Applications of digital technologies in environmental health information systems

Abstract

Background: The world is facing complex environmental health challenges, which require a data science approach for holistic solutions. This indicates a greater need for environmental health information systems (EHISs) to be digitally transformed to facilitate rapid data flow for agile public health interventions. Objective: To consolidate and characterise the body of literature on the application of EHISs in the digital era to inform decision-making and action. Methodology: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Literature was searched from PubMed, Scopus and Google Scholar between January and March 2025. Results: The study analysed 74 articles, with 26% focusing on existing EHISs, 24% on environmental health indicators, 14% on geographic information system tools, and 36% on digital technologies in environmental health data management. Researchers and practitioners, mainly in high-income countries, continue to explore predictive analytics, modelling and machine learning, the Internet of Things, smart devices, and infographics in environmental health. Conclusion: Digital technologies are revolutionizing environmental health by providing actionable insights, enhancing decision-making, and promoting proactiveness. This integration is crucial for improving public health outcomes and must be embraced by low-and-middle-income countries as well, even amid resource-related and interoperability challenges.

Keywords

digital technologies environmental health information systems public health action scoping review

Introduction

The world is faced with complicated environmental health issues that call for innovative and holistic solutions to safeguard the environment and people’s health.¹ Some environmental health challenges include the spread of infectious diseases that emerge or reemerge, industrialisation, rapid urbanisation, poor environmental protection, and climate change.² Thus, in the quest to address these challenges, data must be used to provide planning and decision-making intelligence to develop targeted interventions.³ Traditionally, environmental health problems were addressed systematically by controlling a single pollutant or exposure.¹ However, today’s complex environmental health problems require more innovative, comprehensive and integrated solutions that address not only a single pollutant or exposure but also a variety of environmental factors and their impacts on human health.¹ As a result, many countries have environmental health information systems (EHISs) to centralise data to better understand the link between environmental factors and public health in their settings.⁴ Shakeri Hossein Abad et al. define an EHIS as the ongoing, systematic collection, analysis, and interpretation of environmental health data for timely dissemination of information to drive public health action.⁵ This information system enables environmental health monitoring and improved understanding of the relationship between environmental hazards and public health.¹ Different countries and regions of the world refer to their EHISs in different terms, such as the National Integrated Environmental Health Surveillance System in Nigeria, the Environmental Public Health Tracking Program in the United States of America (USA), the Environmental Public Health Program in Canada, the Environmental Public Health Surveillance System in the United Kingdom, the Environmental Health Intelligence (which incorporates the Environmental Health Indicators Program) in New Zealand, and the European Environment and Health Information System.^4,6 EHISs from country to country and region to region are also shaped by different local environmental health contexts, health priorities and health systems in terms of their scopes and indicators.⁷

In the current digital era, there is a growing need for many countries to also integrate their datasets on environmental health and establish EHISs that are essential for improving governments’ response to environmental health challenges. The literature indicates that EHISs are critical in being a decision-support system by providing valuable information to policymakers and decision-makers to address public health issues and assess the effectiveness of policies and other interventions in the community.^7,8 Therefore, when well designed and successfully executed, EHISs are key in improving public health and yielding better health outcomes in the community.⁹ EHISs enable public health systems to respond to environmental health threats and outbreaks by identifying vulnerable groups at risk of adverse health effects to be targeted for risk communication and other environmental health interventions.^6,10 Therefore, data in an EHIS can be used to support decision-making, from an operational level to a strategic level, which then enables the use of evidence-based information to effectively manage environmental health issues and gauge the delivery of environmental health services by the government to prevent disease occurrence.¹¹ A good EHIS is among the building blocks for an effective health system that prioritises a preventive healthcare approach.¹²

Salathé¹³ postulated that health information systems need to operate in digitalised environments to unlock efficiency and effectiveness in the management and use of health information. For this reason, Shakeri Hossein Abad et al.⁵ assert that digital systems, as opposed to traditional paper-based data collection and storage methods, contain information that can be used to accelerate the detection of disease outbreaks, increase the transparency of outbreak data published by governments, and facilitate public health responses to emerging diseases and population-related risk factors. Digital information and surveillance technologies became more relevant in an unprecedented manner during the COVID-19 pandemic, and they were utilised primarily in high-income countries.¹⁴ Consequently, these countries were able to respond better to the COVID-19 pandemic and prepare for potential future public health threats through the use of data. Therefore, a transformational agenda is needed for the digitalisation of EHISs, and a data science approach is needed for interventions to be proactively enrolled where they are needed the most.^15,16

Given the increase in environmental health challenges in communities, especially in low-to middle-income countries, the environmental health burden of disease has worsened.^2,17 As a result, environmental health data management and utilisation needs to be strengthened to improve the quick flow of data and information to drive agile public health interventions. The use of digital technologies in data management has an ability to automate manual processes and improve operational efficiencies.^13,14 Therefore, in the quest for improved environmental health data management, the objective of this scoping review is to consolidate and characterise how data can be utilised to provide environmental health intelligence in decision-making and guide public health action in the digital era. This can allow researchers, practitioners and policymakers to embrace digital technologies in environmental health data management and explore attached data-driven opportunities for better public health. Furthermore, this scoping review seeks to raise the importance of improved data management and utilisation systems for enhancing the provision of EHS, strengthening the health system, and enabling better health outcomes.

Materials and methods

This scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. These guidelines ensure transparency and rigour in the process of importing, screening, reviewing and extracting articles.¹⁸ A web-based screening and data extraction software called Covidence was used in this review to streamline and record the process.

