These easy-to-navigate tools can reveal important information about community conditions influencing health.
Data, compiled and analyzed with health equity at the top of the agenda, are important for identifying problems, allocating resources, and targeting interventions to those who most need them. They can help tell stories about local communities and pinpoint the health damage done by structural racism.
But data also have pitfalls and must be collected and interpreted cautiously. Too often, data are incomplete. People also don’t know that they are contributing to large datasets or how all their input is being used. And data are not inherently objective; the algorithms used to harness and analyze vast amounts of information can exacerbate existing biases.
We have worked with our partners to address systems-level challenges including how data are collected, shared and broken down by race, ethnicity, gender, age, disability, neighborhood and other factors.
Databases and tools that account for some of these factors already exist. They provide community leaders and residents with local health data, as well as contextual data at the state, county, city, and Census tract levels. Furthermore, these data resources continue to evolve as new evidence and research questions about health equity emerge. They help communities target interventions and measure progress toward ensuring that everyone has a fair and just opportunity to achieve good health.
The Child Opportunity Index tracks neighborhood opportunity using Census data. Its goal is to measure and compare how neighborhoods support healthy child development as well as long-term health, opportunity, and wellbeing. It measures 29 factors, categorized under three broad headings—education, health and environment, and social and economic. Vast disparities in such basic amenities like healthy foods, good schools, safe housing, and playgrounds are revealed in the data, which are available by both Census tracts and ZIP code and can be broken down by race and ethnicity. Policymakers and community-based organizations can use the data to invest in and improve childhood programs that will pay dividends in the long term.
The Life Expectancy Calculator estimates life expectancy at birth by state, county, ZIP code, and all 67,000 Census tracts. Coupled with a companion interactive map, it reveals dramatic disparities that can exist within a few miles of one another. For example, in the aggregate, Attala County, Mississippi, with a life expectancy of 76.2 years, looks better than the statewide average of 74.9 years. But when the data are broken down further, the picture changes dramatically—one Census tract has a life expectancy of 82.8 years while residents in a neighboring tract can expect to live just 66.6 years. That kind of granular information offers a portrait of inequities, helping to build the case for targeting resources where they are needed most.
The CUSP Database (the COVID-19 U.S. State Policy Database) tracks state policies put in place during the pandemic, revealing that well-intentioned COVID-19 policies disadvantaged the very communities most in need of support. For example, the decision to give elderly people, rather than essential workers, priority to vaccines, meant that fewer people of color had early access. Similarly, some government relief programs, including unemployment insurance, excluded certain low-wage, tipped, and gig workers. CUSP also tracks mask mandates, business closures, stay-at-home and quarantine orders, eviction and utility shutoff protections, SNAP waivers, paid leave, workplace safety standards, and more. CUSP data can be used to inform equitable health and social policy decisions, especially when they are combined with health outcomes data.
With these tools, researchers, policymakers, and community activists can take steps to improve health outcomes. And with the National Commission’s guidelines, we have a roadmap to make all data tools far more useful in the future.
Alonzo Plough, RWJF’s chief science officer and vice president, Research-Evaluation-Learning, is responsible for aligning all of the Foundation’s work with the best evidence from research and practice and incorporating program evaluations into organizational learning.