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Creating an Informatics Job Classification Series for Health Departments Creating an Informatics Job Classification Series for Health Departments Ari Whiteman Learn why public health informatics jobs are critical for health departments in this brief. Why a Public Health Informatics Job Classification Matters The push to modernize public health data infrastructure has highlighted that traditional job classifications (e.g., Epidemiologist, IT Specialist, or Developer) do not fully capture the unique skill set and value of public health informaticians. Informatics professionals specialize in integrating, standardizing, and managing health data systems to ensure interoperability across platforms and agencies. Their expertise includes health data standards (e.g., HL7, FHIR, LOINC, USCDI), database management, extract-transform-load (ETL) processes, business intelligence tools, and cross-system data exchange, all of which are skills and proficiencies needed for data modernization transitions and processes. According to the 2022 ASTHO Profile of State and Territorial Public Health, 25 states and territories did not have a dedicated public health informatics job classification series, which would create several key advantages over forcing informaticians into traditional public health classifications that may not adequately reflect their job duties or the expertise needed to perform them: Recruitment: Public health informatics job descriptions are likely to attract more qualified and better-fit candidates for the responsibilities of the role, which differ from that of IT professionals or epidemiologists. Retention: Informaticians often leave for the private sector due to higher pay opportunities, as governmental roles often pay less than similar roles in the private sector. A well-defined classification series with competitive salary benchmarks helps retain talent. Role Clarity: Differentiating informaticians from epidemiologists or IT ensures staff are linked to projects that fit their skills and experience, which can improve performance and morale across disciplines. Future-Readiness: Quality health data infrastructure is a preparedness necessity. Informatics positions enable agencies to respond to emerging health crises by linking response and surveillance data with other state and national reporting systems. Common Challenges and Solutions Anticipating challenges can help maintain progress and understanding across all involved parties. Challenge: Resistance to creating new classifications. Solution: Propose consolidation or sunsetting of outdated roles and highlight national best practices. Challenge: Salary constraints compared to private sector. Solution: Emphasize total rewards (retirement, benefits, meaningful work) and seek flexibility for exceptions. Provide salary data from sources like Lightcast, Healthcare Information and Management Systems Society, American Health Information Management Association, or surveys from the American Medical Informatics Association. Challenge: Limited internal public health informatics expertise. Solution: Build capacity through “train-the-trainer” models, upskilling, and leveraging partnerships with universities. Challenge: Slow civil service processes. Solution: Start early, maintain persistence, and adapt business case arguments to different decision-makers (HR, unions, leadership). See ASTHO’s Data Modernization Primer and Tactical Guides for more information, in particular: See the Tactical Guide on Building, Equipping, and Sustaining a Data Modernization Workforce for strategies for upskilling, training, recruitment, and retention of a data modernization workforce. See the Tactical Guide on Planning Data Modernization Activities for strategies to mobilize a team, conduct current state assessments, develop plans, and gain support from leadership. Challenge: Overlap with other roles (e.g., epidemiologists). Solution: Clearly define distinctions between public health informatics and other roles, then communicate role clarity to benefit morale and efficiency. Conclusion Creating a public health informatics job classification series is both a strategic investment and a practical necessity. By distinguishing informatics as a professional path within public health, states and territories can strengthen their workforce, address long-standing recruitment and retention barriers, and prepare for the rapidly evolving demands of data modernization. While the process requires persistence, clear documentation, and negotiation across HR and leadership, the payoff is a resilient workforce equipped to manage the data infrastructure that underpins modern public health practice. Reference the How-To Guide for more details on creating public health informatics job classification series. Learn More - Brief - Creating an Informatics Job Classification Series OE22-2203 PHIG article yes

Use Partner Mapping to Power Data Modernization Projects Allen Rakotoniaina, Heidi Westermann, Elyssa Stoops, Charlie Ishikawa Learn how to use partner mapping to clarify and understand your data modernization partners, and tailor engagement strategies for shared projects ownership. Partner mapping is a practical way for state, territorial, local, and tribal public health agencies to identify, organize, and engage the wide range of stakeholders involved in data modernization (DM). This resource helps agencies clarify who their DM partners are, understand their perspectives, and tailor engagement strategies to build shared ownership of DM projects. article yes

Delve into the successes, challenges, and lessons learned to inform/advance FHIR adoption among state and local public health agencies and their health care partners.

