Thursday, February 5, 2015

Populations, individuals and imprecise disease prediction

As Michael Gerson writes in the Washington Post, "Preventable infectious disease is making its return to the developed world, this time by invitation." When anti-vaxxers were few, and they chose not to expose their kids to what they consider toxins, their kids benefited from the herd immunity that resulted from most parents choosing to have their kids vaccinated (or, as Gerson puts it, anti-vaxxers chose to be free-riders).  They could claim there were no costs to their action (or non-action), because as long as they were a small minority, there weren't.

But unlike many complex non-infectious diseases, infectious diseases are very predictable. Once the proportion of a population that is immunized falls below a certain threshold, as determined by the rigorous and empirically tested mathematics of infectious disease, the kids of anti-vaxxers are then sitting ducks for disease once they are exposed, and the disease is then likely to spread even to the immunized population because no vaccine is 100% effective.  And this is happening in the US now with measles.  Anti-vaxxers convinced enough of their neighbors not to vaccinate that they can no longer claim no cost, only benefits to their beliefs.

From Mother Jones, 2014

In theory, herd immunity protects a population from measles when at least 90-95% of the population is vaccinated, so the above map would suggest that even in Oregon, with a rate of non medical immunization exemption over 6%, the disease would be unable to gain a foothold.  But, infectious disease researcher Marcel Salathé who is here at Penn State nicely describes herd immunity here, and suggests that something closer to 100% coverage would actually be required to protect against measles because of pockets of lower vaccination rates, and non-random mixing of the population and so forth.

The science on the safety and efficacy of vaccination is well-established. Vaccines can have side-effects, but it's pretty clear the list doesn't include autism.  (Has anyone estimated the prevalence of autism in anti-vaxxer communities yet?  If they were right that the MMR vaccine causes autism, the rate should be a lot lower in unvaccinated kids by now, no?)  The freedom of choice issue, of whether the state has a right to require individuals to be vaccinated, is a live one, and any conscientious objector to the right of society to make decisions for individuals has to be struggling with this one, given the societal consequences.  This is distinctly not the same as an individual's freedom to decide whether to smoke or to drink jumbo soft drinks because in the case of vaccines, what's good for society is also good for individuals, and vice versa.

Measles virus; Wikipedia (Cynthia S Goldsmith Content Provider, CDC) 

But that's not what interests me particularly here.  What interests me is the interplay between population and individual disease dynamics.  Infectious disease dynamics depend on the proportion of susceptible individuals in the population; too few and the disease dies out, enough and the disease sticks around, cyclically infecting people as, say, the flu, or endemic, as, e.g., venereal diseases.  So, in a very real sense infectious disease happens to a group, in a group, and because of a group, at the same time it's happening to individuals in that group.  But chronic non-infectious diseases (CNIDs) don't work that way.  Chronic non-infectious diseases happen to individuals only, irrespective of what's happening to anyone else in the population (though, see below).

But, what we know about and predict for individuals depends on what we know (or think we know) about a CNID in a population.  Epidemiologists collect data in a group on what may be relevant risk factors, and then statistically estimate their importance and impact. Observations on a single individual don't have the power to allow epidemiologists to determine which risk factors are likely to be important.  That requires repeated observations, on many people.

So, calculations of how likely you are to have a heart attack given your age, body mass index, cholesterol levels and so on are based on population associations between such factors and actual heart attacks.  These are based on past observed experience in many individuals.  As we've written many times before, it's not really clear what 'risk' represents, other than the observed proportion of a population with apparent past exposure to tested risk factors who went on to develop a disease.  But, a lot of people with the same risk factors didn't develop disease, or have a heart attack or whatever, and a lot of people without those risk factors did.  So, risk estimates are population-based statistics that may or may not apply to you -- or anyone individually, really.  They are collective data, and clearly don't explain all risk, or allow precise prediction.

Now, social epidemiologists would say that chronic diseases can be as much a result of population factors as infectious diseases are.  Smoking, obesity, drinking, stress-related diseases are correlated with social class, so in a very real sense, population dynamics can affect risk of CNIDs as well.  So, if we want to explain CNIDs by distal rather than proximal risk factors, population dynamics become important, as with infectious diseases.  But they are still population-level factors -- not every low-income individual is obese or has high blood pressure, and not every overweight individual is poor.  But, everyone with measles has been infected by the measles virus.

The population issue, I think, goes a long way toward explaining why it's so hard to predict chronic disease, genetic or otherwise.  We're forced to infer group statistics to individuals, and that's never going to be precise.

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