So says an article in today's Science Daily, which reports on a recent study at Northwestern of children from the Philippines. The study finds that children from the Philippines have much lower levels of C-reactive protein (CRP), which indicates better resistance to disease. Exposure to germs was much higher for the children in the Philippines.
So what's wrong with this study? It's a very tenuous association, and from what I can gather in the articles, no attempt was made to ensure the children in the U.S. that were compared to the children in the Philippines were similar in other ways. They might be different in CRP due to other environmental or hereditary factors. Perhaps it's the weather? The diet? One of any number of things could account for the difference.
In addition, the study appears to ignore the much higher infant mortality rate and much lower life expectancy in the Philippines (you can try www.indexmundi.com for life expectancy and other information by country). In other words, even if higher germ exposure does mean lower CRP, does it actually mean less disease and longer life? The broad indication is that it does not.
In order for the study to be valid, it needs to adjust for whatever inherent differences (in addition to germ exposure) exist between Phillipino and US children, and then see if CRP levels are still different. An even better way to do such a study would be to study children living in similar environments (same place, socio-economic situation, etc.) and determine if the ones exposed to more germs had lower levels of CRP when they reached adult-hood.
I've seen articles (see this for example, but I can't find a more definitive one at this time) that indicate that children with early exposure to farm animals have fewer allergies, but nothing showing exposure to more serious germs is good. And some of the germs that we are exposed to are more than just common germs--they are deadly. It might be that those who are exposed to these deadly germs early, and live, are much better off later in life, but that is no reason to expose them to those germs unnecessarily. Of course, you wouldnt give your child a deadly disease so that, if they survived, they'd be resistant to it later in life.
We live in a society that is sometimes alarmist concerning germs, and I have written about this. Yet this doesn't mean that, on the whole, a clean environment does not promote good health, and the article cited above seems to only have the most tenous of indications that it may not.
Wednesday, December 9, 2009
Thursday, October 29, 2009
Why Swine Flu is not a bunch of hogwash
This updates my previous blog: "Why Swine flu is a bunch of hogwash?"
Things have changed a bit in the months since that blog, and the hysteria I cited has leveled off. President Obama did declare a swine flu emergency a couple days ago, but I think that was a good idea.
Here is what has changed:
1) Swine flu deaths have been at epidemic levels the last three weeks. The chart below (from the CDC) shows flu and pneumonia deaths as a percentage of all deaths. The upper black line indicates epidemic level, and the red line is the current level. The graph shows four years of weekly figures.
While this graph doesn't look too serious, and 2008 levels were much further above the threshold at their peak, the scary thing here is that it is so early in the season. This graph serves as a reminder, too, that every year the flu kills thousands of people, and the flu vaccine could prevent a large number of those deaths.
2) Hospitals are already getting crowded. One of the big problems with a real epidemic is the overcrowding of hospitals. This means that the really sick people cannot get treatment, and that is part of the reason the emergency was declared. See this article in USA Today about over-crowding. ok, so it's USA Today, a paper that loves hyperbole, but, again, it's early in the season and any indication of overcrowding at this point is scary.
3) The vaccine is not yet fully available. The regular flu vaccine has been out for weeks. Unfortunately, almost none of the flu this year seems to be covered by that vaccine. The majority seems to be 2009 H1N1 (the swine flu). See this chart for a breakdown. Note the orange/brown is 2009 H1N1, and note the yellow means it is not tested for sub-type, so almost all typed flu is swine flu.
That's why I am worried. The other concern is that, even when the vaccine does come out, people won't take it. See my brother's blog about why you should and the crazies who say you should not.
Things have changed a bit in the months since that blog, and the hysteria I cited has leveled off. President Obama did declare a swine flu emergency a couple days ago, but I think that was a good idea.
Here is what has changed:
1) Swine flu deaths have been at epidemic levels the last three weeks. The chart below (from the CDC) shows flu and pneumonia deaths as a percentage of all deaths. The upper black line indicates epidemic level, and the red line is the current level. The graph shows four years of weekly figures.
