Job-specific lead exposure gradients and personal protective equipment paradox in informal occupational settings: a case-control study from The Gambia

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Sanyang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444687/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Occupational lead exposure remains a major public health concern in low- and middle-income countries (LMICs), where informal employment structures and weak regulatory oversight create unique exposure patterns. Although auto repair work is recognized as high-risk, within-industry exposure gradients and personal protective equipment (PPE) use patterns remain poorly characterized. This study aimed to assess job-specific blood lead level (BLL) gradients among auto repair workers in The Gambia and examine the relationship between occupational specialty and PPE use. Methods A cross-sectional comparative study enrolled 213 participants in the Greater Banjul Metropolitan Area: 145 exposed auto repair workers (mechanics, electricians, battery repairers, panel beaters/welders, spray painters) and 68 unexposed healthcare worker controls. BLLs were measured using the LeadCare® II system. Questionnaires captured sociodemographic characteristics, work tasks, behavioral factors, and PPE use. Kruskal-Wallis and Mann-Whitney U tests compared BLLs across specialties. Multivariable linear regression estimated adjusted differences in BLL by specialty, and Poisson regression with robust standard errors estimated prevalence ratios for high BLL (≥ 10 µg/dL). Results Exposed workers had significantly higher BLLs than controls (median: 7.40 vs. 5.80 µg/dL; p < 0.001). Within the exposed group, a 5.3-fold gradient was observed: battery repairers (35.62 µg/dL), electricians (15.17 µg/dL), panel beaters/welders (11.45 µg/dL), mechanics (9.08 µg/dL), and spray painters (6.67 µg/dL). All battery repairers (100%) had BLL ≥ 10 µg/dL. Adjusted analyses showed battery repairers had BLLs 27.06 µg/dL higher than mechanics (95% CI: 17.60–36.52; p < 0.001), and electricians had BLLs 6.05 µg/dL higher (95% CI: 1.75–10.35; p = 0.006). A PPE paradox emerged: high-risk specialties (battery repairers, electricians) reported PPE use rates of only 14.3%, compared to 31.8% in lower-risk specialties (p = 0.071). A composite risk score combining smoking, eating at work, no PPE use, and high-risk specialty showed a dose-response relationship with BLL (r = 0.20; p = 0.017). Conclusions Marked within-industry exposure gradients and an inverse relationship between risk and PPE use were identified. Findings underscore the need for targeted, specialty-specific interventions rather than blanket approaches. Battery repair and electrical work should be prioritized for regulatory attention, PPE provision, and health literacy programs in similar low-resource settings. occupational lead exposure blood lead level personal protective equipment informal sector The Gambia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lead exposure remains one of the most significant environmental and occupational health challenges facing low- and middle-income countries (LMICs) [ 1 ]. According to a landmark 2020 report by UNICEF and Pure Earth, approximately one-third of the world's children, up to 800 million globally, have blood lead levels (BLLs) at or above 5 µg/dL, the level previously used by the U.S. Centers for Disease Control and Prevention (CDC) to identify children requiring intervention [ 2 ]. Nearly half of these affected children reside in South Asia and sub-Saharan Africa (SSA), where regulatory frameworks for controlling lead exposure are often inadequate or poorly enforced. The economic consequences are substantial, with childhood lead exposure estimated to cost LMICs nearly $ 1 trillion in lost lifetime productivity [ 1 – 3 ]. Lead is a neurotoxin with no safe exposure level. Low-level chronic exposure has been linked to decreased cognitive function, reduced IQ, attention deficits, behavioral problems, and impaired academic achievement in children[ 4 – 6 ]. In adults, lead exposure is linked to hypertension, cardiovascular disease, chronic kidney disease, and reproductive toxicity[ 7 – 10 ]. Recognizing harm at lower exposure levels, the CDC updated its blood lead reference value (BLRV) from 5 µg/dL to 3.5 µg/dL in October 2021, based on the 97.5th percentile of BLLs in U.S. children aged 1–5 years from 2015–2018 National Health and Nutrition Examination Survey data[ 11 ]. Occupational exposure is a major pathway for lead contamination in LMICs. In The Gambia, lead exposure remains significant due to historical and ongoing environmental sources. Although leaded gasoline was banned in 2008, its use led to widespread contamination of air, soil, and dust, creating persistent exposure in urban areas. Recent studies show 82.1% of auto repair workers have elevated blood lead levels compared to 52.9% of healthcare worker controls, highlighting occupational and environmental risks[ 12 ]. Sources include informal recycling of lead-acid batteries and lead-containing paints in residential and commercial environments[ 13 ]. These factors indicate Gambian workers face risks from occupational and ambient lead exposure, requiring targeted interventions. Workers in lead-acid battery manufacturing and recycling, automobile repair, metal smelting, welding, painting, and electronics recycling face elevated exposure risks[ 14 , 15 ]. In SSA, the informal sector, with unregistered businesses, irregular conditions, limited oversight, and minimal occupational health infrastructure, employs a substantial workforce proportion[ 16 – 18 ]. This informality creates conditions for heightened lead exposure, including poor ventilation, lack of engineering controls, limited PPE access, and poor hygiene practices like eating at worksites[ 14 , 19 ]. The auto repair industry has been identified as a high-risk occupation for lead exposure across multiple African countries [ 20 – 23 ]. Workers in this sector are exposed to lead through contact with lead-acid batteries, leaded components, lead-based paints and primers, solders, and contaminated dust [ 8 ]. However, the auto repair industry is heterogeneous, encompassing distinct specialties with potentially different exposure profiles, including mechanics, electricians, battery repairers, panel beaters/welders, and spray painters [ 24 ]. Despite this heterogeneity, most previous studies have treated auto repair workers as a homogeneous exposure group, potentially masking important within-industry gradients that could inform targeted intervention strategies for this group. While PPE is a critical component of the hierarchy of controls for occupational hazards when engineering controls and administrative measures are insufficient [ 25 ], it is important to first understand what preventive measures are in place to mitigate risks. PPE should primarily address residual risks that cannot be eliminated through other controls. In essence, identifying existing protective practices in informal settings can help build upon these measures and strengthen interventions, with PPE serving as an additional safeguard. In well-resourced settings, the use of PPE is guided by workplace risk assessments, regulatory mandates, and occupational health services. However, in informal sector settings in LMICs, PPE availability and use patterns are shaped by different factors, including cost, accessibility, worker knowledge, risk perception, and workplace culture [ 26 ]. Understanding how PPE use relates to occupational risk within specific industries is essential for designing effective interventions. The Gambia, a small West African nation with a population of approximately 2.4 million, has a growing automotive sector concentrated in the Greater Banjul Area. The auto repair industry operates largely within the informal economy, with workshops ranging from sophisticated urban garages to roadside repair stands. Despite The Gambia's ratification of international conventions on occupational health and safety, the enforcement of workplace regulations remains limited, and comprehensive data on occupational lead exposure among auto repair workers are sparse. This study addresses critical gaps in the literature by (1) characterizing job-specific BLL gradients among different auto repair specialties in The Gambia, (2) examining the relationship between occupational specialty and PPE use patterns, (3) identifying a potential PPE paradox wherein higher-risk workers exhibit lower rates of protective behavior, and (4) developing a composite risk score to assess cumulative occupational risk factors. These findings have implications for designing targeted, specialty-specific interventions for similar informal occupational settings in LMICs. Methods Study design and setting This case-control study was conducted in the Greater Banjul Metropolitan Area, The Gambia, to compare blood lead levels (BLLs) among occupationally exposed workers and unexposed healthcare workers. The Greater Banjul Area encompasses Banjul City, Kanifing Municipality, and parts of the West Coast Region, representing the most urbanized and industrialized zone in The Gambia. The auto repair industry in this region is characterized by informal workshops that engage in vehicle maintenance, welding, painting, battery repair, and related activities. Ethical approval was obtained from the Joint Gambia Government/Medical Research Council Ethics Committee (Protocol Number: SCC 1602v1.1) and more details can be found here [ 12 ]. Participants A total of 213 participants were enrolled, comprising 145 exposed workers (cases) from the auto repair industry and 68 unexposed healthcare workers (controls) from Kanifing General Hospital. Cases were selected using simple random sampling from tax registries maintained by Brikama Area Council, Kanifing Municipal Council, and Banjul City Council. The exposed group included mechanics, panel beaters/welders, spray painters, electricians, and battery repairers. Battery recyclers, who are typically not listed in official registries, were identified using snowball sampling. This recruitment method was used for this sub-population because many operate outside formal registries in The Gambia’s informal sector. While this approach enabled inclusion of a hard-to-reach subgroup, it may limit representativeness and introduce selection bias, as workers with stronger social networks were more likely to be recruited. Consequently, findings for battery repairers should be interpreted with caution and considered indicative rather than population-wide estimates. Controls were randomly selected from the staff roster at Kanifing General Hospital. Healthcare workers were selected as controls because their occupational environment is presumed to have minimal lead exposure compared to auto repair settings. Hospitals and clinics typically lack lead-related processes making them an appropriate reference group for assessing occupational exposure differences. Additionally, healthcare workers share similar geographic, social, and urban living conditions with auto repair workers, reducing confounding from environmental factors. All participants provided informed consent prior to their enrollment [ 12 ]. Data collection Data collection involved two components: blood sample analysis and a structured questionnaire survey. Blood lead levels were measured using the LeadCare® II Blood Lead Testing System, a point-of-care device that reports values between 3.3 and 65 µg/dL [ 12 ]. The system was calibrated at the start of each workday and after every 30 samples to ensure the accuracy of the measurements. Capillary blood samples were collected by trained laboratory technicians. The structured questionnaire captured sociodemographic information (age, gender, ethnicity, marital status, education level, literacy status, family size), occupational characteristics (specialty within the auto repair industry, years of work experience, daily work hours, weekly work days), behavioral factors (smoking status, eating at workplace), specific work tasks performed (radiator repair, battery