In addition to determining the sampling strategy, it is important to collect data on a sufficient number of subjects and to collect a sufficient number of repeated measures on a given subject (Gilbert 1987; Kish 1965). The determination of an adequate sample size and an adequate number of repeated measures is a complex process in this setting because of the factors that contribute to clustering and correlation. In addition, one must consider the temporal and job-associated factors that lead to within-subject variability across repeated measures on an individual. For example, in cases where farmworker tasks vary substantially across different times of day or seasons, repeated samples are necessary to capture the resulting variability. The greater the variability across time, or across tasks, the greater the need for repeated measures on a given subject.

Therefore, one must determine a sampling strategy and appropriate sample size requirements, including within-subject repeat sampling, that maximize the resulting amount of information achieved with respect to the within-subject versus between-subject variability for the given problem (Ryan et al. 2000). Consideration of even a few of these factors commonly leads to complex analytical, theoretical, or simulation-based approaches for determining the appropriate sample sizes (Hendricks et al. 1996; Rochon 1998). However, both collection and measurement costs often limit the ability to collect a large series of longitudinal samples on single or multiple individuals (Rennolls 1991). Some farmworker populations are also highly mobile and perform many different tasks on a single farm or across different kinds of farms, creating additional difficulties in longitudinal exposure studies (Quandt et al. 2002).

The initial extraction from a solid matrix (e.g., dust, wipes, soil) is usually achieved using a solvent extraction technique. Choices include Soxhlet extraction, blender homogenization, sonication, and accelerated solvent extraction (ASE) (Curwin et al. 2003; Rudel et al. 2003; Wilson et al. 2003). Soxhlet extraction and blender homogenization use partitioning of the analyte into the solvent as the means for extraction. With sonication, ultrasonic energy is used to help drive the analytes into solution; with the ASE technique, both elevated temperature and pressure are used to assist in the solubilization of the analyte(s) in the solvent. Advantages of ASE include lower solvent use, faster extraction times, and less apparatus to clean. Dust, soil, dermal and surface wipes, food, air sorbents, and air filters have been extracted successfully with this technique. With any of these techniques, the choice of solvent is the critical factor. Solvents are generally matched in polarity to the target analyte(s) to maximize extraction of the target analyte(s) and minimize extraction of potential matrix interferences.

The extraction of analytes from an aqueous matrix (e.g., water, beverages, urine) is usually accomplished using either solvent partitioning into an immiscible solvent, such as dichloromethane, or solid-phase extraction (SPE). With an SPE method, analytes partition from the liquid phase into or onto the surface of solid sorbent particles. One of the most frequently used SPE phases is C₁₈; this sorbent is composed of octadecyl carbon chains chemically bound to a silica surface. Several relatively new SPE sorbents (e.g., OASIS, NEXUS, STRATA) offer a mixed polarity polymerized phase to allow for simultaneous extraction of diverse chemicals.

Cleanup of the sample extract can be achieved using any one of various chromatographic techniques. Many analytical methods for trace analysis require this step for proper operation. Although there may not be direct interferences from the matrix to a given analyte in the final detection method, a significant amount of co-extracted material may have a deleterious effect on instrument performance. This degradation in instrument performance is seen as a loss of sensitivity, loss of chromatographic resolution, or instability of calibration. Semipreparative cleanup steps such as precipitation, liquid–liquid partitioning, and gel permeation chromatography (GPC) can be used to remove large quantities of co-extracted material. For instance, GPC is routinely used to remove lipids from dietary/food and blood samples; however, this technique has also been used to remove these same types of compounds from house dust (Moate et al. 2002). The SPE technique described above for extraction can also be used as a chromatographic cleanup step (Nishioka et al. 2001). In addition to using a smaller amount of solvent, these cartridges can be stacked to optimize tandem cleanup steps, thus cutting cleanup times considerably.

The sample preparation and cleanup steps are usually the most common source of analytical error, whether systematic or random, because the sample is frequently handled by humans. Automated sample preparation techniques, such as automated SPE or GPC, are usually more precise. If the chemical is inherently incompatible with the analytical system that follows, a chemical derivatization (Bravo et al. 2002, 2004; Hardt and Angerer 2000; Hill et al. 1995; Lin et al. 2002; Moate et al. 1999; Stalikas and Pilidis 2000) or reduction procedure may also be required. The addition of steps into the sample preparation procedure usually increases the overall imprecision of the method.

