Community Risk, Mobility Patterns, and COVID-19 Hospitalization Risk in Texas: ZIP Code–Level Ecological Longitudinal Study
4 affiliations
- 1Department of Mathematics, Tarleton State University, 1333 W Washington St, Stephenville, TX, 76401, United States, 1 (254) 968-9000
- 2Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States
- 3School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
- 4BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States
Abstract
Background: The COVID-19 pandemic revealed substantial disparities in health outcomes across US communities. High-resolution mobility data offer new opportunities for real-time surveillance of behavioral responses, yet less is known about how precautionary behavior varied within cities and how these differences were associated with subsequent COVID-19 hospitalization risk.
Objective: This study assessed how community-level risk shapes heterogeneity in precautionary mobility behavior within cities and whether these differences are associated with subsequent COVID-19 hospitalization risk.
Methods: We analyzed anonymized mobile device data from 623 ZIP codes in the 20 most populous counties in Texas and COVID-19 hospitalization records comprising 65,474 hospital admissions from 620 ZIP codes (February-July 2020). We constructed a ZIP code–level measure of precautionary mobility reduction (PMR) and linked it to a composite community risk score derived from US Centers for Disease Control and Prevention social vulnerability indicators. Generalized additive mixed models estimated associations among community risk, mobility patterns, and weekly hospitalization rates, adjusting for epidemic timing, population size, and spatial clustering.
Results: Mobility declined by approximately 73% relative to baseline following statewide stay-at-home orders but varied substantially across ZIP codes. On April 2, 2020, PMR was negatively associated with community risk (slope −0.16, 95% CI −0.19 to −0.13; P<.001). At peak divergence, high-risk ZIP codes reduced mobility by 12 to 13 percentage points less than low-risk ZIP codes (95% CI 11 to 15). Greater PMR was associated with lower subsequent hospitalization risk (incidence rate ratio [IRR] 0.41, 95% CI 0.31 to 0.53; P<.001), whereas higher community risk was associated with increased hospitalization risk (IRR 3.03, 95% CI 2.57 to 3.58; P<.001). The protective association of mobility reduction was attenuated in higher-risk communities (interaction IRR 2.79, 95% CI 2.08 to 3.73; P<.001).
Conclusions: Mobility data can support real-time surveillance of behavioral responses during epidemics. Within-city differences in mobility reduction were associated with subsequent hospitalization risk, but their protective association was weaker in higher-risk communities. Integrating digital mobility data with community risk metrics may improve targeted and equitable public health responses.
Related
- Multimorbidity Trajectories and Hospitalization Risk Among People With HIV in Sub-Saharan Africa: A Longitudinal Analysis From the African Cohort Study
- Long COVID prevalence among U.S. adults: A state-level ecological analysis of associations with SARS-CoV-2 incidence, COVID-19 hospitalization rates, COVID-19 vaccine coverage, and multimorbidity
- Geographical disparities in intentional self-harm hospitalization risks in Florida: a retrospective ecological study
- Diagnosis-code co-occurrence patterns and their associations with hospitalization costs among patients with lung cancer: a province-wide retrospective study in Sichuan, China
- Persistent prefectural ordering of billed urinary albumin quantification in Japan: a nationwide longitudinal ecological study
BibTeX
@article{herreradiestra2026community,
title = {{Community Risk, Mobility Patterns, and COVID-19 Hospitalization Risk in Texas: ZIP Code–Level Ecological Longitudinal Study}},
author = {Jose Herrera-Diestra and Dongah Kim and Shraddha Ramdas Bandekar and Emily Javan and Spencer J Fox and Remy Pasco and Lauren Ancel Meyers},
journal = {JMIR Public Health and Surveillance},
year = {2026},
month = nov,
doi = {10.2196/97405},
url = {https://doi.org/10.2196/97405},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Community Risk, Mobility Patterns, and COVID-19 Hospitalization Risk in Texas: ZIP Code–Level Ecological Longitudinal Study
AU - Jose Herrera-Diestra
AU - Dongah Kim
AU - Shraddha Ramdas Bandekar
AU - Emily Javan
AU - Spencer J Fox
AU - Remy Pasco
AU - Lauren Ancel Meyers
PY - 2026
DA - 2026/11/04
JO - JMIR Public Health and Surveillance
DO - 10.2196/97405
UR - https://doi.org/10.2196/97405
AB - Background: The COVID-19 pandemic revealed substantial disparities in health outcomes across US communities. High-resolution mobility data offer new opportunities for real-time surveillance of behavioral responses, yet less is known about how precautionary behavior varied within cities and how these differences were associated with subsequent COVID-19 hospitalization risk. Objective: This study assessed how community-level risk shapes heterogeneity in precautionary mobility behavior within cities and whether these differences are associated with subsequent COVID-19 hospitalization risk. Methods: We analyzed anonymized mobile device data from 623 ZIP codes in the 20 most populous counties in Texas and COVID-19 hospitalization records comprising 65,474 hospital admissions from 620 ZIP codes (February-July 2020). We constructed a ZIP code–level measure of precautionary mobility reduction (PMR) and linked it to a composite community risk score derived from US Centers for Disease Control and Prevention social vulnerability indicators. Generalized additive mixed models estimated associations among community risk, mobility patterns, and weekly hospitalization rates, adjusting for epidemic timing, population size, and spatial clustering. Results: Mobility declined by approximately 73% relative to baseline following statewide stay-at-home orders but varied substantially across ZIP codes. On April 2, 2020, PMR was negatively associated with community risk (slope −0.16, 95% CI −0.19 to −0.13; P<.001). At peak divergence, high-risk ZIP codes reduced mobility by 12 to 13 percentage points less than low-risk ZIP codes (95% CI 11 to 15). Greater PMR was associated with lower subsequent hospitalization risk (incidence rate ratio [IRR] 0.41, 95% CI 0.31 to 0.53; P<.001), whereas higher community risk was associated with increased hospitalization risk (IRR 3.03, 95% CI 2.57 to 3.58; P<.001). The protective association of mobility reduction was attenuated in higher-risk communities (interaction IRR 2.79, 95% CI 2.08 to 3.73; P<.001). Conclusions: Mobility data can support real-time surveillance of behavioral responses during epidemics. Within-city differences in mobility reduction were associated with subsequent hospitalization risk, but their protective association was weaker in higher-risk communities. Integrating digital mobility data with community risk metrics may improve targeted and equitable public health responses.
SN - 2369-2960
ER -