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Enregistrement 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 sur 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

Notice bibliographique

RevuemedRxiv · 2021
Typepreprint
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSalmonella and Campylobacter epidemiology
Établissements canadiensUniversity of Toronto
Organismes subventionnairesBill and Melinda Gates Foundation
Mots-clésMedicineIncidence (geometry)PopulationTyphoid feverSalmonellaImmunologySerologyBlood cultureDemographyAntibodyInternal medicineVirologyEnvironmental healthAntibioticsMicrobiologyBiology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,313
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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