Journal Article

Detecting Patterns of Occupational Illness Clustering with Alternating Logistic Regressions Applied to Longitudinal Data

John S. Preisser, Thomas A. Arcury and Sara A. Quandt

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 158, issue 5, pages 495-501
Published in print September 2003 | ISSN: 0002-9262
Published online September 2003 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwg169
Detecting Patterns of Occupational Illness Clustering with Alternating Logistic Regressions Applied to Longitudinal Data

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In longitudinal surveillance studies of occupational illnesses, sickness episodes are recorded for workers over time. Since observations on the same worker are typically more similar than observations from different workers, statistical analysis must take into account the intraworker association due to workers’ repeated measures. Additionally, when workers are employed in groups or clusters, observations from workers in the same workplace are typically more similar than observations from workers in different workplaces. For such cluster-correlated longitudinal data, alternating logistic regressions may be used to model the pattern of occupational illness clustering. Data on 182 Latino farmworkers from a 1999 North Carolina study on green tobacco sickness provided an estimated pairwise odds ratio for within-worker clustering of 3.15 (95% confidence interval (CI): 1.84, 5.41) and an estimated pairwise odds ratio for within-camp clustering of 1.90 (95% CI: 1.22, 2.97). After adjustment for risk factors, the estimated pairwise odds ratios were 2.13 (95% CI: 1.18, 3.86) and 1.41 (95% CI: 0.89, 2.24), respectively. In this paper, a comparative analysis of alternating logistic regressions with generalized estimating equations and random-effects logistic regression is presented, and the relative strengths of the three methods are discussed.

Keywords: agricultural workers’ diseases; generalized estimating equation; logistic models; odds ratio; random effect; repeated measures; Abbreviations: ALR, alternating logistic regression; GEE, generalized estimating equation; GLMM, generalized linear mixed model; GTS, green tobacco sickness; POR, pairwise odds ratio.

Journal Article.  5373 words. 

Subjects: Public Health and Epidemiology

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