Timing characteristics of the epidemic of acute kidney injury involved deaths(unfinished research paper from early in 2025) - Maybe I'll finish by end of year
Hi All, Many know that I do not have faith or reliance in the peer-reviewed journal articles and research papers. In fact, I often state that they are more of the problem than the solution. However, many have pushed me to get some done because reality does not exist to scientists, even on the health freedom side of the marketplace of ideas unless it is peer-reviewed by those who never employed the methods in the paper. Here is just the Abstract from a paper I started early in 2025. I’ll save the rest for when it gets published. AbstractBackground The incidence rate of acute kidney injury (“AKI”) starkly increased during the years 2020-2024 compared to the prior decade. The encumbrance of excess AKI involved deaths has not been epidemiologically and methodically analyzed in the context of inflections in the first derivative in time. Methods A review (2015-2023) of the official electronic death records from U.S. states Connecticut (CT), Massachusetts (MA), and Minnesota (MN) that comprise ~4.9% of the U.S. population is used to model and quantify the timing of excess AKI-involved deaths. Time-based 9-year daily rolling average waveforms depict signal inflections in cohorts of age groups, geographic location, prevalence of cause, and Covid and pneumonia co-involvement with AKI. An “AKI-involved death” is defined as the appearance of ICD-10 (International Classification of Diseases, 10th Revision) prefix “N17” (meaning “acute renal failure” and deemed equivalent to AKI for purposes of this study) in part I (cause-of-death) or II (conditions possibly contributing to death) of a decedent’s electronic death record. For the CT, MA, and MN analyses, linear least squares approximation using base years 2015-2019 was used to generate expected values for 2020-2023. Excess was calculated as the difference between actual and expected values in 2020-2023. Z-Scores were derived from the mean and standard deviation of base years 2015-2019. The daily Z-Score graphs were derived from a day’s mean and standard deviation within a 91-day rolling average across years 2015-2019 for that particular day. Further validation of the excess AKI-involved deaths from CT, MA, and MN is confirmed using official data obtained from the Centers for Disease Control and Prevention (CDC) for all fifty (50) U.S. states, supporting the assertion that CT, MA, and MN are not anomalous relative to the fifty (50) states. CT, MA, and MN were chosen for the simple reason that they are the only states for which record-level source data (RLSD) was acquired. RLSD enables data studies involving permutations of multiple variables modeled as a function of time. Excess deaths and Z-Scores for the 50 states are derived from base years 2010-2019, accordingly. For reasonable economy of research, only annual tables and graphs were calculated for the 50 states. Results Excess AKI-involved deaths across all U.S. states in 2022 in ages 15-44 increased 163% totaling 3,340 excess over expected (expected 95% confidence interval [95% CI], 1,918 to 2,173) and in 2023 in all ages increased 91.1% totaling 56,468 excess over expected ([95% CI], 61,313 to 62,655). The starting points in time of the AKI-involved mortality increase are disparate by geographic location. The excess AKI-involved deaths can be separated into two noticeably independent and distinct “signals,” or functions, leading to a further hypothesis that there are at least two causes of the dire and colossal excess AKI-involved deaths. There is also a signal pattern difference between the United States and other nations, leading to another further hypothesis that some custom or policy differences led to perceptible quantitative differences in deaths involving AKI by nation. Conclusions When compared to incidence rate increases in other causes such as myocarditis, AKI involved death in the Covid era demands attention as one of the greatest mass death events in the past 100 years in the United States. This analysis is a catalyst to raise awareness among the scientific community, healthcare professionals, government officials, and the general public. Interestingly, waveform analyses consistently demonstrate, across location and age, that there are two separate and distinct signal types that begin at different times coincident with policy changes much more than with the ebb and flow of seasonal disease in a given society. Plain Language SummaryAKI beginning in late 2020 is one of the greatest American mass death events in the past 100 years, far greater than polio, smallpox, Hong Kong flu, and H1N1; and even greater than Covid in life-years-lost. AKI is determined by death certifiers empirically through objective test data. Thus, it is not likely in excess due a change in the behavior of death certifiers. Exempli gratia, cardiac arrest certification often results from the subjective behavior of the death certifier and may have little to do with heart disease. A policy change related to the application of “cardiac arrest” to death records can result in false signals. Such a false signal due to policy is less likely in AKI certification because AKI results from laboratory data involving various component levels of urine and blood. There are two different signals of AKI. Signal 1 is seasonal and somewhat sinusoidal. Signal 2 is somewhat linearly rising to a significantly elevated steady-state. The start and end points of the two signals of AKI are not aligned in time with Covid disease in the first wave. Signal 1 is aligned with subsequent Covid waves. Signal 2 is disparate from Covid and contributes to ~100% growth over three years from 2021 through 2023. The author reserves the right to further edit and pare the Abstract and Plain Language Summary expressed above. God bless you all JOHN 14:6 TRUTHYou're currently a free subscriber to The Real CdC’s Newsletter. For the full experience, upgrade your subscription.
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