Modernizing Data Systems in Environmental Public Health: A Blueprint for Action, published by the National Environmental Health Association, October 2025.
Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
October, 2025
Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Acknowledgements
This guide was developed through the collaboration, expertise, and dedication of numerous contributors committed to advancing the field of environmental health through data-informed practice. Primary Authors This guide was written by a team of subject matter experts in environmental public health, informatics, and public health data systems. Their collective experience, insights, and leadership shaped the content and structure of this resource: Serene Ong, GIS Business Systems Analyst III, Innovation and Technology Department, San Bernardino County Andrew Mackey, GIS Business Systems Analyst III, Department of Public Health, San Bernardino County Contributing Authors The following individuals provided research, writing, and technical contributions to various sections of the guide: Becky Labbo, MA, Senior Evaluation Specialist, National Environmental Health Association Christopher Walker, MSEH, REHS, Senior Program Analyst, National Environmental Health Association Rachel Stokes, MPH, DrPH, REHS, Project Coordinator II, National Environmental Health Association Reviewers and Advisors NEHA gratefully acknowledges the reviewers whose thoughtful feedback and critical insights enhanced the accuracy, clarity, and relevance of this guide. Their input ensured that the guide reflects the diverse needs and realities of the environmental public health workforce: Data and Informatics Program Committee, National Environmental Health Association Support and Funding This Data Modernization Guide is one of several projects supported by the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award totaling $300,000 with 100 percent funded by CDC/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by CDC/HHS, or the U.S. Government.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Foreward The environmental public health (EPH) landscape is evolving, driven by complex environmental health challenges and an increase in the frequency of public health emergencies. These challenges call for faster, more coordinated, and more data-driven responses than ever before. In some cases, EPH agencies operate with older data systems and limited technological capacity, which could limit their ability to maximize public health impact. The Centers for Disease Control and Prevention (CDC), aware of these issues, has made data modernization a national public health priority. Through its Data Modernization Initiative (DMI), CDC has raised the importance of integrated, real-time, and interoperable systems that enhance public health surveillance, decision-making, and response capabilities across various disciplines, including environmental health. The National Environmental Health Association (NEHA) conducted a comprehensive data and informatics needs assessment in 2023 to better understand the specific data and informatics challenges faced by EPH profession- als. The results indicated significant gaps in technical infrastructure, workforce capacity, data accessibility, and interoperability—issues that span rural, urban, tribal, and territorial jurisdictions. These insights underscore the need for customized guidance to assist agencies at all levels in navigating the path toward modernization. This guide, Modernizing Data Systems in Environmental Public Health: A Blueprint for Action, was created to address that need. It serves as a practical and strategic resource for EPH professionals who seek to enhance their data understanding, capabilities, and data infrastructure. Organized by thematic sections, the guide pro- vides foundational principles, actionable strategies, real-world case studies, and tools to support jurisdictions of all sizes and capacities to make significant progress toward modern, resilient data systems. Portions of this document and the associated checklist were developed with assistance from ChatGPT (GPT-4, OpenAI, accessed on August 2025). The artificial intelligence (AI) tool was used to reword and edit content for grammar, flow, and clarity; generate illustrative EPH examples; and support the development of final checklist items based on the content of this guide. All AI-assisted material was reviewed, verified, and revised by the authors to ensure accuracy, clarity, and alignment with the standards and mission of NEHA. NEHA extends its deepest thanks to the authors, contributors, reviewers, and partners who brought this guide to fruition. This resource is meant to not only inform but also empower EPH leaders and practitioners with the knowledge, inspiration, and tools necessary to advance data modernization and improve the health of the com - munities they serve.
