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State firearm survey data can inform firearm injury prevention strategies—read recommendations for developing state firearm surveys and applying findings.

Learn key lessons from the DREAM Learning Community about addressing structural racism and driving systemic change, which can help achieve optimal health for all.

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

Explore key actions, indicators, and supports to enhance workforce recruitment and hiring in governmental public health.

Learn how retail program standards improve retail food safety in this infographic.

Overdose Data to Action (OD2A) Case Study: New York State’s Power of Voice Program

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

This brief dives into the impact of the COVID-19 pandemic on the ability of people with disabilities to access vital health care services during the public health emergency.

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

Leveraging Medicaid to Support Community Health Workers astho, association of state and territorial health officials, community health workers, health equity, medicaid coverage, chw workforce, social service, public health, health care system, improve health, individual and community, mental health, achieving health equity, social determinants of health, underserved communities, united states, health disparities, medicaid program, state Medicaid, advance health equity, highest level of health, people of color, community they serve, improve access, people living, increased health Vanessa Finisse, Madison Hluchan How to leverage Medicaid to support community health workers. Community health workers (CHWs) are pivotal in advancing health equity and improving population health, especially for marginalized communities. Today, there is increasing federal investment to better integrate CHWs into the health care system, spurred by post-COVID-19 federal legislation. While the benefits of CHW integration are well-documented, sustainable funding remains a challenge. This brief, developed in partnership with the Center for Health Care Strategies (CHCS), explores Medicaid coverage for CHW-led services and highlights opportunities for state and territorial health agencies (S/THAs) to collaborate with Medicaid to support CHWs. Key Considerations Medicaid-Funded CHW-Led Services Medicaid authorities can finance CHW-led services, such as state plan amendments (SPAs), section 1115 demonstrations (1115 waivers), and managed care flexibilities. States can pick a pathway depending on their goals, timeline, and administrative capacity. As compared to SPAs, 1115 waivers provide states with more flexibility to waive federal Medicaid rules to test innovative approaches (see Table 1). website yes

Centralizing Administrative Functions, with Lessons Learned from Guam Megan Drake-Pereyra Centralizing administrative functions, such as procurement or grants management, is a strategy many organizations utilize. Having administrative functions concentrated with a specific team rather than dispersed or managed within separate teams can work well. There is potential for standardized processes and procedures, increased efficiency and quality, more control and accountability, and consistent data collection and monitoring. This brief details how health departments can utilize existing, evidence-based frameworks to centralize administrative functions and build off lessons learned from others, such as the Guam Department of Public Health and Social Services (Guam DPHSS). Getting Started Considerations When transitioning from a decentralized structure to a centralized structure, it is important to clearly outline the what, why, how, and benefits. Consider the following components to kickstart success: Leadership vision: Start with the leader’s visionary perspective. When the leader allocates sufficient time and consistently reinforces the vision, it allows for the necessary decisions, trust, and support to be established during the transition. Data-driven design: Use data and existing information, such as current standard operating procedures or process diagrams, to understand the decentralized process differences/similarities, and guide effective centralized processes and procedures. Role clarity: Clearly outline and define the new centralized infrastructure, purpose, roles, responsibilities, expectations, and procedures. This helps everyone understand and follow the new processes more consistently, with better results. Performance measures: Establish and use performance measures from the outset (e.g., team knowledge, skills, competency, process time and quality, outcomes, impact, etc.), for