The Effect of the COVID-19 Pandemic on the Elderly: Population Fatality Rates, COVID Mortality Percentage, and Life Expectancy Loss.
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately affected the elderly. This Article provides a detailed analysis of those effects, drawing primarily on individual-level mortality data covering almost three million persons aged 65+ in three Midwest states (Indiana, Illinois, and Wisconsin). We report sometimes surprising findings on population fatality rates ("PFR"), the ratio of COVID to non-COVID deaths, reported as a percentage, which we call the "Covid Mortality Percentage," and mean life expectancy loss ("LEL"). We examine how these COVID-19 outcomes vary with age, gender, race/ethnicity, socio-economic status, and time period during the pandemic. For all persons in the three Midwest areas, COVID PFR through year-end 2021 was 0.22%, mean years of life lost ("YLL") was 13.0 years, the COVID Mortality Percentage was 12.4%, and LEL was 0.028 years (eleven days). In contrast, for the elderly, PFR was 1.03%; YLL was 8.8 years, the COVID Mortality Percentage was 13.2%, and LEL was 0.091 years (thirty-four days). Controlling for gender, PFR and LEL were substantially higher for Blacks and Hispanics than for Whites at all ages. Racial/ethnic disparities for the elderly were large early in the pandemic but diminished later. Although COVID-19 mortality was much higher for the elderly, the COVID Mortality Percentage over the full pandemic period was only modestly higher for the elderly, at 13.2%, than for non-elderly adults aged 25-64, at 11.1%. Indeed, in 2021, this ratio was lower for the elderly than for the middle-aged, reflecting higher elderly vaccination rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".