The third step of the method involves detection and quantification. Although there is usually a semipreparative chromatographic cleanup step in the method, very few methods allow detection of an analyte at trace levels without high-resolution chromatography preceding the detector. Environmental and biological samples are simply too complex. Typical detection methods couple a gas chromatograph (GC) with a mass spectrometer (MS) or with a selective detector (e.g., electron capture detector, nitrogen phosphorus detector), or couple a high-performance liquid chromatograph with MS or tandem MS (MS-MS). The MS-based techniques rely on several features of the system to confirm detection: the retention time (from the chromatographic part of the system) and detection of two or three diagnostic ions that are in the correct ratio to each other. The diagnostic ions must co-maximize at the correct retention time in the proper ratio for an analyte to be considered detected. In the tandem MS-MS techniques where a selected diagnostic ion is passed from one MS through a collision chamber to the next MS, an additional confirmation of identification is obtained by having the transition from a characteristic “precursor ion” to a “product ion” at the correct retention time. This specificity of an MS-MS analysis can be very useful for analytes that have relatively low-mass ions with frequent interferences. Although MS-based systems are very reliable and especially useful for multiresidue methods, a specific detector such as a nitrogen phosphorous detector for organophosphate insecticides or an electron capture detector for organochlorine pesticides can often be used for single analyte detection or for multiresidue methods that cover a narrow compound class range.

Another analytic technique that is often employed for measuring pesticides is immunoassays (IAs) (Biagini et al. 1995; Brady et al. 1989; Dzgoev et al. 1999; Lyubimov et al. 2000; Sanderson et al. 1995; Thurman and Aga 2001). For this technique, a sample preparation step to isolate the chemical from the matrix may or may not be used. Many IAs are commercially available for selected chemicals for some types of sample matrices. However, the development of an IA for a new chemical is a lengthy process that typically requires the generation and isolation of antibodies and then the development of the assay itself. Usually ultraviolet, fluorescence, or radioactivity detection is used for the assays. IAs may be very specific for a given chemical, or they may have a great deal of cross-reactivity that can limit their utility for single pesticide identification. This cross-reactivity may allow assessment of exposure to a class of chemically related pesticides (Kaufman and Clower 1995). The LODs for IAs can vary widely; however, many have adequate sensitivity for measuring low-level exposures, but most are targeted at measuring occupational exposures. The imprecision usually ranges from 10 to 15%, and the throughput is usually quite high (> 100 samples per day).

The quantification of analytes can be accomplished using external calibration, the internal standard (IS) method, or, if MS is used for detection, the isotope dilution method. With the IS method, the IS is added at a fixed level to samples and standards. The relative ratio of response for analyte to IS is used to even out minor variations in injected volume, volatilization in the injector, transport from injector to the column, and column activity in GC-MS analyses. Where feasible, the isotope dilution method is preferable because the addition of the stable isotope at the beginning of the method (at extraction) can be used to account for all analytical method losses and ionization effects. Unfortunately, relatively few compounds that contain stable isotopes are available, and most are costly. In this case, the addition of surrogate recovery standards (SRSs) at the point of extraction and measurement of the recovery of these SRSs is used to assess (and possibly correct for) the method performance on a sample-by-sample basis. Information on extraction efficiency can be used to adjust the reported values to compare values across studies.

Although the pesticides and analytical methods may vary substantially, documented method performance is fundamental to all studies. Documenting method performance entails assessing at a minimum the accuracy, precision, and LOD. Additional useful factors to assess performance include storage stability, ruggedness, and operational range. The accuracy is assessed by measuring the percent relative recovery of analytes. To assess relative recovery, a known concentration of each analyte is added to unused samples or sample matrix to create spiked samples. The amount of analytes added should be representative of the amounts likely to be found in actual field samples. For matrices such as soil, dust, or urine, the actual matrix can be readily obtained. Care must be taken to ensure that background levels of the analytes are sufficiently low so that they do not interfere with low-level spikes to the matrix (typically, spike levels need to be about 4–5 times greater than a background level). For matrices such as dermal wipes, there is a critical need to prepare a matrix that is as similar as possible to the field matrix. To accomplish this for a manual field harvester exposure study, Boeniger et al. (Boeniger MF, Nishioka MG, Carreon T, Sanderson W, unpublished data) ground up several of the typical commodities (e.g., cauliflower leaves, strawberries, lettuce), mixed this pulp with soil, and applied the mixture to pieces of pig skin obtained from the local rendering plant. This “dirty” pigskin was wiped with a wipe moistened with isopropyl alcohol, and the wipe was then inoculated with the pesticides of interest before extraction to provide a robust assessment of method performance (Boeniger MF, Nishioka MG, Carreon T, Sanderson W, unpublished data). Pristine gauze wipes would not have been an accurate reflection of the challenges of the field samples. In many instances, however, it is difficult to simulate all of the materials in a complex matrix unless the matrix both is well characterized and provides repeatable measurements, which is not always the case with farming environments and farmworkers. The precision of the method is then assessed via the relative standard deviation of replicate spike recovery values in the chosen field matrix. Analyte recoveries are usually lower at lower analyte spike levels, particularly approaching the LOD, and this aspect of the method should be assessed in case global recovery adjustments are needed. The variability of relative recoveries among discrete matrix samples should be evaluated.

