Order Ativan No Prescription
By: Lyle FearnleyOrder ativan no prescription, Calculations of the severity of the novel H1N1 influenza pandemic have been sorely lacking. As Laurie Garret and other experts noted early on, the lack of an index of severity in WHO’s pandemic alert system perhaps led some governments—and certainly much of the public—to consider ‘pandemic’ as an indication of danger rather than a reflection of geographical prevalence. H1N1 was responded to as if it were the actualization of the potential H5N1 outbreak everyone was waiting for. Or it was for a moment. For no sooner did some numbers start coming in then the threat began to be downplayed: death rates appeared far lower than seasonal flu.
Now, South Carolina SC S.C. , Cheapest ativan online, in a report issued on December 12th by CDC, we have some more numbers about how many people have caught the pandemic flu, California CA Calif. , Ordering ativan from canada, how many have been hospitalized and how many have died in the United States. The summarized data presented in the media is distilled in the number 9,820: deaths from H1N1. This is usually presented either to downplay the severity, ativan cheap, Texas TX Tex. , by comparing the number to the 36,000 seasonal flu deaths estimated to occur each year; or, buy ativan c.o.d., Order ativan overnight delivery, conversely, to demonstrate the danger of the outbreak through a comparison with the previous estimate, cheap ativan tablets, Purchase ativan online, one month ago, of only 3, αγοράσετε ativan, Osta ativan online, 900 H1N1 deaths. But as we know with influenza, it is important to get behind the numbers. How are these calculations made, cheap ativan online cheap. Idaho ID , The comparison with seasonal flu is complicated because the numbers are calculated in significanly different ways.
1) The Seasonal Flu deaths estimate is a famous and important one, αγοράσετε ativan έκπτωση, Cheap ativan overnight delivery, and I have encountered references to it again and again during fieldwork among syndromic surveillance developers and users (this at the time that they were trying to demonstrate the utility of s.s. by its early detection of seasonal flu outbreaks). The numbers are based on a study by William Thomson published in 2003 in JAMA and updated for intervening years in 2009. The study attempted to correlate excess deaths from circulatory and respiratory illness during flu season with flu isolates collected by viral surveillance laboratories. The mathematics behind the study is explained in simple terms in this recent Slate article. Basically, ativan without a prescription, Osta ativan, the study used a regression analysis to solve a multi-variable equation that looks like this:
[Total R and I deaths] = [R and I Deaths if there were no such thing as flu] + X*[number of confirmed flu cases]
Once the two variables are found that best fit reported data, the product of X*[number of confirmed flu cases] is the number of flu deaths, generic ativan. Ordering ativan, 2) H1N1 deaths are calculated for the recent report with a completely different method. These deaths are calculated using data from the Emerging Infections Program (EIP)—a ‘sentinel’ surveillance system set up in 1994. The EIP is a collaboration of ten state health departments with the CDC. In each state, the health deparmtnet assembles a network of local health departments, buy ativan online cheap, Lowest price ativan, academic institutions, laboratories, Wyoming WY Wyo. , Pharmacie ativan bon marché, and doctors offices or hospitals. The network is especially attuned to monitor for emerging or unusual diseases, and research or active surveillance efforts are undertaken through this network. The H1N1 flu deaths were calculated by first assessing flu hospitalizations in this network. The actual number of hospitalizations in the EIP network are “extrapolated” into ‘national data’ and corrected using a probabalistic multiplier model developed for an earlier CDC estimate of H1N1 prevalence (and originally used to assess the impact of foodborne illness). Then deaths are calculated from the national hospitalization data. This calculation is made using a ratio derived elsewhere of laboratory-confirmed deaths to laboratory-confirmed hospitalizations, Massachusetts MA Mass. .
Discussion: Its hard for me to think how to begin to compare these two numbers based on the vastly different techniques used to calculate them. Certainly it seems premature to use them in the glib fashion that says there have been only 1/3 as many deaths from H1N1 as seasonal influenza. Perhaps more worth noting (in classic nineteenth century, hygiene publique fashion) is the differential mortality associated with H1N1 pandemic. According to CDC numbers, this has been two-fold. First, the H1N1 pandemic disproportionately effects those under 65. A full 7,500 of the estimate 10,000 deaths occurred in the 18-64 age bracket, an age bracket that makes up only a modest portion of the ‘seasonal flu’ deaths. Such an age-shift is a classic sign of pandemic strains and was observed in many previous pandemics. Second, the pandemic H1N1 has caused four times more deaths among “American Indians and Alaskan Natives” according to a recent study. The CDC study attributes this differential mortality to “environmental” conditions, which they go on to specify as poverty, delayed access to healthcare, and low vaccination coverage, along with underlying risk-conditions such as asthma and diabetes.
So to say this outbreak is not severe seems wrong, although an accurate metric of severity is still waiting. Moreover, the delays in calculating severity seem to point up some of the weaknesses of statistical risk calculation for dealing with emerging infections. How would a preparedness system judge severity differently.
Similar posts: Buy ativan. Buy klonopin no prescription. Buy cheap clomid online. Buy acomplia cod. Order acomplia. Buy clomid cod.
Trackbacks from: Order ativan no prescription. Order ativan no prescription. Order ativan no prescription. Order ativan no prescription. Order ativan no prescription. Order ativan no prescription.