repair, panel beating/welding, painting/spraying), and PPE use patterns. Graduate students from the University of The Gambia administered questionnaires under supervision of the principal investigator. Study variables Primary outcome. Blood lead level (BLL) was analyzed as a continuous variable (µg/dL) and categorized into three levels based on established thresholds: normal (< 5 µg/dL), moderate (5–9.9 µg/dL), and high (≥ 10 µg/dL). Although the CDC updated its blood lead reference value (BLRV) to 3.5µg/dL in 2021 for identifying elevated levels in children, this study used 5µg/dL as the categorization threshold for adults based on occupational health literature and prior LMIC studies. The 5µg/dL cutoff remains widely applied in adult occupational exposure research and aligns with earlier CDC guidance, facilitating comparability with existing studies in similar contexts. Additionally, lead exposure in LMICs is likely higher due to the continued legacy of leaded gasoline use. For example, in The Gambia, lead in gasoline was only banned as recently as 2008, indicating its persistence in the mainstream environment and potential contribution to background exposure levels. Primary exposure. Occupational specialties were categorized into five groups: mechanics (reference), panel beaters/welders, spray painters, electricians, and battery repairers. A binary high-risk specialty variable was created by combining electricians and battery repairers based on their known exposure to lead-acid batteries and lead-containing electrical components. PPE use. Personal protective equipment use was assessed through self-reports regarding general PPE use and task-specific PPE use (when working on radiators, batteries, welding, or painting). The PPE types included goggles, respirators/masks, insulated gloves, safety shoes, and work suits/overalls. A binary variable was created to indicate any PPE use versus no PPE use. Covariates. Demographic variables included age (≤ 20, 21–30, 31–40, 41–50, and > 50 years), education level (no formal education, primary, secondary, and tertiary), ethnicity (Fula, Jola, Mandinka, Wollof, and others), and marital status. Occupational covariates included years of work experience ( 8 hours), and days worked per week. Behavioral factors included current/former smoking status and workplace eating. Composite risk score. A cumulative occupational risk score (range 0–4) was constructed by summing binary indicators for current smoking (1 point), eating at the workplace (1 point), no PPE use (1 point), and high-risk specialty (1 point). Statistical analysis Descriptive statistics were calculated for all variables, with continuous variables summarized as means, standard deviations, medians, and ranges and categorical variables as frequencies and percentages. Given the non-normal distribution of BLL (Shapiro-Wilk p < 0.05), non-parametric tests were used for bivariate analyses. The Kruskal-Wallis H test was used to assess differences in BLL across occupational specialties, with post-hoc pairwise comparisons using Mann-Whitney U tests with Bonferroni correction. Chi-square tests were used to examine the associations between categorical variables and BLL categories. Multivariable linear regression was used to estimate the adjusted differences in BLL by occupational specialty, with mechanics as the reference category. Three nested models were constructed: Model 1 (unadjusted, specialty only), Model 2 (adjusted for demographics: age and education), and Model 3 (fully adjusted: age, education, years of work, smoking status, and PPE use). Variance inflation factors (VIF) were calculated to assess multicollinearity, with a VIF > 10 indicating a concern. Poisson regression with robust standard errors was used to estimate prevalence ratios (PR) for high BLL (≥ 10 µg/dL) as a sensitivity analysis, given the high prevalence of the outcome. The PPE paradox was examined by calculating the Spearman correlation between specialty-level PPE use rates and mean BLL and by stratified analyses comparing PPE effects within high-risk versus lower-risk specialty groups. Task-specific analyses examined BLL differences by individual tasks (radiator repair, battery repair, welding, and painting) and task combinations. Trend tests used Spearman correlation between the composite risk score and BLL. All analyses were conducted in Python 3.12 using pandas, scipy, and statsmodels libraries. Statistical significance was set at α = 0.05 (two-tailed). Based on the multivariable regression findings, we developed a practical risk screening tool to identify high-priority workers for intensive interventions. The tool assigns weighted scores to each significant risk factor and stratifies workers into four risk categories (low, moderate, high, very high) with corresponding intervention recommendations ( Supplementary Materials S1 and S2 ). Results Participant characteristics The study included 213 participants: 145 exposed auto repair workers and 68 unexposed healthcare controls. Table 1 presents the sociodemographic and occupational characteristics of exposed workers. All exposed workers were male, with the majority aged 21–30 years (42.1%) or ≤ 20 years (27.6%). Educational attainment was low, with 26.9% having no formal education and 31.7% having only a primary education. Most workers (53.1%) had ≥ 10 years of work experience and worked > 8 hours per day (98.6%). Current or former smoking was reported by 43.4% of exposed workers, and nearly all (99.3%) reported eating at their workplace. The mean BLL among exposed workers was 11.17 µg/dL (SD = 10.96), with a median of 7.40 µg/dL (range: 3.4–65.0 µg/dL). Among the controls, mean BLL was 9.07 µg/dL (SD = 11.01), with a median of 5.80 µg/dL (range: 2.0–65.0 µg/dL). The difference between groups was statistically significant (Mann-Whitney U = 6401, p < 0.001). Among exposed workers, 82.1% had elevated BLL (≥ 5 µg/dL) compared to 52.9% of controls, and 30.3% had high BLL (≥ 10 µg/dL) compared to 23.5% of controls. Figure 5 illustrates the distribution of BLL between exposed and unexposed groups. Table 1 Sociodemographic and occupational characteristics of exposed auto repair workers (N = 145) Variable n % M (BLL) SD Mdn Age (years) ≤ 20 40 27.6 11.60 12.23 7.35 21–30 61 42.1 10.85 12.01 6.90 31–40 25 17.2 10.72 7.18 8.80 41–50 10 6.9 11.59 6.28 10.80 > 50 9 6.2 12.14 12.14 8.40 Education No formal education 39 26.9 11.32 10.17 8.00 Primary 46 31.7 10.65 9.35 7.25 Secondary 55 37.9 12.11 13.00 7.10 Tertiary 5 3.4 4.42 0.74 4.50 Years of work experience < 1 year 6 4.1 8.10 3.72 6.90 1–3 years 19 13.1 14.69 13.77 9.80 4–6 years 31 21.4 13.31 15.91 7.30 7–9 years 12 8.3 6.78 1.99 6.15 ≥ 10 years 77 53.1 10.36 8.47 7.40 Current/former smoker 63 43.4 11.34 10.98 6.90 Uses any PPE 40 27.6 10.40 9.05 7.00 Note. BLL = blood lead level (µg/dL); M = mean; SD = standard deviation; Mdn = median; PPE = personal protective equipment. Blood lead levels by occupational specialty Marked differences in BLL were observed across occupational specialties (Table 2 , Fig. 1 ). Battery repairers had the highest mean BLL (35.62 µg/dL, SD = 22.74), followed by electricians (15.17 µg/dL, SD = 11.78), panel beaters/welders (11.45 µg/dL, SD = 10.51), mechanics (9.08 µg/dL, SD = 8.53), and spray painters (6.67 µg/dL, SD = 2.44). The Kruskal-Wallis test confirmed significant differences across the specialties (H = 26.92, p < 0.001). Post-hoc pairwise comparisons revealed that battery repairers had significantly higher BLL than all other specialties (all p < 0.05), and electricians had significantly higher BLL than mechanics (p < 0.001) and spray painters (p < 0.001). All five battery repairers (100%) had high BLL (≥ 10 µg/dL), compared to 56.7% of electricians, 33.3% of panel beaters/welders, 21.7% of mechanics, and only 4.3% of spray painters. This represents a striking 5.3-fold gradient in mean BLL from the highest-risk (battery repair) to lowest-risk (spray painting) specialty within the same industry. Table 2 Blood lead levels by occupational specialty among auto repair workers Specialty n M SD Mdn Range High BLL (%) Battery repairer 5 35.62 22.74 22.40 17.3–65.0 100.0 Electrician 30 15.17 11.78 11.25 4.1–50.7 56.7 Panel beater/welder 18 11.45 10.51 7.10 4.5–43.8 33.3 Mechanic (reference) 69 9.08 8.53 6.70 3.4–65.0 21.7 Spray painter 23 6.67 2.44 6.70 3.9–14.8 4.3 Note. BLL = blood lead level (µg/dL); High BLL = ≥ 10 µg/dL. Kruskal-Wallis H = 26.92, p < .001. Battery repairers had significantly higher BLL than all other specialties ( p < .05 for all pairwise comparisons). Personal protective equipment paradox Analysis of PPE use patterns revealed a paradoxical inverse relationship between exposure risk and protective behavior (Table 3 ). Overall, only 27.6% of exposed workers reported using any form of PPE. Among high-risk specialties (battery repairers and electricians combined), only 14.3% used PPE compared with 31.8% among lower-risk specialties (χ² = 3.26, p = 0.071). By individual specialty, panel beaters/welders had the highest PPE use rate (72.2%), followed by spray painters (60.9%), battery repairers (20.0%), electricians (13.3%), and mechanics (11.6%). This pattern represents a PPE paradox (Table 3 ; Fig. 2 ): workers in specialties with the highest mean BLL (battery repair: 35.6 µg/dL; electrical work: 15.2 µg/dL) had the lowest rates of PPE adoption, while those in lower-exposure specialties (panel beating: 11.4 µg/dL; spray painting: 6.7 µg/dL) had the highest PPE use rates. The Spearman correlation between the specialty-level mean BLL and PPE use rate was negative (r = − 0.10), although not statistically significant, given the small number of specialty groups. Stratified analysis examining PPE effectiveness within risk strata found no significant difference in BLL between PPE users and non-users within either high-risk specialties (20.18 vs. 17.75 µg/dL, p = 0.56) or lower-risk specialties (9.06 vs. 8.92 µg/dL, p = 0.69), suggesting that the type, quality, or proper use of PPE may be inadequate to provide meaningful protection in this setting. Table 3 PPE use by occupational specialty Specialty n PPE users PPE rate (%) M (BLL) Risk level Battery repairer 5 1 20.0 35.62 High Electrician 30 4 13.3 15.17 High Panel beater/welder 18 13 72.2 11.45 Lower Mechanic 69 8 11.6 9.08 Lower Spray painter 23 14 60.9 6.67 Lower Note. PPE = personal protective equipment; BLL = blood lead level (µg/dL). High-risk specialties (battery repairer, electrician) combined: 14.3% PPE use. Lower-risk specialties combined: 31.8% PPE use (χ² = 3.26, p = .071). Multivariable regression analysis Table 4 and Fig. 4 present the results of multivariable regression models examining associations between occupational specialty and blood lead levels. Panel A shows results from linear regression examining adjusted differences in continuous BLL. In the fully adjusted model, battery repairers had BLL 27.14 µg/dL higher than mechanics (95% CI: 17.69–36.58, p < 0.001), and electricians had BLL 6.07 µg/dL higher than mechanics (95% CI: 1.77–10.36, p = 0.006). The differences between panel beaters/welders (β = 2.24, p = 0.452) and spray painters (β = −2.23, p = 0.408) were not statistically significant. The model explained 24.8% of variance in BLL (R² = 0.248, F(9, 135) = 4.95, p < 0.001). Panel B shows Poisson regression results with robust standard errors estimating prevalence ratios (PR) for high BLL (≥ 10 µg/dL). Battery repairers had 4.4-fold higher prevalence of high BLL compared to mechanics (PR = 4.39, 95% CI: 2.61–7.36, p < 0.001), and electricians had 2.6-fold higher prevalence (PR = 2.58, 95% CI: 1.50–4.43, p = 0.001). Spray painters showed a non-significant 80% lower prevalence compared to mechanics (PR = 0.20, 95% CI: 0.03–1.62, p = 0.132). Notably, after adjusting for specialty, the effects of age, education, smoking, and PPE use were not statistically significant in either model, suggesting that occupational specialty is the primary determinant of lead exposure in this population. The variance inflation factors were all below 5.5, indicating no substantial multicollinearity. Table 4 Multivariable regression