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Contents
Section 5: Lessons from the Field — Case Studies and Strategic Insights.....................21 California – Traffic Exposure Mapping ......................................................................................... 22 San Bernardino County, CA – Environmental Health Services Modernization.............22 Texas – SHARP Platform ................................................................................................................. 23 Tennessee – Source Water Assessment Modernization......................................................23 Synthesis and Key Insights from Case Studies........................................................................24 Section 6: Emerging Strategies and Technologies in Data Modernization.....................25 Secure Organizational AI..................................................................................................................25 Data and Cybersecurity: Protecting Information Assets.......................................................26 Cloud Computing: Infrastructure for Modernization...............................................................26 Data Sharing: Enhancing Collaboration and Transparency..................................................26 Section 7: Charting the Path Forward: Strategic Guidance and the Road to Implementation..................................................................27 Call to Action.........................................................................................................................................27 Getting Started: A Checklist for Data Modernization.............................................................28 Environmental Health Data Modernization: Getting Started Checklist............................28 Key Success Factors Throughout the Process..........................................................................29 Phase 1: Assessment and Planning..........................................................................................29 1. Current Readiness Assessment................................................................................................29 Inventory Existing Data Systems ....................................................................................... 29 Evaluate Data Quality and Gaps........................................................................................29 Assess Organizational Readiness.....................................................................................29 2. Strategic Planning..........................................................................................................................29 Define Modernization Goals and Priorities ..................................................................... 29 Conduct Stakeholder Analysis............................................................................................29 Develop a Phased Implementation Plan.........................................................................30 Phase 2: Foundation Building.....................................................................................................30 3. Data Literacy and Skills Development....................................................................................30 Assess Current Capabilities.................................................................................................30 Deliver Foundational Training..............................................................................................30 4. Governance and Standards........................................................................................................30 Establish Data Governance Framework..........................................................................30 Adopt Standardization Practices.......................................................................................30
Acknowledgments............................................................................................................................ ii Primary Authors......................................................................................................................................ii Contributing Authors.............................................................................................................................ii Reviewers and Advisors.......................................................................................................................ii Support and Funding.............................................................................................................................ii Foreward............................................................................................................................................. iii Section 1: Introduction to Data Modernization and Environmental Health Informatics....1 The Case for Data Modernization....................................................................................................1 Strategic Role of Data Modernization in Environmental Health............................................2 Core Principles to Guide Data Modernization..............................................................................2 Barriers and Challenges to Data Modernization.........................................................................3 Section 2: Data Literacy and Foundational Skills......................................................................4 Environmental Public Health Data Overview...............................................................................4 Key Data Sources...................................................................................................................................6 Data Quality and the Role of Metadata..........................................................................................8 Managing Data with Integrity, Privacy, and Compliance......................................................10 The Importance of Data Standards..............................................................................................10 Key Standards Supporting Modern Environmental Health Systems................................10 Visualization and Communication.................................................................................................11 Section 3: Core Principles of Data Modernization.................................................................12 The FAIR Principles for Optimal Data Use.................................................................................12 Timeliness and Real-Time Access................................................................................................13 Role of Consistency............................................................................................................................13 Accessibility and Usability...............................................................................................................13 Governance and Security.................................................................................................................14 A Data-Centric Culture......................................................................................................................14 Section 4: Essential Tools and Technologies for Data Modernization..............................15 Foundational Technology Needs in Environmental Health..................................................15 Geographic Information Systems (GIS).......................................................................................16 Data Visualization Platforms...........................................................................................................17 Data Processing and Analysis Tools............................................................................................18 Mobile Data Collection and Field Tools.......................................................................................19 Artificial Intelligence (AI) ................................................................................................................... 20
Contents
5. Technology Infrastructure Planning.........................................................................................30 Evaluate Technology Options..............................................................................................30 Plan Security and Compliance Measures........................................................................30 Phase 3: Core Implementation....................................................................................................30 6. System Selection and Deployment..........................................................................................30 Select Core Platforms.............................................................................................................30 Implement Foundational Systems.....................................................................................30 7. Data Migration and Refine Data Quality ................................................................................ 30 Plan Data Migration................................................................................................................30 Implement Quality Assurance Processes........................................................................31 Phase 4: Advanced Capabilities.................................................................................................31 8. Analytics and Visualization.........................................................................................................31 Develop Reporting Capabilities..........................................................................................31 Enhance Spatial Analysis......................................................................................................31 9. Public Access and Transparency..............................................................................................31 Develop Public-Facing Tools...............................................................................................31 Enable Easy Access to Records and Services...............................................................31 Support Data Understanding and Use............................................................................31 Phase 5: Optimization and Innovation......................................................................................31 10. Advanced Analytics and AI......................................................................................................31 Explore Predictive Capabilities ............................................................................................ 31 Enhance Automation..............................................................................................................31 11. Continuous Improvement.........................................................................................................32 Establish Feedback Mechanisms.......................................................................................32 Plan for Future Growth..........................................................................................................32 Glossary of Terms...........................................................................................................................33 Sources.............................................................................................................................................. 35
Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Section 1: Introduction to Data Modernization and Environmental Health Informatics The Case for Data Modernization
Environmental public health (EPH) agencies play a vital role in protecting communities from preventable harm. EPH is essential to safeguarding the well-being of every community. From ensuring safe food, water, and waste systems to preventing vector-borne diseases and man- aging hazardous materials, environmental health pro - tects lives and sustains the environments we depend on. To achieve this protection effectively, the work of EPH agencies depends on collected and utilized data. Data are more than just a record of past actions and serve as a resource that informs decision-making, guides resource allocation, supports transparent communication, and promotes continuous improvement. Data modernization involves transforming how public health systems gather, handle, analyze, and share data. For EPH programs, it replaces traditional methods like paper workflows, spreadsheets, and isolated databases with ad- vanced systems that support real-time decisions, commu- nity involvement, and collaboration across agencies. This shift signifies a change in mindset and operations, viewing data as not only an administrative chore but also a vital element of effective public health efforts. To demonstrate how data serves as a continuous driver of improvement, the data management lifecycle depicted in Figure 1 illustrates the full arc of data use, from initial sur - veillance and community input to analysis, insight, action, and re-evaluation. This cycle highlights the interconnected steps that transform raw data into meaningful, actionable information to support informed and responsive deci - sion-making in environmental health.
