Toward a New Strategic Public Health Science for Policy, Practice, Impact, and Health Equity

Advancing Data Science Among the Federal Public Health Workforce: The Data Science Upskilling Program, Centers for Disease Control and Prevention

CONTEXT

Data can guide decision-making to improve the health of communities, but potential for use can only be realized if public health professionals have data science skills. However, not enough public health professionals possess the quantitative data skills to meet growing data science needs, including at the Centers for Disease Control and Prevention (CDC).

PROGRAM

The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023.

EVALUATION

All 2022-2023 cohort respondents to the end-of-project survey reported the program increased their data science knowledge. In addition, 90% agreed DSU improved their data science skills, 93% agreed it improved their confidence making data science decisions, and 96% agreed it improved their ability to perform data science work that benefits CDC.

DISCUSSION

DSU is an innovative, inclusive, and successful approach to improving data science literacy at CDC. DSU may serve as an upskilling model for other organizations.

Domains of Excellence: A CDC Framework for Developing High-Quality, Impact-Driven Public Health Science Publications

CONTEXT

The Centers for Disease Control and Prevention (CDC) has a long history of using high-quality science to drive public health action that has improved the health, safety, and well-being of people in the United States and globally. To ensure scientific quality, manuscripts authored by CDC staff are required to undergo an internal review and approval process known as clearance. During 2022, CDC launched a scientific clearance transformation initiative to improve the efficiency of the clearance process while ensuring scientific quality.

PROGRAM

As part of the scientific clearance transformation initiative, a group of senior scientists across CDC developed a framework called the Domains of Excellence for High-Quality Publications (DOE framework). The framework includes 7 areas ("domains") that authors can consider for developing high-quality and impactful scientific manuscripts: Clarity, Scientific Rigor, Public Health Relevance, Policy Content, Ethical Standards, Collaboration, and Health Equity. Each domain includes multiple quality elements, highlighting specific key considerations within.

IMPLEMENTATION

CDC scientists are expected to use the DOE framework when conceptualizing, developing, revising, and reviewing scientific products to support collaboration and to ensure the quality and impact of their scientific manuscripts.

DISCUSSION

The DOE framework sets expectations for a consistent standard for scientific manuscripts across CDC and promotes collaboration among authors, partners, and other subject matter experts. Many aspects have broad applicability to the public health field at large and might be relevant for others developing high-quality manuscripts in public health science. The framework can serve as a useful reference document for CDC authors and others in the public health community as they prepare scientific manuscripts for publication and dissemination.

SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators' research use in public discourse

BACKGROUND

While prior work has revealed conditions that foster policymakers' use of research evidence, few studies have rigorously investigated the effectiveness of theory-based practices. Specifically, policymakers are most apt to use research evidence when it is timely, relevant, brief, and messaged appropriately, as well as when it facilitates interactive engagement. This study sought to experimentally evaluate an enhanced research dissemination intervention, known as the SciComm Optimizer for Policy Engagement (SCOPE), implemented during the COVID-19 pandemic among US state legislators.

METHODS

State legislators assigned to health committees and their staff were randomized to receive the SCOPE intervention. This involved providing academic researchers with a pathway for translating and disseminating research relevant to current legislative priorities via fact sheets emailed directly to officials. The intervention occurred April 2020-March 2021. Research language was measured in state legislators' social media posts.

RESULTS

Legislators randomized to receive the intervention, relative to the control group, produced 24% more social media posts containing research language related to COVID-19. Secondary analyses revealed that these findings were driven by two different types of research language. Intervention officials produced 67% more COVID-related social media posts referencing technical language (e.g., statistical methods), as well as 28% more posts that referenced research-based concepts. However, they produced 31% fewer posts that referenced creating or disseminating new knowledge.

CONCLUSIONS

This study suggests that strategic, targeted science communication efforts may have the potential to change state legislators' public discourse and use of evidence. Strategic science communication efforts are particularly needed in light of the role government officials have played in communicating about the pandemic to the general public.

