What I Learned About Health From Reporting It

Photo by Jeremy Bronson  https://www.flickr.com/photos/jbrons/5216172956

Photo by Jeremy Bronson.

Some people find themselves nodding along with articles like 25 Struggles Only People Addicted to Diet Coke Will Understand. Me, I got that same feeling from reading this Vox piece by Julia Belluz and Sarah Kliff: No more dieting, and 7 other things we do differently after reporting on health care.

I’d like to add a few more things to the list based on my own experience writing about health. But first, the top nodding-along items on the Vox list:

Dieting (#1 on their list): Despite the fads, science hasn’t consistently found any one diet to be better than others for weight loss. But here’s the kicker: none of them work in the long term. Measured on a scale of years (rather than weeks or months), dieting tends to leave people heavier than when they started. You’re better off eating and exercising for health, which works even if your weight doesn’t budge.

Screening (#7 on their list): If you go looking for problems, you just might find ones that aren’t really there. Screening tests have risks as well as benefits, and making a healthy person go through cancer treatment for a harmless “incidentaloma” is a pretty awful fate. I don’t turn down every sort of test, but I do choose carefully.

New Studies (#2 on their list): Work the news cycle long enough, and things stop feeling like news. Another superfood? Yawn. Another potential new antibiotic? Put it over there with all the others that haven’t worked out. I love studies that bust a long-held myth, but I almost never take the bait anymore. A single study tells us very little. Now, a Cochrane review that concludes a long-held myth is dead? That I’ll pitch.

Here are a few things I’ve learned that weren’t on the Vox list.

Trust guidelines, but only so far. The best recommendations are evidence-based, and can do a world of good. For example, new guidelines recommend more IUDs and less cervical cancer screening. But guidelines are measures meant for public health, and may not apply to you, in particular, as an individual. Sometimes, guidelines simply aren’t based on good quality evidence. And health care providers may be relying on outdated guidelines even when the evidence (and newer guidelines!) prove those old guidelines wrong.

News always comes from somewhere. Just as butterflies come from caterpillars, so do health news stories come from press releases. Savvy readers and reporters know to check for Big Pharma funding a study on, say, a drug—but how many of us look for a funder or publicist on a study that reports on how eggs make your salad healthier? Well, guess what: that study was funded by the American Egg Board. Plenty of studies on antioxidants in fruit are on cranberries, because Ocean Spray funds a lot of scientific research. That doesn’t mean that other fruits don’t have health benefits, but we hear more about cranberries than those others because the cranberry promoters fund and publicize more research. It’s a type of publication bias that affects the public without most of us ever knowing.

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Level up! Mr Epid is now Dr Epid!

My old lab got me a cake to celebrate!

My old lab got me a cake to celebrate!

I’m back! I took an extended hiatus from the blog while I finished up my PhD, but, at the end of March, I successfully defended my PhD, and after making the changes suggested by the examining committee I submitted in the middle of April and started working. Those of you following along on Twitter will recognize the change in my Twitter handle from @MrEpid to @DrEpid; those of you who know me in real life will have heard me go on about it for the last few months as I prepare. For those wondering, I will eventually change the URL of my blog as well so they all match :)

For those unaware of the process, the PhD defence is an oral exam. At Queen’s (the process may differ at other universities), you submit your thesis, and then have to wait (a minimum) of 25 business days for the exam. The exam consists of 4 examiners; an examiner external to your university, one external to your department, one from your department, and the final examiner is your department head (or a department head delegate). You also have a chair from another department from your institution, as well as your supervisors there. After you give a 15-20 minute presentation, the examiners ask their questions. Typically, there are two rounds of questions, after which you leave, and the examiners deliberate. You’re then called back in, and they let you know their decision, and any changes you have to make before submitting your final thesis. My examiners were amazing, and while the questions were tough, they were fair. I actually really enjoyed the discussion I had with my examiners during my defence, and they ranged from the details of my analysis, to the concept of “ethnic identity” and what it actually means in terms of my research.

I want to thank everyone for their support over the past 4 and a bit years. As per prior precedents (Janiszewski, 2010; Saunders, 2013), I will be copy-pasting the acknowledgements section from my thesis below. I’d also like to thank the PLOS Blogs network, especially Victoria Costello for giving me the opportunity to join the network, and Travis and Peter for their support and encouragement when I started blogging. In addition, thank you to my co-authors Beth and Lindsay here who picked up the slack when I took a hiatus this year to focus on finishing up.

