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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

Policy Approaches to Improve State and Local Data Sharing Health officials can pursue organizational policies across key priority areas to advance state and local data sharing. Learn about these policy approaches. Several factors impact effective data sharing between state and local health departments—which is vital to public health decision-making—such as legislation and regulations, limited funding, IT and workforce resources, departmental processes, leadership buy-in, and data governance. Health officials can pursue organizational policies across these key priority areas to advance data sharing practices. Get the Infographic (PDF) website yes

Arizona Department of Health Services Pursues Policies to Advance Data Sharing with Tribal Nations Erik Skinner, Christina Severin, Reema Mistry The Arizona Department of Health Services is pursuing policies to advance data sharing with tribal nations, centered around partnerships, education, and more. With leadership support and funding to modernize its public health infrastructure, the Arizona Department of Health Services (ADHS) is pursuing policies to advance data sharing with tribal nations. This includes investing in partnerships with tribal leaders, educating the public health workforce about tribal governments and tribal health care, and working to improve data identification processes to support effective data sharing between the state and tribal nations. Data sovereignty is an important consideration for ADHS, as there are 22 federally recognized tribal nations in Arizona. ADHS recognizes the inherent right of tribal nations to access their citizens’ public health data and is developing a tribal data sovereignty policy that both acknowledges their unique data needs and aligns with state requirements around tribal engagement. Leadership Support and Effective Tribal Engagement ADHS leadership understands the importance of making strong connections with tribal nations and recognizing each nation’s public health priorities while meeting its statutory requirement to develop tribal consultation policies. To that end, ADHS developed the tribal liaison position to serve as a resource, advocate, and communication link between ADHS and Arizona’s Native American health care community partners, including tribal community leaders, health and epidemiology directors, Indian Health Service (IHS), and Tribal Epidemiology Centers (TECs). Understanding cultural norms is essential to building trust with tribal partners; the tribal liaison role has been vital to ADHS engagement with tribal nations on data sovereignty topics. People and processes are important to establishing data sharing policies, and a well-informed workforce is essential for effective collaboration with sovereign tribal nations. ADHS is working with the Native Nation Institute to provide training on tribal sovereignty and cultural humility for staff. It has also developed a tribal handbook for public health staff on sovereignty, cultural trauma, and the roles of IHS and TECs. Identifying Tribal Affiliation within Datasets and Tribal Public Health Priorities ADHS conducted a data assessment to identify instances in which data sharing was active and ongoing between ADHS and tribal nations, and instances in which it had expired. A notable technical challenge was identifying tribal members within existing datasets, as many public health datasets are incomplete (e.g., do not include tribal affiliation) or rely on IT systems that are unable to aggregate data appropriately—making it difficult to ensure tribal authorities receive relevant, comprehensive public health data for their communities. In addition, because each tribal nation’s public health priority areas and data needs could differ from the data that state health information systems collect, sharing relevant data with tribal nations can be challenging. ADHS is working with each nation to identify tribal public health priority areas, find solutions to identify tribal data within state collected datasets, and share it with the respective nations. Ken Komatsu - Brief - AZ DHS Pursues Policies to Advance Data Sharing with Tribal Nations Honoring Sovereignty in Data Sharing Relationships Data sharing agreements with public health agencies often establish that the state agency controls the disposition and use of the data, and that each party benefits. Acknowledging that tribal partners are entitled to their citizens’ data without conditions differs from how ADHS has historically approached data-sharing relationships with others. ADHS plans to formally establish a non-transactional data sharing policy with tribal public health partners, and establish data sharing agreements that align with this approach going forward. Implementation Considerations Considerations for state health agencies in fostering strong relationships and effective engagement with tribal partners around data-sharing efforts include: Center tribal sovereignty when framing data sharing agreements with tribal nations. Engage tribal liaisons in data-sharing efforts with tribal nations. They maintain close relationships with tribes and can help develop mutual cultural understanding, which is essential to engaging tribal partners. Assess datasets to determine data completeness with regards to tribal affiliation and identify opportunities to improve comprehensive data sharing with tribal authorities. Invest in state health agency staff training on tribal sovereignty and cultural humility, so staff can be well-prepared when engaging in data sharing conversations with tribal partners. Gerilene Haskon - Brief - AZ DHS Pursues Policies to Advance Data Sharing with Tribal Nations OT18-1802 website yes

