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A method,
not a verdict.

This section describes the structured, evidence-based methodology for determining causation. It is grounded in Chapter 4, “Methodology,” of the AMA Guides to the Evaluation of Disease and Injury Causation, 2nd edition, and informed by related scientific and occupational medicine sources.

The method proceeds in a disciplined sequence. It begins with the individual case, including the diagnosis, exposure, timing, objective findings, prior history, and competing explanations. It then evaluates the relevant scientific literature to determine whether the claimed exposure is capable of causing the condition and whether the individual’s exposure was sufficient in dose, intensity, and timing.

The conclusion is not assumed at the outset. It comes last, after the facts have been identified, the evidence has been organized, the science has been assessed, and alternative explanations have been considered. A defensible causation opinion follows the method; it does not reason backward from the desired conclusion.

Reference: Melhorn JM, Hegmann KT, Talmage JB, Hyman MH, Ackerman WE III. Methodology. In: Melhorn JM, Ackerman WE III, Talmage JB, Hyman MH, eds. AMA Guides to the Evaluation of Disease and Injury Causation. 2nd ed. American Medical Association; 2014:115–138.

What any analysis must still show.

Historically, a condition was considered occupational when three criteria were met. They remain a plain-language summary of what the modern method operationalizes.

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Developed by NIOSH, modified by ACOEM, and integrated into the AMA Guides causation Blue Book, this stepwise method provides a disciplined framework for determining work-relatedness. Each step must be satisfied by credible evidence before a causation opinion can be supported. If a required step cannot be satisfied in a way that credibly supports relatedness, the claim of work-related causation cannot stand.

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The rule The method moves in one direction: from the individual case, to the scientific literature, and then to a defensible conclusion. The conclusion follows the evidence — never the other way around.

What “strong evidence” actually means.

Step 2 asks what the literature shows. Answering that question credibly requires understanding how the literature is weighed. We recommend using a standardized evidence-rating framework that converts a body of studies into a defensible, evidence-based foundation.

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The most consequential number.

Relative risk compares the rate of disease in an exposed group with the rate in an appropriate unexposed or reference group. An RR of 1.0 means no excess risk is associated with the exposure. As RR increases above 1.0, the portion of disease among exposed individuals that may be attributed to the exposure also increases, assuming the association is valid and causal. This bridge is expressed as the attributable fraction among the exposed: (RR − 1) / RR. At relative risk (RR) 2.0, the attributable fraction reaches 50%; above RR 2.0, it exceeds the more-likely-than-not threshold.

MET-05

Two examples

Epidemiology describes groups; a claim concerns one person. Population association establishes only whether an exposure is capable of causing a condition — and at what strength.

Most conditions are multifactorial. Where several causes contribute, apportionment estimates the relative weight of each — occupational and non-occupational. Even where an exposure is an established risk factor, the science may show its contribution to be small, warranting only a correspondingly small apportionment.

Integrity means applying the method consistently and reporting the results without regard to the requesting party. The science is the science regardless of who asks for the analysis; the conclusion must follow the evidence, not the interests of the person or party requesting the review.

Adapted from The Science of Causation: Principles and Best Practices for Determining Injury and Disease Relatedness and The Science of Causation: An Introduction to Biostatistics — foundational educational source documents for OpenCausation.org^SM — by J. Mark Melhorn, MD.