 1997 Nov;47(1-2):69-73. [Medline]
    382. Sawai T, Uzuki M, Watanabe M. [Telepathology at presence and in the future]. Rinsho Byori 2000 May;48(5):458-462. [Medline]
    383. Sood S, Mbarika V, Jugoo S, Dookhy R, Doarn CR, Prakash N, et al. What is telemedicine? A collection of 104 peer-reviewed perspectives and theoretical underpinnings. Telemed J E Health 2007 Oct;13(5):573-590. [CrossRef] [Medline]
    384. Stoloff P, Garcia FE, Thomason JE, Shia DS. A cost-effectiveness analysis of shipboard telemedicine. Telemed J 1998;4(4):293-304. [CrossRef] [Medline]
    385. Sudhamony S, Nandakumar K, Binu P, Issac Niwas S. Telemedicine and tele-health services for cancer-care delivery in India. IET Commun 2008;2(2):231. [CrossRef]
    386. Tachakra S, Wang XH, Istepanian RSH, Song YH. Mobile e-health: the unwired evolution of telemedicine. Telemed J E Health 2003;9(3):247-257. [CrossRef] [Medline]
    387. Takahashi T. The present and future of telemedicine in Japan. Int J Med Inform 2001 May;61(2-3):131-137. [Medline]
    388. Takizawa M, Miyashita T, Murase S, Kanda H, Karaki Y, Yagi K, et al. [Mobile hospital -real time mobile telehealthcare system with ultrasound and CT van using high-speed satellite communication-]. Igaku Butsuri 2003;23(1):51-58. [Medline]
    389. Takizawa M, Sone S, Takashima S, Feng L, Maruyama Y, Hasegawa M, et al. The mobile hospital--an experimental telemedicine system for the early detection of disease. J Telemed Telecare 1998;4(3):146-151. [CrossRef] [Medline]
    390. Tan J, Cheng W, Rogers WJ. From Telemedicine to E-Health: Uncovering New Frontiers of Biomedical Research, Clinical Applications and Public Health Services Delivery. Journal of Computer Information Systems 2016;42(5):7-18. [CrossRef]
    391. Tyrer HW. Specifications for a satellite based wide area network. Biomed Sci Instrum 1999;35:111-116. [Medline]
    392. Tyrer HW, Wiedemeier PD, Cattlet RW. Rural telemedicine: satellites and fiber optics. Biomed Sci Instrum 2001;37:417-422. [Medline]
    393. VandenBos G, Williams S. The Internet versus the telephone: What is telehealth anyway? Professional Psychology: Research and Practice 2000;31(5):490-492. [CrossRef]
    394. Vargas A, Ugalde M, Vargas R, Narvaez R, Geissbuhler A. [Telemedicine in Bolivia: RAFT-Altiplano project, experiences, future prospects, and recommendations]. Rev Panam Salud Publica 2014;35(5-6):359-364 [FREE Full text] [Medline]
    395. Watson D. Telemedicine. Med J Aust 1989 Jul 17;151(2):62-6, 68, 71. [Medline]
    396. Yamaguchi T. Performance tests of a satellite-based asymmetric communication network for the 'hyper hospital'. J Telemed Telecare 1997;3(2):78-82. [CrossRef] [Medline]
    397. Yamashita S, Shibata Y, Takamura N, Ashizawa K, Sera N, Eguchi K. Satellite communication and medical assistance for thyroid disease diagnosis from Nagasaki to Chernobyl. Thyroid 1999 Sep;9(9):969. [CrossRef] [Medline]
    398. Yokota K, Takamura N, Shibata Y, Yamashita S, Mine M, Tomonaga M. Evaluation of a telemedicine system for supporting thyroid disease diagnosis. Stud Health Technol Inform 2001;84(Pt 1):866-869. [Medline]
    399. Zhao J, Zhang Z, Guo H, Ren L, Chen S. Development and recent achievements of telemedicine in china. Telemed J E Health 2010 Jun;16(5):634-638. [CrossRef] [Medline]
    400. Casalino NAD, Alessandro and Fadda C. Organizational Impact and Exploitation of the Results of an Italian Research Project for E-Health and Medical Training. 2005 Presented at: European Conference on Information Systems; May 1, 2005; Regensburg, Germania.
