The choosen/random notes provide additional context for the three core methods without disclosing proprietary calculation details: ...Even only 1/2 of the mathematical 'Method A' is a method inside the method; it does not let get very precise results, but is enough to test and debunk any age structure -if it is not composed of truly premature deaths, showing a degree of inconsistencies. Sensitivity tests incorporating potential disease-specific risk factors (prevalence and severity) for every* CCW-listed condition (*with the possible exception for Alzheimer's Disease and Non-Alzheimer’s Dementia), show that such adjustments cannot reduce the observed inconsistencies in the official COVID-19 death structure (concerns the U.S.); on the contrary, they would strengthen the required impossibly large suppression of mortality in healthier subgroups within an age band, bolstering that the official structure diverges strongly from biological and actuarial patterns, and making necessary to decrease the total average age of victims yet more to counteract. An additional layer of refinement (sensitivity tests) arises from synergy among conditions; even when matching only severe CCW conditions between a younger highly multimorbid individual and an importantly older one (with the same RLE, as less multimorbid), the younger case carries a substantially higher competing-risk burden mainly from the larger volume of concurrent mild conditions (thus his severe conditions must be more severe/risky), but also from other severe ones; this implies that the severe conditions in younger highly multimorbid fatal victims must be even more lethal - further increasing the importance of the frail younger tail and strengthening the inconsistencies in observed age structures. This additional layer is the reason that only for Alzheimer’s Disease and Non-Alzheimer’s Dementia, which have the steepest prevalence gradients, strong synergy effects have chances not to offset or exceed the older ones' advantage in raw severity prevalence (in 'Method A'). /To make the construction yet better we finally took into accout 'a share of men seeking care (IfR x severe fraction) to a share of women seeking care' (M/F), which 'M/F' was ~1.15 for aged 85+, ~1.30 for aged 75-84, but huge ~1.55 for aged 65-74, ~ 1.25 for aged 55-64; the effect of a smaller share of older men having high numbers of CCW-listed conditions is weaker than the contrary effect of men having a smaller RLE for the same number of conditions./. However, in this case (Covid-19 in the U.S.) the structure gets essentially disproved even before any RMT (and adjusted illness rates for frail subgroups) is/are applied, and the required suppression is already much too strong; this demonstrates that the implausibility is structural and not dependent on the precise value of the multiplier. ...The methods are new ideas. Even something as obvious as the relationship between the increase in average age and the number of conditions in premature virus-driven deaths has never been properly considered (a starting point for the easier and shorter 'Method C'). Consensus explanations must reconcile the observed age shift with realistic chronic-condition gradients - an area where pure medical intuition often falls short of rigorous demographic/actuarial chaining. For high selectivity, shown as a high average age, there must be seen a much increased average number of conditions too (interdependence). We also distinguish a still high RLE due to being still relatively young from a still high RLE in a very healthy importantly older one. RLE at any age depends on the number of chronic conditions, and the differences are still big even among old people - e.g., for Americans aged 75 their RLE was only ~4 years (on average) if they had >15 of the 30 CCW-listed chronic conditions, but 16.5 years if they had 0 - 3 conditions. For younger ones, full data is also available (interestingly, with increasing total condition count, the difference in age-related RLE decreases, to become close to none for very highly multimorbid ones). RLE as low as 100 times smaller than among the frailest ones (with RLE ⩽3 years). ...BASIC RISK MULTIPLIER: BRM represents the additional mortality risk from infection when frailty is atypical for a person’s age. When two individuals have the same short remaining life expectancy (RLE), the younger one usually has more “dense” and pathological frailty - caused by aggressive underlying disease processes and stronger synergies between conditions. This makes younger frail people disproportionately vulnerable to acute infections compared to older individuals with equivalent RLE but more gradual, age-related decline. As a result, the effective risk multiplier is greater than 1 and tends to increase with decreasing age. This effect helps explain why true virus-driven deaths must show an additional younger skew relative to the natural-death age distribution, under observed infection patterns (infection rates not rising, but declining at a very advanced age). ...Two separate layers can be applied. First, the relative volume of a frail subgroup is adjusted upward to reflect their higher (compared to the age-normal level) realized infection probability (due to greater healthcare contact and institutional exposure). Second, the Risk Multiplier is applied as the higher risk of death given infection, capturing a denser frailty typical of younger individuals reaching the same low remaining life expectancy. ...Younger frail individuals aged 'X' have substantially higher RLE-distribution, when comparing with older individuals aged 'Y' (e.g., 85 years) at equivalent average remaining life expectancy, resulting in a greater contribution to 'the low-RLE tail' compared to the older individuals. It increases any Risk Multiplier yet a little more. ...For individuals under age 60, a very small upward adjustment to the average age at death is appropriate to account for conditions such as early-stage cancer and certain liver diseases that strongly shorten remaining life expectancy with relatively limited impact on physiological reserves at the time of infection. In the remaining age-range, even if a share of aged 60+ in deaths is strongly bigger, the effect of early-stage cancer is very small too, due to differing shares (falling slowly at first, but quickly at ages 75+), age distributions of cancer mortality there, and because at ages 60+ aggressive cancers, that drastically shorten RLE, tend to concentrate among people who are already highly multimorbid and so already have short RLE (much shorter than what is normal for age). ...Any real structure of victims of viruses such as influenza or Covid-19, even for the weakest variant of a virus, must give an average age at death significantly lower than the average age of natural deaths (/e.g., injuries or infant mortality do not belong); all such deaths must be premature, corrective factors have a very limited impact, and act in opposite directions; RMT inhibits an additional older skew, on the contrary, RMT enhances a younger skew. Unfortunately, for 'official victims' of Covid-19 in the U.S., there was minimal average prematurity; it is also worth noting that the smaller the expected number of truly premature virus-driven deaths relative to the total excess mortality, the larger the proportion of harvested deaths that are likely caused by secondary effects than the virus itself. ...Comparative data concerning the pre-pandemic normal prevalences of conditions in U.S. society, at different ages, are based on CMS and ICD-coded, often extrapolated with e.g. NHANES; the average %-prevalence of later added CCW conditions is higher, 9 is less than half of the older 2008-CCW conditions, but still the new 9 weight just over 0.6 of the older ones, for the detailed age structure of 'official Covid-19 victims'. ...On Consensus and Rigorous Analysis: Consensus is a valuable practical tool for coordination when rigorous evidence is unavailable. However, when a clear mathematical or actuarial derivation yields specific, bounded, and testable results that conflict with prevailing consensus, the rational approach is to prioritize the stronger method and treat the consensus as provisional. Broad consensus dilutes precision by incorporating opinions of widely varying competence. The methods A/B/C are a mathematically rigorous way to separate attributable mortality from natural baseline and harvesting effects. The methods, including calculations, were repeated and validated, in detail, with leading AI systems.

