Seroprevalence of Coronavirus Disease 2019 (COVID-19) Antibodies in a Vulnerable Neighbourhood, Buenos Aries Argentina

Overview

Background The study aimed to establish the seroprevalence of SARS-COV-2 in an Argentinian slum three months after the first case was reported. Methods Between June 10th and July 1st, a cross-sectional design was carried out on people over 14 years old, selected from a probabilistic sample of households. Finger prick puncture ELISA test

A community-level seroprevalence survey was conducted in a cross-sectional design during a Participatory Action Research (PAR). Starter PAR team: researcher who developed the Elisa Test, virologist that perform the analysis of the test and physician that served as volunteer field epidemiologist at the surveillance system (DetectAr Barrio 31) offered technical cooperation to test people experiencing homelessness. PAR-First step: A sample for convenience of homeless people was tested during 3 days in a popular dining room managed by a non-governmental organization. Samples were collected by a DetectAr nurse and a volunteer epidemiologist. PAR-Second step: feasibility was determined by the DetectAr coordinators of the Community Health Division of the Ministry of Health and the 12 health community workers (HCWs) when two volunteer epidemiologists from a University Institute explained the field organization (sample technique, data collection and database entry) for obtaining a probabilistic sample of residents. The PAR process was a cornerstone for reaching the sample size. PAR-Third step: A cross-sectional study for seroprevalence survey was carried out. Sample size and sample method Sample size was calculated for a seroprevalence of 5% according to evidence. A two-stage random sampling method was applied. First level: sector of the slum, Second level: geographical areas determined by the Department of Statistic and Census. Thirty houses were selected at this level. People over 14 years old were tested at the front door of their houses. Serological test An enzyme linked immunosorbent assay \[ELISA\] developed in Argentina, by a laboratory in Buenos Aires, Argentina, was used. Performance characteristics are a high specificity (\>95%) and a high sensibility (\>95%) for SARS-COV-2 IgG. The test detects antibodies against two viral antigens, trimeric spike and the receptor binding domain (RBD) of the spike. Viral proteins were expressed in human cells. This kit has obtained regulatory approval by Argentina's national drug regulatory agency (ANMAT, National Administration for Drugs, Food and Medical Devices)\[. Blood samples were collected in a capillary tube from a finger prick, taken at the front door of each house. All HCWs were trained and epidemiological data was entered in a database. Samples were processed and analyzed at the virology laboratory in a pediatric hospital of Buenos Aires. Statistical analysis To obtain the weighted prevalence, sample dataset was expanded to that of the last census by 3 factors: at neighborhood level, at household level and at individual level. The calculation of expansion factors at the household level is the inverse of the joint probability of selecting the last sampling unit (a household). The expansion factors at the household level imply three types of adjustments. The first one is related with non-response (given that some households did not want to answer the survey); the second one corresponds to the projection of sample to the entire population, and the third one to calibration techniques with a final adjustment by groups of age and gender, using external information from population census. Therefore, calibration variables were people 14 year-old or more, grouped by sex and by intervals of age: 14-30; 31-45; 46-59; 60 and more. Prevalence of IgG antibodies were adjusted using sampling weights and post-stratification to allow for differences in non-response rates based on age group, sex, and census-tract income. As household members share exposure to COVID-19, thus the outcome (prevalence of COVID-19 IgG) should show some correlation within the household. To test clustering effect, a random effects logistic regression model (multilevel model) was applied as it includes the variation between clusters explicitly in the likelihood and therefore takes account of intracluster correlation.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes