What Was Wrong With My Last Video

After I posted my last video on wait times for getting a doctor’s appointment in Atlanta, Georgia vs. wait times in Boston, Massachusetts despite the fact that insurance premiums are drastically more expensive in Boston, I received a more mixed response than to any of my other videos.

Fortunately, I have commenters who are much smarter than I am. So I want to take the time to make note of what was wrong with that video.

Things that were wrong:

• While I didn’t say this, I left the impression that the universal health coverage program in Mass was solely responsible for the expensive premiums. That is not the case. In the study I referenced, premiums were extremely high in Mass, but that was done before universal health coverage (UHC) was implemented. There has not (to my knowledge) been a similarly comprehensive study done since the implementation of UHC, so I cannot say that UHC inevitably leads to higher premiums.
• Instead of comparing Boston to all the places that don’t have UHC, I picked one place that I liked (I used to live in Atlanta). Atlanta is particularly good on wait times, but it isn’t average. It would have been far better to compare Boston to the rest of the country as a whole.

Things that weren’t wrong, but that people complained about anyway:

• “The cost of living is higher in Boston. That is what drives the insurance premiums higher”

  This statement was done by people who haven’t actually run the numbers. No one has yet explained to me how a 14% increase in cost of living between Atlanta and Boston explains a 300% increase in health insurance premiums. More importantly, no one has pointed out that doctors in Boston make significantly more than doctors in Atlanta (which would be a far more important data point for investigation).

  Also, it doesn’t explain the difference between Boston and Los Angeles. The Los Angeles cost of living is 22% higher than Boston, but they still have cheaper insurance. And they still have wait times that are half of Boston’s.

To me, the most important point of all of this is the fact that we may not have enough data to say that UHC has actually caused Boston to get a lot worse than it already was, but we do have enough data to say that it certainly hasn’t made things better.

Some people would point out such statistics as “Well, nearly everyone in Massachusetts has health insurance now! It’s been a success!” (In fact, that’s exactly what Mitt Romney does when he says we should, like, totally copy the Massachusetts model.)

But this isn’t a single variable issue, it’s a trade-off issue. If we gave everyone in the country health insurance, but it cost $1 trillion per year, everyone would be against it because the benefit outweighs the cost. Similarly, if we provided everyone with health insurance and kept the cost the same as before, but it took five times as long to get a treatment, most people would still be against it.

It is not self-evident to me that 100% health insurance coverage is a good thing because it depends so heavily on the trade-offs. You could have “100% health insurance coverage” as a matter of statistics, but if that means that it takes three years on a waiting list to get a hip replacement, I’ll stick with the 85% we have today.

And I’ve only mentioned three variables here (cost, insurance coverage and wait times). There is a balance between hundreds of variables that has to be maintained. If you ever listen to the President’s speeches on this issue, you come away with the impression that there will be no trade offs and that it will be cheap premiums, universal coverage and no wait times! And the government will make drugs cheaper.

Also, there will be rainbows and ponies.

Call me a cynic, but that kind of thinking about in the UHC plan strikes me as somewhat naive.

What I would like to see is a comprehensive study of the effects on the Massachusetts plan on the health care system. I don’t know that there are really that many positive effects other than “Look, everyone has health insurance!”

Obama Health Care Reform and Wait Times Visualization (In Lego!)

The next couple weeks are insane for me, but I’ve been sitting on this idea for some time and I figure its time to let it loose into the wild, spelling errors and all.

First, my sources.

• Wait time data – Merrit Hawkins and Associates 2009 Survey of Physician Appointment Wait Times
• Cost of Insurance Premiums – AHIP Center for Policy and Research Individual Health Insurance 2006-2007: A Comprehensive Survey of Premiums, Availability, and Benefits

Now for the caveats.

Wait times data are for routine checkups and does not count emergency care or diagnostic testing.

Phyllis Shlafly repeated the line that “The average wait is… the second trimester of pregnancy to see an obstetrician-gynecologist.” It looks like she is using the same documents that I’m using and if that is the case, that statements is absolutely false.

First of all, these wait times apply only to routine checkups (as stated above) and the OB/GYN checkups are “well woman” check-ups. Someone correct me if I’m wrong, but I don’t think that a pregnant woman falls into that category.

Second, the average wait time in that category is 70 days, which is really only the second trimester if you count the “Wait a second, I’m pregnant!” realiziation time, which might be OK if she mentioned that to he readers.

