The impact of integrated genomic surveillance on non-typhoidal Salmonella infection in Australia: an ecological study

Son Nghiem; Nhung Mai; My Tran; Danielle M Cribb; Liliana Bulfone; Patiyan Andersson; Alireza Zahedi; Tuyet Hoang; Tehzeeb Zulfiqar; Angeline Ferdinand; Katie Glass; Martyn D Kirk; Vitali Sintchenko; Amy V Jennison; Benjamin P Howden; Emily Lancsar

doi:10.1016/j.lanwpc.2025.101592

The impact of integrated genomic surveillance on non-typhoidal Salmonella infection in Australia: an ecological study

Lancet Reg Health West Pac. 2025 Jun 17:59:101592. doi: 10.1016/j.lanwpc.2025.101592. eCollection 2025 Jun.

Authors

Son Nghiem^{1

2}, Nhung Mai¹, My Tran¹, Danielle M Cribb¹, Liliana Bulfone¹, Patiyan Andersson^{3

4

5}, Alireza Zahedi⁶, Tuyet Hoang^{3

4

5}, Tehzeeb Zulfiqar¹, Angeline Ferdinand^{3

4

7

5}, Katie Glass¹, Martyn D Kirk¹, Vitali Sintchenko^{8

9}, Amy V Jennison⁶, Benjamin P Howden^{3

4

10

5}, Emily Lancsar¹¹

Affiliations

¹ National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT, Australia.
² Centre for Health Services Research, The University of Queensland, Brisbane, Australia.
³ Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
⁴ Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
⁵ Centre for Pathogen Genomics, The University of Melbourne, Melbourne, VIC, Australia.
⁶ Public and Environmental Health, Pathology Queensland, Queensland Department of Health, Brisbane, Australia.
⁷ Centre for Health Policy, School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
⁸ Sydney Institute for Infectious Diseases, The University of Sydney, Sydney, NSW, Australia.
⁹ Centre for Infectious Diseases and Microbiology-Public Health, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Sydney, NSW, Australia.
¹⁰ Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia.
¹¹ Department of Health Economics Wellbeing and Society, The Australian National University, Canberra, ACT, Australia.

Abstract

Background: Whole Genome Sequencing (WGS) is a powerful technology for monitoring and detecting outbreaks of infectious pathogens, including non-typhoidal Salmonella (NTS). Despite its higher cost than traditional typing methods, WGS offers numerous advantages, including higher resolution and potentially quicker turnaround time. However, evidence regarding its effectiveness in NTS surveillance has predominantly stemmed from micro-simulations or small-scale data. Notably, a recent systematic review identified a lack of real-world, large-scale evidence on the impact of WGS application in NTS surveillance. Our study fills this gap by estimating the effects of WGS on NTS surveillance in Australia using national notifiable disease datasets.

Methods: The main dataset was the National Notifiable Diseases Surveillance System (NNDSS) for NTS from 2009 to 2024. The treatment variable was defined as a binary variable representing the period when WGS was implemented in each jurisdiction of Australia. To minimise the effects of unobserved confounders, we employed a two-stage difference-in-difference (2sDiD) approach. This method estimated the parameters of state and period fixed-effects and then adjusted the observed outcomes from those fixed-effects in the first stage. The average treatment effect was obtained in the second stage by regressing the adjusted outcome against the treatment in the second stage. We also conducted a sensitivity analysis using a multi-period DiD model with a double machine-learning estimator.

Findings: Compared to the pre-WGS periods, the introduction of WGS was associated with an average of 11.6% reduction in NTS cases when a static specification was applied. Results of a dynamic specification were slightly higher, with a 12.7% reduction in NTS cases after WGS. The estimated effects increased to 17.5% when a multi-period DiD model with a double machine learning estimator was applied.

Interpretation: Our study shows that WGS was associated with a significant reduction (11.6%-17.5%) of NTS cases in Australia. Using the cost and break-even point of NTS from previous Australian studies, our findings suggest that WGS is associated with 7200-10,900 cases of NTS averted, saving US$11.3 m-US$17.0 m per year.

Funding: Australian National Health and Medical Research Council, Medical Research Futures Fund (FSPGN00049), and Investigator Grant (GNT1196103) to BPH.

Keywords: Australia; Ecological study; Genomic surveillance; Salmonella infections; Whole genome sequencing.