Various terms and definitions are used to describe the lowest level of accurate identification or quantification: method quantification limit, method detection limit (MDL), limit of quantification, and LOD. Because different definitions exist for each, it is essential to state the method or definition that is used. Given that samples cannot be quantified below a certain level, any nondetected analyte should be reported as < LOD or < MDL, with those limits stated. The frequency of detection is always a function of the method LOD. In later sections we discuss in more detail the issue of calculating or imputing the LOD.

Important elements of the analytical effort also include planning the quality control (QC) samples that are generated in the field and the laboratory. These samples are distinct from, and do not replace, QA samples that are typically analyzed by two different laboratories to help uncover potential bias in the data. It is preferable to use field QC samples, in addition to laboratory QC samples, to qualify method performance, because those samples will have undergone all the handling and storage of the actual field samples. In the field, QC samples include field blanks to assess whether samples have been contaminated in the field; field spikes to assess whether the analyte concentration changes during collection, shipping, and handling; and field duplicates (or replicates) to assess the variability of the concentration in the matrix. Field blanks, field spikes, and duplicates should make up 5–10% of the total field samples, with a minimum of at least three to five of each type. Laboratory QC samples should be included to ensure the laboratory portion of the study is operating correctly. Solvent method blanks, or matrix blanks, need to be processed with each sample set generated in the laboratory to ensure no laboratory contamination. Fortified method blanks and laboratory matrix blanks allow assessment of potential analyte losses from laboratory procedures.

Further QA/QC can also be incorporated into the methodology, especially for well-established methods used on a routine basis (Barr and Needham 2002). QA/QC programs typically comprise formal detailed protocols to ensure adherence to a given method and multiple testing procedures that easily allow the detection of systematic failures in the methodology (Barr and Needham 2002). The testing procedures can include proficiency testing to ensure accuracy as measured against a known reference material, repeat measurements of known matrix materials (laboratory QC) to confirm the validity of an analytical run and to measure analytical precision, “round robin” studies to confirm reproducible measurements among laboratories analyzing for pesticides or metabolites, regular verification of instrument calibration, daily assurance of minimal laboratory contamination by analyzing “blank” samples, and cross-validations to ensure that multiple analysts and instruments obtain similar analytical values. Many laboratories have adopted comprehensive QA/QC programs to ensure valid measurement results (Needham et al. 1983; Schaller et al. 1995). For instance, some public health laboratories in the United States have been certified by the Center for Medicare and Medicaid Services to comply with all QA/QC parameters outlined in the Clinical Laboratory Improvement Amendment of 1988 (1988), and many other laboratories have received International Standard Organization quality registrations. Studies conducted by pesticide industries for U.S. Environmental Protection Agency compliance are performed under Good Laboratory Practice protocols. The Federal Republic of Germany has chosen to implement a rigorous internal and external QA program for biological analyses (Lehnert et al. 1999; Schaller et al. 1991, 1995). Many parameters for implementing or improving a QA program have been published (Schaller et al. 1991; Taylor 1987; Westgard 2002).

Because pesticides are often measured using expensive instrumentation and require highly trained analysts, these measurements are usually costly. The most selective and sensitive methods are usually the most complex and can range in cost from $100 to $500 per sample analyzed. Many of the analyses are multi-analyte panels, so the cost per analyte per sample is much more reasonable. IAs are less specific and less complex; therefore, their cost is usually less than $50 per test. However, usually only one chemical can be measured per test, and new chemicals cannot be easily incorporated into the method. IAs may have potential also for screening samples to determine the necessity of further testing.

