You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Farmworkers can be exposed to a variety of pesticides in their work. Although educational programs such as those based on the U.S. Environmental Protection Agency (EPA) Worker Protection Standard promote preventive behaviors, including the use of personal protective equipment and hygiene, studies indicate that exposure occurs for a significant proportion of workers and their coresident family members ([@b2-ehp-118-635], [@b3-ehp-118-635]; [@b6-ehp-118-635]; [@b13-ehp-118-635]). Monitoring cholinesterase activity provides a means of assessing exposure to organophosphorus and carbamate pesticides ([@b23-ehp-118-635]). These pesticides inhibit acetylcholinesterase activity (AChE; Enzyme Commission number EC 3.1.1.2) and may produce an array of neurotoxic effects (reviewed in [@b12-ehp-118-635]). Because of the essentially irreversible binding of organophosphorus pesticides to red blood cell cholinesterase, recovery from cholinesterase depression is prolonged, reflecting the 120-day half-life of red blood cells. This, plus the wide range of normal values for cholinesterase, makes repeated measures the best means of detecting cholinesterase depression ([@b9-ehp-118-635]; [@b18-ehp-118-635]; [@b19-ehp-118-635]; [@b22-ehp-118-635]). Routine screening for cholinesterase depression is mandated in only a few places \[e.g., California and Washington State ([@b15-ehp-118-635]; [@b18-ehp-118-635])\] and then only for workers applying and handling pesticides and not those carrying out routine fieldwork.

Most research on cholinesterase in farm laborers focuses on applicators or workers exposed to high concentrations of pesticides, often in developing countries, through misuse ([@b10-ehp-118-635]; [@b16-ehp-118-635]; [@b17-ehp-118-635]; [@b21-ehp-118-635]). Although it has been hypothesized that long-term exposure to levels of pesticides too low to cause symptoms of poisoning would also be detectable through cholinesterase sampling, few published studies have used repeated sampling of cholinesterase to document such low-level exposure in workers.

In this article we focus on data collected from Latino farmworkers in North Carolina across an agricultural season. We used data on cholinesterase activity obtained from whole blood samples, pesticide dose data obtained from urine samples, and behavioral data concerning work activities obtained from self-reports. Our goals were to *a*) document patterns and variation in cholinesterase levels across the agricultural season and *b*) explore the association of cholinesterase depression with pesticide exposure across the agricultural season.

Methods
=======

Community Participatory Approach to Measuring Farmworker Pesticide Exposure (PACE3) is an ongoing translational research program addressing the health of Latino farmworkers in eastern North Carolina. PACE3 used a longitudinal design in which data were collected from participants up to four times at monthly intervals in 2007. All sampling, recruitment, and data collection protocols, including signed informed consent, were approved by the Wake Forest University School of Medicine Institutional Review Board, in compliance with all applicable U.S. requirements. Informed consent was obtained from each participant before any study data were collected.

Locale
------

Data collection was completed during the summer of 2007 in 11 counties in North Carolina with large farmworker populations: Brunswick, Columbus, Cumberland, Greene, Harnett, Johnston, Lenoir, Pitt, Sampson, Wayne, and Wilson counties. Conservative estimates by the North Carolina Employment Security Commission (unpublished data) in 2007 put the number of migrant farmworkers in these counties without H-2A (guest-worker) visas at 13,675 and with H-2A visas at 2,995, and the number of seasonal farmworkers at 5,800. The workers in these counties constitute substantial proportions of the total farmworker population in North Carolina, an estimated 36.2% of migrant workers without H-2A visas, 34.3% of migrant farmworkers with H-2A visas, and 22.8% of seasonal workers. The agricultural production in these counties varies, but the major hand-cultivated and hand-harvested crops include tobacco, sweet potatoes, and cucumbers.

