Bibliographic record
Abstract
To the Editor: Geographic disparities in age-adjusted premature mortality have been extensively catalogued across the United States.1 For example, Wayne County, Michigan (Detroit), recently lost 10,263 years of potential life per 100,000 population per year,2 whereas nearby Washtenaw County lost only 5,096 years per 100,000. These large differences have prompted federal public health agencies to attempt to identify the most vulnerable areas for intervention (“hot spotting”).3 However, traditional indicators such as education and health-care access are inadequate to predict geographic disparities in mortality.1 A widening array of new indicators have therefore been developed—from smoking and alcohol consumption rates to density of fast-food restaurants. The proliferation of new indicators presents a further challenge: which are most efficient at predicting vulnerable areas? Here, we “open-source” an approach using readily available data sets to identify key predictors of US geographic disparities in premature mortality. As detailed in the eAppendix, https://links.lww.com/EDE/A775 (which includes full statistical code), we analyzed 50 key indicators of socioeconomic, demographic, behavioral, and environmental conditions available in 20 commonly used, geocoded, publicly available data sets from all US counties. The primary outcome was age-adjusted years of potential life lost before 75 years of age, as computed by the National Center for Health Statistics.2 This end point is a principal target of the US Centers for Disease Control and Prevention for reducing geographic disparities.4 We also investigated alternative outcomes and found similar solutions (see eAppendix, https://links.lww.com/EDE/A775). We analyzed the data using regression tree analysis, which can avoid bias in the presence of multicollinearity.5 This approach tests all possible combinations of interactions among all available indicator variables to identify a logical sequence of indicators associated with mortality rates. A standard complexity parameter was used to prevent overfitting,6 and “random forest” bootstrapping was performed by randomly sampling repeatedly from subsets of the data that consist of approximately two-thirds of the complete data set, then selecting the estimators that have the highest explanation of variance in the remaining one-third of the sample, generating a large number of bootstrapped trees from which we present the convergent solution.7,8 We identified combinations of traditional indicators that, together with some less commonly used indicators, could explain approximately 70% of geographic disparities in premature mortality. (Income, education, and race combined explained only one-third of the variance.) As illustrated in the Figure, the largest division in premature mortality among counties was between those experiencing more or less than 46.5 teen births per 1000 women 15–19 years of age. A second branch of the tree further separated counties by median household income (greater or less than $42,330/year). The wealthier group had the lowest rates of premature mortality (group 1: mean 5,623 years of potential life lost before age 75 per 100,000 population; N = 503 counties). On the right side of the tree are counties with the highest rates of premature mortality. The percent Native American population was key among counties with a high teen birth rate. The 14 US counties with the highest rates of premature mortality (group 10: mean 19,102 per 100,000) had a teen birth rate above 46.5 per 1,000, Native Americans as more than 46.6% of the population, and more than 12.5% of children uninsured. The eAppendix tables (https://links.lww.com/EDE/A775) provide summary statistics, further diagnostic and cross-validation plots, additional trees with alternative outcomes and subsamples, and complete code that requires less than 5 minutes on a standard laptop computer. As shown here, just a few parsimonious combinations of key indicators can quickly identify vulnerable counties.FIGURE: Data-mining results showing combinations of key indicators explaining disparities in premature mortality among all US counties. N indicates number of counties; YPPL, years of potential life lost before age 75 per 100,000 population.Sanjay Basu Stanford Prevention Research Center Department of Medicine Stanford University School of Medicine Stanford, CA [email protected] Arjumand Siddiqi Division of Epidemiology and Division of Social and Behavioral Sciences Dalla Lana School of Public Health University of Toronto Toronto, ON, Canada
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".