Bridging the gap: enhancing data science and leadership knowledge and skills in the context of the public health workforce

Introduction: This study addresses critical gaps in public health workforce development, focusing on data science and leadership skills amidst increasing data accessibility and complexity in public health practice.

Methods: Quantitative and qualitative research methods were employed, including listening sessions with diverse public health professionals. Recruitment and post-session surveys were conducted, and session transcripts were analyzed using phenomenological and categorical coding methods based on the PPP Framework.

Results: The research identified three core concepts in public health data science and leadership: data science, data literacy, and data-informed leadership. Clear definitions for each concept were developed. Significant gaps in workforce capacity, accessibility, and training were highlighted, particularly in aligning academic curricula with real-world public health needs.

Analysis: The study revealed a disconnect between current public health programs and workforce preparedness, performance, and practice. Participants emphasized the need for comprehensive leadership development programs and integration of data science and leadership components into existing curricula.

Discussion: This work provides a foundation for strengthening the public health workforce by identifying key concepts and gaps in data science and leadership. The findings have implications for policy development, resource allocation, and building a competent workforce capable of leveraging data for improved public health outcomes. This research contributes to advancing the field of public health by communicating scientific knowledge crucial for future breakthroughs in workforce development.

1 Introduction

1.1 Challenges in public health data

Data constitutes a critical resource in the domain of public health, underpinning evidence-based practices, intervention strategies, and the enhancement of community health and safety. Public Health data is used to understand the burden of disease, predict and improve health outcomes, recognize solutions to disparities, and provide evidence of the impact of interventions (1). Since its formal establishment as a field, public health has leveraged data to fulfill its mission to enhance overall health and wellbeing for all people. This mission was highlighted in the 1988 Institute of Medicine (IOM) report, The Future of Public Health (2). The updated 2002 IOM report’s call to action was to strengthen governmental public health through advancements in their capabilities in surveillance (3). To achieve this, data collection and analysis required modernization, the pursuit of which developed the specialty of Public Health Informatics and Data Science. Over the past two decades, with greater attention on public health data, surveillance, collection and application for decision-making, the role and vulnerabilities of data science have become increasingly apparent. During the inaugural Data Science in Public Health Summit, participants described data science as an overarching term for four emerging trends: increased data availability and complexity, development of computational methods, advances in computational infrastructure, and growing concern around scientific rigor.

1.2 Challenges in the public health workforce

The COVID-19 pandemic underscored numerous deficiencies in public health systems, including inadequate workforce capacity exacerbated by years of underfunding, as highlighted by a reduction of 40,000 public health jobs between the 2008 Great Recession and the pandemic’s onset (4, 5). The lack of support for national public health efforts negatively impacts efforts to effectively deliver the Foundational Public Health Services, described as the bare minimum communities should be offered.

1.3 Examining intersecting gaps between the public health workforce and public health data science

The public health workforce interests and needs survey (PH WINS) has brought forth gaps within the field (6) including training needs in strategic thinking, diversity, equity, and inclusion (DEI), data decision-making, and policy engagement. As Fraser et al. (7) pointed out, PH WINS is not only a dataset but an advancement that can promote data-driven workforce planning in governmental public health. PH WINS reveals that one of the biggest gaps in public health is the lack of multisector and crosscutting leadership and the need to transition from individual to collective-focused leadership. The current public health workforce is further challenged by the widespread availability of big data and user-generated data that requires new analytical methods and data science developments to guide public health practice and policy. Data science plays an essential role in informing public health decision making, however inadequate investments in the workforce and infrastructure, including those related to data science leadership development, present challenges to the workforce’s ability to keep pace with the dynamic and rapidly evolving landscape of data availability (8). Inadequate systems in surveillance, information technology, and data processing prevent time-appropriate interventions centered in scientifically sourced data and free of misinformation to be provided to the public (5).

1.4 The role of academics in addressing gaps in the public health data science workforce

The new shifts and advances in data science and public health leadership are reflected in current trends of academic offerings. Data science undergraduate, graduate, and upskilling programs are increasing in popularity (1). The increasing accessibility of data and the need for collection, analysis, and visualization highlight the disconnect between the public health workforce knowledge and needs (8), pressuring academic and training institutions to address how to adequately prepare students. However, there is little information on the employment outcomes of recent public health graduates or knowledge and skills matching between their education and the core public health workforce (5). The absence of this data makes it challenging for the governmental public health workforce to understand recent graduates’ data science and leadership skills. A better understanding of the misalignment between academic training and the workforce needs is required to establish a sustainable, competent pipeline.

