Years of Potential Life Lost (YPLL)
Years of potential life lost (YPLL) is a measure of the relative impact of various diseases and other forces on a population when an individual dies at an age younger than life expectancy. The underlying notion is that the loss of a productive life (if the loss could have been prevented) is a waste to society.
While the concept of YPLL is primarily related to public health and studies that take on the societal perspective, it is also quite relevant to employers and disease management. The preventable loss of a productive worker results in costs associated with replacement hiring and training that are borne by employers.
The rate of years of potential life lost is a numeric representation of potential life lost per 1,000 population for the healthy productive work years of life. Comparisons of premature deaths across different populations use the YPLL rate.
Here the workforce is defined as those over 16 and under age 65 because that is the population of interest to employers. Alternatively, the average life expectancy can be used in the denominator.
Rate of years of potential life lost:
|YPLL =||Years of potential life lost||x 1,000|
|Population over 16 and under age 65|
A 25-year-old man dies in a motorcycle accident because he did not wear a helmet. The death could have been prevented. Since he could have theoretically lived to an average life expectancy of about 72 years, 47 years of potentially productive life are lost.
Note, at the societal level, if 2,000 such motorcycle deaths occur in 25-year-olds, 94,000 potential years of life are lost (2,000 preventable deaths times 47 years lost).
A 50-year-old woman dies from renal failure because she has not followed the treatment regimen for her kidney disease. The death could have been prevented by chronic care management interventions. Since she could have theoretically lived a productive life until retirement at age 65, 15 years of potentially productive work life have been lost.
Timmreck, T. An Introduction to Epidemiology. 2nd ed. Sudbury: Jones and Bartlett Publishers (1998).
Zero (Missing in Graphical Depiction)
Graphical depictions of data may appear misleading if zero is missing from the scale (i.e., does not appear on the Y axis).
In the example, a variable is observed in the pre- and post-periods, and the results are charted in a format with a missing zero. The post-experience appears to indicate a significant reduction compared with the pre-value. A different depiction of the same data is shown in the graphic below.
Although the reduction is the same in both cases, when the zero is included in the scale, a different conclusion is reached from the presentation of the data.
Bland, M. Introduction to Medical Statistics. Oxford: Oxford University Press (1987).
Iezzoni, L. Risk Adjustment for Measuring Health Care Outcomes, 3rd ed. Chicago: Health Administration Press (2003).