Modeling the Potential for Vaccination to Diminish the Burden of Invasive Non-typhoidal <i>Salmonella</i> Disease in Young Children in Mali, West Africa

<div><p>Background</p><p>In sub-Saharan Africa, systematic surveillance of young children with suspected invasive bacterial disease (e.g., septicemia, meningitis) has revealed non-typhoidal <i>Salmonella</i> (NTS) to be a major pathogen exhibiting high case fatality (~20%). Where infant vaccination against <i>Haemophilus influenzae</i> type b (Hib) and <i>Streptococcus pneumoniae</i> has been introduced to prevent invasive disease caused by these pathogens, as in Bamako, Mali, their burden has decreased markedly. In parallel, NTS has become the predominant invasive bacterial pathogen in children aged <5 years. While NTS is believed to be acquired orally via contaminated food/water, epidemiologic studies have failed to identify the reservoir of infection or vehicles of transmission. This has precluded targeting food chain interventions to diminish disease transmission but conversely has fostered the development of vaccines to prevent invasive NTS (iNTS) disease. We developed a mathematical model to estimate the potential impact of NTS vaccination programs in Bamako.</p><p>Methodology/Principal Findings</p><p>A Markov chain transmission model was developed utilizing age-specific Bamako demographic data and hospital surveillance data for iNTS disease in children aged <5 years and assuming vaccine coverage and efficacy similar to the existing, successfully implemented, Hib vaccine. Annual iNTS hospitalizations and deaths in children <5 years, with and without a <i>Salmonella</i> Enteritidis/<i>Salmonella</i> Typhimurium vaccine, were the model’s outcomes of interest. Per the model, high coverage/high efficacy iNTS vaccination programs would drastically diminish iNTS disease except among infants age <8 weeks.</p><p>Conclusions/Significance</p><p>The public health impact of NTS vaccination shifts as disease burden, vaccine coverage, and serovar distribution vary. Our model shows that implementing an iNTS vaccine through an analogous strategy to the Hib vaccination program in Bamako would markedly reduce cases and deaths due to iNTS among the pediatric population. The model can be adjusted for use elsewhere in Africa where NTS epidemiologic patterns, serovar prevalence, and immunization schedules differ from Bamako.</p></div

Number of Severe iNTS Cases and Deaths within the First 36 Months after Introduction of Routine NTS Vaccination and Various Catch-up Campaign Schedules.

<p>Number of Severe iNTS Cases and Deaths within the First 36 Months after Introduction of Routine NTS Vaccination and Various Catch-up Campaign Schedules.</p

Total Cases and Fatal Cases due to iNTS Disease within a Birth Cohort over Time.

<p>(a) in unvaccinated conditions, (b) with a vaccine administered at 6 weeks of life with 100% coverage and 100% efficacy, and (c) with vaccines administered at 6, 10, and 14 weeks of life with vaccine coverage and efficacy as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005283#pntd.0005283.t004" target="_blank">Table 4</a>.</p

Number of Cases Prevented and Deaths Averted per Year⁺ Depending on Vaccine Efficacy, Serovar Distribution, and Vaccination Schedule.

<p>Number of Cases Prevented and Deaths Averted per Year<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005283#t005fn001" target="_blank">⁺</a> Depending on Vaccine Efficacy, Serovar Distribution, and Vaccination Schedule.</p

Changes in the Number of Fatal iNTS Cases across the Probable Ranges of Model Parameters.

<p>Changes in the Number of Fatal iNTS Cases across the Probable Ranges of Model Parameters.</p

Average Number of Cases per Month by Age Group Occurring in Even and Odd Years of Surveillance and in Model Simulations.

<p>Average Number of Cases per Month by Age Group Occurring in Even and Odd Years of Surveillance and in Model Simulations.</p

Background Birth and Mortality Rates^* from DHS, Bamako [20–22].

<p>Background Birth and Mortality Rates^{<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005283#t001fn001" target="_blank">*</a>} from DHS, Bamako [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005283#pntd.0005283.ref020" target="_blank">20</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005283#pntd.0005283.ref022" target="_blank">22</a>].</p

Changes in the Number of Hospitalized iNTS Cases across the Probable Ranges of Model Parameters.

<p>Changes in the Number of Hospitalized iNTS Cases across the Probable Ranges of Model Parameters.</p

NTS Vaccination Characteristics.

<p>NTS Vaccination Characteristics.</p

iNTS Case Fatality Rates from Even Years of HGT Surveillance (η[a]).

<p>iNTS Case Fatality Rates from Even Years of HGT Surveillance (η[a]).</p