models for blood lead level by occupational specialty (fully adjusted model) Panel A : Linear regression (Outcome: BLL in µg/dL) Variable β SE 95% CI p Battery repairer 27.14 4.78 [17.69, 36.58] < .001*** Electrician 6.07 2.17 [1.77, 10.36] .006** Panel beater/welder 2.24 2.97 [− 3.63, 8.12] .452 Spray painter −2.23 2.68 [− 7.53, 3.08] .408 Age (years) 0.05 0.10 [− 0.14, 0.24] .587 Education (ordinal) 0.34 1.02 [− 1.67, 2.36] .736 Current smoker 0.11 1.70 [− 3.25, 3.47] .948 Uses PPE −0.06 2.28 [− 4.56, 4.44] .980 Note. Reference category: Mechanic. Model R ² = .248, adjusted R ² = .198, F (9, 135) = 4.95, p < .001. β = unstandardized coefficient. Panel B : Poisson regression with Robust SE (Outcome: High BLL ≥ 10 µg/dL) Variable PR SE 95% CI p Battery repairer 4.39 1.15 [2.61, 7.36] < .001*** Electrician 2.58 0.71 [1.50, 4.43] .001** Panel beater/welder 1.39 0.58 [0.61, 3.17] .430 Spray painter 0.20 0.22 [0.03, 1.62] .132 Age (years) 1.02 0.01 [1.00, 1.04] .117 Education (ordinal) 0.87 0.11 [0.67, 1.13] .310 Current smoker 0.96 0.24 [0.59, 1.56] .877 Uses PPE 0.87 0.29 [0.45, 1.68] .675 Note. Reference category: Mechanic. PR = prevalence ratio; SE = robust standard error; CI = confidence interval. High BLL defined as ≥ 10 µg/dL. Model deviance = 83.07; Pearson χ² = 97.43. ** p < .01. *** p < .001. Composite occupational risk score A dose-response relationship was observed between the composite occupational risk score and BLL (Table 5 ; Fig. 3 ). Workers with the lowest risk score (score = 1) had a mean BLL of 8.81 µg/dL (n = 23), while those with the highest score (score = 4) had a mean BLL of 16.10 µg/dL (n = 13). The Spearman correlation between risk score and BLL was positive and statistically significant (r = 0.20, p = 0.017), indicating a modest but meaningful trend of increasing BLL with cumulative risk factor exposure. Using the composite risk algorithm based on the four classified risk strata, a field-ready screening tool for operationalizing this risk stratification is provided in Supplementary Material S1and S2 . Table 5 Blood lead levels by composite occupational risk score Risk score n M SD Mdn Components 1 23 8.81 5.36 7.20 1 risk factor only 2 55 9.15 9.80 6.90 2 risk factors 3 54 13.03 13.28 7.50 3 risk factors 4 13 16.10 10.26 14.20 All 4 risk factors Note. Risk score components: current smoking (1 point), eating at workplace (1 point), no PPE use (1 point), high-risk specialty (1 point). Spearman r = .20, p = .017. Discussion This study provides novel evidence of marked within-industry blood lead level gradients among auto repair workers in The Gambia and identifies a paradoxical inverse relationship between occupational risk and personal protective equipment use. These findings challenge the conventional approach of treating informal occupational sectors as homogeneous exposure groups and highlight the need for targeted, specialty-specific interventions in similar low-resource settings. Job-specific lead exposure gradients The most striking finding of this study was the 5.3-fold gradient in the mean BLL across occupational specialties within the same industry, ranging from 35.62 µg/dL among battery repairers to 6.67 µg/dL among spray painters. This gradient persisted after adjusting for demographic factors, work duration, smoking status, and PPE use, indicating that occupational specialty is the primary determinant of exposure in this population. The magnitude of this within-industry variation exceeds that typically observed between broad occupational categories in LMIC settings. Battery repairers exhibited dramatically elevated BLLs, with all five workers in this specialty having BLL ≥ 10 µg/dL and a mean nearly four times the exposure threshold. This finding is consistent with the known toxicity of lead-acid battery work, where direct contact with lead plates, lead oxide paste, and lead sulfate creates multiple exposure pathways, including inhalation of lead dust and fumes and dermal absorption [ 20 , 27 ]. The small sample size of battery repairers (n = 5) reflects their relative scarcity in the formal sampling frame, as many battery recyclers operate outside registered business registries and are identified through snowball sampling. Despite the limited sample size, the consistent elevation of BLLs among all battery repairers (range: 17.3–65.0 µg/dL) underscores the severity of exposure in this specialty. Electricians represented the second-highest exposure group (mean BLL = 15.17 µg/dL), with over half (56.7%) having a BLL ≥ 10 µg/dL. Automotive electrical work involves frequent handling of lead-acid batteries, lead-soldered connections, and lead-containing components. Unlike battery repairers, who work exclusively with batteries, electricians perform diverse tasks that may dilute their exposure through time spent on non-lead-related activities [ 8 , 28 ]. However, cumulative exposure from regular battery handling appears to be sufficient to substantially elevate BLLs. The relatively lower BLLs among spray painters (mean = 6.67 µg/dL) likely reflect the phase-out of lead-based automotive paints in many commercial products, although legacy lead paint may still be used in some informal settings. Panel beaters/welders (mean = 11.45 µg/dL) and mechanics (mean = 9.08 µg/dL) showed intermediate exposure levels, possibly due to contact with lead-containing components and contaminated workshop environments. Personal protective equipment paradox A central finding of this study was the identification of a PPE paradox: workers in the highest-risk specialties demonstrated the lowest PPE adoption rates. Among battery repairers and electricians combined, only 14.3% reported using any form of PPE, compared to 31.8% among lower-risk specialties. Conversely, spray painters (60.9%) and panel beaters/welders (72.2%) had the highest PPE use rates, despite having lower mean BLLs. This counterintuitive pattern has important implications for occupational health programs. Several factors may explain this paradoxical finding. First, the visibility of hazards differs across specialties: spray painting generates visible particles and noxious fumes that prompt immediate protective behavior, whereas lead exposure from battery handling is less perceptible [ 29 , 30 ]. Second, spray painters and welders work with equipment that necessitates respiratory and eye protection for immediate comfort, whereas battery work can be performed without such equipment despite its insidious long-term toxicity. Third, workplace culture and peer norms may vary across specialties, with protection being normalized in some trades but not others. The finding that PPE use did not significantly reduce BLLs within either risk stratum (high-risk: 20.18 vs. 17.75 µg/dL among PPE users vs. non-users, p = 0.56; lower-risk: 9.06 vs. 8.92 µg/dL, p = 0.69) suggests that the PPE being used may be inadequate for lead protection, improperly fitted, inconsistently worn, or insufficient to overcome the intensity of exposure in these settings. Standard cotton gloves and surgical masks, which are common PPE in informal settings, provide minimal protection against lead contamination. This finding echoes observations from other LMIC studies showing that PPE availability does not guarantee effective protection without proper selection, fitting, training, and consistent use [ 26 , 31 , 32 ]. Cumulative risk and composite score The development of a composite occupational risk score integrating smoking, eating at workplace, PPE non-use, and high-risk specialty yielded a significant dose-response relationship with BLL (Spearman r = 0.20, p = 0.017). Workers with all four risk factors had mean BLLs nearly twice those of workers with only one risk factor (16.10 vs. 8.81 µg/dL). This cumulative risk model has potential applications in identifying high-priority individuals for targeted interventions and health monitoring in resource-limited settings. The nearly universal practice of eating at the workplace (99.3% of exposed workers) represents a major, modifiable risk factor. Eating and drinking in lead-contaminated workspaces facilitates the ingestion of lead particles that accumulate on hands, surfaces, and food items [ 22 ]. The prevalence of this behavior reflects both long working hours (> 8 hours/day for 98.6% of workers) and the informal nature of these workplaces, which typically lack designated break areas separated from work zones. Comparison with regional and international data The mean BLL among Gambian auto repair workers in our study (11.17 µg/dL) falls within the range reported in other LMIC settings, but the specialty-specific gradients observed here are particularly striking. For example, in Ethiopia (Jimma), garage workers exhibited mean BLLs of 11.7–36.5 µg/dL, with manual spray painters disproportionately affected (≥ 20 µg/dL). In Addis Ababa, exposed bus-garage workers had a mean BLL of 29.7 µg/dL compared to 14.8 µg/dL in matched controls. In Nigeria (Lagos), comparative analyses reported median BLLs of 43.5–66.0 µg/dL among roadside vs. organized auto technicians, with higher levels in organized garages attributed to prolonged exposure and poorer ventilation. Kenyan research further demonstrated task-based exposure differences, with battery-repair tasks yielding airborne lead concentrations exceeding WHO limits and corresponding mean BLLs of ~ 25 µg/dL. Taken together, these studies confirm that informal auto-repair sectors across LMICs share elevated lead exposure risks; however, the PPE paradox we observed in The Gambia where the highest-risk specialties (battery repairers/electricians) had the lowest PPE adoption has been less explicitly documented elsewhere, highlighting a critical gap in behavioral and implementation research on protective practices. Notably, even our healthcare-worker control group exhibited substantial exposure, with 52.9% having BLLs ≥ 5 µg/dL, suggesting background environmental sources in the Greater Banjul Area. Potential contributors include legacy lead paint and contamination from informal used lead-acid battery activities, both documented nationally and regionally. The higher BLLs we observed among controls with lower education (mean = 20.3 µg/dL) may reflect socio-environmental pathways (housing quality, occupational proximity, health literacy) that warrant targeted community interventions alongside workplace controls. Strengths and limitations This study had several strengths. First, the novel analysis of within-industry exposure gradients provides actionable data for targeted interventions. Second, the use of a validated point-of-care BLL testing system enabled field-based assessments. Third, the comprehensive questionnaire captured diverse risk factors, enabling multivariable analysis and risk stratification. Fourth, the inclusion of a control group from a non-lead-exposed occupation provided a reference population. This study has several limitations. The cross-sectional design precluded causal inference regarding the temporal relationship between specialty, PPE use, and BLL. The small sample size of battery repairers (n = 5), reflecting their scarcity in formal registries, limits the precision of estimates for this critical subgroup. Self-reported PPE use may be subject to social desirability bias, potentially overestimating actual usage. The LeadCare® II system has an upper detection limit of 65 µg/dL, which may have led to censoring of the highest exposures. Finally, the study was conducted in a single urban area, and the findings may not be generalizable to rural settings or other countries. Public health and policy implications These findings have important implications for occupational health policies and practices in The Gambia and similar LMIC settings. First, occupational health programs targeting the auto repair industry should adopt specialty-specific rather than industry-wide approaches, with highest priority given to battery repairers and automotive electricians. Second, regulatory attention should be focused on licensing requirements, workplace standards, and mandatory PPE provisions for battery-related work. Third, the PPE paradox underscores the need for