December 23rd, 2009 at 10:39 am
Lyle,
Many thanks for this informative post. It is analytically clear. It leaves us where we belong conceptually– clear that we don’t have a metric or even a single venue to decide what is going on.
I suppose it is also possible that the label H1N1 covers over a range of diversity as well?
Paul
December 24th, 2009 at 7:56 am
Lyle — Thanks for this post. It is really quite helpful. It seems that in addition to uncertainty about the number of people killed, and the demographics of mortality from H1N1, there is something to be said about the way that public health experts have to deal with a current unfolding event in relation to an uncertain future. On the one hand, it seems to me that I have read numerous references to an anxiety among public health experts and officials not to cry wolf, and thus lower their credibility when the next event comes. On the other hand, it seems that a crucial feature of a novel strain is that the likelihood of mutations that move things in a much more serious direction is great, such that as a matter of preparedness rather than as response to current severity it makes sense to do things like undertake vaccination programs that may (if I understand things correctly) have some chance of giving some herd immunity to more virulent future mutations. I guess the point in this case would be that “severity” is not the only relevant metric for determining what kind of response is appropriate.
December 26th, 2009 at 2:57 am
Many thanks for this interesting post, Lyle! I have a couple of comments.
“Such an age-shift is a classic sign of pandemic strains and was observed in many previous pandemics.” As far as I know, an age-shift was primarily observed during the 1918 pandemic. The famous W curve. I think this is not true for the 1957 and the 1968 pandemics. The other point is that during the current pandemic, the important thing may not be the age of these people, but the fact that most of them had underlying conditions. In 1918, however, the 18-64 age bracket referred to a healthy population. Experts were stunned that so many healthy people in their best age died. Hence the comparison doesn’t really work, because you may be comparing two different kinds of populations.
What is really surprising with this pandemic is that senior people have not really been affected a lot. That’s really interesting, but seems not to be very much discussed these days. The obsession with the 18-64 bracket, by contrast, is due to the on-going controversy about the “mildness” of the current pandemic.
In terms of vaccination, a better sense of severity doesn’t necessarily mean a better vaccination plan (see my paper). At the very least, this is an open question. More and better information doesn’t always and necessarily mean that the intervention will be better, too.
As far as Laurie Garrett is concerned, her argument seems a bit strange, especially if you re-read her articles. She has certainly played the game, made a lof cash, increased her authority as an “expert,” etc.. And if she would like to accuse someone for confusing pandemic influenza with severity I think it would be best if she would start with herself.
Stephen: “On the other hand, it seems that a crucial feature of a novel strain is that the likelihood of mutations that move things in a much more serious direction is great.” Different viruses have different mutations rates, but this is not related to epidemic vs. pandemic viruses and mutations can go both ways (i.e. more or less virulence, transmissibility, pathogenicity, etc.). Finally, the virus is just one side of the equation, the other side is the host (and the biomedical means of intervention, etc.).
What is also interesting, is the question of what happens when there is a shift in medium, i.e. when numbers become public. What is interesting with this pandemic is its incredible publicity. That has a certain impact on numbers and how they circulate. Here is a recent pro-med post:
“Not so, says the head of the CDC, Dr Thomas Frieden. “I think we’ve
been completely transparent with what we think is happening. I think
we have a difference of opinion on whether that is mild or severe,”
he says. He points out that the CDC has counted more than 250 deaths
among children. “Any flu season that kills at least 3 times more
children than a usual flu season I think it would be very misleading
to describe that as mild,” he says. But Frieden agrees that
perception is what matters. The more that people think the pandemic
threat is over, the fewer who will get vaccinated.”
So, to go back to Lyle, there are different methods of calculation, which all seem to measure different things. And different people have different stakes in these numbers. It’s therefore very hard to say what this intensive debate about numbers is really about.
December 27th, 2009 at 11:31 am
I have always heard that age-shift was associated with all three twentieth-century pandemics. From an article co-authored by Don Olson of New York, “A characteristic feature of influenza epi-
demiology has historically been that the burden of excess mortality in interpandemic seasons occurs primarily in older age groups, whereas the burden in pandemic seasons shifts disproportionately to younger ages (19). During and following each of the three major 20th-century pandemics in which this pattern occurred, in subsequent epidemic seasons, the burden of mortality shifted proportionally back to older age groups.” This is why New York City was (back a couple of years ago) developing syndromic surveillance systems that monitored for age-shifts as a ‘sign’ of a possible pandemic event. Of course, as it turned out, viral surveillance detected pandemic potential (by identifying ‘novelty’) long before any syndromic surveillance system produced any information at all.
December 27th, 2009 at 12:48 pm
If you look at the “pandemic” of 1977, when H1N1 re-emerged after a 20 year absence, there is no shift in age-related mortality pattern. The 1977 “pandemic” is, of course, not considered a true pandemic by experts today, for reasons that are not entierely consistent. It certainly was an antigenic shift and not an antigenic drift. So you then get into an argument about what is and what is not considered a pandemic and what kinds of events you are comparing.
If you compare the 1918 pandemic with the pandemics of 1957 and 1968, the W-shaped age-specific morality curve is unlike those of all other official pandemics. The 1918 pandemic is associated with a massive shift in age specific mortality which is very different from all that followed.
As far as I have been able to follow the current events, the most significant factor seems to have been that most people, who were severely affected, were people with other medical conditions.