Data Modernization Primer and Tactical Guides Dive into these data modernization reports for strategies and detailed steps to move from siloed systems to a connected, resilient data ecosystem. Public health data modernization is a collective effort by federal, state, local, and tribal organizations to strengthen public health data and surveillance systems. The ultimate goal is to move from siloed public health data systems to a connected, resilient, adaptable, and sustainable “response ready” data ecosystem. The primer provides state and territorial health officials with a high-level understanding of the objective and significance of data modernization as well as the roles that they play in a successful data modernization initiative. The five tactical guides detail key strategies and tactics for implementing and maintaining data modernization initiatives within public health agencies. article yes

Learn about how public health is employing ethical frameworks to support and improve wastewater surveillance and other community health monitoring measures.

Supporting Incarcerated People’s Recovery: Linkage to Care Policies for People Entering and Exiting Incarceration with Substance Use Disorder Linkage to Care Policies for Incarcerated People with SUD Explore linkage to care policies for incarcerated people with SUD across states, including SUD screening upon entry, treatment referrals, linkage post-release, and naloxone access. Individuals with substance use disorder (SUD) are more likely to be involved in the criminal justice system and experience higher rates of incarceration. They also experience disproportionately high rates of death, and the risk of overdose death does not cease upon release. Jurisdictions looking to reduce the risk of recidivism, overdose, and premature death among this population can consider policies that improve linkage to care across all stages of the criminal justice system. This report delves into these policies by state, which include screening for SUD upon entry, treatment referrals, care linkage post-release, and naloxone access. Get the Report (PDF) website yes