Finally, a special thank you to all the readers of the blog. It’s been a privilege to write for you, and it means a lot when you tell me how much you enjoy my work. Thank you, and I’m looking forward to getting back into writing more regularly.


I would like to start by thanking my supervisors, Dr. Will Pickett and Dr. Ian Janssen. I am grateful to have had the opportunity to learn from you both, and appreciate your support through my PhD journey. Your honesty, integrity, and willingness to always provide me feedback and support was always appreciated. Will, I look forward to our teams meeting in the playoffs again (hopefully with better results for me this time!)

I would also like to thank those in the Department of Community Health and Epidemiology/Public Health Sciences and the Clinical Research Centre for their support, with a special thank you to Lee Watkins and Deb Emerton for their help. Thank you also to the Clinical Research Centre Student Group. Your antics, customized t-shirts, snack breaks, and random dance parties always kept me entertained, and it’s been a pleasure working with all of you. The Thought Tub is richer for having you.

This work would not have been possible without the financial support of Queen’s University, the Ministry of Colleges, Training and Universities Ontario Graduate Scholarship, and the Canadian Institutes of Health Research Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award.

I would also like to thank my friends and colleagues, especially Anne, Kim, Raymond, Sarah, Alison, Hidé and Marion who have been unwavering in their support over the years. I also owe a special debt of gratitude to Rim, Lydia, Liam, Hoefel, Brian and the Gong Show/Danger Zone family for ensuring that I always get some physical activity, and that yes, I do even lift.

Finally, thank you to my family. Your love, support, guidance, and willingness to listen to me at all times of the day have allowed me to complete this project. Thank you.

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The ‘autism epidemic': increasing cases or increasing diagnoses?

Increase_in_autism_diagnosis (1)

“Increase in autism diagnosis” by fightingautism.org – Licensed under Public Domain via Wikimedia Commons

 An interesting new study was recently published in the British Medical Journal about the ‘autism epidemic’ we have been experiencing in recent years (1). The Swedish authors of the study used data from children born between 1993 and 2002 to compare time trends in the rates of the autism symptom phenotype (i.e. the symptoms upon which a diagnosis of autism is based) and registered clinical diagnoses of autism spectrum disorder.

Why was this study done?

The prevalence of autism has jumped dramatically since about the 1970s. A review of U.S. studies found that prevalence increased 10-fold between the 1970s and 1990s, from less than 3 cases per 10,000 children to over 30 cases per 10,000 children (2). The U.S. CDC also found rising cases throughout the 1990s, but that prevalence has actually declined between 2000 and 2010 (3,4).

The reasons for this ‘autism epidemic’ are controversial. The falsified autism-vaccine link received torrid media coverage over the past decade, causing a sharp drop in childhood vaccination rates and subsequent rise in measles among children (5). Other potential causes are thought to be environmental, given that the human genome is stable over a 30-40 year period. If some secular environmental exposure is causing increased rates of autism, then it would be a major public health crisis.

Bucking this ‘environmental causes’ line of thought, the authors of the current study hypothesised that the ‘autism epidemic’ may be due to how autism has been diagnosed and registered over time. They provide four reasons for this hypothesis:

  • The rise in prevalence was reported during the same time period that the diagnostic criteria widened;
  • Increasing awareness of autism spectrum disorder causes ‘diagnostic substitution’: when children who would have previously been diagnosed with a learning disability or other mental illness or retardation are now diagnosed with autism;
  • Patient referral and availability of services increases due to increasing awareness; and,
  • Differential availability of case records and the way in which cases are diagnosed between similar geographical regions leads to wide variation in measured and actual prevalence.

What did they do?

Data from the Swedish national patient register on over 1 million Swedish children born between 1993 and 2002 were used. The register contains data on registered, diagnosed cases of autism spectrum disorder based on ICD-9 and ICD-10 codes (international classification of diseases, 9th and 10th revisions).

The comparison data came from the Child and Adolescent Twin Study in Sweden (1). The twins’ parents were interviewed over the telephone, using the ‘Autism-Tics, ADHD, and other Comorbidities’ (CATSS) inventory, which is designed to be administered by a layperson over the telephone to assess symptoms of autism spectrum disorder, including language and communication, social interaction, and restricted and repetitive behaviours (1). It is independent of the interviewer’s clinical knowledge or preferences, does not disclose which symptoms belong to which disorder, and evaluates lifetime symptoms. Hence, it should remove the biases listed by the authors in their hypotheses about the ‘autism epidemic’ (1).

The prevalence of registered diagnosed cases in the general population of Swedish children was compared to the prevalence of autism symptoms in the Child and Adolescent Twin Study over a ten-year period.

