Culture of Data 2025

This presentation highlights the development of a streamlined data collection and sharing system for Adams County Health Department’s (ACHD) Harm Reduction Program, in partnership with the Colorado Department of Public Health and Environment (CDPHE). Supporting multiple programs, the Harm Reduction Program faced complex reporting requirements and relied on a time-intensive, error-prone process: collecting client data on paper and manually entering it into both an internal Excel document and CDPHE’s REDCap database. Seeking to improve efficiency and accuracy, the Harm Reduction team collaborated with ACHD’s Health Data and GIS (HDGIS) team to create the ACHD Harm Reduction Data Ecosystem.

Methods
   The HDGIS team designed the Harm Reduction Data Ecosystem using ESRI software to streamline data workflows and facilitate accurate reporting. To safeguard sensitive client data, the HDGIS team created a temporary data collection database on ArcGIS Online and a permanent secure database on ArcGIS Enterprise. An automated process then securely transferred data to the internal database, reducing manual entry. The team also built two client lookup dashboards for easy record access and updates, enhancing client care continuity. Finally, a monthly data export feature automated ACHD-to-CDPHE data uploads, improving data accuracy and timely reporting.

Results
The Harm Reduction team’s transition to the ACHD Harm Reduction Data Ecosystem has resulted in higher data quality, improved client tracking, enhanced resource referrals, and greater program efficiency. Staff spend less time on data entry, allowing more focus on client services. The team has also expanded their use of program data for insights and informed decision-making.

Recommendations/Practical Applications/Future Goals
   The Harm Reduction Data Ecosystem has strengthened ACHD’s partnership with CDPHE and enhanced data management for the Harm Reduction team. This project serves as a model for similar data solutions across ACHD programs, demonstrating the impact of an integrated, client-centered data approach."

Behavioral health disparities in rural areas are significantly influenced by social determinants of health (SDOH), such as economic stability, access to care, and social support networks. Northeast Colorado, like many rural regions, faces unique challenges, including provider shortages, geographic isolation, and systemic barriers that exacerbate mental health inequities. This presentation explores how data science can be a powerful tool for addressing these disparities, offering actionable insights into the complex relationships between SDOH and behavioral health outcomes. By leveraging data-driven approaches, stakeholders can identify at-risk populations, design targeted interventions, and advocate for policy changes that prioritize health equity.

Through a combination of case studies, local data analysis, and community engagement strategies, participants will gain a comprehensive understanding of how to integrate data science into behavioral health planning. The presentation will highlight practical tools for mapping SDOH, employing predictive analytics, and fostering collaborative partnerships to implement tailored solutions. Attendees will leave equipped with actionable strategies to harness data for advancing health justice and improving behavioral health outcomes in Northeast Colorado’s rural communities.

The American Telemedicine Association launched a Disparities Toolkit, which includes an online data system that generates a Digital Infrastructure Score (DIS) available via an interactive map. The composite DIS was derived through multiple regression analysis that integrates diverse datasets encompassing telehealth accessibility factors (including affordability and internet speeds) and social drivers of health (such as the social vulnerability index), along with health outcomes. The DIS provides a place-based snapshot of technology and social factors and their relation to health outcomes. The data's hierarchical geographic structure, ranging from micro-level units (zip codes) to macro-level regions (states), facilitates comparative analyses both within Colorado and across other states. The tool allows users to conduct analysis and create visualizations within the system, and the data can be exported in a variety of formats.

Digital inclusion has emerged as a “super social driver of health” that intersects with multiple health-related social needs. An expanding ecosystem of technology-enabled health platforms and tools, including patient portals, health apps, and remote monitoring devices, supports diverse aspects of health information access and healthcare delivery. The future of public health and the achievement of reducing inequities lies, to a great degree, in the use of technology. 

This session will explain the tool's data sources and development methodology, offering practical examples of its visualization features and analytical capabilities. The DIS analytical framework serves diverse stakeholders including policymakers, program directors, evaluators, educators, and community organizations. These partners can utilize the tool for identifying areas of access barriers and health disparities, implementing evidence-based programs, monitoring and evaluating intervention outcomes, developing educational resources, and guiding community health improvement initiatives.

This presentation will feature Family Connects; an evidence-based postpartum nurse home visiting program that has been implemented in several counties across Colorado. During the visit, nurses conduct an assessment of families’ strengths and needs and connect them to appropriate community-based resources. Because the program is universal rather than targeted, and aims to serve 60% of the eligible population, we have a unique opportunity to gather data around the support needs of our perinatal community and their ability to successfully connect to community-based resources to meet those needs. Specifically, participant data collected includes demographic data such as caregiver and infant race and ethnicity, caregiver education level, primary language spoken, and caregiver and infant insurance type. Additionally, participants’ strengths and needs are assessed in 12 domains and assigned a matrix score of 1-4 depending on risk-level. Participants are referred to community-based resources and supports for any matrix ratings of 3 or 4, signifying high need or risk for the family. Participants receive a follow-up call 30 days after their visit, and data is collected on whether or not the participant successfully connected to community resources. This data is being used to identify systems-level gaps in services and improve community alignment efforts across systems of service. Specifically, this presentation will present data from Denver and Jefferson counties. Denver county launched their program in late 2024 and Jefferson County launched their program in 2023. As both programs move through the implantation phase, they are serving approximately 17% and 11% of the eligible birthing population, respectively.
Through this presentation, the audience will better understand the current needs of families with young children in Denver and Jefferson counties and how well the counties’ systems are supporting families through available resources. It will also provide an example of how to use programmatic data to identify areas that need system level improvement. The presentation will feature intentional strategies for engaging community voice in program implementation and data interpretation in order to reduce disparities among marginalized communities and improve health equity.

The fight against food insecurity is led by diverse distribution agencies, often with limited
coordination and intentional design across providers. Data science can be used to guide
decisions and make the system more effective at tackling inequities. In New Jersey a
cross-sector partnership has employed an innovative methodology powered by data to address
food insecurity. Trenton Health Team (THT), in partnership with local stakeholders, constructed
a food insecurity index to highlight the intensity of food need across the county. Using publicly
available data, geospatial methodologies, and cluster analysis, the Index assigned a relative
food insecurity score to each census block group. Stakeholder feedback was centered
throughout the process, resulting in an interactive map which transformed fifteen variables into
an accessible, visual tool.
The Food Insecurity Index has been utilized for both short and long term change. THT
worked with the regional food bank to deploy mobile pantries to areas with higher need but
limited resources, and plans are being finalized for a new brick and mortar food hub located in
the center of the most under-resourced neighborhood. Emphasizing the project as a
collaborative process not only allowed THT to incorporate valuable local insight in the tool, but
also established trust and increased buy-in from community partners. This framework of
participatory data-based decision making to build consensus will continue to be used by THT as
the Index expands to cover the entire state of New Jersey, and is applicable to an array of
projects across public health and nationwide.
Attendees of this presentation will learn how to apply the Index methodology to their
local geography, as its backbone of census data allows it to be utilized anywhere in the USA.
Attendees will also learn how they can translate data projects into actions via participatory
processes with diverse stakeholders.