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 syndromMethods 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) orAttributable 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 RR-1)/RR X 100% 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 RegulationsCausal 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'Hill viewpoints
Biological plausibility, 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 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 andTest accuracy
Test accuracy investigation is a standard practice in clinical epidemiology. In this setting, a diagnostic test is scrutinized to determine by various measures how often a test result is correct. In FE the same principles are used to evaluate the accuracy of proposed tests leading to conclusions that are central to fact finder determinations of guilt or innocence in criminal investigations, and of causality in civil matters. The utility of a test is highly dependent on its accuracy, which is determined by a measure of how often a positive or negative test result truly represents the actual status that is being tested. For any test or criterion there are typically four possible results: (1) a true positive (TP), in which the test correctly identifies tested subjects with the condition of interest; (2) a true negative (TN), in which the test correctly identifies test subjects who do not have the condition of interest; (3) a false positive (FP), in which the test is positive even though condition is not present, and; (4) a false negative (FN) in which the test is negative even though the condition is present. Fig. 3.19 is a contingency table illustrating the relationships between test results and condition presence, as well as the following test accuracy parameters: * Sensitivity (the rate at which the test is positive when the condition is present) TP/(TP + FN) * Specificity (the rate at which the test is negative when the condition is absent) TN/(TN + FP) * Positive predictive value (the rate at which the condition is present when the test is positive) TP/(TP + FP) * Negative predictive value (the rate at which the condition is absent when the test is negative) TN/(TN + FN)Bayesian reasoning
Probability is used to characterize the degree of belief in the truth of an assertion. The basis for such a belief can be a physical system that produces outcomes at a rate that is uniform over time, such as a gaming device like a roulette wheel or a die. With such a system, the observer does not influence the outcome; a fair six-sided die that is rolled enough times will land on any one of its sides 1/6th of the time. An assertion of a probability based in a physical system is easily tested with sufficient randomized experimentation. Conversely, the basis for a high degree of belief in an asserted claim may be a personally held perspective that cannot be tested. This does not mean that the assertion is any less true than one that can be tested. As an example, one might truthfully assert that “if I eat a banana there is a high probability that it will make me nauseous” based upon experience unknown to anyone but one’s self. It is difficult to test such assertions, which are evaluated through collateral evidence of plausibility and analogy, often based on similar personal experience. In forensic settings, assertions of belief are often characterized as probabilities, that is, ''what is most likely'', for a given set of facts. For circumstances in which a variety of conditions exist that may modify or “ condition” the probability of a particular outcome or scenario, a method of quantifying the relationship between the modifying conditions and the probability of the outcome employs Bayesian reasoning, named for Bayes’ Theorem or Law upon which the approach is based. Most simply stated, Bayes’ Law allows for a more precise quantification of the uncertainty in a given probability. As applied in a forensic setting, Bayes’ Law tells us what we want to know given what we do know. Although Bayes’ Law is known in forensic sciences primarily for its application to DNA evidence, a number of authors have described the use of Bayesian reasoning for other applications in forensic medicine, including identification and age estimation.Post-test probability
TheExamples of investigative questions
* What is likelihood that the asbestos exposure that Mr X experienced during his employment at company Z caused his lung cancer? * How likely is it that the DNA found on the forensic scene belongs to Mr X? What is the chance that you are wrong? Could you in your probability calculation take into account the other evidence that points towards the identification of Mr X? * Could you estimate the probability that the leg amputation of Mrs Y could have been prevented if the delay in diagnosis would not have occurred? * How likely is it that the heart failure of Mrs Y was indeed caused by the side effect of this drug? * What is the chance that the death that followed the administration of the opiate by 20 minutes was due to the drug and not to other (unknown) factors? * What is the chance that Mr. X would have needed neck surgery when he did if he had not been in a minor traffic crash the prior month? * How likely is it that the bladder cancer of Mrs Y was caused by passive smoking during her imprisonment given the fact that she was an ex-smoker herself? * Which liability percentage is reasonable in the given circumstance? * What would be the life expectancy of Mr X at the time of his death if the wrongful death not occurred? * How long is Mr X expected to survive, given his brain/ spinal cord injury, on a more probable than not basis? * Given the medical and non-medical evidence at hand regarding the circumstances of this traffic crash, what is the probability that Mrs Y was the driver? * Given the medical and non-medical evidence at hand regarding the circumstances of this car accident, what is the probability that Mr X was wearing a seat belt? * What is the probability that Mrs Y’s need for surgery resulted from the crash, vs. that it would have occurred at the same time if the crash had not happened?References
External links
Further reading
* {{cite journal , last1 = Meilia , last2 = Dianita Ika , first2 = Putri , last3 = Freeman , first3 = Michael D. , last4 = Zeegers , first4 = Maurice P. , year = 2018 , title = A Review of the Diversity in Taxonomy, Definitions, Scope, and Roles in Forensic Medicine: Implications for Evidence-Based Practice , journal = Forensic Science, Medicine, and Pathology , volume = 14 , issue = 4, pages = 460–68 , doi=10.1007/s12024-018-0031-6, pmid = 30276619, pmc = 6267374, doi-access = free