Data Collection
Data Access
Accountability
DATA MANAGEMENT LIFE CYCLE
Interpretation
Data Analysis
Figure 1
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Strategic Role of Data Modernization in Environmental Health
Core Principles to Guide Data Modernization As a general practice, modernization efforts are guided by several core prin - ciples to enhance their effectiveness (Table 1). These principles form the basis for planning, implementation, and ongoing improvement. They ensure that modernization not only introduces new technologies but also enhances the overall functionality, fairness, and impact of data on EPH services. When applied consistently, these principles enable agencies to protect public health better, respond to emerging threats, and serve communities with transpar - ency and efficiency. Each of these principles will be explored in more detail in later sections of this guide, which will provide practical examples and action - able steps for implementation.
Environmental health informatics sits at the intersection of environmental health practice, information systems, and data analytics. When agencies mod- ernize their data systems, they unlock the ability to detect environmental ex - posures sooner, analyze risks more effectively, and deploy interventions more strategically. They also gain tools to strengthen partnerships with community organizations, emergency response teams, and other public health units. Mod- ern informatics capabilities enable predictive modeling, automated reporting, geospatial analysis, and public-facing dashboards that promote transparency and trust. Modernized environmental health data systems contribute to public health re - silience in the face of current and emerging threats. From the impacts of cli- mate change to the spread of new contaminants and diseases, today’s environ - mental challenges require agile and data-driven responses. Real-time access to high-quality data supports efforts ranging from heat emergency response coordination to flood zone inspection reroutes and outbreak source tracing. In this context, modernization is not a luxury, it is a public health necessity.
Table 1
CORE PRINCIPLE DESCRIPTION Timeliness
Data reflect current conditions to inform proactive enforcement, guide emergency response, and support real-time decision-making. Shared definitions, formats, and data dictionaries promote consistency, reduce errors, ensure reliability and validity, and support comparisons across programs. Data are intended to be accessible to analysts, field staff, administrators, and the public through user-friendly dashboards, mobile access, and direct query interfaces that allow authorized users to access the underlying or raw data securely. Systems are expected to integrate in ways to reduce duplication, improve data flow, enable data aggregation, and enhance overall quality. Data are to be protected through privacy laws (e.g., the Health Insurance Portability and Accountability Act [HIPAA]) and ethical stewardship, especially for sensitive records such as complaints or inspections.
Standardization
Accessibility
Interoperability
Security and Ethics
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Barriers and Challenges to Data Modernization While data modernization offers many advantages, it often encounters nota- ble challenges. Agencies might face budget constraints, limited IT resources, fragmented systems, and regulations that have not kept pace with technology. These challenges can be further complicated by the varied regulatory envi - ronment across local and state EPH agencies, which can slow efforts toward standardization and interoperability.
Modernization also elevates the role of data as a strategic asset in organiza - tional strategy. In a modernized EPH agency, data serves multiple strategic and operational purposes--data are not just collected and stored, it is lever- aged to inform decisions, drive performance, and communicate value. Table 2 shows the diverse functions data can serve in a modernized agency, demon - strating how areas ranging from operations and workforce management to public engagement and funding justification are supported by data.
Table 2
FUNCTION PURPOSE System of Record Documents all past and ongoing activities to create a reliable institutional memory.
Performance Measurement
Tracks inspection rates, violations, turnaround times, and compliance outcomes to evaluate effectiveness. Guides field prioritization, resource allocation, and policy development through evidence-based insights. Enhances transparency for the public, informs leadership, and delivers operational insights for staff. Identifies disparities in services, environmental exposures, and health outcomes across populations; supports emergency communication and program advocacy efforts. Helps supervisors balance workloads, assign inspections, and monitor team performance. Uses dashboards, maps, and visual reports to highlight progress and advocate for continued investment. Enables residents to explore data and provide feedback through interactive tools. Strengthens funding proposals by documenting needs, gaps, and outcomes.
