MS NOW95%

JD Vance wants you to believe fraud is only a blue state problem 75%

By Paul Waldman0%

5/14/2026, 5:54:58 PM

BS Summary: This article contains 16 faulty reasoning types, including Politically Left Leaning Bias, Negativity Bias, and Pessimism Bias, with Biased Writer Voice as the most egregious example at 55.4% saturation with 636 hits. Analysis detected 2,658 faulty-reasoning hits from 1,147 analyzed words, generating a BS Score of 66.3% and a BS Rank of 75% (5,667 of 21,887 articles). This article is worse (more manipulative) than 74.10% of the article peer group.

Last week, former All-American Ole Miss football star Rufus French was sentenced to 16 years in prison after the Department of Justice indicted him in 2024 for defrauding Medicare of $200 million in payments for durable medical equipment, or DME, in a scam that involved “overseas call centers, sham telemedicine companies, and straw‑owned DME suppliers.” 
French was not from a blue state, he is not a Somali immigrant, and his prosecution began when Joe Biden was president. 
Which makes him the opposite of everything the Trump administration  especially Vice President JD Vance, who is running President Donald Trump’s high-profile crackdown on fraud in social services  apparently wants you to believe about the nature of fraud in America. 
Putting the public spotlight solely on fraud in social services has the benefit of discrediting programs conservatives already despise  and justifying future budget cuts. 
On Wednesday, Vance and the Centers for Medicare & Medicaid Services chief, Dr. 
Mehmet Oz, held a press conference to announce that the fraud busting had kicked into gear. 
In addition to stopping $1.3 billion in Medicaid payments to hospices in California “because the state of California has not taken fraud very seriously,” as Vance put it, all 50 states are officially on notice: If they don’t work more closely with the administration to combat Medicare and Medicaid fraud, the administration will cut off funds meant to conduct that very enforcement. 
Despite occasional nods to the fact that this is a nationwide problem, the Trump administration clearly wants people to think fraud happens solely, or at least mostly, in blue states. 
The anti-fraud campaign started when the Trump administration decided to go after Minnesota, focusing specifically on Somali immigrants, a frequent target of nakedly racist attacks from Trump himself. 
Or, as Vance said at a recent speech in Iowa: “Somali fraudsters” are stealing money from the government. 
At Wednesday’s press conference, Vance was only slightly more conciliatory toward the parts of the country that didn’t choose to vote for Trump. 
“We have red states and blue states that go after fraud aggressively,” he said. 
“But we also unfortunately have some states, mostly blue states, unfortunately, that do not take Medicaid fraud seriously.” 
Andrew Ferguson, the ultra-MAGA chair of the Federal Trade Commission who is participating in this initiative, reiterated the point. 
“This is the result of decades of corruption, particularly in blue states,” he said. 
But it’s a little hard to take the Trump administration’s word that this is a blue state problem. 
“There’s too much money in the system for it not to be appealing for fraudsters,” said Tricia Neuman, executive director of the Program on Medicare Policy at KFF  and the money is everywhere. 
“There have been pockets of fraud that have gotten attention in Florida and in Texas,” she pointed out. 
“In 25 years of focusing on Medicaid,” said Joan Alker, executive director of the Center for Children and Families at Georgetown University’s McCourt School of Public Policy, “there has been no discernible pattern to me that it’s more common in blue states.” 
The only pattern so far is that the Trump administration is going after states where the president has public disputes with the governor: Minnesota with Tim Walz and California with Gavin Newsom, for example. 
Here’s the truth: Fraud in government contracting of all kinds is a genuine and long-standing problem that costs taxpayers billions of dollars a year. 
But there are many reasons to be skeptical that this administration really cares about it. 
The problem starts with the structure of the healthcare system. 
