{"id":"HHS-OASH-2026-0397-2932","number":"2932","name":"Jack Mahogov","organization":"","posted":"2026-10-14T04:00Z","received":"2026-10-11T04:00Z","body":"To Whom It May Concern:\n\nI appreciate the opportunity to provide information regarding electromagnetic fields, radiofrequency radiation, and wireless technologies.\n\nI am not an expert in epidemiology, toxicology, biophysics, dosimetry, oncology, neurology, or radiation science. However, I understand that personal observations are being solicited, and I am therefore pleased to contribute data of approximately the same scientific rigor as any other uncontrolled anecdotal report.\n\nOver the past several years, I have experienced repeated exposure to Wi-Fi, Bluetooth, cellular networks, and other forms of radiofrequency radiation.\n\nDuring that same period, I have observed a number of concerning health-related outcomes.\n\nFor example, after spending prolonged periods in Wi-Fi-enabled environments, I frequently experience fatigue, reduced concentration, irritability, and a strong desire to stop working.\n\nThe association is especially pronounced Monday through Friday.\n\nSymptoms tend to improve substantially on weekends and during vacations.\n\nThis reproducible dechallenge phenomenon may indicate a biologically meaningful relationship between wireless exposure and employment.\n\nI have also observed a strong temporal association between 5G exposure and hunger.\n\nFollowing several hours of exposure, I reliably develop an urge to eat. Administration of food results in rapid symptom improvement.\n\nThe consistency of this response, together with its recurrence on re-exposure, raises concerns regarding a possible radiofrequency-associated metabolic syndrome.\n\nAdditional observations include:\n\n- Exposure to Bluetooth is frequently followed by depletion of headphone batteries.\n- Prolonged screen use in Wi-Fi environments is associated with eye strain.\n- Cell-phone exposure is strongly associated with receiving unwanted phone calls.\n- Individuals exposed to wireless networks continue to age over time.\n- Every person I know who has died was, at some point in life, exposed to electromagnetic fields.\n\nThe last finding is particularly troubling because the observed association with mortality is 100%.\n\nI recognize that several methodological limitations should be acknowledged.\n\nExposure was not quantified.\n\nSymptoms were not prospectively defined.\n\nThere was no control group.\n\nThere was no blinding.\n\nThere was no randomization.\n\nPotential confounders such as age, sleep, caffeine, stress, occupation, screen time, underlying illness, and the ordinary passage of time were not evaluated.\n\nSelection bias was substantial because I selected myself for participation.\n\nRecall bias was unavoidable because I am recalling the events.\n\nReporting bias is possible because I am reporting only the observations I find interesting.\n\nThe hypothesis was formulated after the outcomes occurred.\n\nNo statistical analysis was performed.\n\nNevertheless, because the events occurred after exposure, a causal relationship cannot be ruled out.\n\nI therefore recommend that federal agencies consider a streamlined evidentiary framework for future public-health policy.\n\nUnder this approach, causation could be established when the following conditions are met:\n\n1. An individual believes an exposure caused a symptom.\n2. The symptom occurred at some point after the exposure.\n3. The individual reports improvement after doing something else.\n4. The story sounds compelling.\n5. Enough similar stories can be collected.\n\nThis framework would offer several advantages over conventional epidemiology.\n\nIt would eliminate the need for control groups, exposure measurement, preregistered hypotheses, statistical power, replication, dose-response analysis, and tedious arguments about confounding.\n\nIt would also dramatically accelerate policy development.\n\nThe same methodology could be extended beyond radiofrequency radiation.\n\nFor example, I have personally observed that rain is strongly associated with umbrellas, fire trucks are frequently present at large fires, and nearly every person with arthritis has previously consumed water.\n\nThese associations merit immediate federal review.\n\n\nRespectfully submitted,\n\nJack Mahogov, A Member of the Continuously Exposed General Population","attachments":[],"attachment_inventory":true,"withheld":false,"source":"https://www.regulations.gov/comment/HHS-OASH-2026-0397-2932","tags":["health-experiences","sleep-cognition","biology-metabolism","exposure-places","policy-standards","research-evidence"],"source_hash":"bc356d6e3b60b952be458171443b7f9aa8bc59e3bd7493b68f2ba68150d0d6bd","slug":"health-experiences-comment-2932","classification":{"id":"HHS-OASH-2026-0397-2932","body_sha256":"5bdf64e2280e8cef62f28942e1c03a0b801963d96f4029358a5bba9dadd1d4ad","status":"ai_reviewed","primary_topic":"research","topics":[{"id":"research","evidence":"ology.\n\nIt would eliminate the need for control groups, exposure measurement, preregistered hypotheses, statistica"}],"summary":"Uses examples of everyday events occurring after wireless exposure to question causal conclusions from uncontrolled anecdotes and emphasize measurement, controls, confounding and replication.","reviewed_at":"2026-10-15T03:51:16Z","review_method":"Source-based AI review of complete published comment bodies; exact topic evidence; attachments and external links not reviewed.","review_source_sha256":"14e1529ff6f6f361ee6ec8f75372da416f94bce2215f83275450d2803b23af14","source_url":"https://rfsafe.net/hhs-comments/data/catalog.json","scope":"Original comment body; attachments excluded"},"forum_topics":["research"],"primary_topic":"research","forum_title":"Uses examples of everyday events occurring after wireless exposure to question causal conclusions from un…"}