There is a large literature on the importance of frequent hand washing in hospitals, to prevent spreading infectious diseases among patients. It’s a major problem, since hospital-caused infections are growing, and have nasty effects.
Brad Stats recently sent me two papers he co-authored on the topic. Both are based on an analysis of behavior by 4100 caregivers. They led me to ask two sets of questions. First, if everyone did comply with the recommendations on hand-washing frequency and duration, how much time would it take out of their work day? Second, while there have been lots of projects using electronics for monitoring compliance, has there been any work on straightforward manufacturing-style interventions to make compliance easier?
Here are my questions in more detail, taken from an email to Brad.
Separating historical truth from myth is as hard in science as anywhere else. This article has several examples, including whether Darwin got his ideas from someone else, and a dispute about whether Semmelweis was really ignored after his discovery of the link between hand-washing and disease.
Semmelweiss teaches doctors to wash their hands c 1850 – it is still an issue today
The Hamblin article [about a supposed misplaced decimal point], unscholarly and unsourced, would become the ultimate authority for all the citations that followed. (Hamblin graciously acknowledged his mistake after Sutton published his research, as did Arbesman.)
In 2014, a Norwegian anthropologist named Ole Bjorn Rekdal published an examination of how the decimal-point myth had propagated through the academic literature. He found that bad citations were the vector. Instead of looking for its source, those who told the story merely plagiarized a solid-sounding reference: “(Hamblin, BMJ, 1981).” Or they cited someone in between — someone who, in turn, had cited Hamblin. This loose behavior, Rekdal wrote, made the transposed decimal point into something like an “academic urban legend,” its nested sourcing more or less equivalent to the familiar “friend of a friend” of schoolyard mythology. Source: Who Will Debunk The Debunkers? | FiveThirtyEight
I found a similar myth about aviation checklists. It’s a myth that they were invented because of the crash of a B-17 bomber prototype in 1935. The first B-17 checklist was in 1937, and by then many Navy aircraft had more complete checklists. Including one published before the 1935 crash.
As far as I could tell when I researched this, the B-17 checklist story was first told in a 1965 book by Edward Jablonski. Since then the myth has been passed from article to article to book, such as Atul Gawande’s generally excellent book, Checklist. The crash did happen, but checklists were invented independently of it.
Here’s a column by a Forbes blogger about Zika saying that “we should not wait so long to develop vaccines against tropical diseases.” He concludes:
Many pharmaceutical companies don’t focus on a disease until it becomes common enough to be highly profitable. The trouble is the vaccine world has become a bit like the plot line for “She’s All That” or “Cinderella.” Attention towards a person or thing does not occur until a cool person notices he or she or it. But when it comes to disease and stock market opportunities, as the saying goes, once your grandmother knows about it, it is usually too late.
Source: Zika Vaccine: Another Example Of Waiting Until It’s Too Late? – Forbes
This is not news. And it’s a classic situation where market forces are not enough to give socially desirable behavior. Developing a vaccine for a disease that is not in rich countries has low expected profitability. Even if the disease goes epidemic, pharma company will have to sell at a price near marginal cost.
The only solution is to use a different way to fund development. Contests, grants (Gates foundation), purchase guarantees (used by US DoD) all work. But waiting for the traditional patent system + pharma profit motive won’t lead to timely development of medication for poor-country diseases.
I guess a Forbes columnist is not allowed to point this out.
I was teaching the Virginia Mason VMMC case in Tech & Operations Management yesterday, and made a loose comment about busy urban hospitals being better than suburban ones. For example in the UC San Diego system, when someone is my family is really sick I try to take them to the downtown (dilapidated, overcrowded) UCSD hospital before I’d go to the one near campus (hotel-like, luxurious).
A student asked “why”, forcing me to do a little research. Here is my answer to her. Continue reading
I have long argued that the FDA has an incentive to delay the introduction of new drugs because approving a bad drug (Type I error) has more severe consequences for the FDA than does failing to approve a good drug (Type II […]
Source: Is the FDA Too Conservative or Too Aggressive?
My take: this paper by Vahid Montazerhodjat and Andrew Lo is interesting, but it only looks at one issue, and there are many other problems that make overapproval more likely. There are many biases in the drug pipeline and FDA approval process, most of which are heavily in favor of approving drugs that do nothing (and yet, still have side effects). To mention one of many, the population used to test drugs is younger, healthier, more homogeneous, and more compliant than the population that ends up actually taking the drug. A second bias is that the testing process screens out people who have major side effects – they stop taking the drug, and are dropped from the sample (and from the statistical analysis at the end). So we only see the people with moderate or no side effects. Both of these problems lead to biases, which better statistical methods cannot remove.
The paper is interesting, but it is working from an idealized model of the drug research process, and I would not take its quantitative results seriously. The basic logic seems sound, though: there should be different approval standards for different diseases.
When the doctor’s away, the patient is more likely to survive | Ars Technica.
Very surprising. When cardiologists are away from the hospital, deaths after heart failure or cardiac arrest declined. I’ll probably use this in my course this Spring. (Or perhaps in both courses: Big Data, and Operations Quality in Healthcare.)
No, a study did not link GM crops to 22 diseases.
And a candidate for worst graph of the year, appearing to show that deaths from a certain class of diseases grew in parallel with some farming trends. ! (Figure 16 in the article, which is at http://www.organic-systems.org/journal/92/JOS_Volume-9_Number-2_Nov_2014-Swanson-et-al.pdf ). Any steadily increasing time series can be plotted so that they lie approximately on top of each other, if you distort the scales enough. Other “causes” they could have plotted, with approximately the same results: cell-phone per capita, percentage of cars on the road with ABS brakes, and (for all I know) average campaign spending per Congressional race.