Same data, different conclusion

September 4, 2020. Correlation is not causality. Assigning a cause of death is not science, just a subjective, often political, judgment.

Using the same data as the mainstream media and the 'experts' -- and as much as I despise spin -- I would like to write a Covid story quite unlike the one promoted by the illiberal, progressive media and Democrat party.

I would use the same measures and, unlike, the current narrative, use them consistently. And Covid test results 'confirmed' but not audited will be viewed with appropriate skepticism. I will look at comparable demographic groups rather than compare apples (e.g., the United States) with oranges (e.g., Belgium). I will also look at events in the first two months of 2020 that facilitated the spread of Covid rather than stop it. I will assign responsibility to the appropriate government level for local health, rather than cede authority or assign responsibility to the World Health Organization, the CDC, or the president or prime minister of a country.

And I would ask questions that need to be asked yet see left unasked, like "How many people who tested negative for Covid subsequently died within two weeks of Covid?" This is the test of false negative test results. And, "What is the co-morbidity pattern on the death certificates of comparable people dying without Covid, as compared to those dying with Covid on their death certificate?" And, what is the economic or financial incentive for attributing Covid as a cause of death?" Related, "What have been the 2020 mortality rates for the co-morbidities found on 94% of Covid death certificates, as compared to pre-Covid trends or patterns? And, "What are the Covid impacts for comparable countries, regions, states or other units of governance, between those that had no lockdown and those with a significant lockdown? And, "What has been the incidence and impact of 'superspreader' Covid events, and could or should have they been prevented from happening?

The biggest problems with the reporting and responses on Covid tend to stem from some basic flaws: Changing the metrics used to track 'progress,' from counting hospitalizations and deaths, to counting only the absolute number of 'confirmed' positive test numbers. Relying on unreliable data, especially from faulty Covid test kits, labs and data centers. Inability to use inferential statistics to establish patterns, trends and (significant) differences. Failure to compare mortality rates for this year with previous years. Unwillingness to recognize the long-term human health (and economic) costs of short-term responses to Covid. Calling one opinion from one expert 'science'. Hypocrisy of top officials and politicians in not following their own mandates. Ignoring the long-term trend of an increasing number of deaths each year in the United States, about 50,000 more deaths each year, primarily the result of an aging population, with more lifestyle caused chronic morbidities.

Here is an example of a more comprehensive study of the pandemic in the United States. First identify the following groups: [1] People with severe Covid symptoms; [2] people with symptoms suggesting presence of Covid; [3] people with some suspicion of contact with a Covid carrier; [4] unsymptomatic people volunteering for testing; and [5] a random, respsentative sample from people not in any of the previous four groups. Next, for each group test for Covid and record the results; better still, audit or double check the test result for accuracy before posting, i.e., not just confirmed, but audited and verified. Next, for verified positive tests, identify subjects in one of three groups: those requiring intensive medical treatment, those requiring hospitalization, and those quarantined or otherwise untreated. Next, track all those subjects tested, regardless of their test results. One month after testing, identify all subjects as on oof the following: [1] never symptomatic; [2] recovered; [3] currently ill but not hospitalized; [4] hospitalized, not ICU; [5] hospitalized and in ICU; or [6] deceased. Add in the answer to the earlier questions about death certificate conditions, causes and comparisons with non-Covid deaths.

These data would provide a more informed view of the impact of the pandemic and allow public health officials to identify the critical pathways that lead to death or survival. These pathways would inform policy in a fashion not available with simple counts of the absolute number 'confirmed' positive tests, be they reported over time or across regions/jurisdictions.