8 ms·

> "1 in 1000 uninsured people die each year. It's not an exaggeration to say that due to the work we're doing here, 5,000-10,000 people will live to see the end

• slurry 13y ago
  Why would you blame the study authors for doing a correlation study? I know "CORRELATION IS NOT CAUSATION!!!" is the go-to takedown on the internet, but correlation studies actually can have significant value - otherwise peer-reviewed journals wouldn't publish them. I'm also skeptical of Politifact's competence to adjudicate public health scholarship.
  • aaronbrethorst 13y ago
    I'm also skeptical of Politifact's competence. FTFY
  • aamar 13y ago
    I agree that correlation studies can be valuable, but I see many correlation studies as (intentionally or not) exploiting a propensity (arguably a bug) in human reasoning that conflates it with causation. In this situation, we have thorough documentation of multiple people clearly making the error. I'm actually not sure the study authors should be blamed. But: given how politicized this question is, they could have reasonably anticipated the misuse of their results, and thus could have written their results in such a way as to avoid this. Or they could have publicly corrected non-experts who cited them for causation. Or: they could have controlled for factors that would make mortality and insurance-status independent. This last option is difficult & requires complex judgement calls (see [1] for a reasonable attempt), but even if you feel the other two aren't required of academics, this last one very much may be. (Separately, I agree that Politifact should not be trusted automatically, but these seem like reasonable analyses, and I did not quickly find anything better.) [1] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739025/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739025/
  • Fomite 13y ago
    Not only do they have significant value, but they're the only type of evidence you'll ever have for something like this - it's impossible, and almost certainly unethical - to run a randomized trial of keeping people from having health insurance. Also, while "Correlation is not causation" is, as you mentioned, a tired canard on the internet, people often seem to forget that all things that do have a causal relationship with have some form of association. Association doesn't prove causation, but it's a damned fine first step, and miles above "guessing", which is what happens without evidence.
    • ekianjo 13y ago
      Sorry, but no. 100 times no. Correlation is very often linked to a third factor or multiple factors which are not visible, nor measured in observational studies. Besides, let's not disregard the fact that correlation still has some good chance to be pure luck. Even correlation with 95% confidence statistical significance can be a random result in a non-nil number of times. So, no, you never prove anything nor imply anything at all with correlation. You're still guessing.
• dannyr 13y ago
  "But I don't think a statistic like this should be left unchallenged on HN." Yeah, thanks for fighting for the cause! #sarcasm Is HN supposed to be a place where we try to find a flaw on every statement and make sure it doesn't go unnoticed? What you pointed out doesn't even diminish an ounce of what Brandon and his teammates are doing.
  • aamar 13y ago
    > Is HN supposed to be a place where we try to find a flaw on every statement and make sure it doesn't go unnoticed? A recurring and important (to me, anyway) question here is how technical skill may be leveraged to provide real value to the world. This is a hard and unanswered problem. This [brandonb's] statement is so strong that, if it were true, it would eliminate a vast territory of alternate [possibly correct] paths to answers ("oh, you did X? My code saved 50 lives this year."). So it [brandonb's statement] is worth challenging (or correcting) more so than any random statement here. And yes, I do view promoting accuracy (even disillusion) as fighting for the cause. I hope Brandon will accept my sincere thanks for working on something that is important. Irrespective of health impact, the financial risk borne by the uninsured is an important issue and not controversial. edit: clarifications in []
    • dannyr 13y ago
      I'm curious what do you think would have happened if you just let it go. "This is a hard and unanswered problem. This statement is so strong that, if it were true, it would eliminate a vast territory of alternate paths to answers ("oh, you did X? My code saved 50 lives this year.")." It seems like you are more of the problem rather than Brandon's statement. No matter what Brandon says, accurate or not, people will still find a flaw in it. People will see what they want to see. If you want to eliminate alternate paths to answers, the sure way to do that is not say anything at all.
