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Record W3205523677 · doi:10.1101/2021.10.20.21265277

Estimating typhoid incidence from community-based serosurveys: A multicohort study in Bangladesh, Nepal, Pakistan and Ghana

2021· preprint· en· W3205523677 on OpenAlexaff
Kristen Aiemjoy, Jessica C. Seidman, Senjuti Saha, Sira Jam Munira, Mohammad Saiful Islam Sajib, Syed Muktadir Al Sium, Anik Sarkar, Nusrat Alam, Farha Nusrat Jahan, Md. Shakiul Kabir, Dipesh Tamrakar, Krista Vaidya, Rajeev Shrestha, Jivan Shakya, Nishan Katuwal, Sony Shrestha, Mohammad Tahir Yousafzai, Junaid Iqbal, Irum Fatima Dehraj, Yasmin Ladak, Noshi Maria, Mehreen Adnan, Sadaf Pervaiz, Alice Carter, Ashley T Longley, Clare Fraser, Edward T. Ryan, Ariana Nodoushani, Alessio Fasano, Maureen M. Leonard, Victoria Kenyon, Isaac I. Bogoch, Hyon Jin Jeon, Andrea Haselbeck, Se Eun Park, Raphaël M. Zellweger, Florian Marks, Ellis Owusu‐Dabo, Yaw Adu‐Sarkodie, Michael Owusu, Peter Teunis, Stephen P. Luby, Denise O. Garrett, Farah Naz Qamar, Samir K. Saha, Richelle C. Charles, Jason R. Andrews

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of Toronto
FundersBill and Melinda Gates Foundation
KeywordsMedicineIncidence (geometry)PopulationTyphoid feverSalmonellaImmunologySerologyBlood cultureDemographyAntibodyInternal medicineVirologyEnvironmental healthAntibioticsMicrobiologyBiology

Abstract

fetched live from OpenAlex

Abstract Background 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. Methods 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 serosurveys 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 antibody 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. Findings The longitudinal antibody kinetics for all antigen-isotypes were similar across countries and did not vary by clinical severity. The seroincidence of typhoidal Salmonella infection among children <5 years ranged between 58.5 per 100 person-years (95% CI: 42.1 - 81.4) in Dhaka, Bangladesh to 6.6 (95% CI: 4.3-9.9) in Kavrepalanchok, Nepal, and followed the same rank order as clinical incidence estimates. Interpretation 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. Funding This work was supported by the Bill and Melinda Gates Foundation (INV-000572). Research in context Evidence before this study Previous studies have identified serologic responses to two antigens (Hemolysin E [HlyE] and Salmonella lipopolysaccharide [LPS]) as promising diagnostic markers of acute typhoidal Salmonella infection. We reviewed the evidence for seroepidemiology tools for enteric fever available as of November 01, 2021, by searching the National Library of Medicine article database and medRxiv for preprint publications, published in English, using the terms “enteric fever”, “typhoid fever”, “ Salmonella Typhi”, “ Salmonella Paratyphi”, “typhoidal Salmonella ”, “Hemolysin E”, “ Salmonella lipopolysaccharide”, “seroconversion”, “serosurveillance”, “seroepidemiology”, “seroprevalence” and “seropositivity.” We found no studies using HlyE or LPS as markers to measure the incidence or prevalence of enteric fever in a population. Anti-Vi IgG responses were used as a marker of population seroprevalence in cross-sectional studies conducted in South Africa, Fiji, and Nepal, but were not used to calculate population-based incidence estimates. Added value of this study We developed and validated a method to estimate typhoidal Salmonella incidence in cross-sectional population samples using antibody responses measured from dried blood spots. First, using longitudinal dried blood spots collected from over 1400 blood culture-confirmed cases in four countries, we modeled the longitudinal dynamics of antibody responses for up to two years following infection, accounting for heterogeneity in antibody responses and age-dependence. We found that longitudinal antibody responses were highly consistent across four countries on two continents and did not differ by clinical severity. We then used these antibody kinetic parameters to estimate incidence in population-based samples in six communities across the four countries, where concomitant population-based incidence was measured using blood cultures. Seroincidence estimates were much higher than blood-culture-based case estimates across all six sites, suggestive of a high incidence of asymptomatic or unrecognized infections. Still, the rank order of seroincidence and culture-based incidence rates were the same, with the highest rates in Bangladesh and lowest in Ghana. Implications of all the available evidence Many at-risk low- and middle-income countries lack data on typhoid incidence needed to inform and evaluate vaccine introduction. Even in countries where incidence estimates are available, data are typically geographically and temporally sparse due to the resources necessary to initiate and sustain blood culture surveillance. We found that typhoidal Salmonella infection incidence can be estimated from community-based serosurveys using dried blood spots, representing an efficient and scalable approach for generating the typhoid burden data needed to inform typhoid control programs in resource-constrained settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.313
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes1
Has abstractyes

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