Mirin et al. (9) reviewed educational programs in public health data science across top domestic and global public health schools, finding that most data science degrees are awarded at the master’s level, primarily within epidemiology, biostatistics, and health informatics. They noted a significant gap in access to data-intensive courses for students in socio-behavioral fields, suggesting a need for further research to align public health data science curricula with the technical and essential skills required for successful careers in data science and leadership (9). This is exemplified in a recent study that identified data science and leadership as two of the four domains of skills required by local public health departments for responding to community needs and improving health outcomes (10).

Through a commitment to advancing the public health workforce to meet community needs, the National Network for Public Health Institutes (NNPHI) began exploring the landscape of data science and leadership in public health. NNPHI aims to address both the internal needs of the workforce and their ability to meet the evolving demands of the communities they serve. In partnership with the Centers for Disease Control and Prevention’s Public Health Leadership and Education, Advancing Health Equity and Data Science (Public Health LEADS) program, NNPHI collaborated with Health Communications Consultants, Inc. to focus on the data science and leadership skills across current public health career pathways for mid to senior-level professionals (11).

This exploration served as a foundational effort to enhance the public health workforce. Through listening sessions, we identified and defined key concepts in public health data science and leadership, as well as gaps in capacity, accessibility, education, and training. This paper discusses these concepts and methods, synthesizing participant insights into three core areas: data science, data literacy, and data-informed leadership.

2 Materials and methods

An observational, cross-sectional evaluation was conducted in July 2023. The data collection methods included three tools: (1) recruitment survey (Table 1), (2) listening sessions, and (3) post listening session survey (Table 2).

Table 1

Table 1. Recruitment survey.

Table 2

Table 2. Post listening session survey.

2.1 Measurement tools

The recruitment survey was comprised of 21 questions: 13 multiple choice questions, seven open-ended, and one text-limited. Utilizing Qualtrics, the recruitment survey was administered to a convenience sample recruited via social media and personal connection outreach from June 11, 2023, through July 11, 2023.

A semi-structured conversational tool (Table 3) was created to help guide the listening sessions. Listening sessions were recorded and transcribed for use.

Table 3

Table 3. Semi-structured conversational tool.

The post listening session survey was comprised of 14 questions: 4 multiple choices, 9 open-ended and 1 demographics-related question. Utilizing Qualtrics, the post listening session survey was administered from July 12, 2023, through July 27, 2023.

2.2 Participants and procedure

2.2.1 Recruitment

The recruitment survey used the online Qualtrics platform, targeting members of the public health workforce from academia, government health departments, public health nonprofits, and related organizations. Convenience and snowball sampling methods were used through the research team’s networks to expand the participant pool. A recruitment email and flyers were sent to the participant pool with consent to participate assessed by their continuation through the recruitment process. Follow-up email communications were sent to individuals who responded to the initial contact. Participants were given the option of selecting one of four 90-min listening sessions in which to participate. A review of recruitment survey responses was done to ensure that potential supervisory and subordinate coworkers were not in the same sessions. Once a session selection was made, an email invitation with a unique identifier was sent to the participant. A reminder email was sent 1 day prior to the scheduled date to improve the participation rate and ensure timely attendance. Persons unable to make their initial selection were communicated with to reschedule for their second selection or for a later listening session date.

2.2.2 Listening sessions

We applied phenomenological methods of qualitative research for the listening sessions to encourage open conversation on the topics to be explored. Phenomenological methods are used for the purpose of understanding the nature of the experiences while avoiding interviewers inducing speculation (e.g., “Why do you think your supervisor wanted you to do that?” or “How would you feel if you had received a different kind of training?”) (12–14). The approach allows for naturalistic conversations that flow with the participants’ responses as opposed to traditional focus group methods which utilize the same questions for each focus group and can bias participants’ responses in the direction of the specific question. It encourages participants to share their stories and lived experiences in a safe and supportive context while the conversation flow provides insights into the priorities of specific decisions and their consequences. The approach also provides benefits for participants by providing opportunities to be authentically “seen and heard” (13, 14). A semi-structured conversational tool was created to help guide the sessions (Table 3). Prior to entering the Zoom platform, participants were renamed using the unique identifier assigned to them to ensure anonymity in the evaluation processes. Participants were encouraged to turn their cameras on during the session, but it was not required. A PowerPoint slide deck with welcome and thank you slides containing access to the post listening session survey were created to initiate and end the conversation. The survey link was provided to participants at the conclusion of the listening session and provided again prior to the closure of the survey. To be eligible for the participant incentive, participants needed to complete the post listening session survey which was also hosted on a Qualtrics platform. Listening sessions were recorded and transcribed with a transcription service. The evaluation team observed and took notes during each session. The MPHI (Michigan Public Health Institute) Institutional Review Board determined all research methods presented were exempt from further review.