Now for the insurance cost data. This was a statistic I struggled with for quite some time. The reason is because the latest comprehensive data available was collected at the end of 2006 and beginning of 2007. This was so soon after the passage of the Massachusetts health care reform that it is very unlikely that it accurately reflects the results of that reform (which is something the study authors freely admit).

However, I’ve search high and low and cannot find any indication that the premiums have decreased at all. To the best of my knowledge, they have increased faster than the country average.

If this is true,  then the average individual health insurance premium in Massachusetts is somewhere around $830 per month.

But I figured I might as well underestimate in order to flush out people who might complain, so I used the non-specific and drastically reduced number of $600+ per month.

Finally, the most important question:

How close to the Massachusetts health reform is the Obama health reform plan?

Because, honestly, if they weren’t anything like each other, there would be no point in comparing them, would there?

The sad fact of the matter is that the Massachusetts model provides the closest real life approximation to the Obama plan that there is available.

They both have a government agency for providing health care exchanges. They both require business over a certain size to provide insurance for their employees or face penalties. They both require individuals to purchase insurance or face tax penalties.

Like it or not, I think we can look to Massachusetts as a miniature crystal ball to provide a glimpse into the future of health care in the US if the Obama health care plan is passed.

The Obama Stimulus: Predictions vs. Reality

In this video, I take a look at the economic predictions that President Obama made in February regarding the stimulus plan and how those predictions are corresponding to reality.

The answer is: Not well.

But first, some references.

• I got all my data from one extremely boring source, the Bureau of Labor Statistics. At the moment, all the data is in the economic summary, but I assume that will change as the weeks go on, so you can also check the historical unemployment tables.
• The chart that President Obama used is in this document on how the stimulus will effect the economy.
• Special thanks to Geoff at Innocent Bystanders. Last month, he started charting how the numbers Obama used to sell the stimulus were matching to the actual numbers. So I can’t claim the idea as my own.

OK… now into the math. The chart that everyone is using does not have a corresponding table with hard number (at least no table that I could find), so I had to guess-timate what they were predicting the unemployment rates would be in May. I assumed that, because their graph divergence began immediately after the Q1, 2009 line, that that line represented the beginning of Q1 2009 (as opposed to the middle). So I estimated that the May would be just a shade before Q3, which is about the same place that Geoff put his May data.

Based on that, I estimated the points on the line like so:

	Unemployment Rate	Unemployed Population
Predicted Unemployment without the Stimulus	8.7%	13,492,000
Predicted Unemployment with the Stimulus	7.9%	12,251,000
Actual Unemployment with the Stimulus	9.4%	14,511,000

Now… here is the problem. In order to make our data symmetrical, we would have to have another row… a row called “Actual Unemployment without the Stimulus”. This, of course, is a row we cannot have because we sadly live in a space-time of collapsed quantum possibilities. We can never know what that row would hold.

This is where I start getting a little less analytical and a little more irritated. The president’s predictions have been shown to be completely off the mark… almost laughably so. And yet he acts as if he alone knows what would have happened if we hadn’t passed the stimulus because he keeps making statements like “we’ve saved 150,000 jobs“.

It is clear that, if he is referring to the chart we were presented with above, such a claim is absurd. What the president is doing is ignoring the fact that his predictions in the past were horribly inaccurate and simply moving ahead with new predictions. The big difference is that his new predictions can’t be judged against any set of objective reality. He is pitting the actual universe in which the stimulus bill passed against the imaginary universe in which it did not pass. Not surprisingly, the imaginary universe is worse that the real universe and the result is that the President is a hero for saving us from that imaginary universe.

I am not a very anti-Obama person. Predicting the future is tricky business and I think his team should get some leeway on this.

However…

Their predictions were not just kinda wrong. They were horrifically, disasterously wrong. If President Obama is going to use statistics and charts to push nearly $800 billion in spending, I think we should be able to expect his numbers to at least kinda match the reality that comes out of his policies.

At the very least, I’d like to know how his team got those numbers. More importantly, I’d like to know how they have changed their method of prediction. President Obama is fond of saying that we tried tax cuts and they didn’t work, so we should try something else. In that same vein, his team tried predicting the effect of the stimulus and that didn’t work. So I would like to know if they are using the same failed methods they used before or if they are doing something different.