insight into the value, or return on investment, of the centralized model. This will help indicate the quantity, quality, and impact of programs/processes. Documentation: Capture and share decisions, vision, goals, structure, standard operating procedures, and relevant details in writing for new team members and users of the centralized functions. Communication: Transparently share plans, timelines, and additional knowledge to maximize utilization and value. Additionally, anticipate and proactively address resistance to change to help everyone embrace and adhere to the new, centralized approach. The Plan-Do-Study-Act Method Change management, quality planning, and process improvement models can also support organizational and process change. For example, the quality improvement methodology, Plan-Do-Study-Act (PDSA), offers an effective framework for centralizing administrative functions and complements many of the aforementioned considerations: Step one, plan, relies on leaders to decide the vision, scope, structure, roles/responsibilities, goals, and purpose of the centralized team. Here leaders establish and reinforce the leadership vision, using existing data to guide the design of the new centralized team. Step two, do, is dedicated to onboarding centralized team members, defining their work processes and procedures, and ensuring effective communication with all stakeholders. This requires thorough documentation and strong communications plans. Step three, study—an often overlooked but crucial building step—is for testing the processes, procedures, roles, and responsibilities, to confirm and build confidence that this centralized structure will yield the desired results. Performance measures provide clarity into what is working well and what is not. Step four, act, is for launching and rolling out the structure, ongoing monitoring of performance, and continuing to educate and coach for successful, sustainable improvements. Lessons Learned from Guam Guam DPHSS, a joint health and social services agency, is working to centralize its administrative functions to reduce inefficiencies and redundancies as well as improve quality and consistency. This has been a big change for Guam DPHSS, but leadership vision, documentation, role clarity, and communication have proven to be key throughout the process. In 2021, Guam DPHSS established a centralized Office of Grants Management (OGM). In its early stages, programmatic teams saw OGM as a regulatory body that would audit and direct their work, while OGM’s true objective was to provide support and ease administrative burden, allowing program staff to focus on accomplishing their goals and deliverables. By clarifying and documenting the vision, roles, and responsibilities as well as focusing on communication, the OGM built trust, addressed specific concerns, and established a shared vision of their role as supportive and helpful. In 2023, Guam DPHSS began the process of establishing a centralized Procurement Management Office (PMO). While Guam DPHSS reorganized and co-located staff into the new, centralized PMO, Guam was undergoing a governmentwide business process improvement (BPI) project focused on procurement—presenting an opportunity for Guam DPHSS to involve new PMO staff and other key DPHSS team members in improving its functions and centralizing the procurement process. Through the BPI project, which utilized PDSA, DPHSS clarified roles and responsibilities, defined work processes and procedures, and developed training and communications plans that supported process improvement and centralization of procurement functions. Guam DPHSS has learned many lessons throughout their journey to create a centralized OGM and PMO, including that change of this magnitude is hard—more specifically, balancing change management while ensuring maintenance of key operations. Ultimately, they found that the aforementioned considerations and methods for getting started were critical in supporting the change to centralized administrative functions. Establishing the Ideal Structure for Administrative Functions Determining if and how centralized administrative functions will work for an organization is multifaceted. An organization’s culture, size, infrastructure (including technology and systems), and workforce and skills all play crucial roles in shaping the ideal structure. The methods and considerations noted previously can help health departments determine and support the best path forward for each unique organization. ASTHO has several additional resources and tools that can support administrative change and improvement. Visit the ASTHO STAR Center to learn more. website yes

