**Background:** Agricultural workers are exposed to numerous occupational hazards including ultraviolet radiation, pesticides, herbicides, diesel exhaust, and microbial agents, many of which are known or suspected carcinogens. While previous research in Western populations has shown lower overall cancer risk among farmers due to healthier lifestyles, certain cancers (e.g., lymphoma, leukemia, prostate, skin) are elevated. However, data from Asian populations, where farming practices and exposures differ, are limited. This study aimed to assess cancer risk among Taiwanese agricultural workers compared to the general population using a large national cohort.
**Methods:** The study utilized Taiwan's Farmers' Health Insurance (FHI) database, which enrolls full-time farmers meeting strict criteria (age >15, no full-time off-farm employment, ≥90 farm work days/year, ≥0.1 hectares of farmland). Between 2000-2009, 1,232,604 farmers were enrolled (718,881 men, 513,723 women; mean age 50.3 years). After excluding 57,455 individuals with cancer before enrollment, the final cohort included 1,175,149 farmers and 1,175,149 matched general population controls (matched on age, gender, township, enrollment year). The study population was linked to the National Cancer Registry (TCR) to identify new cancer cases from 2000-2018. The TCR captures 98.1% of cancer cases with high quality (0.70% death certificate only, 93.72% microscopically verified). Cox proportional hazards models estimated hazard ratios (HR) and 95% confidence intervals (CI), adjusted for area-level smoking, drinking, betel nut chewing rates, healthcare access, and ultraviolet exposure. Sensitivity analyses excluded solid tumors diagnosed within 10 years of enrollment and hematological tumors within 2 years to account for latency periods.
**Key Results:** During follow-up, 136,913 new cancers occurred among farmers and 130,248 among controls. Male farmers had significantly increased risks for several cancers after accounting for latency: overall cancer (HR 1.11, 95% CI 1.10-1.13), lymphocytic leukemia (HR 1.12, 95% CI 1.04-1.21), chronic myelogenous leukemia (HR 1.26, 95% CI 1.03-1.55), non-Hodgkin's lymphoma (HR 1.10, 95% CI 1.01-1.20), oral cancer (HR 1.29, 95% CI 1.23-1.35), lip cancer (HR 1.55, 95% CI 1.27-1.89), esophageal cancer (HR 1.29, 95% CI 1.21-1.38), rectal cancer (HR 1.12, 95% CI 1.06-1.18), liver cancer (HR 1.19, 95% CI 1.15-1.23), lung cancer (HR 1.17, 95% CI 1.13-1.22), trachea/bronchi cancer (HR 1.36, 95% CI 1.00-1.86), and other non-melanoma skin cancer (HR 1.22, 95% CI 1.14-1.31). Female farmers had elevated risks for multiple myeloma (HR 1.29, 95% CI 1.02-1.63) and other non-melanoma skin cancer (HR 1.20, 95% CI 1.09-1.32). Notably, lymphoma, NHL, other lymphoid neoplasms, and multiple myeloma showed increased risks across different insurance enrollment periods, suggesting a persistent effect. Male farmers had a protective effect for colon cancer (HR 0.95, 95% CI 0.91-0.99), and female farmers had lower risks for breast cancer (HR 0.84, 95% CI 0.81-0.88).
**Clinical Implications:** This large population-based study provides robust evidence that Taiwanese agricultural workers face significantly elevated risks for several cancers, particularly hematological malignancies (leukemia, lymphoma, multiple myeloma) and non-melanoma skin cancer. The findings underscore the need for targeted cancer screening and prevention programs for farmers, including regular skin examinations, oral cancer screening, and awareness of hematological cancer symptoms. The elevated risks for oral, esophageal, and liver cancers in male farmers may be partly attributable to betel nut chewing, a common practice in Taiwan. The protective effect for colon and breast cancers likely reflects higher physical activity levels and possibly lower smoking rates among farmers. These results highlight the importance of occupational health surveillance and exposure reduction strategies (e.g., pesticide safety training, UV protection) in agricultural settings. Future studies should incorporate individual-level exposure data and lifestyle factors to better understand causal mechanisms.