Cargando…
High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study
Latin America has been severely affected by the COVID-19 pandemic. The COVID-19 burden in rural settings in Latin America is unclear. We performed a cross-sectional, population-based, random-selection SARS-CoV-2 serologic study during March 2021 in the rural population of San Martin region, northern...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612248/ https://www.ncbi.nlm.nih.gov/pubmed/34817236 http://dx.doi.org/10.1128/mSphere.00685-21 |
_version_ | 1784603430991429632 |
---|---|
author | Moreira-Soto, Andres Pachamora Diaz, Johanna Maribel González-Auza, Lilian Merino Merino, Xiomara Jeanleny Schwalb, Alvaro Drosten, Christian Gotuzzo, Eduardo Talledo, Michael Arévalo Ramirez, Heriberto Peralta Delgado, Roxana Bocanegra Vargas, Spassky Drexler, Jan Felix |
author_facet | Moreira-Soto, Andres Pachamora Diaz, Johanna Maribel González-Auza, Lilian Merino Merino, Xiomara Jeanleny Schwalb, Alvaro Drosten, Christian Gotuzzo, Eduardo Talledo, Michael Arévalo Ramirez, Heriberto Peralta Delgado, Roxana Bocanegra Vargas, Spassky Drexler, Jan Felix |
author_sort | Moreira-Soto, Andres |
collection | PubMed |
description | Latin America has been severely affected by the COVID-19 pandemic. The COVID-19 burden in rural settings in Latin America is unclear. We performed a cross-sectional, population-based, random-selection SARS-CoV-2 serologic study during March 2021 in the rural population of San Martin region, northern Peru. In total, 563 persons from 288 houses across 10 provinces were enrolled, reaching 0.2% of the total rural population of San Martin. Screening for SARS-CoV-2 IgG antibodies was done using a chemiluminescence immunoassay (CLIA), and reactive sera were confirmed using a SARS-CoV-2 surrogate virus neutralization test (sVNT). Validation of the testing algorithm using prepandemic sera from two regions of Peru showed false-positive results in the CLIA (23/84 sera; 27%) but not in the sVNT, highlighting the pitfalls of SARS-CoV-2 antibody testing in tropical regions and the high specificity of the two-step algorithm used in this study. An overall 59.0% seroprevalence (95% confidence interval [CI], 55 to 63%) corroborated intense SARS-CoV-2 spread in San Martin. Seroprevalence rates between the 10 provinces varied from 41.3 to 74.0% (95% CI, 30 to 84%). Higher seroprevalence was not associated with population size, population density, surface area, mean altitude, or poverty index in Spearman correlations. Seroprevalence and reported incidence diverged substantially between provinces, suggesting regional biases of COVID-19 surveillance data. Potentially, limited health care access due to environmental, economic, and cultural factors might lead to undetected infections in rural populations. Additionally, test avoidance to evade mandatory quarantine might affect rural regions more than urban regions. Serologic diagnostics should be pursued in resource-limited settings to inform country-level surveillance and vaccination strategies and to support control measures for COVID-19. IMPORTANCE Latin America is a global hot spot of the COVID-19 pandemic. Serologic studies in Latin America have been mostly performed in urban settings. Rural populations comprise 20% of the total Latin American population. Nevertheless, information on COVID-19 spread in rural settings is scarce. Using a representative population-based seroprevalence study, we detected a high seroprevalence in rural populations in San Martin, northern Peru, in 2021, reaching 41 to 74%. However, seroprevalence and reported incidence diverged substantially between regions, potentially due to limited health care access or test avoidance due to mandatory quarantine. Our results suggest that rural populations are highly affected by SARS-CoV-2 even though they are sociodemographically distinct from urban populations and that highly specific serological diagnostics should be performed in resource-limited settings to support public health strategies of COVID-19 control. |
format | Online Article Text |
