An AI-powered study has revealed that the increase in the elderly population and rise in smoking are the main factors behind the surge in global rheumatoid arthritis burden since 1980.

Rheumatoid arthritis is a chronic autoimmune disease that primarily affects joints, causing pain, swelling, and stiffness.

The analysis published in the Annals of the Rheumatic Diseases highlights significant socioeconomic disparities and worsening inequalities in disease burden.

The study found that demographic ageing, population growth, and uneven healthcare infrastructure exacerbate rheumatoid arthritis burdens differently across regions.

Utilizing data from the Global Burden of Disease (GBD), the study integrated the largest spatiotemporal rheumatoid arthritis dataset spanning 953 global to local locations from 1980 to 2021 with a novel deep learning framework.

From 1980 to 2021, the global rheumatoid arthritis burden continued to rise, particularly among younger age groups and a wider range of geographic locations worldwide.

Disability-adjusted life years (DALYs)-related inequality increased by 62.55 per cent from 1990, with Finland, Ireland, and New Zealand identified as the most unequal countries in 2021.

While economic factors were not the sole determinants of rheumatoid arthritis disease burden, high sociodemographic index (SDI) regions like Japan and the UK showed contrasting patterns in disease burden.

Japan's declining DALY rates despite high SDI may be attributed to early diagnosis programs, widespread use of biologic therapies, and a diet rich in anti-inflammatory components.

Researchers predict that by 2040, low-middle SDI regions may experience increasing DALYs due to ageing and population growth, while high SDI areas may see a decrease.

Controlling smoking could reduce rheumatoid arthritis deaths by 16.8 per cent and DALYs by 20.6 per cent in high-smoking regions such as China, providing significant benefits for medium/high SDI areas.

Despite these findings, many regions worldwide still lack the necessary evidence base to inform precision health policy and targeted interventions.