Search strategy

An extensive search for relevant literature was performed from January 2025 to March 2025 from three major databases. These databases included PubMed, Scopus and Google Scholar. The search focused on peer-reviewed articles published between 2015 and 2025. To refine the search results in the databases, the following key terms were used in conjunction with general words in digital transformation: “artificial intelligence”, “big data”, “digitalisation”, “digital era”, “digitisation” and “electronic”. The key terms include “Environmental Public Health Information System”, “Environmental Public Health Tracking”, “Environmental Health Tracking System”, “Environmental and Health Information System”, “Environmental Health Intelligence”, “Environmental Health Surveillance”, “Health and Environment Information System”, “Environmental Health Indicators Program”, “Environmental Health Information Systems”, “Big data in environmental health”, “Use of environmental health data for public health action” and “Use of data for environmental health interventions”. The variety of the key terms was based on the fact that the information systems used in environmental health are referred to in different terms in different parts of the world.^4,6

Inclusion and exclusion criteria

The inclusion and exclusion criteria depicted in Table 1 were applied in this review to obtain the included articles. This was important to ensure that the chosen articles are relevant to the subject and research question of this review and to promote its quality and integrity.

Table 1.

Inclusion and exclusion criteria table.

Inclusion criteria	Exclusion criteria
Articles written in English	Non-English articles
Published peer-reviewed articles (full-text)	Abstract-only articles (including abstracts for conferences)
Articles that are related to the topic and purpose of the review	Articles not relevant to the topic and purpose of the review
Articles that are related to environmental health and digitalisation	Articles not relevant to environmental health and digitalisation
Articles published between 2015 and 2025	Articles published before 2015
	Duplicate articles

Selection of relevant articles

To select relevant articles to form part of this review, a PRISMA-ScR framework was followed. As shown in Figure 1 (PISMA-ScR flow chart), 221 articles were identified from the databases and imported into Covidence. Thereafter, 41 duplicates were identified and excluded, leaving 180 articles for screening. The screening process was conducted by reading titles and abstracts. As a result, 33 irrelevant articles were excluded because they did not meet the inclusion criteria. A total of 147 articles were then sought for full-text review. In the full-text review, a deeper review through reading the full-text articles was performed, and 73 articles were excluded. Ultimately, 74 articles were found to be eligible. These eligible articles were then included for data extraction and synthesis.

Figure 1.

PRISMA-ScR flow chart depicting the search and selection process (extracted from Covidence).

Data extraction and synthesis

The bibliographic characteristic data from the included articles were extracted via a standard data extraction form via the Covidence online tool. The form included fields such as author, title, country where the study was conducted (or the country of the first affiliation of the first author in articles such as reviews), continent, type of study, year of publication and the name of a studied EHIS or surveillance system. Other fields that were used include the development of indicators and systems, the application of GIS tools, and the use of digital technologies. All the fields assisted in providing a characteristic breakdown of the studies that formed part of this review, as well as their focus and outcomes. Deductive thematic analysis was conducted using the above-mentioned thematic areas. Inductive thematic analysis was also conducted to enable further analysis, interpretation and generate more results on digital technologies, in line with the purpose of the study. As a result, data on different types of digital technologies in environmental health data management and utilisation was identified, segmented and inductively coded until code saturation was reached. The use of deductive and inductive thematic analysis enabled the researchers to describe and analyse the application of digital transformation in environmental health data management and utilisation for improved decision-making and public health action. Data extraction and synthesis was an iterative process, verified by the three authors until consensus was reached. All the included articles were reviewed and synthesised as narratively presented in the results and discussion.

Results

Among the 74 articles, 35 (47.2%) were from the USA, 7 (9.4%) from Canada, 5 (6.7%) from China, 4 (5.4%) from the United Kingdom, 4 (5.4%) from Australia, 2 (2.7%) from India, 2 (2.7%) from New Zealand, 2 (2.7%) from Switzerland, 2 (2.7%) from Iran, 1 (1.3%) from Malaysia, 1 (1.3%) from Thailand, 1 (1.3%) from Afghanistan, 1 (1.3%) from the Republic of Korea, 1 (1.3%) from the State of Palestine, 1 (1.3%) from the Pacific Island Countries, 1 (1.3%) from Nigeria, 1 (1.3%) from Malawi, 1 (1.3%) from Japan, 1 (1.3%) from Germany and 1 (1.3%) from Italy. Twenty-six (n = 26; 35.1%) articles were technical reports, 22 (29.7%) were reviews, 11 (14.8%) were commentaries, 8 (10.8%) were experimental studies, and 7 (9.4%) were cross-sectional studies. A breakdown of all the articles that were reviewed is attached in additional file no. 1.

The review of all the included articles indicates how environmental health data gets utilised to improve public health. The included articles dealt with EHISs utilised by governmental agencies (Table 2), the development of environmental health indicators (Table 3), exploration of biomonitoring,^19–21 data-driven environmental health intelligence, application of geographic information system (GIS) tools (Table 4), and the use of digital technologies (artificial intelligence, Internet of Things, big data, infographics, dashboards, etc.) to increase the efficiency of environmental health surveillance, monitoring and response (Table 5).

Table 2.

Reviewed articles on specific existing Environmental Health Information or Surveillance Systems utilised by government agencies.

