Estimating the incidence of typhoidal Salmonella from cross-sectional serology using antibody dynamics following infection

Aiemjoy, K.; Seidman, J. C.; Saha, S.; Munira, S. J.; Sajib, M. S. I.; Al Sium, S. M.; Sarkar, A.; Alam, N.; Nusrat Jahan, F.; Kabir, M. S.; Tamrakar, D.; Vaidya, K.; Shrestha, R.; Shakya, J.; Katuwal, N.; Shrestha, S.; Yousafzai, M. T.; Iqbal, J.; Dehraj, I. F.; Ladak, Y.; Maria, N.; Adnan, M.; Pervaiz, S.; Carter, A. S.; Longley, A.; Fraser, C.; Ryan, E. T.; Nodoushani, A.; Fasano, A.; Leonard, M.; Kenyon, V.; Bogoch, I. I.; Jeon, H. J.; Haselbeck, A.; Park, S. E.; Zellweger, R.; Marks, F.; Owusu-Dabo, E.; Adu-Sarkodie, Y.; Owusu, M.; Teunis, P.; Garrett, D. O.; Luby, S.; Qamar, F. N.; Saha,

The incidence of enteric fever, an invasive bacterial infection caused by typhoidal Salmonellae, is largely unknown in regions lacking blood culture surveillance. New serologic markers have proven accurate in diagnosing enteric fever, but whether they could be used to reliably estimate population-level incidence is unknown. We collected longitudinal blood samples from blood culture-confirmed enteric fever cases enrolled from surveillance studies in Bangladesh, Nepal, Pakistan and Ghana and conducted cross-sectional sero-surveys in the catchment areas of each surveillance site. We used ELISAs to measure quantitative IgA and IgG antibody responses to Hemolysin E (HlyE) and S. Typhi lipopolysaccharide (LPS). We used Bayesian hierarchical models to fit two-phase power-function decay models to the longitudinal sero-responses among enteric fever cases and used the joint distributions of the peak antibody titers and decay rate to estimate population-level incidence rates from cross-sectional serosurveys. The longitudinal antibody kinetics for all antigen-isotypes were similar across countries and did not vary by clinical severity. The incidence of typhoidal Salmonella infection ranged between 41.2 per 100 person years (95% CI: 34.0-50.1) in Dhaka, Bangladesh to 5.8 (95% CI: 4.8-7.1) in Kavrepalanchok, Nepal and followed the same rank order as clinical incidence estimates. The approach described here has the potential to expand the geographic scope of typhoidal Salmonella surveillance and generate incidence estimates that are comparable across geographic regions and time.