History of FE 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. FE 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. 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. The 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 of error that may threaten the validity of a study by producing results that are systematically different from the true results. Two main categories of bias in epidemiologic studies are selection bias, which occurs when study subjects are selected as a result of another unmeasured variable that is associated with both the exposure and outcome of interest; and information bias, which is systematic error in the assessment of a variable. While useful when assessing a previously unexplored association, there is no combination or minimal number of these criteria that must be met in order to conclude that a plausible relationship exists between a known exposure and an observed outcome. 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 and Positive Predictive Value The post-test probability is a highly useful Bayesian equation that allows for the calculation of the probability that a condition is present when the test is positive, conditioned by the pretest prevalence of the condition of interest. This equation is given in box to the right: Post-test probability equation The equation results in a positive predictive value for a given pre-event or pretest prevalence. In a circumstance in which the pretest prevalence is considered “indifferent” the prevalence and (1-prevalence) values cancel out, and the calculation is a simplified to a positive predictive value. Examples of Investigative Questions Addressed by Forensic Epidemiologists 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?
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. 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. 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. 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 of error that may threaten the validity of a study by producing results that are systematically different from the true results. Two main categories of bias in epidemiologic studies are selection bias, which occurs when study subjects are selected as a result of another unmeasured variable that is associated with both the exposure and outcome of interest; and information bias, which is systematic error in the assessment of a variable. While useful when assessing a previously unexplored association, there is no combination or minimal number of these criteria that must be met in order to conclude that a plausible relationship exists between a known exposure and an observed outcome. 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 The post-test probability is a highly useful Bayesian equation that allows for the calculation of the probability that a condition is present when the test is positive, conditioned by the pretest prevalence of the condition of interest. This equation is given in box to the right: Post-test probability equation The equation results in a positive predictive value for a given pre-event or pretest prevalence. In a circumstance in which the pretest prevalence is considered “indifferent” the prevalence and (1-prevalence) values cancel out, and the calculation is a simplified to a positive predictive value. Examples 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?
D
of FE
R
FE Methods and Principles
Methods and principles
R
Risk Ratio
risk ratio
R
Methodology
methodology
R
The Hill Viewpoints
Hill viewpoints
R
Reasoning
reasoning
R
Probability and Positive Predictive Value
probability
R
Investigative Questions Addressed by Forensic Epidemiologists
investigative questions