Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period. OBJECTIVE: We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period. METHODS: Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google's Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison. RESULTS: Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=-0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001). CONCLUSIONS: The Transportation Security Administration's travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States.
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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 | 0.079 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".