Mortality Before and After Border Wall Construction Along the US–Mexico Border, 1990–2017
Bibliographic record
Abstract
Objectives. To evaluate changes in mortality in US counties along the US–Mexico border in which there was substantial new border wall construction after the Secure Fence Act of 2006 relative to border counties in which there was no such border wall construction. Methods. Using complete 1990 to 2017 mortality microdata and a quasi-experimental difference-in-differences design, we evaluated changes in overall (all-cause) mortality, mortality from drug overdose, and mortality from homicide in the 10 counties with substantial new border wall construction and 11 counties with no such construction. We fit a linear model, adjusting for population characteristics and county and year fixed effects, with Bonferroni adjustments for multiple comparisons. Sensitivity analyses included the addition of adjacent inland counties and modifications to the statistical model. Results. Relative to counties without substantial new border wall construction, counties in which a substantial amount of new border wall was constructed exhibited a nonsignificant 0.02-percentage-point increase (95% confidence interval [CI] = −0.06, 0.10; P > .99) in overall mortality after construction. Border wall construction was not associated with changes in either deaths from overdose or deaths from homicide. Conclusions. Wall construction along the US–Mexico border after the Secure Fence Act of 2006 was not associated with discernible changes in mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".