Biomonitoring of Acetylcholinesterase Inhibition among Agricultural Workers Exposed to Pesticides in Tanzania: A Cross-Sectional Study

Biomonitoring of Acetylcholinesterase Inhibition among Agricultural Workers Exposed to Pesticides in Tanzania: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biomonitoring of Acetylcholinesterase Inhibition among Agricultural Workers Exposed to Pesticides in Tanzania: A Cross-Sectional Study Raphael J Mwezi, Jones A Kapeleka, Rosevera Kombo, Yusuph Owanga, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9215864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Agricultural workers in Tanzania face significant health risks from pesticide exposure, yet national biomonitoring data remain limited. This study evaluated acetylcholinesterase (AChE) inhibition as a biomarker of pesticide exposure among agricultural workers across diverse farming systems. Methods: A cross-sectional study was conducted from January 2020 to December 2025 within a safety assessment framework by the Tanzania Plant Health and Pesticides Authority. A total of 1,387 agricultural workers from five regions (Arusha, Kilimanjaro, Morogoro, Mbeya/Songwe, and Coastal areas) were recruited using multistage stratified sampling. Participants were classified as exposed (direct pesticide handlers) or apparently unexposed (reference). AChE levels were measured using the Test-mate ChE Cholinesterase Test System (Model 400). Statistical analyses included t-tests, ANOVA, mixed effects models, and correlation analyses, with adjustment for age, sex, and years of exposure where available. Results: Exposed individuals had significantly lower AChE levels than the reference group (21.5 ± 2.4 vs. 28.5 ± 6.9 U/g Hb; mean difference 7.0 U/g Hb; 95% CI: 6.5–7.5; p < 0.001; Cohen's d = 1.34). Regional variations were observed, with Morogoro showing the lowest levels (22.5 U/g Hb). Sugarcane and flower farming sectors were associated with greater enzyme inhibition. Age showed a small positive correlation with AChE levels (r = 0.06, p = 0.039). Mixed effects modeling confirmed that exposure status remained predictive after accounting for farm-level clustering (β = 6.51, p < 0.001). Conclusions: Despite the lack of detailed health information, this study provides critical biomonitoring data demonstrating significant AChE inhibition among pesticide-exposed agricultural workers in Tanzania. Routine cholinesterase biomonitoring should be incorporated into national occupational health programs. Acetylcholinesterase (AChE) Pesticide exposure Agricultural workers Biomonitoring Regional variations Tanzania Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Agriculture is the backbone of the Tanzanian economy, employing a majority of the population and contributing substantially to national GDP (URT, 2019 ). Alongside intensification and commercialization of agriculture, pesticide use has increased in Tanzania and across low- and middle-income countries, raising concerns about occupational and environmental health (Kapeleka et al., 2019b ; Manyilizu et al., 2016 ). Organophosphate and carbamate pesticides, frequently used in many crop systems, inhibit AChE, an essential enzyme for cholinergic neurotransmission (Lekei et al., 2016 ; Mwezi et al., 2020 ). AChE inhibits a well-established mechanistic biomarker of exposure and early effect for these pesticide classes (Chiaia-hernandez et al., 2017 ; Lionetto et al., 2013 ; Mwezi et al., 2020 ). Field-based AChE testing has been used in diverse settings to monitor exposures, identify overexposed workers, and guide preventive actions (Cotton et al., 2018 ; Suarez-Lopez et al., 2013 ). Point-of-care and portable AChE measurement systems (e.g., Test-mate) permit rapid assessment in remote or resource-limited contexts and have proven feasible in adolescents and agricultural communities (Chiaia-hernandez et al., 2017 ; Trueblood et al., 2019 ). Nonetheless, field assays may show greater variability compared with laboratory methods, underscoring the need for rigorous quality control and appropriate interpretation (Chen et al., 2012 ; Lionetto et al., 2013 ). Epidemiological research worldwide has repeatedly demonstrated that agricultural workers involved in pesticide handling exhibit lower cholinesterase activity and higher prevalence of related symptoms than non-handlers or reference populations (Bakand, 2012 ; Garabrant et al., 2008 ; Pascal et al., 2021 ; Vikkey et al., 2017 ). Regional studies in East Africa and neighboring regions have documented AChE suppression among flower farm workers, smallholder horticulturalists, and other agricultural populations (Mwangi, 2024 ; Rune et al., 2020 ). In Tanzania specifically, prior smaller studies have reported occupational cholinesterase inhibition and associations with safety practices and seasonal application patterns (Kapeleka et al., 2019a ; Manyilizu et al., 2016 ). However, nationally representative biomonitoring data integrating multiple regions and diverse cropping systems are limited. This multi-region cross-sectional study addresses that gap by measuring erythrocyte AChE across five regions and multiple agricultural field types in Tanzania, comparing pesticide handlers to an apparently unexposed reference group. The aims were to (1) compare AChE levels between pesticide-exposed and apparently unexposed agricultural workers; (2) investigate regional variations; (3) assess differences by agricultural field type; (4) evaluate the consistency of exposure effects across regions and field types; and (5) explore the relationship between age and AChE levels. By situating these data within regional and international evidence, this study intends to inform occupational health surveillance and targeted interventions in Tanzania. 2. Materials and Methods 2.1. Study Design and Setting This cross-sectional study was conducted from January 2020 to December 2025 within the framework of a safety assessment by the inspection sector of the Tanzanian Plant Health and Pesticides Authority (TPHPA), Arusha, Tanzania. The study was designed as a single cross-sectional survey rather than repeated sampling or surveillance; the extended timeframe reflects the phased regional rollout of the safety assessment program across five regions. Participants were recruited from five regions selected for their diverse crops, significant farming systems, and varying climates: Arusha, Kilimanjaro, Morogoro, Mbeya/Songwe, and Coastal regions (including Dar es Salaam and Coastal region) (Fig. 1 ). Arusha and Kilimanjaro primarily focus on coffee and horticulture, Morogoro on sugarcane, Mbeya/Songwe on mixed crops, and Coastal areas on horticulture and subsistence farming. 2.2. Sample Size Determination Sample size was calculated based on an anticipated mean AChE difference of 6 U/g Hb between exposed and apparently unexposed workers, with a standard deviation of 8 U/g Hb from previous studies (Kapeleka et al., 2019b ). A minimum of 190 participants per group was required to detect this difference with 95% confidence and 90% power. A design effect of 2.0 was applied to account for multistage stratified sampling and clustering at the farm level, increasing the required sample to 380 per group. To facilitate stratified analyses across regions and field types, the target sample was increased to approximately 1,400 participants, accounting for a 10% non-response or exclusion rate. 2.3. Study Population and Sampling A total of 1,387 farm workers were recruited using multistage stratified sampling. In the first stage, farms were randomly selected from regional agricultural registries, stratified by primary crop type (sugarcane, coffee, flowers, horticulture, or mixed/other). In the second stage, workers were randomly selected from each farm's employee list, with selection proportionate to farm size. 2.4. Inclusion and Exclusion Criteria Inclusion criteria were: (1) current employment in agriculture for at least six months, (2) age 18 years or older, and (3) ability to provide informed consent. Exclusion criteria included: (1) pre-existing neurological disorders (to avoid confounding unrelated to pesticide exposure), (2) current pregnancy (to avoid physiological changes in cholinesterase levels), and (3) refusal to participate. Participants were classified as exposed (n = 408) if they engaged in direct handling of pesticides, including mixing, loading, or application. Conversely, apparently unexposed (reference) workers (n = 979) included those not in direct contact with pesticides and working in areas where pesticides had not been used recently (e.g., administrative personnel or organic farming). The term "apparently unexposed" acknowledges that complete absence of pesticide exposure is unlikely in the general environment, as even non-handlers may experience environmental or bystander exposure. 2.5. Data Collection Trained research assistants conducted structured interviews to gather demographic information, including age, sex, and years of agricultural employment. Agricultural field type was recorded, and farm names were documented to account for farm-level clustering in multilevel modeling. Detailed health questions were not asked, as the study was designed as an occupational health screen rather than a clinical assessment. 2.6. Blood Sample Collection and AChE Measurement Capillary blood samples were obtained via finger puncture using sterile lancets. Samples were collected into heparinized capillary tubes and processed within 5 minutes to minimize diurnal variation in enzyme activity. Blood was analyzed using the Test-mate ChE Cholinesterase Test System (Model 400) with the AChE Erythrocyte Cholinesterase Assay Kit (Model 460) (EQM Research Inc., Cincinnati, OH, 2003). This system measures hemoglobin-adjusted erythrocyte AChE activity, a standard biomarker for monitoring exposure to organophosphate and carbamate pesticides. 2.7. Statistical Analysis Data was analyzed in R (version 4.2.1). Descriptive statistics were produced for continuous and categorical variables, and distributional assumptions were checked using Shapiro–Wilk tests and visual inspection of Q–Q plots for normality and Levene’s test for homogeneity of variances. Group comparisons of AChE levels were performed with independent-samples t-tests, with Cohen’s d reported as a measure of effect size, and regional differences were examined using one-way ANOVA followed by Tukey’s Honest Significant Difference post-hoc comparisons. A two-way ANOVA was used to evaluate main effects and the interaction between exposure status and field type. To account for clustering at the farm level, mixed-effects models with farm included as a random intercept were fitted, adjusting for age, sex, and years of exposure where those covariates were available. Pearson correlation coefficients were calculated to assess the relationship between age and AChE. Statistical significance was set at α = 0.05, with Bonferroni adjustments applied for multiple comparisons where appropriate, and 95% confidence intervals were reported for all parameter estimates. 