Author(s), year	Title	Country	Name of an environmental health information or surveillance system
Amos et al. 2023	Public health data applications using the CDC tracking network: Augmenting environmental hazard information with lower-latency NASA data	USA	National environmental public health tracking program
Ban et al. 2019	Environmental health indicators for China: Data resources for Chinese environmental public health tracking	China	Chinese environmental public health tracking
Borman 2022	Environmental health intelligence New Zealand (EHINZ): Intelligence for public health action	New Zealand	Environmental health intelligence New Zealand
Charleston 2015	Environmental public health tracking: Driving environmental health information	USA	National environmental public health tracking program
Eatman et al. 2017	CDC’s national environmental public health tracking program in action: Case studies from state and local health departments	USA	National environmental public health tracking program
Edokpolo et al. 2019	Developing a conceptual framework for environmental health tracking in Victoria, Australia	Australia	Environmental health tracking system
Fox 2017	Building the future of environmental public health tracking: Proceedings and recommendations of an expert panel workshop	USA	National environmental public health tracking program
Kearney et al. 2015	A decade of environmental public health tracking (2002-2012): Progress and challenges	USA	National environmental public health tracking program
Latshaw et al. 2017	Advancing environmental health surveillance in the US through a national human biomonitoring network	USA	CDC’s state-based biomonitoring program
Martin-Olmedo 2018	Environmental and health data needed to develop national surveillance systems in industrially contaminated sites	USA	National environmental public health tracking program
Namulanda 2015	Biomonitoring and environmental public health tracking	USA	National environmental public health tracking program
Namulanda et al. 2018	Electronic health record case studies to advance environmental public health tracking	USA	National environmental public health tracking program
Namulanda et al. 2022	Environmental public health tracking, an untapped resource for occupational health	USA	National environmental public health tracking program
Qualters et al. 2015	Data to action: Using environmental public health tracking to inform decision-making	USA	National environmental public health tracking program
Richardson 2017	Environmental health tracking improves pesticide use data to enable research and inform public health actions in California	USA	National environmental public health tracking program
Wall & Kassinger 2015	Multiple measures on the environmental public health tracking network	USA	National environmental public health tracking program
Wall 2015	Together at last: Exploring health and environmental information on the national environmental health tracking network	USA	National environmental public health tracking program
Wang et al. 2018	Assessing the public health impact of using poison center data for public health surveillance	USA	National poison data system
Wilson et al. 2017	Environmental public health tracking program advances and successes: Highlights from the first 15 years	USA	National environmental public health tracking program

Table 3.

Studies on the development of environmental health indicators and information systems.

Author(s), year	Country	Data use outcome(s)
Wang et al. 2021	China	Quantification and assessment of the health impacts of air pollution in China to evaluate the environmental health status, identify areas needing attention, and inform targeted interventions
Brereton et al. 2018	Pacific Island countries	Use of data to depict the state of children’s environmental health and highlight priorities for actions to promote healthy environments and thus achieve the ‘healthy pacific Island countries’ vision
Behbod 2016	United Kingdom	Development of a new International network on public health and environment tracking to support local, national and international initiatives for the prevention and management of non-communicable diseases and the protection and promotion of planetary health
Mason et al. 2018	New Zealand	Prioritisation and selection of environmental health indicators using public health impact criteria and an environmental burden of disease approach
Maroosi et al. 2019	Iran	Improved understanding of the relationship between the environment and health and the prioritisation of policymaking, using environmental health indicators based on the causal effect model
Kyle 2019	USA	Integration of data from public health surveillance, environmental monitoring, community health, and environmental protection to create a cohesive and functional information system for modern environmental public health
Yu et al. 2025	USA	Use of biomonitoring to address environmental contaminant exposures and develop targeted public health interventions in New Jersey
Lin et al. 2020	USA	Use of school environmental health indicators to help school officials and policymakers initiate prevention programmes for better health
Sly et al. 2016	Australia	Use of exposure and outcome indicators to monitor Australian children’s environmental health, establish public health interventions and inform public health policy
Jung et al. 2017	Republic of Korea	Use of targeted children’s environmental health indicators to improve monitoring systems of children’s environmental health for better public health action in low- and middle-income countries in Asia
Joas et al. 2018	Europe (continental-based study)	Use of human biomonitoring as part of european environmental health surveillance to inform public health policies about risks from environmental chemicals and for monitoring exposure burden from environmental chemicals
Braggio et al. 2015	USA	Environmental health community profiling in Baltimore City using a community-based environmental public health tracking outreach approach for targeted interventions
Di Napoli et al. 2022	United Kingdom	Use of climate change impacts, exposures, and vulnerability indicators to enhance the monitoring systems to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies
Liu et al. 2021	USA	Use of an integrated climate change and health indicator system to capture the complex dynamics that define climate-attributable health outcomes to generate useful data to inform policymakers
Saunders et al. 2017	United Kingdom	Establishment of effective data sharing, horizon scanning systems and the use of novel statistical methods and targeted interventions informed by local concerns to influence public health practice
Lauriola et al. 2020	USA	Use environmental and public health tracking insights to drive public health actions, reduce exposure and prevent the occurrence of disease through an international network for practitioners and researchers
Bassil et al. 2015	USA	Integration of environmental and human health databases to further the understanding of environmental health associations in the great lakes for improved decision-making
Noaeen et al. 2024	Canada	Use of open environmental data platforms to enhance research and public awareness and foster strategic collaborations and the generation of actionable insights that promote health

Table 4.

Application of GIS tools in environmental health data management and utilisation.

