Where to begin? I got an email with a letter from the commissioner of the NY DOH that covered three topics:
1. Flu Season
2. Synthetic Cannabinoids
3. NY's Medical Marijuana Program
The bit about Synthetic Cannabinoids started with, "The surging popularity of these man-made drugs has created
a serious and sustained public health problem in New York State..."
It went on to describe the problem, and ended the next paragraph with, "Be on the lookout for the use of these drugs by your patients. We need to work
together to stop this scourge."
This was immediately followed by:
If you have patients with medical conditions that may benefit from the use of medical
marijuana, I would also like to remind you that next month is the launch of New York’s Medical
Marijuana Program. I encourage you to enroll in the online course to become a registered
physician, so you can certify eligible patients to receive medical marijuana. For more
information, please access at:
Granted, synthetic cannabinoids are not the same thing as medical marijuana, and the letter even remarks on this point. But you'd think someone would have noticed the irony in the juxtaposition, and the complete absence of any comment about the problems with medical marijuana.
However, this post is really about flu vaccination. I know I've written about this topic before, and it's not a psychiatric topic, but the interpretation of research findings is a psychiatric and clinically relevant topic, and this turned into an exercise in understanding the literature.
The commissioner's comment on Flu includes the following:
It’s impossible to predict the severity and timing of any flu season. Every
year, however, flu causes widespread illness. Last year’s flu sickened approximately 51,000
people in New York, causing more than 11,000 hospitalizations and six pediatric deaths. The
Centers for Disease Control and Prevention recommends an annual vaccine for everyone over
six months of age... One study showed that flu
vaccination reduced flu-related hospitalizations among adults of all ages by 71 percent.
No reference was given for the "one study".
This topic holds personal relevance for me because I don't get the flu vaccination, and as a result, I have to agree that I will not be around patients in my affiliated hospital without wearing a mask.
The reason I don't get the flu vaccine is the Cochrane Review (the link goes to the summary of the review, from which you can access the article). Briefly, they looked at a total of 90 studies, 20% of which had a high risk of bias, and 10% of which had good quality methodology. The evidence is current through May of 2013.
They found that the Number Needed to Vaccinate (NNV) to prevent one case of influenza-like illness (ILI) was 40 (CI: 26-128), and 71 (CI: 64-80) to prevent one case of confirmed influenza, in the case of parenteral inactivated vaccine. For live aerosol vaccine the NNV for ILI was 46 (CI: 29-115).
In addition, "Vaccination had a modest effect on time off work and had no effect on hospital admissions or complication rates." I view time off work as a good measure of severity of illness, because if you're really sick, you don't go to work.
That's why I don't get the flu vaccine.
But I was curious about this statement from the DOH letter that, "One study showed that flu vaccination reduced flu-related hospitalizations among adults of all ages by 71 percent." I mean, according to Cochrane, vaccination has no effect on hospitalization. And according to this study, it reduces flu-related hospitalization by 71%.
The first thing I looked at were the CDC recommendations, and none of their references seemed relevant. Please note that I was only looking for information about adults. There may be some stuff there about kids.
So then I looked for the 71% study, and I found it through a link on NPR, of all places. The study is entitled,
Effectiveness of influenza vaccine for preventing laboratory-confirmed influenza
hospitalizations in adults, 2011-2012 influenza season, by Talbot et al. It was funded by the CDC, and ultimately published in Clinical Infectious Diseases.
Talbot, et al did a case-positive control-negative analysis of 169 adult patients admitted to a hospital for something that looked like flu. That is, after eliminating patients who didn't meet eligibility criteria, they had 169 left. They were able to track down the vaccination status for these patients, and they tested them for flu.
It turned out that 11 of 65 (17%) non-vaccinated patients were positive for flu, and 6 of 104 patients (6%) were positive for flu. There were confounding factors, e.g. the vaccinated group was older, and the non-vaccinated group smoked more.
"Unadjusted vaccine effectiveness was 71.1% (95%CI: 17.3%, 89.9%) for all adults and 76.8% (24.1%, 92.9%) for adults ≥50 years. Adjusted vaccine effectiveness for preventing influenza associated hospitalizations was 71.4% (95%CI: 17.1%, 94.9%) for all adults and 76.8% (24.0%, 97.9%) for adults ≥50 years."
This is where I got stuck. There was a table at the end, but it didn't help all that much, except to give me the 11 and 6 numbers above. I didn't know how they got their 71%, and I didn't know how to reconcile their findings with Cochrane's conclusions.
So I asked for help. I emailed Mickey, who writes the 1boringoldman blog. It's a great psychiatry blog-you'll see a link listed on my blog roll, to the right. Please visit it early and often. An embarrassing fact is that when I first started reading it, I thought Mickey's last name must be, "Goldman". Or, "Oldman". It's not.
Three of Mickey's recent posts, In the Land of Sometimes 1, In the Land of Sometimes 2, and In the Land of Sometimes 3, are excellent statistics tutorials. Mickey is also one of the people responsible for republishing Paxil Study 329, which involved quite a bit of statistical knowledge. So I thought he'd probably be able to help.
That I thought he'd be interested in and willing to help is probably a testament to his decency, which comes through in his blog posts. We'd had a couple of email exchanges in the past, so I wasn't contacting him completely cold, but he really didn't need to help at all. And within a few hours of emailing him, he got back to me with an explanation, computation, and references, and I am close to tears over his generosity as I type this.
I'm going to stop here, and continue in the next post with how to compute Vaccine Efficacy, how the 71% statistic came to be, and my discussion with Mickey about the conclusions.