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Summary

Local general practitioner density can reduce bias in disease incidence estimates from voluntary health surveillance networks. Accounting for this factor improved accuracy in the French Sentinelles network, showing incidence changes of 1.6-9.9%.

Keywords:
Epidemiological surveillanceGP densityGeneral practitionersIncidence estimationInfluenza-like illnessSurveillanceSurveillance networks

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Biostatistics

Background:

  • Voluntary health surveillance networks often lack control over participant characteristics.
  • External data, such as local physician density, can mitigate bias in incidence estimates.
  • Understanding participant selection bias is crucial for accurate disease monitoring.

Purpose of the Study:

  • To assess the impact of local general practitioner (GP) density on influenza-like illness (ILI) case reporting.
  • To compare ILI incidence estimates derived from surveillance data with varying participant characteristics.
  • To evaluate the effectiveness of incorporating GP density in reducing bias in incidence estimates.

Main Methods:

  • Formulated ILI incidence estimates based on the inverse association between reported cases and GP density.
  • Simulated epidemics using a spatially explicit disease model.
  • Observed simulated epidemics with surveillance networks of diverse characteristics (e.g., random, maximum coverage, largest cities).

Main Results:

  • A 3.6% decrease in reported ILI cases was observed for every additional GP per 10,000 inhabitants in the French Sentinelles network.
  • ILI incidence estimates varied significantly based on participant selection scenarios.
  • Adjusting for GP density reduced bias, with overall incidence changes ranging from 1.6% to 9.9% in the Sentinelles network.

Conclusions:

  • Local GP density is a practical metric for reducing bias in general practice disease incidence estimation.
  • This approach enhances disease monitoring, particularly when participant selection cannot be controlled.
  • Incorporating GP density improves the reliability of surveillance data.