health education programs that address risk perception, making invisible hazards, such as lead toxicity, tangible to workers through blood lead testing with immediate feedback. Fourth, the near-universal practice of eating at the workplace suggests that creating designated lead-free break areas and promoting handwashing before eating are feasible, low-cost interventions with the potential for significant impact. Fifth, the composite risk score could be adapted as a screening tool to identify high-priority individuals for intensive interventions. Finally, the elevated BLLs among healthcare worker controls suggest that occupational interventions alone are insufficient, and broader environmental lead reduction strategies are needed. To facilitate the translation of these findings into occupational health practice, we developed a risk screening score sheet suitable for use in The Gambia's informal sector ( Supplementary Materials S1 and S2 ). This tool enables community health workers, environmental health officers, and trade union representatives to rapidly identify high-priority individuals for blood lead testing and intensive intervention without requiring laboratory resources. The color-coded, field-ready format requires only basic addition and can be completed in under five minutes, making it feasible for use during routine workplace visits or community health outreach activities. Conclusions This study demonstrates marked heterogeneity in lead exposure within the informal auto repair sector in The Gambia, with battery repairers and electricians bearing disproportionate exposure burdens to lead. The identification of a PPE paradox, wherein the highest-risk workers exhibit the lowest protective behavior, challenges the assumptions underlying conventional occupational health interventions and calls for innovative approaches that address risk perception and make invisible hazards tangible. These findings support targeted, specialty-specific interventions prioritizing battery and electrical work, alongside broader efforts to reduce environmental lead contamination. The composite risk score developed in this study may be used as a practical tool for identifying high-priority workers in resource-limited settings. Declarations Ethics approval and consent to participate : This study was reviewed and approved by the Joint Gambia Government–Medical Research Council Ethics Committee (Protocol No. SCC 1602v1.1). All eligible participants provided written informed consent prior to enrollment. Following consent, participants completed questionnaires and agreed to venous blood collection for lead testing. Consent for publication: Not applicable Competing interests: The authors declare no competing interest. Funding: This research received no external funding. Author Contribution Conceptualization, E.S., M.D., and A.J.; methodology, E.S., M.D., A.B., and A.J.; Software, A.B., E.S., A.M.S., and A. N.; validation, E.S., M.D., W.N.M., and A.B.; formal analysis, A.B. and E.S.; investigation, A.J., A.M.S., A.N., and E.S.; data curation, E.S., A.B., and A.J.; writing – original draft preparation, E.S., A.B., M.D., and A.J.; writing – review and editing, E.S., A.B., A.J., M.D., A.M.S., A.N., and W.N.M. Acknowledgements: The authors gratefully acknowledge the staff and faculty of the University of The Gambia, Department of Public and Environmental Health, School of Medicine and Allied Health Sciences, for their assistance with data collection and entry. We also extend our sincere thanks to the staff of the Gambia National Public Health Laboratory, Ministry of Health, for their support in blood sample collection and analysis. Data Availability All data are available upon request from the corresponding author. References Pure Earth. 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Lead Exposure in Battery Manufacturing and Recycling in Developing Countries and Among Children in Nearby Communities. J Occup Environ Hyg. 2011;8:520–32. https://doi.org/10.1080/15459624.2011.601710 . Abebe MT, Kumie A, Ayana SW, Assefa T, Ambaw W. Assessment of occupational exposure to lead among workers engaged in a city bus garage in Addis Ababa, Ethiopia: a comparative cross-sectional study. J Occup Med Toxicol. 2024;19:26. https://doi.org/10.1186/s12995-024-00422-9 . Locke SJ, Deziel NC, Koh D, Graubard BI, Purdue MP, Friesen MC. Evaluating predictors of lead exposure for activities disturbing materials painted with or containing lead using historic published data from U.S. workplaces. Am J Industrial Med. 2017;60:189–97. https://doi.org/10.1002/ajim.22679 . CDC. Understanding Your Risk for Lead Exposure. Lead in the Workplace. 2024. https://www.cdc.gov/niosh/lead/risk-factors/index.html . Accessed 26 Nov 2025. Drouard SHP, Ahmed T, Amor Fernandez P, Baral P, Peters M, Hansen P, et al. Availability and use of personal protective equipment in low- and middle-income countries during the COVID-19 pandemic. PLoS ONE. 2023;18:e0288465. https://doi.org/10.1371/journal.pone.0288465 . Obeng-Gyasi E. Sources of Lead Exposure in West Africa. Sci. 2022;4:33. https://doi.org/10.3390/sci4030033 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8444687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567577122,"identity":"8c29a9e4-8fbc-45b2-8607-44f290f6861d","order_by":0,"name":"Edrisa Sanyang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFCCBIYDSDwbBgkQxUOCljTitCCDw4S18LPnPjz4o4bBnn92j+Hngl/nE2fOSGB88LYNtxbJnucGh3mOMSTOuHPGWHpm3+3E2RIJzIZz8WgxuJEGdAwb0Hk3cgykeXtu586TSGCT5sWjxR6o5eCPfwz28jdyjH/z9pwDaWH/jU+LgUQawwGgAsYNN3LMpHl+HMgFOoyNGZ8WiTPPGA7z9kkkbryRVmbN25BcP7PnYbPknHO4tfC3pzF//PHNxl7uRvLm2zx/7Iwljicf/PCmDLcWmGVAzGHAwAh2D2MDQfVQwP6AgeEPsYpHwSgYBaNgJAEASlJTsAXSId0AAAAASUVORK5CYII=","orcid":"","institution":"Western Kentucky University","correspondingAuthor":true,"prefix":"","firstName":"Edrisa","middleName":"","lastName":"Sanyang","suffix":""},{"id":567577126,"identity":"3f6d8609-5c00-4ca1-bea6-189ea6cb0d04","order_by":1,"name":"Amadou Barrow","email":"","orcid":"","institution":"University of the Gambia","correspondingAuthor":false,"prefix":"","firstName":"Amadou","middleName":"","lastName":"Barrow","suffix":""},{"id":567577129,"identity":"646bb345-c6f1-428c-970d-c9655b42d468","order_by":2,"name":"Alhaji Jabbi","email":"","orcid":"","institution":"University of the Gambia","correspondingAuthor":false,"prefix":"","firstName":"Alhaji","middleName":"","lastName":"Jabbi","suffix":""},{"id":567577134,"identity":"4a4bfef7-aaee-4676-94d1-2a351977205b","order_by":3,"name":"Musa Drammeh","email":"","orcid":"","institution":"United Nations Children's Fund","correspondingAuthor":false,"prefix":"","firstName":"Musa","middleName":"","lastName":"Drammeh","suffix":""},{"id":567577135,"identity":"95ce93be-4b96-452b-811a-b9b2a3d27d13","order_by":4,"name":"Abdoulie M. 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Individual data points are jittered for visualization. Dashed lines indicate CDC reference level (5 μg/dL) and elevated threshold (10 μg/dL). Specialties are ordered by descending mean BLL. Kruskal-Wallis H = 26.92, p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/1cbe4e1b677a08b6659a49d6.png"},{"id":99480813,"identity":"44951768-0e6e-4681-a9da-66c29bcad1ed","added_by":"auto","created_at":"2026-01-04 23:44:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNote: Blood lead levels (red bars, left axis) and PPE use rates (blue bars, right axis) by occupational specialty. Higher-risk workers (battery repairers, electricians) demonstrate lower PPE adoption (14.3% combined) despite elevated exposure levels (mean BLL = 18.09 μg/dL), while lower-risk workers (panel beaters/welders, spray painters) show higher PPE use rates (66.3% combined) despite lower mean BLL (8.96 μg/dL).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/ba815692048dc3fecd5e75d0.png"},{"id":99790145,"identity":"3842decf-f55e-47fb-96c9-9ddebac63cb9","added_by":"auto","created_at":"2026-01-08 12:56:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNote: Error bars represent standard error of the mean. Risk score components: current smoking (+1 point), eating at workplace (+1 point), no PPE use (+1 point), and high-risk specialty (+1 point; maximum score = 4). The positive trend indicates increasing BLL with cumulative risk factor exposure (Spearman ρ = 0.20, p = 0.017).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/5c9bd67318a1d1999e79b6f9.png"},{"id":99480811,"identity":"ce33bba7-3de6-49e5-b861-06c43e8aa7da","added_by":"auto","created_at":"2026-01-04 23:44:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNote: Reference category: Mechanic. Coefficients represent the adjusted difference in BLL (μg/dL) compared to mechanics, controlling for age, education, smoking status, and PPE use. Error bars indicate 95% confidence intervals. Model R² = 0.248, F(9, 135) = 4.95, p \u0026lt; 0.001. ***p \u0026lt; 0.001; **p \u0026lt; 0.01.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/8b9b53623dd2c2a364d60972.png"},{"id":99480818,"identity":"80254f08-ca81-4ac1-b55a-2bcd7ac501af","added_by":"auto","created_at":"2026-01-04 23:44:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":214334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNote: Panel A shows box plot distributions with median (horizontal line), interquartile range (box), and whiskers extending to 1.5× IQR. Panel B shows categorical distribution of BLL. The difference between groups was statistically significant (Mann-Whitney U = 6401, p \u0026lt; 0.001).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/5c5297c522a1180e03799473.png"},{"id":100788580,"identity":"3b95d5e8-2735-4db8-8be2-7e228b9ab24d","added_by":"auto","created_at":"2026-01-21 12:06:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1833332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8444687/v1/f1c1fa4c-9e49-4705-bd24-afb6ab6590c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Job-specific lead exposure gradients and personal protective equipment paradox in informal occupational settings: a case-control study from The Gambia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLead exposure remains one of the most significant environmental and occupational health challenges facing low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to a landmark 2020 report by UNICEF and Pure Earth, approximately one-third of the world's children, up to 800\u0026nbsp;million globally, have blood lead levels (BLLs) at or above 5 \u0026micro;g/dL, the level previously used by the U.S. Centers for Disease Control and Prevention (CDC) to identify children requiring intervention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nearly half of these affected children reside in South Asia and sub-Saharan Africa (SSA), where regulatory frameworks for controlling lead exposure are often inadequate or poorly enforced. The economic consequences are substantial, with childhood lead exposure estimated to cost LMICs nearly \u003cspan\u003e$\u003c/span\u003e1 trillion in lost lifetime productivity [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLead is a neurotoxin with no safe exposure level. Low-level chronic exposure has been linked to decreased cognitive function, reduced IQ, attention deficits, behavioral problems, and impaired academic achievement in children[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In adults, lead exposure is linked to hypertension, cardiovascular disease, chronic kidney disease, and reproductive toxicity[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recognizing harm at lower exposure levels, the CDC updated its blood lead reference value (BLRV) from 5 \u0026micro;g/dL to 3.5 \u0026micro;g/dL in October 2021, based on the 97.5th percentile of BLLs in U.S. children aged 1\u0026ndash;5 years from 2015\u0026ndash;2018 National Health and Nutrition Examination Survey data[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Occupational exposure is a major pathway for lead contamination in LMICs. In The Gambia, lead exposure remains significant due to historical and ongoing environmental sources. Although leaded gasoline was banned in 2008, its use led to widespread contamination of air, soil, and dust, creating persistent exposure in urban areas.