Fig 1.
Mapping locations of homeless people participating in the study, Marseille, France.
A base map was extracted from OpenStreetMap (http://www.openstreetmap.org) [18]. OpenStreetMap is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). The map was generated using QGIS 3.16 software (GNU licence) [19].
Fig 2.
Flow chart of the SARS-CoV-2 seroprevalence study among people experiencing homelessness, Marseille, France.
a: Although comprehensive homelessness prevalence data for Marseille are still lacking, we used data from local Integrated Reception and Orientation Service (IROS–SIAO in French) for emergency and transitional accommodations, and estimations of NGOs for slums/squats and streets; b: estimations of NGOs at around 30% of squat inhabitants; c: Reasons for not being included in the study were difficult to distinguished as most of people living in squats or streets cumulated the three main reasons (refusals, comprehension issue or unreachable at the time of the study took place); *: ETHOS: the European typology for homelessness and housing exclusion; ETHOS1: living rough; ETHOS2: living in emergency accommodations (emergency shelters and hotels); ETHOS3: living in transitional accommodations for homeless persons; and ETHOS8: living in insecure accommodations (i.e., illegal occupation of lands, squat/slum or temporarily with family/friends).
Table 1.
Sociodemographic characteristics of the study population (n = 1,156).
Table 2.
Rapid serological testing results for SARS-CoV-2 (N = 1,156).
Table 3.
Seroprevalence of SARS-CoV-2 according to demographic characteristics, living conditions, health characteristics and comorbidities (N = 1,156).