What did they find?


Annual prevalence of autism spectrum disorder in Child and Adolescent Twin Study in Sweden (CATSS), national patient register (NPR), and NPR diagnoses in Swedish twins. *Prevalence calculated on 19 993 people responding in twin study born 1993-2002. †Prevalence calculated on all twins, irrespective of response in CATTS (n=26 953). Diagnosis in NPR was ascribed before the children’s 10th birthday. ‡Prevalence calculated on all births in Sweden 1993-2002 (n=1 078 975). Diagnosis in NPR was ascribed before the children’s 10th birthday. Regression lines are depicted within 95% confidence intervals. Reproduced from (1).

In the Swedish national patient register, the authors found that the population prevalence of autism spectrum disorder was 0.42%, ranging from 0.23% to 0.60% at different time points (1). They observed a linear increase in prevalence over the ten-year period (P for trend <0.0001). By contrast, the population prevalence of autism symptom phenotype was 0.95%, ranging from 0.52% to 1.59% at different time points (1). This prevalence was stable over time (P for trend = 0.85).

The key point to note is that, while there were no significant differences in the prevalences of diagnosed autism spectrum disorder and autism symptom phenotype, the prevalence of the former (diagnosed cases) steadily increased with time, while the prevalence of the latter (undiagnosed symptoms) was stable with time.

What do the results mean?

The study authors concluded that their data do not support a secular increase in the rate of autism spectrum phenotype, meaning that the way in which autism cases are diagnosed and recorded may explain the rising cases of autism observed over recent decades (1). These results should be good news, as they do not indicate that a secular environmental exposure accounts for a large proportion of new autism cases in recent times. However, the findings from the study are likely to be taken as controversial by anti-vaccine supporters. Hopefully, this kind of research can be translated effectively into the public domain to improve public awareness and knowledge. More research from other geographical locations such as the United States testing the same hypotheses as in this study would also be valuable.



  1. Lundström S, Reichenberg A, Anckarsäter H, Lichtenstein P, GillbergAutism phenotype versus registered diagnosis in Swedish children: prevalence trends over 10 years in general population samples. BMJ 2015;350:h1961.
  2. Blaxill MF. What’s going on? The question of time trends in autism. Public Health Rep 2004;119:536-51.
  3. Yeargin-Allsop M, Rice C, Karapurkar T, Doernberg N, Boyle C, Murphy C. Prevalence of autism in a US metropolitan area. JAMA 2003;289(1):49-55.
  4. Centers for Disease Control and Prevention. Autism Spectrum Disorder (ASD): Data and Statistics. http://www.cdc.gov/ncbddd/autism/data.html (accessed 06 May 2015).
  5. Centers for Disease Control and Prevention. Measles cases in the United States reach 20-year high. http://www.cdc.gov/media/releases/2014/p0529-measles.html (accessed 06 May 2015).
Category: Epidemiology, Health systems, Time trends | Tagged , , , , | 7 Comments

When a Company Drops a Controversial Ingredient, They’re Not Doing It For Your Health

This week, Pepsi announced they were dropping aspartame from their flagship diet drink in the US.

Last week it was Chipotle swearing off GMO ingredients.

Before that, Kraft dropped certain dyes, Subway took the “yoga mat chemical” out of its bread, Gatorade stopped using brominated vegetable oil, and these are just a few of the dubiously health-related but loudly trumpeted food reformulations of recent years.

Companies reformulate their products all the time, but when they make a big announcement about the change, I’m skeptical that it’s anything other than a marketing move (or a swap they were going to make anyway, with a convenient reason). It turns out there are reasons, not exactly hidden, to question the do-good intentions of recent reformulations.

Diet Pepsi

I wrote today at Lifehacker about the science behind Pepsi’s shift away from aspartame. Short version: there’s no reason to believe that aspartame is dangerous, and people who want to err on the side of caution won’t find the replacement sweeteners any more reassuring.

But Pepsi acknowledged that although they were bowing to consumer concerns about safety, they didn’t believe the sweetener was unsafe. Sure, it makes sense that a company would be careful not to state that they’d been knowingly poisoning customers (even if that were true). But their actions suggest that health concerns are a red herring:

  • Pepsi’s CEO discussed the shift as part of a conference in which he also explained that sales have been lagging and a nod to consumer demand is part of the strategy to bring sales up.
  • Pepsi is only changing the formula for Diet Pepsi and two variations on the drink, and only in the US. Meanwhile, Diet Mountain Dew and Pepsi Max will keep their aspartame, because consumers aren’t complaining specifically about aspartame in those drinks.


