CHANGES IN LIFE EXPECTANCY BETWEEN 2019 AND 2021 IN THE UNITED STATES AND 21 PEER COUNTRIES
Bibliographic record
Abstract
ABSTRACT BACKGROUND Prior studies reported large decreases in US life expectancy during 2020 as a result of the COVID-19 pandemic, disproportionately affecting Hispanic and Black populations and vastly exceeding the average change in life expectancy in other high-income countries. Life expectancy estimates for 2021 have not been reported. This study estimated changes in life expectancy during 2019-2021 in the US population, in US racial/ethnic groups, and in 21 peer countries. The study compared outcomes across five US racial/ethnic groups and is the first to estimate changes in life expectancy during the pandemic in non-Hispanic American Indian/Alaska Native and Asian populations. METHODS US and peer country death data for 2019-2021 were obtained from the National Center for Health Statistics, the Human Mortality Database, and overseas statistical agencies. The 21 peer countries included Australia, Austria, Belgium, Canada, Denmark, England and Wales, Finland, France, Germany, Israel, Italy, Netherlands, New Zealand, Northern Ireland, Norway, Portugal, Scotland, South Korea, Spain, Sweden, and Switzerland. Life expectancy was calculated for 2019 and 2020 and estimated for 2021 using a previously validated modeling method. RESULTS US life expectancy decreased from 78.85 years in 2019 to 76.98 years in 2020 and 76.44 years in 2021, a net loss of 2.41 years. In contrast, peer countries averaged a smaller decrease in life expectancy between 2019 and 2020 (0.55 years) and a 0.26-year increase between 2020 and 2021, widening the gap in life expectancy between the United States and peer countries to more than five years. The decrease in US life expectancy was highly racialized: whereas the largest decreases in 2020 occurred among non-Hispanic (NH) American Indian/Alaska Native, Hispanic, NH Black, and NH Asian populations, in 2021 the largest decreases occurred in the NH White population. DISCUSSION The US mortality experience during 2020 and 2021 was more severe than in peer countries, deepening a US disadvantage in health and survival that has been building for decades. Over the two-year period between 2019 and 2021, US NH American Indian/Alaska Native, Hispanic, and NH Black populations experienced the largest losses in life expectancy, reflecting the ongoing legacy of systemic racism as well as inadequacies in the US handling of the pandemic. The crossover in racialized outcomes between 2020 and 2021, in which the NH White population experienced the largest decreases, likely has multiple explanations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".