Real World Occupational Epidemiology, Part 1: Odds Ratios, Relative Risk, and Asbestosis

Beh, Eric J.; Smith, Derek

doi:10.1080/19338244.2011.564233

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…All analyses were performed using IBM SPSS Statistics 19. As the outcome of interest (the incidence of household transmission) was common and this can affect the approximation of relative risk [12], we used Zhang’s formula to correct the odds ratios [13]. …”

Section: Methodsmentioning

confidence: 99%

Wearing face masks in public during the influenza season may reflect other positive hygiene practices in Japan

2012

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BackgroundAlthough the wearing of face masks in public has not been recommended for preventing influenza, these devices are often worn in many Asian countries during the influenza season. In Japan, it is thought that such behavior may be an indicator of other positive hygiene practices. The aim of this study, therefore, was to determine if wearing a face mask in public is associated with other positive hygiene practices and health behaviors among Japanese adults.MethodsWe initially recruited around 3,000 Japanese individuals ranging from 20 to 69 years of age who were registered with a web survey company. Participants were asked to recall their personal hygiene practices during the influenza season of the previous year. Logistic regression analysis was then used to examine the associations between wearing a face mask in public and personal hygiene practices and health behaviors.ResultsA total of 3,129 persons responded to the survey, among whom 38% reported that they had worn a face mask in public during the previous influenza season. Wearing a face mask in public was associated with various self-reported hygiene practices including: frequent hand washing (adjusted Odds Ratio [OR]: 1.67; 95% Confidence Interval [95%CI]: 1.34-1.96), occasional hand washing (OR: 1.43; 95%CI: 1.10-1.75), frequently avoiding crowds (OR: 1.85; 95%CI: 1.70-1.98), occasionally avoiding crowds (OR: 1.65; 95%CI: 1.53-1.76), frequent gargling (OR: 1.68; 95%CI: 1.51-1.84), occasional gargling (OR: 1.46; 95%CI: 1.29-1.62), regularly avoiding close contact with an infected person (OR: 1.50; 95%CI: 1.33-1.67), occasionally avoiding close contact with an infected person (OR: 1.31; 95%CI: 1.16-1.46), and being vaccinated of influenza in the last season (OR: 1.31; 95%CI: 1.17-1.45).ConclusionsOverall, this study suggests that wearing a face mask in public may be associated with other personal hygiene practices and health behaviors among Japanese adults. Rather than preventing influenza itself, face mask use might instead be a marker of additional, positive hygiene practices and other favorable health behaviors in the same individuals.

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Section: Methodsmentioning

confidence: 99%

Wearing face masks in public during the influenza season may reflect other positive hygiene practices in Japan

2012

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“…As a result, many governments have introduced laws that ban the manufacturing, and importation, of products containing asbestos fibres. This data, summarized in Table 2, has also been a topic of statistical discussion by Beh and Smith (2011) and Tran, Beh and Smith (2012); the latter studied the data in terms of the AAI. Table 2 also summarises the expected cell frequencies under independence between the variables (in parentheses on the left hand side) and under the LIS (in parentheses on the right hand side).…”

Section: Applicationmentioning

confidence: 98%

On issues concerning the assessment of information contained in aggregate data using the F-statistic

Cheema

Beh

Hudson³

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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The analysis of aggregate data has been gaining momentum in the statistics and allied disciplines, (including public policy, political science and epidemiology) for more than 20 years. As a result, the issue has received an increasing amount of attention by categorical data analysts. Performing aggregate data analysis is quickly becoming unavoidable in many situations, especially when individual level data is unavailable. For example, the U.S. Justice Department uses aggregate data to formulate the public policies against racial discrimination, political scientists are always interested in exploring the political or ideological preferences of different demographic groups while social scientists use aggregate data to study the relationship between crime and unemployment. The availability of aggregate data has increased due to strict confidentiality restrictions imposed upon by government and corporate organisations who are reluctant to release individual level information. There is a wealth of contributions on this issue that is available in the ecological inference (EI) literature which considers the association structure between categorical variables (at the individual level) given only the aggregate information. The main difficulty in EI arises due to the loss of information during the process of aggregation and results in aggregation bias. It is also a matter of concern for aggregate data analysts that the interpretation of the parameters from EI models might be entirely different to analogous parameters for the study of individual level data. An alternative strategy to EI is to consider the recently proposed Aggregate Association Index (AAI) that allows the analyst to quantify the overall extent of association between two dichotomous variables given only the aggregate, or marginal, information of a 2x2 table. Unlike EI, the AAI does not estimate, or model, the conditional proportions but focuses instead on gauging the extent of association between the variables. The AAI can also be further partition into positive and negative association terms thus enabling the analysts to understand the more likely direction of the association given only the aggregate data. However, the major issue with the performance of AAI is the impact the sample size has on its magnitude. In this paper we investigate the informativeness of the aggregate data for inferring an association exists between the variables of a 2x2 table. This article introduces development of an F-test to determine the statistical significance of the information contained in the aggregate data for inferring a statistically significant association between the variables. Unlike Pearson's chi-squared statistic, the F-statistic is robust to any change in the sample size and depends only on the aggregate information in the contingency table. Thus this statistic provides an opportunity to understand the structure of a 2x2 table without being influenced by sample size. The applicability of this test is demonstrated by using the Selikoff's (1981) asbestosis data which...

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“…Some of the simplest techniques include calculation of the odds ratio, correlation and chi-squared statistic. 13 In a previous article, for example 5 we described an analysis of Table 1 from the point of view of the odds ratio, thereby providing insight into the calculation and the interpretation of simple statistical measures. Alternatively, graphical techniques such as correspondence analysis may also be used to analyse Selikoff's '20 year rule'.…”

Section: Measuring Associationmentioning

confidence: 99%

Real-World Occupational Epidemiology, Part 3: An Aggregate Data Analysis of Selikoff's “20-Year Rule”

Tran

Beh

Smith

2012

Archives of Environmental & Occupational Health

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Real World Occupational Epidemiology, Part 1: Odds Ratios, Relative Risk, and Asbestosis

Cited by 14 publications

References 19 publications

Wearing face masks in public during the influenza season may reflect other positive hygiene practices in Japan

Wearing face masks in public during the influenza season may reflect other positive hygiene practices in Japan

On issues concerning the assessment of information contained in aggregate data using the F-statistic

Real-World Occupational Epidemiology, Part 3: An Aggregate Data Analysis of Selikoff's “20-Year Rule”

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