Excess Mortality and Years of Potential Life Lost Among Black People in the US from 1999 to 2020
Bibliographic record
Abstract
ABSTRACT Importance Amid efforts in the United States to promote health equity, there is a need to assess progress in reducing excess deaths and years of potential life lost (YPLL) among Black people compared with White people. Objective To evaluate trends in excess mortality and YPLL among Black people compared with White people. Design Serial cross-sectional. Setting National data from the Centers for Disease Control and Prevention, 1999-2020 Participants Non-Hispanic White and non-Hispanic Black people Exposures Race as documented in the death certificates. Main outcomes and measures Excess age-adjusted all-cause and disease-specific mortality rate (per 100,000 individuals) and YPLL among Black people compared with White people. Results From 1999 to 2020, the total number of excess deaths was 658,356 and 1,154,108 among Black females and males, representing 34,938,070 and 47,005,048 excess YPLL among Black females and males. The excess deaths and YPLL were largest among infants and non-elderly adults. Heart disease had the most excess deaths. From 1999, the age-adjusted excess mortality rate declined at an annual average of -9.0 (95% CI: -10.0, -8.0; P<0.001) until 2015 among Black women and at an annual average of -16.3 (95% CI: -20.9, -11.6; P<0.001) until 2012 among Black men, followed by no significant change until 2019 in either group. From 2019 to 2020, excess deaths increased from 90.4 to 192 per 100,000 Black women and from 209.8 to 395 per 100,000 Black men, reaching rates approximating those of 1999. The trends in rates of excess YPLL followed a similar pattern. Conclusions and relevance Over a recent 22-year period, Black people in the US lost more than 80 million years of life when compared with White people. After a period of progress, improvements stalled, and most gains were eliminated in 2020. KEY POINTS Question How many excess deaths and years of potential life lost (YPLL) for Black people, compared with White people, occurred in the United States from 1999 through 2020? Findings Based on Centers for Disease Control and Prevention data, excess deaths and YPLL persisted throughout the period, with initial progress followed by little improvement, and then worsening in 2020 to about 1999 levels. Black people had 1.8 million excess deaths and over 80 million YPLL over the study period. Meaning After initial progress, excess mortality and YPLL among Black people stagnated and then worsened, indicating a need for new approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".