A novel approach used outcome distribution curves to estimate the population-level impact of a public health intervention

Objectives: To provide an analytical framework within which public health interventions can be evaluated, present its mathematical proof, and demonstrate its use using real trial data.

Study design and setting: This article describes a method to assess population-level effects by describing change using the distribution curve. The area between the two overlapping distribution curves at baseline and follow-up represents the impact of the intervention, that is, the proportion of the target population that benefited from the intervention.

Results: Using trial data from a parenting program, empirical proof of the idea is demonstrated on a measure of behavioral problems in 355 preschoolers using the Gaussian distribution curve. The intervention group had a 12% [9%-17%] health gain, whereas the control group had 3% [1%-7%]. In addition, for the subgroup of parents with lower education, the intervention produced a 15% [6%-25%] improvement, whereas for the group of parents with higher education the net health gain was 6% [4%-16%].

Conclusion: It is possible to calculate the impact of public health interventions by using the distribution curve of a variable, which requires knowing the distribution function. The method can be used to assess the differential impact of population interventions and their potential to improve health inequities.