High-resolution estimates of tuberculosis incidence among non-U.S.-born persons residing in the United States, 2000–2016
Why this work is in the frame
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Bibliographic record
Abstract
In the United States, new tuberculosis cases are increasingly concentrated within non-native-born populations. We estimated trends and differences in tuberculosis incidence rates for the non-U.S.-born population, at a resolution unobtainable from raw data. We obtained non-U.S.-born tuberculosis case reports for 2000-2016 from the National Tuberculosis Surveillance System, and population data from the American Community Survey and 2000 U.S. Census. We constructed generalized additive regression models to estimate incidence rates in terms of birth country, entry year, age at entry, and number of years since entry into the United States and described how these factors contribute to overall tuberculosis risk. Controlling for other factors, tuberculosis incidence rates were lower for more recent immigration cohorts, with an incidence risk ratio (IRR) of 10.2 (95 % confidence interval 7.0, 14.7) for the 1950 entry cohort compared to its 2016 counterpart. Greater years since entry and younger age at entry were associated with substantially lower incidence rates. IRRs for birth country varied between 8.86 (6.78, 11.52) for Somalia and 0.02 (0.01, 0.03) for Canada, compared to all non-U.S.-born residents in 2016. IRRs were positively correlated with WHO predicted incidence rate and negatively associated with wealth level for the birth country. Lower country wealth level was also associated with shallower declines in tuberculosis over time. Tuberculosis risks differ by several orders of magnitude within the non-U.S.-born population. A better understanding of these differences will allow more effective targeting of tuberculosis prevention efforts. The methods presented here may also be relevant for understanding tuberculosis trends in other high-income countries.
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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 | 0.012 |
| 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 | 0.001 |
| 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 it