While this graph doesn't look too serious, and 2008 levels were much further above the threshold at their peak, the scary thing here is that it is so early in the season. This graph serves as a reminder, too, that every year the flu kills thousands of people, and the flu vaccine could prevent a large number of those deaths.2) Hospitals are already getting crowded. One of the big problems with a real epidemic is the overcrowding of hospitals. This means that the really sick people cannot get treatment, and that is part of the reason the emergency was declared. See this article in USA Today about over-crowding. ok, so it's USA Today, a paper that loves hyperbole, but, again, it's early in the season and any indication of overcrowding at this point is scary.
3) The vaccine is not yet fully available. The regular flu vaccine has been out for weeks. Unfortunately, almost none of the flu this year seems to be covered by that vaccine. The majority seems to be 2009 H1N1 (the swine flu). See this chart for a breakdown. Note the orange/brown is 2009 H1N1, and note the yellow means it is not tested for sub-type, so almost all typed flu is swine flu.
That's why I am worried. The other concern is that, even when the vaccine does come out, people won't take it. See my brother's blog about why you should and the crazies who say you should not.
Thursday, October 15, 2009
Redskins are lucky to play bad teams, but how lucky?
A recent article in Yahoo Sports pointed out that the Washington Redskins are the first team in history to play six winless teams in a row. Here is their schedule so far (also according to the article cited above):
The calculation assumes, plausibly, that the Redskins have the same chance of playing any given team (unlike some college teams, who purposely make their schedules easy, this is not possible in the NFL).
The calculation also assumes, not plausibly, that teams that have thus far won no games have a 50-50 chance of winning each game. The implicit assumption there is that all NFL teams are evenly matched. The fact is that there are a few really good teams, a few really bad teams, and a bunch of teams in the middle. Thus, there are likely to be a bunch of winless teams after 5 games, and not, as the incorrect calculation below implies, only 1 winless team of 32 after 5 games.
Finally, the calculation, apparently in a careless error, assumes the chances of playing a winless team the first week are 50-50, when, of course, all teams are winless the first week.
So the Mr. Chase's (incorrect) calculation is
Week 1 chances: 50% ( 1 in 2)
Week 2 chances: 50% (1 in 2)
Week 3 chances: 50%*50%=25% (1 in 4)
Week 4 chances: 50%*50%*50%=12.5% (1 in 8)
Week 5 chances: 50%*50%*50%=12.5% (same as week 3 because the team they played had only played three games)
Week 6 chances: 50%*50%*50%*50%*50%=3.125% (1 in 32)
A law of probability is that the chance of two unrelated events happening is the product of their individual chances. Thus, if the chance of rain today is 50% and the chance of rain tomorrow is 50%, the chance of rain both days is 25%, if those chances are unrelated (which, by the way, they probably aren't). This is why the chances for multiple losses are multiplied together.
But back to the football schedule. To calculate the chances of 6 straight games against winless teams, Mr. Chase reasonably multiplied the 6 individual chances (again it assumed the 6 matchups were unrelated):
50% * 50% * 25% * 12.5% * 12.5% * 6.25% = .003%, or 1 in 32,768.
SO, the 32,768 is the number reported in the article.
The easy correction is that the chances of playing a winless team in the first game is 100%, so the calculation should be:
100% * 50% * 25% * 12.5% * 12.5% * 6.25% = .006%, or 1 in 16,384.
This error has been pointed out in comments on the article.
In addition, other comments point out the other major flaw: teams do not have equal probability of losing. Thus the chance that a team will be, say, 0-2 is not 25% (50%*50%) but something else, depending on the quality of the teams. At the extreme, half the teams lose every game and half win every game (this of course assumes losing teams only play against winning teams, but it is possible).
The reality is certainly not this extreme, which would imply a 50-50 chance each week of playing all losing teams (and thus a 1 in 32 chance of playing 6 in a row). So, how do we figure out the reality?
The easiest way is to look at, each week the percent of teams that are winless. If we assume the Redskins have an equal chance of playing each team, then we can compute the odds each week (click on the week to see the linked source). Note that everything is out of 31 teams instead of 32 because the Redskins can't play themselves.