Engaging Communities Is a Critical Tobacco Control Strategy Community Engagement Tobacco Control, Menthol Cigarette Disparities, Tobacco Control Learning Collaborative, Culturally Tailored Tobacco Interventions, Flavored Tobacco Product Legislation, United States, Flavored Tobacco Product, Health Equity, Youth and Young Adults, Tobacco Free, Cigarettes Smoked, Community Partners, Young People, Community Health, Youth Tobacco Survey, Flavored E-Cigarettes, Smoking Cessation, Tobacco Industry, Smoking Behavior, Study Showed, African American, Smoking Rates, Tobacco Marketing, Minority Populations, Hispanic Black, ASTHO, Association of State and Territorial Health Officials Charla Sutton, Matta Sannoh, Josh Berry, Kenny Ray, Ashley Hebert, Iman Byfield For decades, the tobacco industry has disproportionately targeted communities of color increasing rates of menthol cigarette use and tobacco-related health disparities. By prioritizing community efforts, health agencies can confront these disparities by fostering trust, inclusivity, and cultural responsiveness. Funded by CDC’s Office of Smoking and Health (OSH) and in partnership with The Center for Black Health & Equity (The Center), ASTHO initiated the Increasing State Menthol Capacity Learning Collaborative consisting of eight state tobacco use prevention teams each paired with a local community-based organization. The program fosters strong linkages between state commercial tobacco control programs and community-based partners to reduce menthol and flavored product use. The Role of Community Engagement Community-based initiatives are pivotal in tobacco control efforts, as they enable stakeholders to: Understand history, context, culture, and geography. Underserved communities possess a keen awareness of the origins of their problems and how decision-making processes affect them. Embrace community voices. “No one asked us” is the most common feeling communities most impacted by a problem share when decision-makers act without including them. Build organizational capacity that sustains change, creates credibility with decision-makers, and empowers communities to meet challenges head-on and garner support for their initiatives. Barriers to effective community engagement include insufficient training, funding, communication, and planning, plus disorganization, under-acknowledged communities, over-committed leaders, and inability to change course. Learning Collaborative at a Glance Eight state health teams (IN, MN, NY, PA, RI, MI, WA, WI)—each paired with a community-facing organization—kicked off the Increasing State Menthol Capacity Learning Collaborative in January 2023 with a shared vision and plan to reduce menthol and flavored product use. The Collaborative worked to: (1) improve capacity to identify and implement strategies to prevent menthol and other flavored tobacco product use, (2) strengthen collaboration between state commercial tobacco control programs and community-based partners, (3) tailor interventions to those most affected, and (4) understand the role of policy interventions and/or systems change and culturally-appropriate cessation strategies. ASTHO, OSH, and The Center provided peer-to-peer learning, technical assistance, and networking opportunities to help project teams draw from the group’s various resources, expertise, and experiences. For example, each state team participated in five virtual, expert-led learning sessions, which provided training on SMARTIE goals, equity-centered community engagement strategies, and effective communication messages for policies that restrict or eliminate the sale of flavored tobacco products. In addition, technical assistance provided the project teams guidance on their established workplan objectives and helped them navigate community-specific challenges. Menthol Capacity Building Strategies Each team worked to address health inequities of their chosen target population with culturally-tailored actions in one of three strategies: (1) Policy, Systems, and Environmental Change, (2) Menthol Cessation, or (3) Counter Marketing/Public Education. Teams focused on African Americans (nearly two-thirds of whom start by using tobacco with menthol), youth, Latinx, immigrant populations, and the LGBTQ+ community. Each team curated state-specific infographics, factsheets, webpages, and media campaigns to examine the role of policy in reducing menthol and flavored tobacco product use. Others engaged legislators or held educational events. Key Takeaways and Next Steps Community Engagement and the Menthol Landscape: Despite challenges, preemption should not stop community engagement work. While state or federal laws and regulations may change, the communities most impacted—and their voices, experiences, and advocacy efforts—remain and are essential in driving meaningful change. Ongoing awareness of the disproportionate impact of menthol and other flavored tobacco products on marginalized communities underscore a continuous need for community engagement and policies that prioritize health equity. Partnering for Influence and Advocacy: Community engagement fosters awareness of the unique challenges that marginalized populations face, ensuring that initiatives are tailored accordingly. In the face of preemption and other regulatory challenges, community voices are critical for national change. Mobilizing Support through Collaboration: Partnerships between state agencies and local organizations allow capacity building and resource sharing. Such partnerships help mobilize broader support with both constituents and legislators, share best practices/lessons learned, and collectively address challenges. Funding Local Initiatives: Effective community engagement often requires financial resources. Examples include facilitating quality meetings as needed, developing educational tools for community dissemination, using paid and social media, and obtaining individuals to implement key activities (e.g., employees or subject matter experts). The collaborative’s participants further encourage: Sustaining and strengthening partnerships with community-based organizations, state health agencies, and national partners to leverage stakeholder expertise and insights. Investing in ongoing capacity building efforts to equip communities with the knowledge, skills, and resources to address tobacco-related challenges effectively (e.g., training, resource sharing, offering technical assistance, and funding community-led initiatives). Engaging with policymakers, community leaders, and others to raise awareness about the negative impact of menthol and other tobacco products. Advocate for evidence-based policies (e.g., e-cigarette flavor restriction) at the local and state level to inform national discussion. Sharing lessons learned—both successes and challenges—with others. website yes

