Sound science and, more importantly, sound reasoning about science have slowly been making their way into appellate decisions for two decades now, but last year's Ford v. Dixon was something special. We called it the best causation opinion of 2012 and without saying so thought it a Palsgraf for this age of Big Data. Acknowledging the data available for estimating a given asbestos exposure and the risk attendant to that exposure the opinion simply asked of plaintiffs that they 1) demonstrate that the risk complained of is not merely de minimis; and, 2) explain how they estimated the risk.
The opinion recognized the centrality of risk in so-called substantial factor causation analysis. It also held that establishing the existence of an infinitesimal risk cannot suffice to carry plaintiff's burden of showing a substantial one. Finally it required that plaintiff estimate the risk by reasonable inference drawn from sound quantitative science. Essentially it replaced crude (and easily distorted) proxies for risk like Lohrmann's frequency, proximity and regularity test (now more than a quarter century old) with a modern approach based on the data and the statistical inferences that it warrants. Its analysis was, as the dissent in last week's opinion stated, excellent.
But it didn't reflect the law in Maryland according to the state's highest court (Dixon v. Ford). Reciting that it had held as recently as 2011 that the "'frequency, regularity, and proximity' test remains 'the common law evidentiary standard used for establishing substantial-factor causation in negligence cases alleging asbestos exposure'", and that a decade before that it had declined to hold that a plaintiff must "present expert testimony as to the amount of respirable asbestos fibers emitted by a particular product", the court found that the plaintiff had satisfied her burden by showing that her husband had done 1,000 brake jobs and by her expert's conclusion that the resulting in-home exposures were "high" and so "a substantial factor" in the cause of her mesothelioma.
Apparently Ford's main argument was that plaintiff's expert had hung her hat on the dubious "every fiber / every breath" theory. The court found that argument disingenuous as the evidence established years of work on asbestos-containing brakes and routine contamination of the home from the clothing of plaintiff's husband. Ford's better argument (described by the court as "a fallback"), was that the failure (or more likely refusal) of plaintiff's expert to estimate the risk from such exposures meant that the question of whether the exposures were a substantial factor (i.e. "but for" cause plus a not insubstantial risk) went unanswered. So the testimony, lacking any information about risk, could not possibly assist a fact finder to determine whether the risk was substantial. The only answer the court could muster to this objection was that they already had a substantial factor test - the frequency, proximity and regularity test; and it was satisfied by evidence of an asbestos-related disease, 1,000 brake jobs and an exposure opined to be "high".
Ironically the court then went on to render many more pixels in a discussion of the Dixon plaintiffs' damages. Imagine what would happen if a plaintiff were to try to hold an award for lost wages when his evidence consisted only of the following: a) "I planned to keep working"; b) "I worked six days a week"; and, c) "I was a very hard worker." Without some numbers behind these statements no jury could sensibly answer a binary question like "Has she lost more than $50,000?" much less "How much has she lost?" And even if an economist showed up to support whatever sum plaintiff's counsel intended to blackboard/whiteboard/PPT/etc no court would let him testify unless he could at least opine about the incomes of people who have jobs like the plaintiff's.
So why in a time when when it's easy to find data about the asbestos exposures of people who had jobs like the plaintiff's don't we demand that experts say what they are? And why in a time when it's easy to estimate the risk posed by a given exposure don't we demand that experts say what it is? We know why the plaintiffs don't want to have to quantify dose and risk in low dose cases - either the calculated risk is too small or it's too easy to call BS on the way it was calculated if it's high. But why so many courts continue to resist quantitative data on the question of substantial factor causation in asbestos cases remains to us a mystery; especially when there's so much data available.
In the end Ford v. Dixon sought to introduce the law to the sort of decision-making tools that are revolutionizing everything from medical diagnoses to weather forecasting in hopes of making justice a little less rough and a little more just. If it had a flaw it was that it was ahead of its time.