Dropping GMO ingredients from the Chipotle menu, likewise, can’t be based on any serious health concerns. Dan Charles at NPR explains some of the reasons why the move is hypocritical:

  • They’re keeping sodas, which often include GMO corn syrup.
  • They’re keeping sodas, which have actual known health risks much larger and more definite than any of the suspected health risks of GMO products.
  • They cite concerns about the pesticide resistance in farming practices associated with GMO soy plants, but are switching away from soy to an oil that, while non-GMO, has very similar pesticide concerns.

It looks like Chipotle is making the switch simply because consumers want it, and it’s easy. They’re only changing two ingredients: Cooking oil, and corn flour tortillas. If GMO corn and soy were sprinkled across their menu, would they be as committed to the change?

The bottom line

While giving in to consumer pressure may help a company’s bottom line, the implication that it’s for health reasons (which is usually not stated outright because, you know, it’s usually not based in science) may be a bad thing for scientific understanding among the public. The likes of the #FoodBabeArmy get their concerns addressed without any reference to the actual science involved. (The Food Babe’s first objection to Kraft’s yellow dye? It’s made from petroleum. No, that’s not the same as eating asphalt or gasoline. Sorry.)

I understand wanting to change ingredients, but that can happen without a media splash. The neon-colored dyes in Kraft products kind of gross me out too, especially next to the grayish color of the noodles. I’m not concerned for my kids’ health, but as I stir the cup (on the days we’ve bought Kraft’s brand instead of Annie’s–both end up in our kitchen), I wonder who decided cheese should be this color anyway? And the truth: It was probably because, years ago, consumers liked it better that way.

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Why Isn’t Sex Ed Preparing Students for Adulthood?

Uterus Anatomy Embroidery Hoop Art Wall Decor in Orange

When I was in high school, I took a class that taught me how to dissect a worm. We were not expected to go home and dissect worms or somehow put to use our newly found expertise on worm anatomy. Still, I remember that dissection teaching me ideas and skills I used later on: How to carefully cut something open and learn about its insides; why exactly a worm is shaped the way it is; the similarities between worms and humans (we both have hearts! Sort of).

In another class, I learned how to write a check and balance a checkbook. I didn’t have a bank account in my name at the time, but the information stuck.

Algebra felt useless at first, but gave me the tools I needed to crunch numbers in later classes like physics and population genetics.

So why, when we talk about sex ed classes for teenagers, do we focus on what students will do with the information they learn right now​? A conservative arguing for abstinence-only education, or a more evidence-minded person talking about which type of sex ed “works” better, are only focusing on immediate outcomes: Do kids go home and have sex? If they do, are they getting pregnant or contracting STDs?

Imagine if we set curricula this way for all subjects: Choose the content for math classes based on the math we expect kids to do in their daily life. Pare down art curricula so that kids won’t be able to use what they learn in making graffiti.

Or, more shockingly, imagine that we taught sex ed with the same goals as academic subjects: Preparing students for life after high school.

Even in the fantasy world where students never have sex while they’re young enough to still be taking sex ed classes, almost all will have sex later on. They’ll need to know about consent and pleasure and risks. Many will need to know about fertility and how pregnancy works.

What happens then?

It turns out they might remember what they learned in sex ed, and that’s not a good thing. Take this 2012 study by Chelsea Polis and Laurie Zabin of Johns Hopkins. They found that 19% of young women and 13% of young men believed they were infertile. Those numbers are far higher than the actual prevalence of infertility in that age group, which means there could be a significant chunk of sexually active young people who believe, incorrectly, that they can’t get pregnant. Here’s the likely reason why:

Polis and Zabin point out that some public health messages designed to encourage consistent contraceptive use focus on the fact that pregnancy can occur after a single act of unprotected intercourse and do not adequately explain the probability of pregnancy.

…an oversimplified message may inadvertently lead some individuals to assume they are infertile if pregnancy does not occur after one or several acts of unprotected sex, and may result in reduced motivation to use contraceptives. [quote from the study’s press release]

Focusing on the actual statistics involved in pregnancy would help young people understand their fertility. They also need enough information to understand the real risk of pregnancy from different contraceptives. (Not “condoms fail all the time” but how often do they fail with perfect and with typical use, and how do you achieve perfect use?) A few lucky women might stumble across a copy of Taking Charge of Your Fertility, but why isn’t a basic understanding of the human reproductive system standard issue?