    401. Dańda J, Juszkiewicz K, Leszczuk M, Loziak K, Papir Z, Sikora M, et al. Medical video server construction. Pol J Pathol 2003;54(3):197-204. [Medline]
    402. Geissbuhler A, Bagayoko CO, Ly O. The RAFT network: 5 years of distance continuing medical education and tele-consultations over the Internet in French-speaking Africa. Int J Med Inform 2007;76(5-6):351-356. [CrossRef] [Medline]
    403. Graschew G, Roelofs TA, Rakowsky S, Schlag PM. The Virtual Hospital as a Digital Tool for e-Health. 2006 Presented at: World Congress on Medical Physics and Biomedical Engineering; September 1, 2006; Seoul, Korea p. 358-361.
    404. Groves T. SatelLife: getting relevant information to the developing world. BMJ 1996;313(7072):1606-1609 [FREE Full text] [Medline]
    405. Jones R, Skirton H, McMullan M. Feasibility of combining e-health for patients with e-learning for students using synchronous technologies. J Adv Nurs 2006 Oct;56(1):99-109. [CrossRef] [Medline]
    406. Jossif A, Pattichis CS, Kyriakides M, Pitsillides A, Kyriacou E, Dikaiakos M. Selected eHealth applications in Cyprus from the training perspective. Methods Inf Med 2007;46(1):84-89. [Medline]
    407. Kiuchi T, Takahashi T. High speed digital circuits for medical communication; the MINCS-UH project. Methods Inf Med 2000 Dec;39(4-5):353-355. [Medline]
    408. Mahapatra AK, Mishra SK, Kapoor L, Singh IP. Critical issues in medical education and the implications for telemedicine technology. Telemed J E Health 2009;15(6):592-596. [CrossRef] [Medline]
    409. Masic Z, Novo A, Masic I, Kudumovic M, Toromanovic S, Rama A, et al. Distance learning at biomedical faculties in bosnia & herzegovina. Stud Health Technol Inform 2005;116:267-272. [Medline]
    410. Moahi KH. Health Information Networks for Telehealth in Africa ? Challenges and Prospects: a Review of the Literature. Libri 2009;49(1):43-58. [CrossRef]
    411. Singh I, Kapoor L, Daman R, Mishra SK. Comparative study of connectivity in telemedicine. Telemed J E Health 2008 Oct;14(8):846-850. [CrossRef] [Medline]
    412. Tangalos E, McGee R, Bigbee AW. Use of the new media for medical education. J Telemed Telecare 1997;3(1):40-47. [CrossRef] [Medline]
    413. Yamauchi K, Ikeda M, Ota Y, Yang S, Ishigaki T, Itouji E, et al. Evaluation of the Space Collaboration System: its history, image quality and effectiveness for joint case conference. Nagoya J Med Sci 2000 May;63(1-2):19-24. [Medline]
    414. Angood PB, Satava R, Doarn C, Merrell R, E3 Group. Telemedicine at the top of the world: the 1998 and 1999 Everest extreme expeditions. Telemed J E Health 2000;6(3):315-325. [CrossRef] [Medline]
    415. Berek B, Canna M. Telemedicine on the move: health care heads down the information superhighway. Hosp Technol Ser 1994;13(6):1-65. [Medline]
    416. Brown-Connolly N, Concha JB, English J. Mobile health is worth it! Economic benefit and impact on health of a population-based mobile screening program in new Mexico. Telemed J E Health 2014 Jan;20(1):18-23 [FREE Full text] [CrossRef] [Medline]
    417. Guo T, Laksanasopin T, Sridhara AA, Nayak S, Sia SK. Mobile device for disease diagnosis and data tracking in resource-limited settings. Methods Mol Biol 2015;1256:3-14. [CrossRef] [Medline]
    418. Kishi Y. [The current situation of i-STAT (portable clinical analyzer) and its future view]. Rinsho Byori 2007 May;55(5):460-464. [Medline]