Week 1: 31 out of 31 teams winless. Chances: 31/31=100%
Week 2: 15 out of 31 teams winless. Chances 15/31=48% (I am assuming no byes first week and I know redskins lost their first game).
Week 3: 8 out of 31 teams winless. Chances: 8/31=26%
Week 4: 6 out of 31 teams winless. Chances: 6/31 = 19%
Week 5: 6 out of 31 teams winless. Chances 6/31 = 19%
Week 6: 4 of 31 teams winless. Chances: 4/31 = 13%
So the actual chances, assuming the Redskins have an equal chance of playing each team each week and cannot play themselves, are: 100%*48%*26%*19%*19%*13% = 0.06%, or 1 in about 1,700. Much more likely than 1 in 32,000 but still pretty unlikely.
And after all these easy games, how are they doing? Unluckily for Redskins fans, not too well...they're 2-3 going into Sunday's game against the winless Chiefs.
Week 1 -- at New York Giants (0-0)
Week 2 -- vs. St. Louis Rams (0-1)
Week 3 -- at Detroit Lions (0-2)
Week 4 -- vs. Tampa Bay Buccaneers (0-3)
Week 5 -- at Carolina Panthers (0-3)
Week 6 -- vs. Kansas City Chiefs (0-5)
The author of the article, Chris Chase (or, as he notes, his dad-let's call him Mr. Chase), calculates the odds of this as 1 in 32,768. This calculation is incorrect and far too high for several reasons, which I get to below. But first, let me explain how the calculation was likely performed.The calculation assumes, plausibly, that the Redskins have the same chance of playing any given team (unlike some college teams, who purposely make their schedules easy, this is not possible in the NFL).
The calculation also assumes, not plausibly, that teams that have thus far won no games have a 50-50 chance of winning each game. The implicit assumption there is that all NFL teams are evenly matched. The fact is that there are a few really good teams, a few really bad teams, and a bunch of teams in the middle. Thus, there are likely to be a bunch of winless teams after 5 games, and not, as the incorrect calculation below implies, only 1 winless team of 32 after 5 games.
Finally, the calculation, apparently in a careless error, assumes the chances of playing a winless team the first week are 50-50, when, of course, all teams are winless the first week.
So the Mr. Chase's (incorrect) calculation is
Week 1 chances: 50% ( 1 in 2)
Week 2 chances: 50% (1 in 2)
Week 3 chances: 50%*50%=25% (1 in 4)
Week 4 chances: 50%*50%*50%=12.5% (1 in 8)
Week 5 chances: 50%*50%*50%=12.5% (same as week 3 because the team they played had only played three games)
Week 6 chances: 50%*50%*50%*50%*50%=3.125% (1 in 32)
A law of probability is that the chance of two unrelated events happening is the product of their individual chances. Thus, if the chance of rain today is 50% and the chance of rain tomorrow is 50%, the chance of rain both days is 25%, if those chances are unrelated (which, by the way, they probably aren't). This is why the chances for multiple losses are multiplied together.
But back to the football schedule. To calculate the chances of 6 straight games against winless teams, Mr. Chase reasonably multiplied the 6 individual chances (again it assumed the 6 matchups were unrelated):
50% * 50% * 25% * 12.5% * 12.5% * 6.25% = .003%, or 1 in 32,768.
SO, the 32,768 is the number reported in the article.
The easy correction is that the chances of playing a winless team in the first game is 100%, so the calculation should be:
100% * 50% * 25% * 12.5% * 12.5% * 6.25% = .006%, or 1 in 16,384.
This error has been pointed out in comments on the article.
In addition, other comments point out the other major flaw: teams do not have equal probability of losing. Thus the chance that a team will be, say, 0-2 is not 25% (50%*50%) but something else, depending on the quality of the teams. At the extreme, half the teams lose every game and half win every game (this of course assumes losing teams only play against winning teams, but it is possible).
The reality is certainly not this extreme, which would imply a 50-50 chance each week of playing all losing teams (and thus a 1 in 32 chance of playing 6 in a row). So, how do we figure out the reality?