I recently taught a nutrition course to a group of students fresh out of high school. When we got to the part about pregnancy and infant nutrition, they asked some honest questions that made me back up and start from the basics: for example, they weren’t clear on what the beginnings of pregnancy have to do with a woman’s monthly cycle. The 40-week accounting of a 38-week pregnancy was unfathomable to them, because they didn’t realize there was this thing called ovulation that happens two weeks into the cycle. They didn’t realize that when you get pregnant, there’s a lag of several weeks before you’re even able to test for pregnancy. And like I was taught years ago, they thought it was possible for a woman to become pregnant at any time in her cycle. (Possible, perhaps, but extremely rare—and focusing on that possibility means ignoring the biology of how the cycle typically works.)

Along the same lines, kids also need real information and statistics on types of contraceptives, including how to choose one when you need it. So what if they don’t need it right away? They don’t need algebra right now either. But someday they’ll be using math to program the next Angry Birds, and someday they’ll be using their knowledge of contraceptives to decide whether or not to use condoms that night, or whether they should be asking their doc for an IUD.

We have national debates on how best to scare teens away from sex, and sometimes step back enough to debate whether we should scare teens away from sex. But there’s a bigger question we’re missing: what happens when those teens grow up?

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An ‘apple a day’ keeps the prescription medications away?



Last week, a research article titled “Association between apple consumption and physician visits: appealing the conventional wisdom than an apple a day keeps the doctor away” was published online in the journal JAMA Internal Medicine. A great premise for a health research study, albeit the long research title. The article is written up in a clever, straightforward way – the authors propose, that,

“Although some may jest, considering the relatively low cost of apples (currently $1.13 per pound of Red Delicious apples), a prescription for apple consumption could potentially reduce national health care spending if the aphorism holds true.”

What did they do?

The article presents the results of a classic epidemiological study – a cross-sectional analysis of the U.S. National Health and Nutrition Examination Survey (NHANES). NHANES is a long-running nationally-representative survey of civilian, non-institutionalised American adults. The survey is conducted every few years, when a new random sample of the population is drawn. The authors of the study pooled together respondents from the 2007-08 and 2008-09 rounds of the survey, to get an eligible sample of 12,755 survey respondents (1). From there, they used data from 24-hour dietary recall questionnaires, demographics, health examinations, and health questionnaires completed by the survey respondents (1). After accounting for missing data, excluding all survey respondents who reported that their 24-hour diet questionnaire that they filled out was not representative of their usual diet, and those whose apple consumption came solely from apple juice and apple sauce, the study included 8,399 study participants (1).

The researchers estimated the total grams of raw apple consumed by each study participant during the 24-hour dietary recall period, by linking the reported foods to the U.S. Environmental Protection Agency’s Food Commodity Intake Database (1). They defined study participants as ‘apple eaters’ if the daily amount of apple they reported eating was equivalent to 149 grams (1 small apple). They investigated whether there could be a dose-response relationship between apple consumption and avoidance of health care visits by comparing the effect of eating 1 small vs. 1 medium vs. 1 large apple per day against eating no daily apples.

“Keeping the doctor away” was defined as successfully avoiding more than one visit to a physician in the past year. One physician visit was allowed, as American adults are recommended to visit their doctor each year for an annual health check-up.

What did they find?

The researchers estimated that 19.3 million U.S. adults are ‘apple eaters’, who consume about 26.9 million small apples daily, weighing 8.8 million pounds (1). In contrast, 207.2 million adults were estimated to be non-apple eaters – the majority of the population. Apple eaters were no more likely to avoid visiting the doctor than non-apple eaters (Odds Ratio = 1.19; 95% Confidence Interval: 0.93-1.53), regardless of the size of the daily apple (p-value for trend = 0.06). There was also no difference in overnight hospital stays or mental health visits between apple eaters and non-apple eaters. However, people who ate a daily apple seemed more likely to avoid taking prescription medications than non-apple eaters. When comparing ‘small’ and ‘large’ apple eaters against non-eaters, there were no differences in medication-taking, as the odds ratios were of small (but positive) magnitude and the 95% confidence intervals were wide, indicating imprecise estimates due to small sample size. However, the odds ratio for ‘medium’ apple eaters was positive and statistically significant for avoiding taking prescription medications, and the overall trend in the dose-response relationship between size of daily apple and odds of avoiding medications was statistically significant (p-value for trend = 0.02). Trend analyses, while reliant on p-values, are useful in small sample size situations where a linear dose-response relationship is plausible, as they indicate the overall relationship across categories of exposure (a.k.a. size of apple).



What does it mean?

