This comment is well outside the scope of what I usually write about here. But the recent controversy about President Trump’s call to the family of a serviceman killed in Niger began while I was with a group of friends with whom I served in the Air Force.

I’m limiting my comments here to the phrasing Trump used, “He knew what he signed up for.”

Imagine four cases:

Case 1: “I know what I signed up for.”

Case 2: A family member or someone close to the killed or injured says, “She (or he) knew what they signed up for.”

Case 3: A person who did not know personally the person killed or injured says “She (or he) knew what they signed up for.”

Case 4 is a variant of Case 3, in that the speaker is someone with a political stake in how the death or injury is perceived.

The phrasing is identical in each case except the first, when the speaker is referring to him or herself, but they may have different meanings and certainly may be perceived differently.

In each case, we can consider not only military deaths and injuries, but also police, fire, and other similar occupations that carry more than common risks, and which are on behalf of the public. (Imagine, for example, that President Obama had used the same phrasing regarding a policewoman, killed in the line of duty.)

Whether the speaker personally knows the person killed or injured, and has direct knowledge of their motivation, is central to how the phrasing is perceived. At a minimum, presuming one knows about an individual when one has no direct knowledge carries extra risk of being misperceived. That is especially so in matters both personally and politically sensitive.

Case 1 has a unique perspective, in that the speaker is acknowledging the extra risks entailed in their own work and is perhaps being modest about their own character in accepting those risks.

In Case 2, the speaker very likely knows that the person killed or injured knew the extra risks and accepted them. Generally, this case is close but certainly not identical to Case 1.

In Case 3, the speaker knows nothing directly about the person killed or injured, but is presuming they do. Thus, in Case 3, the spirit behind the speaker’s comment is unclear or ambiguous. It can be taken as acknowledging courage, or it can be shrugging off the loss of or injury to someone the speaker does not know personally. This is quite different, even from Case 2.

And, in Case 4, the speaker is not only presuming something, they may have an interest in discounting the loss, making it more likely to be taken as shrugging it off. But, even if not, the comment is presumptuous. The burden of clarity, especially in Cases 3 and 4 is on the speaker.

And a history of carelessness with language or with the fate of others reduces or eliminates whatever benefit of the doubt might usually be given to the speaker in Cases 3 and 4. Thus, there should be no surprise that this call initiated another controversy.

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Cathy O’Neil’s Weapons of Math Destruction, How Big Data Increases Inequality and Threatens Democracy, is a terrific and important book. 

O’Neil has the credentials and the cred. Her Ph.D. in mathematics is from Harvard and she subsequently taught at Barnard. She took her analytical skills to D. E. Shaw, a hedge fund and then to other private sector organizations.

O’Neil sees the risks in overuse, especially of opaque mathematical models, not so much from a technical perspective, but because of the economic and political power of such models. She also sees the risks of how using such models and analyses creates feedback loops, especially vicious cycles that disadvantage the disadvantaged. And she’s acutely critical – rightly so in my judgement – of analyses and measurement systems that may be technically sound, but are far from conceptually sound. 

Example: measurement of teacher performance based on how their students do on high-stakes, non-routine tests. O’Neil’s explanation is not mere rant. Because she understands the technical and conceptual framework, she sees and helps the reader see how fragile and unreliable (thus, unfair) the system is. (And, I would add that, as a matter of public policy, how counterproductive such systems are.) O’Neil tells the story of Tim Clifford, an experienced teacher whose model-based score in one year was 6 out of 100. Protected by tenure, but baffled by his dismal score, he continued teaching the same way. The following year his score was 96 out of 100. The arithmetic might have been right. A measurement system that produces those kinds of swings is dangerous to rely on.

(Note that education is not my domain, but health care has been and I’ve observed that education policy makers could and should have paid attention to the lessons about measurement systems that health care folks learned with a 20-30 year head start. Perhaps I’ll expand on that one of these days.)

Don’t assume that because O’Neil is very much the mathematician, that she can’t write. She can. She has a nice, accessible style and uses storytelling well to make her points. And anyone who goes by the moniker, mathbabe takes her work, but not herself too seriously.

I’d put this book in a group with Michael Lewis’s Moneyball, Nate Silver’s The Signal and the Noise as eminently readable books on modern analysis. Lewis is about “why,” Silver’s mostly about “how” and O’Neil is about “be careful” about using today’s tools.

Don’t just take my word for it. Here’s Evelyn Lamb’s review in Scientific American.

For the lay reader who wants to know what some of the downside are, for the policymaker who wants to measure performance, but wants also to be fair and responsible, and for the data maven who’s concerned about the ethical parameters of their work, Weapons of Math Destruction is a must read.