The easiest way is to look at, each week the percent of teams that are winless. If we assume the Redskins have an equal chance of playing each team, then we can compute the odds each week (click on the week to see the linked source). Note that everything is out of 31 teams instead of 32 because the Redskins can't play themselves.
Week 1: 31 out of 31 teams winless. Chances: 31/31=100%
Week 2: 15 out of 31 teams winless. Chances 15/31=48% (I am assuming no byes first week and I know redskins lost their first game).
Week 3: 8 out of 31 teams winless. Chances: 8/31=26%
Week 4: 6 out of 31 teams winless. Chances: 6/31 = 19%
Week 5: 6 out of 31 teams winless. Chances 6/31 = 19%
Week 6: 4 of 31 teams winless. Chances: 4/31 = 13%
So the actual chances, assuming the Redskins have an equal chance of playing each team each week and cannot play themselves, are: 100%*48%*26%*19%*19%*13% = 0.06%, or 1 in about 1,700. Much more likely than 1 in 32,000 but still pretty unlikely.
And after all these easy games, how are they doing? Unluckily for Redskins fans, not too well...they're 2-3 going into Sunday's game against the winless Chiefs.
Wednesday, August 12, 2009
Unemployment down but joblessness is up?
There was a bit of interesting news that came out Friday--the nations unemployment rate actually declined, from 9.5% to 9.4%. This is true despite the fact that there was a net loss of jobs of 247,000 (see the NY Times article). How could this happen?
Well, the unemployment rate is calculated by taking the number unemployed and dividing by the labor force: Unemployment Rate= Number Unemployed / Labor Force.
The numerator in the equation, Number Unemployed, is defined as the number of people not employed minus anyone who hasn't looked for a job in the last 4 weeks. The denominator of the equation, Labor Force, is defined as the Number Unemployed plus the number of people currently working (either full or part-time).
Thus, if people give up (and giving up is defined as not looking for the last 4 weeks), they are no longer counted in either the numerator or denominator of the equation. And that is exactly what happened between June and July of this year. According to the BLS (bureau of labor statistics), 637,000 people left the labor force between June and July. Thus, even though the number of people employed fell (by a seasonally adjusted 155,000), the unemployment rate also fell, because the number of people looking for work fell also (267,000). The net result was a drop in unemployment even though fewer people were working and more people lost jobs than found jobs.
A note about the math. At first blush, you may wonder whether it matters, since the people not looking are removed both from the numerator (Number Unemployed) and denominator (Labor Force). But mathematically, it does matter. Suppose we have a ratio 2/10, which equals 20%. Subtract 1 from the numerator and 1 from the denominator and you have 1/9, which equals 11.1%. Thus we subtracted the same number from the numerator and denominator but we did not end up with the same 20%. Instead we ended up with far less (11.1%).
The general rule is that the ratio falls when subtracting the same number from the numerator and denominator as long as the ratio is less than 1. So, 2/10>1/9 but 20/10<19/9, for example. What this means for the unemployment rate (which is always less than 1 since 1 is 100% unemployment) is that when people leave the work force, the unemployment rate is somewhat artificially reduced. This is why we had more people losing their jobs but a decline in unemployment last month.
I would guess that the labor force drop-offs would be far higher during deeper recessions where many despair of getting work or decide to take a break from their search, and this guess is borne out by recent information on the BLS site, which cites the increase in discouraged workers this last year: "Among the marginally attached, there were 796,000 discouraged workers in July, up by 335,000 over the past 12 months. (The data are not seasonally adjusted.) Discouraged workers are persons not currently looking for work because they believe no jobs are available for them."
This NY Times chart of unemployment uses a more reasonable definition and shows unemployment far higher than the official 9.4% rate. It includes all those who have looked for a job in the past year as well as part-time workers who want full-time work as part of the unemployed, and the unemployment rate is between 10 and 20%, depending on the state.
Well, the unemployment rate is calculated by taking the number unemployed and dividing by the labor force: Unemployment Rate= Number Unemployed / Labor Force.