Although there are limitations of this study – think of all the possible ways in which apple eaters could differ from non-apple eaters, that might also affect whether they might take prescription medications or not (aside from age, sex, race/ethnicity, education, body mass index, smoking status, and health insurance type, which were accounted for). Unmeasured and subsequently uncontrolled confounding in studies like this can always skew results. The study authors propose a prospective or experimental design for the future, to help reduce the likelihood of residual confounding and establish the correct temporality of the relationship – that apple eating comes before not taking medications, and not the other way around. Based on their results, the authors suggest an update of the well-known aphorism to be,

“If anything, apple eating may help keep the pharmacist away.”

It doesn’t quite roll off the tongue, but certainly gets one thinking about how and why these aphorisms come into use in the first place.



1) Davis MA, Bynum JPW, Sirovich BE. Association between apple consumption and physician visits: appealing the conventional wisdom than an apple a day keeps the doctor away. JAMA Intern Med 2015; doi: 10.1001/jamainternmed.2014.5466

Image sources

Fuji Apples: By Scott Bauer, USDA ARS [Public domain], via Wikimedia Commons

Prozac Pills: By Tom Varco (Own Work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

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Games That Teach You Something About Public Health

Reading is great, but sometimes it’s more fun to learn by playing. Here are a few games that will end up teaching you something about public health:

Screenshot from Spent game

Spent: This game is for anybody who feels like they know how they would live if they were poor. Just don’t buy as much stuff, right? The game, created by an ad agency for Urban MInistries of Durham, starts you off with $1000 in the bank and asks you to choose a job. From there, you have 30 days’ worth of expenses and decisions. You win if you can make it through the month without going broke.

The game was informative, not just because of the statistics and facts sprinkled throughout, but because it hit hard emotionally. I won (one of the times I played it), and was even able to afford to help a relative buy medication toward the end, and take a community college course that could help me earn more money in the future. But to get there, I had to forgo health insurance, stop paying my gas bill, deny my kid the opportunity to participate in a sport (I couldn’t afford the uniform and physical), and when my pet got sick, it was cheaper to put him down than to pay for treatment.

For some decisions, you can ask a friend for help (to do laundry at their place for free, for instance)–but that required posting to Facebook or Twitter, something I hate to do because who wants to annoy their friends or explain to their friends what they’re doing? Point taken.

Screenshot of Vax game

Vax: This is a short, fast-paced game that pits you against an infectious disease. Your playing field: a network of susceptible people.

You get a head start, with a limited number of vaccinations you can give before the disease starts to spread. When you vaccinate somebody, they drop out of the network, their dot disappearing and the network breaking apart. Once the outbreak begins, your only tool is quarantine, which likewise drops people out of the network.

Strategies that win: vaccinating (or quarantining) people who have the most connections, which has the biggest impact on the route the disease can travel. If you can completely split the network into pieces, that helps you too. But beware: When you reach the “hard” level, you’ll find that some people in the network refuse to be vaccinated. The game was developed by Marcel Salathe’s epidemiology research group.


Gut Check – This one was designed by microbiologist Jonathan Eisen. It’s a “real” game, the website says, but “one might accidentally learn about concepts such as antibiotic resistance, hospital-acquired infections, prebiotics, probiotics, opportunistic infections and more.”

In this card game (which regretfully I have not played yet), you and friends each try to develop your own microbiome, filling it with beneficial species—but you can also play pathogens on your opponents, pass around antibiotic resistance plasmids, and spread germs in crowded places with the “airplane trip” and “go to work sick” cards. There’s even a homeopathy card for those turns when you’d rather not play anything at all.

The game is available as free downloads to print, and the makers are working on a professionally printed version too.

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Drink makers are squirrely about ingredients, even when they share nutrition info

Alcoholic beverage manufacturer Diageo made headlines recently for announcing they will put nutrition labels on their products, including Guinness and Smirnoff brands. But the buzz about nutrition information (which I wrote about, briefly, for Lifehacker) skipped over what should be considered a major omission: Drink makers still aren’t disclosing their ingredients in many cases. For at least one type of sugary liqueur, Diageo forgot(?) to list the sugar.

I took a look at Diageo’s nutrition website, www.drinkIQ.com. Nutrition information, including alcohol content, calories, and other nutrients like carbohydrates, is all there. Unlike typical US “Nutrition Facts” labels, the types of carbohydrates and fats aren’t broken down, but totals are given. Many drink makers have this information available on their websites; it’s putting labels on the drinks themselves that’s rare. In fact, until 2013 they weren’t allowed at all in the US. (Meanwhile, some drinks, like cider and gluten-free beer, fall into a loophole requires nutrition labels.)