Decision Support
In addition, varying levels of data literacy among environmental health field staff can make it harder to fully leverage new tools, even when they are available. Targeted training and workforce development help ensure that modernization leads to meaningful operational and public health improvements rather than re - maining solely a technical upgrade. Leadership transitions, uncertainty around change, and competing short-term priorities can also slow progress—particular- ly in agencies without a clear, long-term modernization strategy. Awareness of these barriers is essential for creating a realistic, phased ap - proach that balances ambition with practicality. Successful strategies combine technology adoption with capacity building to ensure environmental health professionals are equipped with not only advanced tools but also the skills and confidence to use them effectively. Ultimately, the goal of modernization is to create stronger, smarter, and more resilient environmental health systems. It aims to give EPH professionals the tools needed to protect communities efficiently, fairly, and transparently. Equal- ly important, it seeks to re-center EPH within the larger public health system, making sure it is not sidelined by technological limitations but empowered by modern, mission-driven data systems that reflect the urgency and complexity of the work.
Communication
Analytical Lens
Workforce Management
Storytelling
Community Engagement
Grant Justification
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Section 2: Data Literacy and Foundational Skills
EPH data span multiple domains and disciplines, requiring professionals to be fluent in a diverse set of information sources. To thrive in a modern data environ- ment, EPH professionals benefit from strong data literacy and foundational skills. These competencies enable practitioners to access, interpret, and effectively communicate data, ultimately improving public health outcomes and supporting informed, data-driven decision-making that benefits all communities. Environmental Public Health Data Overview EPH professionals benefit from familiarity with the following key types of data—each serving a distinct purpose, yet often interconnected: exposure data, health outcome data, surveillance data, demographic and socioeconomic data, geospatial data, regulatory and compliance data, and financial and budgetary data. Communication records data are a cross-cutting element that support and enhance all these categories by linking technical findings to the outreach, notifications, and engagement activities that give them real-world context and drive action. Exposure data capture environmental measurements such as air quality, water contaminants, chemical exposures, and radiation levels. These indicators are essential to assess risk in residential and occupational settings. Health outcome data include records of illnesses and conditions potentially linked to environmental factors, such as asthma, lead poisoning, heat-related illness, and certain cancers, which serve as a basis for epidemiological analysis. Surveillance data are collected continuously to monitor trends and detect emerging public health threats. These data might include patterns in vector- borne diseases, foodborne illness outbreaks, or violations of drinking water standards. Demographic and socioeconomic data provide context by offering information on age, race, income, education, and housing. These data help assess communi- ty vulnerability and highlight disparities in exposure and health outcomes.
Geospatial data , often visualized using GIS, enable EPH professionals to map risks, identify spatial trends, and define service areas or inspection zones.
Regulatory and compliance data include documentation of inspections, permit statuses, violations, and enforcement actions. These records confirm adherence to EPH regulations, aid agencies in tracking program performance, and improve inspector deployment strategies. Financial and budgetary data encompass permit and license fee payments, fines and penalties, and budget allocation and expenditure tracking, while also functioning as a public health surveillance and compliance tool that integrates with accounting systems to reconcile revenues and expenses.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Table 3 presents real-world examples of each type of data to illustrate how they are applied in practice and how they support core functions within environmental public health programs. Table 3 TYPE OF DATA DESCRIPTION REAL-WORLD EXAMPLE Exposure Data Measurements of environmental conditions that pose potential health risks. South Coast Air Quality Management District,
California: Provides real-time air quality measurements and forecasts, including nitrogen dioxide and ozone levels for Southern California residents. New York City Department of Health: Publishes lead poisoning reports showing elevated blood lead levels in children by neighborhood. Florida Department of Health’s Arbovirus Surveillance: Tracks mosquito-borne diseases such as dengue and West Nile virus with weekly updates. University of Wisconsin’s County Health Rankings: Aggregates data on income, education, housing, and access to care to assess community health drivers across U.S. counties. Mapping Inequality Project (a collaboration with universities): Uses historical redlining maps to study associations with current environmental exposures and health disparities. Massachusetts Department of Environmental Protection: Posts public records of hazardous waste site inspections and enforcement actions against noncompliant facilities. A typical environmental public health department might integrate permitting and inspection systems with payment processing and budget tracking to align operational, financial, and compliance data.