3. Results 3.1. Participant Characteristics Participant characteristics by exposure status are summarized in Table 1 . A total of 1,387 participants were enrolled, including 408 exposed and 979 apparently unexposed individuals. The mean age was similar between groups (exposed: 34.2 ± 10.5 years; reference: 34.8 ± 10.8 years). Most of the participants were male (94.1% in the exposed group and 92.2% in the reference group). Years of agricultural employment were also comparable (exposed: 8.4 ± 6.2 years; reference: 7.9 ± 5.8 years). Table 1 Participant Characteristics by Exposure Status Characteristic Exposed (n = 408) Apparently Unexposed (n = 979) Age (years), Mean ± SD 34.2 ± 10.5 34.8 ± 10.8 Sex (% Male) 94.1% 92.2% Years of Employment, Mean ± SD 8.4 ± 6.2 7.9 ± 5.8 AChE Level (U/g Hb), Mean ± SD 21.52 ± 2.41 28.47 ± 6.93 AChE Level (U/g Hb), Median (IQR) 22.0 (20.2–23.5) 27.7 (26.0–30.0) 95% CI for Mean [21.3, 21.8] [28.0, 28.9] 3.2. AChE Levels by Exposure Status Mean AChE levels in the exposed group were 21.52 ± 2.41 U/g Hb, significantly lower than the 28.47 ± 6.93 U/g Hb observed in the reference group (Fig. 2 ). The mean difference was 6.95 U/g Hb (95% CI: 6.54–7.36), which was statistically significant (t = 22.8, df = 1385, p < 0.001) and reflected a large effect size (Cohen's d = 1.34). The median AChE level among exposed individuals (22.0 U/g Hb) was below the 25th percentile of the reference group (26.0 U/g Hb), suggesting very little overlap between the two distributions. Figure 1 shows a boxplot comparing AChE levels by exposure status, visually highlighting the marked difference with minimal overlap. Specifically, exposed workers (n = 408) had notably lower AChE levels (median = 22.0 U/g Hb) than those in the unexposed group (n = 979; median = 27.7 U/g Hb). Again, the median AChE level in the exposed group was below the 25th percentile of the reference distribution, reinforcing the minimal overlap between the two groups. 3.3. Regional Variations The regional distribution of participants and their corresponding AChE levels are presented in Table 2 . Morogoro recorded the highest proportion of exposed workers (64.1%) and had the lowest mean AChE level (22.5 ± 4.30 U/g Hb). One-way ANOVA identified significant differences in AChE levels across regions (F = 37.2, p < 0.001). Subsequent Tukey’s HSD post-hoc analysis indicated that Morogoro’s mean AChE level was significantly lower than those of all other regions (p < 0.05 for every comparison except Coastal, where p = 0.045). No significant differences were found between Arusha, Kilimanjaro, and Mbeya/Songwe. Figure 3 displays regional differences in mean AChE levels, stratified by exposure status across the five regions. In Arusha and Kilimanjaro, exposed individuals exhibited relatively higher mean AChE levels compared to other regions, although still lower than their unexposed counterparts. Morogoro had the lowest AChE levels among exposed workers (21.5 U/g Hb), whereas Arusha and Kilimanjaro recorded comparatively higher values for exposed workers (around 24–25 U/g Hb). The pattern of lower AChE levels among exposed workers compared to unexposed individuals was consistent in all regions. Table 3 details the Tukey’s HSD post-hoc results, confirming that Morogoro’s AChE levels were significantly lower than those in every other region. Table 2 Regional Distribution of Participants and AChE Levels Region N Exposed n (%) Mean AChE (U/g Hb) SD 95% CI Arusha 612 146 (23.9%) 26.8 8.72 [26.1, 27.5] Kilimanjaro 525 137 (26.1%) 27.1 4.39 [26.7, 27.5] Morogoro 142 91 (64.1%) 22.5 4.30 [21.8, 23.2] Mbeya/Songwe 66 15 (22.7%) 26.9 3.73 [26.0, 27.8] Coastal 42 19 (45.2%) 25.7 3.64 [24.6, 26.8] Table 3 Tukey's HSD Post-hoc Comparisons Between Regions Comparison Mean Difference (U/g Hb) 95% CI p-value Morogoro - Arusha -4.30 [-5.99, -2.62] < 0.001 Morogoro - Kilimanjaro -4.66 [-6.37, -2.95] < 0.001 Morogoro - Mbeya/Songwe -4.40 [-7.10, -1.71] < 0.001 Morogoro - Coastal -3.23 [-6.41, -0.05] 0.045 Arusha - Kilimanjaro -0.30 [-1.65, 1.05] 0.98 Arusha - Mbeya/Songwe -0.10 [-2.50, 2.30] 0.99 Arusha - Coastal 1.10 [-1.80, 4.00] 0.87 Kilimanjaro - Mbeya/Songwe 0.20 [-2.21, 2.61] 0.99 Kilimanjaro - Coastal 1.40 [-1.51, 4.31] 0.70 Mbeya/Songwe - Coastal 1.20 [-2.40, 4.80] 0.90 3.4. Field-Type Differences AChE levels varied by agricultural field type, with sugarcane workers showing both the highest proportion of exposed individuals (68.5%) and the lowest mean AChE level (21.9 ± 4.10 U/g Hb). In comparison, flower workers exhibited higher mean AChE levels (27.5 ± 10.00 U/g Hb) but also greater variability (Table 4 ). Figure 4 displays these differences, contrasting exposed and apparently unexposed individuals across five field categories, with error bars representing the standard error of the mean. Among exposed workers, sugarcane field workers had the lowest AChE levels (21.9 U/g Hb), while coffee and flower workers generally had higher levels. The difference in AChE levels between exposed and unexposed individuals was consistent across all field types. Results from a two-way ANOVA (summarized in Table 5 ) showed that exposure status had a strong effect on AChE levels (η² = 0.22), and field type also had a smaller but significant effect (η² = 0.01), while the interaction between exposure status and field type was not significant. Table 4 AChE Levels by Field Type Field Type N Exposed n (%) Mean AChE (U/g Hb) SD 95% CI Sugarcane 124 85 (68.5%) 21.9 4.10 [21.2, 22.6] Coffee 704 189 (26.8%) 26.6 4.06 [26.3, 26.9] Flower 451 89 (19.7%) 27.5 10.00 [26.6, 28.4] Horticulture 38 14 (36.8%) 26.3 4.38 [24.9, 27.7] Other 70 31 (44.3%) 25.8 3.46 [25.0, 26.6] Table 5 Two-way ANOVA Results Source DF Sum Sq Mean Sq F p η² Exposure Status 1 13,909 13,909 392.43 < 0.001 0.22 Field Type 4 530 133 3.74 0.005 0.01 Exposure × Field Type 4 11 3 0.07 0.990 < 0.001 Residuals 1,377 48,805 35 3.5. Mixed Effects Model Accounting for Farm-Level Clustering Using a mixed effects model with farm included as a random intercept (n = 187 farms, median of 7 workers per farm, range 1–24), exposure status continued to be a significant predictor of AChE levels, even after adjusting for clustering and potential confounding factors (see Table 6 ). The estimated difference in AChE levels between the reference and exposed groups was 6.51 U/g Hb (95% CI: 5.79–7.23; p < 0.001). Age demonstrated a small but significant positive association (β = 0.036; 95% CI: 0.002–0.071; p = 0.039), while sex and years of exposure were not significant predictors in the adjusted model. Table 6 Mixed Effects Model Results (Farm as Random Intercept) Fixed Effect Estimate SE 95% CI t p (Intercept) 20.52 0.98 [18.77, 22.28] 20.88 < 0.001 Exposure Status (reference vs. exposed) 6.51 0.37 [5.79, 7.23] 17.66 < 0.001 Age (per year) 0.036 0.018 [0.002, 0.071] 2.07 0.039 Sex (male vs. female) -0.21 0.54 [-1.27, 0.85] -0.39 0.697 Years of exposure (per year) -0.02 0.03 [-0.08, 0.04] -0.64 0.525 *Note: Random intercept variance (farm) = 2.84; residual variance = 35.2; intraclass correlation coefficient = 0.075* 3.6. Consistency of Exposure Effect Across Strata The findings from stratified analyses, as detailed in Tables 7 and 8 , provide compelling evidence for the robustness and consistency of the exposure effect on AChE levels across diverse agricultural settings. In every region examined (Table 7 ), exposure was associated with a highly significant reduction in mean AChE levels, with mean differences ranging from − 6.06 to -7.30 U/g Hb (all p < 0.001). The strength of these associations is further supported by the narrow 95% confidence intervals, indicating precise and reliable estimates. Similarly, the results of agricultural field type (Table 8 ) reinforce the universality of this effect. Significant mean reductions in AChE levels were observed across all field types, with differences ranging from − 5.84 to -6.85 U/g Hb (all p < 0.001). The magnitude and statistical significance of these reductions, regardless of crop or agricultural context, underscore the pervasive impact of occupational exposure on cholinesterase levels. Table 7 Stratified t-tests by region Region Mean Difference (U/g Hb) t df p 95% CI Arusha -6.07 -13.0 564 < 0.001 [-6.99, -5.15] Kilimanjaro -7.30 -28.5 331 < 0.001 [-7.80, -6.79] Morogoro -6.94 -15.2 117 < 0.001 [-7.84, -6.04] Mbeya/Songwe -6.20 -10.9 45.2 < 0.001 [-7.34, -5.06] Coastal -6.06 -10.1 38.6 < 0.001 [-7.28, -4.85] Table 8 Stratified t-tests by Field Type Field Type Mean Difference (U/g Hb) t df p 95% CI Coffee -6.58 -31.0 435 < 0.001 [-7.00, -6.17] Flower -6.67 -11.3 420 < 0.001 [-7.84, -5.51] Sugarcane -6.58 -13.4 88.5 < 0.001 [-7.56, -5.61] Horticulture -6.85 -8.09 35.9 < 0.001 [-8.57, -5.13] Other -5.84 -13.6 66.0 < 0.001 [-6.70, -4.98] 3.7. Relationship Between Age and AChE Levels A clear distinction emerges between exposed and apparently unexposed individuals in the relationship between age and AChE levels. Among exposed workers, there is a weak but statistically significant positive correlation between age and AChE levels (Pearson's r = 0.11, p = 0.027), while no significant correlation is observed in the reference group (r = 0.04, p = 0.21). Figure 5 shows the visualization, exposed individuals are represented by red circles and a solid regression line, while apparently unexposed individuals are depicted with blue circles and a dashed regression line; shaded bands indicate the 95% confidence intervals. These results highlight a minor age-related increase in AChE levels among those exposed, a pattern not evident in the unexposed group. 3.8. Distribution of AChE Levels by Exposure Status The distribution of AChE levels varies markedly by exposure status, with exposed individuals exhibiting values concentrated at the lower end of the scale, predominantly between 15 and 25 U/g Hb, resulting in a pronounced peak (Fig. 6 ). In contrast, apparently unexposed individuals have AChE levels that cluster around higher values, with a peak near 28 to 30 U/g Hb. Density curves superimposed on the histogram red for exposed and blue for apparently unexposed workers further illustrate this separation. The exposed group demonstrates a left-shifted distribution, while the unexposed group is centered at higher AChE levels. This substantial separation between the two distributions underscores the strong inhibitory effect of pesticide exposure on AChE activity. 3.9. Temporal Trends in AChE Levels Mean AChE levels remain consistently lower in the exposed group (21–22 U/g Hb) compared to the apparently unexposed group (27–28 U/g Hb) across the entire period from 2020 to 2025 (Fig. 7 ). Throughout these years, there is no indication of temporal trends in either group. Error bars indicate the standard error of the mean. This persistent difference highlights the enduring impact of pesticide exposure on AChE activity, with exposed workers consistently exhibiting reduced enzyme levels over time compared to their unexposed counterparts. 4. Discussion This study provides robust, multi-regional biomonitoring evidence of significant AChE inhibition among pesticide-exposed agricultural workers in Tanzania. Exposed workers had substantially lower mean erythrocyte AChE (21.52 ± 2.41 U/g Hb) than apparently unexposed workers (28.47 ± 6.93 U/g Hb), a mean difference of 6.95 U/g Hb (95% CI: 6.54–7.36), p < 0.001, with a large effect size (Cohen’s d = 1.34). The exposed-group median falling below the 25th percentile of the reference distribution highlights the minimal overlap between groups and the clinical and public-health relevance of the finding. 