The numerator in the equation, Number Unemployed, is defined as the number of people not employed minus anyone who hasn't looked for a job in the last 4 weeks. The denominator of the equation, Labor Force, is defined as the Number Unemployed plus the number of people currently working (either full or part-time).
Thus, if people give up (and giving up is defined as not looking for the last 4 weeks), they are no longer counted in either the numerator or denominator of the equation. And that is exactly what happened between June and July of this year. According to the BLS (bureau of labor statistics), 637,000 people left the labor force between June and July. Thus, even though the number of people employed fell (by a seasonally adjusted 155,000), the unemployment rate also fell, because the number of people looking for work fell also (267,000). The net result was a drop in unemployment even though fewer people were working and more people lost jobs than found jobs.
A note about the math. At first blush, you may wonder whether it matters, since the people not looking are removed both from the numerator (Number Unemployed) and denominator (Labor Force). But mathematically, it does matter. Suppose we have a ratio 2/10, which equals 20%. Subtract 1 from the numerator and 1 from the denominator and you have 1/9, which equals 11.1%. Thus we subtracted the same number from the numerator and denominator but we did not end up with the same 20%. Instead we ended up with far less (11.1%).
The general rule is that the ratio falls when subtracting the same number from the numerator and denominator as long as the ratio is less than 1. So, 2/10>1/9 but 20/10<19/9, for example. What this means for the unemployment rate (which is always less than 1 since 1 is 100% unemployment) is that when people leave the work force, the unemployment rate is somewhat artificially reduced. This is why we had more people losing their jobs but a decline in unemployment last month.
I would guess that the labor force drop-offs would be far higher during deeper recessions where many despair of getting work or decide to take a break from their search, and this guess is borne out by recent information on the BLS site, which cites the increase in discouraged workers this last year: "Among the marginally attached, there were 796,000 discouraged workers in July, up by 335,000 over the past 12 months. (The data are not seasonally adjusted.) Discouraged workers are persons not currently looking for work because they believe no jobs are available for them."
This NY Times chart of unemployment uses a more reasonable definition and shows unemployment far higher than the official 9.4% rate. It includes all those who have looked for a job in the past year as well as part-time workers who want full-time work as part of the unemployed, and the unemployment rate is between 10 and 20%, depending on the state.
Friday, June 12, 2009
Riding a bike? Wear a helmet.
Now that the sun has finally come out in NYC today after what seems like weeks of rain and cold weather, it seems an appropriate time to talk about one of my favorite summer recreational activities--riding a bike.
Growing up in the 1970s, I don't think I ever saw a helmet, much less wore one. However, in the same way we've figured out that seatbelts (and airbags) save lives, we also now know that biking with a helmet makes you safer. The Consumer Product Safety Council reported that wearing a helmet can decrease risk (of head injury) by as much as 85%.
Sadly, there are still a lot of enthusiasts out there that have a take no prisoners type attitude about wearing helmets, even implying that they are less safe (see for instance the helmet section of this web page in bicycle universe). Yet I think anyone who understands the statistics will see that the "freedom" of riding without a helmet is far outweighed by the risk.
The Insurance Institute for Highway Safety (IIHS) has long been a great source for safety information. They've got the same goal that hopefully most of us do, reducing deaths and injuries. In a 2003 report, the IIHS reports that child bicycle deaths has declined by more than 50% since 1975 (despite increased biking and presumably because most children wear helmets now). In addition, about 92% of all bicycle deaths were cyclists not wearing helmets (see this report). The same report also shows that while child bicycle deaths have declined precipitously(from 675 in 1975 to 106 in 2007), adult deaths have increased since 1975 (from 323 to 583).
Helmet usage is harder to figure out, but most sources put overall use around 50%, with children's use higher. This means that, given that 92% of deaths are cyclists not wearing helmets, you stand about 11 times the chance of getting killed if you don't wear a helmet. This number can be played with a little and wittled down if you assume, say, that cyclists not wearing helmets bike more dangerously, but there would have to be enormous differences for helmets to be shown to be ineffective. Moreover, all the major scientific studies show large positive effects from helmet usage (see this ANTI-helmet site for a summary of the case-control studies).