Author(s), year	Application of GIS tools and methodologies in environmental health data management and utilisation
Chinnaswamy 2015	Development of an environmental health information system based on a GIS platform for the spatial analysis of the distribution of cardiovascular cases and air pollution, to identify hotspots to roll out interventions to priority areas and populations at risk
Idowu 2015	Spatial display of environmental health conditions to enable decision-making and the application of targeted interventions. The design of the spatial environmental health data model was conducted using the unified modelling language
Fletcher-Lartey & Caprarelli 2016	Application of geographical analysis using satellite and remote sensing data to map and study parasitic diseases to enable the understanding of parasite ecology and their associations with disease distribution, enabling the development of effective control and prevention interventions, mainly in developing regions
Sharifi et al. 2024	Examination of ambient air quality monitoring and control from a socio-economic and environmental perspective, using satellite imagery, air quality monitoring stations, and GIS to provide recommendations to policymakers and urban planners on improving air quality and public health
Mueller et al. 2023	Use of ArcMap software to map mosquito larval, adult habitats and human populations at risk for mosquito exposure and possible arbovirus transmission. The goal was to strengthen mosquito control programmes and create treatment area maps to improve strategic planning
Baldwin et al. 2022	Importation of data with GPS coordinates into ArcGIS 10.3 for mapping tick exposure risk at a scale for public health interventions and land management practices to reduce human exposure
Boonnuk et al. 2022	Implementation of a GIS and structural equation model to simulate public health impacts during floods, to improve preparedness and aid plans in response
Oyoshi et al. 2019	Use of earth-observing satellites to provide geo-specific environmental data on shortwave radiation, rainfall, soil moisture, vegetation index, aerosol optical thickness, land surface temperature and altitude to support epidemiological research
Ceccato et al. 2018	Use of GIS and remote sensing to improve knowledge of climatic, environmental, and biodiversity factors influencing vector-borne diseases. The knowledge and products informed resource allocation and climate action
Faruque 2019	Use of geospatial data, smartphone applications and machine learning to bring environmental health information closer to health professionals, to address environmental hazards and to improve public health and wellbeing

Table 5.

The use of digital technologies in environmental health data management and utilisation.

Author(s), year	Focus of the article or digital technologies studied	Outcomes
Budd et al. 2022	Use of mobile phones, online datasets, and machine learning in the public-health response to COVID-19	Strengthening pandemic management and future preparedness for public health threats through the alignment of international strategies for the regulation, evaluation and use of digital technologies
Iyamu et al. 2021	Definition of digital public health and the role of digitisation, digitalisation, and digital transformation	Clarification of the conceptual definitions to harness opportunities to integrate digital technologies into public health in a way that maximises their potential to improve public health outcomes
Odone et al. 2019	Reflection on the application of digital tools to public health and the conceptualisation of a successful European strategy for public health digitalisation	Acknowledgement of digitisation as an asset for public health, and the identification of key pillars for the European strategy, which include political commitment, normative frameworks, technical infrastructure, targeted economic investments, education, research, monitoring and evaluation
Adefeni et al. 2023	Machine learning, sensor networks, satellite imagery, predictive analytics and big data-driven models, and their significance in public health	Identification of challenges and opportunities for professionals and specialists in leveraging artificial intelligence for sustainable and resilient communities amidst evolving environmental and public health challenges
Birkhead et al. 2015	Description of electronic health records-based surveillance, and the analysis of the barriers to more effective use for public health surveillance	Improvement of public health surveillance through electronic health records and the possibility of bridging the gap between public health practice and clinical medicine to develop meaningful surveillance measures that are useful at both the clinical population level and the total population health level
Brown 2019	Strengths and limitations of using hospital records to assess environmental health	The use of existing information in low-resource settings to generate needed health data for urban populations, as well as to enhance urban health intelligence and urban policy formulation
Syamimi Masrani et al. 2022	Exploration of how the digital environment should be part of environmental health and its cascading effects on healthcare services, societal livelihood, symbiotic environment, and individual health	Timely transformation of environmental health to include the digital environment as part of its main components
Ibrahim 2020	Reflection on the role of the fourth Industrial revolution in combating the COVID-19 pandemic in smart and sustainable cities	Improved preparedness for future public health threats through reflections from the application of digital technologies and transformation in the control of the COVID-19 pandemic
Du et al. 2024	Construction of advanced machine learning model using environmental health big data to predict the daily indoor PM2.5 concentration data in typical air polluted cities	Establishment of a machine learning model and the accurate prediction of indoor PM2.5 big data to support research of indoor PM2.5 and human health and improve indoor air quality
McNabb et al. 2017	Exploration of ICT trends for prevention, detection, and response, laboratory reporting, push notification, analytics, predictive surveillance, and using new data sources to enhance public health surveillance	Recognition of people, politics, and policies as key issues that mostly challenge progress for implementation of ICT solutions in public health surveillance
Shakeri Hossein Abad et al. 2021	Characterisation of the use of digital media in different aspects of public health surveillance	Leveraging digital data to help portray the dynamics of public health surveillance systems and allow public health professionals to pinpoint public concerns and needs to develop location specific interventions
Lerman Ginzburg et al. 2021	Development and evaluation of an infographic about the dangers of ultrafine particles from traffic pollution, and public health actions that readers in affected communities can take to protect their health	Use of infographics as a viable tool to communicate traffic pollution, its effects and public health action to affected community members
Liu 2022	Gains and challenges of data-driven machine learning in environmental pollution (air and water) surveillance and control	Application of problem specific machine learning models to improve environmental (air and water) pollution surveillance and control
Choirat et al. 2019	Review of how big data and data science can improve environmental research capabilities	Advocacy for collaborations between environmental health researchers, biostatisticians and data scientists for environmental health to embrace data science and ensure robust data analytics and interpretation
Dehghani et al. 2022	Introduction of a process-mining framework for measuring the status of environmental health in institutions	Improved analysis of big data and risks to provide more realistic decisions in the environmental health system
Comess et al. 2020	Challenges and recommendations on bringing big data and machine learning in environmental health	Presentation of actionable recommendations for leaders in the public and environmental health fields to foster big data revolution for improved environmental health and action
Mooney & Pejaver 2017	Review of the role of big data in public health research and practice, ethical implications on privacy, as well as the use of related terminologies and machine learning	Introduction of a specialised team science model in training and practice to strengthen public health through big data analytics
Benke & Benke 2018	Description of fundamentals of artificial intelligence and big data in the context of potential applications to data analytics in public health and the medical sciences	Improved application of data analytics and clarification of ethical issues and the future roles of professionals and specialists in the age of artificial intelligence
Singh et al. 2021	Review of the use of remote sensing, geospatial and computational technologies in air quality assessment monitoring and management	Improvement of air quality assessment and monitoring and human health management through the integration of ground-based smart sensors, satellite remote sensing sensors, geospatial technologies, and computational technologies
Chan & Chang 2022	Use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice	Construction of innovative decision models for improved decision-making for public health
Gamache et al. 2018	Synergistic bridging of electronic data to benefit communities and other populations	Proactive use of population health-centric information exchange and analytics activities to benefit public health
Sahu et al. 2021	The use of internet of things as a data source for public health surveillance	Utilisation of internet of things based data sources to complement and strengthen public health surveillance and response systems
Barros et al. 2020	Application of internet-based sources for public health surveillance (infodemiology and infoveillance)	Use of internet-based health information to study illnesses affecting the population to benefit public health through community profiling and the application of targeted interventions
Jia et al. 2023	The use of GIS, spatial modelling, artificial intelligence, data science, and digital twin in a national notifiable disease reporting system	Improved timeliness, completeness, and accuracy of public health actionable information through technology-driven innovative public health surveillance systems
Stieb et al. 2017	Application of big data to occupational and environmental health	Advancement of big data and data sharing to inform occupational and environmental
Stingone et al. 2021	Use of a n interdisciplinary data science approach to advance environmental health research to improve birth outcomes	Development of the interdisciplinary framework for preterm and low weight births complexities of multiple exposures and biological responses to provide evidence for informed public health policy and strengthened preventative population-level interventions
Wallar et al. 2018	Centralisation and digitalisation of environmental public health data and information to strengthen the utility and impact of the environmental public health tracking program	Development of a centralised and modernised source of public health data for academia, practice and partnerships