    419. Latifi R, Ong C, Peck K, Porter J, Williams M. Telepresence and telemedicine in trauma and emergency care management. Eur Surg 2005 Oct;37(5):293-297. [CrossRef]
    420. Lin C. Mobile telemedicine: a survey study. J Med Syst 2012 Apr;36(2):511-520. [CrossRef] [Medline]
    421. Maglaveras N, Chouvarda I, Koutkias V, Lekka I, Tsakali M, Tsetoglou S, et al. Citizen centered health and lifestyle management via interactive TV: The PANACEIA-ITV health system. AMIA Annu Symp Proc 2003:415-419 [FREE Full text] [Medline]
    422. Mupela EN, Mustarde P, Jones HLC. Telemedicine in primary health: the virtual doctor project Zambia. Philos Ethics Humanit Med 2011 May 13;6:9 [FREE Full text] [CrossRef] [Medline]
    423. Nagatuma H. Development of an Emergency Medical Video Multiplexing Transport System (EMTS): aiming at the nation-wide prehospital care in ambulance. J Med Syst 2003 Jun;27(3):225-232. [Medline]
    424. Nakajima I. Japanese telemedical concept of ambulatory application. J Med Syst 2011 Apr;35(2):215-220. [CrossRef] [Medline]
    425. Rizou D, Sachpazidis I, Salvatore L, Sakas G. TraumaStation: a portable telemedicine station. In: Conf Proc IEEE Eng Med Biol Soc. 2009 Presented at: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 3-6 Sept. 2009; Minneapolis, MN, USA p. 1254-1257. [CrossRef]
    426. Shimizu K. Telemedicine by mobile communication. IEEE Eng Med Biol Mag 1999;18(4):32-44. [Medline]
    427. Sibert K, Ricci MA, Caputo M, Callas PW, Rogers FB, Charash W, et al. The feasibility of using ultrasound and video laryngoscopy in a mobile telemedicine consult. Telemed J E Health 2008 Apr;14(3):266-272. [CrossRef] [Medline]
    428. Chronaki CE, Berthier A, Lleo MM, Esterle L, Lenglet A, Simon F, et al. A satellite infrastructure for health early warning in post-disaster health management. Stud Health Technol Inform 2007;129(Pt 1):87-91. [Medline]
    429. Garshnek V. Applications of space communications technology to critical human needs: rescue, disaster relief, and remote medical assistance. Space Commun 1991 Jul;8(3-4):311-317. [Medline]
    430. Garshnek V, Burkle FM. Telecommunications systems in support of disaster medicine: applications of basic information pathways. Ann Emerg Med 1999 Aug;34(2):213-218. [Medline]
    431. Gomez E, Poropatich R, Karinch M, Zajtchuk J. Tertiary Telemedicine Support During Global Military Humanitarian Missions. Telemedicine Journal 1996 Jan;2(3):201-210. [CrossRef] [Medline]
    432. Meade K, Lam DM. A deployable telemedicine capability in support of humanitarian operations. Telemed J E Health 2007 Jun;13(3):331-340. [CrossRef] [Medline]
    433. Merrell R, Cone SW, Rafiq A. Telemedicine in extreme conditions: disasters, war, remote sites. Stud Health Technol Inform 2008;131:99-116. [Medline]
    434. Merrell RC, Doarn CR. Disasters--how can telemedicine help? Telemed J E Health 2005 Oct;11(5):511-512. [CrossRef] [Medline]
    435. Otsu Y, Choh T, Yamazaki I, Kosaka K, Iguchi M, Nakajima I. Experiments on the quick-relief medical communications via the Japan's domestic communication satellite CS-2 for the case of disasters and emergencies. Acta Astronaut 1986;13(6-7):459-466. [Medline]
    436. Sadiq MA, Nagami K, Nakajima I, Juzoji H, Igarashi K, Tanaka K. Mobile telemedicine package for disasters. 2006 Presented at: HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services; 17-19 Aug. 2006; New Delhi, India. [CrossRef]
