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Public Health Informatics by Rita Kukafka

Public Health (PH)​

The canonical definition and goals of "Public Health":

Public Health is the science of protecting and improving the health of families and communities through promotion of healthy lifestyles, research for disease and injury prevention and detection and control of infectious diseases.

You can achieve the stated goals via activities like research, health promotion, surveillance, monitoring, developing policies on prevention and access to care, and so on. It may seem pretty broad but that's the idea: the key emphasis is on improving the lot of the population and not single individuals (which is the domain of Medicine).

The "Population Based Perspective" of PH maintains that

  1. Disease Risks (and not diseases themselves!) are on a continuum: there no binary/categorical notion of "You're at risk" or "You're free of risk" for a given disease.

  2. With the lovely Normal distribution, it stands to reason that you'll have people on the tail ends. PH is concerned with applying itself to the chunky middle. It concerns itself with interventions applied to a large group of people at small risk, rather than the inverse. This may seem like we're excluding people but what we're really doing with this focus is shifting the mass of the distribution (e.g. the mean) in a direction we'd like. Dr. Kukafka gives an example of this with obesity interventions. Medicine generally concerns itself with high-risk groups.

  3. Regarding exclusion again: We are very much products of our environment (this can be physical (e.g. sociocultural) or genetic) and you cannot isolate an individual from the population to which they belong. So you first start with asking why a population has a particular disribution of disease risk first, and then explore why an individual as it.

Considering Social determinants of health have 'leaked' into clinical practice as well.

Tools: If you use CT Scans and thermometers (for example) in the medical domain, you use Epidemiology, Surveillance, vital statistics (and others) in the PH domain.

Public Health Informatics (PHI)​

You apply computers and informatics theories to solve PH problems. It's about as interdisciplinary as you can imagine. Started seriously as a discipline in early 2000s.

The Data​

There's an initiative called "Healthy People 2030" that's set some goals. They publish a list of Data Sources you can look at. These sources do have some common standards (e.g. they must be nationally representative, must be reliable, public, etc).

As with most data, there are a lot of challenges. The usual suspects: interop, storage standards, and so on. A big problem is that reporting is slow and does not keep up with events like COVID (or worse, a bioterrorism attack). The fact that public health and medicine evolved separately is a problem too.

Syndromic Surveillance​

(A syndrome is a collection of symptoms that occur consistently and indicate the presence of a disease.)

You use data that's collected for other purposes to establish some kind of PH emergency. This was developed in the wake of 9/11 when we realized we were ill-equipped to deal with a bioterrorism attack. You identify that people are ill (in clusters) and report this immediately (i.e., don't wait to diagnose/confirm) to react quickly to reduce mobidity (you have the disease) and mortality (you've died of the disease). E.g. there's a suspiciously large increase in calls to ambulances. NYC used this to go from detection to a stay-at-home order in two weeks.

Reporting and the speed of reporting are still big challenges. You can certainly use data at point-of-capture for PH interventions or research. Like when you go see a nurse and he measures your BP. It doesn't even have to be at a clinic: Collect data from companies about cigaratte price increases and see what happens to the number of people who smoke!

A Risk-Stratified Approach​

As stated earlier, and speaking broadly (and with the Bell Curve), Medicine focuses on tail-ends, PH focuses on shifting the hump. But you can certainly slice the curve up and tailor interventions based on who falls where.

Lots of Challenges!​