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ASTHO Legislative Prospectus | Previewing 2024 state legislative actions on data modernization and privacy.

Learn about the importance of exploring intermediaries that work alongside existing data platforms in addressing ongoing public health challenges.

Learn about the Vermont Department of Health's efforts to prioritize data modernization initiatives through its Data Modernization Advisory Committee.

Discover how improving public health data infrastructure can create more robust care for people with disabilities in this blog post.

Learn about the importance of immunization information systems to public health in this Health Policy Update.

Learn about Pima County's journey to advance data modernization efforts through an elevated partnership with County IT.

Get insight into the successes and challenges of integrating race/ethnicity data in public health and future directions in this field.

What Public Health Leaders Need to Know About HTI-2 Proposed Rule How Proposed HTI-2 Rule Will Benefit Public Health Data Exchange Lillian Colasurdo, Lana McKinney, Alexandra Woodward Read how the HTI-2 rule improves upon HTI-1 standards and criteria for data exchange among public health, health care, and data providers to benefit public health. On July 10, 2024 HHS’s Assistant Secretary for Technology Policy/Office of the National Coordinator of Health Information Technology (ASTP) published the proposed rule: Health Data, Technology, and Interoperability: Patient Engagement, Information Sharing, and Public Health Interoperability (HTI-2). This rule, which advances interoperability and supports access, exchange, and use of electronic health information (EHI), represents a significant step towards strengthening public health data infrastructure and promotes interoperability between health care and public health entities. It proposes significant changes impacting public health agencies (PHAs) and removing barriers to EHI exchange, while attempting to streamline health IT processes. Background Two of the most significant federal laws passed in the past fifteen years for health data exchange are the Health Information Technology for Economic and Clinical Health (HITECH) Act and the 21st Century Cures Act. Together, these laws provided ASTP with authority to set standards for and certify heath information technology. ASTP’s certification program mandates that electronic health records (EHR) comply with the new standards and eligible providers and hospitals must use certified technology to be fully reimbursed by the Centers for Medicare & Medicaid Services. Additionally, these laws grant ASTP rulemaking authority. Earlier this year, ASTP finalized the HTI-1 rule, taking substantial steps toward improving public health and health care data exchange. This rule requires EHR systems to support either HL7 Clinical Document Architecture (CDA) or Fast Healthcare Interoperability Resources (FHIR) standards for electronic case reporting, both of which enhance the data quality and timeliness for public health reporting. HTI-1 also directs EHR developers to track and report on the amount of data electronically submitted to Immunization Information Systems (IISs); that information helps improve vaccine product distribution and availability. While the rule sets the stage for a future transition to a FHIR-based approach for case reporting, there must be an adequate transition period for public health agencies. While HTI-1 took significant steps toward improving the way data is exchanged, primarily in the health care setting, the proposed rule HTI-2 aims to expand these standards to further benefit public health. The HTI-2 expands upon the interoperability standards established in HTI-1 and aims to improve interoperability by revising the ONC Health IT Certification Program. It specifically proposes two new sets of certification criteria for IT developers that will benefit public health entities and payers. Public health agencies face many barriers to efficiently exchanging data with health care providers and other entities including lack of common data standards, inconsistent reporting requirements, limited system interoperability, and inadequate public health data infrastructure. HTI-2 addresses these challenges by establishing certification criteria for public health technologies, creating a common floor to support data exchange. In addition, HTI-2 