THE COMPUTATIONAL METHODS REVEALING TRUE COVID-19 DEADLY BURDENS

The choosen 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; external medical opinions are not especially necessary, as opinions cannot withstand pure mathematics and logic. Sensitivity tests incorporating potential disease-specific risk factors (prevalence and severity) for every* CCW-listed condition (*with the potential 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 (concerns sensitivity tests) arises from synergy among conditions; even when matching the number of severe CCW conditions between a younger highly multimorbid individual and an average importantly older one (with the same RLE), the younger case carries a substantially higher competing-risk burden from the larger volume of concurrent mild conditions (thus his severe conditions must be more severe/risky); 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 group's 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. Differences in care settings between age and multimorbidity groups introduce opposing biases (e.g., LTC exposure vs treatment intensity), which mostly offset at population level./. However, in this specific case (Covid-19 in the U.S.) the structure gets essentially disproved even before any RMT is 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 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 a high selectivity, shown as a high average age, there must be seen a much increased average number of conditions too (interdependence); we distinguish a still high RLE due to being quite young from a still high RLE due to being a very healthy old one. For a specific age, shares of younger and older ones are external data. RLE at any age depends on the number of chronic conditions, and the differences are 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, a 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 90 and yet older ones, however younger of them usually must additionally be extremely multimorbid and the rest highly multimorbid, thus their share is negligible. Not only 'Method C', but also the Part A module enables direct verification by actuarial teams, relying solely on demographic and statistical inputs to quantify plausibility ('Method A' is constructed for RLE vs. MD, and fully works even without any knowledge about disease burden among actual epidemic victims, but can be used in the context of disease burden too). /For the analysis, systems overload during major events, and its impact on the average number of conditions per death certificate, are included./ ...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 much/importantly 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 a visible 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. ...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). ...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. ...The methods were repeated and validated, fragment-by-fragment, with leading AIs.

...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.