Showing posts with label absolute risk. Show all posts
Showing posts with label absolute risk. Show all posts

Thursday, May 17, 2018

Increasing Disparities in Infant Mortality? How a Narrative Can Hinge on the Choice of Absolute and Relative Change

An April, 11th, 2018 article in the NYT entitled "Why America's Black Mothers and Babies are in a Life-or-Death Crisis" makes the following alarming summary statement about racial disparities in infant mortality in America:
Black infants in America are now more than twice as likely to die as white infants — 11.3 per 1,000 black babies, compared with 4.9 per 1,000 white babies, according to the most recent government data — a racial disparity that is actually wider than in 1850, 15 years before the end of slavery, when most black women were considered chattel.
Racial disparities in infant mortality have increased since 15 years before the end of the Civil War?  That would be alarming indeed.  But a few paragraphs before, we are given these statistics:

In 1850, when the death of a baby was simply a fact of life, and babies died so often that parents avoided naming their children before their first birthdays, the United States began keeping records of infant mortality by race. That year, the reported black infant-mortality rate was 340 per 1,000; the white rate was 217 per 1,000.
The white infant mortality rate has fallen 217-4.9 = 212.1 infants per 1000.  The black infant mortality rate has fallen 340-11.3 = 328.7 infants per 1000.  So in absolute terms, the terms that concern babies (how many of us are alive?), the black infant mortality rate has fallen much more than the white infant mortality rate.  In fact, in absolute terms, the disparity is almost gone:  in 1850, the absolute difference was 340-217 = 123 more black infants per 1000 births dying and now it is 11.3-4.9 = 6.4 more black infants per 1000 births dying.

Analyzed a slightly different way, the proportion of white infants dying has been reduced by (217-4.9/217) 97.7%, and the proportion of black infants dying has been reduced by (340-11.3/340)= 96.7%.  So, within 1%, black and white babies shared almost equally in the improvements in infant mortality that have been seen since 15 years before the end of the Civil War.  Or, we could do a simple reference frame change and look at infant survival rather than mortality.  If we did that, the current infant survival rate is 98.87% for black babies and 99.51% for white babies.  The rate ratio for black:white survival is .994 - almost parity depending on your sensitivity to variances from unity.

It's easy to see how the author of the article arrived at different conclusions by looking only at the rate ratios in 1850 and contemporaneously.  But doing the math that way makes it seem as if a black baby is worse off today than in 1850!  Nothing could be farther from the truth.

You might say that this is just "fuzzy math" as our erstwhile president did in the debates of 2000.  But there could be important policy implications also.  Suppose that I have an intervention that I could apply across the US population and I estimate that it will save an additional 5 black babies per 1000 and an additional 3 white babies per 1000.  We implement this policy and it works as projected.  The black infant mortality rate is reduced to 6.3/1000 and the white infant mortality rate is 1.9/1000.  We have saved far many black babies than white babies across the population.  But the rate ratio for black:white mortality has increased from 2.3 to 3.3!  Black babies are now 3 (three!) times as likely to die as white babies!  The policy has increased disparities even though black babies are far better off after the policy change than before it.

It reminds me of the bias where people would rather take a smaller raise if it increased their standing relative to their neighbor.  Surprisingly, when presented with two choices:
  1. you make $50,000 and your peers make $25,000 per year
  2. You make $100,000 and your peers make $250,000 per year
many people choose 1, as if relative social standing is worth $50,000 per year in income.  (Note that relative social standing is just that, relative, and could change if you arbitrarily change the reference class.)

So, relative social standing has value and perhaps a lot of it.  But as regards the hypothetical policy change above, I'm not sure we should be focusing on relative changes in infant mortality.  We just want as few babies dying as possible. And it is disingenuous to present the statistics in a one-sided, tendentious way.

Thursday, April 6, 2017

Why Most True Research Findings Are Useless

In his provocative essay in PLOS Medicine over a decade ago, Ioannidis argued that most published research findings are false, owing to a variety of errors such as p-hacking, data dredging, fraud, selective publication, researcher degrees of freedom, and many more.  In my permutation of his essay, I will go a step further and suggest that even if we limit our scrutiny to tentatively true research findings (scientific truth being inherently tentative), most research findings are useless.

My choice of the word "useless" may seem provocative, and even untenable, but it is intended to have an exquisitely specific meaning:  I mean useless in an economic sense of "having zero or negligible net utility", in the tradition of Expected Utility Theory [EUT], for individual decision making.  This does not mean that true findings are useless for the incremental accrual of scientific knowledge and understanding.  True research findings may be very valuable from the perspective of scientific progress, but still useless for individual decision making, whether it is the individual trying to determine what to eat to promote a long healthy life, or the physician trying to decide what to do for a patient in the ICU with delirium.  When evaluating a research finding that is thought to be true, and may at first blush seem important and useful, it is necessary to make a distinction between scientific utility and decisional utility.  Here I will argue that while many "true" research findings may have scientific utility, they have little decisional utility, and thus are "useless".

Saturday, November 16, 2013

The Cardiologist Giveth, then the Cardiologist Taketh Away: Revision of the Cholesterol Guidelines

There has been quite a stir this week with the publication of the newest revision of the ACC/AHA guidelines for the treatment of cholesterol.  The New York Times is awash with articles summarizing or opining on the changes and many of the authors are perspicacious observers:
As the old Spanish proverb states, "rio revuelto, ganancia de pescadores" - when the river is stirred up, the fishermen benefit.  I will admit that I'm gloating a bit since I consider the new guidelines to be a tacit affirmative nod to several posts on the topic of the cholesterol hypothesis (CH).  (More posts here and here and here, among several others - search for "cholesterol" or "causal pathways" on the Medical Evidence Blog search bar.)

Wednesday, October 24, 2012

A Centrum a Day Keeps the Cancer at Bay?


Alerted as usual by the lay press to the provocative results of a non-provocative study, I read with interest the article in the October 17th JAMA by Gaziano and colleagues: Multivitamins in the Prevention of Cancer in Men. From the lay press descriptions (see: NYT summary and a less sanguine NYT article published a few days later,) I knew only that it was a positive (statistically significant) study, that the reduction in cancer observed was 8%, that a multivitamin (Centrum Silver) was used, and the study population included 14,000 male physicians.

Needless to say, in spite of a dormant hope something so simple could prevent cancer, I was skeptical. Despite decades, perhaps eons of enthusiasm for the use of vitamins, minerals, and herbal remedies, there is, to my knowledge (please, dear reader, direct me to the data if this is an omission) no credible evidence of a durable health benefit from taking such supplements in the absence of deficiency. But supplements have a lure that can beguile even the geniuses among us (see: Linus Pauling). So before I read the abstract and methods to check for the level of statistical significance, the primary endpoint, the number of endpoints, and sources of bias, I asked myself: "What is the probability that taking a simple commercially available multivitamin can prevent cancer?" and "what kind of P-value or level of statistical significance would I require to believe the result?" Indeed, if you have not yet seen the study, you can ask yourself those same questions now.