Sample
------

PACE3 used a two-stage procedure to select farmworkers. Details are presented elsewhere ([@b2-ehp-118-635]). Briefly, three partnering agencies prepared lists of farmworker camps for the counties that they served. Camps were randomized and approached in order until each agency recruited a minimum number of camps and a specified number of participants. All 44 camps that were approached agreed to participate. In camps with seven or fewer residents, all farmworkers were invited to participate. In camps with more than seven residents, 8--10 farmworkers were recruited. In total, 287 farmworkers were recruited: 261 at the first round of data collection, and 26 at the second round of data collection. Of all farmworkers approached by the interviewers, 13 chose not to participate, for a participation rate of 95.7%. At the second round of data collection, 41 participants were lost to follow-up; 20 were lost at the third round, and 12 were lost at the fourth round. Four rounds of data collection were completed with 197 farmworkers, only three rounds with 27, only two rounds with 14, and only one round with 49.

Data collection
---------------

Data collection relevant to these analyses included a detailed interview, a finger-stick blood sample to measure cholinesterase, and a first morning urine void to measure pesticide metabolites. Participants were given an incentive valued at \$20 when they completed data collection for each round.

Data collection was completed from May through September 2007 by teams of data collectors who were fluent in Spanish. A detailed interview was completed with the farmworker participants at each round of data collection. At every contact, the questionnaire included items on work conditions in the 3 days before the interview and risk factors for pesticide exposure. At the first contact, the questionnaire also included items on participant personal characteristics (e.g., age, educational attainment) and current health status. The questionnaire used in these interviews was developed in English, translated by an experienced translator who was a native Spanish speaker familiar with Mexican Spanish, reviewed by four fluent Spanish speakers familiar with farmwork, and then pretested with 16 Spanish-speaking farmworkers and revised as needed.

Blood samples to measure cholinesterase activity were collected at each of the four interviews. The sample collector first cleaned one finger of the farmworker well with an alcohol wipe and then pricked the finger using a sterile lancet. The resulting blood drops were applied to 903 Protein Saver paper (Whatman Ltd., Piscataway, NJ), soaking through a printed half-inch circle that holds 75--80 μL blood. Samples were labeled, allowed to dry, placed in a paper envelope, and then sealed in a plastic bag for transport to the laboratory. Random duplicates were also collected and sent to the laboratory for analysis. Samples were labeled so that the laboratory could not identify individuals and duplicates.

The dried blood samples were delivered to the National Health and Environmental Effects Research Laboratory of the U.S. EPA's Cellular and Molecular Toxicology Branch, Neurotoxicology Division (Research Triangle Park, NC), and analyzed as previously described by [@b14-ehp-118-635]. Dried blood spots were punched out of the filter paper with a standard office hole-punch. Only those that were 98--100% saturated with blood on both sides of the filter paper were used for the final analysis. Previous work had estimated that each punch contained approximately 15 μL of fresh blood. The punch was then added to 500 μL Triton/Ellman buffer (0.1 mM sodium phosphate buffer, pH 8.0, plus 1% Triton X-100). To allow the whole blood to elute, each vial containing buffer and punch was placed in a refrigerator for 17 hr and then in a shaking water bath (26°C) for an additional 4 hr. The Triton/Ellman buffer was then removed from the vial and analyzed for cholinesterase activity. Cholinesterase activity was assessed in 15 μL of each sample using a basic Ellman assay ([@b8-ehp-118-635]) modified for use in a microtiter plate reader ([@b24-ehp-118-635]). All samples from a participant were analyzed on the same day. A reference sample (rat brain homogenate) was run on each plate to ensure consistency among plates; the coefficient of variation for these reference samples was 5%. Activity is reported as nanomoles of acetylthiocholine hydrolyzed per minute per milliliter of whole blood.

For the measurement of pesticide urinary metabolites, at the end of each interview the interviewer gave the participants urine collection containers with labels attached. Participants were instructed to fill the containers with their first void upon rising the next morning. They were assured that the urine samples would be tested for agricultural chemicals and metals only and not for the use of alcohol, drugs, or any health conditions. They were asked specifically to provide only their own urine in the containers, not that of any other workers in the camp. Participants placed their urine containers in a cooler with blue ice that was provided to them, and these coolers were retrieved by project staff. Coolers were transported to the nearest of the three collaborating community partners, and samples were aliquoted into labeled containers and placed in a laboratory freezer, where they were stored at −20°C. The urine samples were shipped on dry ice to the Centers for Disease Control and Prevention (Atlanta, GA) via overnight delivery. These samples were analyzed for pesticide metabolites as described elsewhere ([@b2-ehp-118-635]). Metabolites included those for organophosphorus insecticides (parent chemicals: acephate, chlorpyrifos and chlorpyrifos methyl, coumaphos, diazinon, isazophos, malathion, parathion and methyl parathion, pirimiphos methyl, *o*-methoate, dimethoate), carbamates (parent chemicals: bis-dithiocarbamates), pyrethroid insecticides, and herbicides.

