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Learn step by step how to modernize data infrastructure at your health agency to improve public health outcomes, with helpful examples and resources.

Read a detailed summary of the FY26 Senate Appropriations Bill, which was released on July 31.

The Senate released its version of the FY25 LHHS appropriation bill on August 1, 2024, with significant changes in proposed public health funding than the House's proposed bill.

ASTHO Legislative Prospectus | Previewing 2025 state legislative actions on data modernization and privacy.

ASTHO’s legislative prospectus on Immunization Information Systems, Health Information Exchanges, and balancing data privacy with sharing

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.

Two reports explore opportunities for improved public health action through immunization data sharing with health information exchanges, in addition to the broader legal landscape of public health data.

The Suicide, Overdose, Adverse Childhood Experiences Prevention Capacity Assessment Tool (SPACECAT) compiled a national report and accompanying infographic, that break down the biggest findings from the data, and highlight the biggest barriers facing health agencies today.

This report analyzes the results from a survey of state and territorial health agency staff to collect information regarding health agency efforts to address climate change and extreme weather.

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

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

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

What I Wish I Knew Before Linking Data ASTHO, Association of State and Territorial Health Officials, linking data, data linkage, family and child health, public health, data linkage research, public health agencies, north Carolina, children's data network, child welfare indicators, california health and human services, child welfare, child welfare policy, maternal and child health, alaska division of public health, analytics and epidemiology, data analysis, applied surveillance, data systems, child protection, birth data, vital records, medicaid data, record registries, child advocacy, data pieces, connecting records, data sources, medical histories 45:57 Data linkage experts share insights and recommendations for leveraging data linkage projects to explore and make an impact on public health issues. PH Conversations Series - What I Wish I Knew Before Linking Data This episode features a conversation between two data linkage experts—Jared Parrish, PhD, MS, and Emily Putnam-Hornstein, PhD—highlighting their lessons learned and sharing recommendations for those seeking to use data linkage projects to examine key public health issues, such as: The thought process behind choosing which datasets to link, which linkage tools and methods to use, and how to bring intentionality to these choices when considering a research question. The benefits of using data linkage to enhance datasets and build a comprehensive and robust collection of information for new insights. Lessons learned for navigating data linkages with important considerations for preparation, analysis, and the uses of data linkage. Show Notes Interviewer Stephany Strahle, MPH, Maternal and Child Health Contractor, ASTHO Guests Jared Parrish, PhD, Senior Epidemiologist, State of Alaska, DHSS, Division of Public Health Emily Putnam-Hornstein, PhD, Distinguished Professor for Children in Need, University of North Carolina at Chapel Hill <!-- Resources Braiding and Layering Funding to Address the Social Determinants of Health --> PHC Podcast Transcript - What I Wish I Knew Before Linking Data website yes

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