| Summary: | Abstract Background It has been shown that COVID-19 affects people at socioeconomic disadvantage more strongly. Previous studies investigating the association between geographical deprivation and COVID-19 outcomes in Italy reported no differences in case-hospitalisation and case-fatality. The objective of this research was to compare the usefulness of the geographic and individual deprivation index (DI) in assessing the associations between individuals' deprivation and risk of Sars-CoV-2 infection and disease severity in the Apulia region from February to December 2020. Methods This was a retrospective cohort study. Participants included individuals tested for SARS-CoV-2 infection during the study period. The individual DI was calculated employing polychoric principal component analysis on four census variables. Multilevel logistic models were used to test associations between COVID-19 outcomes and individual DI, geographical DI, and their interaction. Results In the study period, 139,807 individuals were tested for COVID-19 and 56,475 (43.5%) tested positive. Among those positive, 7902 (14.0%) have been hospitalised and 2215 (4.2%) died. During the first epidemic wave, according the analysis done with the individual DI, there was a significant inversely proportional trend between the DI and the risk of testing positive. No associations were found between COVID-19 outcomes and geographic DI. During the second wave, associations were found between COVID-19 outcomes and individual DI. No associations were found between the geographic DI and the risk of hospitalisation and death. During both waves, there were no association between COVID-19 outcomes and the interaction between individual and geographical DI. Conclusions Evidence from this study shows that COVID-19 pandemic has been experienced unequally with a greater burden among the most disadvantaged communities. The results of this study remind us to be cautious about using geographical DI as a proxy of individual social disadvantage because may lead to inaccurate assessments. The geographical DI is often used due to a lack of individual data. However, on the determinants of health and health inequalities, monitoring has to have a central focus. Health inequalities monitoring provides evidence on who is being left behind and informs equity-oriented policies, programmes and practices. Future research and data collection should focus on improving surveillance systems by integrating individual measures of inequalities into national health information systems.
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