THE COMPUTATIONAL METHODS REVEALING TRUE COVID-19 DEADLY BURDENS

The choosen/random notes provide additional context for the three core methods without disclosing proprietary calculation details:

 ...Even only 1/2 of the main 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 a bit easier '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 <2 years (after excluding those already in their terminal state) is hardly predictable, unless among aged 90+, however younger of them usually must additionally be extremely multimorbid and the rest highly multimorbid, thus their share is negligible. 'Method C' proves that the required average number of chronic conditions for aged 75 or 67 needed to support the observed very high average death age of epidemic decedents (76.58 y. in 2020 in the U.S.) remain very strongly elevated (even with conservative RMs and basing only on total count), compared to the population norm at that age (...the construction of this method does not take into account other types of limitations resulting from other methods, otherwise any big increase in condition count might prove insufficient). The very powerful sensitivity test, assuming no Risk Multipliers (or where needed, RM = 1.0) and a conservative adjustment of illness rates for frail subgroups, was also performed; although visibly lower, the required average number of chronic conditions at age 75 remains strongly elevated (by x1.70 - x1.75) compared to the population norm; this further supports the conclusion that the official COVID-19 death structure diverged strongly from expected biological and actuarial patterns. There are two different ways to perform 'Method C'; the second version is more difficult and is only needed if only more rough baseline data is available. /Even if 'Method A' is constructed for RLE vs. MD, and fully works without any knowledge about disease burden among actual epidemic victims, it can be used in the context of disease burden too./

...Attempts to explain the observed official structure of 'Covid-19 victims' in the U.S. through specific high-risk diseases or combinations of high-risk conditions (theoretically e.g., diabetes + heart disease + obesity) does not help rescue it. The wider combination the yet bigger another constraint (on top of: total condition count - severity - synergy effects) that pushes the required average total condition count yet higher, not lower; only at much higher total condition counts does the mathematical likelihood of having a potential high-risk specific combination rise substantially. When the total number of conditions is low, the probability of any particular high-risk condition being present is lower than its average prevalence at a given age.

...Death certificate data provides an additional consistency check. In 2020, official COVID-19 deaths listed an average of approximately 3.8 conditions (in the U.S.). Systems overload during major events, and its impact on the average number of conditions per death certificate, are included by us. Even allowing for known under-documentation (also outside overwhelmed periods) and including some mostly secondary conditions (Pneumonia, Respiratory failure, Adult respiratory distress syndrome, Sepsis), this number remains much lower than would be expected if the majority of deaths were truly premature virus-driven cases. This supports the view that the official death structure is dominated by natural and harvested (of those who were in a terminal state before infection) deaths. In fact, secondary conditions should not be counted as pre-existing chronic conditions when assessing true frailty (without them the number should fall to about 3.0). ...Further enrichment of Method C using detailed clinical records (beyond death certificates) would be valuable, but such data faces significant access barriers for non-governmental entities in the United States. Access to comprehensive hospital, claims, or electronic health records would allow for yet more precise quantification of the true share of premature virus-driven deaths; large entities in other countries may have better access to such detailed data in their jurisdictions, potentially enabling it, after accounting for age structure and selection effects.

...Two separate layers should 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.

...As the average years of life lost (YLL) increases, the dispersion in remaining life expectancy among victims must disproportionately strong widen. A pathogen capable of causing higher average prematurity cannot become less able to kill the frailest individuals. Higher average YLL necessarily implies a greater contribution (share) from younger and less multimorbid groups; this relationship is a natural mathematical consequence of the bounded distribution of remaining life expectancy within old age bands. An average number of lost years of life could not be only 4 - 5 years for the smallest plausible average prematurity, as the mortality rate among the rest of the elderly cannot be >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 over 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).

...Younger frail individuals aged 'X' have higher RLE-distribution, when comparing with older individuals aged 'Y' (e.g., 85 years) at equivalent average remaining life expectancy, resulting in a bigger contribution to 'the low-RLE tail' compared to the older individuals. It increases any Risk Multiplier yet a bit more.

...For individuals under age 60, a very small upward adjustment to the average age at death is appropriate (in Method A) 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 very 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, for younger ones 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 were repeated and validated, in detail, with leading AI systems; there are no mistakes in the calculations.