Data WMD O Neil

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by John W Rodat on April 7, 2017

Hey, data mavens:


JWR License Plate

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The US Senate changed their rules today, requiring only a simple majority to end debate over Supreme Court nominations. This so-called “nuclear option” reduced the votes required from 60 percent plus one to 50 percent plus one. The change was accompanied by much drama. 

It’s probably fair to assume that a similar change will come regarding legislation. (That’s already the case for Budget Reconciliation.)

Though, there’s much hue and cry, in the long run, this will make the Senate less undemocratic. As Senate seats are allocated two for each state, people in states with smaller population have proportionately more representation in the Senate that people in other states.

With the 60 percent rule, Senators (from the smallest states) representing about 11 percent of the country’s total population could block action in the Senate. With a simple majority (plus one), that number increases to about 17 percent. It’s still undemocratic, but less so.

Public Signals States Able to Block Senate Action

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A key consideration in the current fracas, but which is not part of the discussion is that the effect of Medicaid on county finances in New York is quite variable.

Here are some graphics for background

County Cost of Medicaid Trend


Medicaid as Percent of Total Expenditures


Medicaid as Percent of Property Taxes


Medicaid as Percent of Sales Taxes


Medicaid as Percent of Sales Taxes Net of Distributions to Cities Towns Villages


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I’m analyzing NYS options if the AHCA becomes law including the amendment pushed by Congressmen Collins and Faso. I’ll post that later today.

For some context and history, it will be useful to read this post from five years ago.

Note that the number in the earlier post is $8 billion while the estimated effect of the Collins/Faso amendment is around $3.2 billion. The difference is that the larger number includes New York City, for which they would provide no relief.

More to come

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I’ve been working on health coverage issues since the late 70’s. Here are some of the key things that WE have learned during the time since.

  1. Prior to Medicare, private insurers did not offer coverage to the elderly. Most elderly simply got dropped when they turned 65. That’s why we have Medicare.
  2. Prior to the Affordable Care Act (ACA), most people without coverage were in families with at least one working person. Many were in families with two working persons. But the employer/s didn’t offer coverage or the wages were too low to take advantage of what was offered. And, in many states, Medicaid did not offer coverage either.
  3. Those uncovered families included a lot of kids.
  4. There were lots of people without coverage and the numbers were growing, even after Medicare and Medicaid and until the ACA was implemented
  5. Coverage makes a difference in whether and when you get health care. And the more subtle (or insidious) the condition, the bigger the difference.
  6. If you seek care without coverage, it’s usually later in the disease process and thus, worse and more expensive to care for. It’s usually later because you don’t know you’re sick or the nature of your illness. Think late-stage cancer vs. cancer that’s detected early.
  7. Getting care makes a difference in whether and how well you live.
  8. If you get care without coverage, you’re more likely to go bankrupt.
  9. If you get care without coverage, people with coverage cover the cost through higher prices that eventually insurers and government pay.
  10. If you seek care without coverage, you usually go to the least efficient provider, namely a hospital emergency room.
  11. Not covering kids, especially with broad coverage that includes preventive care, is especially dumb.
  12. Higher numbers and percentages of uninsured patients undermine the financial health and viability of hospitals – including those that serve the insured. So coverage won’t protect you from indirect effects.
  13. People with infectious diseases, but without coverage, are still infectious and more likely to spread the infection. So coverage won’t protect you from indirect effects.You can’t hide from all those diseases in some gated community. 

Well, I hope I’ve learned other things too, but I think this list covers the key things. 

So repealing the Affordable Care Act without a credible replacement is downright foolish. 

And, no, I’m not a liberal and not even a fan of single-payer structures. But increasingly, I think I’d take single payer over a system collapsing of its own weight, both financial and political.

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We’ve updated our visualized Medicaid enrollment data. These data run through May of 2016.

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From The Secret War: Spies, Cyphers, and Guerrillas, 1939-1945, by Max Hastings:


“The first requirement for successful use of secret data is that commanders should be willing to analyze it honestly. Herbert Meyers, a veteran of Washington’s National Intelligence Council, defined his business as the presentation of ‘organized information’; He argued that ideally intelligence departments should provide a service for commanders resembling that of ship and aircraft navigation systems. Donald McLachlan, a British naval practitioner, observed: ‘Intelligence has much in common with scholarship, and the standards which one demanded in scholarship are those which should be applied to intelligence.’ “ 

Hastings goes on to reference German commanders after the war blaming their intelligence failures on Hitler’s “refusal to countenance objective assessment of evidence” – especially if the reports were unfavorable [to his views].’


Useful lessons here even outside the military and national security domains, but seems especially timely these days. Bad enough to try to fool others. Fooling yourself is especially dangerous.

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Visualized Medicaid enrollment trends, by health plan (and fee-for-service) along with mix by demographic characteristics

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