As the Centers for Disease Control and Prevention (CDC) prioritizes health equity, bridging data gaps in routine and emergency surveillance is crucial to assess the public health needs of marginalized groups, including people experiencing homelessness, individuals with substance use disorder, and people living with disabilities. Incomplete or unknown data on diseases and conditions for affected populations hinder public health agencies and partners from addressing health inequities effectively. In today’s data-driven world, accurate information is vital for shaping policies and interventions that promote health equity and inclusivity. However, data collection methods often fall short when it comes to representing the needs and experiences of marginalized communities, particularly the disability community. Local health departments (LHDs) play a crucial role in addressing this gap by implementing strategies to improve data collection practices. In this blog, we will explore why enhancing data collection around the disability community is essential and suggest actionable steps for LHDs to take.

CDC’s Public Health Informatics Office (PHIO) is collaborating with partners to collect high-quality and comprehensive data capable of identifying barriers to inclusion, establish data standards, and improve data exchange to better achieve health equity among various disproportionately affected populations and strengthen data-driven actions.

It is an exciting time in public health, with new innovative approaches transforming how health data is collected, stored, shared, and analyzed. These approaches leverage data science to produce more efficient and timely data for actionable decision-making. Addressing health disparities of underserved populations, such as those with disabilities, is not a one-size-fits-all challenge. The ability to access geographic and demographic specific data will equip local health departments (LHDs) with the right information to provide the best possible interventions and health services.

CDC is assisting LHDs with technical assistance to improve the quality of data for actionable decision-making. Two major challenges to address the public health needs of disproportionately affected populations, such as the disability community, are lack of data and lack of awareness of these populations and their needs. To bolster health surveillance and preparedness, evaluating data systems is essential to establish and sustain robust information streams for disproportionately affected populations. Data collection should prioritize incorporating an equity lens to ensure capturing the necessary data for understanding the challenges and requirements of underserved populations. Examples of how LHDs can improve their data collection efforts thru DMI include:

• Automating race, ethnicity, and other demographic data collection and bringing it closer to the point of care to get this information faster and more accurately.
• Collecting more specific non-health data on smaller populations that have been historically underserved and under-represented in the data.
• Using new tools and approaches to reduce and account for biases in public health data and analytics and improve understanding of Social Determinants of Health.
• Working to understand the impacts of social and demographic factors on health by linking data in new ways.

Community health assessment and improvement plans play a key role in understanding the health status and needs at the community level. A collaborative approach among disability-led organizations provides an opportunity for precise data collection that is inclusive of the disabled population. Several LHDs have found success in leveraging NACCHO’s MAPP strategic planning process in collecting disability inclusive data. For example, the Champaign-Urbana Public Health District used the MAPP framework in creating a survey in multiple formats to collect primary data on the needs of those with a disability. A key to their success was building collaborative partnerships with local organizations serving the disability population. Another example is the Northwestern Michigan Health Department which used NACCHO’s MAPP framework to collect secondary data indicators for a community health status assessment. Both primary and secondary data collection methods are useful with obtaining critical information in determining how resources should be allocated to meet the needs of the disability community. The data collection process on the disability community should have an emphasis on community engagement and collaboration with disability-led organizations for system level planning to achieve health equity for all at the local level.

By automating demographic data collection and enhancing health information exchange, LHDs can provide more targeted and efficient health services to community members. This ensures that individuals receive the care they need in a timely manner. Engaging with local communities in the data collection process fosters a sense of empowerment and ownership. It allows community members to have a voice in shaping public health policies and programs that directly affect them. Overall, these efforts contribute to building healthier and more resilient communities by addressing the root causes of health disparities and ensuring that everyone has equal access to quality healthcare and resources.