4.1. AChE levels between pesticide-exposed and apparently unexposed agricultural workers Our primary objective demonstrated a pronounced and statistically robust difference in AChE activity between handlers and apparently unexposed workers. The magnitude of the mean difference (~ 7 U/g Hb) and the large effect size are consistent with occupational cohorts where organophosphate and carbamate pesticides are commonly used (Farahat et al., 2016 ; Kapeleka et al., 2019a ). Comparable field studies include Egyptian cotton workers who showed substantial cholinesterase depression in relation to chlorpyrifos exposure (Farahat et al., 2016 ) and adolescent Egyptian agricultural cohorts where longitudinal biomonitoring linked urinary metabolites to AChE reductions (Crane et al., 2014 ). In Ghana, rice applicators exposed to chlorpyrifos showed elevated exposure biomarkers and health risk indicators, supporting similar occupational risk patterns in West Africa (Atabila et al., 2018). Community-level and household proximity studies further reinforce these occupational findings: Chilean rural inhabitants living near intensive agriculture exhibited blood cholinesterase alterations (Ramírez-santana et al., 2020 ) and Ecuadorian studies have shown concurrent urinary organophosphate metabolites and reduced AChE activity in adolescents (Skomal et al., 2022 ). Studies from Mexico’s subsistence farmers similarly documented AChE inhibition tied to pesticide use (Osten et al., 2010 ). These examples indicate that both direct occupational handling and community exposure can produce biologically significant cholinesterase suppression. Field-based AChE testing like the Test-mate system used in our study is widely adopted for pragmatic occupational screening (Cotton et al., 2018 ; Trueblood et al., 2019 ). However, assay variability and timing of sampling relative to exposure are important considerations; confirmatory laboratory assays and paired urinary metabolite measures improve attribution and interpretation (Chen et al., 2012 ; Garabrant et al., 2008 ). 4.2. Regional variations in AChE levels The findings of this study show significant regional heterogeneity (F = 37.2, p < 0.001): Morogoro had the lowest mean AChE (22.5 ± 4.30 U/g Hb) and the highest proportion of exposed workers (64.1%). Stratified analyses showed exposure-associated reductions in every region (mean differences − 6.06 to -7.30 U/g Hb), indicating the exposure effect is pervasive but varies in intensity. Comparable geographic patterns have been documented elsewhere. In Ethiopia, concentrated floriculture zones reported substantial AChE suppression among flower workers linked to intense organophosphate usage (Shentema et al., 2020 ). In Thailand and Myanmar, regional studies found variation in cholinesterase depression and associated symptoms among migrant and local farmworkers, reflecting differences in crop, practices, and the use of personal protective equipment (PPE) (Thetkathuek et al., 2017 ; Zaw et al., 2020 ). In Kenya and Uganda, workplace and regional heterogeneity likewise influenced exposure biomarkers (Macharia, 2015 ; Rune et al., 2020 ). Such international examples support targeted regional surveillance and interventions in zones with intensive high-input agriculture such as sugarcane in Morogoro. 4.3. Differences in AChE by agricultural field type Field-type differences in our data show sugarcane workers had the lowest mean AChE (21.9 ± 4.10 U/g Hb) and highest proportion of handlers (68.5%), while floriculture showed higher mean AChE but greater variability (27.5 ± 10.00 U/g Hb). Exposure status explained most variance (η² = 0.22), with field type contributing a smaller but significant effect (η² = 0.01). International reports echo crops specific patterns. High-input monocultures such as rice in Ghana and tea in India are associated with higher biomarker evidence of exposure (Atabila et al., 2018; Dutta & Bahadur, 2018 ). Floriculture and greenhouse crops often show variable exposures; studies in Ethiopia, Kenya, and Peru reveal marked exposure in many floriculture operations but heterogeneity tied to farm size, regulation, and worker protections (Ortiz-delgado et al., 2025 ; Shentema et al., 2020 ). Studies in Brazil and Southern India also document crop-specific exposure profiles in small-scale workers (Ademuyiwa et al., 2007 ; Assis et al., 2018 ). These global comparisons indicate that crop type and the organization of production strongly influence exposure intensity and variability. 4.4. Consistency of exposure effects across regions and field types The handler-associated reduction in AChE was consistent across regions and crop types: stratified mean differences clustered around 6–7 U/g Hb, and mixed-effects modeling (farm random intercept) affirmed exposure status as a robust predictor (β = 6.51, 95% CI: 5.79–7.23; p < 0.001). The intraclass correlation (~ 0.075) indicates some farm-level clustering but confirms that individual handler status is the primary determinant. This uniformity parallels multi-site studies where handler role, task, and intensity of application dominate over regional variation in predicting cholinesterase depression (Crane et al., 2014 ; Farahat et al., 2016 ). Programmatic experiences from Washington State and Australia show that national surveillance combined with local implementation can effectively detect and reduce overexposure (Cotton et al., 2018 ; Hofmann et al., 2011 ). In contrast, settings without systematic biomonitoring frequently document unrecognized chronic suppression and health effects (Firestone et al., 2005 ; Ramírez-santana et al., 2020 ). 4.5. Relationship between age and AChE levels Age showed a small positive association with AChE overall (r = 0.06, p = 0.039), slightly stronger among exposed workers (r = 0.11, p = 0.027). Mixed-effects modeling identified a modest age effect (β = 0.036 per year, 95% CI: 0.002–0.071; p = 0.039). Possible explanations include healthy-worker selection, task allocation differences with seniority, or physiological baseline variation (Lionetto et al., 2013 ; Zhou et al., 2021 ). International comparisons are mixed. Some child and adolescent studies indicate greater vulnerability and stronger associations between exposure and biomarker or health outcomes (Phillips et al., 2022 ; Skomal et al., 2022 ; Suarez-lopez et al., 2012 ), while adult occupational cohorts often show minimal age effects once exposure intensity is accounted for (Garabrant et al., 2008 ). Thus, while age merits recording and analytic adjustment, occupation and exposure intensity remain the dominant drivers of AChE suppression. 4.6. Policy, practice, and research recommendations The consistent and substantial AChE suppression observed across Tanzania underscores the need for urgent, coordinated action in surveillance, prevention, and research. Routine cholinesterase monitoring should be integrated into national occupational health programs, including baseline and periodic testing, clear action thresholds, and established referral pathways (Cotton et al., 2018 ; Hofmann et al., 2011 ; Karrms et al., 2008 ). Targeted interventions should prioritize high-burden regions such as Morogoro and high-risk crop sectors, including sugarcane and floriculture, through improved access to PPE, strengthened training, adoption of safer technologies such as closed mixing systems, and, where feasible, substitution with less toxic pesticides (Atabila et al., 2018; Kumar et al., 2021 ; Manyilizu et al., 2016 ; Singh & Gautam, 2021 ). Enhanced exposure assessment in sentinel populations should combine AChE monitoring with urinary biomarkers (e.g., TCPy), environmental measurements, and detailed task records to better characterize exposure–response relationships (Farahat et al., 2016 ; Garabrant et al., 2008 ; Skomal et al., 2022 ). In parallel, future research should focus on longitudinal and intervention studies to evaluate recovery patterns and the effectiveness of PPE and training, as well as investigations into genetic susceptibility and nutritional modifiers, while ensuring inclusion of informal agricultural workers and vulnerable groups such as adolescents and pregnant women. 5. Conclusions Despite the absence of detailed clinical health data, this study provides strong evidence of significant AChE inhibition among pesticide-exposed agricultural workers across diverse farming systems in Tanzania. Exposed workers consistently exhibited markedly lower AChE levels than their apparently unexposed counterparts across regions and crop types. These findings underscore the urgent need to integrate routine cholinesterase biomonitoring into national occupational health programs, including baseline and periodic testing, referral systems, and targeted training. Interventions should be tailored to high-risk sectors such as sugarcane and floriculture. Future research should prioritize longitudinal study designs, quantitative exposure assessment, evaluation of intervention effectiveness, and investigation of genetic susceptibility to better understand and mitigate pesticide-related health risks. Additionally, future studies could explore the clinical effects of pesticide exposure using both prospective and retrospective approaches. There is also an opportunity to utilize hospital records to examine patterns and outcomes of pesticide poisoning cases across different regions. Declarations Author Contributions RJM – Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. JAK – Conceptualization, Methodology, Investigation, Writing – review & editing. RK and YO – Investigation, Data curation, Writing – review & editing. SJU, MCD, WPH, RW and JMV – Conceptualization, Methodology, Supervision, Writing – review & editing – Conceptualization, Writing – review & editing and JB – Conceptualization, Investigation, Writing – review & editing. Funding: This research was supported by the Tanzania Plant Health and Pesticide Authority (TPHPA). 2.8. Ethical Considerations The research involving human participants received ethical approval from the Tanzania National Institute for Medical Research (NIMR), reference number NIMR/HQ/R.8a/Vol. IX/2742. The study was conducted in accordance with the principles of the Declaration of Helsinki and the applicable Tanzanian regulations and guidelines for health research. Data Availability Statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional data sharing policies and ethical approvals. Acknowledgments The authors express their gratitude to the Nelson Mandela African Institution of Science and Technology in Tanzania for their academic support. They also appreciate the Tanzania Plant Health and Pesticides Authority for their assistance with screening and data extraction Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Author’s Affiliations ¹Department of Global Health and Biomedical Sciences, Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania ²Department of Internal Medicine, Kilimanjaro Christian Medical Centre, Moshi, Tanzania ³Department of Internal Medicine, KCMC University, Moshi, Tanzania ⁴Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom ⁵Directorate of Pesticide Management, Tanzania Plant Health and Pesticide Authority (TPHPA), P.O. Box 3024, Arusha, Tanzania ⁶Food and Agriculture Organization of the United Nations (FAO), UN Liaison Office, Plot 13 Area D, Along Mlimwa C Road, Dodoma, Tanzania References Ademuyiwa, O., Ugbaja, R. N., Rotimi, S. O., Abam, E., Okediran, B. S., Dosumu, O. A., & Onunkwor, B. O. (2007). Erythrocyte acetylcholinesterase activity as a surrogate indicator of lead-induced neurotoxicity in occupational lead exposure in Abeokuta , Nigeria . 