Can We Find A Trend In The Fungal Meningitis Outbreak?

Epidemiologists have some of the most intriguing jobs in the world. No other branch of science can make an immediate impact akin to that of the study of diseases. The fruit of diligent research can be life- no, world-changing. And when an epidemic strikes, their fast actions can save thousands of lives.

Kudos to the epidemiologists on the case of the recent outbreak of fungal meningitis. I heard about the outbreak, and then one day later they had already located the cause and were doing work to minimize the damage done by the tainted steroids. Unfortunately, they can’t save every life, and nearly 300 cases have been reported with a fatality rate of about 8%. However, their quick work surely saved many more lives.

For my latest post, I’ve decided to play epidemiologist to try an isolate a trend among the data for the meningitis outbreak. Is there a reason that certain states have been hit harder than others? (Besides, of course, the states that haven’t received the infected drugs, and obvious comparisons like population)

_______________________________________________________________________________________________

The CDC website doesn’t have information on former outbreaks of fungal meningitis (or if they do, they’re hiding it very well), and this made my first idea- does this outbreak parallel previous outbreaks?- very short-lived. However, the CDC did have interesting statistics on another topic, which redirected my plan for this post.

The fungal meningitis outbreak has apparently been classified as a “Healthcare-Associated Infection” or “HAI”. The CDC tracks HAIs in a variety of ways; one of them is an SIR value: Standardized Infection Ratio. This value is found by taking the number of actual HAIs and dividing it by a predicted number of HAIs. Lower numbers are better, and values under 0.5 are very good. Similarly, values over 1.0 are very bad- this means that the included facilities are actually causing more infections than they’re projected to.

Here is a chart of the SIR values (in 2010, the most recent I could find) for all of the states where the tainted steroids have been sent:

 These values are mostly good; Indiana is the only state with a value above 1, and Michigan and West Virginia both have values under 0.5 (Remember that!)

You may have noticed that three states- Idaho, Minnesota, and Rhode Island- don’t have SIR values on the graph. This is because SIR values are independently submitted by health care centers, and some states don’t have enough centers submitting information to the CDC for effective calculation of SIR. These three states are some examples- less than five centers submitted information over 2010, whereas most states have several dozen.

Since the outbreak is a HAI, it would be reasonable to assume that most of the infections occurred in states with high SIR values. But that isn’t the case:

STATE	CASES
ILLINOIS	1
NEW YORK	1
IDAHO	1
PENNSYLVANIA	1
TEXAS	1
NORTH CAROLINA	2
MINNESOTA	7
FLORIDA	17
MARYLAND	16
NEW JERSEY	16
OHIO	11
NEW HAMPSHIRE	10
INDIANA	38
MICHIGAN	53
VIRGINIA	41
TENNESSEE	69
CALIFORNIA	0
CONNECTICUT	0
GEORGIA	0
NEVADA	0
RHODE ISLAND	0
SOUTH CAROLINA	0
WEST VIRGINIA	0
TOTAL	285

Indiana, which had the highest SIR value, has quite a few cases compared to other states. Michigan, however, had the lowest SIR value- and has more cases than any state except Tennessee. But West Virginia, which had the second-lowest SIR value, has zero cases.

Why is there variability in the data? One reason is because not every state received the same amount of the infected drug. Only one facility in West Virginia received the drug, compared to six in Indiana. Based on the data, each facility that received a shipment of the steroid had about 3.8 infections. From this average, we can predict how many cases will occur in each state:

This isn’t very good. We can take it our prediction one step further by applying a state’s SIR to the predicted number of cases (for example, Illinois: 11 predicted cases x 0.678= 8 predicted cases with SIR):

 

There’s still no strong correlation here between SIR and the number of cases. We can calculate our own SIR values for these states using our predicted number of cases and the number of actual cases. Unfortunately, when we do this, only four states- the ones in blue on the data table and the following graph- have SIR values that fall within the standard range of scores (that is, the range of scores for all 50 states.):

M-SIR represents my own calculated SIR value based solely on the meningitis statistics

Quite simply, there’s no correlation between HAI SIR values and the recent fungal meningitis outbreak. The only explanation I can come up with for this is that most facilities used the tainted steroid believing it to be safe, whereas with most HAIs the healthcare center should know how to avoid the problem.

My data:

https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdDVETDlPQ0dTcUFXVU1TXzcxS2h1TFE

Note: All data as of October 21, 2012

How Successful Will The iPhone 5 Be?

