Disability-Adjusted Life Years Following Adult Out-of-Hospital Cardiac Arrest in the United States
Bibliographic record
Abstract
Background Disability-adjusted life years (DALY) are a common public health metric used to consistently estimate and compare health loss because of both fatal and nonfatal disease burden. The annual number of DALY because of adult out-of-hospital cardiac arrest (OHCA) in the United States is unknown. Our objective was to estimate the DALY after adult nontraumatic, emergency medical services-treated OHCA, and to compare OHCA DALY to other leading causes of death and disability in the US. Methods and Results The DALY were calculated as the sum of years of life lost and years lived with disability. The years of life lost were calculated using all adult nontraumatic emergency medical services-treated OHCA with complete data from the national Cardiac Arrest Registry to Enhance Survival database for 2016, and actuarial data for remaining life expectancy at the age of death. Cerebral performance category scores from the Cardiac Arrest Registry to Enhance Survival database and previously established disability weights were used to estimate years lived with disability. The cohort's calculated DALY were extrapolated to a national level to estimate total US DALY. Data were reported as total, mean, and DALY per 100 000 individuals. A total of 59 752 OHCA met study inclusion criteria. The DALY for the study population were 1 194 993 (years of life lost, 1 194 069; years lived with disability, 924) in 2016. The estimated total DALY following adult nontraumatic emergency medical services-treated OHCA in the US were 4 354 192 (years of life lost, 4 350 825; years lived with disability, 3365) for the index year 2016. The rate of OHCA DALY were 1347 per 100 000 population, which ranked third in the US behind ischemic heart disease (2447) and low back and neck pain (1565). Sensitivity analyses yielded similar findings. Conclusions Adult nontraumatic OHCA is a leading cause of annual DALY in the US and should be a focus of public health policy and resources.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".