24 , 183–188. https://doi.org/10.1016/j.etap.2007.05.002 Assis, C. R. D., Linhares, A. G., Cabrera, M. P., Oliveira, V. M., Silva, K. C. C., Marcuschi, M., Carvalho, E. V. M. 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Exposure to cholinesterase inhibiting insecticides and blood glucose level in a population of Ugandan smallholder farmers . 58 , 713–720. https://doi.org/10.1136/oemed-2020-106439 Shentema, M. G., Kumie, A., Bråtveit, M., & Deressa, W. (2020). Pesticide Use and Serum Acetylcholinesterase Levels among Flower Farm Workers in Ethiopia — A Cross-Sectional Study . Singh, N., & Gautam, P. (2021). Neurodegenerative diseases: Impact of pesticides. Journal of Experimental Biology and Agricultural Sciences , 9 (5), 572–579. https://doi.org/10.18006/2021.9(5).572.579 Skomal, A. E., Zhang, J., Yang, K., Yen, J., Tu, X., Suarez-torres, J., Lopez-paredes, D., Calafat, A. M., Ospina, M., Martinez, D., & Suarez-lopez, J. R. (2022). Concurrent urinary organophosphate metabolites and acetylcholinesterase activity in Ecuadorian adolescents. Environmental Research , 207 (September 2021), 112163. https://doi.org/10.1016/j.envres.2021.112163 Suarez-Lopez, J. R., Himes, J. H., Jacobs, D. R., Alexander, B. H., & Gunnar, M. R. (2013). Acetylcholinesterase Activity and Neurodevelopment in Boys and Girls. Pediatrics , 132 (6), e1649–e1658. https://doi.org/10.1542/peds.2013-0108 Suarez-lopez, J. R., Jacobs, D. R., Himes, J. H., Alexander, B. H., Lazovich, D., & Gunnar, M. (2012). Lower acetylcholinesterase activity among children living with flower plantation workers. Environmental Research , 114 , 53–59. https://doi.org/10.1016/j.envres.2012.01.007 Test-mate ChE Cholinesterase Test System (Model 400) - Instruction Manual. (2003). EQM Research, Inc. , Model 400 , 18. http://www.eqmresearch.com/Manual-E.pdf Thetkathuek, A., Yenjai, P., Jaidee, W., & Jaidee, P. (2017). Pesticide Exposure and Cholinesterase Levels in Migrant Farm Workers in Thailand Pesticide Exposure and Cholinesterase Levels in Migrant Farm Workers in. Journal of Agromedicine , 22 (2), 118–130. https://doi.org/10.1080/1059924X.2017.1283276 Trueblood, A. B., Ross, J. A., Shipp, E. M., & Mcdonald, T. J. (2019). Feasibility of Portable Fingerstick Cholinesterase Testing in Adolescents in South Texas . https://doi.org/10.1177/2150132719838716 URT. (2019). Economic Impact Assessment Services for Agribusiness Policy Reforms in Tanzania . October . Vikkey, H. A., Fidel, D., Elisabeth, Y. P., Hilaire, H., Hervé, L., Badirou, A., Alain, K., Parfait, H., Fabien, G., & Benjamin, F. (2017). Risk Factors of Pesticide Poisoning and Pesticide Users ’ Cholinesterase Levels in Cotton Production Areas : Glazoué and Savè Townships , in Central Republic of Benin . https://doi.org/10.1177/1178630217704659 Zaw, T., Phyu, M. P., & Kyaw, S. (2020). Erythrocyte Acetylcholinesterase Enzyme Activity , Serum Interleukin-6 Level and Respiratory Function of Myanmar Agricultural Workers Exposed to Organophosphate Pesticides . 7 (3), 107–111. Zhou, X., Zhang, M., Wang, Y., Xia, H., Zhu, L., Li, G., Dong, H., Chen, R., Tang, S., & Yu, M. (2021). Cholinesterase homozygous genotype as susceptible biomarker of hypertriglyceridaemia for pesticide-exposed agricultural workers hypertriglyceridaemia for pesticide-exposed agricultural workers. Biomarkers , 26 (4), 335–342. https://doi.org/10.1080/1354750X.2021.1893815 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 24 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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3","display":"","copyAsset":false,"role":"figure","size":45630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean acetylcholinesterase (AChE) levels (U/g Hb) by region and exposure status.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/1e4542306c57f22209e7bf1b.png"},{"id":106533684,"identity":"efe02b3d-930a-44b7-b7ea-9047ce14647d","added_by":"auto","created_at":"2026-04-09 14:57:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean acetylcholinesterase (AChE) levels (U/g Hb) by field type and exposure status.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/62e0ef56de1f2e3fd1f1d402.png"},{"id":106533711,"identity":"e07099d6-861a-4914-b52d-2240ce721148","added_by":"auto","created_at":"2026-04-09 14:58:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of acetylcholinesterase (AChE) levels (U/g Hb) versus age (years) by exposure status.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/8c2e5797a6d784df69213bcd.png"},{"id":106533708,"identity":"171c337b-e0df-4004-ba79-9b37f422cb2a","added_by":"auto","created_at":"2026-04-09 14:58:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of acetylcholinesterase (AChE) levels (U/g Hb) by exposure status.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/4e8a62ca9d79a4b8ec006833.png"},{"id":106533712,"identity":"7c37a3d7-2ec9-4ad5-a4ed-001ad2eada9a","added_by":"auto","created_at":"2026-04-09 14:58:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":84781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends in mean acetylcholinesterase (AChE) levels (U/g Hb) by exposure status from 2020 to 2025.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/390223ee78665e7e2b977f13.png"},{"id":106533735,"identity":"30b11872-72c6-4ca8-acb0-60df6c78c264","added_by":"auto","created_at":"2026-04-09 14:58:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2298570,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9215864/v1/c4fc2407-671c-4c85-8fcf-981f8392efb3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biomonitoring of Acetylcholinesterase Inhibition among Agricultural Workers Exposed to Pesticides in Tanzania: A Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgriculture is the backbone of the Tanzanian economy, employing a majority of the population and contributing substantially to national GDP (URT, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alongside intensification and commercialization of agriculture, pesticide use has increased in Tanzania and across low- and middle-income countries, raising concerns about occupational and environmental health (Kapeleka et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Manyilizu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Organophosphate and carbamate pesticides, frequently used in many crop systems, inhibit AChE, an essential enzyme for cholinergic neurotransmission (Lekei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mwezi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AChE inhibits a well-established mechanistic biomarker of exposure and early effect for these pesticide classes (Chiaia-hernandez et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lionetto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mwezi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Field-based AChE testing has been used in diverse settings to monitor exposures, identify overexposed workers, and guide preventive actions (Cotton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Suarez-Lopez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Point-of-care and portable AChE measurement systems (e.g., Test-mate) permit rapid assessment in remote or resource-limited contexts and have proven feasible in adolescents and agricultural communities (Chiaia-hernandez et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Trueblood et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, field assays may show greater variability compared with laboratory methods, underscoring the need for rigorous quality control and appropriate interpretation (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lionetto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpidemiological research worldwide has repeatedly demonstrated that agricultural workers involved in pesticide handling exhibit lower cholinesterase activity and higher prevalence of related symptoms than non-handlers or reference populations (Bakand, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Garabrant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pascal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vikkey et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Regional studies in East Africa and neighboring regions have documented AChE suppression among flower farm workers, smallholder horticulturalists, and other agricultural populations (Mwangi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rune et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Tanzania specifically, prior smaller studies have reported occupational cholinesterase inhibition and associations with safety practices and seasonal application patterns (Kapeleka et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Manyilizu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, nationally representative biomonitoring data integrating multiple regions and diverse cropping systems are limited.\u003c/p\u003e \u003cp\u003eThis multi-region cross-sectional study addresses that gap by measuring erythrocyte AChE across five regions and multiple agricultural field types in Tanzania, comparing pesticide handlers to an apparently unexposed reference group. The aims were to (1) compare AChE levels between pesticide-exposed and apparently unexposed agricultural workers; (2) investigate regional variations; (3) assess differences by agricultural field type; (4) evaluate the consistency of exposure effects across regions and field types; and (5) explore the relationship between age and AChE levels. By situating these data within regional and international evidence, this study intends to inform occupational health surveillance and targeted interventions in Tanzania.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted from January 2020 to December 2025 within the framework of a safety assessment by the inspection sector of the Tanzanian Plant Health and Pesticides Authority (TPHPA), Arusha, Tanzania. The study was designed as a single cross-sectional survey rather than repeated sampling or surveillance; the extended timeframe reflects the phased regional rollout of the safety assessment program across five regions. Participants were recruited from five regions selected for their diverse crops, significant farming systems, and varying climates: Arusha, Kilimanjaro, Morogoro, Mbeya/Songwe, and Coastal regions (including Dar es Salaam and Coastal region) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Arusha and Kilimanjaro primarily focus on coffee and horticulture, Morogoro on sugarcane, Mbeya/Songwe on mixed crops, and Coastal areas on horticulture and subsistence farming.