Occupational health is a vital component of public health focused on preventing and managing diseases, injuries, and other health issues caused by work conditions. It plays a critical role in safeguarding well-being and productivity, especially in low- and middle-income countries where safety standards may be weak or poorly enforced. Rural populations engaged in manual labour, agriculture, and small-scale industries face multiple hazards including chemical exposure, physical strain, poor ergonomics, unsafe work environments, and inadequate awareness of personal protective measures [1]. Irungattukottai, a semi-urban village in Tamil Nadu, has experienced rapid industrial growth, shifting employment from agriculture to tyre, steel, and other manufacturing sectors. While improving economic opportunities, this shift has brought prolonged working hours, repetitive tasks, unsafe machinery, dust, noise, chemical fumes, and weak safety oversight—factors contributing to a substantial, underreported burden of occupational diseases [2,3].   Work-related musculoskeletal disorders (WRMSDs) are among the most common occupational health problems, affecting bones, muscles, joints, ligaments, tendons, nerves, and related systems. They are often caused or worsened by repetitive or forceful tasks, awkward postures, and sustained exertion. Prolonged exposure to such physical demands can result in long-term disability, absenteeism, and reduced quality of life [4,5]. Oral health, another crucial component of overall health, includes the condition of teeth, gums, oral mucosa, and supporting structures, and is increasingly linked to systemic diseases such as cardiovascular disease, diabetes, and respiratory infections [6]. However, it remains neglected in many vulnerable groups, particularly rural industrial workers, due to limited access to care and lack of awareness [7].   In industrial areas like Irungattukottai, long working hours, environmental pollutants, and restricted hygiene facilities can negatively impact oral health. Risk factors such as poor diet, tobacco and betel nut use, and inadequate oral hygiene contribute to conditions including periodontal disease, dental caries, oral mucosal lesions, and even precancerous changes [8]. Occupational exposures to fine dust, metallic particles, or acidic vapours may lead to dental erosion and gingival inflammation [9], while job stress and fatigue can further reduce self-care. The lack of routine dental check-ups and affordable treatment options accelerates disease progression in these populations [10]. The intersection of occupational hazards and oral health remains underexplored in Indian public health research, making it vital to develop integrated workplace programs incorporating oral health education, preventive services, and timely treatment [11]. This study investigates WRMSDs, oral health status, BMI, and physical activity readiness (PAR-Q) in tyre and steel factory workers, addressing an under-researched intersection of occupational and dental public health.

Study Design and Setting A cross-sectional study was conducted from September to December 2023 in Irungattukottai, Tamil Nadu. Ethical approval was obtained from the Institutional Ethics Committee of Saveetha University, and permissions were granted by factory management. Inclusion criteria: industrial workers aged ≥18 years, employed ≥6 months in tyre/steel factories, consenting to participate. Exclusion: unwilling participants or those with cognitive impairments.   Sample size (n=122) was calculated using an expected WRMSD prevalence of 50%, 95% confidence, and 5% margin of error, with simple random sampling from employee lists. A pilot study (n=15) confirmed questionnaire reliability (κ=0.87).   Data Collection Tools Demographic/occupational questionnaire Oral examination using WHO DMFT/def and gingival index under natural light BMI measurement PAR-Q Nordic Musculoskeletal Questionnaire [12,13]   The investigators received training in public health dentistry and clinical examination techniques from Saveetha Dental College, Chennai, to ensure standardization of data collection and minimize inter-observer variability.   Statistical Analysis Chi-square tests, one-way ANOVA with post-hoc tests for DMFT/def, Kendall’s tau-b correlation for ordinal associations (SPSS v26, p<0.05).