Environmental health information or surveillance systems utilised by government agencies

A total of 19 articles, as depicted in Table 2, were on existing EHISs or surveillance systems utilised by government agencies. This table indicates studies on environmental health surveillance and monitoring by government agencies, reflecting best practices, lessons to be learnt and paving a way for continuous improvement. Seventy-four percent (74%; n = 14) of these 19 articles were on the USA’s National Environmental Public Health Tracking Program. Others (26%; n = 5) included the Chinese Environmental Public Health Tracking, Environmental Health Intelligence New Zealand, Australia’s Environmental Health Tracking System, as well as the USA’s biomonitoring program and the national poison data system. These articles signify the importance of environmental health surveillance for governmental agencies to be informed on environmental health issues and develop targeted interventions. It is also notable that all the articles in Table 2 are from high-income countries, which indicates a research gap in low- and middle-income countries on the evaluation of environmental health information systems and their advancements.

Development of environmental health indicators and information systems

Articles on the development of environmental health indicators and information systems (Table 3) shown that various efforts are being made in different parts of the world to continue to explore the use of data to identify and address key environmental public health impacts. Children’s environmental health indicators are part of the projects that researchers are exploring to promote healthy environments that support the wellbeing and development of children as part of vulnerable groups (n = 4).^22–25 Other studies on the establishment and application of environmental health indicators and systems were on air pollution (n = 1),²⁶ general environmental health conditions and outcomes (n = 5),^7,27–30 biomonitoring (n = 1),²¹ climate change (n = 2),^31,32 planetary health (n = 3)^1,3,33 and community-based environmental health tracking (n = 1).³⁴ The wide distribution of these studies indicates the inclusive approach to protect human health in all environments with public health threats.

Application of GIS tools in environmental health data management and utilisation

The reviewed articles charted in Table 4 show the importance of GIS tools and their application in environmental health data management and utilisation. Taking into consideration the manner in which the GIS tools were used in these studies. Which include data mapping for the spatial display of environmental health data (n = 5),^35–39 spatial data analysis (n = 1),⁴⁰ epidemiological analysis (n = 1),⁴¹ simulation of public health impacts (n = 1),⁴² as well as modelling and remote sensing (n = 2).^10,43 The application of the GIS tools in these studies pinpoints problematic public health areas and enables targeted interventions to be launched. However, the use of GIS tools in the articles outlined in Table 4, are more on air quality, broader environmental health conditions, vector control and climate change impacts.

Digital technologies in environmental health data management and utilisation

Table 5 projects 27 (36%) reviewed articles that are specific to digital technologies in environmental health data management and utilisation. This table also shows the focus of these studies and their related outcomes, indicating the advantages of integrating digital technologies into environmental health information management to facilitate data-driven healthy and sustainable developments in the community. Most articles (n = 15) were on big data, predictive analytics, modelling and machine learning to leverage artificial intelligence to improve environmental and public health surveillance and research, promote data-driven decision-making and enable agile response to public health threats.^{14–16,44–54} Other articles (n = 12) were on the use of Internet of Things, smart devices, digital twins, infographics and the general use of digital technologies and systems to maximise public health opportunities for better health outcomes.^5,14,55–64

The results of this scoping review indicate the power of data and digital technologies to bring decision-making intelligence, proactiveness and efficiency in environmental health to strengthen public health action and build sustainable communities. Furthermore, the results present various opportunities to be leveraged in different public health settings, which signify the urgency for countries to adopt new technologies and embrace a data science approach, especially in the low- and middle-income countries.