Olmsted County Pilots a Regional Population Health Data Hub to Improve Data Accessibility Gelila Tamrat, Sara Black, Reema Mistry, Christina Severin Olmsted County, Minnesota, pilots a regional population health data hub to improve data accessibility, which supports improved decision-making and interventions. Historically, Olmsted County and other local counties in southeast Minnesota have faced barriers to accessing timely and actionable public health data, including limited data analytics workforce capacity, lack of data-sharing agreements (DSAs), and misaligned data suppression standards. To address these challenges, Olmsted County Public Health Services (OCPHS) piloted a regional population data hub, in partnership with the Minnesota Department of Health (MDH) and 10 local health departments (LHDs). OCPHS procured resources to develop a regional data-sharing platform, expanded their epidemiology team, and pursued DSAs. As a result, they gained access to critical data that supports informed decision-making and tailored interventions at the local level. Tina Jordahl - Brief - Olmsted County MN DMI Hub Developing a Regional Population Health Data Hub With financial support from the Minnesota legislature in 2021, OCPHS collaborated with MDH and its regional counterparts to develop a regional population health data hub for smaller LHDs to access community-level public health data. OCPHS maintains the hub by managing data from the state, regional partners, and 10 LHDs, and creating data dashboards to support southeast Minnesota counties’ population health data needs. This effort involved building and expanding relationships with MDH unit-specific epidemiologists, working closely with public health system consultants at MDH, and raising awareness of the need for sustained data analytics workforce support. Following the initiative’s success, OCPHS plans to engage with state and local leaders to identify funding sources that can sustain the hub beyond the pilot funding cycle. Promoting Data Accessibility through Strategic Partnerships and Agreements MDH’s Center for Public Health Practice supports public health system consultants, who offer technical assistance and consultation services to strengthen public health infrastructure across Minnesota. The consultant for the southeast region of the state was crucial in linking state and local staff to advance the development of the regional population health data hub. They helped triage and expedite requests from OCPHS by identifying the right points of contact for datasets and legal counsel within MDH. The collaboration of MDH, OCPHS, and participating LHDs facilitated the development of DSAs, which allowed for proper data flow and enabled OCPHS to request data from MDH on behalf of participating counties, reducing the need for each county to request data. It also helped OCPHS to become the first county in the state to adopt CDC’s ESSENCE tool to monitor hospital visits for syndromic surveillance across Minnesota and neighboring states, better enabling LHDs to address the needs of communities residing along state borders. Hiring Strategies for the Data Analytics Workforce OCPHS focused on hiring staff to support the regional population health data hub with data expertise, strong communication skills, and a particular interest in population health and social determinants of health. OCPHS created two permanent epidemiologist positions to promote sustainability for that position in the future. To expand their hiring pool, OCPHS relied on Olmsted County’s updated remote work policies following the COVID-19 pandemic when many shifted to remote or hybrid work. They also invited leaders from partner counties to help vet candidates who could support other LHDs’ needs. Meaghan Sherden - Brief - Olmsted County MN DMI Hub Advancing Equity Through Data Accessibility Due to data suppression rules, counties in southeast Minnesota had limited access to county-level data for certain statewide datasets. OCPHS worked with MDH to identify appropriate data suppression standards that supported access to community-level public health data and preserved privacy and security, and collaborated with the county IT department to develop the regional data hub with public-facing and internal dashboards, aligned with the required privacy and security standards. The public-facing dashboards show aggregate data with appropriate suppression standards at county, regional, and state levels. The internal dashboards provide complete data summaries and are protected with appropriate permissions and multi-factor authentication for LHD staff to perform population-level analysis. Providing timely, granular data to participating counties allows LHD staff to develop tailored strategies to address emerging health issues promptly, bridging health equity gaps. OCPHS also integrates standard demographic data on race, sex, gender, and age into its dashboards, enabling regional LHDs to gain deeper insights into their communities and fine-tune equity-centered public health initiatives and interventions. Jenny Passer - Brief - Olmsted County MN DMI Hub Implementation Considerations Foster collaborative relationships across state and local health departments to identify opportunities to share resources when advancing data-sharing efforts. Models in which larger LHDs support key data infrastructure needs on behalf of smaller LHDs may bolster data analytics/epidemiology capacity across multiple LHDs and streamline coordination with key partners at the state health department. Consider how state health department consultant or liaison roles charged with providing technical assistance to state or local partners may help facilitate key connections between state and local health department staff pursuing cross-jurisdictional data-sharing efforts. Invest in data analytics/epidemiology workforce strategies that help address specific needs related to population health and relationship building, along with technical skills. Cross-jurisdictional data-sharing efforts require staff with strong data analytics and communication skills, as they work with multidisciplinary leaders and across jurisdictions to inform community-based interventions. Collaborate proactively with legal and IT departments to identify data governance solutions and technical approaches to adhere to required privacy and security standards. Establishing DSAs is important, as it allows sharing of data within required legal guardrails. Similarly, IT leaders can identify technological solutions that support effective access to data. OT18-1802 website yes

