After completing my doctorate, my most cherished "congrats" gift was a mug whose simple decoration was a large-lettered sentence: "Trust me, I'm a doctor." The irony still makes me smile as it always seemed clearly created with an MD, not a PhD, in mind. Many of my clients want to know the difference between the two degrees.
Different Doctors with Different Lenses
Let's start with the most fundamental: epidemiologists focus on populations while physicians diagnose and treat illness in individual patients. For example, a doctor can help determine which antibiotic best treats a patient’s UTI; epidemiologists track thousands of UTIs across populations, analyze which treatments work best against specific pathogens, and investigate why some fail. Different doctors, different lenses.
Epidemiologists are Detectives who Decode Causation
But here's where it gets interesting - and where attorneys often find epidemiologic expertise particularly valuable: we're the detectives of causal inference1. While physicians excel at diagnosis and treatment, epidemiologists excel at untangling the complex web of cause and effect.
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1 The process of determining whether a relationship between variables represents true cause-and-effect, rather than mere correlation.
Imagine you're investigating whether pilots are more prone to vision problems due to UV radiation exposure. At first glance, a study showing that active pilots have fewer vision problems than the age-equivalent general population might seem reassuring. However, an epidemiologist would immediately spot the methodological trap: pilots must pass strict vision tests to receive and maintain their certification. Those who develop vision problems likely leave aviation, meaning they're systematically removed from the study population. You’ve got a selection bias called “the healthy worker effect.”
While a physician might focus on an individual pilot's vision issues, an epidemiologist asks questions like: Are we looking at the right population? Have we accounted for the systematic exclusion or removal of affected individuals? How can we accurately capture the true relationship between aviation exposures and vision outcomes? Are there other populations with the same exposure who are less prone to selection bias?
Epidemiologists conduct systematic evidence reviews
When attorneys ask what epidemiologists bring to a case that a clinician cannot, my answer is a methodological toolkit for systematically assessing causal inference. We're trained to ask questions like:
Is there a true causal relationship, or are we seeing “noise” from confounding2 factors?
We know that low physical activity is a risk factor for dementia, but so is low education. Often, the two are correlated; how do we tease this apart?
Is there a known biological mechanism associated with an exposure that could explain the outcome observed3? And if so, does the timing align with what we know about this exposure-disease relationship?
We know the mechanisms by which ionizing radiation can cause cancer. Still, it is not likely cancer will arise in a time period of a few months after the exposure but instead a timeline of many years.
Are there alternative explanations we need to consider?
Did that person with ionizing radiation exposure live in older housing stock in which radon exposure might be an issue? Did their socioeconomic status play a factor (lower socioeconomic status is an independent risk factor for cancer)?
How do multiple causes interact?
Smoking and radon exposure are independent risk factors for cancer. But smokers with radon exposure experience a level of risk much greater than adding the risk of radon plus the risk of smoking. This is called effect modification in epidemiology.
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2 A confounder is a factor that is:
Associated with the exposure of interest
Independently associated with the outcome (through a causal pathway separate from the exposure)
Not in the causal pathway between the exposure and outcome (not a mediating factor)
3 The lack of a known biological mechanism does not rule out causation - many causal relationships were established before their mechanisms were understood. Scientific knowledge evolves and today's mystery may be tomorrow's well-explained phenomenon.
Epidemiologists make causal inference less tricky
Causal pathways are rarely simple. Physicians document individual cases. Epidemiologists unravel webs of causation by mapping out competing pathways, analyzing temporal relationships, and systematically evaluating evidence to reveal causal links between exposures and health outcomes.
Causal pathways are rarely simple. Physicians document individual cases. Epidemiologists unravel webs of causation by mapping out competing pathways, analyzing temporal relationships, and systematically evaluating evidence to reveal causal links between exposures and health outcomes.
Through systematic evidence reviews, we follow rigorous methodologies to evaluate the quality of research and synthesize findings. We analyze medical records, transmission chains, and timelines. We consider biological plausibility and statistical patterns. But perhaps most importantly, we identify and account for common methodological challenges that make causal inference tricky - effect modification, confounding, and competing causal pathways*.
So yes, I am a doctor - just not that kind. My prescription pad is filled with robust evidence synthesis and methodologic rigor. While I can't cure your headache, I can help you understand why headaches occur in certain populations and under what circumstances. And in legal cases, this might just be the kind of doctor that you need.
*These are great terms for a Google search or your favorite LLM. Or, for the very curious, feel free to schedule some time with me, and I will walk you through examples relevant to your field of expertise.