    437. Simmons S, Alverson D, Poropatich R, D'Iorio J, DeVany M, Doarn CR. Applying telehealth in natural and anthropogenic disasters. Telemed J E Health 2008 Nov;14(9):968-971. [CrossRef] [Medline]
    438. Simmons SC, Murphy TA, Blanarovich A, Workman FT, Rosenthal DA, Carbone M. Telehealth technologies and applications for terrorism response: a report of the 2002 coastal North Carolina domestic preparedness training exercise. J Am Med Inform Assoc 2003;10(2):166-176 [FREE Full text] [Medline]
    439. Woodall J. Official versus unofficial outbreak reporting through the Internet. Int J Med Inform 1997 Nov;47(1-2):31-34. [Medline]
    440. Ziadlou D, Eslami A, Hassani HR. Telecommunication Methods for Implementation of Telemedicine Systems in Crisis. 2008 Presented at: 2008 Third International Conference on Broadband Communications, Information Technology & Biomedical Applications; 23-26 Nov. 2008; Gauteng, South Africa.
    441. Algaet M, Noh Z, Shibghatullah A, Milad A, Mustapha A. Provisioning Quality of Service of Wireless Telemedicine for E-Health Services: A Review. Wireless Pers Commun 2014 Apr 10;78(1):375-406. [CrossRef]
    442. Crump A. Satellite teleHealth: good for the cutting edge and in the bush? Geospat Health 2006 Nov;1(1):7-10. [CrossRef] [Medline]
    443. DeBakey M. Telemedicine Has Now Come of Age. Telemedicine Journal 1995 Jan;1(1):3-4. [CrossRef]
    444. Duplaga M, Zieliński K. Evolution of IT-Enhanced Healthcare: From Telemedicine to e-Health. In: Zieliński K, Duplaga M, Ingram D, editors. Information Technology Solutions for Healthcare. London: Springer; 2006:1-21.
    445. Durrani H, Khoja S. A systematic review of the use of telehealth in Asian countries. J Telemed Telecare 2009;15(4):175-181. [CrossRef] [Medline]
    446. Graschew G, Roelofs TA, Rakowsky S, Schlag PM. Network design for telemedicine--e-health using satellite technology. Stud Health Technol Inform 2008;131:67-82. [Medline]
    447. Kocian A, De Sanctis M, Rossi T. Hybrid satellite/terrestrial telemedicine services: Network requirements and architecture. 2011 Presented at: Aerospace Conference, 2011 IEEE; 5-12 March 2011; Big Sky, MT, USA. [CrossRef]
    448. Lamminen H. Mobile satellite systems. J Telemed Telecare 1999;5(2):71-83. [CrossRef] [Medline]
    449. Merrell RC, Lee A, Kwankam SY, Mwape B, Chinyama C, Latifi R, et al. Satellite applications for telehealth in the developing world. J Telemed Telecare 2006;12(6):321-324. [CrossRef] [Medline]
    450. Srivastava S, Pant M, Abraham A, Agrawal N. The Technological Growth in eHealth Services. Comput Math Methods Med 2015;2015:894171 [FREE Full text] [CrossRef] [Medline]
    451. Tulu B, Chatterjee S, Maheshwari M. Telemedicine taxonomy: a classification tool. Telemed J E Health 2007 Jun;13(3):349-358. [CrossRef] [Medline]
    452. Vouyioukas D, Maglogiannis I, Pasias V. Pervasive E-health services using the DVB-RCS communication technology. J Med Syst 2007 Aug;31(4):237-246. [Medline]
    453. Zaidan B, Zaidan A, Mat Kiah M. Impact of Data Privacy and Confidentiality on Developing Telemedicine Applications: A Review Participates Opinion and Expert Concerns. International J. of Pharmacology 2011 Mar 1;7(3):382-387. [CrossRef]
    454. Graschew G, Roelofs TA, Rakowsky S, Schlag PM. Design of Satellite-Based Networks for u-Health - GALENOS, DELTASS, MEDASHIP, EMISPHER. 2007 Presented at: 2007 9th International Conference on e-Health Networking, Application and Services; 19-22 June 2007; Taipei, Taiwan p. 19-22.
