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Enregistrement W2123931989 · doi:10.1258/jrsm.96.6.311

Drinking Water and Infectious Disease: Establishing the Links

2003· article· en· W2123931989 sur OpenAlexaboutno aff
Sandy Cairncross

Notice bibliographique

RevueJournal of the Royal Society of Medicine · 2003
Typearticle
Langueen
DomaineNursing
ThématiqueChild Nutrition and Water Access
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInfectious disease (medical specialty)Data scienceComputer scienceDiseaseWorld Wide WebMedicinePathology

Résumé

récupéré en direct d'OpenAlex

Water has a special significance for epidemiologists, whose very science was founded by John Snow's studies of waterborne cholera in nineteenth century London. Many people believe that such outbreaks have been consigned to history, and that water treatment and disinfection have stopped waterborne disease transmission in developed countries once and for all, except on odd occasions when things go wrong. This book will correct that impression; research in the past decade, by some of the authors collected here, has shown that populations in developed countries, drinking water which has been treated to meet WHO guidelines and EU or USEPA quality standards, can still experience a substantial amount of waterborne enteric infection and disease. The book is based on an OECD expert group meeting held in July 2000 to address the need for international coordination for improved surveillance and outbreak investigation. The focus is on issues of epidemiological methodology, with sections on surveillance systems, on outbreak investigation, and on the investigation of sporadic disease. Nearly half the authors are public officials. That sounds like a recipe for something dry and dreary, but the book makes exciting reading. This is partly because the discussions of methodology are liberally illustrated with cases from the field, and good accounts of epidemiological detective work always impart some of the thrill of the chase. Most of the chapters are also well written, or at least adroitly edited to be very readable. It is unusual for practitioners to be good writers, and this group has done well. The theoretical parts, such as the listing of the factors to bear in mind when designing a surveillance system or an outbreak investigation, should be valuable for teaching. Written as they are by practitioners (or by ex-practitioners who have defected to academia) they are rooted in actual practice but trenchant in describing the weaknesses of current arrangements. Statements such as, ‘The surveillance systems of many European countries are incapable of detecting waterborne disease’ and ‘There is very little evidence that coliform monitoring is predictive of disease associated with drinking water’ bring one up short. Clever ideas, such as the suggestion that one ask the cases in a case-control study to suggest their friends as controls, enliven the text. There are accounts of most of the significant events in the field in the past decade, such as the Milwaukee cryptosporidiosis epidemic with over 400 000 cases, and the court case following the outbreak in Torbay, UK. The cause of the former and the outcome of the latter are given in the book, but I mustn't spoil those stories for you. Others, such as Pierre Payment's prospective studies in Canada, are mentioned in passing and the relevant references are cited. The discussion of recent findings about Cryptosporidium is particularly good on what we don't yet know about it. The book thus offers an effective update on recent developments in the study and prevention of waterborne disease. The shortcomings of the book are minor. The discussion of surveillance system design does not describe how parallel systems can be used to increase sensitivity, or the relative advantage of sentinel versus exhaustive systems; there is no mention of the E. coli O157 outbreak in Walkerton, Ontario; and, though there is one chapter about the developing countries, there is no account of the community-based surveillance systems for detecting cases of guinea-worm disease in Africa, whose effectiveness under difficult conditions—remote and inaccessible communities, severe resource constraints, illiterate frontline workers, multiple language communities, and so on—puts the health authorities of Milwaukee to shame.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,168
Score d'incertitude au seuil0,254

Scores Codex et Gemma par catégorie

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

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 Opus0,010
Tête enseignante GPT0,249
Écart entre enseignants0,239 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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

Citations14
Publié2003
Routes d'admission1
Résumé présentoui

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