Dr. Annie Andrews US Inactive
DR. ANNIE ANDREWS FOR SENATE
LINDSEY GRAHAM MUST GO >>
CHIP IN
Ad copy
Lindsey Graham ripped healthcare away from 1 in 5 children because it's a... money laundering scheme??? My name is Dr. Annie Andrews and as a mom and pediatrician, I know that the children who were relying on Medicaid were NOT laundering money. Let’s be real: Lindsey Graham gutted Medicaid to make his billionaire donors even richer. Help me kick Lindsey Graham out of office and flip the Senate by rushing a donation to my campaign right now >>
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 66.2/100 |
| Spend / Reach / Long. / Eff. | 73.7 · 97.1 · 39.9 · 53.9 |
| Spend range | USD 45,000–49,999 |
| Impressions | 700,000 – 799,999 |
| CPM (≈ $/1k impr) | USD 63.33 |
| Est. audience size | — |
| Days live | 146 (2025-07-18 → 2025-12-11) |
| Created | 2025-07-17 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | SC — Annie Andrews |
| Surfaced by | “senate:Annie Andrews” |
🧠 AI analysis
| Issue | healthcare |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | diy amateur |
| Who pictured | candidate, medical |
| Symbols | healthcare, money cash |
| Call to action | donate |
| Standout element | bold yellow-and-white 'NOT LAUNDERING MONEY' subtitle overlaid on a close-up selfie-style video of a pediatrician in scrubs |
| Tactic | The ad weaponizes Graham's own rhetoric by repeating his 'money laundering' framing to absurdity, using a credentialed doctor-mom as a trusted messenger to make the Medicaid cuts feel personally outrageous and drive immediate donations. |
Where it ran (delivery by region)
| California | 11.8% |
| New York | 5.8% |
| Florida | 5.6% |
| South Carolina | 5.0% |
| North Carolina | 4.6% |
| Texas | 4.5% |
| Pennsylvania | 4.2% |
Who saw it (delivery)
| 18-24 · female | 0.1% |
| 45-54 · male | 3.7% |
| 65+ · male | 21.9% |
| 65+ · female | 34.2% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 9.7% |
| 55-64 · female | 15.6% |
| 45-54 · unknown | 0.2% |
| 45-54 · female | 6.4% |
| 18-24 · male | 0.1% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 1.9% |
| 35-44 · female | 3.3% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 0.7% |
| 25-34 · female | 1.0% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.8% |
