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Using Data Strategies to Advance Health and Racial Equity

Data analysis is an essential tool for advancing health and racial equity. Geographic analysis of  disease burden has illuminated the impact of racism and identified areas to target interventions. Spurred by the disproportionate impact of COVID-19 on communities of color, states are exploring new strategies to use COVID-19 and other public health data to advance health and racial equity. Convergence analysis, using publicly available data sets, is one such strategy. 

During a recent meeting of NASHP’s health equity workgroup, a cross-sector group of state officials exploring and discussing approaches to advance health and racial equity, state officials discussed the need to examine health equity comprehensively as opposed to using a siloed approach that often results from programmatic and grant constraints. A comprehensive focus requires thinking in different dimensions to understand the dynamic interplay  of various health conditions and the historical, social and economic context which drive them out of control. Harmonizing publicly available data sets and using GIS mapping tools makes this possible.   

Publicly available data sets:

Convergence analysis helps to depict the extent that different health conditions coexist at their worst level. Any dataset that is geographically referenced can be used for convergence analysis. State officials, and community groups, can conduct convergence analysis using publicly available datasets including those maintained by the Centers for Disease Control and Prevention (CDC) and the free version of ArcGIS.

In Denver, CO, data from the CDC Behavioral Risk Factor Surveillance System (BRFSS) 500 Cities project was used to determine burdens of the following diseases by census tract: asthma, chronic obstructive pulmonary disease (COPD), coronary heart disease, diabetes, high blood pressure, kidney disease, obesity, and stroke. Convergence analysis revealed that disease burden was greatest in six census tracts that included 32,019 people. African American and Latinx populations were overrepresented in these areas.

Similar convergence analysis was completed for Columbus, OH. Thirty-seven census tracts with a population of 114,835 had at least four of the health conditions at the highest level. In the identified census tracts, 54% of the population is African American, compared to 29% in Columbus overall. These census tracts also face 7.5% unemployment compared to 4.7% citywide.

Inequality and Social Vulnerability

The Mapping Inequality dataset from the University of Richmond can be incorporated into convergence analysis to provide a historical context of the impact of structural racism on health. The Mapping Inequality project digitized the Home Owners’ Loan Corporation (HOLC) maps used between 1935 and 1940. These maps assigned grades to neighborhoods based on their perceived mortgage security to banks and served as a tool for redlining and government-backed residential segregation.

In Denver, CO, areas that had the lowest HOLC grades also had the highest social vulnerability according to the CDC/ATSDR Social Vulnerability Index. The CDC/ATSDR Vulnerability Index identifies communities that may need the most assistance before, during or after a natural or human disaster. This analysis highlights the legacy of structural racism and its impact on health outcomes, especially regarding a community’s ability to respond to an emergency.

The Health Opportunity Index

The Health Opportunity Index is another data strategy for stakeholders invested in health equity. The Index was developed by Rexford Anson-Dwamena, MPH, an epidemiologist at the Virginia Department of Health. The tool can be used to identify which social determinants of health (SDOH) may be driving health disparities in communities at the census tract level.

In an analysis of Ohio’s major cities, the drivers of adverse health outcomes were income inequality, concentrated poverty, food insecurity and segregation. The Health Opportunity Index identifies the challenges and opportunities available to improve health.

State Health Equity Data Tools

The following are some examples of how states are using data to inform their health equity approaches:

  • Massachusetts’ Public Health Data Warehouse is a “unique surveillance and research tool that provides access to timely, linked, multi-year data to enable analyses of health priorities and trends, such as the current opioid epidemic and persistent inequities in maternal and child health.” The commonwealth also operates the Population Health Information Tool, a health data portal that includes a health equity dashboard.
  • New York maintains a Prevention Agenda Dashboard to track progress on the state’s health improvement plan. Data can be viewed at the sub county level.
  • North Carolina created a map of areas with the highest rates of social vulnerability and lowest rates of COVID-19 vaccination. The state encourages vaccine providers to prioritize these areas for outreach and mobile efforts.
  • Pennsylvania’s Health Equity Analysis Tool (PA HEAT) includes a dashboard and geographic analysis of health and social determinants of health information at the regional, county, zip code and census tract levels. The purpose of the tool is to “provide a granular geographic perspective of areas that have significant opportunities to improve equity.”
  • Rhode Island used geographic analysis of COVID-19 cases during its response to focus on high density areas.
  • Virginia’s Health Equity Leadership Taskforce created two dashboards to advance health equity. The Equity in Action dashboard describes how the state is prioritizing equity in COVID-19 response and recovery and the Equity-at-a-Glance dashboard is an assessment of six social determinants of health at the state and local level. The dashboard shows data on income and poverty, educational attainment, food access, unemployment, broadband access, and housing insecurity.

Implications

States looking to engage in data analysis to advance health equity face barriers including missing data, inconsistent standards, and poor tracking of data by race and ethnicity. These issues were illuminated by the COVID-19 pandemic. During the first month of COVID-19 vaccination, data for race and ethnicity was missing for roughly half of people who initiated vaccination against COVID-19. Complete data by race and ethnicity is essential for states to identify gaps in vaccination efforts and better reach historically marginalized populations with COVID-19 vaccines.

Current race and ethnicity categories also limit state officials’ full understanding of disparities among a subpopulation. For example, the category “Asian” covers many subpopulations. A recent Executive Order from the Biden Administration, encourages the Equitable Data Working Group to “expand the collection and use of disaggregated data at the Federal, State, and local level on Asian American, Native Hawaiian, and Pacific Islander communities.”

Further federal guidance could standardize categories and allow for clarity and consistency across federal, state, and local levels. Funding from the American Rescue Plan provides some opportunities for states to invest in their public health data infrastructure. The House Committee on Energy and Commerce is also considering legislation on data, social determinants of health, and health equity.

Access to data and the ability to analyze it geographically is important for helping state officials to equitably distribute resources to address health related social needs. Data analysis is a critical first step for state officials to use their financing, regulatory, and other levers to advance health equity. Creating data maps allows state leaders to visualize areas to prioritize for health equity strategies.

To read more about state initiatives to address health equity, explore NASHP’s toolkit,?Resources for States to Address Health Equity and DisparitiesIf you are a state official interested in joining NASHP’s equity workgroup, please contact Allie Atkeson. 

Support for this work was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the foundation. NASHP would like to credit Johnnie (Chip) Allen, MPH for sharing the referenced images and data strategies to advance health equity with NASHP’s health equity workgroup. 

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