Category Archives: Health Care

The Inadequacy of the Obama Administration – Part II

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Via Brad DeLong, a memo from David Cutler to Larry Summers on health reform implementation – at the time Cutler was advising Pres. Obama on health economics and Summers was the Director of the National Economic Council:

Date: May 11, 2010
To: Larry Summers
From: David Cutler
Subject: Urgent Need for Changes in Health Reform Implementation

I am writing to relay my concern about the way the Administration is implementing the new health reform legislation. I am concerned that the personnel and processes you have in place are not up to the task, and that health reform will be unsuccessful as a result.

Let me start by reminding you that I have been a very active supporter of reform. In addition to being the senior health care advisor to the President’s campaign, I worked closely with the Administration, helped Congress draft the legislation, met with countless Members of Congress and interest groups to explain the reform effort, conducted numerous radio and television interviews, walked hundreds of reporters through health care, and wrote a number of op-eds and issue briefs supporting reform. I am told that the President and senior members of the Administration valued my input, though I was never offered a position in the Administration. I say this to illustrate that I have thought about the issues a good deal and have discussed them with many people.

You should also note that while this memo is my own, the views are widely shared, including by many members of your administration (whose names I will omit but who are sufficiently nervous to urge me to write), as well as by knowledgeable outsiders such as Mark McClellan (former CMS administrator) and Henry Aaron (Brookings). Indeed, I have been at a conference on health reform the past two days, and have found not a single person who disagrees with the urgent need for action.

My general view is that the early implementation efforts are far short of what it will take to implement reform successfully. For health reform to be successful, the relevant people need a vision about health system transformation and the managerial ability to carry out that vision. The President has sketched out such a vision. However, I do not believe the relevant members of the Administration understand the President’s vision or have the capability to carry it out.  Let me illustrate the problem you face and offer some solutions. Continue reading →

Max Sawicky: Obamacare Isn’t Causing an Increase in Part-Time Employment, In One Chart

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Via Brad DeLong:  Max Sawicky, Obamacare Isn’t Causing an Increase in Part-Time Employment, In One Chart:

One of the more baffling messages in the current debate over the economy and “Obamacare” is the hue and cry over the trend in part-time employment. The fact is that since the end of the Great Recession, the trend in part-time employment has been down, not up. The black line in the chart below shows the share of part-time workers in the labor force. The light blue region shows the level of workers who are part-time due to economic reasons. The navy blue region show the level of workers who are part time due to “non-economic” reasons (health, child care responsibilities, etc.). The vertical bars denote recessions, from peak to trough.
max chart

ThinkProgress.org: 20 Questions You Have About Obamacare But Are Too Afraid to Ask

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Update 10/2/2013:  Link to the Massachusetts Health Connector.

Via Brad DeLong, Annie-Rose Strasser and Tara Culp-Ressler, 20 Questions You Have About Obamacare But Are Too Afraid to Ask:

If you’re confused about Obamacare, you’re not alone. Over the past several years, every survey on the subject has revealed that Americans consistently fail to correctly identify the provisions that are actually in the Affordable Care Act…  As the open enrollment period draws near, you may be wondering how it affects you or what you need to do. Or you may simply want to understand more about the law that’s dominating the news. Here are simple answers to 20 questions about Obamacare that may have you mystified (click on each question to jump down to the answer, or just scroll down to read all of them):

Continue reading →

Rep. Capuano: Obamacare is facing death by a thousand cuts

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Mike Capuano’s op-ed in today’s Globe re ACA implementation:

Outright repeal is one way to sabotage health care reform, but most opponents recognize they don’t have the votes for that, let alone enough to override a certain presidential veto. Of course, this has not prevented House Republicans from voting three dozen times to repeal health care reform. A 37th attempt took place earlier this month. All of this represents nothing more than political grandstanding. Since they haven’t been able to achieve an outright repeal, Republicans are also working to thwart implementation.

Over the last three years, the Obama administration and leaders in many states have been preparing for the full implementation of health care reform. At every step, the Republican majority in the House, their committed counterparts in the Senate, and their allies in many state houses across the country have sought to delay, obstruct, and undermine these efforts. Rather than fix the parts of the law that need to be fixed, they plot new ways to kill it by a thousand cuts.