2.3 Analysis

Descriptive analysis was conducted on the recruitment survey and post listening survey data results. Open-ended questions in both surveys specified that participants’ answers addressed two factors: (a) public health data science, and (b) public health leadership. The resulting binary code used in coding and analysis could be interpreted as discrete or unified parts.

After the first coding phase, categorical coding was used on the listening session transcripts by three independent coders to break the data down further into three discrete parts: (1) People, (2) Process, and (3) Products (The “PPP Framework”). The framework is one frequently used in organizational improvement. For our analysis, the PPP Framework parts were described as follows:

• People: people within the public health workforce and how they engage with one another.

• Process: processes and procedures within public health organizations.

• Products: products or services offered, provided, or needed by the public health organizations.

• Environment: external or internal environmental factors that affect the PPP Framework (e.g., political climate, social variables, workforce culture, etc.).

The exploratory nature of the evaluation warranted an inductive approach. Open coding was conducted to create additional themes/sub-categories. Codes were aggregated and condensed across coders per listening session and then across all listening sessions. There were eight axial categories:

• Collaboration: activities of two or more people or organizations working toward a common goal, which could include products, processes, desired effects, outcomes, etc.

• Funding/Resources: finances, materials, staff, or other assets needed to function effectively.

• Recruitment and retention: the process of identifying, attracting, interviewing, selecting, and hiring people to join a public health organization and then keeping those employees within the agency and/or in the public health workforce.

• Systems improvement: activities to make the public health system or related public health processes, products, or people more effective, efficient, transparent, fair, inclusive, etc. (e.g., improvements in data modernization, policy, development, etc.).

• Tools/technology: devices, software, hardware, applications, programming, programming languages, information technology (IT) systems.

• Workforce: people within the public health workforce and their work-related attributes.

• Workforce development: processes and products that educate and train individuals and groups to effectively meet the current and future needs of the agencies involved in planning for and delivering public health services.

• Other: includes responses that fell outside of the eight axial codes (e.g., factors such as awareness, value, time, etc.).

3 Results

3.1 Participant profile

There were 267 unique email invitations sent to persons identified in the public health workforce during the recruitment timeframe resulting in a 25.8% response rate (69/267). Sixty-five (65) respondent surveys were retained for analysis; of which twenty-six (26) respondents participated in a listening session (LS). The average age of respondents was 40 years with a range of 22–64 years of age, while the age of listening session participants was an average of 43 years and range of 31–63 years of age. Respondents and listening session participants were majority white [Survey: 55% (36); LS 53% (21)] and female [Survey: 70% (46); LS 54% (14)]. Respondents and listening session participants are highly educated with bachelor’s degrees [Survey: (36); LS (1)], masters’ degree [Survey: 74% (48): LS 50% (13)] and doctoral degrees [Survey: 28% (18); LS: 46% (12)]. Forty-nine percent of respondents identified that their advanced degree was in the field of public health (n = 32). Respondents identified their experience in public health programs with data science (n = 55), public health leadership (n = 41), public health workforce development (n = 38), public health recruitment and retention (n = 24), and public health leadership curricula (n = 14) as >2 years of experience (n = 59). Their current work environment included local health agency (n = 16), state health agency (14), educational/academic institute (12), private nonprofit organization (10), private for profit (5), federal health agency (2) and other (5) which they classified as urban (30), suburban (18), Other (12, mixed settings), rural (3) and tribal area (1). See Supplementary Table 1 for additional information on participant profiles.

3.1.1 Motivation for participation

Of the 26 listening session participants, 14 responded to the post listening session survey (54% response rate). All survey respondents (100%) indicated that they participated in the listening sessions due to altruistic motivation, 50% (n = 7) had an intellectual motivation, 36% (n = 5) were motivated because the request came from a peer or someone they respected, while 29% (n = 4) participated for the camaraderie or shared experience.

