HIV Health Equity Leadership Development Program
Talk/Presentation · June 2024
Ben Hammond, Eric Warren, and I presented together as the Health Equity Tracker team for the fifth month of the HIV Health Equity Leadership Development Program — a Gilead Sciences-funded program run in partnership with Xavier University of Louisiana. The session, officially titled "End the Epidemic: Examining the health equity implications of health systems, policy, and data gaps for people living with HIV," used the same "Data is Power" framing SHLI builds into its learning programs, but this time aimed squarely at how HET's HIV-specific data could sharpen the fellows' own policy advocacy work.
The Program
The HIV Health Equity Leadership Development Program is a Gilead Sciences-funded initiative run in partnership with Xavier University of Louisiana. By month five, fellows had already been building their own health equity policy projects, and this session — "End the Epidemic" — was designed to give them a working tool for the data side of that work, not just a lecture on data literacy in the abstract.
The Tracker, From COVID to HIV
Ben opened with HET's origin story: it started as a COVID-era response to a specific gap — marginalized communities were dying at disproportionate rates, but the data behind that was scattered and inconsistent, so SHLI built a tracker, with support from Google and other partners, positioned deliberately between an oversimplified infographic and a raw, 900-dropdown government database. He demoed the incarceration data as a case in point: in Texas, Black residents make up 12% of the population but 33% of the prison population, while white residents make up 40% of the population and the same 33% of the prison population — the same disparity, shown two different ways. He also walked through HET's open-science posture: every dataset is downloadable row by row, links back to original sources, and — deliberately — data gaps and mislabeling get flagged rather than smoothed over.
Making the Invisible Visible: HIV-Specific Features
Eric went deeper into what HET does specifically for HIV. The tracker flags where HIV data is missing or suppressed rather than hiding the gap — for example, CDC sex data is aggregated as male/female only, so trans populations are effectively invisible in that dataset, and HET says so explicitly instead of pretending the picture is complete. He also walked through a rate-framing choice that matters more than it sounds: HIV prevalence is shown per 100,000 people, but PrEP coverage is shown as a percentage, because that pairing gives the clearest read on how many people who could benefit from PrEP are actually on it. HIV stigma is tracked as its own measure — a 1–10 score from an epidemiologist-designed survey — so fellows could compare stigma directly against prevalence or PrEP coverage in the same state.
My Role: Turning the Tracker Into a Working Tool
My piece was the interactive half of the session. Before we met, I read through every homework assignment the fellows had submitted and pulled direct language from their own policy work — including one fellow's case study built around making an argument to a chairman — as the jumping-off point for the activity. I put together a how-to guide (posted to Canvas and dropped as a live link) and sent fellows into breakout rooms with a specific task: use HET to find the data that would actually strengthen the case they were already trying to make, not a generic tour of the tool.
What Fellows Found
The breakout report-backs surfaced real methodology questions, not just tool tips:
- One group looked at COVID reporting and noticed some states published only total case counts without race-segregated data — which doesn't just create a gap, it can actively mask which populations weren't getting equitable treatment.
- A second group pulled HIV stigma scores by state and was surprised to find Texas, Michigan, and New Jersey reporting the highest scores. They also flagged that HET's "two or more races" category likely undercounts people who identify with more than two racial identities.
- One fellow raised a sharper methodology question: heavier African American representation in Medicaid HIV data might partly reflect that Medicaid patients are simply easier to track than privately insured patients, not just where HIV is more prevalent — a caution about reading disparity data at face value that echoes HET's own "correlation isn't causation" framing.
- Another fellow pointed to the reporting gap between the Ryan White Program and the AIDS Drug Assistance Program (ADAP) as its own signal of disparities in access to antiretroviral treatment and adherence.
- A fellow working in retail pharmacy noted that medication therapy management (MTM) programs typically track Medicare patients for cardiovascular and diabetes medications, but rarely for HIV adherence outside commercial insurance — a real blind spot in who gets flagged for outreach that HET's data could help name.
Where It's Headed
In response to that discussion, the team previewed what's coming next for HET: gun violence data (shaped by a recent community workshop on visualizing disparities across data sources with different levels of legal restriction), plus two new pharmacy-data phases — medication adherence for schizophrenia and mental health conditions, and adherence and rate data across multiple cancer types.
We closed by offering fellows ongoing HET user-interview sign-ups for UX feedback, and standing offers to run smaller workshops for their own community groups, coworkers, or students going forward.
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