Monitoring and Surveillance of Behavioral Health in the Context of Public Health Emergencies

A Toolkit for Public Health Officials

I: Equity Considerations with Implications for Behavioral Health Surveillance

Focus on Equity

Ongoing PH data modernization efforts and technology advances could help contribute to more-equitable and more-person-centered PH surveillance that effectively integrates BH while protecting individual rights and privacy. However, there are several key requirements for improving equity in BH surveillance.

First, surveillance data sources will need to intentionally include data that capture different kinds of equity (e.g., procedural, distributive, and contextual). For example, the Equity Indicators Initiative (undated) in New York City monitors how older adults, immigrants, people from racial and ethnic minority groups, and women experience significant disparities in access to and quality of care, as well as in mortality and well-being. Capturing these types of equity is a complex task and will require not only new data sources but also changes to the way that surveillance data are collected. For example, the CDC Foundation Health Equity Strategy Office has identified five data equity principles to help PH officials better center equity in their data life cycle (Summary 1).

Summary 1 Data Equity Principles and Their Application to the Data Life Cycle

Data Equity Principles

  1. Recognize and define systemic, social, and economic factors that affect individual health outcomes and communities’ ability to thrive.
  2. Use equity-mindedness as the guide for language and action in a continual process of learning, disaggregating data, and questioning assumptions about relevance and effectiveness.
  3. Proactively include participants from the communities of interest in research and program design to allow for cultural modifications to standard data collection tools, analysis, and sharing.
  4. Collaborate with agencies and the community to create a shared data development agenda that ensures a plan for data completeness, access, and prioritized use to answer high-interest questions.
  5. Facilitate data sovereignty by paving the way for communities to govern the collection, ownership, dissemination, and application of their own data.

Equitable Actions Throughout the Data Life Cycle

Circular flow chart with five arrows. The arrows are labelled and flow in this order: planning, collection, access, analysis, and dissemination. Dissemination points back to the planning arrow.

A larger circle outlines the arrows with text where each arrow starts.

At the top of the circle, at the start of the planning arrow, the text says: Recognize and define systemic factors.

At the start of the collection arrow, the text says: Use equity mindedness for language and action.

At the start of the access arrow, the text says: Allow for cultural modifications.

At the start of the analysis arrow, the text says: Create a shared data agreement.

At the start of the dissemination arrow, the text says: Facilitate data sovereignty.

Source: Adapted from Health Equity Strategy Office et al., undated.

Second, partnerships with data-science and technology providers will be needed to augment transparency and power in data access, use, and decisionmaking and to enhance surveillance dashboards. Data-science and technology providers can offer analytic storytelling capacities in ways that balance speed and precision that are often not available to PH agencies (e.g., to support platforms that help explain the links of racism and other inequities to health outcomes) and that can help train members of the future PH workforce to organize their surveillance dashboards in ways that encourage social change and structural action. (For example, this dashboard [Robert Wood Johnson Foundation, undated] shows how zip code predicts life expectancy and how that compares with other zip codes in your state and across the country.)

Third, PH officials will need to adopt key practices that capture equity and facilitate agency among key stakeholders and the public. In addition to the data equity principles mentioned, the Kirwin Institute offers guidance on ways to ensure equitable and inclusive civic engagement—which is critical for the partnerships needed to collect and interpret surveillance data (Holley, 2016). One example would be building partnership engagement efforts based on the unique contributions that partners bring (i.e., their gifts of diversity) through bridging efforts, mapping assets, and understanding the varied types of community leaders (e.g., connectors, catalysts).

Taken together, these requirements (Summary 2) can help ensure that BH surveillance is not only timely and accurate but also more equity-centered.

Summary 2 Key Requirements for Improving Equity in Behavioral Health Surveillance

Data sources that capture

  • Procedural equity
    • What: The perceived fairness of processes and procedures to make decisions
    • Why: Builds trust between public health leaders and the public
  • Distributive equity
    • What: How social welfare and need are balanced
    • Why: Supports choices about resource and other investment allocation
  • Contextual equity
    • What: How preexisting social conditions (e.g., accumulation of risk exposures, the legacy of injustice, and systemic barriers to accessing BH care) influence equity
    • Why: Helps create structure for public health action

Data science and tech partnerships to

  • Augment transparency and power in data access, use, and decisionmaking
  • Enhance surveillance dashboards to
    • Include and explain drivers of health inequity
    • Communicate data in ways that can lead to social change and structural action (e.g., tying inequities to policies and other paths for intervention)

Adoption of key practices that highlight

  • Differences in BH and its determinants associated with social position
  • Social and structural determinants of BH at multiple levels of measurement
  • The rationale for methodological choices made and measures chosen
  • Comparison of groups classified by multiple social statuses
  • Stakeholders and their communication needs

Equity is about more than just collecting data on race, sexual orientation, ethnicity, gender identity, disability status, and preferred language.

Source: Chandra et al., 2022.