3.2 Key public health data science and leadership concepts for the workforce

Coding methods revealed several concepts under public health data science and public health leadership. Public Health Data Science concepts included data analysis, data management, data interpretation, data visualizations, data-driven decision making, data literacy, surveillance and monitoring, communication with data, data modernization, storytelling with data, data access and sharing, emergent technology, data integration, and data interoperability. Public Health Leadership concepts included policy development, strategic planning, communications, and advocacy, interdisciplinary collaboration and partnership building, crisis management and emergency response, leadership development, health equity and social determinants of health, innovation, systems thinking, and interpersonal skills (Table 4).

Table 4

Table 4. Concepts identified under public health data science and leadership.

The emerging concepts were compatible with resulting concepts from a previous NNPHI public health workforce development project that resulted in a taxonomy used to describe essential skills in the workforce. The framework from that project was applied to clarify and organize the data. Through this application it became clear that data literacy should be recognized as a fundamental concept rather than a subset of data science. As a result, three core concepts emerged: data science, data literacy, and data-informed leadership (15).

3.2.1 Data science

3.2.1.1 Data science defined

While there is variation across sources and organizations regarding definitions of “data science,” all suggest a combination of math and statistics skills often executed using software and/or programming. Data science overlaps with areas like advanced analytics, artificial intelligence, and machine learning.

Work conducted in data science will generally fall into the following process sequence:

• Data intake: collecting data from primary or secondary sources and using structured (such as a database of prescription distributions) or unstructured (like focus group transcripts, or online traffic).

• Data storage: organizing data so that it can be reliably accessed. Proper storage enables workflow and processes so that the data is usable when and how it needs to be used.

• Data analytics: testing and manipulating data using a scientific method. This includes hypothesis testing as well as predictive and generative applications anchored in replicable practices. In this area of work, data visualization can be both process and product as it can help the data scientist assess, but it also is a crucial tool for assisting others who must interpret the data.

• Data communication: similar to scientists in other fields, data scientists must use communication skills. The results of their analyses must be presented in a way that empowers administrators to make decisions and community members to act on data results and implications.

3.2.1.2 Data science gaps

Participants from the listening sessions offered many observations related to data science; these included identifying several gaps in the field that impede the workforce. Many public health staff within their departments lacked formal coursework in data science. Additionally, training to support staff in a rapidly changing environment, such as in advances in tools and technology, was absent. Many saw a need to improve the data infrastructure and a need to align academic curriculum with on-the-job needs to better recruit and retain staff. Additionally, participants identified leadership support to provide opportunities for mentorship and career advancement.

In the listening sessions, participants noted that there needed to be a holistic interdisciplinary approach to training/education, to enable current public health students to gain skills like prioritizing practical applications, engaging in data analysis projects, and gaining experience in data management and interpretation. Participants also mentioned a need for formal training in leadership and management; in general, and as it relates to data science, with additional discussion on the challenges of confidently communicating data science results to leaders and by leaders.

The listening sessions also suggested that data science involves high levels of expertise in tool use, i.e., SAS, Excel, machine learning, statistical techniques, experimental methods, and technical writing, all of which were identified as domains in the Essentials Framework (15). “Data science practice concepts and skills that must be conveyed to students right out of grad school” a listening session participant noted.

The participants discussed the demand for data-driven courses and curricula for workforce development, noting that current academic curricula did not sufficiently incorporate data science skills. There was a perceived lack of complete and standardized curriculum exclusively focused on data science skills. For example, one participant stated that “teaching them not only how to do the data analysis, but how to sort through what’s data and what’s noise” is a key skill. See Table 5 for a complete listing of “Data Science Gaps” identified by participants.

Table 5

Table 5. Data science gaps identified by participants.

3.2.2 Data literacy

3.2.2.1 Data literacy defined

Data literacy is the ability to find meaning in data. Just as language literacy is far more than the technical component of letter sounds and grammar, data literacy is the synthesis of a spectrum of knowledge, skills, and attitudes (KSAs) that enable clear communication that retains consistent meaning. Data literacy tasks typically include a combination of the following:

• Data quality assessment: knowing when data meets requirements for reliability and appropriate representation of a given phenomenon. When advising leadership, quality assessment helps to know the degree of trust that can be placed in the data analysis results. This includes understanding the appropriateness of certain mathematical and statistical techniques for given data types and objectives.