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample Size Determination\u003c/h2\u003e \u003cp\u003eSample size was calculated based on an anticipated mean AChE difference of 6 U/g Hb between exposed and apparently unexposed workers, with a standard deviation of 8 U/g Hb from previous studies (Kapeleka et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). A minimum of 190 participants per group was required to detect this difference with 95% confidence and 90% power. A design effect of 2.0 was applied to account for multistage stratified sampling and clustering at the farm level, increasing the required sample to 380 per group. To facilitate stratified analyses across regions and field types, the target sample was increased to approximately 1,400 participants, accounting for a 10% non-response or exclusion rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Study Population and Sampling\u003c/h2\u003e \u003cp\u003eA total of 1,387 farm workers were recruited using multistage stratified sampling. In the first stage, farms were randomly selected from regional agricultural registries, stratified by primary crop type (sugarcane, coffee, flowers, horticulture, or mixed/other). In the second stage, workers were randomly selected from each farm's employee list, with selection proportionate to farm size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria were: (1) current employment in agriculture for at least six months, (2) age 18 years or older, and (3) ability to provide informed consent. Exclusion criteria included: (1) pre-existing neurological disorders (to avoid confounding unrelated to pesticide exposure), (2) current pregnancy (to avoid physiological changes in cholinesterase levels), and (3) refusal to participate. Participants were classified as exposed (n\u0026thinsp;=\u0026thinsp;408) if they engaged in direct handling of pesticides, including mixing, loading, or application. Conversely, apparently unexposed (reference) workers (n\u0026thinsp;=\u0026thinsp;979) included those not in direct contact with pesticides and working in areas where pesticides had not been used recently (e.g., administrative personnel or organic farming). The term \"apparently unexposed\" acknowledges that complete absence of pesticide exposure is unlikely in the general environment, as even non-handlers may experience environmental or bystander exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Collection\u003c/h2\u003e \u003cp\u003eTrained research assistants conducted structured interviews to gather demographic information, including age, sex, and years of agricultural employment. Agricultural field type was recorded, and farm names were documented to account for farm-level clustering in multilevel modeling. Detailed health questions were not asked, as the study was designed as an occupational health screen rather than a clinical assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Blood Sample Collection and AChE Measurement\u003c/h2\u003e \u003cp\u003eCapillary blood samples were obtained via finger puncture using sterile lancets. Samples were collected into heparinized capillary tubes and processed within 5 minutes to minimize diurnal variation in enzyme activity. Blood was analyzed using the Test-mate ChE Cholinesterase Test System (Model 400) with the AChE Erythrocyte Cholinesterase Assay Kit (Model 460) (EQM Research Inc., Cincinnati, OH, 2003). This system measures hemoglobin-adjusted erythrocyte AChE activity, a standard biomarker for monitoring exposure to organophosphate and carbamate pesticides.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical Analysis\u003c/h2\u003e \u003cp\u003eData was analyzed in R (version 4.2.1). Descriptive statistics were produced for continuous and categorical variables, and distributional assumptions were checked using Shapiro\u0026ndash;Wilk tests and visual inspection of Q\u0026ndash;Q plots for normality and Levene\u0026rsquo;s test for homogeneity of variances. Group comparisons of AChE levels were performed with independent-samples t-tests, with Cohen\u0026rsquo;s d reported as a measure of effect size, and regional differences were examined using one-way ANOVA followed by Tukey\u0026rsquo;s Honest Significant Difference post-hoc comparisons. A two-way ANOVA was used to evaluate main effects and the interaction between exposure status and field type. To account for clustering at the farm level, mixed-effects models with farm included as a random intercept were fitted, adjusting for age, sex, and years of exposure where those covariates were available. Pearson correlation coefficients were calculated to assess the relationship between age and AChE. Statistical significance was set at α\u0026thinsp;=\u0026thinsp;0.05, with Bonferroni adjustments applied for multiple comparisons where appropriate, and 95% confidence intervals were reported for all parameter estimates.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participant Characteristics\u003c/h2\u003e \u003cp\u003eParticipant characteristics by exposure status are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 1,387 participants were enrolled, including 408 exposed and 979 apparently unexposed individuals. The mean age was similar between groups (exposed: 34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 years; reference: 34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8 years). Most of the participants were male (94.1% in the exposed group and 92.2% in the reference group). Years of agricultural employment were also comparable (exposed: 8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2 years; reference: 7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant Characteristics by Exposure Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed (n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApparently Unexposed (n\u0026thinsp;=\u0026thinsp;979)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (% Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Employment, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAChE Level (U/g Hb), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAChE Level (U/g Hb), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0 (20.2\u0026ndash;23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7 (26.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI for Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[21.3, 21.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[28.0, 28.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AChE Levels by Exposure Status\u003c/h2\u003e \u003cp\u003eMean AChE levels in the exposed group were 21.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41 U/g Hb, significantly lower than the 28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93 U/g Hb observed in the reference group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean difference was 6.95 U/g Hb (95% CI: 6.54\u0026ndash;7.36), which was statistically significant (t\u0026thinsp;=\u0026thinsp;22.8, df\u0026thinsp;=\u0026thinsp;1385, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reflected a large effect size (Cohen's d\u0026thinsp;=\u0026thinsp;1.34). The median AChE level among exposed individuals (22.0 U/g Hb) was below the 25th percentile of the reference group (26.0 U/g Hb), suggesting very little overlap between the two distributions. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a boxplot comparing AChE levels by exposure status, visually highlighting the marked difference with minimal overlap. Specifically, exposed workers (n\u0026thinsp;=\u0026thinsp;408) had notably lower AChE levels (median\u0026thinsp;=\u0026thinsp;22.0 U/g Hb) than those in the unexposed group (n\u0026thinsp;=\u0026thinsp;979; median\u0026thinsp;=\u0026thinsp;27.7 U/g Hb). Again, the median AChE level in the exposed group was below the 25th percentile of the reference distribution, reinforcing the minimal overlap between the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Regional Variations\u003c/h2\u003e \u003cp\u003eThe regional distribution of participants and their corresponding AChE levels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Morogoro recorded the highest proportion of exposed workers (64.1%) and had the lowest mean AChE level (22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30 U/g Hb). One-way ANOVA identified significant differences in AChE levels across regions (F\u0026thinsp;=\u0026thinsp;37.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequent Tukey\u0026rsquo;s HSD post-hoc analysis indicated that Morogoro\u0026rsquo;s mean AChE level was significantly lower than those of all other regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for every comparison except Coastal, where p\u0026thinsp;=\u0026thinsp;0.045). No significant differences were found between Arusha, Kilimanjaro, and Mbeya/Songwe. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays regional differences in mean AChE levels, stratified by exposure status across the five regions. In Arusha and Kilimanjaro, exposed individuals exhibited relatively higher mean AChE levels compared to other regions, although still lower than their unexposed counterparts. Morogoro had the lowest AChE levels among exposed workers (21.5 U/g Hb), whereas Arusha and Kilimanjaro recorded comparatively higher values for exposed workers (around 24\u0026ndash;25 U/g Hb). The pattern of lower AChE levels among exposed workers compared to unexposed individuals was consistent in all regions. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the Tukey\u0026rsquo;s HSD post-hoc results, confirming that Morogoro\u0026rsquo;s AChE levels were significantly lower than those in every other region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegional Distribution of Participants and AChE Levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExposed n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean AChE (U/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArusha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.1, 27.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKilimanjaro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.7, 27.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[21.8, 23.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbeya/Songwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.0, 27.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[24.6, 26.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTukey's HSD Post-hoc Comparisons Between Regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Difference (U/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro - Arusha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-5.99, -2.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro - Kilimanjaro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-6.37, -2.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro - Mbeya/Songwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-7.10, -1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro - Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-6.41, -0.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArusha - Kilimanjaro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-1.65, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArusha - Mbeya/Songwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-2.50, 2.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArusha - Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-1.80, 4.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKilimanjaro - Mbeya/Songwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-2.21, 2.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKilimanjaro - Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-1.51, 4.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbeya/Songwe - Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-2.40, 4.