Age Distribution We had 55.7% aged 18-30, 36.9% aged 31-50, 7.4% aged 51-65; 90.2% male. Gingivitis was most prevalent in the youngest group (40.98%), followed by 31-50 years (36.89%) (Table 1).   Table 1: Demographic Distribution of Study Participants Category Subcategory Frequency (n) (%) Age Group 18–30 years 68 55.7 31–50 years 45 36.9 51–65 years 9 7.4 Gender Male 110 90.2 Female 12 9.8 Socioeconomic Status Lower middle 49 40.2 Upper middle 62 50.8 Upper 11 9.0   Oral Health Gingivitis was most common in the 18-30 Age group (40.98%) (Figure 1) DT and DMFT/def scores increased significantly with age (p<0.001); MT increased without significance (p=0.061); FT consistently low (p=0.680) (Table 2).     Figure 1: Distribution of Gingivitis Across Age Groups Among Study Participants     Table 2: Age-wise comparison of dental caries experience among study participants Variable Age Group Mean Std One-way Anova p value DT 18–30 years 1.10 ± 1.70 10.258 0.000 31–50 years 1.09 ± 1.69 51–65 years 3.89 ± 2.667 MT 18–30 years 0.04 ± 0.20 2.860 0.061 31–50 years 0.22 ± 0.90 51–65 years 0.67 ± 2.00 FT 18–30 years 0.07 ± 0.49 0.386 0.680 31–50 years 0.16 ± 0.767 DMFT/def 18–30 years 1.22 ± 1.907 8.779 0.000 31–50 years 1.47 ± 2.242 51–65 years 4.56 ± 4.157   BMI Overweight/obesity more prevalent in 31-50 years. For PAR-Q Item 4, 40.7% of overweight participants answered “Yes,” indicating potential limitations to physical activity, with this association nearing statistical significance (χ² = 5.361, p = 0.055) (Table 3). Significant association between BMI and neck discomfort (p<0.001) (Figure 2 and Table 4).     Figure 2: Distribution of BMI Categories Across Different Age Groups   Table 3: Association Between BMI Categories and Physical Activity Readiness (PAR-Q Items 4 & 5) PAR-Q Item BMI Category No Yes χ² Value p-value PAR-Q 4 Under weight 35(36.8%) 9(33.3%) 5.361 0.055 Normal 34(35.8%) 6(22.2%) Over weight 19(20%) 11(40.7%) Obese 7(7.4%) 1(3.7%) PAR-Q 5 Under weight 37(35.6%) 7(38.9%) 1.454 0.105 Normal 36(34.6%) 4(22.2%) Over weight 24(23.1%) 6(33%) Obese 7(6.7%) 1(5.6%)   Table 4: Association Between BMI and Reported Discomfort in Various Body Regions Body Region BMI Category Yes No χ² Value p-value Neck Underweight 0 (0.0%) 44 (62.0%) 80.621 0.000 Normal 14 (27.5%) 26 (36.6%) Overweight 29 (56.9%) 1 (1.4%) Obese 8 (15.7%) 0 (0.0%) Shoulders Underweight 23 (32.4%) 21 (41.2%) 3.506 0.320 Normal 23 (32.4%) 17 (33.3%) Overweight 18 (25.4%) 12 (23.5%) Obese 7 (9.9%) 1 (2.0%) Upper back Underweight 22 (32.8%) 22 (40.0%) 0.760 0.859 Normal 23 (34.3%) 17 (30.9%) Overweight 17 (25.4%) 13 (23.6%) Obese 5 (7.5%) 3 (5.5%) Hip Underweight 24 (33.3%) 20 (40.0%) 2.167 0.539 Normal 22 (30.6%) 18 (36.0%) Overweight 21 (29.2%) 9 (18.0%) Obese 5 (6.9%) 3 (6.0%) Knees Underweight 10 (27.0%) 34 (40.0%) 2.728 0.435 Normal 12 (32.4%) 28 (32.9%) Overweight 12 (32.4%) 18 (21.2%) Obese 3 (8.1%) 5 (5.9%)   Musculoskeletal Work experience negatively correlated with neck (τ=-0.528, p<0.001) and lower back pain (τ = -0.155, p = 0.046) - consistent with healthy worker effect. Weekly work hours positively correlated with shoulder, elbow, and wrist pain (Table 5). PAR-Q: No significant correlations between work experience and most items; only Item 1 showed a weak negative correlation (Table 6).   Table 5: Correlation of Work Experience, Weekly Hours, and Musculoskeletal Symptoms Correlations Experience Hours of work/week Neck Shoulders Elbows Wrist Upper back Lower back Hip Knees Ankles Kendall's tau_b Experience Correlation Coefficient 1.000 -0.528** 0.025 -0.239** . . -0.155* . -0.019 -0.075 . Sig. (2-tailed) - 0.000 0.745 0.002 . . 