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Spillover effects in epidemiology: parameters, study designs and methodological considerations.

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Public health interventions often have spillover effects, benefiting unvaccinated individuals. Accurately measuring these herd effects is crucial for understanding the full public health impact of interventions like vaccines.

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Public health interventions can generate benefits beyond direct recipients, known as spillover effects.
  • Herd protection from vaccines is a key example, where unvaccinated individuals benefit from widespread vaccination.
  • Existing studies on spillover effects often lack standardized methods and causal inference assumptions, particularly outside of vaccine research.

Purpose of the Study:

  • To standardize nomenclature for spillover parameters in public health interventions.
  • To articulate causal inference assumptions for estimating spillover effects.
  • To advance methods for measuring and reporting spillover effects in public health.

Main Methods:

  • Conducted a systematic review of spillover effects in low- and middle-income countries.
  • Classified commonly reported spillover parameters into a standard notation.
  • Illustrated parameter classes with empirical examples and described study designs for causal inference.

Main Results:

  • Identified and classified key spillover parameters: cluster-level, conditional on density/distance/network, and vaccine efficacy-related.
  • Highlighted the underestimation of public health benefits when only direct effects are considered.
  • Provided a framework for causal inference in spillover effect estimation.

Conclusions:

  • Standardizing spillover parameter nomenclature is essential for consistent reporting and analysis.
  • Robust causal inference methods are needed to accurately quantify spillover effects.
  • Improved estimation and reporting of spillover effects will lead to a better understanding of public health intervention impact.