introduces changes to the Information Blocking Rule and provides transparency to Trusted Exchange Framework and Common Agreement (TEFCA) requirements. Micky Tripathi - What PH Leaders Must About HTI-2 Proposed Rule Key HTI-2 Provisions Certification Criteria, Standardizing Application Programming Interfaces (APIs) HTI-2 proposes four standards and certification criteria that may impact public health systems. Depending on the criteria, ASTP proposes that many of them be implemented by the beginning of 2027 and 2028. Updating naming conventions and standards for existing functional criteria. There are currently nine functional (or “f”) criteria EHRs must meet to exchange data with public health agencies. The updated naming conventions and standards point EHR vendors and public health systems to the latest standards for implementation. Additionally, these updates include two new criteria for birth reporting and bi-directional exchange with a prescription drug monitoring program. Establishing new certification “f” criteria for Health IT so Public Health certified systems can receive, validate, parse, and filter standardized data. These functions will apply to immunization, syndromic, laboratory, cancer pathology, case, birth, and prescription drug monitoring program data. Adopting the United States Core Data for Interoperability (USCDI) version 4, a standardized set of health data classes and elements for interoperable health information exchange. Version 4 includes several new data elements relevant to public health, such as health status assessments (e.g., alcohol and substance use). Standardizing HL7 FHIR-based API for public health data exchange by creating new certification criteria to support ongoing development and transition to FHIR for patient and population-level data exchange. CDC and ASTP have noted potential benefits of increasing public health access to critical data while reducing the reporting burden on both health care organizations and developers. HTI-2 proposes similar certification standards and alignment for reporting to payers that comply with existing CMS API requirements. Information Blocking Updates The Information Blocking Rule requires that patients have timely access to their own electronic health records and prohibits health care providers and networks, HIEs, and developers from interfering with said access. When a public health agency serves as a provider, it is crucial to ensure that patients can access records in compliance with the existing rule. HTI-2 clarifies what constitutes “interfering” with the access and exchange and provides a non-exhaustive list of examples. HTI-2 also proposes a new exception to information blocking—the Protected Care Access Exception—that would “apply to acts or omissions likely to interfere with access, exchange, or use of particular EHI that an actor believes could create a risk of exposing patients, care providers, and other persons who assist in access or delivery of health care to potential administrative, civil, or criminal investigations or other actions on certain bases.” This exception is particularly relevant for jurisdictions with more restrictive laws for sharing reproductive health data. TEFCA Governance Rules As ASTP and its Recognized Coordinating Entity (RCE), the Sequoia Project, seek to establish standards for implementing the Trusted Exchange Framework and Common Agreement (TEFCA), the proposed HTI-2 rule would codify one portion of the framework by establishing the qualifications for Qualified Health Information Networks (QHINs), onboarding and designation processes, the attestation process, termination and appeal rights, and ASTP’s formal authority to delegate responsibility to the RCE. Conclusion The HTI-2 proposed rule represents a significant step towards strengthening public health data infrastructure and promotes interoperability between health care and public health entities. It is specifically designed to “address gaps in public health data and help the nation become response-ready, promote health equity, and improve health outcomes for all.” The Joint Public Health Informatics Taskforce (JPHIT), coordinated by ASTHO and consisting of 14 member organizations including public health associations, gathered comments and input from constituent members and submitted consolidated feedback on the proposed rule in October and awaits responses and the final rule from ASTP. OE22-2203 PHIG article yes

