Tag Archives: Conditions and Diseases

Tailors and seamstresses at risk of chronic bronchitis

4 Jun

tailors and seamstresses could experience à higher risk of chronic bronchitis a Chinese study found:

English: A tailor at work in Hong Kong. Deutsc...

English: A tailor at work in Hong Kong. Deutsch: Ein Schneider bei der Arbeit, in Hong Kong (Photo credit: Wikipedia)

English: Banjul, tailor

English: Banjul, tailor (Photo credit: Wikipedia)


Tailor (Photo credit: nibaq)

A tailor shop in Phuket

A tailor shop in Phuket (Photo credit: sama sama – massa)

English: Tailor in Nizwa Français : Tailleur p...

English: Tailor in Nizwa Français : Tailleur pour dames à Nizwa (Oman) (Photo credit: Wikipedia)

Acute bronchitis schema

Acute bronchitis schema (Photo credit: Wikipedia)

(quoted) “Persons reporting exposure to cotton dust more often indicated work in the cotton textile industry (14 percent) or as a tailor or seamstress (71 percent) than in the other industries or occupations listed on our questionnaire. We found an association between being a tailor or seamstress and chronic bronchitis (OR = 1.35, 95 percent CI: 0.88, 2.06), chronic phlegm (OR = 1.39, 95 percent CI: 1.14, 1.70), or chronic dry cough (OR = 1.32, 95 percent CI: 0.88, 1.99)” end of quotation.
According to the authors of the study the causes could be probably found in the exposure to cotton dust: (quoted ) “Cotton dust exposure was associated with non chronic cough or phlegm (OR = 1.40, 95 percent CI: 1.19, 1.64), chronic phlegm (OR = 1.36, 95 percent CI: 1.09, 1.69), and chronic bronchitis (OR = 1.71, 95 percent CI: 1.13, 2.60)” (end of quotation).

More information:

1) reference in Vancouver convention format:
Levan TD, Koh WPP, Lee HPP, Koh D, Yu MC, London SJ. Vapor, dust, and smoke exposure in relation to adult-onset asthma and chronic respiratory symptoms: the Singapore Chinese Health Study. American journal of epidemiology. 2006 Jun;163(12):1118-1128. Available from: http://dx.doi.org/10.1093/aje/kwj144.

2) Full text pdf of the article:

Am. J. Epidemiol.-2006-LeVan-1118-28

Subgroup analyses

11 Dec

Heterogeneity in treatment effects

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Subgroup analysis is the core of interpretation of random controlled trials. But it must respect some strictly defined rules otherwise it will lead the reader to dangerous misinterpretation. A recent article (2010) by Kent et al exposes a very useful checklist for authors as well as for readers.

A given treatment induces a 25% Relative Risk Reduction of a given disease; a subgroup analysis implemented on one hand in a low risk of disease group of subjects versus in an other hand in a high risk of disease group of subjects will lead to an Absolute Risk Reduction of 1% versus 5% and a number of subjects needed to treat to prevent one additional disease of 100 versus 20. What is at stakes for health policy decision makers, care providers and patients is no less than efficacy, efficiency and harms of treatments grounded on evidence based results.

In their open access article the authors, from the Institute for Clinical Research and Health Policy Studies (Boston, MA, USA) and from the Center for statistics in Medicine (University of Oxford, UK) and from the department of Hygiene and Epidemiology (Ioannini, Greece) clearly expose the advantages and limits of the subgroup analysis techniques, weather they aim at exploratory research without immediate clinical implication or they attempt to further confirm an already strong a priori pathophysiological or empirical knowledge.

According to those authors, for reporting on subgroup analysis and heterogeneity in treatment effects, health services authors should:

Evaluate the distribution of the risk of disease in the overall study population before any treatment using a pre-specified externally developed risk prediction model (eg: risk score).

Pre-specify the subgroups including the threshold values for continuous or ordinal variables (except for clearly labelled exploratory purposes which are potentially useful for hypothesis generation and informing future research but having little or no immediate relevance to patient care).

Report the statistical significance between subgroups using interaction terms (testing for the significance of a treatment effect within a subgroup is inappropriate due to poor statistical power).

Correct the statistical comparisons for the number of the number of primary subgroup analysis performed.

The full text of the entire article is available in open access here.

Cite this article as: Kent et al.: Assessing and reporting heterogeneity in
treatment effects in clinical trials: a proposal. Trials 2010 11:85

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