\u003c/p\u003e \u003cp\u003eRecent studies show 82.1% of auto repair workers have elevated blood lead levels compared to 52.9% of healthcare worker controls, highlighting occupational and environmental risks[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sources include informal recycling of lead-acid batteries and lead-containing paints in residential and commercial environments[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These factors indicate Gambian workers face risks from occupational and ambient lead exposure, requiring targeted interventions. Workers in lead-acid battery manufacturing and recycling, automobile repair, metal smelting, welding, painting, and electronics recycling face elevated exposure risks[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In SSA, the informal sector, with unregistered businesses, irregular conditions, limited oversight, and minimal occupational health infrastructure, employs a substantial workforce proportion[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This informality creates conditions for heightened lead exposure, including poor ventilation, lack of engineering controls, limited PPE access, and poor hygiene practices like eating at worksites[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe auto repair industry has been identified as a high-risk occupation for lead exposure across multiple African countries [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Workers in this sector are exposed to lead through contact with lead-acid batteries, leaded components, lead-based paints and primers, solders, and contaminated dust [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the auto repair industry is heterogeneous, encompassing distinct specialties with potentially different exposure profiles, including mechanics, electricians, battery repairers, panel beaters/welders, and spray painters [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite this heterogeneity, most previous studies have treated auto repair workers as a homogeneous exposure group, potentially masking important within-industry gradients that could inform targeted intervention strategies for this group. While PPE is a critical component of the hierarchy of controls for occupational hazards when engineering controls and administrative measures are insufficient [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], it is important to first understand what preventive measures are in place to mitigate risks. PPE should primarily address residual risks that cannot be eliminated through other controls. In essence, identifying existing protective practices in informal settings can help build upon these measures and strengthen interventions, with PPE serving as an additional safeguard. In well-resourced settings, the use of PPE is guided by workplace risk assessments, regulatory mandates, and occupational health services. However, in informal sector settings in LMICs, PPE availability and use patterns are shaped by different factors, including cost, accessibility, worker knowledge, risk perception, and workplace culture [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Understanding how PPE use relates to occupational risk within specific industries is essential for designing effective interventions.\u003c/p\u003e \u003cp\u003eThe Gambia, a small West African nation with a population of approximately 2.4\u0026nbsp;million, has a growing automotive sector concentrated in the Greater Banjul Area. The auto repair industry operates largely within the informal economy, with workshops ranging from sophisticated urban garages to roadside repair stands. Despite The Gambia's ratification of international conventions on occupational health and safety, the enforcement of workplace regulations remains limited, and comprehensive data on occupational lead exposure among auto repair workers are sparse. This study addresses critical gaps in the literature by (1) characterizing job-specific BLL gradients among different auto repair specialties in The Gambia, (2) examining the relationship between occupational specialty and PPE use patterns, (3) identifying a potential PPE paradox wherein higher-risk workers exhibit lower rates of protective behavior, and (4) developing a composite risk score to assess cumulative occupational risk factors. These findings have implications for designing targeted, specialty-specific interventions for similar informal occupational settings in LMICs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis case-control study was conducted in the Greater Banjul Metropolitan Area, The Gambia, to compare blood lead levels (BLLs) among occupationally exposed workers and unexposed healthcare workers. The Greater Banjul Area encompasses Banjul City, Kanifing Municipality, and parts of the West Coast Region, representing the most urbanized and industrialized zone in The Gambia. The auto repair industry in this region is characterized by informal workshops that engage in vehicle maintenance, welding, painting, battery repair, and related activities. Ethical approval was obtained from the Joint Gambia Government/Medical Research Council Ethics Committee (Protocol Number: SCC 1602v1.1) and more details can be found here [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eA total of 213 participants were enrolled, comprising 145 exposed workers (cases) from the auto repair industry and 68 unexposed healthcare workers (controls) from Kanifing General Hospital. Cases were selected using simple random sampling from tax registries maintained by Brikama Area Council, Kanifing Municipal Council, and Banjul City Council. The exposed group included mechanics, panel beaters/welders, spray painters, electricians, and battery repairers. Battery recyclers, who are typically not listed in official registries, were identified using snowball sampling. This recruitment method was used for this sub-population because many operate outside formal registries in The Gambia\u0026rsquo;s informal sector. While this approach enabled inclusion of a hard-to-reach subgroup, it may limit representativeness and introduce selection bias, as workers with stronger social networks were more likely to be recruited. Consequently, findings for battery repairers should be interpreted with caution and considered indicative rather than population-wide estimates. Controls were randomly selected from the staff roster at Kanifing General Hospital. Healthcare workers were selected as controls because their occupational environment is presumed to have minimal lead exposure compared to auto repair settings. Hospitals and clinics typically lack lead-related processes making them an appropriate reference group for assessing occupational exposure differences. Additionally, healthcare workers share similar geographic, social, and urban living conditions with auto repair workers, reducing confounding from environmental factors. All participants provided informed consent prior to their enrollment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData collection involved two components: blood sample analysis and a structured questionnaire survey. Blood lead levels were measured using the LeadCare\u0026reg; II Blood Lead Testing System, a point-of-care device that reports values between 3.3 and 65 \u0026micro;g/dL [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The system was calibrated at the start of each workday and after every 30 samples to ensure the accuracy of the measurements. Capillary blood samples were collected by trained laboratory technicians. The structured questionnaire captured sociodemographic information (age, gender, ethnicity, marital status, education level, literacy status, family size), occupational characteristics (specialty within the auto repair industry, years of work experience, daily work hours, weekly work days), behavioral factors (smoking status, eating at workplace), specific work tasks performed (radiator repair, battery repair, panel beating/welding, painting/spraying), and PPE use patterns. Graduate students from the University of The Gambia administered questionnaires under supervision of the principal investigator.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003ePrimary outcome.\u003c/b\u003e Blood lead level (BLL) was analyzed as a continuous variable (\u0026micro;g/dL) and categorized into three levels based on established thresholds: normal (\u0026lt;\u0026thinsp;5 \u0026micro;g/dL), moderate (5\u0026ndash;9.9 \u0026micro;g/dL), and high (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL). Although the CDC updated its blood lead reference value (BLRV) to 3.5\u0026micro;g/dL in 2021 for identifying elevated levels in children, this study used 5\u0026micro;g/dL as the categorization threshold for adults based on occupational health literature and prior LMIC studies. The 5\u0026micro;g/dL cutoff remains widely applied in adult occupational exposure research and aligns with earlier CDC guidance, facilitating comparability with existing studies in similar contexts. Additionally, lead exposure in LMICs is likely higher due to the continued legacy of leaded gasoline use. For example, in The Gambia, lead in gasoline was only banned as recently as 2008, indicating its persistence in the mainstream environment and potential contribution to background exposure levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrimary exposure.\u003c/b\u003e Occupational specialties were categorized into five groups: mechanics (reference), panel beaters/welders, spray painters, electricians, and battery repairers. A binary high-risk specialty variable was created by combining electricians and battery repairers based on their known exposure to lead-acid batteries and lead-containing electrical components.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePPE use.\u003c/b\u003e Personal protective equipment use was assessed through self-reports regarding general PPE use and task-specific PPE use (when working on radiators, batteries, welding, or painting). The PPE types included goggles, respirators/masks, insulated gloves, safety shoes, and work suits/overalls. A binary variable was created to indicate any PPE use versus no PPE use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates.\u003c/b\u003e Demographic variables included age (\u0026le;\u0026thinsp;20, 21\u0026ndash;30, 31\u0026ndash;40, 41\u0026ndash;50, and \u0026gt;\u0026thinsp;50 years), education level (no formal education, primary, secondary, and tertiary), ethnicity (Fula, Jola, Mandinka, Wollof, and others), and marital status. Occupational covariates included years of work experience (\u0026lt;\u0026thinsp;1, 1\u0026ndash;3, 4\u0026ndash;6, 7\u0026ndash;9, \u0026ge;\u0026thinsp;10 years), hours worked per day (4\u0026ndash;5, 6\u0026ndash;8, \u0026gt;\u0026thinsp;8 hours), and days worked per week. Behavioral factors included current/former smoking status and workplace eating.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComposite risk score.\u003c/b\u003e A cumulative occupational risk score (range 0\u0026ndash;4) was constructed by summing binary indicators for current smoking (1 point), eating at the workplace (1 point), no PPE use (1 point), and high-risk specialty (1 point).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were calculated for all variables, with continuous variables summarized as means, standard deviations, medians, and ranges and categorical variables as frequencies and percentages. Given the non-normal distribution of BLL (Shapiro-Wilk p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), non-parametric tests were used for bivariate analyses. The Kruskal-Wallis H test was used to assess differences in BLL across occupational specialties, with post-hoc pairwise comparisons using Mann-Whitney U tests with Bonferroni correction. Chi-square tests were used to examine the associations between categorical variables and BLL categories.