Republicans have denied funding for essential preparations and made it plain they will resist confirmation of presidential nominees needed to administer it. Moreover, the Republican-led House has voted to defund the law and targeted specific aspects of it. They have voted to withhold salaries for employees who will set up health care exchanges, the marketplace where consumers will go to choose a plan. They have voted to repeal funding for school-based health care centers and voted numerous times to eliminate the Prevention Trust Fund. You don’t have to be a doctor to recognize that preventing illness is cheaper than treating it. Eliminating funding for programs like this will result in higher health care costs.

The evidence is overwhelming that Republicans are undermining health care reform whenever possible so that it will either be delayed, incredibly messy, or impossible to administer responsibly. Then, in the run-up to the next national election, they will simply blame Obama and Democrats for passing a bad law while denying they did anything to sabotage it.

Continue reading →

Understanding the Oregon Medicaid experiment: Part 4

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UPDATED 5/25/2013

Part 4 – in which I believe I make progress in understanding the details but still end up wrapped around the axle.

First off, but having nothing directly to do with the NEJM paper, I understand significantly more about logistic regression than I did a week ago.  I’m used to thinking about continuous variables.  Logistic regression facilitates modeling of binary outcomes, i.e., enables you to predict the probability of an outcome being true (or false) given a particular set of conditions which affect the outcome.

Under the logit model, the probability of an outcome being true given a set of conditions defined as vector \mathbf{x} is:

(1)   \begin{equation*} p(\mathbf{x}) = \frac{1}{ 1+exp(- \mathbf{x}^{T} \mathbf{b} )} \end{equation*}

The vector \mathbf{x} consists of m elements whose values are the independent variables which affect the outcome.  For example, in the Oregon Medicaid experiment, p could be the probability of elevated GH level in an individual and the elements of \mathbf{x} could be ones or zeros depending upon whether the individual had Medicaid, was a member of a one-person household, a two-person househol, liked dogs, stated that pancakes were their favorite breakfast food, etc.  (The last two are intentionally silly – I made them up – but you get the idea,  \mathbf{x} is a vector which describes the “state” of the individual.)  The vector \mathbf{b} consists of the sensitivities of the probability to the independent variables in \mathbf{x} .  Our goal is to determine the ‘best’ values of the fit coefficients and the accuracy of the logistic regression model.

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Understanding the Oregon Medicaid experiment: Part 3

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In thinking about it a bit more and reading the Supplemental Appendix, I realize that this is a logistic regression problem as much as it is an exercise in Bayesian analysis.  (The Supplemental Appendix suggests that they’re using linear regression instead of logistic regression – which puzzles me.) That said, if the particulars of your analysis involve a small number of binary independent variables (i.e.., lottery winner/loser, on Medicaid/off Medicaid) and no continuous independent variable then it also seems like it would be easy to recast the logistic regression problem as a linear regression problem – need think more about that though.

In terms of analyzing the data, I’d combine logistic regression with bootstrapping to get uncertainties in estimated probabilities (and differential probabilities) of outcome with and without treatment.   From there you should be able to get the ‘overlap of pdfs’ approach I described in my previous post.  Although the details aren’t clear to me yet, I think this (if I follow through on it) will turn out to be complementary to Steve Pizer and Austin Frakt’s “Loss of Precision with IV” calculation.

UPDATE:

Continue reading →

Understanding the Oregon Medicaid experiment: Part 2

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Part 2 – in which I acknowledge an embarrassing misunderstanding of the data.  Yeah, about that…  My previous post about how the proper way to assess Medicaid’s effect on patient-level quantities like GH level would be to examine a correlation plot of GH levels and the start and end of the experiment?  Can’t be done.  Can’t be done because there is no baseline data.  It’s right there in the Methods summary:

Approximately 2 years after the lottery, we obtained data from 6387 adults who were randomly selected to be able to apply for Medicaid coverage and 5842 adults who were not selected. Measures included blood-pressure, cholesterol, and glycated hemoglobin levels…

I just presumed there was baseline data.  Nope.  Reading comprehension shortcoming on my part.  (Thanks to Austin Frakt and the lead study author Katherine Baicker for politely pointing that out to me.  Yah.  Nothing like demonstrating one’s ignorance in front of people who know what they’re doing.  Moving on…)  Lack of baseline data certainly complicates interpretation of the t=2 years data.  I maintain that comparison of before and after measurements is what you’d like to use as the basis for your conclusions but, to appropriate a line from Rumsfeld, “You analyze the data you’ve got not the data you’d like to have.”