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Field-Type Differences\u003c/h2\u003e \u003cp\u003eAChE levels varied by agricultural field type, with sugarcane workers showing both the highest proportion of exposed individuals (68.5%) and the lowest mean AChE level (21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10 U/g Hb). In comparison, flower workers exhibited higher mean AChE levels (27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00 U/g Hb) but also greater variability (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays these differences, contrasting exposed and apparently unexposed individuals across five field categories, with error bars representing the standard error of the mean. Among exposed workers, sugarcane field workers had the lowest AChE levels (21.9 U/g Hb), while coffee and flower workers generally had higher levels. The difference in AChE levels between exposed and unexposed individuals was consistent across all field types. Results from a two-way ANOVA (summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that exposure status had a strong effect on AChE levels (η\u0026sup2; = 0.22), and field type also had a smaller but significant effect (η\u0026sup2; = 0.01), while the interaction between exposure status and field type was not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAChE Levels by Field Type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExposed n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean AChE (U/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[21.2, 22.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.3, 26.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[26.6, 28.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorticulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[24.9, 27.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[25.0, 26.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-way ANOVA Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eη\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e392.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure \u0026times; Field Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Mixed Effects Model Accounting for Farm-Level Clustering\u003c/h2\u003e \u003cp\u003eUsing a mixed effects model with farm included as a random intercept (n\u0026thinsp;=\u0026thinsp;187 farms, median of 7 workers per farm, range 1\u0026ndash;24), exposure status continued to be a significant predictor of AChE levels, even after adjusting for clustering and potential confounding factors (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The estimated difference in AChE levels between the reference and exposed groups was 6.51 U/g Hb (95% CI: 5.79\u0026ndash;7.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age demonstrated a small but significant positive association (β\u0026thinsp;=\u0026thinsp;0.036; 95% CI: 0.002\u0026ndash;0.071; p\u0026thinsp;=\u0026thinsp;0.039), while sex and years of exposure were not significant predictors in the adjusted model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed Effects Model Results (Farm as Random Intercept)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[18.77, 22.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure Status (reference vs. exposed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[5.79, 7.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.002, 0.071]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male vs. female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[-1.27, 0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of exposure (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[-0.08, 0.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Note: Random intercept variance (farm)\u0026thinsp;=\u0026thinsp;2.84; residual variance\u0026thinsp;=\u0026thinsp;35.2; intraclass correlation coefficient\u0026thinsp;=\u0026thinsp;0.075*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Consistency of Exposure Effect Across Strata\u003c/h2\u003e \u003cp\u003eThe findings from stratified analyses, as detailed in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, provide compelling evidence for the robustness and consistency of the exposure effect on AChE levels across diverse agricultural settings. In every region examined (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), exposure was associated with a highly significant reduction in mean AChE levels, with mean differences ranging from \u0026minus;\u0026thinsp;6.06 to -7.30 U/g Hb (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The strength of these associations is further supported by the narrow 95% confidence intervals, indicating precise and reliable estimates. Similarly, the results of agricultural field type (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reinforce the universality of this effect. Significant mean reductions in AChE levels were observed across all field types, with differences ranging from \u0026minus;\u0026thinsp;5.84 to -6.85 U/g Hb (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The magnitude and statistical significance of these reductions, regardless of crop or agricultural context, underscore the pervasive impact of occupational exposure on cholinesterase levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified t-tests by region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Difference (U/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArusha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-6.99, -5.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKilimanjaro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.80, -6.79]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorogoro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.84, -6.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbeya/Songwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.34, -5.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.28, -4.85]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified t-tests by Field Type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Difference (U/g Hb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.00, -6.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.84, -5.51]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-7.56, -5.61]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorticulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-8.57, -5.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e[-6.70, -4.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Relationship Between Age and AChE Levels\u003c/h2\u003e \u003cp\u003eA clear distinction emerges between exposed and apparently unexposed individuals in the relationship between age and AChE levels. Among exposed workers, there is a weak but statistically significant positive correlation between age and AChE levels (Pearson's r\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.027), while no significant correlation is observed in the reference group (r\u0026thinsp;=\u0026thinsp;0.04, p\u0026thinsp;=\u0026thinsp;0.21). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the visualization, exposed individuals are represented by red circles and a solid regression line, while apparently unexposed individuals are depicted with blue circles and a dashed regression line; shaded bands indicate the 95% confidence intervals. These results highlight a minor age-related increase in AChE levels among those exposed, a pattern not evident in the unexposed group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Distribution of AChE Levels by Exposure Status\u003c/h2\u003e \u003cp\u003eThe distribution of AChE levels varies markedly by exposure status, with exposed individuals exhibiting values concentrated at the lower end of the scale, predominantly between 15 and 25 U/g Hb, resulting in a pronounced peak (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, apparently unexposed individuals have AChE levels that cluster around higher values, with a peak near 28 to 30 U/g Hb. Density curves superimposed on the histogram red for exposed and blue for apparently unexposed workers further illustrate this separation. The exposed group demonstrates a left-shifted distribution, while the unexposed group is centered at higher AChE levels. This substantial separation between the two distributions underscores the strong inhibitory effect of pesticide exposure on AChE activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Temporal Trends in AChE Levels\u003c/h2\u003e \u003cp\u003eMean AChE levels remain consistently lower in the exposed group (21\u0026ndash;22 U/g Hb) compared to the apparently unexposed group (27\u0026ndash;28 U/g Hb) across the entire period from 2020 to 2025 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Throughout these years, there is no indication of temporal trends in either group. Error bars indicate the standard error of the mean. This persistent difference highlights the enduring impact of pesticide exposure on AChE activity, with exposed workers consistently exhibiting reduced enzyme levels over time compared to their unexposed counterparts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides robust, multi-regional biomonitoring evidence of significant AChE inhibition among pesticide-exposed agricultural workers in Tanzania. Exposed workers had substantially lower mean erythrocyte AChE (21.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41 U/g Hb) than apparently unexposed workers (28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93 U/g Hb), a mean difference of 6.95 U/g Hb (95% CI: 6.54\u0026ndash;7.36), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with a large effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.34). The exposed-group median falling below the 25th percentile of the reference distribution highlights the minimal overlap between groups and the clinical and public-health relevance of the finding.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1. AChE levels between pesticide-exposed and apparently unexposed agricultural workers\u003c/h2\u003e \u003cp\u003eOur primary objective demonstrated a pronounced and statistically robust difference in AChE activity between handlers and apparently unexposed workers. The magnitude of the mean difference (~\u0026thinsp;7 U/g Hb) and the large effect size are consistent with occupational cohorts where organophosphate and carbamate pesticides are commonly used (Farahat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kapeleka et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Comparable field studies include Egyptian cotton workers who showed substantial cholinesterase depression in relation to chlorpyrifos exposure (Farahat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and adolescent Egyptian agricultural cohorts where longitudinal biomonitoring linked urinary metabolites to AChE reductions (Crane et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In Ghana, rice applicators exposed to chlorpyrifos showed elevated exposure biomarkers and health risk indicators, supporting similar occupational risk patterns in West Africa (Atabila et al., 2018). Community-level and household proximity studies further reinforce these occupational findings: Chilean rural inhabitants living near intensive agriculture exhibited blood cholinesterase alterations (Ram\u0026iacute;rez-santana et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Ecuadorian studies have shown concurrent urinary organophosphate metabolites and reduced AChE activity in adolescents (Skomal et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies from Mexico\u0026rsquo;s subsistence farmers similarly documented AChE inhibition tied to pesticide use (Osten et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These examples indicate that both direct occupational handling and community exposure can produce biologically significant cholinesterase suppression. Field-based AChE testing like the Test-mate system used in our study is widely adopted for pragmatic occupational screening (Cotton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trueblood et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, assay variability and timing of sampling relative to exposure are important considerations; confirmatory laboratory assays and paired urinary metabolite measures improve attribution and interpretation (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Garabrant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Regional variations in AChE levels\u003c/h2\u003e \u003cp\u003eThe findings of this study show significant regional heterogeneity (F\u0026thinsp;=\u0026thinsp;37.