Wastewater surveillance for Salmonella Typhi and its association with seroincidence of enteric fever in Vellore, India

Abstract

Background: Blood culture-based surveillance for typhoid fever has limited sensitivity, and operational challenges are encountered in resource-limited settings. Environmental surveillance targeting Salmonella Typhi (S. Typhi) shed in wastewater (WW), coupled with cross-sectional serosurveys of S. Typhi-specific antibodies estimating exposure to infection, emerges as a promising alternative.

Methods: We assessed the feasibility and effectiveness of wastewater (WW) and sero-surveillance for S. Typhi in Vellore, India, from May 2022 to April 2023. Monthly samples were collected from 40 sites in open drainage channels and processed using standardized protocols. DNA was extracted and analyzed via quantitative PCR for S. Typhi genes (ttr, tviB, staG) and the fecal biomarker HF183. Clinical cases of enteric fever were recorded from four major hospitals, and a cross-sectional serosurvey measured hemolysin E (HlyE) IgG levels in children under 15 years of age to estimate seroincidence.

Results: 7.50% (39/520) of grab and 15.28% (79/517) Moore swabs were positive for all 3 S. Typhi genes. Moore swab positivity was significantly associated with HF183 (adjusted odds ratio (aOR): 3.08, 95% CI: 1.59-5.95) and upstream catchment population (aOR: 4.67, 1.97-11.04), and there was increased detection during monsoon season - membrane filtration (aOR: 2.99, 1.06-8.49), and Moore swab samples (aOR: 1.29, 0.60-2.79). Only 11 blood culture-confirmed typhoid cases were documented over the study period. Estimated seroincidence was 10.4/100 person-years (py) (95% CI: 9.61 - 11.5/100 py). The number of S. Typhi positive samples at a site was associated with the estimated sero-incidence in the site catchment population (incidence rate ratios: 1.14 (1.07-1.23) and 1.10 (1.02-1.20) for grab and Moore swabs respectively.

Conclusions: These findings underscore the utility and effectiveness of alternate surveillance approaches to estimating the incidence of S. Typhi infection in resource-limited settings, offering valuable insights for public health interventions and disease monitoring strategies where conventional methods are challenging to implement.

Fig 1. Location of the study area in Vellore, India.

The yellow-shaded area in the map shows the study area in Vellore urban city with the mapped sewage network consisting of open drainage channels. The blue dots in the map represent the 40 wastewater sampling locations. The base layer for the Country boundary was obtained from geoBoundaries (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231866) [20]. The GIS layers for the study area and sewage network were mapped by the study team based on ground truthing and field surveys (https://github.com/drvenkatm/ES_Salmonella_Vellore-May22_Apr23).

Fig 2. Salmonella Typhi positivity in wastewater (WW) and average monthly rainfall during the study period.

The overall wastewater positivity for S. Typhi in percentage and the average monthly rainfall in millimeters is plotted over the study period from May 2021–April 2022. The height of the bar represents the average rainfall millimeters in each month during the study period, and the line graph represents the overall positivity for S. Typhi during the same months.

Fig 3. Salmonella typhi detection at wastewater sampling sites during different seasons of the year during (A) summer months (March–July), (B) monsoon months (August–November), and (C) winter months (December–February).

Seasonal detection of S. Typhi detection at wastewater sampling sites is depicted in Fig 3. Panel A illustrates the positivity during the summer months (March–July), panel B during the monsoon months (August November), and panel C during the winter months (December–February). The area of the green circles represents the proportion (%) of overall detection for each period. The base layer for the Country boundary was obtained from geoBoundaries (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231866) [20]. The GIS layers for the study area and sewage network were mapped by the study team based on ground truthing and field surveys (https://github.com/drvenkatm/ES_Salmonella_Vellore-May22_Apr23).

Fig 4. Seroincidence of typhoid and S.

Typhi positivity in wastewater across independent and nested catchments. Maps illustrate the 20 catchment areas (out of 40) included in the serosurvey. Panel A shows both independent catchment areas and larger overlapping catchments, with the gradient of the coloured polygons representing the estimated seroincidence. Panel B shows the coloured polygons indicating overall wastewater positivity across the catchment areas. The base layer for the Country boundary was obtained from geoBoundaries (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231866) [20]. The GIS layers for the study area and sewage network were mapped by the study team based on ground truthing and field surveys (https://github.com/drvenkatm/ES_Salmonella_Vellore-May22_Apr23).