A disease kills 10,000 people per year, all at age 75. Another disease kills 5,000 people per year, all at age 25. Which disease causes more Years of Life Lost (YLL), assuming a reference life expectancy of 85 years?
AThe first disease: 10,000 × (85-75) = 100,000 YLL
BThe second disease: 5,000 × (85-25) = 300,000 YLL
CThey are equal because life-years matter equally regardless of age
DYLL cannot be calculated without knowing the disease name
YLL = deaths × remaining life expectancy. The first disease causes 10,000 × 10 = 100,000 YLL. The second causes 5,000 × 60 = 300,000 YLL — three times more life-years lost despite half the number of deaths. This illustrates why DALY-based burden estimation can reorder priorities relative to death counts: diseases that kill young people generate more YLL per death than diseases of old age. This metric values years of life equally regardless of age but recognizes that premature death costs more total life-years.
Question 2 True / False
Depression is a leading cause of global DALYs despite having a low mortality rate. This is because depression generates substantial YLD (years lived with disability) through its high prevalence and moderate disability weight.
TTrue
FFalse
Answer: True
Depression rarely kills directly but affects hundreds of millions of people worldwide at any given time. Even a moderate disability weight (0.15-0.40 depending on severity) multiplied by the enormous number of affected person-years produces a large YLD burden. The GBD Study has consistently ranked depression among the top 5 causes of DALYs globally, alongside ischemic heart disease, lower respiratory infections, and road injuries. This finding — largely invisible to death-focused health statistics — has been influential in arguing for greater investment in mental health services.
Question 3 Short Answer
Explain why burden of disease evidence can reveal mismatches between health spending priorities and actual health needs.
Think about your answer, then reveal below.
Model answer: Health spending priorities are influenced by political advocacy, media attention, industry lobbying, and historical precedent — not just disease burden. Burden of disease measurement provides an objective benchmark of which conditions cause the most health loss. When spending is compared to burden, systematic mismatches emerge: some well-funded conditions (with strong advocacy organizations or pharmaceutical industry interest) receive disproportionate resources relative to their burden, while highly burdensome conditions (mental illness, musculoskeletal disorders, neonatal conditions in LMICs) are systematically underfunded. This evidence creates a basis for evidence-informed reallocation.
The classic example is the HIV/AIDS funding response in Africa: while HIV received massive donor funding (justified by its extraordinary burden in sub-Saharan Africa), other major killers like pneumonia, diarrhea, and malaria in children received comparatively less attention relative to their burden. The GBD data made these disparities visible and informed subsequent rebalancing of global health investments.