Lessons from previous emergencies, like Zika, can help states create an effective response plan for future emergencies and protect maternal and child health.

In addition to PFAS exposure assessments, state and territorial health agencies may also consider the role of social stressors during the risk assessment process.

Linking Datasets to Address Racial Equity in Maternal and Child Health Outcomes astho, association of state and territorial health officials, data sources, people of color, centers for disease control, racial inequities, advance racial equity, maternal morbidity, maternal death, maternal health, child health, participate in prams, risk assessment monitoring system, disease control and prevention, maternal and child, morbidity and mortality, pregnancy risk assessment monitoring, pregnancy related death, racial justice, linked data, achieve health equity, advancing health equity, racial equity, maternal and child health, maternal mortality and morbidity, racial disparities, health equity, data linkages, vital records, pregnancy risk assessment monitoring system Stephany Strahle ASTHO | Strategies for promoting racial equity in maternal and infant health through data linkages. Racial disparities in maternal and child health outcomes impact populations across the United States. Having robust data to understand these disparities may inform more comprehensive initiatives and policies that address the impacts and root causes of inequities. Looking at administrative datasets, such as hospital discharges and vital records, allows health professionals to monitor inequities by racial and ethnic communities. Often not captured in these data, however, is the complex interaction of social determinants—such as access to social support, racial discrimination, insurance coverage throughout pregnancy and postpartum, and access to paid family and medical leave—and their impact on health outcomes. Public health surveillance systems monitor these outcomes and aim to answer questions on a broad range of contextual experiences. These systems can be combined with administrative data through data linkage, “a process that matches records representing the same person or entity derived from different data sources in order to generate new and more comprehensive datasets.” These linkages can help identify areas for patient-centered outcomes research and inform policy recommendation and programs that address maternal and child health disparities across racial and ethnic groups. State Approaches to Data Linkages Linking Vital Records with Income Data California In a recent working paper on maternal and infant health inequities in California, researchers linked administrative vital records with parental income data. This research found that “infant and maternal health in Black families at the top of the income distribution is markedly worse than that of White families at the bottom of the income distribution.” Linking vital records, a source that typically does not capture income information, with data sources that do, provided a novel and robust dataset illuminating the exacerbated disparities experienced by racial and ethnic minorities at all income levels. Using PRAMS to Monitor Health Outcomes The Pregnancy Risk Assessment Monitoring System (PRAMS) allows jurisdictions to monitor various maternal and infant health indicators before, during, and after pregnancy. As one of the few public health surveillance systems collecting data on race-related experiences and discrimination, it also provides a better understanding of disparities among racial and ethnic groups. As part of ASTHO’s Linking PRAMS and Clinical Outcomes Data Multi-Jurisdiction Learning Community, two state teams from Massachusetts and Georgia used data linkage of PRAMS to explore racial disparities in maternal and child health outcomes. Massachusetts The Division of Maternal and Child Health Research and Analysis at the Massachusetts Department of Public Health linked PRAMS data with the Pregnancy to Early Life Longitudinal Data System (PELL), a data system linking birth files to hospital discharge records that can be later used to link hospital-based service records, data on early intervention services, and other data documenting maternal and infant health experiences beyond birth. Previously, both PRAMS and PELL data informed Massachusetts’s 2022 report from the Special Commission on Racial Inequities in Maternal Health, which provided policy-related recommendations on doula workforce development and equitable implementation of paid family and medical leave within the state. Sarah Stone, PhD, MPH, the director of the Massachusetts Office of Data Translation, notes that linking PRAMS, which provides insights into the social determinants shaping people’s experiences during pregnancy, with the more administrative data included in PELL can further inform additional evidence-based initiatives to address inequities in maternal mortality and severe maternal morbidity. Georgia At the Maternal and Child Health Section of the Division of Epidemiology in the Georgia Department of Public Health, linkages between PRAMS and Georgia Vital Record data can provide insight into the observed differences in health outcomes among the state’s diverse population. Jenna Self, MPH, Georgia’s PRAMS project director and health surveys team lead, explains that “the linkages will help explore the association between maternal postpartum behaviors and negative infant health outcomes (e.g., mortality, hospitalization, emergency department visits) with the goal of understanding the health disparities” to inform future equity-focused initiatives. The development of a linked data environment will allow the Georgia Department of Public Health to ask and answer previously time and resource prohibitive questions. Recommendations Data linkage can be a powerful tool to create enhanced datasets that better inform state initiatives to improve racial equity in maternal and infant health outcomes. To use data linkages that identify areas needing equitable public health efforts, states should: Build and strengthen cross-collaborative relationships within and between various state agencies owning the datasets to facilitate data sharing. Consider the racial equity impacts of performing data linkages by exploring research questions that lead to more evidence-based decision-making. Understanding the linked data using a racial equity lens can better inform equitable policy recommendations and programmatic planning. Examine which data sources, when linked, could fill in gaps of understanding and provide a wealth of information to identify disparities and point to specific gaps in quality health care. Brief - Linking Datasets to Address Racial Equity in Maternal and Child Health Outcomes - Special Thanks website yes

This ASTHOBrief addresses the importance of developing robust, culturally competent risk-appropriate care systems for American Indian and Alaska Native communities.

