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

Abstract Background Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate s...

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Main Authors: Alane Izu, Gaurav Kwatra, Shabir A. Madhi, Fabio Rigat
Format: Article
Language:English
Published: BMC 2022-03-01
Series:BMC Medical Research Methodology
Subjects:
Online Access:https://doi.org/10.1186/s12874-022-01529-5
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author Alane Izu
Gaurav Kwatra
Shabir A. Madhi
Fabio Rigat
author_facet Alane Izu
Gaurav Kwatra
Shabir A. Madhi
Fabio Rigat
author_sort Alane Izu
collection DOAJ
description Abstract Background Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera. Methods Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies. Results MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates. Conclusions MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth.
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spelling doaj.art-c8cc3d1c7a6841aa940ca764b5e4f6a32022-12-22T03:06:26ZengBMCBMC Medical Research Methodology1471-22882022-03-0122111610.1186/s12874-022-01529-5Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studiesAlane Izu0Gaurav Kwatra1Shabir A. Madhi2Fabio Rigat3South African Medical Research Council: Vaccines and Infectious Diseases Analytical Research Unit (VIDA), University of the Witwatersrand, Faculty of Health ScienceSouth African Medical Research Council: Vaccines and Infectious Diseases Analytical Research Unit (VIDA), University of the Witwatersrand, Faculty of Health ScienceSouth African Medical Research Council: Vaccines and Infectious Diseases Analytical Research Unit (VIDA), University of the Witwatersrand, Faculty of Health ScienceStatistics and Decision Sciences, Janssen Pharmaceuticals R & DAbstract Background Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera. Methods Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies. Results MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates. Conclusions MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth.https://doi.org/10.1186/s12874-022-01529-5Group B Streptococcus infectionsCase-control studySerological correlates of protectionRelative risk reductionAbsolute disease riskMixture modelling
spellingShingle Alane Izu
Gaurav Kwatra
Shabir A. Madhi
Fabio Rigat
Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
BMC Medical Research Methodology
Group B Streptococcus infections
Case-control study
Serological correlates of protection
Relative risk reduction
Absolute disease risk
Mixture modelling
title Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
title_full Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
title_fullStr Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
title_full_unstemmed Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
title_short Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies
title_sort estimation of invasive group b streptococcus disease risk in young infants from case control serological studies
topic Group B Streptococcus infections
Case-control study
Serological correlates of protection
Relative risk reduction
Absolute disease risk
Mixture modelling
url https://doi.org/10.1186/s12874-022-01529-5
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