{"doi":"10.1136/bmjopen-2023-077153","title":"Geography versus sociodemographics as predictors of changes in daily mobility across the USA during the COVID-19 pandemic: a two-stage regression analysis across 26 metropolitan areas","abstract":"OBJECTIVE: We investigated whether a zip code's location or demographics are most predictive of changes in daily mobility throughout the course of the COVID-19 pandemic. DESIGN: We used a population-level study to examine the predictability of daily mobility during the COVID-19 pandemic using a two-stage regression approach, where generalised additive models (GAM) predicted mobility trends over time at a large spatial level, then the residuals were used to determine which factors (location, zip code-level features or number of non-pharmaceutical interventions (NPIs) in place) best predict the difference between a zip code's measured mobility and the average trend on a given date. SETTING: We analyse zip code-level mobile phone records from 26 metropolitan areas in the USA on 15 March-31 September 2020, relative to October 2020. RESULTS: While relative mobility had a general trend, a zip code's city-level location significantly helped to predict its daily mobility patterns. This effect was time-dependent, with a city's deviation from general mobility trends differing in both direction and magnitude throughout the course of 2020. The characteristics of a zip code further increased predictive power, with the densest zip codes closest to a city centre tended to have the largest decrease in mobility. However, the effect on mobility change varied by city and became less important over the course of the pandemic. CONCLUSIONS: The location and characteristics of a zip code are important for determining changes in daily mobility patterns throughout the course of the COVID-19 pandemic. These results can determine the efficacy of NPI implementation on multiple spatial scales and inform policy makers on whether certain NPIs should be implemented or lifted during the ongoing COVID-19 pandemic and when preparing for future public health emergencies.","journal":"BMJ Open","year":2024,"id":500687,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9582,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1149128,"name":"Rohan Arambepola","orcid":"0000-0003-2833-8786","position":1,"is_corresponding":false},{"id":1145854,"name":"Catherine Schluth","orcid":null,"position":2,"is_corresponding":false},{"id":228278,"name":"Alain Labrique","orcid":"0000-0003-2502-7819","position":3,"is_corresponding":false},{"id":228276,"name":"Shruti H. Mehta","orcid":"0000-0002-2523-0959","position":4,"is_corresponding":false},{"id":228277,"name":"Sunil S. Solomon","orcid":"0000-0001-7947-3051","position":5,"is_corresponding":false},{"id":107139,"name":"Derek A. T. Cummings","orcid":"0000-0002-9437-1907","position":6,"is_corresponding":false},{"id":107138,"name":"Amy Wesolowski","orcid":"0000-0001-6320-3575","position":7,"is_corresponding":false},{"id":699279,"name":"Kathryn L. Schaber","orcid":"0000-0002-6669-9534","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T02:10:08.435215Z","pmid":"38986558","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}