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Cited by 61Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologyOccupational Health and Safety in Workplaces

Detecting patterns of occupational illness clustering with alternating logistic regressions applied to longitudinal data

Preisser, J. S.·2003·American Journal of Epidemiology
DOI 10.1093/aje/kwg169
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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.

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  1. [3]3. McKnight RH, Kryscio RJ, Mays JR, et al. Spatial and temporal clustering of an occupational poisoning: the example of green tobacco sickness. Stat Med 1996;15:747-57.
  2. [11]11. tion-averaged and subject-specific approaches for analyzing repeated binary outcomes. Am J Epidemiol 1998;147:694-703.
  3. [11]11. Zeger SL, Liang KY, Albert PS. Methods for longitudinal data: a generalized estimating equations approach. Biometrics 1988; 44:1049-60.
  4. [13]13. Ananth CV, Preisser JS. Bivariate logistic regression: modelling the association of small for gestational age births in twin gestations. Stat Med 1999;18:2011-23.
  5. [24]24. Little RJA, Rubin DB. Statistical analysis of missing data. New York, NY: John Wiley & Sons, 1987.

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Authors
Preisser, J. S.
Affiliations
University of North Carolina at Chapel Hill (US)
Corresponding author
Preisser, J. S.
ORCID
Preisser, J. S.
Venue
American Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
2003-09-01
Volume
158
Issue
5
Pages
495-501
ISSN
0002-9262 (ISSN-L)
DOI
10.1093/aje/kwg169
OpenAlex
W2134717052
Cited by
61
References
24
Citations by year
2021: 2 · 2020: 3 · 2019: 1 · 2018: 2 · 2017: 2 · 2016: 1 · 2015: 1 · 2014: 4 · 2013: 3 · 2012: 5
FWCI
4.09
Type
journal article
Language
en
Open access
closed
Locations
  • American Journal of Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute for Occupational Safety and Health (R01-OH03648)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Safety in Workplaces
Keywords
longitudinal data, green tobacco sickness, generalized estimating equations, random effects logistic regression
MeSH terms
Agricultural Workers' Diseases, Epidemiologic Methods, Hispanic or Latino, Humans, Longitudinal Studies, Nicotine, North Carolina, Occupational Diseases, Population Surveillance, Risk Factors, Nicotiana, Transients and Migrants, Incidence, Cluster Analysis, Logistic Models, Odds Ratio, Occupational Exposure

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