Assessment of a Regression Method to Reclassify Deaths Attributable to Heart Failure
Bibliographic record
Abstract
<p><strong>BACKGROUND: </strong>Evaluation of cause-specific mortality for public health research depends on accurate death certificates and vital records. However, ill-defined causes of death (termed garbage codes), such as heart failure, are often listed as the underlying cause of death. We examined a regression method proposed by Ahern and colleagues for redistributing deaths attributed to heart failure and compared it to a simulation of the regression method by bootstrapping. </p><p><strong>METHODS: </strong>Deaths attributed to heart failure in four U.S. states (Maryland, Minnesota, Mississippi and North Carolina) were redistributed to a set of underlying causes of death using regression models that identified the proportion of deaths for each target code within a given state-age-sex-education group using ICD-10 mortality data. The results were compared with 3,000 bootstrapped samples with replacement regression.</p><p><strong>RESULTS: </strong>The odds of death from heart failure in the population studied increased with age, was higher in whites and lower in decedents with greater than a high school education compared to those with less than high school education. There were 18 (29.0%) subgroups that showed no significant redistribution targets for the Ahern regression method and 28 (45.2%) for the bootstrapped regression method. Ischemic heart disease was a distribution target for 28 (45.2%) of the Ahern regression subgroups and 22 (35.5%) of the bootstrapped regression subgroups. The Ahern regression method and bootstrapped regression methods were discordant in 19 (30.6%) out of the 62 subgroups examined.</p><p><strong>CONCLUSION: </strong>The Ahern regression method tended to redistribute deaths attributed to heart failure to more target groups compared with the bootstrapped regression method. Both the Ahern regression and the bootstrap regression methods were computationally intensive and inefficient, and results appeared to be influenced by the choices of sex-age-education group strata. Other methods such as coarsened exact matching and improvements to the Ahern approach are desirable additions to the tools available to mitigate the impact of garbage codes on the accuracy of death certification.</p>
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".