Health Outcome Data
Records of diseases or conditions linked to environmental factors.
Surveillance Data
Ongoing monitoring to detect patterns and threats.
Demographic and Socioeconomic Data
Community-level statistics that inform health vulnerability.
Geospatial Data
Spatial information is used to analyze environmental and public health phenomena.
Regulatory and Compliance Data
Documentation of environmental law enforcement and industry compliance.
Financial and Budgetary Data Financial and budgetary data, including fees, fines, and budget tracking, are integrated with public health surveillance and compliance systems.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Key Data Sources EPH professionals operate in a complex and rap - idly evolving landscape where accurate, timely, and contextually relevant data are essential for effective decision-making. No single source can provide the whole picture. To conduct strong analyses and take well-informed action, EPH professionals draw from multiple trusted sources to ensure thorough analyses and informed de - cision-making. The following is a sample of the key sources commonly used in EPH practice: Federal Agencies: Centers for Disease Control and Prevention (CDC), U.S. Environmental Pro- tection Agency (U.S. EPA), U.S. Geological Sur- vey (USGS), National Oceanic and Atmospheric Administration (NOAA). State and Local Agencies: Health and en - vironmental protection departments, urban planning divisions, and regional data portals. Grassroots monitoring efforts, participatory science projects, local nonprofits, and academic partnerships can provide valuable hyperlocal insights. Community-Based Sources: Internal Systems: Data collected directly from in - spections, mobile apps, surveys, and permitting systems within an agency’s own environmental health management system (EHMS). Table 4 provides more detailed information and real-world examples of each source type and highlights how they contribute to effective EPH practice.
Table 4
SOURCE TYPE
SUBCATEGORY (IF APPLICABLE)
DESCRIPTION
REAL-WORLD EXAMPLES
KEY CONSIDERATIONS Broad coverage, scientifically validated; could lack local specificity or timeliness.
Federal Agencies
-
National data and tools for tracking environmental conditions, health outcomes, and trends.
• CDC Environmental Public Health Tracking Network • U.S. EPA Envirofacts and ECHO • USGS Water Data • NOAA Climate Data Online Environmental Health Tracking Program • Massachusetts Asthma Surveillance • NYC Environment & Health Data Portal • California • Air Louisville: Residents monitor pollution with smart inhalers. • CoCoRaHS (Community Cooperative Rain, Hail, and Snow Network): Tracks precipitation via volunteer observers. • iNaturalist: Biodiversity mapping through public submissions.
State and Local Agencies
-
Regional or city- level environmental health data tied to specific populations and geographies.
More localized and policy-relevant, quality and availability can vary by jurisdiction.
Community-Based Sources
Grassroots Monitoring
Informal resident- led data collection using low-cost tools.
Provides hyperlocal context; data could lack standardization or scientific rigor without oversight. Expanding reach and community engagement requires training and quality control to be essential.
Participatory Science Projects
Public volunteers assist in scientific data collection and analysis.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Table 4 (continued)
SOURCE TYPE
SUBCATEGORY (IF APPLICABLE) Local Nonprof- its and Non -
DESCRIPTION
REAL-WORLD EXAMPLES
KEY CONSIDERATIONS Trusted in local contexts; often issue- driven, with deep community ties but limited scalability. High research quality; dependent on funding cycles and time-limited studies. Rich qualitative insight; requires strong community trust and labor- intensive data collection. Directly actionable and operationally relevant; accessibility depends on data infrastructure and staff training
Community-Based Sources
Community organizations that conduct studies or advocacy tied to health and the environment.
• GreenRoots (Massachusetts): Heat and flooding risk mapping.
governmental Organizations
Academic- Community Partnerships
Collaborations between universities and local communities to co-produce data and research. Community-driven assessments using interviews, focus groups, or neighborhood audits. Data from within an agency’s operations—collected via inspections, apps, permitting, and surveys.
• Flint Water Study: Exposed lead via resident sampling.
Hyperlocal Surveys and Reports
• Boston Chinatown Heat Study: Tracked temperature variation with handheld sensors and resident input. • Mobile food safety inspections • Environmental health management system dashboards • Public complaints and heat-related illness reports
Internal Systems
-
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Data Quality and the Role of Metadata Data Quality
Reliable data are essential for credible environmental health efforts. Since agen- cies rely on various sources, data quality is crucial for effective analysis and de - cision-making. Inaccurate data, be it incomplete, outdated, or inconsistent, can lead to false conclusions and missed opportunities for action. Therefore, the goal is for data to be accurate, complete, consistent, and timely. EPH professionals can evaluate datasets using four key dimensions: 1) accuracy, 2) completeness, 3) consistency, and 4) timeliness. These criteria help determine if data can be trusted to inform programs, policies, and public communications.