Designing a Public Health Informatics Job Classification Series: A How-To Guide How To Design a Public Health Informatics Job Series Public health agencies can use this actionable guide to develop data informatics jobs and fill critical gaps in their workforce. As part of state and territorial data modernization programs and data system improvement, many public health agencies are realizing that their existing job classifications don’t always reflect the specialized work of public health informatics professionals. While some positions may resemble traditional roles like epidemiologists, they require a distinct set of skills. Informatics professionals focus on integrating and managing data, building stronger data systems, and improving how information flows across programs and partners. They often use advanced tools such as SQL and Tableau, and they work with national data standards — like HL7, FHIR, LOINC, and USCDI — to ensure that health information systems can “speak the same language.” Existing classifications (such as epidemiologist or information technology specialist) rarely capture these technical and cross-disciplinary responsibilities. Creating a dedicated public health informatics job classification series helps agencies attract and retain experts with the right mix of data, technology, and public health skills. It also provides a clear structure for professional growth, defining duties and qualifications for entry-, mid-, senior-, and managerial-level positions. This clarity supports both staff development and long-term workforce planning. However, establishing a new job classification is not a simple task. It requires careful planning, collaboration across departments, and formal approval processes that can take time. This guide outlines the key decisions, documentation, and steps needed to determine whether a new classification is right for your agency and how to develop one effectively. Each health department’s structure and policies are unique, so the process described here should be adapted to fit local circumstances. Steps in the Process 1. Identifying the Need for a New Job Classification The first step is to determine whether your agency truly needs a new public health informatics job classification. Many health departments find that existing job titles — like epidemiologist or clinical application coordinator — don’t align well with the skills required for informatics work. A new classification may be warranted if current titles have different job requirements, pay structures, or barriers that make it difficult to attract and retain qualified candidates. Begin by asking key questions: Does your department already employ staff performing informatics functions? If so, what are their current classifications, and are they effective in recruitment and retention? How flexible are your civil service rules for posting positions with modified requirements or titles? If there’s sufficient flexibility, you may not need a new classification. Are you relying on temporary staff or contractors for informatics work? If so, analyze whether creating permanent positions could reduce long-term costs. If a new classification isn’t immediately feasible, what options exist to train, promote, or compensate staff doing informatics work within the current framework? 2. Understanding the Approval Process and Requirements Once a need is established, identify the steps and timeline required to create a new classification. This process varies widely by jurisdiction and can take several months, or even years, to complete. Start by determining who has decision-making authority and which offices must approve the proposal. This may include local HR departments, statewide HR or civil service offices, commissioners, or budget authorities such as the Office of Management and Budget. If your HR department has a moratorium on creating new classifications, consider negotiating or repurposing existing ones. Document the rationale thoroughly, highlighting recruitment challenges, misaligned duties, and how informatics roles support data modernization goals. Include evidence such as prolonged vacancies or the impact on program performance. Prepare necessary documentation, which may include: A list of required competencies. Subject matter expert reviews. A work study or position analysis. A “career ladder” illustrating differences between entry, mid, senior, and managerial roles (see example: Nebraska’s Informatics Series (PDF)). Comparisons with other job families, such as Nebraska’s Epidemiology Series (PDF). Additionally, determine whether other state agencies also require informatics roles, as cross-agency collaboration can strengthen the case for a new classification. When it comes to salary justification, find out who sets pay scales — some states use centralized systems, while others allow departmental flexibility. In unionized settings, salary changes may also require negotiation. For example, Minnesota uses the Hay methodology through Korn Ferry to determine compensation levels. 3. Gathering Job Descriptions, Competencies, and Key Skills Developing an accurate and competitive job description is crucial. Start by analyzing the knowledge, skills, abilities, and other characteristics required for informatics work. Assess whether existing staff possess these competencies and where skill gaps may exist. Collect and review comparable job descriptions and frameworks from trusted sources, such as: Public Health Informatics Institute and Council of State and Territorial Epidemiologists Region V Public Health Training Center O*Net Online Health Informatics Profile PublicHealthCareers.org Commonly required skills include: SQL R SAS Python Tableau Snowflake ETL processes Interoperability standards (HL7, FHIR, LOINC, USCDI) Project management tools (e.g., Agile and LEAN) Certifications such as HL7 CDA Specialist, FHIR Fundamentals, or Tableau Desktop Specialist may also be valuable. For competency alignment, reference the following professional frameworks: HIMSS Global Health Informatics Competency Frameworks Applied Public Health Informatics Competency Model (PDF) Council on Education for Public Health (PDF) Council of State and Territorial Epidemiologists Competencies Toolkit Public Health Accreditation Board Council on Linkages Core Competencies CDC Competencies for Public Health Professionals You can also consult the Lightcast workforce analytics reports for skills and salary trends and review the literature review repository for additional insights. 4. Conducting Salary Benchmarking Competitive compensation is essential to attracting informatics professionals. Begin by identifying your HR department’s comparison states or agencies and reviewing their pay plans. Compare rates with large local jurisdictions when possible. Showing cost savings from converting contractors to permanent employees can strengthen your justification. If union negotiations are part of your environment, plan early to align with contract timelines. Many agencies use external benchmarking tools aligned with the location of the job, such as Salary.com, Payscale, Glassdoor, or Indeed. Large-scale workforce datasets from Lightcast — a large-scale data vendor that includes data collected from millions of job descriptions — can be useful as well. Data from Lightcast queries include examples of competitor employers, top technical and soft skills, example job titles, and salary ranges for public health informatics positions. Searching and summarizing reference data like this is vital for establishing baseline job information that is in-line with successful public health informatics positions elsewhere, which can help build a case for why your new series is different than existing jobs and requires a specific pay range. For deeper insights into salary trends and disparities, review the following: Salary and Job Requirement Differences for Jobs in Local and State Health Departments Versus the Private Sector: Analysis of Large-Scale Job Postings Data demonstrates that public-sector informatics roles can pay up to 50% less than private-sector equivalents. Salary Disparities in Public Health Occupations: Analysis of Federal Data (2021–2022) shows that informatics jobs pay between 25-45% less in local or state government compared to all other sectors, a difference of up to $71,000 per year in wages. When Money Is Not Enough: Reimagining Public Health Requires Systematic Solutions to Hiring Barriers explores structural barriers beyond pay, such as slow hiring processes or outdated job descriptions, that cause barriers to hiring in health departments. Also review relevant salary surveys from professional associations: HIMSS Nursing Informatics Survey (2022) American Health Information Management Association Salary Survey Report (2019) (PDF) American Medical Informatics Association Salary Survey iMercer Healthcare Informatics and Technology Compensation Data Other Considerations Establishing a new public health informatics job classification requires more than paperwork — it requires persistence, partnerships, and long-term planning. Building relationships with key stakeholders, such as HR leaders, civil service administrators, union representatives, and department executives, is essential. Each may have unique perspectives and concerns about creating new classifications, so tailoring your approach can make a difference. Persistence is often key. If one strategy or argument isn’t successful, try another. For example, some departments have found success by framing informatics roles as critical to data modernization, interoperability, and cost efficiency, while others emphasize workforce development or public health impact. Having distinct classifications for public health informaticians and epidemiologists also supports workforce morale and clarity. It helps staff and partners understand who to contact for specific issues (e.g., analytical versus technical data

