IMPORTANCE The incidence of invasive infections caused by group B Streptococcus (GBS) continues to increase in the United States. Although diabetes is a key risk factor for invasive GBS, the influence of long-term glycemic control is not well characterized; other risk factors and mortality rates associated with specific types of invasive GBS infections are unknown. OBJECTIVE To investigate risk factors and mortality rates associated with specific invasive GBS infectious syndromes.

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.

The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.

BackgroundSurveillance from the US Center for Disease Control and Prevention (CDC) has detected an increase in the prevalence of invasive Group B streptococcus (GBS) infections between 2008 and 2016 among non-pregnant adults. Here, we use data from the US Veterans Health Administration (VHA) to assess the underlying clinical characteristics and outcomes associated with specific types of invasive GBS infection among veterans.MethodsWe used the VA Corporate Data Warehouse to identify patients with invasive GBS infection diagnosed between 2008–2017 using CDC’s surveillance definitions. Data on the microbiological source of infection (e.g., GBS in cultures from blood, bone or sterile fluids) and associated International Classification of Disease (ICD) codes were used to classify the type of invasive infection. We determined associated co-morbid conditions and 30-day all-cause mortality for incident cases.ResultsBetween 2008 and 2017, there were 4780 incident cases of invasive GBS infection in veterans with a mean age of 66.6 years (±11.7) and30-day all-cause mortality of 8%. The most common syndrome was osteomyelitis (23%, N = 1078) with 30-day mortality of 1%. Other common infections, such as bacteremia (20%; N = 972), skin and soft-tissue infections (18%, 853), and pneumonia (14%, N = 664), had higher mortality (13%, 4% and 17%, respectively; Figure). In patients with GBS peritonitis, present in 3% (N = 138) incidence cases, 46% had chronic liver disease with a 30-day mortality of 28%. Diabetes mellitus (DM) occurred in 66% of patients with any invasive GBS infection and in 86% of patients with GBS osteomyelitis. Chronic heart, kidney, or lung disease affected >25% of patients (table).ConclusionInvasive GBS infection is a burden for veterans with DM and other high-risk conditions, with some types of infections associated with substantial mortality. Osteomyelitis, the most common type of infection, was associated with lower mortality compared with other invasive GBS infections. DM and chronic lung, kidney and heart disease are common among veterans with invasive GBS infection. Disclosures All authors: No reported disclosures.