So why, when you search the internet for helmet effectiveness, or read through the literarture of a number of pro-cycling organizations, do they cast dispersions upon helmet use? This one, for me, is an enigma. I understood why the auto industry was against airbags and seatbelts (they cost money) and why the cigarette and gun manufacturers are against regulation, but why do people care so much about us not wearing helmets. I can think of only a couple of things: a) cyclists want bike lanes and other safety measures without committing to anything on their own, and b) some are too lazy/cool to bother with a helmet. Of course, I'm a cyclist and clearly, I'm all for helmets (and yes, laws requiring them). I also think that if we want state and city governments to take us seriously about increasing cyclist safety through new bike lanes, changing traffic patterns, and building of greenways, we need to do our part, too.
Growing up in the 1970s, I don't think I ever saw a helmet, much less wore one. However, in the same way we've figured out that seatbelts (and airbags) save lives, we also now know that biking with a helmet makes you safer. The Consumer Product Safety Council reported that wearing a helmet can decrease risk (of head injury) by as much as 85%.
Sadly, there are still a lot of enthusiasts out there that have a take no prisoners type attitude about wearing helmets, even implying that they are less safe (see for instance the helmet section of this web page in bicycle universe). Yet I think anyone who understands the statistics will see that the "freedom" of riding without a helmet is far outweighed by the risk.
The Insurance Institute for Highway Safety (IIHS) has long been a great source for safety information. They've got the same goal that hopefully most of us do, reducing deaths and injuries. In a 2003 report, the IIHS reports that child bicycle deaths has declined by more than 50% since 1975 (despite increased biking and presumably because most children wear helmets now). In addition, about 92% of all bicycle deaths were cyclists not wearing helmets (see this report). The same report also shows that while child bicycle deaths have declined precipitously(from 675 in 1975 to 106 in 2007), adult deaths have increased since 1975 (from 323 to 583).
Helmet usage is harder to figure out, but most sources put overall use around 50%, with children's use higher. This means that, given that 92% of deaths are cyclists not wearing helmets, you stand about 11 times the chance of getting killed if you don't wear a helmet. This number can be played with a little and wittled down if you assume, say, that cyclists not wearing helmets bike more dangerously, but there would have to be enormous differences for helmets to be shown to be ineffective. Moreover, all the major scientific studies show large positive effects from helmet usage (see this ANTI-helmet site for a summary of the case-control studies).
So why, when you search the internet for helmet effectiveness, or read through the literarture of a number of pro-cycling organizations, do they cast dispersions upon helmet use? This one, for me, is an enigma. I understood why the auto industry was against airbags and seatbelts (they cost money) and why the cigarette and gun manufacturers are against regulation, but why do people care so much about us not wearing helmets. I can think of only a couple of things: a) cyclists want bike lanes and other safety measures without committing to anything on their own, and b) some are too lazy/cool to bother with a helmet. Of course, I'm a cyclist and clearly, I'm all for helmets (and yes, laws requiring them). I also think that if we want state and city governments to take us seriously about increasing cyclist safety through new bike lanes, changing traffic patterns, and building of greenways, we need to do our part, too.
Monday, May 18, 2009
Why Swine Flu is a bunch of hogwash.
I first thought of writing about this a couple of weeks ago, when the nationwide hysteria concerning swine flu was just beginning, but then, as quickly as it came, it went. Now, with the first death from swine flu in NY, the front pages of the major newspapers have returned to the topic. The New York Times article and headline, was, as always, something close to languid. However, the NY post's article and photos, are, also as usual, a bit hysterical. My son's school, apparent readers of the post, have covered all the water fountains with plastic bags, perhaps unaware that the CDC clearly states there seems to be little or no chance of infection through drinking water.
What's more is that, so far, this flu has been a very minor flu, with about 5,000 documented cases and 6 deaths. The blog of record relays that the "regular" flu has already killed something like 13,000 people in the US this year (it's not clear whether this is derived from the CDC's annual estimate of 36,000). This amounts to about 100 people a day.
