forensic epidemiology forensic epidemiology the discipline of forensic epidemiology (fe) is a hybrid of principles and practices common to both forensic medicine and epidemiology. fe is directed at filling the gap between clinical judgment and epidemiologic data for determinations of causality in civil lawsuits and criminal prosecution and defense. part of a series onforensic science physiological anthropology biology bloodstain pattern analysis dentistry dna phenotyping dna profiling entomology epidemiology limnology medicine palynology pathology podiatry toxicology social psychiatry psychology psychotherapy social work criminalistics accounting body identification chemistry colorimetry election forensics facial reconstruction fingerprint analysis firearm examination footwear evidence forensic arts profiling gloveprint analysis palmprint analysis questioned document examination vein matching forensic geophysics forensic geology digital forensics computer exams data analysis database study malware analysis mobile devices network analysis photography video analysis audio analysis related disciplines electrical engineering engineering fire investigation fire accelerant detection fractography linguistics materials engineering polymer engineering statistics traffic collision reconstruction related articles crime scene csi effect perry mason syndrome pollen calendar skid mark trace evidence use of dna inforensic entomology outline category forensic epidemiologists formulate evidence-based probabilistic conclusions about the type and quantity of causal association between an antecedent harmful exposure and an injury or disease outcome in both populations and individuals. the conclusions resulting from an fe analysis can support legal decision-making regarding guilt or innocence in criminal actions, and provide an evidentiary support for findings of causal association in civil actions. applications of forensic epidemiologic principles are found in a wide variety of types of civil litigation, including cases of medical negligence, toxic or mass tort, pharmaceutical adverse events, medical device and consumer product failures, traffic crash-related injury and death, person identification and life expectancy. history the term forensic epidemiology was first associated with the investigation of bioterrorism in 1999, and coined by dr. ken alibek, the former chief deputy of the soviet bioweapons program. the scope of fe at that time was confined to the investigation of epidemics that were potentially man-made. after the us anthrax attacks of 2001 the cdc defined forensic epidemiology as a means of investigating possible acts of bioterrorism. at the present time fe is more widely known and described as the systematic application of epidemiology to disputed issues of causation that are decided in (primarily) civil, but also criminal courts. the use of epidemiologic data and analysis as a basis for assessing general causation in us courts, particularly in toxic tort cases, has been described for more than 30 years, beginning with the investigation of the alleged relationship between exposure to the swine flu vaccine in 1976 and subsequent cases of guillain–barré syndrome. more recently fe has also been described as an evidence-based method of quantifying the probability of specific causation in individuals. the approach is particularly helpful when a clinical differential diagnosis approach to causation is disputed. examples covering a wide variety of applications of fe are listed below under examples of investigative questions addressed by forensic epidemiologists. methods and principles comparative risk ratio the metric of a case-specific fe analysis of cause is the comparative risk ratio (crr). the crr is a unique metric to fe; it allows for the comparison of probabilities applicable to the investigated circumstances of an individual injury or disease. because a crr is based on the unique circumstances surrounding the injury or disease of an individual, it may or may not be derived from a population-based relative risk (rr) or odds ratio (or). an example of an rr analysis that could be used as a crr is as follows: for an unbelted driver who was seriously injured in a traffic crash, an important causal question might be what role the failure to use a seat belt played in causing his injury. a relevant rr analysis would consist of the examination of the frequency of serious injury in 1000 randomly selected unbelted drivers exposed to a 20 mph frontal collision versus the frequency of serious injury in 1000 randomly selected restrained drivers exposed to the same collision severity and type. if the frequency of serious injury in the group exposed to the presumptive hazard (failure to use a seat belt) was 0.15 and the frequency in the unexposed (belted) group was 0.05, then the crr would be the same thing as the rr of 0.15/0.05. the rr design of the analysis dictates that the populations that the numerator and denominator of the crr are substantially similar in all respects, with the exception of the exposure to the investigated hazard, which was the failure to use a seat belt in the example. in some instances encountered in a legal setting, however, the numerator and denominator risk must be derived from dissimilar populations in order to fit the circumstances of an investigated injury or disease. in such a case the crr cannot be derived from either an rr or or. an example of such a situation occurs when the numerator is a per event risk, and the denominator is a per-time risk (also known as a cumulative risk). an example of this type of analysis would be the investigation of a pulmonary embolism (pe) that occurred a week after a patient sustained a lower extremity fracture in a traffic crash. such complications often result from blood clots forming in the legs and then traveling to the lungs. if the patient had a history of deep vein thrombosis (dvt) in the lower extremities prior to the crash, then a crr might consist of the comparison between the risk of a pe following a lower extremity fracture (a per event rate) and the 1-week risk of pe in a patient with dvt (a time-dependent probability). another example of a crr based on dissimilar populations is when there are only a limited number of potential causes to be compared. an example is the investigation of the cause of an adverse reaction in a person who took two different drugs at the same time, both of which could have caused the reaction (and which, for the example, do not interact with each other). in such a situation, the crr