Cumulative Risk and Population Attributable Fraction in Prevention Caroline H. Davis, David P. MacKinnon, Amy Schultz, and Irwin Sandler Department of Psychology, Arizona State University Compares the use of relative risk versus population attributable fraction in determin- ing the target population for multirisk prevention programs in psychology. Results show that relative risk generally increases as a function of cumulative risk. Guided by this measure, prevention programs should target populations with the largest cumula- tive risk. However, relative risk does not account for the prevalence of a particular level of cumulative risk in the population. Therefore, because the largest cumulative risk is experienced by only a small portion of the population, prevention programs guided by this measure will not always have the greatest public health benefit to re- duce the incidence of problem outcomes in the population. On the other hand, the population attributable fraction, which does take into account the prevalence of a particular level of cumulative risk, does not increase appreciably after a cumulative risk of one, two, or three because the majority of people in the population will experi- ence these levels of cumulative risk. Guided by this measure, prevention programs that target the higher proportion of people who have a more moderate level of risk would have the maximum impact on the population. National data sets from Great Britain (the British Births Cohort Study [BCS]) and the United States (National Lon- gitudinal Study of Youth [NLSY]) are used to explore this pattern of effects. For prevention programs to deliver their services ef- fectively, they must determine the largest possible tar- get group that will benefit from their services. Typi- cally, this is the group with the most important risk factors for the outcome variable the program is de- signed to prevent. The importance of a risk factor is of- ten determined by measures of association such as re- gression coefficients, correlations, relative risk, and odds ratios, which quantify the relation between a risk factor and an outcome variable for an individual. Pre- vention programs are based on these risk factors (Hawkins, Catalano, & Miller, 1992). For example, a program designed to prevent childhood behavior prob- lems might select risk factors based on problems given bereavement, divorce, poverty, and parental depres- sion. If divorce posed the highest relative risk for child- hood behavior problems, then the program would tar- get children who experienced divorce before children who experienced any of the other less important risk factors. This approach is economical in that the cost of delivering the prevention is limited to those individuals in the population who are at the greatest risk of devel- oping childhood behavior problems. The drawback of this approach is that measures of association such as those listed previously fail to take into account the prevalence of a risk factor in the popu- lation when determining its importance. This has im- plications for community-level decisions about select- ing what risk factors to target to have the maximum impact on the public health of their citizens. A pre- vention program that determines its target population based on the importance of a risk factor as defined solely by the strength of association between a risk fac- tor and an outcome while ignoring the prevalence of that risk factor may find that the maximum impact on Journal of Clinical Child and Adolescent Psychology 2003, Vol. 32, No. 2, 228–235 Copyright © 2003 by Lawrence Erlbaum Associates, Inc. 228 This research was supported by the National Institute of Mental Health Grant P30 MH39246–16 to Irwin Sandler to fund a Preven- tive Intervention Research Center at Arizona State University. For the use of the BCS 5-year follow-up data we acknowledge J. Golding as depositor; N. Butler, S. Dowling, and A. Osborn as prin- cipal investigators; the Medical Research Council as sponsor; and the UK Data Archive as distributor. The above bear no responsibility for their further analysis or interpretation. For the use of the BCS 10-year follow-up data we acknowledge J. M. Bynner as depositor; N. Butler and J. M. Bynner as principal investigators; the Joseph Rowntree Memorial Trust, the Department of Education and Sci- ence, the Department of Health and Social Security, the Manpower Services Commission, and the National Institute of Child Health and Development as sponsors; and the UK Data Archive as distributor. The above bear no responsibility for their further analysis or inter- pretation. For the use of the BCS 16-year follow-up data we ac- knowledge J. M. Bynner as depositor; N. Butler and J. M. Bynner as principal investigators; the Home Office, the Cancer Research Cam- paign, Beechams, Kelloggs, Westland, HTV, Channel 4, Allied Ly- ons, the WT Grant Foundation, the Sir J. Knott Settlement, the Hay- ward Foundation, the Daily Star, the New Moorgate Trust, the Lankelly Foundation, the Laura Ashley Trust, and other public and private bodies of private donations as sponsors; and the UK Data Ar- chive as distributor. The above bear no responsibility for their further analysis or interpretation. Requests for reprints should be sent to Caroline H. Davis, Depart- ment of Psychology, Arizona State University, Tempe, AZ 85283. E-mail: caroline.davis@asu.edu