Table 5 provides examples to illustrate how each data quality dimension plays out in practice.
Table 5
DATA QUALITY DIMENSION
DEFINITION
EXAMPLE
Accuracy
Does the data accurately reflect real-world conditions?
A health department installs calibrated air monitors near a school. If the sensor reads 35 µg/m³ on a visibly clear day, it could be an inaccurate reading. Investigating sensor drift ensures valid results. Missing GPS data in inspection records prevents mapping violation hotspots, limiting enforcement strategies. One hospital codes asthma emergency department visits as “respiratory illness,” while others use ICD-10. Inconsistent coding makes state-level comparisons unreliable.
Completeness
Are there missing fields or gaps that could distort the analysis? Are data collection methods and definitions uniform across time and systems? Are the data recent enough to support timely decisions?
Consistency
Timeliness
Using 2019 census data for 2025 can lead to screening misses of recent demographic shifts, potentially overlooking high-risk populations.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Metadata Equally important as the data itself is the presence of metadata, the descrip - tive information that explains how the data was collected, its source, limitations, structure, and update frequency. Metadata allow analysts to understand the context and make informed decisions about appropriate uses of the data. Com- prehensive metadata support data transparency, accuracy, interoperability, and reusability, enabling EPH professionals to use and share data confidently. Metadata are often described as “data about data.” It provides critical details about the origin, content, format, and evolution of a dataset. Well-documented metadata helps users to understand what the data represents, how the data was created, its current relevance, and how the data should or should not be used. Different types of metadata serve various purposes: Descriptive metadata identify and summarize the dataset, which includes el - ements such as the dataset title, a summary, and relevant tags that help users understand the general content and context. Structural metadata explain how the data are organized. This includes details such as field names, data types, file formats, and the overall layout of the dataset, making it easier to navigate and interpret. Administrative metadata provide information about the creation, ownership, and access rights of the dataset. Examples include the author’s name, version history, and any applicable licenses or usage permissions. Provenance metadata document the origin and history of the dataset. It de- scribes how the data were collected, processed, or transformed, such as data cleaning steps or quality control checks that were performed. Statistical metadata outline the methods used during data collection or anal - ysis, which might involve sampling strategies, aggregation techniques, or any statistical procedures that influence how the data can be interpreted.
Table 6 provides a simple example of metadata for an EPH dataset.
Table 6
METADATA FIELD EXAMPLE Dataset Title
2022 Food Safety Inspections: County A
Description
All restaurant inspection results in County A for 2022
Collected By
County A Environmental Health
Date Range
January 1, 2022–December 31, 2022
Last Updated
February 15, 2023
Format
CSV
Variables
Facility_ID, Inspection_Score, Violation_Code, Date
Confidential?
No (public dataset)
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Managing Data With Integrity, Privacy, and Compliance Responsible data use is crucial for maintaining public trust and ensuring en - vironmental health initiatives are both effective and ethical. As data systems grow more sophisticated, it is important to maintain clear standards for privacy, fairness, and transparency throughout data collection and analysis. Responsible data stewardship is more than a legal requirement—it demonstrates respect for the communities involved. Ethical and thoughtful data practices enhance deci- sion-making, strengthen relationships, and produce more reliable results in envi- ronmental health efforts. This stewardship includes: Privacy protection involves compliance with laws and policies that govern the handling of personally identifiable information, protected health information, and informed consent. These safeguards ensure that sensitive data are used respon- sibly and ethically. Fair representation means taking deliberate steps to avoid reinforcing inequities through data practices. Disaggregating data by race, geography, or income (e.g., analyzing heat illness rates by ZIP Code and income level) can surface hidden patterns and ensure that no population is overlooked in planning or response. Transparency is achieved by open communication about how data are collect - ed, used, and shared. This openness builds community trust and strengthens public engagement by showing that data are used not only effectively but also responsibly. The Importance of Data Standards Data standards serve as fundamental frameworks that establish how informa - tion is gathered, structured, classified, and shared. They operate as a universal language to enable EPH professionals from diverse organizations and regions to understand and utilize data with consistency and precision. Just as standardized measurements such as inches, cups, or degrees facilitate clear communication about physical quantities, data standards reduce confusion in complex public health issues. When a local health department documents a major violation or reports a foodborne outbreak, standardized definitions ensure that colleagues elsewhere understand precisely what these terms mean, elimi - nating uncertainty and preventing misinterpretation.