    455. Arbeille P, Herault S, Roumy J, Porcher M, Besnard S, Vieyres P. 3D realtime echography and echography assisted by a robotic arm for investigating astronauts in the ISS from the ground. J Gravit Physiol 2001 Jul;8(1):P143-P144. [Medline]
    456. Arbeille P, Poisson G, Vieyres P, Ayoub J, Porcher M, Boulay JL. Echographic examination in isolated sites controlled from an expert center using a 2-D echograph guided by a teleoperated robotic arm. Ultrasound Med Biol 2003 Jul;29(7):993-1000. [Medline]
    457. Arbeille P, Ruiz J, Ayoub J, Vieyres P, Porcher M, Boulay J, et al. The robot and the satellite for tele-operating echographic examination in Earth isolated sites, or onboard ISS. J Gravit Physiol 2004 Jul;11(2):P233-P234. [Medline]
    458. Chiao L, Sharipov S, Sargsyan AE, Melton S, Hamilton DR, McFarlin K, et al. Ocular examination for trauma; clinical ultrasound aboard the International Space Station. J Trauma 2005 May;58(5):885-889. [Medline]
    459. Jones JA, Sargsyan AE, Barr YR, Melton S, Hamilton DR, Dulchavsky SA, et al. Diagnostic ultrasound at MACH 20: retroperitoneal and pelvic imaging in space. Ultrasound Med Biol 2009 Jul;35(7):1059-1067. [CrossRef] [Medline]
    460. Sargsyan AE, Hamilton DR, Jones JA, Melton S, Whitson PA, Kirkpatrick AW, et al. FAST at MACH 20: clinical ultrasound aboard the International Space Station. J Trauma 2005 Jan;58(1):35-39. [Medline]
    461. Vieyres P, Poisson G, Courreges F, Merigeaux O, Arbeille P. The TERESA project: from space research to ground tele-echography. Ind Rob 2003;30(1):77-82. [Medline]
    462. Cermack M. Monitoring and telemedicine support in remote environments and in human space flight. Br J Anaesth 2006 Jul;97(1):107-114 [FREE Full text] [CrossRef] [Medline]
    463. Doarn CR. Telemedicine in extreme environments: analogs for space flight. Stud Health Technol Inform 2003;97:35-41. [Medline]
    464. Doarn CR, Nicogossian AE, Merrell RC. Applications of telemedicine in the United States space program. Telemed J 1998;4(1):19-30. [CrossRef] [Medline]
    465. Feliciani F. Medical care from space: Telemedicine. ESA Bull 2003 May;114:54-59. [Medline]
    466. Grigoriev AI, Orlov OI. Telemedicine and spaceflight. Aviat Space Environ Med 2002 Jul;73(7):688-693. [Medline]
    467. Ross MD. Medicine in long duration space exploration: the role of virtual reality and broad bandwidth telecommunications networks. Acta Astronaut 2001;49(3-10):441-445. [Medline]
    468. Wilke D, Padeken D, Weber TH, Gerzer R. Telemedicine for the International Space Station. Acta Astronaut 1999;44(7-12):579-581. [Medline]
    469. Haidegger T, Sándor J, Benyó Z. Surgery in space: the future of robotic telesurgery. Surg Endosc 2011 Mar;25(3):681-690. [CrossRef] [Medline]
    470. Friedman DS. Biomedical applications of NASA technology. Med Des Mater 1991 Feb;1(2):54-57. [Medline]
    471. Hertzfeld HR. Measuring the economic returns from successful NASA life sciences technology transfers. J Technol Transf 2002 Dec;27(4):311-320. [Medline]
    472. Hines JW. Medical and surgical applications of space biosensor technology. Acta Astronaut 1996;38(4-8):261-267. [Medline]
    473. Hughes S. Opportunities for the transfer of astronomical technology to medicine. Australas Phys Eng Sci Med 2007 Dec;30(4):292-294. [Medline]
