International travellers from New Jersey: piloting a travel health module in the 2011 Behavioral Risk Factor Surveillance System survey
Bibliographic record
Abstract
BACKGROUND: In 2011, the Centers for Disease Control and Prevention and the New Jersey Department of Health used the New Jersey Behavioral Risk Factor Survey (NJBRFS), a state component of the national Behavioral Risk Factor Surveillance System (BRFSS) to pilot a travel health module designed to collect population-based data on New Jersey residents travelling internationally. Our objective was to use this population-based travel health information to serve as a baseline to evaluate trends in US international travellers. METHODS: A representative sample of New Jersey residents was identified through a random-digit-dialing method and administered the travel health module, which asked five questions: travel outside of USA during the previous year; destination; purpose; if a healthcare provider was visited before travel and any travel-related illness. Additional health variables from the larger NJBRFS were considered and included in bivariate analyses and multiple logistic regression; weights were assigned to variables to account for survey design complexity. RESULTS: Of 4029 participants, 841 (21%) travelled internationally. Top destinations included Mexico (10%), Canada (9%), Dominican Republic (6%), Bahamas (5%) and Italy (5%). Variables positively associated with travel included foreign birth, ≥$75 000 annual household income, college education and no children living in the household. One hundred fifty (18%) of 821 travellers with known destinations went to high-risk countries; 40% were visiting friends and relatives and only 30% sought pre-travel healthcare. Forty-eight (6%) of 837 responding travellers reported travel-related illness; 44% visited high-risk countries. CONCLUSIONS: Approximately one in five NJBRFS respondents travelled internationally during the previous year, a sizeable proportion to high-risk destinations. Few reported becoming ill as a result of travel but almost one-half of those ill had travelled to high-risk destinations. Population-based surveillance data on travellers can help document trends in destinations, traveller type and disease prevalence and evaluate the effectiveness of disease prevention programmmes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".