What Happens Without Standards When jurisdictions lack shared data standards, collaboration becomes difficult and analysis less reliable. For example, one county might classify restaurant vi- olations numerically (e.g., 1, 2, 3) while its neighbor uses qualitative terms (e.g., minor, major, critical). Similarly, public health surveillance varies. One state might track only lab-confirmed cases of foodborne illness, while another might include probable or suspected cases. These differences make it hard to compare data or coordinate regionally. What Happens With Standards Shared standards streamline environmental health work across jurisdictions. Clear definitions allow key terms to be interpreted consistently. Structured data formats enable databases and systems to exchange information auto - matically. The use of a common vocabulary ensures comparisons are mean- ingful and reporting is accurate. Most importantly, interoperable systems fos- ter seamless data sharing, whether during day-to-day operations or public health emergencies. Key Standards That Support Modern Environmental Health Systems Standardization is crucial to ensure that data remain interoperable, reusable, and meaningful across platforms. Consistent formats, definitions, and structures allow environmental health data to be easily shared, understood, and applied across systems and organizations. These standards facilitate interoperability— the ability of different tools and platforms to exchange and utilize data without errors or miscommunication. Reusable indicates that data can be employed multiple times for various pur - poses, such as analysis, reporting, or community engagement, without need - ing to be restructured or reinterpreted. Alignment with established standards enhances collaboration across departments and jurisdictions while simplifying integration with national systems, ultimately strengthening the effectiveness and reach of EPH programs. Relevant frameworks and coding systems that support these standardization goals are important for EPH professionals to understand, including: HL7 (Health Level Seven) is an international standard for health-related data exchange, commonly used in electronic health records, laboratory reporting, and disease surveillance.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
LOINC (Logical Observation Identifiers Names and Codes) is a universal stan - dard for identifying health measurements, observations, and documents. It is primarily used to code laboratory tests and clinical observations. ISO (International Organization for Standardization) provides international - ly recognized frameworks to ensure quality, consistency, and interoperability across various domains. In environmental health, ISO standards are commonly used for: • Environmental management (e.g., ISO 14001): To help organizations man- age their environmental responsibilities systematically. • GIS (e.g., ISO 19115): To standardize how spatial data are collected, stored, and shared. • Metadata (e.g., ISO 19115, ISO 19139): To ensure that descriptive infor- mation about datasets is structured and interoperable across systems. Visualization and Communication The ability to effectively communicate complex data is just as important as an - alyzing it. Visualization tools, ranging from charts and dashboards to GIS maps and infographics, help bring data to life, revealing patterns, trends, and priorities that might otherwise go unnoticed. Proficiency in the following areas is valuable for EPH professionals: Creation of intuitive visualizations using clear charts, tables, graphs, and info - graphics that highlight key messages. Development of GIS maps to visualize spatial relationships, risk concentrations, and service gaps. Design of dashboards that support real-time monitoring and communicate pro- gram performance to interested parties and partners. Translation of complex findings into actionable insights that resonate with non - technical audiences, including agency leaders, policymakers, and community members.
Strong communication through data visualization helps drive action, inform pol - icy, and secure buy-in from leadership and community members.
Improved data literacy and foundational skills equip EPH professionals to navi - gate modern systems, interpret complex datasets, and use data as tools for pre - vention, engagement, and impact. These competencies are not optional in the era of modernization and are essential for building healthier, more informed, and more resilient communities.
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Section 3: Core Principles of Data Modernization
Table 7
FAIR PRINCIPLE
DEFINITION
GENERAL EXAMPLE
USE CASE
Findable
Ideally, data are easily located and understood by humans and machines, which includes metadata, standardized tagging, and centralized access points. Systems allow secure but user-friendly access to data, including explicit permissions, documentation, and compatibility with assistive technology.
A state public health agency creates a
Local community organizations use the tool to find data for grant applications and environmental justice efforts.
Modernizing environmental health data systems involves more than technology and is a strategic process informed by a clear set of principles. These core principles ensure that modernization efforts are purposeful, sustainable, and capable of delivering value to public health profes - sionals and the communities they serve. A well-structured modernization strategy can enhance service accessibility, improve operational efficiency, and strengthen long-term resilience through effective data management practices. The FAIR Principles for Optimal Data Use A foundational framework for data modernization is the FAIR principles—findable, accessible, interoperable, and reusable. Initially introduced in 2016 in Scientific Data to guide scientific data stewardship, these principles serve as a global standard for making digital resources more valuable and meaningful across different systems and users. In EPH, these principles help ensure data can be used across jurisdictions, are accessible, and apply in both routine operations and emergency response. Table 7 il- lustrates how each component of the FAIR framework is applied in EPH, with practical examples and real-world use cases to support its implementation. The FAIR framework improves not only internal data manage - ment but also external transparency, making environmental health data more discoverable, shareable, and impactful.