When it comes to technology- and specifically, new products- perhaps nothing is more anticipated than the iPhone. When the original iPhone was released in 2007, it was the beginning of the Age of the Smartphones. Since then, Apple has stayed on the cutting-edge when it comes to their iPhones, and excitement and anticipation over technological leaps and bounds precede each release.

Last weekend, Apple released their new iPhone, the iPhone 5. Apple had had nearly a year since its most recent phone, the iPhone 4S, to work on improvements. The major selling points Apple hit on in its press release were the physical features, hailing the new product as the “Thinnest, Lightest iPhone Ever”. For this post, I thought I’d take a look at that claim, and examine the evolution of the iPhone. Is it really the thinnest, lightest iPhone ever? Is there anything that prior incarnations of the smartphone did better? Just how much money is Apple making on the iPhone, anyway?

_________________________________________________________________________________________________

Apple has released a new iPhone fairly regularly- about once a year. I won’t be discussing the technological advancements of the iPhone in this post because it’s clear the iPhone has progressed substantially in terms of technology (Siri, anyone? It’s something Sci-Fi authors could only dream about even recently). Instead, we’ll be taking a look at the physical aspects of each phone.

The following graph represents the change in width for the iPhone over time:

As you can see, there’s clearly no change between this generation and the previous two. However, the other dimensions of the iPhone 5- height, depth, and weight- have changed somewhat. Here are those graphs:

The iPhone 5 is significantly larger in terms of height over its previous incarnations. However, the change shouldn’t be terribly noticeable for anyone using the phone: Only about eight millimeters, or a little more than the length of a red ant. It also results in a potential increase in screen size, though again, not terribly noticeable.

The iPhone 5 is also easily the thinnest iPhone yet, with a depth of only 7.6 mm. But is it really that significant of a change? The new phone has only shed 1.7 mm. Remember that ant from earlier? 1.7 mm is about the length of its head, maybe a little smaller. The change probably won’t make any significant difference in the future.

The weight of the new iPhone is something Apple is significantly proud about. They claim to have eliminated 20% of the weight of the iPhone 4S (and they have, actually), but since the iPhone 4S was so light in the first place (140 grams), is it really such a big deal? Let’s examine: the iPhone 5 is 28 grams lighter, so imagine three pencils, or five quarters. I suppose this could make up a fairly noticeable change- I’ve never actually held and compared the two, so I’m just making an estimation. But again, the iPhone 4S was already very light, so Apple isn’t actually saving the backs of millions of their customers (thank you, the Onion).

I also measured some characteristics of the iPhone that aren’t exactly physical: memory and battery life. We can easily see the change in memory over the generations simply by looking at what was sold- the original iPhone was sold in 4, 8, and 16 GB versions, and while every iPhone generation has had a 16 GB version, it’s the smallest memory option for the iPhone 4S and 5, which have 32 GB and 64 GB variants.

Battery life is more interesting than storage memory. Audio and Video battery life has gradually increased over the years (40 hours of audio since the iPhone 4, and 10 hours of video since the iPhone 3G), but standby life has a different trend. The iPhones 3G, 3GS, and 4 all had the most standby life, at 300 hours. The iPhone 5 has three days less life than that. Apple can still claim that it has increased the battery power, however, since the iPhone 4S only had 200 hours of battery life.

 _____________________________________________________________________________________

Has Apple been doing enough to attract new customers and retain their old ones? How have their iPhone sales done over the years? The answer may surprise you:

Black plots in the above graphs represent the first quarter of Apple’s fiscal year. Their first quarter falls over the holiday season, and so yields significantly higher sales and revenue.

The iPhone has been a complete and utter success for Apple. Sales per quarter after the release of each phone have, at the very least, doubled. In the quarter after Apple released the iPhone 3G, it saw an 861% increase in units sold. Yes, you read that number right: 861%. While each iPhone slowly trends downward after release (I estimated a 25% loss each quarter when a newer model was on the market), Apple still pulls in billions of dollars each month, and could hit $100 billion of revenue for this fiscal year.

Even more promising for Apple is the first-weekend sales of its new iPhone 5. Five million phones were sold, bringing in about $1.5 billion- more revenue than the original iPhone made in its entire run, and nearly as many units sold.

Here is my data: https://docs.google.com/spreadsheet/ccc?key=0AnZrkjWWJajQdFNLdmtnYUxOR1ZlY2lfVHR1ZlpGZ0E

Bounds were determined in several ways, but should be viewed as the maximum and minimum possible totals for each phone. We know that no phone has sold $0, so we have to estimate the lower bound. The estimated exact total is based on the 25% decay rate mentioned above and, barring the discovery of the actual data, is a good ballpark figure for each phone’s sales totals.