\u003c/p\u003e \u003cp\u003eMultivariable linear regression was used to estimate the adjusted differences in BLL by occupational specialty, with mechanics as the reference category. Three nested models were constructed: Model 1 (unadjusted, specialty only), Model 2 (adjusted for demographics: age and education), and Model 3 (fully adjusted: age, education, years of work, smoking status, and PPE use). Variance inflation factors (VIF) were calculated to assess multicollinearity, with a VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating a concern. Poisson regression with robust standard errors was used to estimate prevalence ratios (PR) for high BLL (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL) as a sensitivity analysis, given the high prevalence of the outcome.\u003c/p\u003e \u003cp\u003eThe PPE paradox was examined by calculating the Spearman correlation between specialty-level PPE use rates and mean BLL and by stratified analyses comparing PPE effects within high-risk versus lower-risk specialty groups. Task-specific analyses examined BLL differences by individual tasks (radiator repair, battery repair, welding, and painting) and task combinations. Trend tests used Spearman correlation between the composite risk score and BLL. All analyses were conducted in Python 3.12 using pandas, scipy, and statsmodels libraries. Statistical significance was set at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003cp\u003eBased on the multivariable regression findings, we developed a practical risk screening tool to identify high-priority workers for intensive interventions. The tool assigns weighted scores to each significant risk factor and stratifies workers into four risk categories (low, moderate, high, very high) with corresponding intervention recommendations (\u003cem\u003eSupplementary Materials S1 and S2\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n \u003cp\u003eThe study included 213 participants: 145 exposed auto repair workers and 68 unexposed healthcare controls. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic and occupational characteristics of exposed workers. All exposed workers were male, with the majority aged 21\u0026ndash;30 years (42.1%) or \u0026le;\u0026thinsp;20 years (27.6%). Educational attainment was low, with 26.9% having no formal education and 31.7% having only a primary education. Most workers (53.1%) had\u0026thinsp;\u0026ge;\u0026thinsp;10 years of work experience and worked\u0026thinsp;\u0026gt;\u0026thinsp;8 hours per day (98.6%). Current or former smoking was reported by 43.4% of exposed workers, and nearly all (99.3%) reported eating at their workplace.\u003c/p\u003e\n \u003cp\u003eThe mean BLL among exposed workers was 11.17 \u0026micro;g/dL (SD\u0026thinsp;=\u0026thinsp;10.96), with a median of 7.40 \u0026micro;g/dL (range: 3.4\u0026ndash;65.0 \u0026micro;g/dL). Among the controls, mean BLL was 9.07 \u0026micro;g/dL (SD\u0026thinsp;=\u0026thinsp;11.01), with a median of 5.80 \u0026micro;g/dL (range: 2.0\u0026ndash;65.0 \u0026micro;g/dL). The difference between groups was statistically significant (Mann-Whitney U\u0026thinsp;=\u0026thinsp;6401, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among exposed workers, 82.1% had elevated BLL (\u0026ge;\u0026thinsp;5 \u0026micro;g/dL) compared to 52.9% of controls, and 30.3% had high BLL (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL) compared to 23.5% of controls. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the distribution of BLL between exposed and unexposed groups.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eSociodemographic and occupational characteristics of exposed auto repair workers (N\u0026thinsp;=\u0026thinsp;145)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (BLL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMdn\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of work experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent/former smoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUses any PPE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e BLL\u0026thinsp;=\u0026thinsp;blood lead level (\u0026micro;g/dL); \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard deviation; \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;median; PPE\u0026thinsp;=\u0026thinsp;personal protective equipment.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBlood lead levels by occupational specialty\u003c/h3\u003e\n\u003cp\u003eMarked differences in BLL were observed across occupational specialties (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Battery repairers had the highest mean BLL (35.62 \u0026micro;g/dL, SD\u0026thinsp;=\u0026thinsp;22.74), followed by electricians (15.17 \u0026micro;g/dL, SD\u0026thinsp;=\u0026thinsp;11.78), panel beaters/welders (11.45 \u0026micro;g/dL, SD\u0026thinsp;=\u0026thinsp;10.51), mechanics (9.08 \u0026micro;g/dL, SD\u0026thinsp;=\u0026thinsp;8.53), and spray painters (6.67 \u0026micro;g/dL, SD\u0026thinsp;=\u0026thinsp;2.44). The Kruskal-Wallis test confirmed significant differences across the specialties (H\u0026thinsp;=\u0026thinsp;26.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc pairwise comparisons revealed that battery repairers had significantly higher BLL than all other specialties (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and electricians had significantly higher BLL than mechanics (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and spray painters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eAll five battery repairers (100%) had high BLL (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL), compared to 56.7% of electricians, 33.3% of panel beaters/welders, 21.7% of mechanics, and only 4.3% of spray painters. This represents a striking 5.3-fold gradient in mean BLL from the highest-risk (battery repair) to lowest-risk (spray painting) specialty within the same industry.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eBlood lead levels by occupational specialty among auto repair workers\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecialty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMdn\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh BLL (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery repairer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.3\u0026ndash;65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u0026ndash;50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel beater/welder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u0026ndash;43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanic (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u0026ndash;65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpray painter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u0026ndash;14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e BLL\u0026thinsp;=\u0026thinsp;blood lead level (\u0026micro;g/dL); High BLL\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL. Kruskal-Wallis H\u0026thinsp;=\u0026thinsp;26.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Battery repairers had significantly higher BLL than all other specialties (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all pairwise comparisons).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePersonal protective equipment paradox\u003c/h2\u003e\n \u003cp\u003eAnalysis of PPE use patterns revealed a paradoxical inverse relationship between exposure risk and protective behavior (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, only 27.6% of exposed workers reported using any form of PPE. Among high-risk specialties (battery repairers and electricians combined), only 14.3% used PPE compared with 31.8% among lower-risk specialties (\u0026chi;\u0026sup2; = 3.26, p\u0026thinsp;=\u0026thinsp;0.071). By individual specialty, panel beaters/welders had the highest PPE use rate (72.2%), followed by spray painters (60.9%), battery repairers (20.0%), electricians (13.3%), and mechanics (11.6%).\u003c/p\u003e\n \u003cp\u003eThis pattern represents a PPE paradox (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e): workers in specialties with the highest mean BLL (battery repair: 35.6 \u0026micro;g/dL; electrical work: 15.2 \u0026micro;g/dL) had the lowest rates of PPE adoption, while those in lower-exposure specialties (panel beating: 11.4 \u0026micro;g/dL; spray painting: 6.7 \u0026micro;g/dL) had the highest PPE use rates. The Spearman correlation between the specialty-level mean BLL and PPE use rate was negative (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.10), although not statistically significant, given the small number of specialty groups.\u003c/p\u003e\n \u003cp\u003eStratified analysis examining PPE effectiveness within risk strata found no significant difference in BLL between PPE users and non-users within either high-risk specialties (20.18 vs. 17.75 \u0026micro;g/dL, p\u0026thinsp;=\u0026thinsp;0.56) or lower-risk specialties (9.06 vs. 8.92 \u0026micro;g/dL, p\u0026thinsp;=\u0026thinsp;0.69), suggesting that the type, quality, or proper use of PPE may be inadequate to provide meaningful protection in this setting.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003ePPE use by occupational specialty\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecialty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPE users\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPE rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (BLL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery repairer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel beater/welder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpray painter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e PPE\u0026thinsp;=\u0026thinsp;personal protective equipment; BLL\u0026thinsp;=\u0026thinsp;blood lead level (\u0026micro;g/dL). High-risk specialties (battery repairer, electrician) combined: 14.3% PPE use. Lower-risk specialties combined: 31.8% PPE use (\u0026chi;\u0026sup2; = 3.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.071).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariable regression analysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e present the results of multivariable regression models examining associations between occupational specialty and blood lead levels. Panel A shows results from linear regression examining adjusted differences in continuous BLL. In the fully adjusted model, battery repairers had BLL 27.14 \u0026micro;g/dL higher than mechanics (95% CI: 17.69\u0026ndash;36.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and electricians had BLL 6.07 \u0026micro;g/dL higher than mechanics (95% CI: 1.77\u0026ndash;10.36, p\u0026thinsp;=\u0026thinsp;0.006). The differences between panel beaters/welders (\u0026beta;\u0026thinsp;=\u0026thinsp;2.24, p\u0026thinsp;=\u0026thinsp;0.452) and spray painters (\u0026beta; = \u0026minus;2.23, p\u0026thinsp;=\u0026thinsp;0.408) were not statistically significant. The model explained 24.8% of variance in BLL (R\u0026sup2; = 0.248, F(9, 135)\u0026thinsp;=\u0026thinsp;4.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003ePanel B shows Poisson regression results with robust standard errors estimating prevalence ratios (PR) for high BLL (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL). Battery repairers had 4.4-fold higher prevalence of high BLL compared to mechanics (PR\u0026thinsp;=\u0026thinsp;4.39, 95% CI: 2.61\u0026ndash;7.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and electricians had 2.6-fold higher prevalence (PR\u0026thinsp;=\u0026thinsp;2.58, 95% CI: 1.50\u0026ndash;4.43, p\u0026thinsp;=\u0026thinsp;0.001). Spray painters showed a non-significant 80% lower prevalence compared to mechanics (PR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.03\u0026ndash;1.62, p\u0026thinsp;=\u0026thinsp;0.132).\u003c/p\u003e\n \u003cp\u003eNotably, after adjusting for specialty, the effects of age, education, smoking, and PPE use were not statistically significant in either model, suggesting that occupational specialty is the primary determinant of lead exposure in this population. The variance inflation factors were all below 5.5, indicating no substantial multicollinearity.