applicable to the unique circumstances experienced by the individual could be estimated by comparing the adverse reaction rate for the two drugs. attributable proportion under the exposed the attributable proportion under the exposed (ape ) is an indication of the proportion of patients who were exposed to the potential cause and got sick because of this exposure. it can only be used if the rr >1 and can be calculated by . when the crr is based on an rr, these formulae also apply to the crr. the result of the analysis, given as an rr, crr, or ape , meets the legal standard of what is “more likely true than not,” when the rr or crr is >2.0 (with a 95% confidence interval lower boundary of >1.0), or the ape is >50%. the ape is also known as the "probability of causation (pc)" a term that is defined in the us code of federal regulations (federal register / vol. 67, no. 85 / thursday, may 2, 2002 / rules and regulations p. 22297) and elsewhere. causal methodology analysis of causation, particularly for injury or other conditions with a relatively short latency period between exposure and outcome, is accomplished using a 3-step approach, as follows: plausibility: this first step addresses whether it is biologically possible for the injury event to have caused the condition (a.k.a. general causation), and follows a special application of the viewpoints set forth by hill (see below). a finding of plausibility is unrelated to the frequency of the injury, because even if the injury occurs in only 1 in 100 or fewer cases of exposure to the event, it is still plausibly caused by the event. plausibility is a relatively low hurdle to clear in a causal analysis, and is largely satisfied by the lack of evidence of implausibility of the relationship. plausibility is often, but not necessarily, established with epidemiologic data or information. temporality: this second step examines the clinical and other evidence of the timing between the onset of the symptoms of injury and the injury event, and must be satisfied to assess specific causation. first, it must be established that the sequence of the injury and the event is appropriate; the symptoms cannot be identically present prior to the event. further, the onset of the symptoms of injury cannot be either too latent or insufficiently latent, depending on the nature of the exposure and outcome. lack of a more probable alternative explanation: this final step examines the probability of the injury condition occurring at the same point in time in the individual, given what is known about the individual from the review of medical records and other evidence, but in the absence of the injury event (a.k.a. differential diagnosis). first, evidence of competing injury events must be evaluated, and compared for risk (often via analysis of epidemiologic data). then, the likelihood of the condition occurring spontaneously must be assessed, given the known history of the individual. united states case law on injury causation methodology the 3-step methodology was challenged in united states district court for the district of colorado in etherton v auto-owners insurance company. the defendant challenged, among other things, the reliability and fit of the methods described by the expert. after an extensive examination and discussion of the 3-step process used by the expert, the court found that the methodology appropriately fit the specific facts of the case, and that a population-based (epidemiologic) approach was an appropriate part of the causal methodology. the court denied the defendant’s motion to strike the expert’s testimony in the order, which was entered on 3/31/14. the defendant appealed the ruling from the district court, and in july 2016, the tenth circuit u.s. court of appeals affirmed the 3-step causal methodology as generally accepted and well established for assessing injury causation, under the daubert standard. see etherton v. auto-owners insurance company, no. 14-1164 (10th cir, 7/19/16). hill viewpoints plausibility of an investigated association can be assessed in an fe investigation, in part, via application of the hill criteria, named for a 1965 publication by sir austin bradford-hill, in which he described nine “viewpoints” by which an association described in an epidemiologic study could be assessed for causality. hill declined to call his viewpoints “criteria” lest they be considered a checklist for assessing causation. the term “ hill criteria” is used widely in the literature, however, and for convenience is used in the present discussion. of the nine criteria, there are seven that have utility for assessing the plausibility of an investigated specific causal relationship, as follows: coherence: a causal conclusion should not contradict present substantive knowledge. it should “ make sense” given current knowledge analogy: the results of a previously described causal relationship may be translatable to the circumstances of a current investigation consistency: the repeated observation of the investigated relationship in different circumstances or across a number of studies lends strength to a causal inference specificity: the degree to which the exposure is associated with a particular outcome biological plausibility: the extent to which the observed association can be explained by known scientific principles experiment: in some cases there may be evidence from randomized experiments (i.e., drug trials) dose response: the probability, frequency, or severity of the outcome increases with increased amount of exposure the triangular relationship between exposure, outcome, and confounder. when investigating whether there is a causal relationship between an exposure and outcome of interest, the influence of extraneous variables needs to be taken into account. a confounder is defined as a concurrent cause of the outcome under investigation that is related to, but not a consequence of, the exposure of interest. subsequent authors have added the feature of challenge/ dechallenge/ rechallenge for circumstances when the exposure is repeated over time and there is the ability to observe the associated outcome response, as might occur with an adverse reaction to a medication. additional considerations when assessing an association are the potential impact of confounding and bias in the data, which can obscure a true relationship. confounding refers to a situation in which an association between an exposure and outcome is all or partly the result of a factor that affects the outcome but is unaffected by the exposure. bias refers to a form