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Enregistrement W2946496786 · doi:10.1002/ijc.32403

Author's reply to: Meta‐analysis of cancer risks of professional firefighters

2019· letter· en· W2946496786 sur OpenAlexaboutno aff
Hamed Jalilian, Mansour Ziaei, Elisabete Weiderpass, Yahya Khosravi, Kristina Kjærheim, Corina S. Rueegg

Notice bibliographique

RevueInternational Journal of Cancer · 2019
Typeletter
Langueen
DomaineHealth Professions
ThématiqueOccupational Health and Performance
Établissements canadiensnon disponible
Organismes subventionnairesWorld Health Organization
Mots-clésPoolingMedicineConfidence intervalLung cancerDemographyStandardized mortality ratioIncidence (geometry)CancerCohort studyMeta-analysisInternal medicineOncologyMathematics

Résumé

récupéré en direct d'OpenAlex

We would like to thank Dr Casjens and colleagues for their critical and thorough comments on our article: cancer incidence and mortality among firefighters.1 We are happy to discuss all the points raised below. The authors argue for stratification of the results by study design and disagree with the pooling of different risk estimates. We agree that pooling of different risk estimates can lead to biased results. However, if the outcome is rare (such as all cancers studied in our review) and the risk estimates are close to one (almost all cancers among firefighters) estimates from case–control and cohort studies can be pooled without bias.2, 3 Because of the few numbers of studies in some of the cancer types, we therefore preferred to not stratify by study design. Moreover, the proportion of case–control studies among all included studies was maximally 30% (for incidence/mortality of lung cancer). It is true that using multiple estimates of specific cancer sites from one study would give more weight to this one study. However, since it was only two studies that reported two estimates based on the same number of cases, we do not think that this distorts our results. Because the estimates reported differed, we thought it was important to include both of them. For example, Ahn et al.4 reported a significantly elevated standardized mortality ratio of 1.56 (95% confidence interval [95% CI] 1.01–2.41) for kidney cancer, with no significant elevation of standardized rate ratio (0.69; 95% CI 0.16–2.99) for this organ. It is correct that the method by Hamling et al.5 was originally developed to aggregate categories of exposure within one variable but also to aggregate estimates of different categories of disease. We therefore think that it can be used without bias to aggregate estimates of cancers of different ICD codes. The alternative would have been to combine them with fixed effects meta-analysis, but this approach treats each estimate as an independent measure, which we think is not correct. The method of Hamling et al. takes the correlation between the estimates into account and this is why we preferred this method. In any case, out of approximately 800 extracted risk estimates, only one standardized mortality ratio, five standardized incidence ratios and three odds ratios were calculated with the method of Hamling et al. We therefore think that using another method would not have changed the overall results of this meta-analysis. We fully agree with Casjens and colleagues’ comment on our criteria to assess the strength of association between firefighting occupation and cancer. The method uses arbitrary thresholds (similar to the widely used significance level of p < 0.05) to interpret findings. This is why we show all the individual estimates with confidence intervals and just add the interpretation by applying the method of LeMasters et al. This gives the reader an easily understandable idea on the strength of associations and makes the results comparable to the last meta-analysis of LeMasters et al.6 In addition, we tried to enhance the method by separating incidence and mortality risk estimates. We used the DerSimonian–Laird estimator to calculate the between-study variance. We agree that studies have shown that the Paule and Mandel estimator might be more accurate under some circumstances. We decided to use the DerSimonian–Laird estimator because it is most widely used and makes our results comparable. The DerSimonian–Laird estimator performs well with low mean squared errors when τ2 is small.7 Although the Newcastle–Ottawa Scale (NOS) is not the best measure to assess the quality of studies in systematic reviews, it is widely used and accepted for this aim and has been suggested by several investigators.8, 9 However, evidence suggests that quality scales can be problematic and the conclusions of a meta-analysis can be affected by the choice of the quality scale.9 We agree that the NOS was not sensitive to differentiate well between the studies included in our review. We have therefore also put little emphasis on the NOS rating when interpreting our results. In our meta-analysis, we primarily extracted effect sizes of male firefighters but included studies were the estimates included both sexes combined. Casjenns and colleagues were concerned about this approach and suggested to add an analysis in men only. We agree that this approach could be problematic if there were many studies including men and women. However, we extracted more than 800 risk estimates through 48 studies and only six estimates included both sexes combined. Furthermore, the populations in the underlying studies included less than 5% women. We therefore think that a bias because of some estimates including also women is unlikely. We confirm that there is a problem with the graphical representation of the confidence intervals in two studied cancers (intestine and colon) of Figure 2. The underlying numbers for the calculations and reporting of these numbers in the text are, however, correct. Additionally, we spotted that in Figure 2, the number of studies for colon cancer must be rectified to 10. Finally, we tried to find and include all relevant studies by using a broad search strategy including a wide range of words for the population (firefighters) and outcome (cancer) of interest in three important databases (see Supporting Information Table S2 of the article), identifying 2,630 records. In addition, we screened the reference lists of previous reviews on the same topic. However, we can of course not guarantee that we have not missed some publications. As described in Figure 1 of our article, we excluded seven publications that overlapped with other studies (based on the same data material). Moreover, we exclude additional nine studies, including Demers et al.,10 because they reported risk estimates that include more than one occupation. For example, Demers et al.10 reported an odds ratio of 1.90 (95% CI 0.50–9.40) for multiple myeloma among “firefighting and prevention occupations.” Yours sincerely, Hamed Jalilian Mansour Ziaei Elisabete Weiderpass Yahya Khosravi Kristina Kjaerheim Corina S. Rueegg Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

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 candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,054
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0110,000

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 Opus0,255
Tête enseignante GPT0,582
Écart entre enseignants0,327 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations4
Publié2019
Routes d'admission1
Résumé présentoui

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