The pages for Bailey’s Irish Cream liqueurs look good at first glance, giving this information as well as an ingredients list: “Cream, Whisky, Natural Flavors.” But a major ingredient is missing: sugar.

There’s no way that any of cream, whisky, or natural flavors contain the listed amount of carbohydrates. And in fact, if you look up Bailey’s on its main website, a nutrition breakdown specifies there are 20 grams of sucrose per 100 mL (or about two teaspoons per shot)–which could only come from added sugar. (Cream doesn’t contain much sugar, and it’s not in the form of sucrose anyway.) Diageo did not respond to my requests for comment.

The rest of the DrinkIQ website is hit-or-miss, with ingredients lists sometimes reading simply “N/A” and, for the Johnnie Walker brands of scotch, the ingredients list is replaced with a short paragraph about the use of oak barrels and caramel coloring.

I searched in vain for drink manufacturers that consistently do a good job of publishing their ingredients (especially for non-beer drinks; some breweries do list theirs). Know of any you’d like to call out for doing a good job? Leave a note in the comments.

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A healthy society is a disaster resilient society

Today, we warmly welcome to the blog Professor Shinichi Egawa from the Division for International Cooperation for Disaster Medicine, International Institute of Disaster Science at Tōhoku University. His bio can be found at the bottom of this post.

Disasters are usually measured by the number of deaths, injuries and damage to property that they cause. But, do these measures truly express the magnitude of damage to people’s health and quality of life?


In 2011, the earthquake that struck the Pacific coast of Tōhoku, with a magnitude of 9.0 (Mw), was the largest to ever hit Japan and the fourth most powerful in recorded history. Strong national building codes protected most of the buildings in Japan from this devastating earthquake and saved many lives as a result, but the people affected by that disaster still suffer from chronic illnesses, mental problems, loss of family and exposure to nuclear power plant accidents—most notably Fukushima, which continues to pose contamination risks due to the severe infrastructure damage it incurred during the earthquake. The public health situation of a community is a key factor in measuring their resilience against disasters, and accordingly, the strengthening of mental and physical health must be made a priority when looking to curb the risks posed such disasters in the future.


Damage at Akai, Higashimatushima, Miyagi | Wikimedia Commons CC BY-SA 3.0 ChiefHira

On the other side, the process of rebuilding after a disaster is just as important as the capacity and resilience building processes that happen prior to a disaster. Health professionals have an important role to play in facilitating mutual, cooperative relationships with non-health professionals as they work together to help rebuild communities. Specialists, such as those involved in disaster medicine, need to forge partnerships with general health providers to create a unified approach to community resilience and rebuilding programs. The role the health professional plays is not only important in the acute response to injuries caused by disasters, but also in preparedness work, which is crucial to later making an efficient response to any disaster.


In Japan, for example, nation-wide disaster medical response systems have saved many lives. With the Tōhoku earthquake in 2011, systems such as disaster-base hospitals, disaster medical assistant teams (DMAT), staging care units (SCU), wide-area transportation systems, emergency medical information systems (EMIS) and disaster medical-public health coordinators worked efficiently in the immediate aftermath of the earthquake, saving countless lives and limiting the impact of the disaster.


Despite these efforts, however, the medical and public health needs of the affected people exceeded the relief capacities in place at the time and, indeed, for several months after the event. Mental health problems including post-traumatic stress disorder (PTSD), depression and alcoholism are still huge problems in those affected, and it will take years to find solutions to these issues. Medical and public health preparedness should be emphasized and prioritized in order to build resilience to disasters in the form of long-running, systematized global health programs.


During the International Symposium for Disaster Medicine and Public Health Management that took place last May, scientists and experts in disaster medicine and public health reached a consensus that health concerns should be imperative in the formulation of disaster risk reduction interventions. Prioritization of the people’s mental and physical health in the process of disaster risk reduction should be in included in frameworks and policy at all levels. Preparation for people who need special assistance, such as those with disabilities, children and women, elderly people, people with chronic illnesses, foreigners and travelers, cannot be made without their own participation in the process of disaster risk reduction planning.


To achieve the above, education and training of general health-care providers and the continued development of the field of disaster medicine and public health are the only methods that will lead to long-lasting implementation.