      • aamar 13y ago
        I hope my clarifications help explain what I meant in my parent comment. I believe the following are potentially bad consequences of Grayson's/Mikey's claim spreading: - People work on insuring others, at the expense of other activities that they would otherwise believe to be more valuable. - Insuring people (or the ACA) is deemed a failure because mortality rates do not come down as "expected", plausibly leading to the ACA's repeal. - A developer expends time working on the project expecting mortality rates to improve; when it doesn't, the uncritical idealist becomes an uncritical cynic, rejecting any future promise of saving lives/improving things.
• thisisdave 13y ago
  >It did not control for many factors that may be relevant (e.g. smoking). Unless I'm misreading the polifact article you linked to in [2], it says that the 2009 study did control for smoking, and that they did a better job of controlling for such factors than previous studies. > Still, their work stands out from previous efforts because it used more recent survey data and presented a more apples-to-apples analysis between the uninsured and insured populations. For example, it compared deaths rates for uninsured smokers with insured smokers, as well as other factors such as drinking, obesity, income and education.
  • aamar 13y ago
    You are right, the 2009 Wilpers paper does bucket out current and former smokers, as well as look at BMI and other factors. The authors should be credited for that. But when those factors are considered, the null hypothesis is only barely rejected at 95% confidence. The Kronick paper uses a much larger dataset and discusses the issue of what is controlled for more extensively.
    • Fomite 13y ago
      So wait, now not only are we using hypothesis testing as your sole means of evaluating whether or not an effect exists, but you're moving the threshold around because...you want to?
      • aamar 13y ago
        No, I don't think I'm arguing that. It's not a question of any one methodology always being better than others, or correlation studies always being wrong. The question is what we should reasonably believe in light of several analyses of various strengths.
• Fomite 13y ago
  > It did not control for many factors that may be relevant (e.g. smoking). From the abstract: "After additional adjustment for race/ethnicity, income, education, self- and physician-rated health status, body mass index, leisure exercise, smoking, and regular alcohol use" > On the authors of the correlation study, who should never have studied this question without looking at extensive control variables or without more specifically studying causation? How do you suggest studying causation in this setting? A randomized controlled trial where we deprive people of health insurance? Even that will likely not yield a true causal estimate, because randomization only helps for pre-randomization differences in the population, and behavior change from lacking health insurance will occur post randomization. The authors do extensively discuss their control variables, and important to remember is the fact that most papers only control for variables which ended up doing something, a subset of all variables that were tried. The NHANES data the study was pulled from includes a staggering number of covariates. --- While not an ironclad study, I found the paper itself vastly more compelling than the politifact analysis of it, which boils down to "Well, observational studies might be wrong because reasons".
  • yummyfajitas 13y ago
    A randomized controlled trial where we deprive people of health insurance? Yes. Before instituting Obamacare/Romneycare/$POLICY, we should have run a pilot program based on random assignment with clear predefined success metrics. But that's politically dangerous - after all, what if the experiment shows that $POLICY doesn't work? We did that, by accident, in Oregon (google Oregon Health Experiment). There were no statistically significant results beyond the placebo effect [1]. Strangely, none of our fact based politicians have proposed scrapping the medicaid expansion based on that. [1] People with insurance perceived themselves to be healthier before actually consuming any medical care and became less depressed. But no statistically significant difference was observed in any of the objective metrics chosen before the study started.
    • Fomite 13y ago
      Medical and public health researchers are bound to ethical guidelines that would prevent something like this, because the preponderance of evidence is that having health insurance is a net positive for someone's health - the only reason it made sense in Oregon is the fact that they needed a lottery anyway. As for the Oregon study, the results of that study are still relatively new (the idea that any measure focused on preventative health will show results after two years is pretty suspect). The authors of the study discuss this for diabetes: "Medicaid significantly increased the probability of being diagnosed with diabetes after the lottery (by 3.8 percentage points, relative to a base rate of 1.1) and use of diabetes medication (by 5.4 percentage points, relative to a base rate of 6.4). As discussed in the paper, based on clinical trial evidence on diabetes medication, we would expect this increase in the use of medication for diabetes to decrease the average glycated hemoglobin level in the study population by 0.05 percentage points, which is well within our 95% confidence interval for the impact of Medicaid on the level of glycated hemoglobin."