Trends and associated characteristics for Chagas disease among women of reproductive age in the United States, 2002 to 2017
Bibliographic record
Abstract
BACKGROUND: ). Although those affected are mainly in Latin America, Chagas has been detected in the United States (US), Canada and in many European countries due to migration. Few studies have explored the epidemiology of Chagas within the US or changes in disease burden over the past decade. The objective of this study was to explore the trends and associated characteristics for Chagas disease among hospitalized women of reproductive age in the US. METHODS: We analyzed admissions data including socio-demographic and hospital characteristics for inpatient hospitalization for women of reproductive age (15-49 years) in the US from 2002 through 2017. We employed Joinpoint regression analysis to determine trends in the prevalence of Chagas disease over this period. RESULTS: A total of 487 hospitalizations of Chagas disease were identified, corresponding to 3.7 per million hospitalizations over the study period. The rate statistically increased from 1.6 per million in 2002 to 7.6 per million hospitalizations in 2017. Chagas was most prevalent among older women, Hispanics and those in the highest zip income bracket. The in-hospital mortality rate was about 10 times greater among women with Chagas compared to those without the condition (3.1% versus 0.3%), and the condition tended to be clustered in women treated at large, urban teaching hospitals in the Northeastern region of the US. CONCLUSION: Chagas disease diagnosis appears to be increasing among hospitalized women of reproductive age in the US with a 10-fold elevated risk of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".