0.046 . 0.808 0.331 . Hours of work/week Correlation Coefficient -0.528** 1.000 0.080 0.482** . . 0.262** . 0.163 0.182* . Sig. (2-tailed) 0.000 . 0.372 0.000 . . 0.003 . 0.069 0.042 .   Table 6: Correlation of Work Experience, Weekly Hours, and PAR-Q Responses Correlations Experience Hours of work/week PAR-Q 1 PAR-Q 2 PAR-Q 3 PAR-Q 4 PAR-Q 5 PAR-Q 6 PAR-Q 7 Kendall's tau_b Experience Correlation Coefficient 1.000 -0.528** . . . -0.060 -0.047 . . Sig. (2-tailed) . 0.000 . . . 0.438 0.547 . . Hours of work/week Correlation Coefficient -0.528** 1.000 . . . 0.011 -0.048 . . Sig. (2-tailed) 0.000 . . . . 0.898 0.595 . .

This study demonstrates a high prevalence of oral disease and WRMSDs in industrial workers. The negative correlation between work experience and certain pain sites may reflect the healthy worker effect, where less fit individuals leave the workforce earlier. Longer working hours were associated with upper limb pain, aligning with evidence from repetitive-task industries.   The strong BMI-neck pain association is consistent with prior research linking excess weight to increased cervical spine loading. Oral health disparities reflect cumulative effects of limited access to dental care, poor hygiene practices, and occupational exposures. The findings provide a comprehensive insight into the health risks associated with occupational exposure in a rural industrial setting, shedding light on the critical factors that contribute to the deterioration of health in these workers [14-18]. A study by Moreira-Silva et al. [19] found that overweight and obese workers reported higher pain intensity in the shoulders and wrist/hand regions compared to their lean counterparts. Research by Sethi et al. [20] demonstrated a significant association (p<0.001) between high BMI and increased scores of musculoskeletal discomfort and occupational stress among computer workers in Bangalore, India.   Moreover, oral health problems, such as gingivitis and caries, can be aggravated by factors such as dietary habits, which may be influenced by socioeconomic status and access to healthcare [21]. The study also highlighted the impact of socioeconomic status on occupational health outcomes. Socioeconomic disparities play a significant role in determining the overall health of workers, as individuals from lower socioeconomic backgrounds often have limited access to healthcare, nutrition, and preventive measures [22]. Our study findings on the impact of socioeconomic status (SES) on oral health outcomes are echoed in a systematic review and meta-analysis by Knorst et al. [23], which concluded that individuals of lower SES had poorer oral health-related quality of life, regardless of the country's economic classification, SES indicator, or age group.   This calls for a multi-dimensional approach that includes not only workplace health interventions but also broader social determinants of health interventions, such as improving healthcare access and addressing the underlying socioeconomic disparities.

The co-occurrence of WRMSDs and oral diseases in this workforce calls for integrated workplace interventions, including ergonomic redesign, physical activity promotion, nutrition education, and on-site dental screening.   Limitations Limitations include cross-sectional design, male-dominated sample, and reliance on self-report for musculoskeletal symptoms. Recommendations include Implement workplace ergonomics and rest-break schedules. Provide regular dental check-ups at factory clinics. Offer weight management and physical fitness programs. Conduct longitudinal studies to assess intervention effectiveness.