The general issue of missing data often represents a substantial consideration for analysis of pesticide exposures. They can pose serious problems in the data study by reducing statistical power and the statistical efficiency of estimation and may ultimately result in bias of estimates or distortion of the nominal type I error in tests. Potential approaches for handling missing data include use of complete data only, standard imputation, multiple imputation, and/or propensity score adjustment (Baker et al. 2006). The most simplistic approach of using only subjects with complete data reduces the effective sample size and subsequently reduces statistical power. To address this concern, a common approach is to then impute the mean value for any missing data. Doing so, however, underestimates the variability of the data (because a single value is imputed for multiple points) and subsequently produces liberal estimates of statistical significance. To circumvent this limitation, multiple imputation (Little and Rubin 2002) uses sampling from a distribution of values to appropriately estimate the variability and statistical significance. For any of these approaches, however, one must consider the underlying mechanism (Rubin 1976) leading to missing data. For instance, the most basic assumption of missing at random is typically unrealistic, especially in the context of assessing pesticide exposures; more complex mechanisms may invalidate results, producing biased estimates and inferences. One approach to evaluate potential bias is to model the probability of being missing and incorporate the results as a model covariate (Baker et al. 2006). In summary, a review of these issues and potential approaches to addressing missing data is necessary before beginning any subsequent analyses.

A special classification of missing data is those that fall below the analytical method LOD (i.e., censored data). In many studies, pesticide concentrations are often low, and frequently in a set of samples, some will have no detectable levels of a pesticide. The choice of what to do with this information is not straightforward, and investigators may choose to assume that a nondetected level is 0 or some value between 0 and the analytic LOD. The study design choices related to this issue, such as whether to collect a convenience sample or to oversample “high-end” individuals (and then adjust the data using statistical weights), are important considerations that cannot always be predicted if there are few data on a specific active ingredient or few representative measurements in the environmental medium of interest. The use of any single specific value as a substitute for missing values has potentially significant implications on measures of central tendency or variance, as do more rigorous statistical treatments (e.g., bootstrap methods or Bayesian approaches to approximate the censored distributions) that may provide more scientifically defensible answers but may still not provide insight into exposure–effect associations or differences between groups of interest. A recent report examining the effect of nondetectable values on exposure–disease associations in an epidemiologic study of non-Hodgkin lymphoma is a good illustration of this point (Lubin et al. 2004). The general approach has been to use one-half of the LOD; however, data have also been analyzed excluding the samples below the LOD, or ranges have been given. Hornung and Reed (1990) suggest that when most of the data are below the LOD, reporting a mean and standard deviation is a questionable practice and that a better description of the data would be to simply report the percentage of samples below the LOD and the range of the remaining samples.

Measurements below the LOD pose a significant challenge in biomonitoring and are not easily reconciled. The problem can be generally classified as either measuring a signal that falls within the range of instrument error or failure to measure a potentially informative signal at some low range of the data. As public health concerns continue to emphasize protection against even small exposures, detection limits represent an increasingly significant concept. The definition of a LOD, however, has not been well defined and varies conceptually and practically between studies (Currie 1988). In general, the LOD may be based on repeated background measurements (and subsequent prediction intervals), knowledge about the technical limitations of the monitoring device, or other knowledge about the nature of the measurements. Such data can be analyzed via several different strategies. Possible approaches include treating all undetectable measurements as zero, assuming all undetectable measurements are less than the minimum detectable data or assuming all undetectable measurements are less than some specified threshold.

Using a simple imputed value for each undetectable measurement, such as the one-half the LOD, can lead to bias and loss of power (Hughes 2000). He discusses the resulting bias and power and incorporation of standard survival methods for more appropriately treating the undetectable measurements as censored values. Assumptions concerning existence of a specific threshold can also be flawed because an actual separation may not exist between detectable and undetectable measurements. To address this issue, a distribution can be assigned to the probability of detection for a given measurement (Cohen and Ryan 1989). Lambert et al. (1991) used local logistic regression to estimate these probabilities and determine whether a reliable threshold existed and, if it did, estimated that threshold.

