Analyses of academician cohorts generate biased pandemic excess death estimates

Death data from cohorts of academicians have been used to estimate pandemic excess deaths. We aimed to evaluate the validity of this approach.

Data were analyzed from living and deceased member lists from Mainland China, UK and Greece academies; and Nobel laureates (and US subset thereof). Samples of early elected academicians were probed for unrecorded deaths; datasets overtly missing deaths were excluded from further analyses. Actuarial risks were compared against the general population in the same country in respective age strata. Relative incidence risk increases in death in active pandemic periods were compared to population-wide pandemic excess death estimates for the same country.

Royal Society and Academy of Athens datasets overtly missed deaths. Prepandemic death rates were 4- to 12-fold lower in the Chinese Academy of Engineering (CAE) vs respective age strata of the Mainland China population. A +158% relative increase in death risk was seen in CAE data during the first 12-month of wide viral spread. Both increases (+34% in British Academy) and decreases (-27% in US Nobel laureates) in death rates occurred in pandemic (2020-22) vs prepandemic (2017-19) years; point estimates were far from known excess deaths in the respective countries (+6% and +14%, respectively). Published excess death estimates for urban-dwelling Mainland China selectively analyzed CAE that had double the pandemic death rates than another Chinese academy (Chinese Academy of Sciences).

Missingness, lack of representativeness, large uncertainty, and selective analysis reporting make data from academy rosters unreliable for estimating general population excess deaths.