Measures
--------

To define a depression of cholinesterase in a situation where no baseline cholinesterase values were available, the percent change from an individual's maximum value was calculated at each time point. Participants' maximum values were assumed to be the most proximal indicator of cholinesterase recovery. Possible cut points of 10%, 15%, and 20% reductions from each individual's maximum value were explored. A 15% reduction was considered a significant change, because it approximates the 20% reduction from baseline used by Washington State to trigger workplace and worker inquiries to address the problem, and because a true "baseline" or period of nonexposure was not available ([@b33-ehp-118-635]). Further, [@b20-ehp-118-635] recently observed that differences of 12.1% in red blood cell cholinesterase activity have a 95% probability of being significant departures from baseline, and [@b11-ehp-118-635] suggest that defining cholinesterase depression in terms of a 20% change results in a large number of false positives. We compared a total of 564 maximum values with another value for the same individual. Approximately half of the comparisons were ≥ 15%.

Data collected were divided into four periods roughly corresponding to months. May included data collected from 1 May to 8 June; June, from 9 June to 7 July; July, from 8 July to 5 August; and August, from 6 August to 4 September.

The number of pesticides detected ranged from 0 to 7, calculated at each data collection time point from the number of organophosphorus (acephate, chlorpyrifos, dimethoate, malathion, methamidophos, pirimiphos methyl) or carbamate (ethylene thiourea) pesticides with values exceeding the limit of detection.

Time waited to shower after returning home (in minutes) was reported by the worker for the most recent day worked before data collection. In most cases, this was the day of the data collection.

Age was treated as a continuous variable.

Farmworkers were coded as working 0, 1, 2, or 3 of the 3 days preceding the data collection. The 3-day look-back period corresponds with the period in which most pesticides are metabolized.

Data analysis
-------------

Duplicate samples from 56 randomly selected individual farmworkers were collected on the same day to assess the reproducibility of cholinesterase measurements. The distribution of cholinesterase concentrations appeared to be normal. We used a mixed-model approach to estimate the within-subject SD. The resulting coefficient of variation for the duplicate samples was 10.4%.

For the main analyses of 231 farmworkers, we first examined the pattern of change in average cholinesterase levels over time. To account for the clustered longitudinal design, we fitted a mixed-effects model that explicitly modeled the correlation of repeated measures for each individual farmworker and the correlation attributed to multiple farmworkers within the same camp. Least-square means were reported for each time period, and Tukey's method was used to evaluate pairwise differences while adjusting for multiple comparisons. Descriptive statistics, frequency counts, and percentages are presented for a depression of ≥ 15% from reference levels.

Finally, a regression model was fit to identify potential predictors for the change in cholinesterase activity. We used the difference in cholinesterase between a current month and its previous month (current -- previous) as the outcome *Y* to delineate whether recent pesticide exposure is associated with contemporaneous change in cholinesterase activity. We adopted an autoregression approach for our analysis. That is, the outcome at time *t* (or *Y~t~*) was regressed on the outcome at *t* − 1 (or *Y~t~*~−1~). The model also included the total number of detects in pesticides in the current and the previous months, and change in cholinesterase from month to month (June vs. May, July vs. June, and August vs. July). Month-to-month change in cholinesterase was included as farmworkers' endogenous variation in cholinesterase to more clearly delineate the potential effects of pesticide exposure. Multivariate models also adjusted for the maximum cholinesterase level and personal characteristics. All analyses were performed using SAS (version 9.2; SAS Institute Inc., Cary, NC), and *p*-value \< 0.05 was considered statistically significant.

Results
=======

Six individuals were removed from the analysis because of missing data due to a laboratory error (2 observations) and blood spots with inadequate saturation (100 observations). This reduced the sample from 287 to 281 farmworkers. Then 50 farmworkers were removed because they had only one cholinesterase measure, resulting in the final sample size of 231.