While one CDC scientist estimates the number of people with the swine flu are 50,000 or so, this estimate assumes that under-reporting of swine flu is the same as under-reporting of flu in general. Given the focus on swine flu, I expect that under-reporting of it is far lower than of general flu, and thus, the true number with the swine flu is far fewer than 50,000. The CDC's currently weekly flu report shows about one-third of the 1,286 new cases as swine flu (novel H1N1). The same report has a great graph, showing an irregular spike in flu diagnosis, just at the time when reported flu usually falls.
There are three pieces of good news, despite the scary spiked graph. First, with spring, flu cases quickly fall, because flu spreads less when people are further away from each other (i.e., outside instead of inside). Second, cases are already falling (though it's only two weeks of data). Third, all types of flu diagnosis increased in the last two weeks versus the several weeks leading up to May), implying that one of the reasons (perhaps the only reason) for the spike is that we are testing much more than usual, due to the swine flu outbreak.
Thus, Swine flu has so far killed a documented 6 people in the U.S. out of more than 5,000 confirmed cases.
In conclusion, though our own hysteria may drive documented cases up some, and lead to my children having to bring a water bottle to school, the swine flu does not appear to be particularly dangerous or deadly.
What's more is that, so far, this flu has been a very minor flu, with about 5,000 documented cases and 6 deaths. The blog of record relays that the "regular" flu has already killed something like 13,000 people in the US this year (it's not clear whether this is derived from the CDC's annual estimate of 36,000). This amounts to about 100 people a day.
While one CDC scientist estimates the number of people with the swine flu are 50,000 or so, this estimate assumes that under-reporting of swine flu is the same as under-reporting of flu in general. Given the focus on swine flu, I expect that under-reporting of it is far lower than of general flu, and thus, the true number with the swine flu is far fewer than 50,000. The CDC's currently weekly flu report shows about one-third of the 1,286 new cases as swine flu (novel H1N1). The same report has a great graph, showing an irregular spike in flu diagnosis, just at the time when reported flu usually falls.
There are three pieces of good news, despite the scary spiked graph. First, with spring, flu cases quickly fall, because flu spreads less when people are further away from each other (i.e., outside instead of inside). Second, cases are already falling (though it's only two weeks of data). Third, all types of flu diagnosis increased in the last two weeks versus the several weeks leading up to May), implying that one of the reasons (perhaps the only reason) for the spike is that we are testing much more than usual, due to the swine flu outbreak. Thus, Swine flu has so far killed a documented 6 people in the U.S. out of more than 5,000 confirmed cases.
In conclusion, though our own hysteria may drive documented cases up some, and lead to my children having to bring a water bottle to school, the swine flu does not appear to be particularly dangerous or deadly.
Monday, April 27, 2009
Facebook and grades
I don't have a long post for today, but I want to briefly discuss the discussion of a study on Facebook and grades. It was the subject of the Wall Street Journal's Numbers Guy blog last week: http://blogs.wsj.com/numbersguy/ .
The basic question is under what conditions should we publicize results, and should we wait for peer review?
Here was my comment:
I think if the caveats were printed along with the study results, then the publication is reasonable. Otherwise, we are being a bit paternalistic by implying that the general public cannot understand the caveats but we researchers can.
The basic question is under what conditions should we publicize results, and should we wait for peer review?
Here was my comment:
I think if the caveats were printed along with the study results, then the publication is reasonable. Otherwise, we are being a bit paternalistic by implying that the general public cannot understand the caveats but we researchers can.
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Suppose instead this was a study linking domestic air travel through a particular city to a new and deadly virus (say, swine flu?). Then there might be more reason to be more cautious (and paternalistic), because the cost of being wrong is very high. Still, there would be the counter-argument that not publishing could endanger people’s lives. We always have this trade-off, I believe, between unintentionally misleading people that a study is correct when it is not, and vice-versa.
In this open era, especially, I think the balance leans towards publishing, where the blogging/commenting public will quickly crucify the poor research and finding supporting evidence for good research.