Medicare and Medicaid are enormous programs that together serve almost 150 million Americans. 
Hundreds of billions of dollars flow through them, most of it from private providers of goods and services. 
There are more than 2 million Medicare providers, including doctors, hospitals, clinics, labs, equipment suppliers, therapists and more. 
The difficulty of closely monitoring every payment makes the programs an inviting target for criminals. 
Which is true of many government programs. 
But the Trump administration is not making a high-profile effort to combat fraud in defense contracting, of which there is plenty. 
One might ask, if the Trump administration is so committed to fighting fraud, why has it dramatically scaled back efforts to stop white-collar crime, shut down enforcement of crypto fraud laws and stopped enforcing the Foreign Corrupt Practices Act  and not bothered to go after wage theft, which may be the largest form of fraud in America, costing workers an estimated $15 billion a year. 
Trump has also pardoned multiple fraudsters, including in the healthcare industry. 
Joseph Schwartz, a nursing home operator who stole millions from his employees and left patients in terrible condition, got a Trump pardon. 
So did Paul Walczack, another nursing home executive who stole millions  but whose mother paid $1 million to attend a dinner at Mar-a-Lago. 
The Trump administration’s slapdash approach and its eagerness to attack first and ask questions later should make us all skeptical. 
Putting the public spotlight solely on fraud in social services has the benefit of discrediting programs conservatives already despise  and justifying future budget cuts. 
“Ultimately, this is to continue to try to build a case, should they retain control of Congress, to make another effort to cap and block grant Medicaid, because that’s the holy grail for these folks and their allies,” Alker said. 
That would mean huge cuts for Medicaid, which is already reeling from the blows it took from the One Big Beautiful Bill Act. 
The current fraud push may “distract from the fact that this administration and this Congress enacted the largest cuts in Medicaid history last year,” Alker added, “and they don’t want to have anyone talk about that in an election season.” 
Finally, the Trump administration’s slapdash approach and its eagerness to attack first and ask questions later should make us all skeptical of whether anything useful will come out of this effort. 
One example: Last month, Oz released a video announcing a probe of Medicare services in New York, falsely claiming that 5 million New Yorkers were getting personal care services (help with things like bathing and food preparation). 
As the administration had to admit, Oz overstated the number, and the justification for his “investigation,” by more than 10 times, because Trump officials misread the billing codes Medicaid uses. 
It had echoes of the DOGE catastrophe, when the administration unleashed a cadre of inexperienced, ignorant dude bros with unaccountable power and a mandate to destroy systems and agencies they didn’t understand, all in the name of “efficiency.” 
Government can always be more efficient; the problem, then as now, was the incompetence and bad faith of those who said they were trying to make things better. 
That may wind up being the real “fraud” we have to worry about. 
Article reasoning-pattern comparisonThis article: 7.3%Paul Waldman: 9.7%MS NOW: 7.5%Confirmation Bias7.3%This article: 0.0%Paul Waldman: 1.6%MS NOW: 1.2%Anchoring Bias0.0%This article: 0.0%Paul Waldman: 4.5%MS NOW: 3.7%Availability Heuristic0.0%This article: 0.0%Paul Waldman: 1.2%MS NOW: 1.1%Representativeness Heuristic0.0%This article: 0.0%Paul Waldman: 0.6%MS NOW: 1.3%Hindsight Bias0.0%This article: 0.0%Paul Waldman: 3.5%MS NOW: 2.6%Overconfidence Bias0.0%This article: 8.5%Paul Waldman: 10.0%MS NOW: 15.4%Framing Effect8.5%This article: 0.0%Paul Waldman: 0.2%MS NOW: 0.7%Loss Aversion0.0%This article: 0.0%Paul Waldman: 0.4%MS NOW: 0.9%Status Quo Bias0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.1%Sunk Cost Effect0.0%This article: 0.0%Paul Waldman: 3.2%MS NOW: 2.4%Optimism Bias0.0%This article: 14.3%Paul Waldman: 4.9%MS NOW: 3.2%Pessimism Bias14.3%This article: 30.6%Paul Waldman: 24.6%MS NOW: 19.2%Negativity Bias30.6%This article: 0.0%Paul Waldman: 0.4%MS NOW: 