Long-COVID trajectories in the unequal city: a multiscalar model from the Guadalajara Metropolitan Area
DOI:
https://doi.org/10.15517/pkz3qm69Keywords:
Urban inequality, Urban studies, Long-COVID, Environmental healthAbstract
Introduction: This study analyzes the influence of sociospatial inequalities on the distribution and behavior of Long-COVID in the Guadalajara Metropolitan Area (AMG). From a critical geographical perspective, it questions the role of the urban environment as a conditioning factor in post-pandemic health. Method: Through a multiscalar analysis, federal COVID-19 records corresponding to the 2020–2024 period were processed. Based on these data, a Long-COVID Risk Coefficient (CRLC) was constructed, and nine spatiotemporal trajectories were classified according to indicators of volatility, change, and linear determination, allowing the characterization of profile behavior across space. Subsequently, the results were projected at the urban block scale and contrasted with socioeconomic variables through linear regression models. Results: Although the models showed moderate explanatory power (R² < 0.20), the trajectories revealed differentiated spatial patterns depending on the scale analyzed: at the national level, growth trajectories were concentrated in peripheral and vulnerable territories, whereas within the AMG the most persistent trajectories were located in historically advantaged areas. Conclusion: The findings highlight the need to incorporate multiscalar methodologies and more complex sociospatial variables to better understand the territorial persistence of Long-COVID in Latin American urban contexts.
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