Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies

Alane Izu1,2, Gaurav Kwatra3,4, Shabir A Madhi3,4

  • 1South African Medical Research Council: Vaccines and Infectious Diseases Analytical Research Unit (VIDA), University of the Witwatersrand, Faculty of Health Science, Johannesburg, South Africa. alane.izu@wits-vida.org.

Insights

A new statistical method, unsupervised mixture model averaging (MMA), accurately estimates infant Group B Streptococcus (GBS) infection risk. This method improves vaccine development by providing reliable serological markers for protection against invasive GBS disease (IGbsD).

Area of Science:

  • Immunology
  • Infectious Diseases
  • Biostatistics

Background:

  • Group B Streptococcus (GBS) causes significant invasive disease (IGbsD) in infants, leading to mortality and morbidity.
  • Immunizing pregnant women against GBS may prevent infant infections, complementing antibiotic prophylaxis.
  • Accurate serological markers are crucial for efficient vaccine development and licensure, avoiding large-scale efficacy trials.

Purpose of the Study:

  • To develop and apply a novel statistical method, unsupervised mixture model averaging (MMA), for accurately estimating infant IGbsD risk.
  • To address limitations in existing statistical models that do not account for differences between GBS-colonized and GBS-infected mothers.
  • To enable more precise risk assessment in case-control studies using maternal and infant antibody concentrations.

Main Methods:

  • Unsupervised mixture model averaging (MMA) was proposed and applied to case-control study data.
  • MMA was used to estimate infant IGbsD risk, considering potential subgroups of antibody concentrations related to maternal GBS status.
  • MMA estimators were compared against non-parametric disease risk estimators through simulations and analysis of published GBS studies.

Main Results:

  • MMA provided more accurate relative risk estimates across various simulation scenarios.
  • MMA yielded more precise absolute disease risk estimates, particularly when high antibody levels were present in IGbsD cases.
  • MMA-derived antibody concentration thresholds for protection against infant IGbsD were consistent with non-parametric estimates and showed improved clinical interpretability.

Conclusions:

  • Unsupervised mixture model averaging (MMA) is a flexible and robust statistical method for designing and analyzing case-control studies.
  • MMA enhances the accurate estimation and clinical interpretation of invasive GBS disease (IGbsD) risk based on antibody concentrations.
  • This method supports the development and evaluation of GBS vaccines by providing reliable serological markers.
Abstract