Racial Disparities in Mortality Among American Film Celebrities: A Wikipedia-Based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: In the United States, well-documented racial disparities in health outcomes are frequently attributed to racial bias and socioeconomic inequalities. However, it remains unknown whether racial disparities in mortality persist among those with higher socioeconomic status (SES) and occupational prestige. OBJECTIVE: As the celebrity population is generally characterized by high levels of SES and occupational prestige, this study aimed to examine survival differences between black and white film celebrities. METHODS: Using a Web-based, open-source encyclopedia (ie, Wikipedia), data for 5829 entries of randomly selected American film actors and actresses born between 1900 and 2000 were extracted. A Kaplan-Meier survival curve was conducted using 4356 entries to compare the difference in survival by race. A Cox semiparametric regression analysis examined whether adjusting for year of birth, gender, and cause of death influenced differences in survival by race. RESULTS: Most celebrities were non-Hispanic white (3847/4352, 88.4%), male (3565/4352, 81.9%), and born in the United States (4187/4352, 96.2%). Mean age at death for black celebrities (64.1; 95% CI 60.6-67.5 years) was 6.4 years shorter than that for white celebrities (70.5; 95% CI 69.6-71.4 years; P<.001). Black celebrities had a faster all-cause mortality rate using Kaplan-Meier survival function estimates and a log-rank test. However, in a Cox semiparametric regression, there was no longer a significant difference in survival times between black and white celebrities (hazard ratio 1.07; 95% CI 0.87-1.31). CONCLUSIONS: There is some evidence that racial disparities in all-cause mortality may persist at higher levels of SES, but this association was no longer significant in adjusted analyses. Further research is needed to examine if racial disparities in mortality are diminished at higher levels of SES among more representative populations.
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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 | 0.009 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".