title: Detecting patterns of occupational illness clustering with alternating logistic regressions applied to longitudinal data.
abstract: 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 farm workers 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.
authors.affiliation: Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. jpreisse@bios.unc.edu
authors.forename: John S
authors.initials: JS
authors.lastname: Preisser
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Arcury
authors.forename: Sara A
authors.initials: SA
authors.lastname: Quandt
mesh_terms: D000382:Agricultural Workers' Diseases / Q000453:epidemiology; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008137:Longitudinal Studies; D009538:Nicotine / Q000506:poisoning; D009657:North Carolina / Q000453:epidemiology; D009784:Occupational Diseases / Q000453:epidemiology*; D016273:Occupational Exposure / Q000706:statistics & numerical data; D016017:Odds Ratio; D011159:Population Surveillance / Q000379:methods; D012307:Risk Factors; D014026:Nicotiana / Q000506:poisoning; D014171:Transients and Migrants / Q000706:statistics & numerical data
chemical_list: D009538:Nicotine