Temporal Trends and Seasonality of Invasive Candidiasis During and After the COVID-19 Pandemic: An Interrupted Time

José M Oñate-Gutiérrez1,2, Carlos A Alvarez-Moreno2,3, Claudia Cañadas-Aragón1,2

  • 1Clínica Colsanitas S.A., Clínica Sebastián de Belalcázar, Cali 760045, Colombia.

Insights

Invasive candidiasis incidence in Colombia showed an increase until mid-2022, then declined, with persistent seasonality. Time-series analysis revealed no significant pandemic-related changes, highlighting the value of epidemiological surveillance.

Area of Science:

  • Infectious Diseases
  • Epidemiology
  • Public Health

Background:

  • Invasive candidiasis is a severe opportunistic infection.
  • Major disruptive events, like the COVID-19 pandemic, can impact infection incidence.
  • Hospital dynamics in Colombia were significantly altered during the pandemic.

Purpose of the Study:

  • To evaluate temporal trends, seasonality, and potential changes in invasive candidiasis incidence in Colombia from 2019 to 2024.
  • To assess the impact of the COVID-19 pandemic on invasive candidiasis epidemiology.
  • To analyze the evolution of invasive candidiasis during and after the pandemic.

Main Methods:

  • Observational time-series study design.
  • Utilized confirmed invasive candidiasis cases from Colombian hospitals (2019-2024).
  • Employed interrupted time-series (ITS) analysis and seasonal decomposition.

Main Results:

  • Analyzed 1294 cases, observing an increasing trend until mid-2022, followed by a decline in 2023.
  • Seasonal decomposition identified persistent seasonality with peaks in Q2 and Q4.
  • ITS analysis showed no statistically significant changes in level or slope post-pandemic, despite observed fluctuations.

Conclusions:

  • Invasive candidiasis in Colombia demonstrated complex temporal dynamics during and after the COVID-19 pandemic.
  • Sustained seasonality was noted, alongside an initial increase followed by a decrease in incidence.
  • While not statistically significant, findings support time-series models for epidemiological surveillance of invasive fungal infections.

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