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Occupation as an associated risk factor for leptospirosis
Cassiana Dahlke Machado1, Louise Bach Kmetiuk2, Alessandra Cristiane Sibim3
1Zoonosis Control Center, City Secretary of Health, São José dos Pinhais, Brazil.
Introduction:
Although several studies have described occupation as an associated risk factor for leptospirosis, most have reported absolute case counts without normalization by the general population, service attendance, or notification capacity.
Methods:
Accordingly, this study assessed the Brazilian National Surveillance System (SINAN) from 2007 to 2022, examining gathered leptospirosis cases analyzed by occupation as an associated risk factor within the working-age population. To ensure homogeneous attendance and notification capacity, we focused our analysis on the 100 largest Brazilian cities, utilizing censitary working occupation data per region as the denominator for normalization. Non-active populations (students and retirees) were excluded from the denominators to prevent bias dilution. Occupational groups were categorized into low, medium, and high risk for leptospirosis.
Results:
Overall, the occupations with the highest absolute frequency included farmers (12.0%), construction workers (5.8%), and recycling material collectors (4.6%), with the work environment (75.5%) identified as the most common site of infection. The highest normalized occurrence of leptospirosis was reported in southern Brazil, and high-risk occupational groups showed significantly higher infection rates compared to lower-risk categories (p < 0.001). Furthermore, professional groups with higher exposure loads presented greater frequencies of severe clinical outcomes, including renal failure, jaundice, and higher case fatality rates. The present study suggests occupation as an important predictor of leptospirosis, varying across regions according to local profe sional frequencies. In this scenario, the Brazilian national surveillance system serves as an effective indicator for public health actions and policies, enabling the dynamic monitoring of occupational exposure over time. Finally, occupation must systematically be analyzed as a predictor after normalizing for professional frequency, ensuring equitable access to healthcare assistance and standardized institutional notification.
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