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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".