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMultivariable regression models for blood lead level by occupational specialty (fully adjusted model)\u003c/em\u003e \u003cstrong\u003ePanel A\u003c/strong\u003e: \u003cem\u003eLinear regression (Outcome: BLL in \u0026micro;g/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery repairer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[17.69, 36.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.77, 10.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel beater/welder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;3.63, 8.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpray painter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;7.53, 3.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;0.14, 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (ordinal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;1.67, 2.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;3.25, 3.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUses PPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[\u0026minus;\u0026thinsp;4.56, 4.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference category: Mechanic. Model \u003cem\u003eR\u003c/em\u003e\u0026sup2; = .248, adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; = .198, \u003cem\u003eF\u003c/em\u003e(9, 135)\u0026thinsp;=\u0026thinsp;4.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. \u0026beta;\u0026thinsp;=\u0026thinsp;unstandardized coefficient.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e: \u003cem\u003ePoisson regression with Robust SE (Outcome: High BLL\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL)\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery repairer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[2.61, 7.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.50, 4.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel beater/welder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.61, 3.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpray painter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.03, 1.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (ordinal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.67, 1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.59, 1.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUses PPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.45, 1.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Reference category: Mechanic. PR\u0026thinsp;=\u0026thinsp;prevalence ratio; SE\u0026thinsp;=\u0026thinsp;robust standard error; CI\u0026thinsp;=\u0026thinsp;confidence interval. High BLL defined as \u0026ge;\u0026thinsp;10 \u0026micro;g/dL. Model deviance\u0026thinsp;=\u0026thinsp;83.07; Pearson \u0026chi;\u0026sup2; = 97.43. **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eComposite occupational risk score\u003c/h2\u003e\n \u003cp\u003eA dose-response relationship was observed between the composite occupational risk score and BLL (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Workers with the lowest risk score (score\u0026thinsp;=\u0026thinsp;1) had a mean BLL of 8.81 \u0026micro;g/dL (n\u0026thinsp;=\u0026thinsp;23), while those with the highest score (score\u0026thinsp;=\u0026thinsp;4) had a mean BLL of 16.10 \u0026micro;g/dL (n\u0026thinsp;=\u0026thinsp;13). The Spearman correlation between risk score and BLL was positive and statistically significant (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.017), indicating a modest but meaningful trend of increasing BLL with cumulative risk factor exposure. Using the composite risk algorithm based on the four classified risk strata, a field-ready screening tool for operationalizing this risk stratification is provided in\u0026nbsp;\u003cem\u003eSupplementary Material S1and S2\u003c/em\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eBlood lead levels by composite occupational risk score\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMdn\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponents\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 risk factor only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll 4 risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Risk score components: current smoking (1 point), eating at workplace (1 point), no PPE use (1 point), high-risk specialty (1 point). Spearman \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel evidence of marked within-industry blood lead level gradients among auto repair workers in The Gambia and identifies a paradoxical inverse relationship between occupational risk and personal protective equipment use. These findings challenge the conventional approach of treating informal occupational sectors as homogeneous exposure groups and highlight the need for targeted, specialty-specific interventions in similar low-resource settings.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eJob-specific lead exposure gradients\u003c/h2\u003e \u003cp\u003eThe most striking finding of this study was the 5.3-fold gradient in the mean BLL across occupational specialties within the same industry, ranging from 35.62 \u0026micro;g/dL among battery repairers to 6.67 \u0026micro;g/dL among spray painters. This gradient persisted after adjusting for demographic factors, work duration, smoking status, and PPE use, indicating that occupational specialty is the primary determinant of exposure in this population. The magnitude of this within-industry variation exceeds that typically observed between broad occupational categories in LMIC settings. Battery repairers exhibited dramatically elevated BLLs, with all five workers in this specialty having BLL\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL and a mean nearly four times the exposure threshold. This finding is consistent with the known toxicity of lead-acid battery work, where direct contact with lead plates, lead oxide paste, and lead sulfate creates multiple exposure pathways, including inhalation of lead dust and fumes and dermal absorption [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The small sample size of battery repairers (n\u0026thinsp;=\u0026thinsp;5) reflects their relative scarcity in the formal sampling frame, as many battery recyclers operate outside registered business registries and are identified through snowball sampling. Despite the limited sample size, the consistent elevation of BLLs among all battery repairers (range: 17.3\u0026ndash;65.0 \u0026micro;g/dL) underscores the severity of exposure in this specialty.\u003c/p\u003e \u003cp\u003eElectricians represented the second-highest exposure group (mean BLL\u0026thinsp;=\u0026thinsp;15.17 \u0026micro;g/dL), with over half (56.7%) having a BLL\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL. Automotive electrical work involves frequent handling of lead-acid batteries, lead-soldered connections, and lead-containing components. Unlike battery repairers, who work exclusively with batteries, electricians perform diverse tasks that may dilute their exposure through time spent on non-lead-related activities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, cumulative exposure from regular battery handling appears to be sufficient to substantially elevate BLLs. The relatively lower BLLs among spray painters (mean\u0026thinsp;=\u0026thinsp;6.67 \u0026micro;g/dL) likely reflect the phase-out of lead-based automotive paints in many commercial products, although legacy lead paint may still be used in some informal settings. Panel beaters/welders (mean\u0026thinsp;=\u0026thinsp;11.45 \u0026micro;g/dL) and mechanics (mean\u0026thinsp;=\u0026thinsp;9.08 \u0026micro;g/dL) showed intermediate exposure levels, possibly due to contact with lead-containing components and contaminated workshop environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePersonal protective equipment paradox\u003c/h2\u003e \u003cp\u003eA central finding of this study was the identification of a PPE paradox: workers in the highest-risk specialties demonstrated the lowest PPE adoption rates. Among battery repairers and electricians combined, only 14.3% reported using any form of PPE, compared to 31.8% among lower-risk specialties. Conversely, spray painters (60.9%) and panel beaters/welders (72.2%) had the highest PPE use rates, despite having lower mean BLLs. This counterintuitive pattern has important implications for occupational health programs. Several factors may explain this paradoxical finding. First, the visibility of hazards differs across specialties: spray painting generates visible particles and noxious fumes that prompt immediate protective behavior, whereas lead exposure from battery handling is less perceptible [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Second, spray painters and welders work with equipment that necessitates respiratory and eye protection for immediate comfort, whereas battery work can be performed without such equipment despite its insidious long-term toxicity. Third, workplace culture and peer norms may vary across specialties, with protection being normalized in some trades but not others.\u003c/p\u003e \u003cp\u003eThe finding that PPE use did not significantly reduce BLLs within either risk stratum (high-risk: 20.18 vs. 17.75 \u0026micro;g/dL among PPE users vs. non-users, p\u0026thinsp;=\u0026thinsp;0.56; lower-risk: 9.06 vs. 8.92 \u0026micro;g/dL, p\u0026thinsp;=\u0026thinsp;0.69) suggests that the PPE being used may be inadequate for lead protection, improperly fitted, inconsistently worn, or insufficient to overcome the intensity of exposure in these settings. Standard cotton gloves and surgical masks, which are common PPE in informal settings, provide minimal protection against lead contamination. This finding echoes observations from other LMIC studies showing that PPE availability does not guarantee effective protection without proper selection, fitting, training, and consistent use [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCumulative risk and composite score\u003c/h2\u003e \u003cp\u003eThe development of a composite occupational risk score integrating smoking, eating at workplace, PPE non-use, and high-risk specialty yielded a significant dose-response relationship with BLL (Spearman r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.017). Workers with all four risk factors had mean BLLs nearly twice those of workers with only one risk factor (16.10 vs. 8.81 \u0026micro;g/dL). This cumulative risk model has potential applications in identifying high-priority individuals for targeted interventions and health monitoring in resource-limited settings. The nearly universal practice of eating at the workplace (99.3% of exposed workers) represents a major, modifiable risk factor. Eating and drinking in lead-contaminated workspaces facilitates the ingestion of lead particles that accumulate on hands, surfaces, and food items [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The prevalence of this behavior reflects both long working hours (\u0026gt;\u0026thinsp;8 hours/day for 98.6% of workers) and the informal nature of these workplaces, which typically lack designated break areas separated from work zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison with regional and international data\u003c/h2\u003e \u003cp\u003eThe mean BLL among Gambian auto repair workers in our study (11.17 \u0026micro;g/dL) falls within the range reported in other LMIC settings, but the specialty-specific gradients observed here are particularly striking. For example, in Ethiopia (Jimma), garage workers exhibited mean BLLs of 11.7\u0026ndash;36.5 \u0026micro;g/dL, with manual spray painters disproportionately affected (\u0026ge;\u0026thinsp;20 \u0026micro;g/dL). In Addis Ababa, exposed bus-garage workers had a mean BLL of 29.7 \u0026micro;g/dL compared to 14.8 \u0026micro;g/dL in matched controls. In Nigeria (Lagos), comparative analyses reported median BLLs of 43.5\u0026ndash;66.0 \u0026micro;g/dL among roadside vs. organized auto technicians, with higher levels in organized garages attributed to prolonged exposure and poorer ventilation. Kenyan research further demonstrated task-based exposure differences, with battery-repair tasks yielding airborne lead concentrations exceeding WHO limits and corresponding mean BLLs of ~\u0026thinsp;25 \u0026micro;g/dL. Taken together, these studies confirm that informal auto-repair sectors across LMICs share elevated lead exposure risks; however, the PPE paradox we observed in The Gambia where the highest-risk specialties (battery repairers/electricians) had the lowest PPE adoption has been less explicitly documented elsewhere, highlighting a critical gap in behavioral and implementation research on protective practices. Notably, even our healthcare-worker control group exhibited substantial exposure, with 52.9% having BLLs\u0026thinsp;\u0026ge;\u0026thinsp;5 \u0026micro;g/dL, suggesting background environmental sources in the Greater Banjul Area. Potential contributors include legacy lead paint and contamination from informal used lead-acid battery activities, both documented nationally and regionally. The higher BLLs we observed among controls with lower education (mean\u0026thinsp;=\u0026thinsp;20.3 \u0026micro;g/dL) may reflect socio-environmental pathways (housing quality, occupational proximity, health literacy) that warrant targeted community interventions alongside workplace controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study had several strengths. First, the novel analysis of within-industry exposure gradients provides actionable data for targeted interventions. Second, the use of a validated point-of-care BLL testing system enabled field-based assessments. Third, the comprehensive questionnaire captured diverse risk factors, enabling multivariable analysis and risk stratification. Fourth, the inclusion of a control group from a non-lead-exposed occupation provided a reference population.