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Clinical surveillance systems obscure the true cholera infection burden in an endemic region.

Sonia T HegdeAshraful Islam KhanJavier Perez-SaezIshtiakul Islam KhanJuan Dent HulseMd Taufiqul IslamZahid Hasan KhanShakeel AhmedTaner BertunaMamunur RashidRumana RashidMd Zakir HossainTahmina ShirinKirsten E WiensEmily S GurleyTaufiqur Rahman BhuiyanFirdausi QadriAndrew S Azman
Published in: Nature medicine (2024)
Our understanding of cholera transmission and burden largely relies on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serological surveillance provides a complementary approach to monitoring infections, although the link between serologically derived infections and medically attended disease incidence-shaped by immunological, behavioral and clinical factors-remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare-seeking and longitudinal serological data through statistical modeling. Combining the serological trajectories with a reconstructed incidence timeline of symptomatic cholera, we estimated an annual Vibrio cholerae O1 infection incidence rate of 535 per 1,000 population (95% credible interval 514-556), with incidence increasing by age group. Clinic-based surveillance alone underestimated the number of infections and reported cases were not consistently correlated with infection timing. Of the infections, 4 in 3,280 resulted in symptoms, only 1 of which was reported through the surveillance system. These results impart insights into cholera transmission dynamics and burden in the epicenter of the seventh cholera pandemic, where >50% of our study population had an annual V. cholerae O1 infection, and emphasize the potential for a biased view of disease burden and infection risk when depending solely on clinical surveillance data.
Keyphrases
  • public health
  • risk factors
  • healthcare
  • primary care
  • sars cov
  • electronic health record
  • coronavirus disease
  • machine learning
  • climate change
  • cancer therapy
  • human health