All of this will be in the spotlight at the 3rd World Conference for Disaster Risk Reduction taking place in Sendai, Japan this week. The Hyogo Framework for Action (HFA), endorsed by the UN General Assembly in 2005 to make the world more disaster resilient, will be revised in Sendai to reflect the post-2015 development agenda and give greater emphasis to the health of those vulnerable to the risk of natural disasters. The original HFA did not do enough to influence the design of existing national social protection mechanisms, particularly with regard to health programs and education schemes, which are crucial to building resilience to disasters. The new framework HFA framework under discussion in Sendai is meant to cover the next 20-30 years and will be expanded to include such important areas of social protection, like heath and education, effectively leading to the scaling-up of disaster resilience before disasters hit.


We, as health professionals, are striving to strengthen community resilience to disasters through the improvement of physical and mental health services. To do this, health professionals must be regarded as a key stakeholder working in concert with other professionals in the field of disaster risk reduction.


Professor Shinichi Egawa is Professor at the Division for International Cooperation for Disaster Medicine, International Institute of Disaster Science in Tohoku University – the epicentre of the 2011 earthquake and tsunami. Prof. Egawa leads efforts in Disaster Risk Reduction through his research in preparedness with a prioritisation in health as a central goal of risk reduction and management. He will be working closely with UNISDR on the new Hyogo Framework for Action that will be adopted this weekend in Sendai. He is also part of the Disaster Risk Reduction scientific committee at the forthcoming Forum 2015.

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How does the FDA regulate mobile medical apps?


Last month, the FDA released new guidance regarding the regulation of mobile medical apps, to replace its earlier version from 2011. Since that time, the amount of health and medical apps on the market has exploded, with the release of Apple’s Health app with iOS 8 cementing mobile health as ‘a thing’. Needless to say, it is about time for a regulatory update.

The FDA defines a ‘mobile medical app’ as a mobile app that is intended to either (1):

–       Be used as an accessory to a regulated medical device; or

–       Transform a mobile platform into a regulated medical device.

What is a regulated medical device? The FDA guidance states that (1):

When the intended use of a mobile app is for the diagnosis of disease or other conditions, or the cure, mitigation, treatment, or prevention of disease, or is intended to affect the structure or any function of the body of man, the mobile app is a device.

Leaving the sexist nature of this terminology aside, we can see that the FDA takes a very clinical perspective on what types of apps they consider as falling in their domain. This approach is practical. It means that they a priori alleviate themselves of responsibility for the numerous health apps continually being produced, which allows them to focus on those apps posing the greatest risks to users. An app which intends ‘to affect the structure or any function of the body of man’ is potentially much more impactful than a simple diet tracking app. Or is it? And is it debatable whether diet and fitness apps intend to alter the structure or function of man?

Apps, which the FDA will not regulate, but will ‘exercise enforcement discretion’ over, are those that help users to self-manage their disease or conditions without providing specific treatment or treatment suggestions, help users track or manage their health (e.g. trackers for diet, exercise, sleep, mood), apps that provide access to electronic health records, apps that help patients communicate with doctors (e.g. by providing videoconference portals or allowing patients to take snapshots of their symptoms), or those that perform calculation for things like body mass index or pregnancy due date (1).

Apps that are regulated are those that, for example, turn the mobile into a control for a medical device such as a blood pressure cuff or insulin pump, or transform the mobile into a medical device such as an electronic stethoscope or a blood glucose reader (1). The rationale for regulation of these latter uses is that these types devices are already FDA-regulated, and would pose a risk to patients if improperly functioning (1).

Does this mean there is no risk associated with the ‘softer’ uses of medical apps, which are not regulated? Of course not. The risk is lower, perhaps, but apps may contain false or misleading health and medical information, which could be improperly used if taken uncritically. The growing knowledge economy of our society means that education and literacy are far more important than ever before, and this is true down to such a small issue as using health apps on a mobile phone to gain a real personal benefit.

Recently, we have seen the FTC fine the makers of apps that purported to aid in detecting malignant melanoma, which would qualify as being a medical device (2). It will be interesting to see how the regulatory landscape evolves in the future, which will depend on whether there is real transformative power of these non-medical device health apps to influence people’s behaviour and health decisions.



1. U.S. Department of Health and Human Services Food and Drug Administration, Center for Devices and Radiological Health, Center for Biologics Evaluation and Research. Mobile medical applications: Guidance for Industry and Food and Drug Administration Staff. U.S. Department of Health and Human Services Food and Drug Administration. 2015.

2. Dredge S. FTC fines app firms for claiming their technology could detect melanoma. The Guardian. Wednesday Feb 25 2015. http://www.theguardian.com/technology/2015/feb/25/ftc-fines-apps-detect-melanoma (accessed 02 March 2015).

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