Most farmworkers studied were male (90.9%) ([Table 1](#t1-ehp-118-635){ref-type="table"}). They ranged in age from 18 to 70 years, with a mean (± SD) of 34.4 ± 10.6 years. More than half (52.0%) had a primary education or less. Most (97.4%) were from Mexico. All spoke Spanish, and 19.1% spoke an indigenous language as a first language. Most had worked in U.S. agriculture before, and 87.9% were migrant workers. More than three-quarters (77.1%) reported having received pesticide safety training at some time. The sample consisted of more than half (62.8%) guestworkers who had H-2A visas.

Cholinesterase levels for the sample varied by month, with the highest level in August and the lowest in June ([Table 2](#t2-ehp-118-635){ref-type="table"}). Almost half (48.1%) of workers for whom more than one measurement was available (*n* = 237) had their highest cholinesterase values in August. Depressions of 15% or more from an individual's maximum cholinesterase activity occurred throughout the season. More than half (50.5%) occurred in June and only 14.3% in August, which is consistent with the trend in mean cholinesterase across the sample. Cholinesterase levels were significantly higher in July than in June, and in August than in all other months ([Table 3](#t3-ehp-118-635){ref-type="table"}).

[Figure 1](#f1-ehp-118-635){ref-type="fig"} shows the study average compared with plots of the cholinesterase levels from 20 randomly selected study participants. These plots indicate substantial variation in patterns of cholinesterase activity among workers during the summer.

In multiple regression adjusted for age, sex, minutes waited to shower, and days worked in the field, reduction from a prior to a later time period was predicted by a greater number of organophosphorus and carbamate pesticides detected in the later time period, as well as the time effects ([Table 4](#t4-ehp-118-635){ref-type="table"}). We conducted analyses (data not shown) that included potential predictors of pesticide exposure (e.g., number of days worked in fields, time waited to shower after work). These variables were not significant, so only the most parsimonious model is shown.

Discussion
==========

These findings indicate a pattern of cholinesterase depression across the agricultural season, with the greatest number of depressions, as well as the lowest mean level of cholinesterase, occurring in June and apparent recovery in July and August. This pattern has face validity, in terms of the local agricultural cycle. [@b3-ehp-118-635] reported that approximately 40% of farmworkers worked in vegetable production and fewer than half worked in tobacco in the early to midsummer; by late summer fully 75--80% of farmworkers worked in tobacco. Most insecticides are applied during the spring and early summer and fewer pesticides late in the summer when harvest occurs. In August, when half of the maximums occurred, many of the workers are harvesting tobacco, which should receive no organophosphorus or carbamate pesticide application for several weeks before harvest ([@b3-ehp-118-635]). The number of organophosphorus and carbamate pesticides detected in urine samples collected at the same time as the cholinesterase samples is associated with cholinesterase depression, providing further evidence that the patterns observed reflect pesticide exposure.

It is likely that the cholinesterase depression detected in the blood samples was attributable solely to inhibition by the exposure to organophosphorus compounds as opposed to the carbamate pesticides. Although both are cholinesterase inhibitors, cholinesterase inhibited by carbamate pesticides will spontaneously reactivate if the sample is extensively diluted or not kept at low temperatures ([@b24-ehp-118-635]; [@b35-ehp-118-635]; [@b36-ehp-118-635]). To elute the dried blood from the filter paper, we had to subject the sample to both extensive dilution and higher temperature (26°C), so we assume that any carbamylated cholinesterase spontaneously reactivated under these conditions. Thus, if there was any cholinesterase inhibition in blood due to carbamate pesticide exposure, that inhibition likely was no longer present by the time we conducted the analysis.