id | pubmed-8612248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-86122482021-11-29 High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study Moreira-Soto, Andres Pachamora Diaz, Johanna Maribel González-Auza, Lilian Merino Merino, Xiomara Jeanleny Schwalb, Alvaro Drosten, Christian Gotuzzo, Eduardo Talledo, Michael Arévalo Ramirez, Heriberto Peralta Delgado, Roxana Bocanegra Vargas, Spassky Drexler, Jan Felix mSphere Observation Latin America has been severely affected by the COVID-19 pandemic. The COVID-19 burden in rural settings in Latin America is unclear. We performed a cross-sectional, population-based, random-selection SARS-CoV-2 serologic study during March 2021 in the rural population of San Martin region, northern Peru. In total, 563 persons from 288 houses across 10 provinces were enrolled, reaching 0.2% of the total rural population of San Martin. Screening for SARS-CoV-2 IgG antibodies was done using a chemiluminescence immunoassay (CLIA), and reactive sera were confirmed using a SARS-CoV-2 surrogate virus neutralization test (sVNT). Validation of the testing algorithm using prepandemic sera from two regions of Peru showed false-positive results in the CLIA (23/84 sera; 27%) but not in the sVNT, highlighting the pitfalls of SARS-CoV-2 antibody testing in tropical regions and the high specificity of the two-step algorithm used in this study. An overall 59.0% seroprevalence (95% confidence interval [CI], 55 to 63%) corroborated intense SARS-CoV-2 spread in San Martin. Seroprevalence rates between the 10 provinces varied from 41.3 to 74.0% (95% CI, 30 to 84%). Higher seroprevalence was not associated with population size, population density, surface area, mean altitude, or poverty index in Spearman correlations. Seroprevalence and reported incidence diverged substantially between provinces, suggesting regional biases of COVID-19 surveillance data. Potentially, limited health care access due to environmental, economic, and cultural factors might lead to undetected infections in rural populations. Additionally, test avoidance to evade mandatory quarantine might affect rural regions more than urban regions. Serologic diagnostics should be pursued in resource-limited settings to inform country-level surveillance and vaccination strategies and to support control measures for COVID-19. IMPORTANCE Latin America is a global hot spot of the COVID-19 pandemic. Serologic studies in Latin America have been mostly performed in urban settings. Rural populations comprise 20% of the total Latin American population. Nevertheless, information on COVID-19 spread in rural settings is scarce. Using a representative population-based seroprevalence study, we detected a high seroprevalence in rural populations in San Martin, northern Peru, in 2021, reaching 41 to 74%. However, seroprevalence and reported incidence diverged substantially between regions, potentially due to limited health care access or test avoidance due to mandatory quarantine. Our results suggest that rural populations are highly affected by SARS-CoV-2 even though they are sociodemographically distinct from urban populations and that highly specific serological diagnostics should be performed in resource-limited settings to support public health strategies of COVID-19 control. American Society for Microbiology 2021-11-24 /pmc/articles/PMC8612248/ /pubmed/34817236 http://dx.doi.org/10.1128/mSphere.00685-21 Text en Copyright © 2021 Moreira-Soto et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Observation Moreira-Soto, Andres Pachamora Diaz, Johanna Maribel González-Auza, Lilian Merino Merino, Xiomara Jeanleny Schwalb, Alvaro Drosten, Christian Gotuzzo, Eduardo Talledo, Michael Arévalo Ramirez, Heriberto Peralta Delgado, Roxana Bocanegra Vargas, Spassky Drexler, Jan Felix High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title | High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title_full | High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title_fullStr | High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title_full_unstemmed | High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title_short | High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study |
title_sort | high sars-cov-2 seroprevalence in rural peru, 2021: a cross-sectional population-based study |
topic | Observation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612248/ https://www.ncbi.nlm.nih.gov/pubmed/34817236 http://dx.doi.org/10.1128/mSphere.00685-21 |
work_keys_str_mv | AT moreirasotoandres highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT pachamoradiazjohannamaribel highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT gonzalezauzalilian highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT merinomerinoxiomarajeanleny highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT schwalbalvaro highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT drostenchristian highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT gotuzzoeduardo highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT talledomichael highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT arevaloramirezheriberto highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT peraltadelgadoroxana highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT bocanegravargasspassky highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy AT drexlerjanfelix highsarscov2seroprevalenceinruralperu2021acrosssectionalpopulationbasedstudy |