I recently participated in the Social Determinants of Health (SDOH) Public Health Use Case Workgroup for Chronic Disease Prevention, which brought together experts from the public and private sector to strengthen our understanding of how end-users across the public health sector can get the most value from SDOH data. Initiatives like this help uncover the role of SDOH and health equity, but, as is often the case, the project did not include a focus on race, ethnicity, sex, primary language, disability, sexual orientation, and gender identity (SOGI) data elements or data exchange standards.

SOGI data elements must be included for substantive understanding how health inequities affect specific populations, but collecting this data is challenging because there is no national standard for capturing the variables needed to assess health inequities, nor is there consensus about how to categorize data related to complex variables like gender identity or disability status.

Another challenge is selecting the data elements that are most relevant to a specific community. The categories most relevant to understanding health patterns are not universal. For example, Abigail Echo-Hawk, chief research officer for the Seattle Indian Health Board, recently pointed out that categories of data that are important for understanding community health among some populations might be much less relevant for American Indian and Alaskan Native populations.

While integrated SOGI data elements won’t happen overnight, public health leaders can adopt the following strategies to accelerate change:

Focus on platform flexibility and scalability

Ensure that your platform and underlying data model are flexible enough to support evolving analysis approaches and changes in the classification and stratifications of race, ethnicity, sex, primary language, disability, sexual orientation, and gender identity data. Given the continuous improvement in data availability and ongoing refinement to demographic stratifications for policy and scientific reasons, any effective platform must support shifting priorities.  As more organizations embrace personalized medicine and as the application of novel statistical methodologies as academia continues to innovate, it’s clear that public health analytics infrastructures must be future proof.  Voyatek’s Community Health Analytics solutions, for example, uses a flexible data model that isn’t specific to any industry vertical, making all stratifications configurable by our clients.

Leverage common standards

Mapping to common standards accepted by a variety of government agencies and service providers (e.g. NIEM or FHIM standards) ensures your platform’s data will be applicable to multiple analyses and a wide variety of end-users—from health insurance case managers to WIC enrollment specialists.

Virginias Framework for Addiction Analysis and Community Transformation (FAACT) program, developed in partnership with Voyatek, is a great example of how adhering to NIEM standards maximizes data usability. By adopting NIEM’s common vocabulary, the platform allows users from government, public safety, social services, and healthcare to look at data from other domains and gain insights that could help them better serve Virginia residents.

FAACT uses the NIEM standard to bring together data across domains through the coding of demographic data. Demographic details, though, can exist in many different forms across datasets. The gender of a patient seeking treatment, for example, may be labeled as “gender,” “gender identity,” or “patient gender,” depending on the dataset.  FAACT uses the NIEM standard to code each of these fields with the same NIEM type. In this way, the NIEM standard clearly defines data elements and eliminates ambiguity as disparate users interact with the data.

Make it easy to stratify by subgroups

Stratifying process and outcome measures by subpopulation is often the approach used to assess disparate impacts of the social determinants of health, so it’s imperative that your analytics approach includes the functionality to easily stratify by subgroups. With Community Health Analytics, for example, calculations for commonly used demographic stratifications and subgroups are precalculated for faster platform performance. For Montana’s Community Health Insights in the 406 platform, Voyatek allows users to simply select filters to view data stratified by subgroup in a matter of seconds.

Engage your stakeholders

As you’re configuring your platform, include consensus and stakeholder engagement activities across a wide partner network. Doing so will help ensure that your solution will have broad applicability for a variety of user types.  For FAACT, Voyatek hosts discovery sessions with user groups across multiple agencies and facilitates regional collaboration sessions so that real-time data trends can guide additional analyses.