We identified depressions in cholinesterase in this group of farmworkers despite the fact that training in pesticide safety is mandated by U.S. EPA for workers. These findings may indicate that training of these workers is inadequate. Indeed, estimates suggest that one-quarter to two-thirds of farmworkers do not receive training ([@b5-ehp-118-635]; [@b31-ehp-118-635]; [@b34-ehp-118-635]). Among those who received training, as many as 70% report they do not understand it ([@b34-ehp-118-635]). It is also possible that workers are not following advocated behaviors. For example, both [@b28-ehp-118-635] in California and [@b34-ehp-118-635] in North Carolina reported that 25% and 15% of workers, respectively, wear the same work clothes on ≥ 2 days, which may lead to reexposure to pesticide residues. Other behaviors, such as limited hand washing and failure to wear long sleeves and long pants, may contribute to pesticide exposure. Pesticide exposure also may reflect environmental conditions of farmworkers and not just individual behaviors. For example, farmworker housing is subject to contamination through drift from nearby fields ([@b26-ehp-118-635]; [@b32-ehp-118-635]). Facilities in a substantial number of farmworker camps have been found not to meet regulations for laundry and bathroom facilities that would allow workers to wash pesticides from clothing and from their skin ([@b34-ehp-118-635]). The cholinesterase data shown here may indicate that protections to minimize exposures are not working and need to be further evaluated and better enforced.

Most other studies of pesticide exposure have used a study design with nonexposed controls (e.g., [@b7-ehp-118-635]; [@b25-ehp-118-635]; [@b27-ehp-118-635]) or a pre--post design ([@b10-ehp-118-635]; [@b30-ehp-118-635]) to identify cholinesterase depression. Such designs are not always practical with migrant workers in the eastern United States. Workers frequently move from one crop to the next, so it is not possible to verify when they arrive at a new worksite that they are unexposed. In addition, they work on multiple crops where individual growers make decisions about applying pesticides based on particular field conditions for specific crops, which makes pre--post testing difficult without the cooperation of multiple growers ([@b2-ehp-118-635]). Also, their often poor-quality housing can expose them to pesticides in nonfield settings ([@b1-ehp-118-635]; [@b26-ehp-118-635]). Thus, this study demonstrates an analytic technique by which multiple cholinesterase measures across a season can be used to identify depressions for individuals. Its advantage is that it does not require a sample with uniform, known exposure or a control group. This approach should be validated in other worker populations.

Measurement of cholinesterase depression is used infrequently in studies of pesticide exposure in U.S. agriculture; the measurement of metabolites for groups of organophosphorus pesticides \[e.g., dialkylphosphate pesticides (DAP)\] or specific pesticides is more common. However, such studies require knowledge of what pesticides have been applied, and there must be an assay for the pesticide in question. Some organophosphorus pesticides (e.g., acephate, which is used extensively on tobacco) do not affect the common DAP metabolites, and few laboratories have appropriate assays to detect exposure. Therefore, using cholinesterase depression is an efficient way to assess pesticide exposure.

The results of this study should be interpreted in light of several shortcomings. First, data come only from farmworkers in eastern North Carolina; their exposures to pesticides and other environmental factors may differ from those in other regions. Second, because of a lack of a baseline value, a reduction in cholinesterase from any value had to be considered a depression, without regard for time sequence. Recent evidence suggests that as many as 17% of presumed cholinesterase depressions, defined in terms of a 20% change in butyrylcholinesterase, are false positives ([@b11-ehp-118-635]). Although early evidence suggests that butyrylcholinesterase is more labile than other forms ([@b29-ehp-118-635]), it is possible that our cut point for defining cholinesterase depression (15% change) may have resulted in misclassification. Third, cholinesterase values are for total cholinesterase; it is not possible to easily divide the cholinesterase into plasma and red blood cell cholinesterase because we used the dried blood spot collection method. Finally, the measure of pesticide metabolites reflects exposure only in the previous few days, whereas cholinesterase reflects cumulative exposure over many weeks. Therefore, additional repeated pesticide exposure measures over many weeks would be necessary to thoroughly characterize pesticide exposure that should be reflected in cholinesterase depressions.

Nonetheless, this study has several strengths. First, we collected data in a relatively large sample with up to four measurement points. This revealed a seasonal pattern of cholinesterase depression, as well as the variability in individual patterns to suggest a variety of exposure sources. The patterns of cholinesterase are related to exposure to organophosphorus and carbamate pesticides, although features of the laboratory analyses point primarily to organophosphorus pesticides.