[ADDENDUM:  I’m also reminded of John Tukey’s line:  “The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.”]

Continue reading →

Understanding the Oregon Medicaid experiment: Part 1

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Several weeks ago a paper came out in the New England Journal of Medicine, The Oregon Experiment – Effects of Medicaid on Clinical Outcomes.   The paper isn’t preceded by an abstract per se but there are several paragraphs of top-level introductory information:

Background

Despite the imminent expansion of Medicaid coverage for low-income adults, the effects of expanding coverage are unclear. The 2008 Medicaid expansion in Oregon based on lottery drawings from a waiting list provided an opportunity to evaluate these effects.

Methods

Approximately 2 years after the lottery, we obtained data from 6387 adults who were randomly selected to be able to apply for Medicaid coverage and 5842 adults who were not selected. Measures included blood-pressure, cholesterol, and glycated hemoglobin levels; screening for depression; medication inventories; and self-reported diagnoses, health status, health care utilization, and out-of-pocket spending for such services. We used the random assignment in the lottery to calculate the effect of Medicaid coverage.

Results

We found no significant effect of Medicaid coverage on the prevalence or diagnosis of hypertension or high cholesterol levels or on the use of medication for these conditions. Medicaid coverage significantly increased the probability of a diagnosis of diabetes and the use of diabetes medication, but we observed no significant effect on average glycated hemoglobin levels or on the percentage of participants with levels of 6.5% or higher. Medicaid coverage decreased the probability of a positive screening for depression (−9.15 percentage points; 95% confidence interval, −16.70 to −1.60; P=0.02), increased the use of many preventive services, and nearly eliminated catastrophic out-of-pocket medical expenditures.

Conclusions

This randomized, controlled study showed that Medicaid coverage generated no significant improvements in measured physical health outcomes in the first 2 years, but it did increase use of health care services, raise rates of diabetes detection and management, lower rates of depression, and reduce financial strain.

Not surprisingly, their conclusions have inspired much commentary in the blogosphere and, perhaps more importantly, amongst those interested in formulating good public policy.  If it’s true that Medicaid coverage doesn’t yield “significant” improvements in physical health outcomes that has tremendous consequences.  NB:  The authors conclude that coverage results in “significant” improvements in non-physical outcomes so even if there were no impact on physical outcomes one might argue that Medicaid coverage is beneficial.  (I put “significant” in quotes in the preceding sentences because it’s being used by the authors as a term-of-art.  More on that below.)

After reading multiple commentaries (see, for example, ones by Kevin Drum, Aaron Carroll and Austin Frakt, and Brad DeLong) I decided to cough up the $15 and download a copy of the paper for myself.  I found it frustrating for two reasons:

  1. How the authors chose to present their conclusions
  2. The authors’ criteria for declaring a result “significant”

The second issue is the easiest to speak to so I’ll address it first.  Continue reading →

Health care cost control

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Austin Frakt at The Incidental Economist:

Let’s make a deal. You shell out just $500 and I’ll pick up the tab for any automobile you care to buy. I’d better protect myself a little, so the deal is only good for Honda, Toyota, and Hyundai. Are you going to get the base model Yaris ($14k)? I seriously doubt it. You’ll probably get something nicer, maybe a souped up Land Cruiser ($80k+). I’m going to pay an awful lot.

Having learned my lesson, let me make a smarter deal. I’ll give you $15k toward a car, any car on the planet. If you want something more expensive, you pay the difference.

Which deal will lead to more prudent shopping, less wasteful car spending? Which will incentivize the market to be more efficient and consumer friendly?

Continue reading →

ACA implementation issues

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I had my reservations about the ACA (a.k.a. Obamacare) when it passed and I still have my doubts that it will be a success.  (It’s going to need some major tweaks.)  Charlie Pierce calls out attention to one of its shortcomings – quoting his post here in its entirety:

Whoops, here’s another part of the Affordable Care Act that has to go back into the shop because it’s starting to leave gears, and bolts, and piston rods all over the road.

Unable to meet tight deadlines in the new health care law, the Obama administration is delaying parts of a program intended to provide affordable health insurance to small businesses and their employees – a major selling point for the health care legislation.  Continue reading →