Learn how to assess public health technology from a legal and policy lens prior to adoption.

Using Water Quality Monitoring Data for Your Building Water Management Program In order to reduce the risk of opportunistic pathogens, such as Legionella, it is important to use water quality monitoring data as part of your building’s water management program (WMP). The first stage in developing a building water management program is to establish an understanding of baseline water quality at the point of entry and other select points in the building. This factsheet outlines how to monitor water quality in facilities to prevent the growth of Legionella and other opportunistic pathogens. It discusses important basic water quality monitoring parameters, including temperature, pH, turbidity, and disinfectant residual. An important aspect of developing and implementing a building WMP is understanding the water quality received by the water supplier at the building point of entry and how the water quality is affected as it is used throughout the building. This resource contains a five-step process for monitoring water quality that can be used to develop a building WMP. Get the Factsheet (PDF) article yes

Public Health Data Disclosure or Request Readiness Assessment Chris Alibrandi O’Connor Explore key steps and lessons for effective public health data sharing. Organizations that want to share or request data to support public health work should take several preparatory steps. An organization’s data sharing efforts will develop more quickly and smoothly if certain considerations have been analyzed, or actions taken before negotiations begin on a data sharing agreement. The same is true for organizations trying to obtain data to support their public health efforts. Consider the following prompts and questions before initiating data sharing or a request for data to prepare for a more efficient and effective negotiation for the data’s disclosure. That consideration will identify barriers to data sharing as well as result in greater clarity around organizational data sharing needs and data governance. For those seeking data, what data do you need? What type of public health work is on the table and what data is needed to achieve the related public health goals? Identify which entity has the needed data Identify the required data elements Specify the type of data needed (e.g., line level, identifiable, Limited Data Sets, de-identified, aggregated) Having identified the data elements and type of data needed will help later in the legal analysis of which laws apply to the disclosure of data Pre-Negotiation Considerations Review organization’s existing written data sharing or data governance policy. Determine whether the project is covered by it and whether any related data disclosures or uses would comply with it. If there is no such policy, consider developing one for adoption. That process will clarify and/or resolve many issues, including data governance guidelines, which could become barriers during data sharing agreement negotiation. Having an organizational “champion” in a position of authority to support a data project may help overcome internal obstacles. Identify a champion — or develop one. Identify and get to know the people who play key roles in data sharing agreements (data stewards, leadership, legal counsel) to facilitate project communication. Pre-Negotiations Review of Legal Issues Evaluate the answers to these questions about disclosing the data at issue: Can I? (i.e., does the law permit the data’s disclosure?) Must I? (i.e., does the law require disclosure?) Should I? (i.e., is disclosing the data the right call when considering legal, policy, and equitable factors) When considering the risks of disclosing data, also consider the risks of not disclosing it for public health purposes; consider what public health efforts will not succeed without the data. Author Funding Disclaimer - Resource - Data Sharing Request Disclosure Alaska article yes

Improving a State’s Process for Disclosing Data for Public Health Purposes to Tribal Public Health Organizations

