Community Risk, Mobility Patterns, and COVID-19 Hospitalization Risk in Texas: ZIP Code-Level Ecological Longitudinal Study

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.

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.

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.

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).

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.