EXTENDED ABSTRACT plus SUPPLEMENT Restricted (5 pages) ... Method A: A structural collapse test using Residual Life Expectancy (RLE) at death and Mortality Difference (MD) across age bands; referred to background data (Population Pyramid, morbidity in different age groups of the population and shares of highly multimorbid ones, illness rates, etc.). E.g. for the U.S., it starts with the official structure of 'Covid-19 deaths' and happens to demonstrate it was by far impossible for premature deaths, as it would require mathematical contradictions even as high as '3 > 20' in one place. The Risk Multiplier Total (RMT), which intensifies the contradictions, is explained. It is calculated by how much the age-groups' shares would have to be changed (and the average age decreased) to make the contradictions disappear. Only a small correction is needed due to 1-2 important diseases causing short RLE, while at an early stage little affecting physiological reserves; deaths at ages ⩽50 are most often driven by severe, aggressive pathologies, the overall share of highly multimorbid ones is minimal; at ages 60+ aggressive cancers tend to concentrate among people who are already highly multimorbid and so already have RLE much shorter than what is normal for age. There is also shown how to calculate the highest plausible (ADmax) average age of natural deaths wrongly attributed to Covid-19 (a little higher than the lowest possible), finally allowing to calculate the highest plausible share of true Covid-19 deaths. Any “single-disease prevalence support” is impossible, every(*) potential genuine CCW-listed risk factor could only strengthen the contradictions. Method B: We calculate average total life expectancy (TLE), adjusted by eliminating injuries and infant mortality, by shares of men/women, by deficits of children+adolescents, etc. -for true victims of the virus, in the case that they would not get infected and would die of a natural cause in the future, as a function of the degree of prematurity (YLL) and mortality rate. For example, in the theoretical case of close to zero prematurity and mortality, TLE would be <77 in the U.S. for infected by Covid-19 ones, in 2020 (less than ADmax); if all deaths had occurred with prematurity approaching zero, the RMT should fall to slightly above 1.0. In contrast, a theoretical scenario of 100% mortality among the infected cohort - using the same age distribution and sex shares as the infected population, plus adjusted RLE from life tables (the adjustment includes also the elimination of remaining fatal injuries for comparability) - yields TLE about 82.5 years. A model with a steady population illustrates why an important rise in TLE requires deaths to penetrate much deeper into younger age groups (killing across health profiles); next, the strengthening layer is the growing population. At the same time, an average number of lost years of life could not be only 4 - 5 years for the smallest plausible average prematurity (it must be bigger); those who were already in their terminal state before becoming infected should not be counted as victims of the virus. RMT rules out other causes of an increase in TLE. Method C: a bit less reliable as partially possible to counteract (a number of conditions can be manipulated during future events). All natural deaths imply a very high average age at death and then almost 100% mortality (if, for instance, injuries are excluded). Unlike for natural deaths (= realization of risks arising in the past), for quick premature deaths, 100% mortality would mean the average age at death falls to that of infected population (⩽43 y. for Covid-19, the U.S. in 2020) - only then would there be no increase in the average number of conditions compared to what is normal for age. For a v. high average age of premature deaths due to the virus (= huge selectivity) the average number of CCW-listed conditions must be strongly increased compared to the age-normal level, and the increase must occur for every separate age (we show how to calculate it for age 67 and 75), as for a specific age shares of younger and older victims are external data; there was never otherwise-RLE close to zero, but dependent on the number of conditions. At the same time, in the case of all natural deaths together this number is only a little increased. We present why total count remains a necessary 'base layer' - severity is unable to slow the required increase in total conditions among older epidemic decedents; there are two interconnected mechanisms of this phenomenon (severe conditions and their share rise disproportionately more with increasing total condition count, even for an already high number of conditions; synergy among conditions is the second mechanism). REMARKS: Restricted CORRECTIONS: Concern the earlier teaser -on a request. KEYWORDS: mortality differentials, longevity risk, pandemic modeling, actuarial inconsistencies, reinsurance reserving LICENSE: Reuse (of the article, 'Supplement', 'Remarks' or of 'Notes') needs a permission (until 01-2027, unless the date is changed). ...The size (the methods): 300 pages. VitalStats@proton.me