Successful Informatics Alignment article no False

Sustaining DMI: A State Health Official’s Guide to Enhanced Funding Sustainable financing strategies for state health officials to support data modernization and Medicaid. What is the relationship between a state’s Medicaid program and its public health data system? Although state implementation of the Medicaid program (Title XIX of the Social Security Act) varies, each state’s program has enrollment and claims data on Medicaid participants, including demographic data on race and ethnicity, age, and service utilization, such as vaccines received. At the same time, a state’s public health system needs to collect, analyze, and report diverse data from public health initiatives and related programs to support its goals to protect and improve the health of individuals and communities by promoting healthy lifestyles, researching and encouraging disease and injury prevention, and detecting, preventing, and responding to infectious diseases. A state’s Medicaid program and public health agency can collaborate to implement a sustained data modernization initiative (DMI) that combines Medicaid and public health data and integrates these data into the state’s health-related data ecosystem. A sustained DMI can yield various improvements to a state’s health-related data ecosystem, such as improved data quality, public health reporting, data storage and resiliency, and analytics to respond to pandemics. It can also set the stage for data sharing with additional data system partners, which can further improve the state’s health-related data ecosystem. Why is sustainable funding necessary to continue DMIs? Sustainable funding to support personnel, processes, and technology is imperative to the continued success of a DMI. Stable funding can increase state Medicaid and public health agencies’ likelihood of recruiting and retaining personnel with advanced degrees, such as biostatisticians and epidemiologists, by enabling the agencies to offer compensation packages that are competitive with job market rates. Stable funding also enables the agencies to maintain and refine new and existing data-sharing processes, and it ensures that technology is maintained and upgraded appropriately to meet evolving needs. Medicaid funding is a potentially large and stable funding stream that can support the personnel, processes, and technology in a DMI that focuses on integrating Medicaid and public health. However, public health funding has historically been an unstable patchwork of federal, state, local, and private funding streams and mechanisms, largely because of changing economic and political priorities and the perceived risk level and severity of major public health threats. What sustainable financing strategies can support the personnel, processes, and technology needed to continue DMIs? State health officials can use the following three strategies when pursuing Medicaid funding to sustain a DMI: Blend and braid funding sources. Optimize existing and potential funding streams by blending or braiding administrative approaches to grow and maintain programs. To blend funding sources, program officials combine funding into a single stream, which results in a loss of award-specific requirements and thus requires statutory authority. In contrast, braiding funds allows program officers to direct funds toward a single strategy or initiative while preserving funding requirements (Box 1). Callout 1 - Resource - Sustaining DMI: A S/THOs Guide to Enhanced Funding Support personnel by using cost allocation through the Advance Planning Document (APD) process or the Administrative Cost Allocation Plan. A DMI team often has people with specialized skills, such as clinical and technical experts, compliance or legal officers, and financial experts. The salary for these people may be cost-allocated via the APD process or the Administrative Cost Allocation Plan described in Social Security Act Section 1903(a)(7) (Box 2). To illustrate, the Administrative Cost Allocation Plan provides 50 percent match for costs that meet a series of requirements to cover personnel costs. In addition to this strategy, state health officials can cover salary costs through blending and braiding approaches. Callout 2 - Resource - Sustaining DMI: A S/THOs Guide to Enhanced Funding Align public health functions with Medicaid business and technical functions. To explore whether a state Medicaid agency could access enhanced federal funding to support public health, a state public health agency must approach the state’s Medicaid program collaboratively and design and implement a DMI that does the following: Meets the Conditions for Enhanced Funding and couples any technical system improvements with measurable outcomes that improve public health and the Medicaid program. Investigates the extent to which the public health technical functions (for example, health care provider enrollment) align with similar Medicaid business functions. Confirms the extent to which the public health functions and Medicaid Enterprise Systems share or could share (that is, reuse) core technical components to support common business functions. Callout 3 - Resource - Sustaining DMI: A S/THOs Guide to Enhanced Funding After this investigation is complete, the state Medicaid agency should explore cost allocation models that apportion costs with the benefits received (Box 3). Box 4 provides examples of public health use cases that successfully acquired enhanced Medicaid funding. Callout 4 - Resource - Sustaining DMI: A S/THOs Guide to Enhanced Funding website yes