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The cross-sectional design precluded causal inference regarding the temporal relationship between specialty, PPE use, and BLL. The small sample size of battery repairers (n\u0026thinsp;=\u0026thinsp;5), reflecting their scarcity in formal registries, limits the precision of estimates for this critical subgroup. Self-reported PPE use may be subject to social desirability bias, potentially overestimating actual usage. The LeadCare\u0026reg; II system has an upper detection limit of 65 \u0026micro;g/dL, which may have led to censoring of the highest exposures. Finally, the study was conducted in a single urban area, and the findings may not be generalizable to rural settings or other countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePublic health and policy implications\u003c/h2\u003e \u003cp\u003eThese findings have important implications for occupational health policies and practices in The Gambia and similar LMIC settings. First, occupational health programs targeting the auto repair industry should adopt specialty-specific rather than industry-wide approaches, with highest priority given to battery repairers and automotive electricians. Second, regulatory attention should be focused on licensing requirements, workplace standards, and mandatory PPE provisions for battery-related work. Third, the PPE paradox underscores the need for health education programs that address risk perception, making invisible hazards, such as lead toxicity, tangible to workers through blood lead testing with immediate feedback. Fourth, the near-universal practice of eating at the workplace suggests that creating designated lead-free break areas and promoting handwashing before eating are feasible, low-cost interventions with the potential for significant impact. Fifth, the composite risk score could be adapted as a screening tool to identify high-priority individuals for intensive interventions. Finally, the elevated BLLs among healthcare worker controls suggest that occupational interventions alone are insufficient, and broader environmental lead reduction strategies are needed.\u003c/p\u003e \u003cp\u003eTo facilitate the translation of these findings into occupational health practice, we developed a risk screening score sheet suitable for use in The Gambia's informal sector (\u003cem\u003eSupplementary Materials S1 and S2\u003c/em\u003e). This tool enables community health workers, environmental health officers, and trade union representatives to rapidly identify high-priority individuals for blood lead testing and intensive intervention without requiring laboratory resources. The color-coded, field-ready format requires only basic addition and can be completed in under five minutes, making it feasible for use during routine workplace visits or community health outreach activities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates marked heterogeneity in lead exposure within the informal auto repair sector in The Gambia, with battery repairers and electricians bearing disproportionate exposure burdens to lead. The identification of a PPE paradox, wherein the highest-risk workers exhibit the lowest protective behavior, challenges the assumptions underlying conventional occupational health interventions and calls for innovative approaches that address risk perception and make invisible hazards tangible. These findings support targeted, specialty-specific interventions prioritizing battery and electrical work, alongside broader efforts to reduce environmental lead contamination. The composite risk score developed in this study may be used as a practical tool for identifying high-priority workers in resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003e \u003cb\u003eEthics approval and consent to participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eThis study was reviewed and approved by the Joint Gambia Government\u0026ndash;Medical Research Council Ethics Committee (Protocol No. SCC 1602v1.1). All eligible participants provided written informed consent prior to enrollment. Following consent, participants completed questionnaires and agreed to venous blood collection for lead testing.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, E.S., M.D., and A.J.; methodology, E.S., M.D., A.B., and A.J.; Software, A.B., E.S., A.M.S., and A. N.; validation, E.S., M.D., W.N.M., and A.B.; formal analysis, A.B. and E.S.; investigation, A.J., A.M.S., A.N., and E.S.; data curation, E.S., A.B., and A.J.; writing \u0026ndash; original draft preparation, E.S., A.B., M.D., and A.J.; writing \u0026ndash; review and editing, E.S., A.B., A.J., M.D., A.M.S., A.N., and W.N.M.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge the staff and faculty of the University of The Gambia, Department of Public and Environmental Health, School of Medicine and Allied Health Sciences, for their assistance with data collection and entry. We also extend our sincere thanks to the staff of the Gambia National Public Health Laboratory, Ministry of Health, for their support in blood sample collection and analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePure Earth. Mitigating Lead Exposure in Low- and Middle-Income Countries. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pureearth.org/project/mitigating-lead-exposure-in-low-and-middle-income-countries/\u003c/span\u003e\u003cspan address=\"https://www.pureearth.org/project/mitigating-lead-exposure-in-low-and-middle-income-countries/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 Nov 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF, Pure Earth. 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Accessed 26 Nov 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrouard SHP, Ahmed T, Amor Fernandez P, Baral P, Peters M, Hansen P, et al. Availability and use of personal protective equipment in low- and middle-income countries during the COVID-19 pandemic. PLoS ONE. 2023;18:e0288465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0288465\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0288465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObeng-Gyasi E. Sources of Lead Exposure in West Africa. Sci. 2022;4:33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/sci4030033\u003c/span\u003e\u003cspan address=\"10.3390/sci4030033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"occupational lead exposure, blood lead level, personal protective equipment, informal sector, The Gambia","lastPublishedDoi":"10.21203/rs.3.rs-8444687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8444687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOccupational lead exposure remains a major public health concern in low- and middle-income countries (LMICs), where informal employment structures and weak regulatory oversight create unique exposure patterns. Although auto repair work is recognized as high-risk, within-industry exposure gradients and personal protective equipment (PPE) use patterns remain poorly characterized. This study aimed to assess job-specific blood lead level (BLL) gradients among auto repair workers in The Gambia and examine the relationship between occupational specialty and PPE use.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional comparative study enrolled 213 participants in the Greater Banjul Metropolitan Area: 145 exposed auto repair workers (mechanics, electricians, battery repairers, panel beaters/welders, spray painters) and 68 unexposed healthcare worker controls. BLLs were measured using the LeadCare\u0026reg; II system. Questionnaires captured sociodemographic characteristics, work tasks, behavioral factors, and PPE use. Kruskal-Wallis and Mann-Whitney U tests compared BLLs across specialties. Multivariable linear regression estimated adjusted differences in BLL by specialty, and Poisson regression with robust standard errors estimated prevalence ratios for high BLL (\u0026ge;\u0026thinsp;10 \u0026micro;g/dL).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eExposed workers had significantly higher BLLs than controls (median: 7.40 vs. 5.80 \u0026micro;g/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Within the exposed group, a 5.3-fold gradient was observed: battery repairers (35.62 \u0026micro;g/dL), electricians (15.17 \u0026micro;g/dL), panel beaters/welders (11.45 \u0026micro;g/dL), mechanics (9.08 \u0026micro;g/dL), and spray painters (6.67 \u0026micro;g/dL). All battery repairers (100%) had BLL\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;g/dL. Adjusted analyses showed battery repairers had BLLs 27.06 \u0026micro;g/dL higher than mechanics (95% CI: 17.60\u0026ndash;36.52; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and electricians had BLLs 6.05 \u0026micro;g/dL higher (95% CI: 1.75\u0026ndash;10.35; p\u0026thinsp;=\u0026thinsp;0.006). A PPE paradox emerged: high-risk specialties (battery repairers, electricians) reported PPE use rates of only 14.3%, compared to 31.8% in lower-risk specialties (p\u0026thinsp;=\u0026thinsp;0.071). A composite risk score combining smoking, eating at work, no PPE use, and high-risk specialty showed a dose-response relationship with BLL (r\u0026thinsp;=\u0026thinsp;0.20; p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMarked within-industry exposure gradients and an inverse relationship between risk and PPE use were identified. Findings underscore the need for targeted, specialty-specific interventions rather than blanket approaches. Battery repair and electrical work should be prioritized for regulatory attention, PPE provision, and health literacy programs in similar low-resource settings.\u003c/p\u003e","manuscriptTitle":"Job-specific lead exposure gradients and personal protective equipment paradox in informal occupational settings: a case-control study from The Gambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-04 23:44:09","doi":"10.21203/rs.3.rs-8444687/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b1149d1-9f53-4148-a800-ad81a4aff8c3","owner":[],"postedDate":"January 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T12:04:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-04 23:44:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8444687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8444687","identity":"rs-8444687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}