    474. Kukkonen CA. NASA high performance computing, communications, image processing, and data visualization-potential applications to medicine. J Med Syst 1995 Jun;19(3):263-273. [Medline]
    475. Lathers CM, Charles JB, Bungo MW. Humans in space: NASA's contributions to medical technology and health care. J Clin Pharmacol 1990 Mar;30(3):223-225. [Medline]
    476. Rouse DJ, Brown JN, Whitten RP. Methodology for NASA technology transfer in medicine. Med Instrum 1981;15(4):234-236. [Medline]
    477. Rouse DJ, Winfield DL, Canada SC. NASA spinoffs to bioengineering and medicine. Acta Astronaut 1991 Feb;25(2):103-110. [Medline]
    478. Winfield D, Silbiger M, Brown GS, Clarke L, Dwyer S, Yaffe M, et al. Technology transfer in digital mammography. Report of the Joint National Cancer Institute-National Aeronautics and Space Administration workshop of May 19-20, 1993. Invest Radiol 1994 Apr;29(4):507-515. [Medline]
    479. Winfield DL. Aerospace technology transfer to breast cancer imaging. Acta Astronaut 1997;41(4-10):515-523. [Medline]
    480. Foale CM, Kaleri AY, Sargsyan AE, Hamilton DR, Melton S, Martin D, et al. Diagnostic instrumentation aboard ISS: just-in-time training for non-physician crewmembers. Aviat Space Environ Med 2005 Jun;76(6):594-598. [Medline]
    481. Garshnek V. Space medicine comes down to Earth. Space Policy 1989 Nov;5(4):330-332. [Medline]
    482. Grigoriev A, Agadjanyan AA, Baranov VM, Polyakov VV. On the contribution of space medicine in the public health care. Acta Astronaut 1997;41(4-10):531-536. [Medline]
    483. Kozlovskaya IB, Egorov AD. Some approaches to medical support for Martian expedition. Acta Astronaut 2003;53(4-10):269-275. [Medline]
    484. Bediang G, Perrin C, Ruiz DCR, Kamga Y, Sawadogo A, Bagayoko CO, et al. The RAFT Telemedicine Network: Lessons Learnt and Perspectives from a Decade of Educational and Clinical Services in Low- and Middle-Incomes Countries. Front Public Health 2014;2:180 [FREE Full text] [CrossRef] [Medline]


    Abbreviations

    COPUOS: Committee on the Peaceful Uses of Outer Space
    GEO: Group on Earth Observations
    GIS: geographic information system
    GLONASS: global navigation satellite systems
    GNSS: global navigation satellite systems
    GP: general practitioner
    GPS: global positioning system
    HCP: health care professional
    MeSH: Medical Subject Headings
    NASA: National Aeronautics and Space Administration
    NCD: noncommunicable disease
    PA: physical activity
    PM: particulate matter
    SAFE: Satellites for Epidemiology
    UNOSAT: United Nations Operational Satellite Applications Program
    UN-SPIDER: United Nations platform for Space-based Information for Disaster Management and Emergency Response
    UNOOSA: United Nations Office for Outer Space Affairs
    WHO: World Health Organization


    Edited by G Eysenbach; submitted 20.11.17; peer-reviewed by D Davies, T Mackey, C Paton; comments to author 24.01.18; revised version received 21.03.18; accepted 22.04.18; published 27.06.18

    ©Damien Dietrich, Ralitza Dekova, Stephan Davy, Guillaume Fahrni, Antoine Geissbühler. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.06.2018.

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