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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., (©Jennifer M Kreslake, Kathleen Aarvig, Hope Muller-Tabanera, Donna M Vallone, Elizabeth C Hair. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 29.08.2023.)

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.