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

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

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.

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

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

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

This brief highlights how the Georgia Department of Public Health and the Ohio Department of Health developed robust opioid overdose surveillance systems.

In February 2019, ASTHO surveyed its members to understand how data is being utilized to address these harms in their jurisdictions. This brief summarizes the results of this survey and highlights data-based approaches used to address four types of opioid-related harms.

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

Project ECHO: Overdose Fatality Investigation Techniques (OD-FIT) Project ECHO: Overdose Fatality Investigation Techniques (OD-FIT) provides coroners, medical examiners, toxicologists, forensic pathologists, and public health personnel opportunities to learn and share their overdose investigation expertise with peers across the United States and territories. Coroners and medical examiners are called after a fatal overdose to investigate the cause and manner of death. Public health agencies and practitioners use this mortality data to better understand trends in fatal overdoses and to inform the allocation of resources, such as fentanyl test strips and naloxone in states with high rates of overdose deaths.  Project ECHO: Overdose Fatality Investigation Techniques (OD-FIT) is a collaboration between ASTHO and the Centers for Disease Control and Prevention (CDC) to provide coroners, medical examiners, toxicologists, forensic pathologists, and public health personnel with an opportunity to learn and share their overdose investigation expertise with peers across the United States and territories. By strengthening the medicolegal death investigation system, state and territorial health agencies can improve the accuracy and reliability of overdose death data to benefit public health and safety programs, law enforcement investigations, and upstream prevention strategies.  Project ECHO OD-FIT consists of live online sessions featuring didactic presentations followed by case study discussions. Didactic recordings and accompanying resources from the most recent series can be found below. website no False

Sustaining DMI: Leveraging Medicaid to Advance Public Health Data and Surveillance A primer for states: leveraging Medicaid to design and execute a sustainable DMI. This primer describes how to leverage Medicaid to design and execute a sustainable data modernization initiative (DMI). It provides guidance, resources, and practical tools for effective management, strategic planning, and skill development to strengthen sustainability planning. Get the Report (PDF) website yes

Policy Options to Improve Data Sharing Between State and Local Health Departments Organizational policies on data sharing between state and local public health agencies. This report explores organizational policies related to data sharing between state and local public health departments. ASTHO, in collaboration with the National Association of County and City Health Officials and the Network for Public Health Law developed this report, which aims to serve as a guide for state and local public health leaders as they consider organizational policy options to improve state and local data-sharing efforts. Get the Report (PDF) website yes

Investing in Indiana’s Public Health Infrastructure Through Community-Driven Policy Change public health infrastructure, community driven policy, indiana state health commissioner, public health system, indiana department of health, outpatient facilities, technical assistance, data and information integration, emergency preparedness, child and adolescent health, legislative action, state and local elected officials, health problems, health care, health system, health departments, federal agencies, essential public health services, centers for disease control, state and local levels, health outcomes, health organization, covid-19 pandemic, health infrastructure, promoting health, public health organizations, states public health, federal funding, astho, association of state and territorial health officials Maggie Davis, Keith Coleman Indiana enacts historic public health funding through community engagement and legislative support. In April 2023, Indiana passed bill SB 4, which was a historic investment in the state's public health funding and restructuring its public health system. This case study shares how the Governor's Public Health Commission and the Indiana Department of Health approached community listening sessions, formulated recommendations, and successfully built legislative support to reform the public health system in the state. Get the Report (PDF) website yes

This ASTHOReport highlights the public health importance of three harm reduction policies and practices to reduce overdoses: facilitating community distribution of naloxone, facilitating community distribution of fentanyl test strips, and overdose prevention centers.