Discussion

In this digital era, technologically modernised EHISs are well positioned to play an ever-increasing role in unlocking efficiencies and intelligence for government agencies, the private sector and communities to counter environmental health threats and protect public health.^14,29,44,59 This modernisation of EHISs speaks to advancements that are driven by the adoption of digital technologies to support public health professionals, decision-makers, policymakers and the community in real time.^31,33 Digitalisation enables precision, automation, data analytics, prediction and interaction to support and improve public health practices.⁵⁶ Therefore, the intersection of technology and environmental health holds the promise of providing real-time, accurate, and actionable insights to reduce the environmental health burden of disease in communities and contribute to the development of healthier communities. This intersection has led to streamlined environmental health surveillance and monitoring, enhanced operational efficiency, improved service delivery and many other benefits in different parts of the world.^4,6 In the midst of opportunities that are provided by digitalisation, there are reportedly various barriers and challenges that threaten the effective implementation and advancement of health information management systems, especially in low-middle-income countries.^{2,17,47,65,66} However, literature indicates that to strengthen the health system and reposition public health, practitioners, public health agencies and policy makers should seek solutions to support and not hinder modernisation.^31,33,67 As there is a dire need for the use of reliable scientific measures and systems to support efforts to create healthy environments and reduce preventable deaths caused by environmental factors, mostly in affected settings like low- and middle-income countries.^23,66,68 The World Health Organization in 2016 pronounced that physical, biological, and chemical environmental factors cause approximately 23% (13.7 million) of all worldwide deaths each year, with low- and middle-income countries being the most affected.⁶⁹ As a result, the opportunities provided by digital transformation needs to be maximised and leveraged in all countries, learning from each other and differently applied systems in the past and currently.

Improving environmental health surveillance and monitoring

In the USA, the nature of the Centers for Disease Control and Prevention’s Environmental Public Health Tracking Program integrated standardised data on environmental hazards; exposures to these hazards; potentially related health effects; and other data, such as socioeconomic and risk factors, for visualisation at the local, state, and national levels.⁷⁰ This EHIS uses these datasets to perform environmental public health surveillance activities, such as identifying and assessing the distribution of hazards in the environment and the health effects resulting from exposure.¹⁹ As a result, scientific evidence gets produced to respond in a timely and accurate manner to environmental hazards, inform interventions and effectively protect people’s health.^6,71 Data from satellite imagery, drones, sensors, CCTVs, environmental monitoring stations, disease surveillance systems and field environmental health practitioners and other public health professionals, when integrated into a single digital system, provide a comprehensive real-time picture of environmental health threats in the community.^{7,24,40,41,44,62,70} In a study by Sharifi et al. on the impact of socioeconomic and environmental factors on air quality in the city of Kabul, the integration of data from satellite imagery, air quality monitoring stations, GIS tools and multiple regression analysis modelling via R programming demonstrated strong relationships between air quality and urban green spaces, population growth, vehicle count, temperature, and electricity availability.⁴³ These findings provided policymakers with practical recommendations to enhance Kabul’s air quality and general public health. Therefore, the integration of digital technologies and related systems in environmental health monitoring and surveillance is helpful for tracking and communicating complex health trends, informing science and policy decisions, and evaluating public health actions.^31,55 The integration of information systems has multiple benefits, including reduced bureaucracy and management costs.^72,73 Furthermore, through the analysis of centralised integrated data, public health agencies can conduct further investigations on the effects of environmental exposure on people and develop targeted interventions as reference sources for other cases. Since emergent environmental health issues grow in complexity, the adoption of digital technologies presents an opportunity for comprehension and interpretation of multiple and cumulative exposure sources and risk factors through data analytics.⁷⁴ However, limited investments in resources that support the effective utilisation of digital technologies can be a hindrance in leveraging the benefits associated with innovation and digital transformation. Resource constrained countries and agencies need to explore collaboration with scientific and technology agencies, as well as the private sector through public-private partnerships to source investments and support.

Streamlining operational efficiencies and improving service delivery

As environmental health data sources evolve and new public health issues emerge, the digitalisation of EHISs and the use of Internet of Things and data analytical techniques are key to improving operational efficiency and service delivery through the automation of manual processes and the use of digital devices.⁵⁹ The Internet of Things is a technological innovation through which devices can communicate with each other in real time through an internet connection, accelerating data collection and dissemination.⁶³ Digital devices such as smartphones now have digital applications that are designed to improve and automate routine tasks and generate informative statistics on the state of environmental health.^59,75 In a study in Nigeria, the digitalisation of the EHIS and the use of linked smart devices with digital inspection tools made it possible for environmental health practitioners to capture environmental health inspection findings in real time while in the field.³⁵ As environmental health practitioners monitored various aspects of environmental health in the field, the data generated were repurposed from inspection reports to consolidated reports on different areas of environmental health. Regulatory processes were also streamlined using digital technologies to track and monitor compliance. Real-time data analysis of air quality enabled targeted pollution control strategies in a study on air pollution exposure among cyclists.⁷⁶ This emphasises the use of real-time data to quickly provide new ideas for urban health planning and scientifically guide decision-making for sustainable urban development. Therefore, digital technologies can facilitate the development of decision support systems that help government agencies analyse complex environmental data through advanced data analytics and make evidence-based decisions.

