EXPOSURE TO EXTREME WEATHER AND HEALTH OF INFORMAL OUTDOOR WORKERS IN FOUR MAJOR CITIES IN VIETNAM

Văn Kính Nguyễn1, , Thu Giang Nguyễn2, Hồng Sơn Trương1,3, Thị Minh Hạnh Vũ4, Việt Anh Lê3
1 Tổng hội Y học Việt Nam
2 Viện Phát triển Sức khỏe Cộng đồng Ánh sáng - LIGHT
3 Viện Y học ứng dụng Việt Nam
4 Viện Chiến lược và Chính sách Y tế, Bộ Y tế

Main Article Content

Abstract

Objective: To describe extreme-weather exposure, health status, and reported climate-change adaptation measures among informal outdoor workers in four major Vietnamese cities.


Methods: This article reports the quantitative component of a cross-sectional study conducted in Hanoi, Da Nang, Ho Chi Minh City, and Can Tho. The survey included 1,208 workers in construction, street vending, motorcycle taxi/delivery, and porter occupations.


Results: Participants worked 8.7 hours/day and 55.3 hours/week on average. Exposure was reported by 95.4% for heat, 71.0% for heavy rain, and 38.7% for flooding; 55.6% reported 6–10 concurrent weather, environmental, and occupational risk factors and 29.1% reported 11–15. After work, 68.5% reported musculoskeletal symptoms, 24.2% eye symptoms, and 23.7% respiratory symptoms. During the previous 12 months, 42.6% self-reported at least one adverse health or work event that they attributed to unusual weather, and 35.7% perceived extreme weather as affecting mental health. The anxiety and stress subscales of DASS-21 identified at least mild symptoms in 32.9% and 17.8%, respectively. Health insurance coverage was 73.5%, while 2.2% participated in voluntary social insurance. In the multivariable linear model, each one-point increase in awareness score was associated with a 0.774-point higher adaptation-measure score (95% CI: 0.735–0.812).


Conclusion: Informal outdoor workers in the achieved sample experienced high and overlapping exposures together with health and social-protection disadvantages. Integrated climate adaptation, occupational health, primary care, and social-protection measures are needed. The cross-sectional design and non-probability recruitment do not permit causal inference or population-level prevalence estimates.

Article Details

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