COVID-19 Scientific Publications From the Centers for Disease Control and Prevention, January 2020-January 2022

OBJECTIVE

High-quality scientific evidence underpins public health decision making. The Centers for Disease Control and Prevention (CDC) agency provides scientific data, including during public health emergencies. To understand CDC's contributions to COVID-19 science, we conducted a bibliometric evaluation of publications authored by CDC scientists from January 20, 2020, through January 20, 2022, by using a quality improvement approach (SQUIRE 2.0).

METHODS

We catalogued COVID-19 articles with ≥1 CDC-affiliated author published in a scientific journal and indexed in the World Health Organization's COVID-19 database. We identified priority topic areas from the agency's COVID-19 Public Health Science Agenda by using keyword scripts in EndNote and then assessed the impact of the published articles by using Scopus and Altmetric.

RESULTS

During the first 2 years of the agency's pandemic response, CDC authors contributed to 1044 unique COVID-19 scientific publications in 208 journals. Publication topics included testing (n = 853, 82%); prevention strategies (n = 658, 63%); natural history, transmission, breakthrough infections, and reinfections (n = 587, 56%); vaccines (n = 567, 54%); health equity (n = 308, 30%); variants (n = 232, 22%); and post-COVID-19 conditions (n = 44, 4%). Publications were cited 40 427 times and received 81 921 news reports and 1 058 893 social media impressions. As the pandemic evolved, CDC adapted to address new scientific questions, including vaccine effectiveness, safety, and access; viral variants, including Delta and Omicron; and health equity.

CONCLUSION

The agency's COVID-19 Public Health Science Agenda helped guide impactful scientific activities. CDC continues to evaluate COVID-19 priority topic areas and contribute to development of new scientific work. CDC is committed to monitoring emerging issues and addressing gaps in evidence needed to improve health.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM_2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM_2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM_2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM_2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Serving the public? A content analysis of COVID-19 public service announcements airing from March - December of 2020 in the U.S

The objective of this research was to examine the health messages conveyed in public service announcements (PSAs) affiliated with the U.S. federal government response to the COVID-19 pandemic in 2020. To do so, we conducted a content analysis of 132 federally-affiliated PSAs that were aired 170,820 times between March 12 and December 16, 2020. Using a quantitative coding instrument, we analyzed health behavioral guidance, messages about groups, people depicted, and other PSA features. We calculated frequencies of exposure to messages at the airing-level to account for the varying number of times each PSA was aired. Far more PSAs aired between March and June than between July and December. The most common health guidance was to stay at home (80.7%), practice social distancing (61.9%), and wash hands (54.5%); 36.1% of airings included guidance to wear masks. Few PSAs referenced group differences in risk of infection or transmission, nor did they reference scientific evidence or the future availability of vaccines. PSAs aired in 2020 missed opportunities to convey important information to the public and to center health equity in public communication.

Towards better Data Science to address racial bias and health equity

Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and communication approaches to be antiracist and focused on serving minoritized groups that have long-standing worse health indicators than majority groups. In this perspective, we propose eight tenets that could shape a Data Science for Racial Health Equity research framework.

A framework for the promotion of ethical benefit sharing in health research

There is an increasing recognition of the importance of including benefit sharing in research programmes in order to ensure equitable and just distribution of the benefits arising from research. Whilst there are global efforts to promote benefit sharing when using non-human biological resources, benefit sharing plans and implementation do not yet feature prominently in research programmes, funding applications or requirements by ethics review boards. Whilst many research stakeholders may agree with the concept of benefit sharing, it can be difficult to operationalise benefit sharing within research programmes. We present a framework designed to assist with identifying benefit sharing opportunities in research programmes. The framework has two dimensions: the first represents microlevel, mesolevel and macrolevel stakeholders as defined using a socioecological model; and the second identifies nine different types of benefit sharing that might be achieved during a research programme. We provide an example matrix identifying different types of benefit sharing that might be undertaken during genomics research, and present a case study evaluating benefit sharing in Africa during the SARS-CoV-2 pandemic. This framework, with examples, is intended as a practical tool to assist research stakeholders with identifying opportunities for benefit sharing, and inculcating intentional benefit sharing in their research programmes from inception.