Defining Disease Forecasting and Modeling Disease forecasting, generated by disease models, helps the public health workforce understand potential future outbreaks. Learn more about disease forecasts and models. Disease forecasting is important in describing potential future outbreaks that will affect the population and demand for health services in a given geographic area. Forecasts pull input from various sources (e.g., disease models, demographic, mobility, and intervention impact data). Individual forecasts can also be part of an ensemble forecast to improve accuracy. Forecasts can cover any length of time, but most target a window of several weeks to a few months. A subset of forecasts, known as nowcasts, seek to estimate present conditions, or those expected to occur imminently. Disease models are mathematical tools that are foundational components of disease forecasts. They estimate quantifiable factors that are impossible or impractical to directly measure, (e.g., future hospitalizations from a given disease, or its infection count in a population). Although models can be useful for specific questions, they do not give as complete a picture as a forecast. There are four major disease model types: Mechanistic. Attempts to simulate biological and/or social processes of transmission based on assumptions from prior or experimental data. Statistical. Relies on past data (such as infections or death) to predict future trends and can incorporate some assumptions about intervention application and uptake. Quality and quantity of past data can be a major limitation, and some models may suggest biological improbabilities. Agent. Simulates individual risks and behaviors in a population. These are highly complex, computationally very expensive to develop and run and require vast amounts of data and strong assumptions. Ensemble. Like their forecasting counterparts, they compile models and outputs, mitigating the risk of relying on one data point. While raising the overall confidence in output, they require coordination of many models to be built and simulated, which can be complex and costly unless the models already exist (such as for COVID-19 case counts). Forecasts and Models Work Together While disease forecasts and models are often conflated, they are discrete concepts. Forecasts offer a general prediction, whereas models are the mathematical pieces forecasters use to create them. Weather forecasts are commonplace, and their weekly predictions are often reasonably accurate. In contrast, predicting a big storm’s individual factors (e.g., rainfall, wind speed, lightning strikes) fall to the job of models. Together, those models help meteorologists better understand the weather and generate a forecast. In a public health context, disease forecasting informs public health officials, health care providers, and policymakers about potential risks and guide decision-making regarding preventive measures, resource allocation, and response strategies. Meanwhile, disease models aim to simulate the behavior of infectious diseases under different scenarios, allowing researchers to explore and evaluate various factors that influence disease transmission. Considerations for Decision-Making Decision-makers should consider scope and limitations of forecasts and models. They may consider adding inputs—such as projections for economic and long-term impacts. Examples include economic impacts of school closures, costs of more staffing ahead of an outbreak, and supply chain shortage forecasts for personal protective equipment (PPE). Decision-makers at all levels should consider using modeling to answer more specific, practical questions rather than predicting overall trends. Forecasts can cover different geographic scales. Public health leaders will need granular, local data to most effectively inform decision-making and communications. Novel conditions and pathogens may not have readily available data to inform models or forecasts, which will affect their predictive ability. Health officials must effectively communicate these limitations to decision-makers and the public. Examples of Forecasts and Models CDC’s COVID-19 Forecast for Hospitalizations (ensemble forecast) shows the number of daily COVID-19 hospitalizations reported in the United States from the prior two months and projected daily COVID-19 hospitalizations over the coming four weeks. Information sources are independent teams meeting submission and data quality requirements. CDC’s FluSight (ensemble forecast) has many contributing teams and models that predicts the upcoming weekly laboratory confirmed influenza hospital admissions both nationally and by state. Johns Hopkins University’s Center for Systems Science and Engineering county-level risk model for COVID-19 in the United States. This model leverages epidemiological data, mobile phone data, demographic and socioeconomic information, and behavioral metrics. The Global Epidemic and Mobility Framework simulates the global spread of infectious diseases by mathematically representing infection dynamics, population geographies, and population mobility patterns. Additional Resources Disease modeling for public health: added value, challenges, and institutional constraints Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples Technology to advance infectious disease forecasting for outbreak management CDC-RFA-OT18-1802 2018-2024 article yes