Sustaining DMI: Medicaid Advanced Planning Document Process How state Medicaid agencies can request enhanced federal funding for Medicaid Enterprise Systems and related activities. Why is the Advanced Planning Document process important? Based on information from the Government Accountability Office (GAO), the Centers for Medicare & Medicaid Services (CMS), and the Federal Register, the Advanced Planning Document (APD) process is a procedure through which states develop a plan of action for their Medicaid information technology (Medicaid IT) projects. These plans are for designing, implementing, or operating Medicaid Enterprise Systems (MES) projects. State Medicaid agencies (SMAs) submit completed APDs to CMS—specifically a designated state officer in the Center for Medicaid and Children’s Health Insurance Program (CHIP) Services (CMCS) Data and Systems Group (DSG)—to request federal financial participation for their activities. The state officer reviews APDs to assess whether states’ requests for federal financial participation for designing, developing, implementing, or maintaining MES activities contribute to the economic and efficient operation of Medicaid and meet specific technical and operational criteria defined in statute, regulation, or sub-regulatory guidance. A state that receives federal financial participation can see increased access to stable federal funding to support MES activities. In addition, APDs are used to monitor a state’s project performance and outcomes. What are the three types of APDs? There are three types of APDs: Planning, Implementation, and Operational (Table 1). Table - Resource - Sustaining DMI: Medicaid Advanced Planning Document Process What are the major steps for states in the APD process? To request enhanced federal funding for MES, SMAs must complete the APD template that aligns with where they are in the development of their project (for example, design or maintenance) and submit it to the designated CMCS DSG state officer. The APD process contains five major steps and can take many months to complete: Meet with key state contacts and decision-makers. Based on information from the Public Health Informatics Institute’s information and tip sheets, before developing the APD, the SMA should identify and engage key state contacts and decision makers to solicit their input about the proposed project and secure their and their staff’s collaboration to complete and submit the APD to the CMCS DSG state officer. The state health agency (SHA) should work closely with the SMA during this process to ensure that they provide needed support to the SMA. For example, the SHA may gather information for the SMA to include in the APD or advise on how to complete particular sections of the APD. During this process, the SMA and SHA should consult with their respective agency leadership to discuss the type of technological solutions Public Health maintains, Public Health’s relationship with the state Medicaid program, and the opportunity to align systems to reduce overall state costs and improve state efficiency through the APD process. The SMA and SHA should also engage the MES lead, who can offer critical information about current MES components and component certification needed to complete the APD. In addition, GAO recommends states involve their chief information official in overseeing Medicaid IT projects because they can play a critical role in decision making related to IT budgets, management, and oversight. Next, the SMA and SHA should engage the CMCS DSG state officer to develop a strong understanding of how the APD can support the Medicaid program and serve a public health interest. Coordinating with the state project management office can help integrate the diverse parties and processes needed to develop and submit the APD for approval. It can also help ensure that states develop a comprehensive and flexible timeline for the APD process, stay aware of approaching deadlines, and meet ad hoc requirements. Develop the appropriate APD. Next, based on 45 C.F.R. § 95.610(c), the SMA and SHA should identify which of the three types of APDs to submit to the CMCS DSG state officer. Planning APDs are recommended for large and complex projects, such as statewide projects. However, if a state can identify a clear and easy pathway to integrate a public health information technology system with a current MES procurement or development phase, it can forgo developing a Planning APD and directly develop or update an existing Implementation APD. For example, if a state is looking to integrate its counties’ public health data into its MES at once, it should develop a Planning APD as the project is large and affects all counties in the state. However, if a state already has most of its counties’ public health data in its MES but is looking to add a single county’s data to its MES using the same process it previously and successfully used to add the other counties’ data, it may not need to submit a Planning APD. If a state has already successfully integrated its counties’ public health data into its MES and is looking to make major technology upgrades and improvements, it should submit an Operational APD. Regardless of the type of APD the state submits, the SMA and SHA should work together to ensure the request meets the Conditions for Enhanced Funding (see separate document Conditions for Enhanced Funding: The Basics). Submit the APD for approval and be available for revisions. Based on information from CMS, GAO, and the Office of Child Support and Enforcement, the state should then submit the APD to the designated CMCS DSG state officer. The SMA and SHA should plan to receive questions and revision requests from the CMCS DSG state officer and ensure that the state has staff capacity to answer questions and revise and resubmit. Approval conditions can be found at 45 C.F.R. § 307.15, but approval criteria might vary by Medicaid IT project and other factors. If approved, implement the plan. Next, the state can carry out the plan described in its Planning and Implementation APDs. After the Medicaid IT project has been operating for at least six months, states can request system certification from CMS. According to CMS, certification is required to receive the enhanced 75 percent federal financial participation for operations. The certification process includes states submitting to CMS an intake form, a certification request letter, and supplemental materials with information on its system. CMS may then start its review to assess whether the state’s system meets certification requirements. If approved, monitor and report progress and submit other APDs as needed. Based on 45 C.F.R. § 95.610(c) and 45 C.F.R. § Part 95 Subpart F and information from CMS, CMCS, Office of Child Support and Enforcement, as the state continues with its Medicaid IT project, it should adhere to monitoring and reporting requirements for enhanced federal funding. It also should submit annual APDs as required. If the state wants to make any major changes to the Medicaid IT project in concept, scope, cost allocation approach, timeline, and other key areas, it must develop and submit an as-needed APD. An as-needed APD is due no later than 60 days after the occurrence of the change. State examples: Medicaid Enterprise System projects Based on information from Alvarez & Marsel, state MES projects will vary based on factors such as the maturity of a state’s technology infrastructure, its specific data needs, and its available resources. As such, projects to design, implement, or operate MES can range in size, complexity, and timeline. For example, the Alabama MES Modernization Program, the Wyoming Integrated Next Generation System Project, and the Florida Health Care Connections project all seek to transform their singular Medicaid Management Information Systems (MMIS) into modular, multi-vendor MES, but differ in approach. In addition, Arizonia and Hawaii are collaborating to modernize their shared MES. For more information on state MES projects, see the Medicaid Enterprise System Solution/Module Contract Status Report. This webpage lists states’ MMIS and Eligibility and Enrollment contract information for their MES projects. It also lists contact information for state officers to reach out to learn more about states’ MES projects. website yes