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): Morogoro had the lowest mean AChE (22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30 U/g Hb) and the highest proportion of exposed workers (64.1%). Stratified analyses showed exposure-associated reductions in every region (mean differences\u0026thinsp;\u0026minus;\u0026thinsp;6.06 to -7.30 U/g Hb), indicating the exposure effect is pervasive but varies in intensity. Comparable geographic patterns have been documented elsewhere. In Ethiopia, concentrated floriculture zones reported substantial AChE suppression among flower workers linked to intense organophosphate usage (Shentema et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Thailand and Myanmar, regional studies found variation in cholinesterase depression and associated symptoms among migrant and local farmworkers, reflecting differences in crop, practices, and the use of personal protective equipment (PPE) (Thetkathuek et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zaw et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Kenya and Uganda, workplace and regional heterogeneity likewise influenced exposure biomarkers (Macharia, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rune et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such international examples support targeted regional surveillance and interventions in zones with intensive high-input agriculture such as sugarcane in Morogoro.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Differences in AChE by agricultural field type\u003c/h2\u003e \u003cp\u003eField-type differences in our data show sugarcane workers had the lowest mean AChE (21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10 U/g Hb) and highest proportion of handlers (68.5%), while floriculture showed higher mean AChE but greater variability (27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00 U/g Hb). Exposure status explained most variance (η\u0026sup2; = 0.22), with field type contributing a smaller but significant effect (η\u0026sup2; = 0.01). International reports echo crops specific patterns. High-input monocultures such as rice in Ghana and tea in India are associated with higher biomarker evidence of exposure (Atabila et al., 2018; Dutta \u0026amp; Bahadur, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Floriculture and greenhouse crops often show variable exposures; studies in Ethiopia, Kenya, and Peru reveal marked exposure in many floriculture operations but heterogeneity tied to farm size, regulation, and worker protections (Ortiz-delgado et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shentema et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies in Brazil and Southern India also document crop-specific exposure profiles in small-scale workers (Ademuyiwa et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Assis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These global comparisons indicate that crop type and the organization of production strongly influence exposure intensity and variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Consistency of exposure effects across regions and field types\u003c/h2\u003e \u003cp\u003eThe handler-associated reduction in AChE was consistent across regions and crop types: stratified mean differences clustered around 6\u0026ndash;7 U/g Hb, and mixed-effects modeling (farm random intercept) affirmed exposure status as a robust predictor (β\u0026thinsp;=\u0026thinsp;6.51, 95% CI: 5.79\u0026ndash;7.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The intraclass correlation (~\u0026thinsp;0.075) indicates some farm-level clustering but confirms that individual handler status is the primary determinant. This uniformity parallels multi-site studies where handler role, task, and intensity of application dominate over regional variation in predicting cholinesterase depression (Crane et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Farahat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Programmatic experiences from Washington State and Australia show that national surveillance combined with local implementation can effectively detect and reduce overexposure (Cotton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hofmann et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, settings without systematic biomonitoring frequently document unrecognized chronic suppression and health effects (Firestone et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ram\u0026iacute;rez-santana et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Relationship between age and AChE levels\u003c/h2\u003e \u003cp\u003eAge showed a small positive association with AChE overall (r\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.039), slightly stronger among exposed workers (r\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.027). Mixed-effects modeling identified a modest age effect (β\u0026thinsp;=\u0026thinsp;0.036 per year, 95% CI: 0.002\u0026ndash;0.071; p\u0026thinsp;=\u0026thinsp;0.039). Possible explanations include healthy-worker selection, task allocation differences with seniority, or physiological baseline variation (Lionetto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). International comparisons are mixed. Some child and adolescent studies indicate greater vulnerability and stronger associations between exposure and biomarker or health outcomes (Phillips et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Skomal et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Suarez-lopez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while adult occupational cohorts often show minimal age effects once exposure intensity is accounted for (Garabrant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, while age merits recording and analytic adjustment, occupation and exposure intensity remain the dominant drivers of AChE suppression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Policy, practice, and research recommendations\u003c/h2\u003e \u003cp\u003eThe consistent and substantial AChE suppression observed across Tanzania underscores the need for urgent, coordinated action in surveillance, prevention, and research. Routine cholinesterase monitoring should be integrated into national occupational health programs, including baseline and periodic testing, clear action thresholds, and established referral pathways (Cotton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hofmann et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Karrms et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Targeted interventions should prioritize high-burden regions such as Morogoro and high-risk crop sectors, including sugarcane and floriculture, through improved access to PPE, strengthened training, adoption of safer technologies such as closed mixing systems, and, where feasible, substitution with less toxic pesticides (Atabila et al., 2018; Kumar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Manyilizu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh \u0026amp; Gautam, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Enhanced exposure assessment in sentinel populations should combine AChE monitoring with urinary biomarkers (e.g., TCPy), environmental measurements, and detailed task records to better characterize exposure\u0026ndash;response relationships (Farahat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Garabrant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Skomal et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In parallel, future research should focus on longitudinal and intervention studies to evaluate recovery patterns and the effectiveness of PPE and training, as well as investigations into genetic susceptibility and nutritional modifiers, while ensuring inclusion of informal agricultural workers and vulnerable groups such as adolescents and pregnant women.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eDespite the absence of detailed clinical health data, this study provides strong evidence of significant AChE inhibition among pesticide-exposed agricultural workers across diverse farming systems in Tanzania. Exposed workers consistently exhibited markedly lower AChE levels than their apparently unexposed counterparts across regions and crop types. These findings underscore the urgent need to integrate routine cholinesterase biomonitoring into national occupational health programs, including baseline and periodic testing, referral systems, and targeted training. Interventions should be tailored to high-risk sectors such as sugarcane and floriculture. Future research should prioritize longitudinal study designs, quantitative exposure assessment, evaluation of intervention effectiveness, and investigation of genetic susceptibility to better understand and mitigate pesticide-related health risks. Additionally, future studies could explore the clinical effects of pesticide exposure using both prospective and retrospective approaches. There is also an opportunity to utilize hospital records to examine patterns and outcomes of pesticide poisoning cases across different regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRJM \u0026ndash; Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. JAK \u0026ndash; Conceptualization, Methodology, Investigation, Writing \u0026ndash; review \u0026amp; editing. RK and YO \u0026ndash; Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing. SJU, MCD, WPH, RW and JMV \u0026ndash; Conceptualization, Methodology, Supervision, Writing \u0026ndash; review \u0026amp; editing \u0026ndash; Conceptualization, Writing \u0026ndash; review \u0026amp; editing and JB \u0026ndash; Conceptualization, Investigation, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was supported by the Tanzania Plant Health and Pesticide Authority (TPHPA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research involving human participants received ethical approval from the Tanzania National Institute for Medical Research (NIMR), reference number NIMR/HQ/R.8a/Vol. IX/2742. The study was conducted in accordance with the principles of the Declaration of Helsinki and the applicable Tanzanian regulations and guidelines for health research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional data sharing policies and ethical approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Nelson Mandela African Institution of Science and Technology in Tanzania for their academic support. They also appreciate the Tanzania Plant Health and Pesticides Authority for their assistance with screening and data extraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Affiliations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026sup1;Department of Global Health and Biomedical Sciences, Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania\u003cbr\u003e\u0026nbsp;\u0026sup2;Department of Internal Medicine, Kilimanjaro Christian Medical Centre, Moshi, Tanzania\u003cbr\u003e\u0026nbsp;\u0026sup3;Department of Internal Medicine, KCMC University, Moshi, Tanzania\u003cbr\u003e\u0026nbsp;⁴Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e⁵Directorate of Pesticide Management, Tanzania Plant Health and Pesticide Authority (TPHPA), P.O. Box 3024, Arusha, Tanzania\u003cbr\u003e\u0026nbsp;⁶Food and Agriculture Organization of the United Nations (FAO), UN Liaison Office, Plot 13 Area D, Along Mlimwa C Road, Dodoma, Tanzania\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdemuyiwa, O., Ugbaja, R. N., Rotimi, S. O., Abam, E., Okediran, B. S., Dosumu, O. A., \u0026amp; Onunkwor, B. O. (2007). \u003cem\u003eErythrocyte acetylcholinesterase activity as a surrogate indicator of lead-induced neurotoxicity in occupational lead exposure in Abeokuta , Nigeria\u003c/em\u003e.