• Data logic: recognizing fallacies and applying logic techniques. Awareness of logical fallacies is a priority for strong data literacy.

• Data communication: emphasizing Emotional Intelligence (EI), data literate personnel communicate about data in a manner that can be received, understood, and acted upon. They also recognize the emotional components in data communications, providing resilience against misinformation, bias, or decontextualization.

3.2.2.2 Data literacy gaps

Data literacy, and its required communication and visualization skills, were mentioned as opportunities for training and were part of the most pressing needs of the workforce. Specific opportunities captured include training in data literacy, health literacy, plain language, risk communication, communicating with data, communicating data to leadership for accurate external reporting, data decolonization and community engagement. In addition to noted training opportunities, there is a strong workforce need to understand and communicate data use goals, modernize data systems and visualization, and use systemic behavioral science frameworks to understand issues, quantify health needs and build the general public’s trust.

The listening sessions highlighted the need for alignment of the workforce needs and current public health programs. Participants mentioned they received no academic training in how to communicate data interpretation results to the community, on-the-job training in general risk communication, or public speaking. Data communication skills were not practiced in education but rather learned through professional practice. Participants emphasized the importance of communicating results in plain language to leadership, funders, and stakeholders to help facilitate and ensure the information is subsequently conveyed effectively and accurately by those collaborators. See Table 6 for a complete listing of “Data Literacy Gaps” identified by participants.

Table 6

Table 6. Data literacy gaps identified by participants.

3.2.3 Data-informed leadership

3.2.3.1 Data-informed leadership defined

Data-informed Leadership can vary according to what the leadership role itself demands. Generally, Data-informed Leadership uses data to inform, guide, and justify decisions. That said, the leadership role itself (e.g., Team Lead vs. Chief Technical Officer) will dictate the extent to which the following aspects of Data-informed Leadership are included:

• Data governance: setting policies that guide data lifecycles. Governance includes standardization and federation of linked information and protocols for handling data at every stage.

• Data operations: placing talent, processes, and access to the right tools and products to conduct all data tasking efficiently and effectively while maintaining data security. While this is obviously coupled with data governance, the focus of data operations is on the effective use of data, whereas governance focuses on the appropriate use.

• Strategic thinking: determining how data can empower better decision-making outcomes.

• Data communication: identifying dissemination policies and practices within and outside the organization.

• Leadership modeling: everything that a leader does is an opportunity for emulation (or a cautionary tale against it). Consequently, mindful leadership within Data Operations can model data behaviors such as seeking clarification, requesting additional information, and using data to manage teams.

3.2.3.2 Data-informed leadership gaps

Participants’ responses identify specific leadership-related systems issues gaps that impact the current workforce. These systems issues gaps were discussed within the context of data science leadership, and include the lack of leadership, especially with the loss of institutional knowledge and mentorship when departments experience leadership transitions, the lack of leadership training and data science training as well as the willingness to identify likely candidates for leadership training, and the need for skill-building opportunities in leadership for mid-senior professionals. In terms of leadership capacity, participants revealed a need for comprehensive leadership development programs with a focus on policy development, health equity, crisis management, and data science/ data science communication. They further reported that there is no leadership training and education or explicit path for leadership skills development currently available to them.

Participants also revealed a lack of alignment between current public health programs with data science and leadership curriculum and the workforce preparedness, performance, and practice. Under the leadership curriculum needs, participants listed path for leadership skills, cross-training in analytical tools and process, training in data literacy, health literacy, plain language, risk communication, communicating with data, data decolonization, and community engagement as opportunity areas for continuous professional development. Furthermore, participants identified a need for comprehensive leadership development programs tailored to public health professionals, investment in professional development (at all professional levels), collaboration between academia and public health agencies, and the integration of data science and leadership components into existing curricula. See Table 7 for a complete listing of “Data-informed Leadership Gaps” identified by participants.

Table 7

Table 7. Data-informed leadership gaps identified by participants.