Disease Forecasting and Modeling Data for Public Health Action Disease Forecasting Benefits Public Health Planning Disease forecasting and modeling help prepare public health departments for future infectious disease outbreaks and epidemics. Disease forecasting and modeling data can be powerful tools for state and local health agencies (S/THAs) that respond to outbreaks, develop appropriate policies, and ensure interventions have maximum impact. Actions for which decision-makers can leverage such data include: Surveillance. Forecasts and modeling help public health agencies anticipate the spread of disease or outbreaks. This advance warning allows public health officials to inform public health recommendations, preparation, and response. Communication. Disease forecasts help relate the risk of disease outbreaks to various audiences accurately and quickly, which, in turn, can inform messages on important preventive measures and encourages compliance with recommended interventions. Resource allocation. Modeling data can help decision-makers better allocate resources by predicting where and when disease outbreaks are likely to intensify and create the greatest need. Evaluation. Forecasts and modeling can help make evaluating the effectiveness of public health policies and interventions more efficient by comparing predicted outcomes with observed data and adjusting as needed. Considerations Informed by S/THA Forecasting Jurisdictions with forecasting experience identified key indicators to monitor as part of outbreak forecasting, which fall into three main categories: Epidemic spread indicators (e.g., symptom monitoring, morbidity and mortality data, percent positivity, regional pictures of transmission). Health care system capacity (e.g., essential and/or surge personnel, available beds, ventilator usage, and supply of personal protective equipment. Public health capacity for testing capacity and contact tracing. Further considerations for S/THAs: Know your strengths. Identify the unique skillsets among partners in public health, academia, and the private sector and consider how they foster reciprocal relationships. Recognize capacity/expertise gaps. Consider leveraging partnerships for specific types of analytics expertise while exploring internal capacity building opportunities (e.g., job shadowing and resource-sharing programs on workflows and methodologies). Engage legal and compliance teams. Ensure policy and practice are aligned among partners. Explore data access/sharing pipelines. Connect public, private, academic partners, and their audiences. Start small. Identify discrete forecasting and modeling projects to demonstrate success. Identify decision-makers’ needs. Provide quick access to analyses, metrics, dashboards. Michigan Used Models and Forecasting for Hep C Cases In response to Hepatitis C virus (HCV) in young adults from 2010-2018, the Michigan Department of Health and Human Services (MDHHS) simulated how HCV treatment could significantly reduce HCV prevalence among young people who inject drugs, especially for those both previously or currently injecting drugs. MDHHS used several novel predictors to paint a local picture of probable HCV diagnoses among residents up to age 40. These predictors included measures related to a variety of population characteristics (e.g., access to transportation, college education, presence of non-family households) and public health indicators (e.g., heroin treatment admissions, newborns with neonatal abstinence syndrome, and sexually-transmitted infections). MDHHS also leveraged county-level assessments of HCV vulnerability to identify locations for new syringe services programs in the state. MDHHS has recognized several modeling and analytics use cases that benefitted their work during responses to HCV and COVID-19: Short-term forecasts (i.e., weeks) helped predict likely transmission patterns and potential ranges of projections. Longer-term forecasts (i.e., months) explored scenarios based on new recommendations and policy changes. Retrospective counterfactuals evaluated the impact of policies or other changes by examining “what-if” situations. MDHHS is considering using forecasts and models for COVID-19, influenza epidemics, tuberculosis vulnerability, and C. auris spread. Resource constraints require decision-makers and public health practitioners to consider how they are using available resources for the highest return on investment. Models generated momentum to respond to threats and evaluate whether interventions were successful. CDC-RFA-OT18-1802 2018-2024 article yes

This brief details innovative uses of geographic information systems (GIS) in public health. It showcases original research conducted by ASTHO staff to better understand the value of GIS in mapping national public health emergencies

The Pennsylvania Department of Health adapted its National Electronic Disease Surveillance System to allow providers to report STI treatment more efficiently.