Despite the development of new pesticides for use in agriculture, organophosphorus pesticides are still the most widely used insecticides. Their health effects are both immediate and long term. Farmworkers are a vulnerable population; because of language barriers and economic pressures, they are frequently not in a position to understand or to request their right to a safe workplace ([@b4-ehp-118-635]). Greater monitoring of pesticide exposure and enforcement of existing regulations is needed.

We thank S. Padilla, D. Hunter, and B. Padnos (Integrated Systems Toxicology Division, U.S. Environmental Protection Agency) for conducting the cholinesterase assays.

This research was supported by grant R01 ES008739 from the National Institute of Environmental Health Sciences.

![Cholinesterase levels by month for 20 randomly selected farmworkers demonstrates between-person variability in seasonal patterns. The thick black line indicates the sample mean.](ehp-118-635f1){#f1-ehp-118-635}

###### 

Personal characteristics of farmworkers, eastern North Carolina, 2007 (*n* = 231).

  Characteristic                            *n* (%)
  ----------------------------------------- -------------
  Sex                                       
   Male                                     210 (90.9)
   Female                                   21 (9.1)
  Age (years)                               
   18--24                                   42 (18.2)
   25--29                                   43 (18.6)
   30--39                                   79 (34.2)
   ≥ 40                                     67 (29.0)
  Educational attainment (years)            
   0--6                                     120 (52.0)
   7--9                                     81 (35.1)
   ≥ 10                                     30 (12.9)
  Country of birth                          
   Mexico                                   225 (97.4)
   United States                            1 (0.4)
   Other                                    5 (2.2)
  Language spoken                           
   English                                  20 (8.7)
   Spanish                                  231 (100.0)
   Indigenous language                      44 (19.1)
  Seasons in U.S. agriculture               
   ≤ 1                                      29 (12.7)
   2--3                                     33 (14.5)
   4--7                                     73 (32.0)
   ≥ 8                                      93 (40.8)
  Ever received pesticide safety training   
   No                                       53 (22.9)
   Yes                                      178 (77.1)
  Worker type                               
   Migrant worker                           203 (87.9)
   Seasonal worker                          28 (12.1)
  H-2A visa                                 
   No                                       86 (37.2)
   Yes                                      145 (62.8)

###### 

Cholinesterase activity (nmol/min/mL), maximum cholinesterase, and cholinesterase depressions, by time period.

  Cholinesterase activity   *n*   May              June             July             August
  ------------------------- ----- ---------------- ---------------- ---------------- ----------------
  Mean ± SE                 231   2145.06 ± 34.3   2079.59 ± 34.2   2181.06 ± 33.6   2357.94 ± 33.6
  Maximum \[*n* (%)\]       231   37 (16.0)        33 (14.3)        50 (21.7)        111 (48.1)
  Depression \[*n* (%)\]    267   73 (39.0)        97 (50.5)        67 (32.5)        30 (14.3)

###### 

Mean pairwise cholinesterase differences (nmol/min/mL), by month.

           May       June    July                                                        
  -------- --------- ------- ----------- --------- ------- ----------- --------- ------- -----------
  June     65.47     28.0    0.0915      ---       ---     ---         ---       ---     ---
  July     −36.00    27.70   0.56        −101.47   27.38   0.0013      ---       ---     ---
  August   −212.88   27.75   \< 0.0001   −278.35   27.45   \< 0.0001   −176.89   26.58   \< 0.0001

###### 

Results of multiple regression predicting change in cholinesterase from a prior to a later time period, adjusted for age, sex, minutes waited to shower, and days worked in the fields.

  Variable                       Regression parameter estimate   SE      *p*-Value
  ------------------------------ ------------------------------- ------- -----------
  Maximum cholinesterase level   0.032                           0.045   0.4748
  No. of pesticides detected                                             
   Current month                 −29.07                          14.25   0.0418
   Previous month                12.62                           14.55   0.3860
  Difference in cholinesterase                                           
   June -- May                   −42.89                          48.75   \< 0.0001
   July -- June                  144.80                          42.43   \< 0.0001
   August -- July                190.15                          41.48   \< 0.0001

[^1]: L.G. is employed by the North Carolina Farmworkers Project, an agency that advocates for better working and living conditions for farmworkers. The remaining authors declare that they have no competing financial interests.