Pesticides are ubiquitous in our environment, and those that persist are present in low concentrations in many areas not subject to pesticide treatment. With the advent of new, high-sensitivity methods, the LOD problem is often superseded by a determination of whether a measured value exceeds what might be considered a “background” concentration. Variation in background levels, as well as background concentrations themselves, can be used to identify a threshold for “elevated” levels that minimizes both type I (false-positive) and type II (false-negative) errors. A variety of statistical procedures can be applied to analyze subsequent data, including both parametric and nonparametric methods. Shumway et al. (2000), for example, review the use of maximum likelihood estimation and regression on order statistics for potentially nonnormal censored data and propose corresponding exact statistics. Others define simple nonparametric statistics based on an average of background measurements (Linnet and Kondratovich 2004).

Censoring may also be a problem for such data. Samples may be below the LOD either because there is no pesticide to be measured or because the method used to assess the pesticide concentration is not sufficiently sensitive. Assessing normality in such situations is often complicated but can be evaluated via methods such as correlating the Kaplan-Meier estimates with normal probability plots (Hawkins and Gehlert 2000). Hornung and Reed (1990) proposed three methods for assessing summary statistics in the presence of significant left-censoring. The best method invokes a complex maximum likelihood statistical imputation method. However, in many cases this is not necessary. If the degree of left-censoring is not large and the data are highly skewed (geometric standard deviation ≥ 3), then substitution of the LOD/2 for the censored data is suggested. For less skewed data, substitution of the LOD divided by the square root of 2 produces reasonable estimates of summary statistics. Each of these methods assumes a certain character for the distribution. If the data are skewed, as most pesticide data are likely to be, the error estimates for the mean and any estimate of the variance are not likely to be influenced strongly by the selection of LOD method; the uncertainty in the parameter estimates is likely to be larger than the effect of the LOD choice. However, in the limit of an infinite number of samples, the results will still be biased. Deciding whether to use parametric or nonparametric methods, however, depends in part on the assessment of normality, which can be complicated by left-censoring. In this setting, normality can be evaluated by correlating the Kaplan-Meier estimates with normal probability plots (Hawkins and Gehlert 2000).

The problem of undetectable measurements may be further complicated by the existence of multiple detection limits across samples, which may, for instance, result from collection at different time points, measurement by different instruments or procedures, or confounding by systematic instrument variation. Insufficient research has been published on this topic because most methods assume a single threshold, although some of the previously described methodology generalizes to multiple censoring points. Hawkins and Gehlert (2000), for example, investigated the cases of single and double censoring for their method. Further methods for handling values below the LODs should be evaluated and applied consistently across studies.

A separate but related set of issues to missing data is the problem of measurement error, subsequent misclassification, and resulting bias. Measurement error of some type often arises in observational studies because of the use of surrogate variables (e.g., overall job task), self-reported behaviors (e.g., frequency of pesticide use), or other inexact measurements (Kauppinen 1994). Even when the surrogate measurements are relatively sensitive to the exposure of interest, substantial bias can result when making inferences about some intervention of other risk factors (Gardner et al. 2000). Various regression models have been proposed as adjustment methods for these and other problems related to measurement error (Lyles and Kupper 1997).

Biomonitoring and environmental data have been reported in the literature in a variety of ways and are a function of the subject population, sample matrix, analytical methods, and data analysis. Currently, no standard way of reporting these data exists, making it difficult to compare data among studies. Four general issues of concern have emerged: normalization, descriptive statistics, demographic categorization, and data censorship. Discussion of inter- and intralaboratory reliability is also included in this section.

One of the previously mentioned complications of evaluating farmworker exposures is the potential for correlation between observations, such as repeated measurements on a given worker or clustering within subgroups of the populations. Ignoring such correlation, that is, treating each measurement as an independent observation, underestimates the variability of the data, thus leading to overestimated precision and significance levels. A wide range of methodologies, however, exist for appropriately analyzing correlated and longitudinal data (Diggle et al. 2002). The most common approach is to adjust variance estimates via generalized estimating equations (Liang and Zeger 1986), thus allowing for appropriate estimation and inference, even when the exact structure of dependence is not known. Another approach is to treat the individual subject as a random effect and explicitly model the correlation structure through mixed effects regression models (Laird and Ware 1982). A number of publications have addressed possible methodologies for quantifying sources of variability specific to given occupational settings (Kromhout et al. 1993; Peretz 2002; Preisser et al. 2003; Rappaport 1995), many of which deal directly or indirectly with exposure assessment and/or other applications relevant to studying pesticides and associated exposures or morbidity.