\u0026nbsp;\u003cem\u003e24\u003c/em\u003e, 183\u0026ndash;188. https://doi.org/10.1016/j.etap.2007.05.002\u003c/li\u003e\n \u003cli\u003eAssis, C. R. D., Linhares, A. G., Cabrera, M. P., Oliveira, V. M., Silva, K. C. C., Marcuschi, M., Carvalho, E. V. M. M., Bezerra, R. S., \u0026amp; Jr, L. B. C. 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(2021).\u0026nbsp;\u003cem\u003eOccupational use of agrochemicals results in inhibited cholinesterase activity and altered reproductive hormone levels in male farmers from Buea , Cameroon\u003c/em\u003e. \u003cem\u003eDecember 2020\u003c/em\u003e, 192\u0026ndash;202. https://doi.org/10.1093/toxres/tfaa113\u003c/li\u003e\n \u003cli\u003ePhillips, S., Suarez-torres, J., Checkoway, H., Lopez-paredes, D., Gahagan, S., \u0026amp; Suarez-lopez, J. R. (2022). \u003cem\u003eHHS Public Access\u003c/em\u003e. 1\u0026ndash;13. https://doi.org/10.1016/j.ijheh.2021.113691.Acetylcholinesterase\u003c/li\u003e\n \u003cli\u003eRam\u0026iacute;rez-santana, M., Z\u0026uacute;\u0026ntilde;iga-venegas, L., Corral, S., Roeleveld, N., Groenewoud, H., Velden, K. Van Der, Scheepers, P. T. J., \u0026amp; Pancetti, F. (2020). \u003cem\u003eReduced neurobehavioral functioning in agricultural workers and rural inhabitants exposed to pesticides in northern Chile and its association with blood biomarkers inhibition\u003c/em\u003e. \u003cem\u003e6\u003c/em\u003e, 1\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003eRune, M., Hansen, H., J\u0026oslash;rs, E., Sandb\u0026aelig;k, A., Sekabojja, D., Ssempebwa, J. C., Mubeezi, R., Staudacher, P., Fuhrimann, S., Burdorf, A., Bibby, B. M., \u0026amp; Schl\u0026uuml;nssen, V. (2020). \u003cem\u003eExposure to cholinesterase inhibiting insecticides and blood glucose level in a population of Ugandan smallholder farmers\u003c/em\u003e. \u003cem\u003e58\u003c/em\u003e, 713\u0026ndash;720. https://doi.org/10.1136/oemed-2020-106439\u003c/li\u003e\n \u003cli\u003eShentema, M. G., Kumie, A., Br\u0026aring;tveit, M., \u0026amp; Deressa, W. (2020). \u003cem\u003ePesticide Use and Serum Acetylcholinesterase Levels among Flower Farm Workers in Ethiopia \u0026mdash; A Cross-Sectional Study\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSingh, N., \u0026amp; Gautam, P. (2021). Neurodegenerative diseases: Impact of pesticides. \u003cem\u003eJournal of Experimental Biology and Agricultural Sciences\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 572\u0026ndash;579. https://doi.org/10.18006/2021.9(5).572.579\u003c/li\u003e\n \u003cli\u003eSkomal, A. E., Zhang, J., Yang, K., Yen, J., Tu, X., Suarez-torres, J., Lopez-paredes, D., Calafat, A. M., Ospina, M., Martinez, D., \u0026amp; Suarez-lopez, J. R. (2022). Concurrent urinary organophosphate metabolites and acetylcholinesterase activity in Ecuadorian adolescents. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e207\u003c/em\u003e(September 2021), 112163. https://doi.org/10.1016/j.envres.2021.112163\u003c/li\u003e\n \u003cli\u003eSuarez-Lopez, J. R., Himes, J. H., Jacobs, D. R., Alexander, B. H., \u0026amp; Gunnar, M. R. (2013). Acetylcholinesterase Activity and Neurodevelopment in Boys and Girls. \u003cem\u003ePediatrics\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(6), e1649\u0026ndash;e1658. https://doi.org/10.1542/peds.2013-0108\u003c/li\u003e\n \u003cli\u003eSuarez-lopez, J. R., Jacobs, D. R., Himes, J. H., Alexander, B. H., Lazovich, D., \u0026amp; Gunnar, M. (2012). Lower acetylcholinesterase activity among children living with flower plantation workers. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e, 53\u0026ndash;59. https://doi.org/10.1016/j.envres.2012.01.007\u003c/li\u003e\n \u003cli\u003eTest-mate ChE Cholinesterase Test System (Model 400) - Instruction Manual. (2003). \u003cem\u003eEQM Research, Inc.\u003c/em\u003e, \u003cem\u003eModel 400\u003c/em\u003e, 18. http://www.eqmresearch.com/Manual-E.pdf\u003c/li\u003e\n \u003cli\u003eThetkathuek, A., Yenjai, P., Jaidee, W., \u0026amp; Jaidee, P. (2017). Pesticide Exposure and Cholinesterase Levels in Migrant Farm Workers in Thailand Pesticide Exposure and Cholinesterase Levels in Migrant Farm Workers in. \u003cem\u003eJournal of Agromedicine\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 118\u0026ndash;130. https://doi.org/10.1080/1059924X.2017.1283276\u003c/li\u003e\n \u003cli\u003eTrueblood, A. B., Ross, J. A., Shipp, E. M., \u0026amp; Mcdonald, T. J. (2019). \u003cem\u003eFeasibility of Portable Fingerstick Cholinesterase Testing in Adolescents in South Texas\u003c/em\u003e. https://doi.org/10.1177/2150132719838716\u003c/li\u003e\n \u003cli\u003eURT. (2019). \u003cem\u003eEconomic Impact Assessment Services for Agribusiness Policy Reforms in Tanzania\u003c/em\u003e. \u003cem\u003eOctober\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eVikkey, H. A., Fidel, D., Elisabeth, Y. P., Hilaire, H., Herv\u0026eacute;, L., Badirou, A., Alain, K., Parfait, H., Fabien, G., \u0026amp; Benjamin, F. (2017). \u003cem\u003eRisk Factors of Pesticide Poisoning and Pesticide Users \u0026rsquo; Cholinesterase Levels in Cotton Production Areas : Glazou\u0026eacute; and Sav\u0026egrave; Townships , in Central Republic of Benin\u003c/em\u003e. https://doi.org/10.1177/1178630217704659\u003c/li\u003e\n \u003cli\u003eZaw, T., Phyu, M. P., \u0026amp; Kyaw, S. (2020). \u003cem\u003eErythrocyte Acetylcholinesterase Enzyme Activity , Serum Interleukin-6 Level and Respiratory Function of Myanmar Agricultural Workers Exposed to Organophosphate Pesticides\u003c/em\u003e. \u003cem\u003e7\u003c/em\u003e(3), 107\u0026ndash;111.\u003c/li\u003e\n \u003cli\u003eZhou, X., Zhang, M., Wang, Y., Xia, H., Zhu, L., Li, G., Dong, H., Chen, R., Tang, S., \u0026amp; Yu, M. (2021). Cholinesterase homozygous genotype as susceptible biomarker of hypertriglyceridaemia for pesticide-exposed agricultural workers hypertriglyceridaemia for pesticide-exposed agricultural workers. \u003cem\u003eBiomarkers\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 335\u0026ndash;342. https://doi.org/10.1080/1354750X.2021.1893815\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-medicine-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmet","sideBox":"Learn more about [Journal of Occupational Medicine and Toxicology](http://occup-med.biomedcentral.com/)","snPcode":"12995","submissionUrl":"https://submission.nature.com/new-submission/12995/3","title":"Journal of Occupational Medicine and Toxicology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acetylcholinesterase (AChE), Pesticide exposure, Agricultural workers, Biomonitoring, Regional variations, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-9215864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9215864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Agricultural workers in Tanzania face significant health risks from pesticide exposure, yet national biomonitoring data remain limited. This study evaluated acetylcholinesterase (AChE) inhibition as a biomarker of pesticide exposure among agricultural workers across diverse farming systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study was conducted from January 2020 to December 2025 within a safety assessment framework by the Tanzania Plant Health and Pesticides Authority. A total of 1,387 agricultural workers from five regions (Arusha, Kilimanjaro, Morogoro, Mbeya/Songwe, and Coastal areas) were recruited using multistage stratified sampling. Participants were classified as \u003cstrong\u003eexposed\u003c/strong\u003e (direct pesticide handlers) or \u003cstrong\u003eapparently unexposed\u003c/strong\u003e (reference). AChE levels were measured using the Test-mate ChE Cholinesterase Test System (Model 400). Statistical analyses included t-tests, ANOVA, mixed effects models, and correlation analyses, with adjustment for age, sex, and years of exposure where available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Exposed individuals had significantly lower AChE levels than the reference group (21.5 ± 2.4 vs. 28.5 ± 6.9 U/g Hb; mean difference 7.0 U/g Hb; 95% CI: 6.5–7.5; p \u0026lt; 0.001; Cohen's d = 1.34). Regional variations were observed, with Morogoro showing the lowest levels (22.5 U/g Hb). Sugarcane and flower farming sectors were associated with greater enzyme inhibition. Age showed a small positive correlation with AChE levels (r = 0.06, p = 0.039). Mixed effects modeling confirmed that exposure status remained predictive after accounting for farm-level clustering (β = 6.51, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Despite the lack of detailed health information, this study provides critical biomonitoring data demonstrating significant AChE inhibition among pesticide-exposed agricultural workers in Tanzania. Routine cholinesterase biomonitoring should be incorporated into national occupational health programs.\u003c/p\u003e","manuscriptTitle":"Biomonitoring of Acetylcholinesterase Inhibition among Agricultural Workers Exposed to Pesticides in Tanzania: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 14:57:34","doi":"10.21203/rs.3.rs-9215864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-17T18:34:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143606068654529121396570203216612154369","date":"2026-04-02T16:03:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66077164325509582433509808232585830293","date":"2026-04-02T14:23:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T11:06:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T08:10:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T05:59:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Occupational Medicine and Toxicology","date":"2026-03-24T20:15:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-medicine-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmet","sideBox":"Learn more about [Journal of Occupational Medicine and Toxicology](http://occup-med.biomedcentral.com/)","snPcode":"12995","submissionUrl":"https://submission.nature.com/new-submission/12995/3","title":"Journal of Occupational Medicine and Toxicology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"02c0fe57-d279-4e3f-89b0-57937e725851","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T14:57:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 14:57:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9215864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9215864","identity":"rs-9215864","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}