Through data analytics and machine learning algorithms, predictive models can be developed for the early detection of environmental trends and potential hazards.^28,31,47,77 Early detection of environmental health risks allows the provision of early alerts to relevant communities and parties and enables proactive application of interventions, which also assists in the identification of new indicators for surveillance and monitoring.^23,25,75 This is the case in New Zealand, where a digital EHIS provides environmental health intelligence through the early detection of risks, as well as effectively conveying and distributing early warnings and response actions to be taken by relevant stakeholders and citizens for the benefit of their health.⁷⁸ This shows that at the community level, predictive models can assist field public health professionals and administrative office staff in identifying environmental health issues that warrant immediate action, triage, or simply informing other relevant stakeholders for interventions or support.^40,43,46,50 At the national level, these models enable the government to quickly identify large-scale issues affecting inter-regions and forecast current public health patterns of certain parameters and future events.⁷⁵ In New Zealand, it was reported that they used a dashboard under their digital EHIS for climate change monitoring to obtain temperature and rainfall data, together with various health effects indicators at the local level.⁷⁸ In this context, digital information systems present online interactive dashboards to persuasively visualise environmental health surveillance data and predict trends and patterns.⁷⁵ Data visualisation is a crucial tool for decision-makers in public health because it can be used to examine patterns, relationships, and trends that may not be immediately understandable in large amounts of raw data.⁵⁰ Dashboards highlight important indicators and display information in charts, graphs, infographics and maps to enable effective communication of data-driven insights and facilitate internal and external collaborations.⁵¹ The enrolment of data analytics and the development and interpretation of trends and patterns requires skilled health practitioners and data analytics experts. A study by Alwan et al. identified a lack of trained personnel in epidemiology, statistics, information technology, and disease surveillance as part of the barriers against effective utilisation of digital technologies in routine health information systems.⁷⁹ Another study conducted in Iran also highlighted the lack of users’ understanding of technology and its use as one of its implementation hurdles.⁸⁰ Health practitioners’ limited computer skills further lead to low morale and resistance to using digital technologies, which affects productivity in terms of data output.⁸¹ This requires capacity-building programs that are accompanied by change management sessions and sharing of best practices to enable continuous improvement, develop digital literacy, and allow the digital transformation journey to be embraced.

Mapping for public health action

The majority of public health data have a location component, which makes GIS tools relevant for conducting map marking and geographic analysis to assist in planning and decision-making.^36,38 GIS tools, as part of public health surveillance and monitoring, provide an opportunity for an improved understanding of the spatial distribution of disease incidence, as well as spatial observations of environmental factors, to analyse the relationships between environmental exposures and health outcomes.^38,39 This makes GIS tools to be important in mapping populations at risk of environmental exposure and applying a spatial focused approach.^40,82 Fletcher-Lartey and Caprarelli explored GIS in public health and outlined that its application improved health professionals’ comprehension of how underlying environmental, social and cultural factors interact with the person, place, and time in the emergence and re-emergence of diseases.³⁶ This approach allows for the identification of hotspots and the development of targeted interventions on the basis of an understanding of population demographics and the distribution of socioeconomic determinants of health.³⁷ This was evident in research conducted in China, where the use of GIS in data collection and visualisation enabled researchers’ understanding of the link between the distribution of cardiovascular diseases and air pollution throughout Bangalore.⁴⁰ Therefore, it asserted that in the environmental health context, GIS tools allow observations of trends as well as the diagnosis and tackling of environmental health issues by enforcing the law in specific areas, educating the communities at risk, mobilising community partnerships, and linking the citizens to needed environmental health services.^6,37,83 GIS technologies also enable short-term and long-term spatial monitoring of environmental health statuses in the community. As a result, a digital EHIS with GIS tools can also be used to compare environmental health statuses and risks in different geographic areas to enable transparency and trust between the public and government, guiding inclusive preventive decision-making for sustainable and healthier communities.^1,7

Improving risk communication and community engagement

Data from digital EHISs present an opportunity for improved risk communication and community engagement on environmental health risks and impacts in communities.⁴² Social media, government entities’ websites, mass-media platforms, push messages and email notifications can be used for public health risk communication and to effectively engage the public in environmental health issues while promoting health awareness at a large scale.⁵⁹ The adoption of big data as EHISs become integrated with more sources of data leads to increased datasets and complexities, leading to the generation of more scientific information that cannot be easily communicated as is to the public.^34,48,49 This then allows the use of dashboards and infographics to communicate complex information effectively, that may be inclusive of early warning messages.^61,78 In a study on traffic-related air pollution in community settings of Massachusetts, in the USA, infographics were proven to be a viable tool for communicating environmental health-related information.⁶¹ Therefore, through digital technologies in environmental health data management and utilisation, government agencies become placed in a better position to quickly disseminate information, solicit feedback, and encourage citizens’ participation in community-led efforts for better health outcomes and environmental protection.^9,27 This approach can empower citizens with information to apply public health measures in their settings. Hence, government agencies and public health professionals have an opportunity to maximise data-driven risk communication and community engagement through digital media and mass media so that the public can be informed.^32,84

