· Public charity
Foundation for Women and Girls with Blood Disorders
Foundation for Women & Girls with Blood Disorders works to ensure all women and girls with blood disorders are correctly diagnosed and optimally managed at every life stage.
What you funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year — across FY2018–2025.
Where the money goes
Your grants by size, and where they go.
The 6 grants below total $143,392 — the rows itemised in this filing. The $153,583 headline is the total grant expense reported on the return, so the remaining $10,191 is giving the schedule does not break out: grants under the $5,000 itemisation floor, grants to individuals, and grants reported on other schedules. Every figure below describes the itemised rows only.
By grant size · FY2025
- Under $10k1 grant · $8k
- $10k–50k5 grants · $136k
| Recipient | Amount |
|---|---|
| University Health Network | $35,000 |
| Massachusetts General Hospita | $35,000 |
| University of North Carolina | $32,309 |
| University of Utah | $17,500 |
| Brigham & Womens Hospital | $16,083 |
| Ottawa Hospital Research Inst | $7,500 |
Do your dollars go where the need is?
Each grant placed by the hardship in its grantee’s ZIP, then read against the area’s typical level.
Your US giving — 89% of grant dollars ($615k). The need read here covers only this US portion; the 11% that went abroad ($73k) is mapped below.
Dollar for dollar, your grants (FY18–25) land where the poverty rate runs at 15%, against an area that typically sits at 14%. 52% of your dollars go to grantees based in above-average-need neighborhoods. Your grants spread fairly evenly across need levels.
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty linein the grantee’s ZIP, averaged from the tracts in that ZIP; grantee ZIP can differ from where services are delivered.
Which US states your grants reach
Beyond the US — 11% of all-years giving ($73k)
2 countries, by the recipient’s country on the filing.
US grants placed by each grantee’s ZIP, which can differ from where services are actually delivered. Need from public data — U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA — shown as context about the area, never attributed to your giving. International giving is mapped by the recipient’s country from the same 990 filings; comparable need data isn’t available at that grain.
Who you back again
Which organizations you funded more than once, and which you funded a single time. Each grantee is matched to its own filings.
5 repeat relationships — 3 still active in FY2025, 2 since wound down; 3 grantees were first funded in FY2025 (too recent to call).
How the two cohorts compare
Organizations
Total granted
Median revenue growth · since first grant
Still filing today
New vs renewed · share of each year
In FY2025, 46% of grant dollars renewed an existing relationship; $78k went to new ones.
Where new relationships form · theme of each grantee’s first grant
First-time = a grantee’s first year in your filing window; renewed = funded in an earlier year too. As a portfolio matures the renewed share naturally climbs — once funded, an org stays “renewable” — so the signal is the years that buck it (a new-grantee intake wave). The earliest year is left-censored: relationships that predate the data read as “new.” Theme is the grantee’s IRS cause.
Backed again, and grew
- UOUNIVERSITY OF UTAH3× · 2021–2025 · $85k
- BCBaylor College of Medicine3× · 2020–2024 · $68k · revenue +47%
- UOUniversity of North Carolina2× · 2023–2025 · $50k
Funded once
- OHOREGON HEALTH & SCIENCE UNIVone grant, 2022 · $35k
- VWVERSITI WISCONSIN INCgraduatedone grant, 2018 · $35k · revenue +77%
- AMANSCHUTZ MEDICAL CAMPUS OFFICE OF GRANT AND CONTRACTSone grant, 2019 · $35k
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
5 grantees tracked through their own filings, 2017–2025.
Each one resolved to its own IRS returns and tracked year by year — your grant beside their revenue from every source. Association, dated; never a causal claim.
Counted here: distinct organizations you funded across 2017–2025, not grant rows in a single year — so this will not match the grant count on the cover. 5 of the 22 grantees resolved in that span have returns of their own we could reconcile; the rest are funded organizations whose filings we could not track year by year.
Where your money sits — by cause, then by grantee
Each org by its size and your share of it — top-left is where you’re load-bearing
Go grantee by grantee — a decade per org, and how each moved after you funded them
A decade per grantee — revenue shaded, your grants as bars, all rows on one timeline.
Government reliance of your grantees
Every dot is one organization Foundation for Women and Girls with Blood Disorders funds. Left–right is the share of its income from government; up–down is the share from you. Bigger dots raise more. Filter to federal, a single department, or state — and drag the year to watch it move.
- President and Fellows of Harvard College — 7% of income from government
Government income is each org’s traced federal awards (USASpending — grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that your grant caused the public funding. Coverage is precision-first — a floor, not a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12–24 months.
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Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.