3.2.3.3 Connecting data science, literacy, and leadership

“Data Science,” “Data Literacy,” and “Data-informed Leadership” are interrelated yet distinct domains. In the conducted listening sessions, participants were encouraged to discuss their most recent engagements with data, which varied across several types:

a) Supervision and review of data analyses conducted by junior personnel, often leading to the provision of feedback or preparation of presentations for stakeholders like organizational leaders or community members.

b) Collection of primary and secondary data, merging technical tasks such as survey design and database management with Emotional Intelligence (EI). Public health workers employ EI to navigate both direct interactions with target populations and interagency communications, accommodating the cultural and organizational norms of all involved entities.

c) Data storytelling, where beyond technical data manipulation, an understanding of data implications is crucial. Participants highlighted the importance of translating data insights into actionable narratives, requiring effective data visualization and robust communication skills.

Given these distinctions, it is vital that workforce development initiatives in public health adequately address the diverse needs and gaps within data science, literacy, and leadership to align with the career goals of public health professionals.

4 Discussion

We identified individuals from academia, state, local, and territorial health departments, national public health non-profits, and other related organizations who self-identified having experience in public health leadership, public health workforce development, data science education, public health recruitment and retention, and/or public health leadership curricula. The participant profile is demographically comparable to the United States public health workforce (16).

The listening sessions revealed several key concepts which we organized and simplified into 3 core concepts: data science, data literacy and data-informed leadership. We provided a layman’s definition for each core concept to help bridge the gap in defining public health data science and leadership. These definitions could now be used to address some of the gaps in curriculum development, to guide the preparation and development of the workforce, and to better align practice with academic interpretations.

Within the data science domain, the exploration highlighted the need for technical expertise and the role academia plays. Identified gaps showed that entry-level staff were not ready/familiar with technical software, data analysis, and management principles. There was a disconnect between academia and workforce needs—again, this work presents an opportunity to shrink the gap due to conceptualization of data science and leadership concepts/KSAs.

The listening sessions highlighted the need to better align workforce needs and current public health programs. Participants mentioned they received no academic training in how to communicate data interpretation results to the community, nor did they receive on-the-job training in general risk communication and public speaking. Data communication skills were not practiced in education but rather gained through professional practice. Participants emphasized the importance of communicating results in plain language to leadership, funders, and stakeholders.

For the data literacy domain, there is a pressing need for training in data literacy, communication, and visualization for the workforce. Key areas shown in Table 6 include understanding and communicating data use goals, modernizing data systems and using behavioral science frameworks to address issues, quantify health needs and build public trust. The workforce needs to align with current public health programs but there is a lack of academic training especially in the areas of data interpretation, risk communication, and public speaking. Specific opportunities captured include training in data literacy, health literacy, plain language, risk communication, communicating with data, data decolonization, and community engagement.

Regarding leadership capacity, participants indicated a necessity for extensive leadership development programs emphasizing knowledge and skills policy formulation, health equity, crisis management, and data science/ data science communication. They highlighted a lack of available leadership training and education, noting the absence of a clearly defined pathway for developing leadership competencies. The increasing accessibility of data and the need for collection, analysis, and visualization highlight the disconnect between the public health workforce knowledge and needs, (8) pressuring academic and training institutions to address how to adequately prepare students.

The role of the public health leader has increasingly adapted to a “data-centric” world. In the data-informed leadership domain, there is a values-based approach for public health leaders; this lack also permeates other public health domains, not just data science. The issues exist with most, if not all, leadership competencies being drafted prior to 2018—we did not find one set of public health leadership competencies that included “data literacy, data awareness, data communication” as a competency/skill public health leaders need to have. Data informed public health leaders must not only utilize data and insights to guide their decision-making but also must understand the growing need for more impactful data, and advocate for it. Data modernization efforts must include the development of data-informed leaders.

The listening session information we received about perceived gaps in leadership training and education and lack of clear pathways to leadership capacity was amplified by comments about entry-level staff’s lack of readiness with technical software, data analysis, and data communication, as well as comments suggesting that some existing senior public health leadership was equally lacking competency in the very same data science-related areas. Listening session participants described a challenging dynamic: one that existed between intermediate-level staff trying to effectively communicate public health data science results to leadership, and leadership’s difficulty understanding those results. Additionally, there was the perception by some that senior leadership would incorrectly transfer the information upstream or selectively communicate the information to external audiences such as government officials, funders, the media, and the public. Whether these perceptions are limited to the listening session participants or can be generalized to the larger workforce, they highlight the need for additional training and education in public health data science for everyone who engages with public health data and data communication within their public health roles, regardless of agency division or rank.

