High SARS-CoV-2 Seroprevalence in Rural Peru, 2021: a Cross-Sectional Population-Based Study

Abstract

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.

Keywords: COVID-19; Latin America; Peru; SARS-CoV-2; rural; serology; seroprevalence.

FIG 1

Epidemiological surveillance and serologic diagnostics of SARS-CoV-2 from San Martin, Peru. (A) Mean altitude (meters above sea level) map of Peru. The department of San Martin is circled in black. (B) Number of confirmed cases in San Martin as of June 2021. The times where incidence data and samples for the serologic study were taken are marked with arrows. Surveillance data were gathered from https://diressanmartin.gob.pe/ and https://www.gob.pe/minsa/. Reactivity of serum samples from SARS-CoV-2 seroprevalence study in San Martin in 2021 (C) and pre-COVID-19 samples from 2013 to 2018 from Peru (D) in a chemiluminescence immunoassay (CLIA), shown in the x axis, and a SARS-CoV-2 surrogate virus neutralization test (sVNT), shown in the y axis. AU/ml, absorbance unit per milliliter.

FIG 2

Correlation of seroprevalence and incidence data with different indicators. (A and B) Comparison of serologic (A) and incidence (B) data from San Martin. Hu, Huallaga; Mo, Moyobamba; ED, El Dorado. (C) Heatmap of Spearman’s rank correlation test using different social, economic, and geographical indicators. Significant correlations are depicted inside the square. (D) Spearman’s rank correlation test of seroprevalence and cumulative incidence per province.