Estimates and Projections of Value of Life Lost From Cancer Deaths in the United States

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Value-of-life methods are increasingly used in policy analyses of the economic burden of disease. The purpose of this study was to estimate and project the value of life lost from cancer deaths in the United States.


We estimated and projected US age-specific mortality rates for all cancers and for 16 types of cancer in men and 18 cancers in women in the years 2000–2020 and applied them to US population projections to estimate the number of deaths in each year. Cohort life tables were used to calculate the remaining life expectancy in the absence of cancer deaths—the person-years of life lost (PYLL). We used a willingness-to-pay approach in which the value of life lost due to cancer death was calculated by multiplying PYLL by an estimate of the value of 1 year of life ($150 000). We performed sensitivity analyses for female breast, colorectal, lung, and prostate cancers using varying assumptions about future cancer mortality rates through the year 2020.


The value of life lost from all cancer deaths in the year 2000 was $960.6 billion; lung cancer alone represented more than 25% of this value. Projections for the year 2020 with current cancer mortality rates showed a 53% increase in the total value of life lost ($1472.5 billion). Projected annual decreases of cancer mortality rates of 2% reduced the expected value of life lost in the year 2020 from $121.0 billion to $80.7 billion for breast cancer, $140.1 billion to $93.5 billion for colorectal cancer, from $433.4 billion to $289.4 billion for lung cancer, and from $58.4 billion to $39.0 billion for prostate cancer.


Estimated value of life lost due to cancer deaths in the United States is substantial and expected to increase dramatically, even if mortality rates remain constant, because of expected population changes. These estimates and projections may help target investments in cancer control strategies to tumor sites that are likely to result in the greatest burden of disease and to interventions that are the most cost-effective.

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