Enhancing multisectoral collaboration

Environmental health issues affect everyone, and interventions are usually required from the integration of stakeholders and disciplines. Therefore, multisectoral collaborations are key for the characterisation of issues and the development of solutions together. Hence, digital EHISs are vital for purposeful multisectoral collaborations through data sharing in developed networks between environmental health-related entities in different spheres of government, the private sector, communities, research institutes, and other interested and affected parties.⁶ Data sharing has the potential to bring together different stakeholders and form novel partnerships to explore nontraditional data sources such as biomonitoring and exploit the collective power of data.^20,54,64 The purpose of these collaborations should be based on addressing cross-cutting, complex, and interdependent issues and consequently promoting a better life for all people. A digital EHIS can enable the sharing of data, experiences and best practices within a country and at a global level to develop integrated environmental health prevention and promotion goals.¹ In Rwanda, one of the their challenges was the design of an architecture to support interoperability between the old information system and the new one, which was as a result of an unclear framework and the lack of technical consensus.⁸⁵ Therefore, this requires countries to contextualise the adoption of digital systems and eliminate the fragmentation of reporting systems. Fragmented reporting systems are due to insufficient integration of stakeholders and coordination of health information, which can affect the availability, adequacy and reliability of data.⁸⁶ Hence, there should be memorandums of understanding between the stakeholders for the roles and interests to be clear in the integration efforts and interoperability of information systems to allow data sharing that benefits all parties. In Australia, environmental health practices are dependent on multisectoral cooperation and collaborations toward practicing a coordinated approach to address environmental risks to health. Their strategy entails sharing data and resources, as well as addressing multisectoral cross-cutting issues such as food control in a principled manner.^6,22 Wilson and Charleston highlighted that the sharing of data and resources internally and with external agencies was beneficial for environmental health agencies in the USA.⁸⁷ These collaborations in the USA brought public health practice and clinical medicine together to explore the opportunity to use electronic health records to increase the breadth, detail, timeliness, and completeness of public health surveillance and thereby provide better data to guide public health interventions.^53,57,88 The same approach was used in Malawi to assess environmental health problems via hospital records.⁵⁸ Therefore, in the adoption of digital systems in the public health sector, an integrated approach is crucial to provide connections between key stakeholders, solve societal challenges and improve the performance of governmental agencies, even in low-resource settings.^21,37,58,89 Environmental health issues in low- and middle-income countries are complicated, and systems-based approaches are necessary to enable effective and efficient interventions.⁹⁰ As much researchers have noted progress in the implementation of digital health information systems in low- and middle-income countries, sustainability and widespread adoption in their public health systems remain elusive.^58,66,91

Environmental health research development

When high-quality data are available and accessible, research into the links between various environmental factors and human health gets encouraged.^6,89 The availability of research and data to support the provision of clear guidance and interventions is essential to ensure that environmental health challenges are effectively managed.⁶⁷ Furthermore, in practice, environmental health practitioners, public health agencies, and other interested parties depend on current, evidence-based recommendations to support their efforts to address environmental health risks. In New Zealand it was reported that their digital EHIS promotes research and informs environmental health practices on the basis of appropriate and contemporary evidence from which to extract information.⁷ In a study by Jung et al. it was concluded that improved monitoring systems using targeted environmental health indicators has an opportunity to mitigate the data gap and enhance data quality in low- and middle-income countries.²³ In the USA, the Centers for Disease Control and Prevention collaborated with academia to advance research on the science and practice of their EHIS.⁹² This strategy yielded positive results, as key challenges were addressed through research.^93–96 Knowledge among practitioners was also enhanced on the links between health and the environment, and valuable networks were developed with the aim of identifying future collaborations for further research. Therefore, a data science approach in the field of environmental health is key to enabling public health practitioners to analyse increasingly high-dimensional and complex datasets from digital systems and various sources.^15,16,30 This calls for data science and environmental health information management to be incorporated into the training of future environmental health scientists and the continuous professional development of practitioners already in practice.

Limitations

This review should be considered in view of its limitations. First, the literature search was conducted in three major databases, namely, PubMed, Scopus and Google Scholar, with a focus on peer-reviewed articles published in English between 2015 and 2025. This may have excluded grey literature, relevant articles published elsewhere or not published at all, and those written in other languages. Therefore, the results and discussion of this review on the use of digital technologies in environmental health information management to guide decision-making and public health action are non-exhaustive.

Conclusion

Embracing digital technologies in environmental health is key to unlocking intelligence to prevent disease-causing and health-limiting factors in communities. Recognising and appreciating the importance of digital technologies in environmental health globally is a matter of urgency to maximise the potential of government agencies to improve public health outcomes. This is especially the case in low- and middle-income countries, which are burdened by various environmental health threats. More (85%; n = 63) studies in this review were from high-income countries. Studies on the development and review of environmental health indicators and information systems to address circumstantial public health issues and be aligned with new technologies emphasise the importance of continuous development of routine health information systems to promote their relevance and performance. However, more research on EHISs is needed in low- and middle-income countries, especially in Africa, to advance environmental health practices. Collaborations between public health agencies, health practitioners, researchers, policymakers, scientific institutions and the private sector are also required to address political commitments, limited investments, lack of resources, interoperability challenges, unclear health information frameworks, lack of training among health practitioners and resistance to change. The findings presented in this review contribute to the broader discourse on leveraging digital technologies to improve people’s health and well-being, as well as sustainability and resilience in the face of evolving environmental health challenges. Future work is recommended on assessing data quality, characterisation of different public health surveillance systems to unlock integration opportunities, and the development of a funding model for digital transformation in environmental health data management in resource-constrained settings.

Supplemental Material

Supplemental Material - Applications of digital technologies in environmental health information systems

Supplemental Material for Applications of digital technologies in environmental health information systems by Siphesihle Siyamukela Masimula, Mpinane Flory Senekane and Nisha Naicker in Health Informatics Journal

Footnotes

Authors’ note

All the authors have read and agreed with this version of the manuscript to be published.

Acknowledgements

The authors wish to thank the University of Johannesburg for providing access to research databases and resources that were imperative for conducting this review.

Siphesihle Siyamukela Masimula

Mpinane Flory Senekane

Nisha Naicker

Ethical considerations

This review was conducted as part of a broader study that received ethical clearance from the University of Johannesburg Faculty of Health Science’s Research Ethics Committee (REC-2469-2023). Consent for participation in this review is not applicable.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting this review can be found in the referenced studies listed in the manuscript.*

Supplemental Material

Supplemental material for this article is available online.

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