Implementing Levels of Maternal Care Improves Access to Risk-Appropriate Care Lexa Giragosian Levels of maternal care support risk-appropriate care for pregnant and birthing populations. Risk-appropriate care (RAC) is a strategy to ensure that pregnant women and infants with high risk of complications receive care at facilities with personnel who offer services at the required level of specialized care. States can use the process of perinatal regionalization to create coordinated care systems based on levels of maternal care to support RAC access. Implementing and strengthening maternal RAC systems can improve health outcomes for pregnant and birthing populations and reduce the incidence of severe maternal morbidity and mortality. Wanda Barfield - Brief - Implementing Levels of Maternal Care Improves Access to RAC website yes

This brief examines the ways states can support certification for community health workers.

Strengthening Maternal and Infant Health Data in the U.S. Territories ASTHO, association of state and territorial health officials, maternal and infant health data, U.S. territories, public health, surveillance programs, pregnancy risk assessment monitoring system, improving the health, live births, health problems, reproductive health, federal government, toggle the centers for disease control and prevention cdc, risk assessment monitoring system, assessment monitoring system prams, pregnancy risk assessment monitoring, maternal and infant health, information collected, table of contents, population based, health status, supreme court, prams data, toggle the table, risk factors, prenatal care, collecting information Stephany Strahle The U.S. territories—Puerto Rico (PR), U.S. Virgin Islands (USVI), Guam, Commonwealth of the Northern Mariana Islands (CNMI), and American Samoa—are largely excluded from most statistical data systems in the United States. This gap leaves island health leaders, national partners, and federal agencies without the surveillance necessary to inform timely and robust public health programs and policies. This is also seen in critical maternal and child health surveillance programs like the Pregnancy Risk Assessment Monitoring System (PRAMS), Maternal Mortality Review Committees, and the Pregnancy Mortality Surveillance System, which either do not include or only recently included territories in their scope of coverage. This incomplete information creates challenges in identifying the aspects health systems need to address to reduce adverse maternal and infant health outcomes. Applying a life course perspective to maternal and infant health data reveals gaps in public health systems that impact outcomes before, during, and after birth. PRAMS provides vital insights into these lived experiences and pregnant people’s interactions with health care services. PRAMS data can also be linked to other administrative datasets, such as Medicaid, child welfare services, and Community Healthy Start programs, to provide a broader understanding of determinants of health across the life course for both the birthing parent and their child. With the breadth of contextual experiences that PRAMS captures in its data and the potential for data linkage projects to explore outcomes and their contributing factors, U.S. territories can leverage this wealth of information to assess the needs of their pregnant communities and their children. Despite its development in 1987, PRAMS has been implemented in only two territories, PR and CNMI, within the past decade. This brief highlights the work of these two islands and the potential to gain further insights into maternal and infant health outcomes using data linkage methods. Island Expansion of Maternal and Infant Health Surveillance Using PRAMS Since beginning PRAMS data collection in 2017, PR has made considerable strides in providing their communities with comprehensive reports on various topics. In 2021, one in eight live births was preterm in PR—the U.S. average is one in 10 live births. This outcome is one example of a potential research area in PR that could leverage PRAMS linkages to clinical administrative data sources to investigate contributing factors. In a special project conducted from 2016 to 2018, PRAMS served as an avenue for assessment of Zika awareness among pregnant people and their partners. Moreover, PRAMS informed numerous reports and educational materials on topics ranging from dental care to lactation and opioid use during pregnancy. Linking PRAMS to other administrative datasets could illuminate more information about health care utilization and access among pregnant people in PR. Although limited research exists on maternal and infant health outcomes in CNMI, available evidence reveals disparities in preterm birth among the territory’s indigenous Chamorro and Carolinian communities and Asian and Pacific Islander groups. Since CNMI started administering PRAMS in 2021, strong relationships with entities outside the territory (e.g., the Hawaii Department of Health) have facilitated PRAMS implementation by helping navigate Internal Review Board regulations and applications—both of which are necessary to conduct PRAMS collection and potential research using PRAMS data, like data linkage projects. Moreover, the CNMI PRAMS team’s deep familiarity with their communities could help identify local administrative data sources that, when linked to PRAMS, capture priority areas for improved health care and social service delivery. Considerations for Future Maternal and Infant Health Data Exploration With the existing gaps in surveillance data available for maternal and infant health, this recent implementation of PRAMS and the potential for data linkages to other data sources could provide enhanced insights for U.S. territories. The following considerations can inform best practices to optimize this data. Building Partnerships to Support a Linked Maternal and Infant Health Data Network To build capacity for further data exploration, building partnerships with other agencies and PRAMS jurisdictions can facilitate the information-sharing necessary to navigate data use agreements and other considerations before successfully linking data. Leveraging these connections can also supply more avenues to administer educational tools about PRAMS and perinatal services, linking their pregnant populations to the services they need. A robust web of partnerships can create a network of linked data capturing the life course perspective to inform high-quality programs for the ongoing care of pregnant people and their infants. Leveraging Community Input and Data on Social Determinants of Health Territories are uniquely positioned to leverage closer community ties to examine how data linkages can inform initiatives that improve experiences surrounding pregnancy and the life course after birth. As with PR, integrating the voices of pregnant people, their families, and the people providing their care into their advisory committees allows for better identification of what communities need. Active engagement ensures agencies can be efficient with their linkage efforts by tailoring their projects to high-priority maternal and infant health outcomes. Moreover, to foster community awareness about PRAMS and possible linked data sources, territories could create dashboards such as those created by Washington D.C.’s PRAMS program to provide a comprehensive and interactive view of the data. Data on social determinants of health collected through PRAMS—such as insurance coverage throughout pregnancy and postpartum as well as access to social support and a wide range of services—can also be leveraged for potential data linkage to identify inequities in health outcomes and the delivery of care. 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.

The Homeland Security Exercise and Evaluation Program includes seven exercise types that are either discussion-based or operations-based.

Hypertension disorders in pregnancy are the leading cause of maternal death, but state and territorial health agencies can address hypertension in pregnancy and reduce maternal morbidity and mortality.