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      A Survey of Laboratory and Statistical Issues Related to Farmworker Exposure Studies

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          Abstract

          Developing internally valid, and perhaps generalizable, farmworker exposure studies is a complex process that involves many statistical and laboratory considerations. Statistics are an integral component of each study beginning with the design stage and continuing to the final data analysis and interpretation. Similarly, data quality plays a significant role in the overall value of the study. Data quality can be derived from several experimental parameters including statistical design of the study and quality of environmental and biological analytical measurements. We discuss statistical and analytic issues that should be addressed in every farmworker study. These issues include study design and sample size determination, analytical methods and quality control and assurance, treatment of missing data or data below the method’s limits of detection, and post-hoc analyses of data from multiple studies.

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          Most cited references63

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          Longitudinal data analysis for discrete and continuous outcomes.

          Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.
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            Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits

            Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma.
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              Phthalates, alkylphenols, pesticides, polybrominated diphenyl ethers, and other endocrine-disrupting compounds in indoor air and dust.

              Chemicals identified as endocrine-disrupting compounds (EDCs) have widespread consumer uses, yet little is known about indoor exposure. We sampled indoor air and dust in 120 homes, analyzing for 89 organic chemicals identified as EDCs. Fifty-two compounds were detected in air and 66 were detected in dust. These are the first reported measures in residential environments for over 30 of the compounds, including several detected at the highest concentrations. The number of compounds detected per home ranged from 13 to 28 in air and from 6 to 42 in dust. The most abundant compounds in air included phthalates (plasticizers, emulsifiers), o-phenylphenol (disinfectant), 4-nonylphenol (detergent metabolite), and 4-tert-butylphenol (adhesive) with typical concentrations in the range of 50-1500 ng/m3. The penta- and tetrabrominated diphenyl ethers (flame retardants) were frequently detected in dust, and 2,3-dibromo-1-propanol, the carcinogenic intermediate of a flame retardant banned in 1977, was detected in air and dust. Twenty-three pesticides were detected in air and 27 were detected in dust, the most abundant being permethrins and the synergist piperonyl butoxide. The banned pesticides heptachlor, chlordane, methoxychlor, and DDT were also frequently detected, suggesting limited indoor degradation. Detected concentrations exceeded government health-based guidelines for 15 compounds, but no guidelines are available for 28 compounds, and existing guidelines do not consider endocrine effects. This study provides a basis for prioritizing toxicology and exposure research for individual EDCs and mixtures and provides new tools for exposure assessment in health studies.
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                Author and article information

                Journal
                Environ Health Perspect
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                0091-6765
                June 2006
                16 February 2006
                : 114
                : 6
                : 961-968
                Affiliations
                [1 ] National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
                [2 ] Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, USA
                [3 ] Battelle Memorial Institute, Columbus, Ohio, USA
                [4 ] National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
                [5 ] National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA
                [6 ] RTI International, Research Triangle Park, North Carolina, USA
                [7 ] Texas A&M University System Health Science Center, College Station, Texas, USA
                [8 ] School of Human Environmental Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [9 ] Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
                Author notes

                Address correspondence to D.B. Barr, CDC, 4770 Buford Hwy., Mailstop F17, Atlanta, GA 30341 USA. Telephone: (770) 488-7886. Fax: (770) 488-0142. E-mail: dbarr@ 123456cdc.gov

                The authors declare they have no competing financial interests.

                Article
                ehp0114-000961
                10.1289/ehp.8528
                1480509
                16760001
                f33b9708-b439-4e71-aa6c-4030d81d2de2
                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI
                History
                : 11 July 2005
                : 16 February 2006
                Categories
                Research
                Mini-Monograph

                Public health
                biomarkers,laboratory,statistics,sample size,omics,analytical methodology,limit of detection,quality control

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