You remember Democrat Rep. Alan Grayson of Florida. A month ago, he was taking refuge in a union hall to shill for Obamacare in a cowardly last-minute meeting with angry constituents.
As you know, he has now traveled down Demagoguery Road and accused Republicans of wanting sick Americans to “DIE QUICKLY.”
The diarrhea of the mouth continued yesterday with Grayson trashing his political opponents as “knuckle-dragging Neanderthals.”
The Democrat etiquette police, of course, are nowhere to be found.
More offensive than Grayson’s incivility is his promotion of that “Harvard study” — authored by avowed single-payer advocates – purporting to show that 45,000 Americans die every year because of lack of insurance.
Reader Dale notes the methodology:
1. Survey a sample group in 1988, ask at that time if the person is insured, and to rate their health, do not verify actual coverage at that time or at any other time during the study
2. Track the deaths of people in the sample group between 1988 and 2000
3. Attribute deaths to lack of insurance for everyone who at the time of the initial interview claimed to be uninsured dies for any reason over the subsequent 12 years
If on the day of the interview I was uninsured, but then was insured for the next 12 years without interruption, my death would be attributed to me being uninsured. How rigorous a study is that?!
Indeed, if you actually read the study, you’ll read this:
December 2009, Vol 99, No. 12 | American Journal of Public Health
Our study has several limitations. NHANES III assessed health insurance at a single point in time and did not validate self-reported insurance status. We were unable to measure the effect of gaining or losing coverage after the interview.
Point-in-time uninsurance is associated with subsequent uninsurance. Intermittent insurance coverage is common and accelerates the decline in health among middle-aged persons. Among the near-elderly, point-in-time uninsurance was associated with significant decline in overall health relative to those with private insurance. Earlier population-based surveys that did validate insurance status found that between 7% and 11% of those initially recorded as being uninsured were misclassified. If present, such misclassification might dilute the true effect of uninsurance in our sample. We excluded 29.5% of the sample because of missing data. These individuals were more likely to be uninsured and to die, which might also bias our estimate toward the null.
We have no information about duration of insurance coverage from this survey. Further, we have no data regarding cost sharing (out-of-pocket expenses) among the insured; cost sharing worsened blood pressure control among the poor in the RAND Health Insurance Experiment, and was associated with decreased use of essential medications, and increased rates of emergency department use and adverse events in a random sample of elderly and poor Canadians. Unmeasured characteristics (i.e., that individuals who place less value on health eschew both health insurance and healthy behaviors) might offer an alternative explanation for our findings.
Reader Kevin adds:
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There’s another huge misrepresentation about the Harvard study that I haven’t seen reported.
Look at Table 1 on page 3. This is the raw data from the study. Item 2 is the breakdown between those who were insured and those who were uninsured. The last column is the percentage of each group that died. 3.3 % of the uninsured group died. The authors then take the 3.3% figure and interpolate it using census data to GUESS how many people in America die “because” they are uninsured. The word “because” is extremely disingenuous, as there would be no way of knowing if those people would have died with health insurance. And, as you mention in your article – whether any of those people received health insurance or health care before they died.
But even worse is when you consider the percentage of people WITH health insurance who died. Remember, 3.3% of the people “without” insurance died. How many people WITH insurance died? 3.0%. Considering the disparity in the sample (6655 insured/2350 “uninsured”) the difference is basically nothing.
So, this study could easily make the argument that while 44,789 (3.3% of total uninsured) died, a whopping 6,000,000 (3.0% of insured) died! Clearly, FEWER people WITHOUT insurance died. Of course, it’s a foolish argument but it highlights the major deficiency with this study which is that people die with or without insurance. And statistically, the numbers seem to be about even.
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