Based on the listening session content analysis, we determined that further research was warranted to explore data science and leadership in the public health workforce. The results of that research are not included in this paper but revealed the importance of integrating competencies in data literacy and data communication into other public health leadership competencies and paving a pathway of public health data science leadership. Such an integration and pathway would not require a significant departure from either subject; a leader in general public health and a leader in public health data science have many traits in common: both must have as a motivating force a desire to affect positive change and transform the status quo; both must know how to develop a vision with and for their teams, programs, and agencies, as well as the knowledge, skills, and abilities (KSAs) to guide others on the path to reach those long-term goals and objectives. These common KSA’s include, but are not limited to, establishing credibility through both the development of trust and technical expertise in their fields and achieving competency in how to effectively communicate in a way that information is transmitted but, more importantly, understood and acted upon. As we explored the many facets of the intersection between public health data science and public health leadership, we ultimately reached the understanding and agreement that there is no public health leadership competency without public health data science competency; data is the foundation of the core functions of public health, and without competency in at least the basic KSA’s of its application and communication, there can be no comprehensive competency in public health leadership.

We further suggest that there are two crucial areas where this integration of public health data science competency into public health leadership competency can take place. The first, at the academic level through curriculum that has been developed in a collaborative effort between academicians and practitioners. This includes academicians helping practitioners understand the academic process and content that must be considered in the development of collaborative curriculum, such as a primary focus on theory and the key relationships between curriculum and accreditation. This equally includes practitioners helping academicians understand the elements of curriculum that must be considered for practice, such as hands-on practical experience and meaningful goal and competency-focused field experiences. A second method for the integration of data science competencies into public health leadership competencies is through standardized mentor programs within the practice agencies; mentorships that include public health data science and leadership training which can then be conveyed in a standardized format to mentees. A program such as this certainly requires resources and prioritization within the already stretched capacity of public health practice agencies, yet that prioritization is ultimately the key to successful integration—the field of public health must prioritize competency in both public health data science and in public health leadership. Without that prioritization competency will not exist in either discipline at the level required in the current and future public health landscape.

While the participant population reflects the US public health workforce at large, it is limited by the lack of diversity of the respondents and has potential for bias with the self-declaration of experience in the topic areas. To ensure inclusivity and diversity, demographic distributions were continuously assessed and focused efforts were made to reach out to specific groups which were underrepresented in responses received. Additionally, selection bias may have occurred based on network proximity. To mitigate selection bias, only 1 evaluation team member was involved in the process of selecting LS participants based on the self-reported experience recruitment question.

Limitations inherent in qualitative methods were controlled by utilizing a mixed methods approach which included the use of theoretically informed processes in data collection and analysis as well as the use of three independent coders to identify themes and categories.

Our results also aligned with research in the field that highlights the lack of investment in foundational public health services. This lack of financial support continues to contribute to the challenges in better equipping the workforce with the skills in data science and leadership required in public health practice.

5 Conclusion

The work presented herein provides a foundational step toward strengthening the public health workforce by identifying key concepts in public health data science and leadership, as well as pinpointing gaps in public health data science and leadership capacity, accessibility, training, and educational needs. Listening sessions were conducted, and participants shared their experiences with data science and leadership. These discussions led to the classification of three fundamental concepts: data science, data literacy, and data-informed leadership. Clear, layman-friendly definitions of these concepts have been developed, aimed at addressing gaps in curriculum development and data modernization efforts, and they serve as essential tools for advocating for sustained resource investment in public health data science and leadership domains.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Informed consent was obtained from the participants in accordance with the national legislation and the institutional requirements.

Author contributions

DH: Conceptualization, Funding acquisition, Project administration, Writing – original draft, Writing – review & editing. MG: Conceptualization, Project administration, Writing – original draft, Writing – review & editing. CG: Conceptualization, Writing – original draft, Writing – review & editing. DL: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. PB: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. EP: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. SM: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This project was supported by the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award (NU36OE000016-01-00, titled Strengthening Environmental Health -Building Capacity for a More Diverse and Representative Workforce) totaling $366,000 with 100% funded by CDC/HHS.

Acknowledgments

The authors would like to acknowledge Ajay Persaud and Jennifer Ventura at the National Network of Public Health Institutes for their contributions. Thank you to the National Network of Public Health Institutes and Centers for Disease Control and Prevention for their support.

Conflict of interest

DL, PB, EP, and SD were employed by the Health Communication Consultants, Inc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2025.1505869/full#supplementary-material

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