searchable online map showing neighborhood- level health burdens (e.g., pollution, asthma rates), supported by indicator definitions and metadata. A city government offers an open data portal with downloadable datasets on air quality complaints, dumping incidents, and water quality— accompanied by plain- language user guides. A regional health department publishes lead and PM2.5 data in standard geospatial formats via an API, enabling other entities to overlay with housing or hospital data. A university-led air monitoring project releases datasets with complete documentation (e.g., sensor
Accessible
Neighborhood coalitions and journalists access these data to track local environmental trends and advocate for policy changes.
Interoperable Data integrates smoothly across systems, agencies, and tools
Urban planners and researchers combine datasets to identify
through standardized formats, vocabularies, and APIs (application programming interfaces).
neighborhoods needing infrastructure upgrades or public health interventions.
Reusable
Datasets are well- documented with
Advocacy groups and educators repurpose the data in public forums, policy briefs, and student projects to raise awareness.
metadata, methods, and licensing so they can be reused in multiple contexts.
calibration, collection dates, reuse terms).
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Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Timeliness and Real-Time Access Environmental health decisions are often time-sensitive, particularly during outbreaks, natural disasters, or environmental emergencies. Real-time data availability enables agencies to deploy inspections, enforce regulations, and inform the public with greater speed and precision. Modernization efforts pri- oritize the following: Infrastructure that supports live data streaming from mobile inspections, sen - sors, or external data feeds Dashboards that update in near real-time can provide operational leaders with actionable information Automated alerts or thresholds allow staff to respond quickly to risks without manual data review Timeliness ensures that data remain relevant and recognizes that late data could have limited value in prevention or threat containment. Role of Consistency While formal standards provide the foundation for data interoperability, equal- ly important is the consistent application of those standards across programs, systems, and time. Standardization provides the foundation for data interoper- ability, aligning formats, definitions, and coding schemes across environmen- tal health systems. It is the consistent application of those standards across programs, departments, and time, however, that truly unlocks their value. Even the best-established frameworks lose their effectiveness if data entry practices, definitions, or naming conventions vary within or between agencies. Why Consistency Matters Consistent data ensure that the same metrics mean the same thing across programs and over time, making it possible to reliably compare information from one year, site, agency/organization, or jurisdiction to the next. It enables analyses, dashboards, and reports to draw from aligned, dependable inputs, thereby avoiding duplicated efforts, contradictory results, and misleading trends. For example, one program might adopt standardized disease codes. Still, if a second program labels asthma-related visits under a general respiratory illness category, the data cannot be easily reconciled. Similarly, inconsistently recording geographic information, using a ZIP Code in one dataset and coordinates in another, can ham - per spatial analyses, even when both methods adhere to an accepted standard.
Consistency in Practice Proper consistency goes beyond selecting the right standards. It means es- tablishing internal protocols for naming conventions, data entry practices, field definitions, and regular quality checks. These practices help maintain coherence across datasets, enabling EPH professionals to draw confident, evidence-based conclusions and share insights seamlessly with teams and external partners. In short, if standards are the blueprint, consistency brings that blueprint to life, ensuring environmental health data are not only structured but also trustworthy, usable, and impactful across the agency and the communities it serves. Accessibility and Usability Data systems are designed with end users in mind. This consideration includes not only analysts and epidemiologists but also program managers, inspectors, community partners, and policymakers. An effective system translates complex data into usable insights. When systems are straightforward to navigate, they help staff work more efficiently, support better decision-making, and strengthen the connection between agencies and the communities they serve. The following design considerations help ensure that systems are user-centered, functional, inclusive, and practical for everyday use. Key features that enhance accessibility and usability include: User-friendly interfaces that prioritize clarity over complexity in portals, dash - boards, and mobile applications. Tiered access controls that assign appropriate levels of visibility to staff, leader - ship, and the public, protecting sensitive information while ensuring relevance. Language and design considerations to ensure that content is understandable and usable by people of varying backgrounds, literacy levels, and abilities, not just technically accessible. Mobile functionality that supports field-based data entry, offline capabilities, and device compatibility for use during inspections, emergencies, or outreach events. Training and help resources , such as onboarding materials, how-to guides, and ongoing technical support, are provided to empower all users, regardless of experience, to navigate and effectively apply system features.
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