Better Defining Disability Will Make Data More Inclusive and Usable ASTHO, association of state and territorial health officials, access to health care, centers for disease control, syndromic surveillance systems, health outcomes, person with a disability, disaster medical assistance team dmat, mental health conditions, people with disabilities, health disparities, mental health, health equity, public health emergencies, syndromic surveillance, disaster medical assistance teams, disability data, people living with disabilities, disability inclusion Margaret Nilz ASTHO | Syndromic surveillance data on disability prevalence will help people with disabilities in emergencies. Over the past two decades, the frequency and intensity of natural disasters have increased — and will continue to do so. While disasters impact whole communities, past incidents highlight specific effects on people with disabilities, as it is more challenging for them to prepare for and recover from an incident. Understanding the prevalence of disability in a jurisdiction helps fully address the population’s needs.​ There is not a universally accepted way to collect data on people with disabilities. However, the need for disaggregated data by disability status is critical to helping measure health disparities and underlying factors contributing to inequities. Such data will support the development and continuous evaluation and improvement of public health programs and policies. Key Considerations for Collecting Data on People with Disabilities Disability data is essential for inclusive public health practice. Several factors are important to keep in mind when gathering data on people with disabilities. Participation is critical as exclusion from research can further marginalize already vulnerable groups and limit access to advancements. Accounting for historical trauma/negative impacts helps people with disabilities who are at increased risk of coercion, inclusion without consent, and other exploitation. Unwarranted disability assessments, particularly those implemented with limited evidence of effectiveness, have been shown to have negative mental health impacts on participants with disabilities. Different models of disability provide a reference as programs, services, laws, and regulations are developed. Primary models of disability include the Medical Model, Functional Model, Social Model, and Medical/Rehabilitative Model. Current Measures and Definitions of Disability - Brief - Better Defining Disability Disability Inclusion in National Syndromic Surveillance Program (NSSP) NSSP includes electronic health record (EHR) data from 73% of the nation’s emergency departments (EDs). However, it contains no systemic way to identify people with disabilities. Including disability data within a system as valuable as NSSP can help close gaps in monitoring the impacts of emergencies on people with disabilities. Syndromic surveillance data can guide decision-making during emergencies and policy formation at the local, state, and national levels. There are limitations of using syndromic surveillance data. First, diagnostic codes may not map directly onto functional limitations. Second, codes do not provide information about residual functioning, loss of functioning, or disability severity. Additionally, reporting in EHRs may not be accurate due to input or data errors. Codes can be related to a visit or encounter, even if it does not end up being true for a patient. Furthermore, diagnostic codes reflecting disability may not be used in every encounter and people with disabilities may be missed through using ED data as it only represents a snapshot in time. Benefits of Expanding Disability Data Access and Use Expanding the collection, access, and use of disability data for public health program development and emergency preparedness promotes health equity for people with disabilities. More specifically, this data can inform fiscal, programmatic, service policy, and public health planning decisions. When Disaster Medical Assistance Teams (DMATs) deployed to shelters in North Carolina, CDC’s NSSP team asked health officials if they wanted to integrate these data. Within 24 hours, data from DMATs were available in NSSP, providing a snapshot of health in those shelters. Data were monitored along with ED visits to give a complete picture of the storm’s health impacts. In 2017, Hurricane Harvey made landfall in Texas, resulting in 88 deaths and $125 billion in infrastructure damage. Public health officials used syndromic surveillance to understand increases in ED visits by those who evacuated to the Dallas–Fort Worth (DFW) area. Area hospitals saw roughly 4,400 more ED visits than normal; at least 600 were evacuees. Syndromic surveillance data demonstrated extensive health care services use outside the affected areas by highlighting the importance of surge capacity planning one to four hours outside the disaster area. Ongoing Efforts Through a cooperative agreement with CDC, ASTHO is working with subject matter experts to create a definition of disability for syndromic surveillance. ASTHO conducted key informant interviews with disability professionals to inform the development of this new diagnostic code-based definition, along with four scientific panels to assess the drafting and review of national and state-level pilot testing. An expansion of this kind benefits jurisdictions through increased data capacity for fiscal, programmatic, and service policy decision-making and supporting longitudinal tracking of prevalence and risk. Conclusion Efforts to expand data about people with disabilities can help build public health capacity to monitor the health and well-being of people with disabilities before, during, and after public health emergencies. However, efforts in data collection on disabilities require interoperability and standardization across all systems to be successful. Efforts to contextualize public health emergency data and gather supporting data on impacted populations allow health officials to better turn data into action in pursuit of health equity across public health emergencies. NU38OT000290 website yes

The Florida Department of Health created an effective algorithm to automate syphilis laboratory result processing that improves case assignment accuracy and prioritization. This tool outlines key steps and considerations for jurisdictions looking to adopt the algorithm.

ASTHO Innovation Advisory Council fosters partnerships between state health agencies and industry leaders to drive public health innovation and data modernization.

Environmental Public Health Tracking Fellowship Program ASTHO's Environmental Public Health Tracking: Peer-to-Peer Fellowship Program, in partnership with CDC, offers non-funded health agencies the opportunity to conduct pilot projects on environmental health issues of importance to their communities, receive mentorship from current CDC grantees, and become familiar with CDC standards and resources for environmental public health tracking. On this page are ASTHO and partner resources highlighting the program’s successes. Tracking Resources Poster Overview of ASTHO's Tracking Fellowship Program (PDF) This poster provides an overview of the impact and successes of ASTHO’s Environmental Public Health Tracking Fellowship. Fellowship Program Factsheet (PDF) This factsheet highlights achievement and success stories from ASTHO’s Environmental Public Health Tracking Fellowship. Building Capacity, Building Community: ASTHO's EPHT Fellowship Reaches the U.S. Territories (PDF) This two-page fact sheet shares successes and lessons learned from the Program’s first reverse site-visit to a territorial health agency. <!-- ASTHO Environmental Public Health Tracking Fellowship Program: 2002-2019 Environmental Public Health Tracking 101 The National Environmental Public Health Tracking Network (Tracking Network) brings together health data and environmental data from national, state, and city sources and provides supporting information to make the data easier to understand. The Tracking Network has data and information on environments and hazards, health effects, and population health. Embed-EH PH Tracking Fellowship Program ARCGIS --> website no