2.4%Self-Serving Bias0.0%This article: 0.0%Paul Waldman: 4.2%MS NOW: 3.1%Fundamental Attribution Error0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.3%Actor-Observer Bias0.0%This article: 0.0%Paul Waldman: 4.2%MS NOW: 3.5%In-Group Bias0.0%This article: 1.6%Paul Waldman: 5.4%MS NOW: 2.5%Out-Group Homogeneity Bias1.6%This article: 0.0%Paul Waldman: 3.4%MS NOW: 2.2%Halo Effect0.0%This article: 0.0%Paul Waldman: 2.6%MS NOW: 1.8%Horn Effect0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Paul Waldman: 1.4%MS NOW: 1.8%Recency Bias0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.6%Primacy Effect0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.2%Blind-Spot Bias0.0%This article: 11.9%Paul Waldman: 9.2%MS NOW: 4.7%Ad Hominem11.9%This article: 4.8%Paul Waldman: 3.7%MS NOW: 1.3%Straw Man4.8%This article: 0.0%Paul Waldman: 3.0%MS NOW: 5.4%Appeal to Authority0.0%This article: 0.0%Paul Waldman: 3.0%MS NOW: 2.2%False Dilemma0.0%This article: 3.5%Paul Waldman: 3.9%MS NOW: 2.2%Slippery Slope3.5%This article: 0.0%Paul Waldman: 0.3%MS NOW: 0.2%Circular Reasoning0.0%This article: 7.0%Paul Waldman: 11.4%MS NOW: 8.1%Hasty Generalization7.0%This article: 3.5%Paul Waldman: 0.6%MS NOW: 0.7%Red Herring3.5%This article: 0.0%Paul Waldman: 1.7%MS NOW: 0.9%Bandwagon0.0%This article: 0.0%Paul Waldman: 13.3%MS NOW: 9.8%Appeal to Emotion0.0%This article: 0.0%Paul Waldman: 3.1%MS NOW: 2.4%Begging the Question0.0%This article: 7.3%Paul Waldman: 5.7%MS NOW: 3.2%Post Hoc (False Cause)7.3%This article: 8.5%Paul Waldman: 1.5%MS NOW: 0.6%Tu Quoque8.5%This article: 0.0%Paul Waldman: 0.1%MS NOW: 0.9%Burden of Proof0.0%This article: 0.0%Paul Waldman: 0.2%MS NOW: 0.1%Appeal to Nature0.0%This article: 0.0%Paul Waldman: 0.4%MS NOW: 0.3%Composition/Division0.0%This article: 0.0%Paul Waldman: 2.4%MS NOW: 2.6%Anecdotal0.0%This article: 0.0%Paul Waldman: 0.2%MS NOW: 0.2%No True Scotsman0.0%This article: 0.0%Paul Waldman: 0.8%MS NOW: 1.7%Ambiguity (Equivocation)0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Paul Waldman: 0.7%MS NOW: 0.1%Middle Ground0.0%This article: 0.0%Paul Waldman: 0.1%MS NOW: 0.3%Personal Incredulity0.0%This article: 0.0%Paul Waldman: 0.1%MS NOW: 0.3%Special Pleading0.0%This article: 0.0%Paul Waldman: 2.1%MS NOW: 1.3%Genetic Fallacy0.0%This article: 5.4%Paul Waldman: 1.7%MS NOW: 2.5%Unattributed Quote5.4%This article: 0.0%Paul Waldman: 1.9%MS NOW: 1.5%Quote-first Misdirection0.0%This article: 55.4%Paul Waldman: 21.6%MS NOW: 14.2%Biased Writer Voice55.4%This article: 6.5%Paul Waldman: 5.8%MS NOW: 2.1%Indoctrination6.5%This article: 55.4%Paul Waldman: 9.4%MS NOW: 5.3%Politically Left Leaning Bias55.4%This article: 0.0%Paul Waldman: 0.9%MS NOW: 0.6%Politically Right Leaning Bias0.0%This article: 0.0%Paul Waldman: 0.0%MS NOW: 0.4%Attempt to Sell a Product or S…0.0%

1147 words analyzed.

Speakers

4speakers21%attributed speech909writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 55 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 42 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 62 words • 100.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageJD Vance • 18 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageJD Vance • 14 words • 0.0% coverageJD Vance • 18 words • 0.0% coverageWriter's voice • 19 words • 100.0% coverageAndrew Ferguson • 14 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageTricia Neuman • 34 words • 0.0% coverageTricia Neuman • 18 words • 0.0% coverageJoan Alker • 42 words • 0.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 15 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 66 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageJoan Alker • 40 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageJoan Alker • 40 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 37 words • 100.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 38 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverage
Selected voice

Joan Alker

66%flagged-word coverage
122 attributed words51% of attributed speech79% writer coverage
0%35.0%70.0%Biased Writer Voice-70.0 ptsWriter: 70.0%Joan Alker: 0.0%0.0%Politically Left Leaning B-70.0 ptsWriter: 70.0%Joan Alker: 0.0%0.0%Indoctrination-8.3 ptsWriter: 8.3%Joan Alker: 0.0%0.0%Unattributed Quote-6.8 ptsWriter: 6.8%Joan Alker: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.