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Differences in SARS-COV-2 seroprevalence in the population of Cusco, Peru

  • Charles Huamaní
    ,
  • Fátima Concha-Velasco
    ,
  • Lucio Velásquez
    ,
  • María K. Antich
    ,
  • Johar Cassa
    ,
  • Kevin Palacios
  • Universidad Andina del Cusco
    ,
  • Dirección de Epidemiología e Investigación - Gerencia Regional de Salud del Cusco
    ,
  • Universidad Continental, Huancayo
    ,
  • Hospital Nacional Adolfo Guevara Velasco, EsSalud
    ,
  • Universidad Peruana Cayetano Heredia
    ,
  • Universidad Nacional de San Antonio Abad del Cusco
Research Output: Contribution to journal Article Peer-review

Open access

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

100131

Journal (Volume, Issue Number)

Global Epidemiology (Volume 7)

Publication milestones

  • Published - 06/2024

Publication status

Published - 06/2024

Publication IDs

  • Scopus: 85181025711

Abstract

Background: The spread of the coronavirus disease 2019 (COVID-19) in Peru has been reported at the regional level, few studies have evaluated its spread at the provincial level, in which the mechanisms could be different. Methods: We conducted an analytical, cross-sectional, multistage observational population study to assess the seroprevalence of SARS-COV-2 at the provincial and urban/rural levels in a high-altitude setting. The sampling unit was the household, including a randomly selected family member. Sampling was performed using a data collection sheet on clinical and epidemiological variables. Chemiluminescence tests were used to detect total anti-SARS-COV-2 antibodies (IgG and IgM simultaneously). The percentages were adjusted to the sampling design. Results: The overall prevalence in the region of Cusco was 25.9%, with considerably different prevalence between the 13 provinces (from 15.9% in Acomayo to 40.1% in Canchis) and between rural (21.1%) and urban (31.7%) areas. In multivariable model, living in a rural area was a protective factor (adjusted prevalence ratio [aPR], 0.68; 95% confidence interval [CI], 0.61–0.76). Conclusions: Geographic diversity and population density determine different prevalence rates, typically lower in rural areas, possibly due to natural social distancing or limited interaction with people at risk.

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Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well