Recipient Orientation Handbook for OD2A State and LOCAL Cooperative Agreements overdose prevention, substance use disorder, overdose crisis, opioid overdoses, principal investigators, project officer, grants management specialist, secure access management services, public health, mental health conditions, opioid use disorder, drug overdose deaths, opioid overdose deaths, medical condition, cooperative agreements, withdrawal symptoms, drugs or alcohol, prescription drugs, health care, overdose deaths involving prescription opioids, treatment for substance use disorders, deaths involved synthetic opioids, astho, association of state and territorial health officials ASTHO | This guide is for new recipient staff working on the CDC Division of Overdose Prevention cooperative agreements OD2A-S and OD2A-LOCAL. Overdose Data to Action (OD2A) supports jurisdictions in collecting high-quality, comprehensive, and timely data on nonfatal and fatal overdoses and in using those data to inform prevention and response efforts. OD2A focuses on understanding and tracking the complex, changing nature of the drug overdose epidemic and highlights the need for seamless integration of data into prevention strategies. The OD2A program page details funded jurisdictions, how this cooperative agreement contributes to CDC’s efforts to prevent opioid overdoses, and more. This orientation handbook serves as a resource for new recipient staff involved in the CDC DOP OD2A awards, in the hopes that it will assist them in managing their award. Get the Guide (PDF) website yes

Explore INSPIRE: Readiness by ASTHO—an innovative hub for public health preparedness. Share strategies, tools, and solutions to boost readiness.

ASTHO, with support from CDC, launched the first cohort of the Linking Pregnancy Risk Assessment Monitoring System and Clinical Outcomes Data Multi-Jurisdiction Learning Community in October 2021. This brief examines themes that emerged from conversations in the learning community about the challenges encountered during data linking activities and discussed lessons they learned.

Information on disability status and type is not systematically collected during emergency department visits and, as such, it cannot be used during surveillance. ASTHO conducted six key informant interviews with disability professionals to inform development of this new diagnostic code-based definition.

ASTHO has been exploring how using GIS and data visualization tools can improve traditional public health work. This brief shares several expert use cases for visualizing and integrating environmental health and electronic health information with the assistance of GIS tools to improve public health decision making.

Tobacco use causes 480,000 premature deaths each year, making it the leading cause of preventable death in the United States. In collaboration with CDC, ASTHO is leading an effort to explore how states can use Medicaid data to quantify tobacco use within this population, identify related best practices, and make recommendations for scaling.