Spatio-temporal methods for assessing the effects of pandemics on health concerning vulnerability, socioeconomic, and Environmental inequalities

ENG- The COVID-19 pandemic not only caused millions of deaths worldwide but also intensified pre-existing health inequalities. Vulnerable groups such as the elderly, patients with chronic illnesses, socioeconomically disadvantaged populations, and ethnic minorities faced the heaviest burdens, with higher infection and mortality rates and reduced access to essential services. This thesis evaluates the pandemic’s impact on health indicators and their association with socioeconomic factors in Catalonia (Spain) and Santiago de Cali (Colombia), using routinely collected primary care data. Specific objectives included: assessing changes in healthcare access and biomarkers in Catalonia; comparing pre- and during-pandemic biomarker levels in chronic populations; identifying biomarker variations linked to longevity in older adults; analysing the spatiotemporal distribution of COVID-19 vaccination in Cali while addressing selection bias; and evaluating the protective effect of vaccination against mortality in contexts of high inequality. Methodologically, Bayesian spatiotemporal models with the Integrated Nested Laplace Approximation (INLA) were applied to large-scale retrospective cohort data from SIDIAP (Catalonia) and SIVIGILA (Cali). Findings revealed that vulnerable populations consistently experienced worsened clinical indicators, higher mortality, and unequal access to interventions such as vaccination. The pandemic also disrupted primary care continuity, with negative effects on cardiometabolic factors and cardiovascular risk